Podcasts about r d

How do you build something nobody asked for—and turn it into an overnight obsession? In this episode of the Innovation Storytellers Show, I speak with Ross Richardson, Chief Design Officer at SharkNinja, about the thinking, testing, storytelling, and consumer obsession behind some of the company's most successful products. Ross leads a global team spanning mechanical and product engineering, industrial design, product innovation, and R&D. But his path into product design began much earlier, from taking things apart and borrowing his father's tools as a child to studying product design engineering and building a career around understanding how people interact with physical products. Our conversation reveals how SharkNinja approaches innovation by starting with consumer behavior rather than technology for technology's sake. Ross explains how the company identifies what he describes as "desirability gaps," finding experiences people already want but cannot easily recreate at home. From frozen drinks and ice cream to coffee and personal care, the opportunity often comes from making something previously difficult, intimidating, or inaccessible feel simple and repeatable. We also discuss the story behind products such as BlendBoss and how observing changing consumer behavior can lead to unexpected opportunities within established categories. Rather than assuming innovation always requires inventing an entirely new market, SharkNinja watches how people live, what they carry, what they share on social media, and how cultural trends are changing their expectations. At the center of this process is a deceptively simple challenge: "prove it." Ross explains why new ideas must withstand technical testing, commercial scrutiny, consumer validation, and internal skepticism before moving forward. He also takes me inside SharkNinja's show-and-tell culture, weekly product demonstrations, in-home user trials, observational research, physical prototyping, and its "red team" approach to bringing fresh perspectives into the development process. In an era where AI can help almost anyone create a polished presentation or convincing visualization, Ross argues that evidence matters more than ever. A compelling story may earn attention, but data, testing, consumer insight, and a working proof of concept are what help turn skepticism into stakeholder buy-in. We also discuss why teams need room to dream before inviting skeptics into the process, how collective enthusiasm can help engineers solve seemingly impossible problems, and why admitting that a favored idea is wrong can sometimes lead to a far better product. As AI changes how we research markets, generate concepts, and communicate innovation, my conversation with Ross is a timely reminder that understanding human behavior remains at the heart of successful product development. The tools available to innovators may be changing rapidly, but our ability to listen to consumers, identify genuine needs, test assumptions, communicate breakthrough ideas, and prove that an idea deserves to exist still determines whether innovation connects with the people it was designed to serve. How can we combine faster, technology-enabled innovation with the deep consumer understanding needed to create products people genuinely want? Listen to the full conversation and share your thoughts.  

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

Who is Gwen?Gwen Acton, PhD, has built her career helping “rocket scientists”—the brilliant scientists, engineers, and technical experts behind today's most innovative companies—become extraordinary leaders. With a background spanning everything from biology to technology, Gwen was inspired by the crucial roles these experts play in industries like biotech, aerospace, AI, big pharma, and government research labs. She saw that while technical leaders are essential for organizational success, many find themselves suddenly tasked with leading teams, often without the tools or training to do so. Gwen made it her mission to bridge this gap, coaching and advising growth-stage companies and R&D groups so their top technical minds can excel both as experts and as leaders.Key TakeawaysSummaryStuart Webb and Gwen Acton PhD discuss* Why technical expertise doesn't equate to leadership proficiency* Common pitfalls technical leaders face (like micromanagement and letting go of control)* The mindset shift required to move from individual contributor to successful team leader* The most essential skills and behaviors for leadership in science and technology-driven organizations* Practical advice for influencing up, engaging teams, and leading transformation* Insights from Gwen's career and highlights from her book, “Leadership for Scientists and Engineers”* Audience questions on changing leaders' minds and the hallmarks of great leadershipWhether you're a new leader, a technical expert on the rise, or simply curious about the bridge between science and leadership, this episode delivers actionable tips and inspirational stories.Timestamps & Overview* 00:00 - Tech glitches and introductions:Stuart Webb kicks off, introducing Gwen Acton PhD, her background, and the episode setup.* 01:24 - Defining the technical leader's challenge:Gwen explains who she helps and the need for leadership skills in technical organizations.* 04:10 - Why technical backgrounds don't prepare you for leadership:Gwen highlights the crucial differences between technical problem-solving and managing people, and why logic alone isn't enough for people problems.* 07:04 - The confusion and blind spots of technical leaders:Discussion about why what worked as a technical expert often fails in leadership, and the importance of learning new interpersonal skills.* 09:16 - Letting go and the pains of delegation:Gwen addresses micromanagement, the struggle to trust others, and methods for successful delegation in high-precision environments.* 11:29 - One piece of leadership advice:Why great leaders ask more (and better) questions instead of always trying to provide answers.* 13:24 - Audience Q&A – Changing minds and influencing up:Gwen shares tips for helping leaders change direction, including the power of genuine questions and one-on-one influence.* 17:05 - Gwen's journey from scientist to leadership expert:Personal story about her path, why group dynamics and leadership in science are so important, and practical solutions for the technical world.* 20:20 - About the book “Leadership for Scientists and Engineers”:What readers will find inside and why it's designed as a practical guide for technical leaders.* 21:34 - What makes a good leader or role model?:Key traits and behaviors ranging from strategic alignment to offering feedback and motivating teams.* 26:48 - The killer question: What separates great leaders from those who plateau?:Gwen's reflections on lifelong learning, intentional skill development, and the humility to accept feedback.* 29:28 - Closing remarks and staying in touch:How to subscribe for future episodes, more about Gwen, and reaching out with questions.Resources and Links*

Một năm trước, trong diễn văn khai mạc Tuần lễ Công nghệ Luân Đôn (London Tech Week 09-13/06/2025), thủ tướng Anh Keir Starmer đã nêu ra chiến lược lấy việc phát triển trí tuệ nhân tạo AI làm bàn đạp thúc đẩy tăng trưởng kinh tế giai đoạn 2025-2030. Sau một năm lên kế hoạch, với khoản đầu tư gần 100 tỷ bảng vào các trung tâm dữ liệu, vào y tế và giáo dục, ngành AI của Anh đã đạt được tiến bộ gì và giúp cho tăng trưởng kinh tế tới đâu? Thông tín viên Nguyễn Giang từ Luân Đôn giải thích: Anh Quốc đánh cược vào AI RFI : Đầu tiên, xin Nguyễn Giang nói về việc triển khai chiến lược AI của Vương quốc Anh?   Nguyễn Giang : Quan sát những gì Anh quốc đã và đang làm hơn một năm qua, tôi thấy họ đặt trọng tâm của kế hoạch phát triển AI vào các Khu vực Tăng trưởng Ai, tức AI Growth Zones, gọi tắt là AHGZ, với ba mục tiêu: hỗ trợ tăng trưởng kinh tế, tạo đột phá công nghệ và cân bằng, kết nối các vùng miền của Vương quốc Anh. Mô hình AI Growth Zones (Khu vực Tăng trưởng AI) tạo ra các cụm siêu trung tâm dữ liệu (hyperscale data centres) và điện toán hiệu năng cao. Chính phủ Anh đã phê duyệt hàng loạt khu vực trọng điểm ở hạt Oxfordshire (Culham - trung tâm đầu tiên), Đông Bắc Anh (Blyth & North Tyneside), xứ Wales (Wylfa) và gần đây nhất là Scotland (khu vực Lanarkshire - tháng 1/2026). Họ chia ra để mọi khu vực địa lý chính của Vương quốc Anh đều có cơ hội tiếp cận AI.  Khu quan trọng nhất là hành lang công nghệ Oxford-Cambridge (thực tế là cùng Luân Đôn lập ra một Tam giác High-Tech). Xin nhắc là từ Luân Đôn tới Cambridge và Oxford khoảng cách chỉ chừng hơn 100 km. Các đại học lớn, kể cả phòng thí nghiệm nguyên tử quan trọng nhất của Anh (Arbington), cũng nằm trong tam giác này. Điều này giảm được chi phí giao thông, hút được nguồn nhân lực chất lượng cao và tạo điều kiện cho việc kết nối băng thông cho hệ thống điện toán. Nhưng Anh cũng lập các vùng AI Growth Zone ở cả Wales, miền bắc Anh và Scotland là có ý duy trì nhiều động cơ kinh tế cho các vùng đất kia. RFI: Cho tới nay thì các khoản đầu tư được khu vực tư nhân đăng ký vào các khu vực tăng trưởng AI này là bao nhiêu? Nguyễn Giang : Mục tiêu chung là phải đạt tới 100 tỷ bảng Anh đầu tư từ khối tư nhân vào hạ tầng AI thông qua cơ chế này. Ở vùng Đông Bắc (North East AIGZ), quỹ đầu tư Blackstone đã cam kết bỏ ra10 tỷ bảng Anh để xây dựng trung tâm dữ liệu tại Blyth, đồng thời mở đường cho 20 tỷ bảng bổ sung từ các đối tác khác. Chính phủ Anh cũng trực tiếp chi ngân sách tài trợ ban đầu hơn 2,5 tỷ bảng để kích hoạt hệ thống điện toán quốc gia. Điều đáng chú ý là phần cam kết từ cả hai phía. Về phía mình, chính phủ Anh cam kết giá điện đặc biệt cho data centre tại các vùng có thặng dư năng lượng (ví dụ: giảm từ 14 đến 24 bảng/MWh tùy khu vực, tiết kiệm tới 80 triệu bảng/năm cho một trung tâm 500MW). Để đẩy nhanh tiến độ thiết kế các trung tâm dữ liệu, họ ưu tiên đưa chúng vào danh mục Hạ tầng Quốc gia Trọng yếu (Critical National Infrastructure - CNI), cho phép rút ngắn thời gian chờ đấu nối vào lưới điện quốc gia lên đến 5 năm và cắt giảm quy trình phê duyệt quy hoạch từ 18 tháng xuống còn 12 tháng. Còn về phía các nhà đầu tư cùng đối tác ở địa phương thì họ tìm cách bố trí quỹ đất tối thiểu 100 mẫu Anh (acres) đã được dọn sẵn hoặc đủ tiêu chuẩn sẵn sàng xây dựng trước 2028, cam kết tạo ra hàng nghìn việc làm chất lượng cao (như vùng North East cam kết tạo 5.000 việc làm), và phối hợp với các đại học địa phương (Newcastle, Durham...) xây dựng chương trình đào tạo nhân lực AI. Họ cam kết sẽ kết nối với các đại học địa phương. Hiện đang làm nghiên cứu trong một đại học ở Luân Đôn và đã dự nhiều khóa học, hội thảo, tôi thấy một xu hướng rõ rệt: các đại học Anh đang xóa bỏ, thậm chí đóng nhiều khoa ở các trường khắp cả nước trong các bộ môn khoa học xã hội, nghệ thuật, ngôn ngữ, nhưng lại đang mở rất nhiều khóa, ngành liên quan đến trí tuệ nhân tạo, từ mảng nghiên cứu, phát triển công nghệ tới ứng dụng Ai trong đủ mọi lĩnh vực. Xem ra đây là tương lai mới cho giáo dục đại học. RFI: Một câu chuyện nhiều nước nói tới là các trung tâm dữ liệu tiêu thụ điện năng rất lớn. Vậy ở Anh, nguồn năng lượng điện đến từ đâu? Có nằm cạnh nhà máy điện hạt nhân? Nguyễn Giang : Đúng thế, năng lượng là "nút thắt" lớn nhất của hạ tầng AI, và chiến lược của Anh là áp sát nguồn phát năng lượng sạch quy mô lớn, nhưng không chỉ giới hạn ở điện hạt nhân. Điện nguyên tử không phải tất cả, nhưng đây là một trục xương sống cho AI. Ví dụ điển hình nhất là North Wales AI Growth Zone, nơi được thiết kế cặp đôi chiến lược (strategically paired) nằm ngay cạnh dự án Lò phản ứng mô-đun nhỏ (SMR) đầu tiên của Anh tại Wylfa. Sự kết hợp này nhằm cung cấp nguồn điện tải nền (baseload) cực kỳ ổn định và khổng lồ cho các cụm chip AI mà không làm quá tải lưới điện dân dụng. Ngoài ra thì tại vùng North East (Blyth), các trung tâm dữ liệu dùng nguồn điện đến từ các trạm tiếp bờ của điện gió ngoài khơi lớn nhất thế giới ở Biển Bắc (North Sea) và mua cả năng lượng qua cáp ngầm kết nối thủy điện từ Na Uy. Tại Scotland, họ tận dụng nguồn năng lượng tái tạo thặng dư (gió và thủy điện) vốn thường bị nghẽn, không thể truyền tải về miền Nam. Các báo Anh gần đây hay đăng các câu chuyện thú vị như nhà nông nào đó ở vùng quê bán trang trại, lò sản xuất bánh mì, trạm xay bột cho công ty Ai để xây data centre. Theo một điều tra của nhà báo Henry Mawhood cho trang báo nghề cá ở Anh thì thực ra câu chuyện không đơn giản như vậy nhưng đúng là các vùng ven biển phía Tây đảo Anh, từ Weston-Super-Mare tới Cumbria (England), rồi miền Nam xứ Wales và duyên hải Đông Bắc Scotland, người ta đang có kế hoạch xây hai thứ: các nhà máy điện nguyên tử mini, các trạm nhận điện qua cáp ngầm từ Bắc Âu (Na Uy) và các trạm đón tuyến cáp truyền dữ liệu xuyên Đại Tây Dương, nối Anh với Hoa Kỳ. Vì thế, đúng là có những nơi nhà nông được mời chào bán lại các miếng đất, nhà xưởng, ít nhất với diện tích 1-2 trăm mét vuông, để chứa data centre. Trong tháng 6 năm nay, có câu chuyện mang tính biểu tượng ở Anh. Thủ tướng Kier Starmer đã thăm một nhà máy xà phòng cũ có 130 năm tuổi ở Warrington, hạt Cheshire, để dự lễ khai trương dự án trung tâm dữ  liệu. Tức là họ biến một điểm công nghiệp rất là cũ thành trung tâm dữ liệu, dùng nguồn điện, nước có sẵn.  RFI: Theo anh, công suất cho điện dùng vào trí tuệ nhân tạo ở Anh có đủ không? Nguyễn Giang : Theo các báo chuyên ngành đánh giá thì điện ở Anh, gồm cả điện nguyên tử, hiện tại là đủ cho lộ trình giai đoạn 1 nhờ chiến lược phân bổ vùng. Ví dụ, riêng trung tâm dữ liệu AI tại Blyth đang tăng tốc để đạt công suất 1,1 GW (Gigawatt) trong vòng 6 năm tới, biến nó thành một trong những cụm dữ liệu lớn nhất châu Âu. Bằng cách đặt data centre tại nơi "nguồn phát" dư thừa, Anh giải quyết được bài toán tổn thất truyền tải và giá điện cao. RFI: Cho tới nay, việc sử dụng AI trong y tế, tài chính và giáo dục tại Anh ra sao? Nguyễn Giang : Theo chỉ số tâm lý AI (EY AI Sentiment Index) tính đến giữa năm 2026, bức tranh ứng dụng của Anh như sau: Trong Dịch vụ Sức khỏe Quốc gia (NHS- tức Y tế công) thì kế hoạch 10 năm của Anh đã đưa AI từ giai đoạn thí điểm sang tích dợp hệ thống toàn diện. AI hiện là "trợ lý số" bắt buộc ở nhiều khâu: các công cụ "Ambient Scribing" (AI ghi âm cuộc thoại bác sĩ - bệnh nhân tự động chuyển thành hồ sơ bệnh án) phổ cập mạnh mẽ; ứng dụng NHS App tích hợp AI thành cổng sàng lọc bệnh tự động. Đặc biệt, Bộ Y tế Anh vừa tung ra mô hình "MHRA AI Airlock / Sandbox" (tháng 6/2026) để thử nghiệm AI trong việc dự đoán tác dụng phụ của thuốc, giúp NHS giảm bớt gánh nặng chi phí 2 tỷ bảng/năm do phản ứng ngược của thuốc gây ra. Mạnh và sâu hơn là ứng dụng trong tài chính dùng công nghệ số (fintech). Trên thực tế thì tỷ lệ ứng dụng AI ở Anh trong ngành này đã đạt 75%. Các ngân hàng và công ty tài chính Anh đang dùng AI sâu trong phát hiện gian lận (anti-frauds), trong giao dịch tự động và tuân thủ pháp lý (compliance). Vào tháng 1/2026, Cơ quan Quản lý Tài chính (FCA) đã mở cuộc điều tra Mills Review để đánh giá toàn diện tác động dài hạn của AI đối với dịch vụ tài chính tiêu dùng, đồng thời tích hợp sâu công cụ Agentic AI (AI tự chủ) vào hệ thống vận hành. Trong giáo dục đại học, Anh đang chuyển mạnh từ các ngành xã hội sang công nghệ. Hiện Bộ Giáo dục đã chuẩn hóa hơn 400 chương trình thạc sĩ chuyên sâu về AI và 28 trung tâm đào tạo tiến sĩ (CDT). Các trường đại học lớn như Edinburgh (với Bayes Centre), Cambridge, Oxford hoạt động như các lõi R&D chuyển giao công nghệ trực tiếp cho doanh nghiệp trong các AI Growth Zones. RFI:  So với Liên Âu thì Anh Quốc đi trước hay sau về công nghệ AI trong ứng dụng vào kinh tế và các ngành nghề? Nguyễn Giang : Có thể nói về mặt thương mại, hạ tầng và đầu tư thì Anh đi trước. Anh hiện đứng đầu châu Âu và thứ 4 thế giới về năng lực nghiên cứu cũng như thu hút vốn đầu tư mạo hiểm cho AI (riêng năm 2024 đạt gần 3 tỷ bảng). Do lợi thế về nguồn dữ liệu tập trung của NHS và thị trường tài chính London, các tập đoàn Mỹ (Google, Microsoft, OpenAI) chọn Anh làm bãi đáp hạ tầng lớn hơn so với Liên Âu Ưu thế của Anh là có tốc độ tích hợp AI vào dịch vụ công như trong y tế công (NHS) đồng bộ hơn nhờ cơ chế quản lý tập trung, trong khi EU bị phân mảnh theo từng quốc gia thành viên. Nhưng so sánh Anh và Pháp thì bức tranh lại hơi khác RFI:  Anh và Pháp được cho là hai quốc gia châu Âu hành đầu, cạnh tranh với Hoa Kỳ, Trung Quốc về AI nhưng cũng cạnh tranh với nhau? Nguyễn Giang : Các thông tin do hai chính phủ Anh và Pháp công bố dần dần cho thấy sự khác biệt, ưu điểm và nhược điểm của mỗi bên trong chiến lược phát triển AI. Tôi xin điểm ra một số nhỏ: Anh đã triển khai các trung tâm dữ liệu nhanh hơn Pháp. Vì ở Pháp, dù Hội nghị thượng đỉnh Choose France cam kết cắt giảm thủ tục hành chính của Pháp, nhưng Pháp cũng bị ràng buộc bởi các quy định quản lý đất đai và bảo vệ môi trường chặt chẽ của Liên Hiệp Châu Âu nên thủ tục làm tốn thời gian hơn trong việc giải phóng mặt bằng quy mô lớn. Pháp muốn đi nhanh nhưng vẫn phải tuân thủ luật Liên Âu (EU AI Act), còn Anh thì có môi trường pháp lý thoáng hơn, gần với Hoa Kỳ hơn, và thái độ ‘pro-innovation' có vẻ đang tốt hơn Pháp. Trong việc xây dựng hệ sinh thái tài chính và thương mại kỹ thuật số thì Anh có sẵn Luân Đôn là trung tâm Fintech số 1 châu Âu, kết nối truyền thống mạnh mẽ trong thế giới nói tiếng Anh với New York, với Singapore, Hồng Kông nên giúp các AI Growth Zone dễ dàng kết nối trực tiếp giải pháp AI vào thị trường tài chính toàn cầu. Thêm vào đó, việc sở hữu Dữ liệu tập trung của Y tế công NHS là lợi thế độc quyền mà Pháp không có. Anh dễ dàng thu hút các tập đoàn y sinh AI lớn đến thiết lập trung tâm tính toán, vì họ có thể tiếp cận kho dữ liệu y tế khổng lồ hàng đầu thế giới thông qua các cơ chế an toàn số (như MHRA AI Airlock). Thế nhưng, về quy mô vốn đầu tư hạ tầng khổng lồ bằng tiền nước ngoài (FDI Magnitude) thì Pháp đang hơn Anh. Ví dụ, Hội nghị Choose France, tập đoàn SoftBank đã cam kết chi tới 75 tỷ euro (trong đó 45 tỷ euro giải ngân trước 2031) để xây dựng cụm trung tâm dữ liệu AI khổng lồ công suất 5 GW tại Dunkerque. Bên cạnh đó là 10 tỷ euro từ Brookfield và 7,5 tỷ euro từ quỹ MGX (UAE). Hiện chưa rõ cuộc chạy đua đường dài về AI (AI race) giữa Anh và Pháp ra sao, nhưng cả hai nước đều đang đổ tiền vào. Tổng thống Emmanuel Macron công bố trong tháng 6/2026 khoản đầu tư 1,55 tỷ euro tiền công vào công nghệ quantum và bán dẫn (semiconductors). Cùng lúc, chính phủ Anh cũng trực tiếp chi ngân sách tài trợ ban đầu hơn 2,5 tỷ bảng để kích hoạt hệ thống điện toán quốc gia kết nối AI. RFI: Nếu làm tốt, các kế hoạch này có giúp kinh tế Anh tăng trưởng giai đoạn 2027-2030 không? Nguyễn Giang : Anh Quốc đang hy vọng kế hoạch AI Growth Zones và chuyển đổi số này được thực thi trơn tru, Nhờ đó mà tác động lên giai đoạn 2027-2030 sẽ có thể phát huy tác dụng qua ba kênh: Một là người ta hy vọng có dòng vốn dịch chuyển mạnh từ Mỹ sang Anh, không chỉ với con số 100 tỷ bảng đầu tư hạ tầng cam kết đến năm 2030 và còn có các cam kết mới. Các đầu tư này sẽ trực tiếp đóng góp vào GDP thông qua xây dựng và mua sắm thiết bị công nghệ cao ở Anh. Hai là AI sẽ làm tăng hiệu suất của y tế và tài chính, dịch vụ công. Lấy ví dụ ứng dụng AI giúp rút ngắn thời gian chẩn đoán của bác sĩ, cắt ngắn thời gian chờ được khám bệnh, và gián tiếp giải phóng hàng tỷ bảng Anh áp lực ngân sách (thời gian, giờ công) cho phép tái đầu tư tiền vào các khu vực kinh tế tạo ra thặng dư cao hơn. Và thứ ba là tác động lan tỏa (Spillover effects). Đây là điều cần chờ xem. Thế nhưng theo các mô hình dự báo, khi các AI Growth Zones đi vào hoạt động ổn định từ năm 2027, các ngành kế toán, tài chính, năng lực tính toán trong sản xuất v.v. sẽ nhờ giải pháp AI mà giảm được chi phí. Qua đó, giá trị chung của toàn chuỗi cung ứng ở Anh sẽ tăng lên, cùng sức cạnh tranh của hàng hóa Anh. Chúng ta sẽ không thấy ngay lợi ích này, nhưng đây là quá trình chuyển đổi, có người nói là cuộc cách mạng cơ sở hạ tầng lớn nhất từ thế kỷ 16 nên cần phải có  thời gian để “hưởng lợi”, nhìn từ góc độ của Anh Quốc.

Today, I'm joined by Stephen Ellsworth, founder of poppi.   Originally launched at farmers markets as "Mother Beverage," poppi rebranded in 2020 and scaled rapidly before being acquired by PepsiCo for nearly $2B.   In this episode, we discuss cultivating consumer obsession.   We also cover:   How 2.5 years at farmers markets became the ultimate R&D Why the $50M revenue milestone matters Landing a deal on Shark Tank Subscribe to the podcast → insider.fitt.co/podcast  Subscribe to our newsletter → insider.fitt.co/subscribe  Follow us on LinkedIn → linkedin.com/company/fittinsider    Website: www.drinkpoppi.com/  Stephen's Instagram: https://www.instagram.com/stephenellsworth_/    -   The Fitt Insider Podcast is brought to you by EGYM. Visit EGYM.com  to learn more about its smart fitness ecosystem for fitness and health facilities. Fitt Talent: https://talent.fitt.co/  Consulting: https://consulting.fitt.co/  Investments: https://capital.fitt.co/    Chapters: (00:00) Introduction (01:49) Background and poppi origin story (02:40) Mother Beverage to poppi journey (04:50) Two-year R&D at farmers market (06:20) Shark Tank and capital inflection (08:12) Rebranding decision and strategy (11:00) 2020 timing and category alignment (12:40) Intuition-driven product development (13:40) COVID acceleration (14:45) $50M inflection point (16:20) Founder preferences: zero to one vs. scaling (17:50) Product obsession and team building (18:40) 15–20% better formula (20:00) poppi playbook and thinking differently (21:50) Democratizing better-for-you foods (25:30) PepsiCo acquisition concerns (28:45) Large incumbents vs. innovation (31:00) Post-acquisition emotions (35:15) Preferment vs. retirement (37:30) What's next (39:15) Conclusion

A24 announced a $75 million research partnership with Google DeepMind and framed it as R&D for production tools, not a content deal. Nobody cared about the distinction. Chris, Daniel, and James Blevins spend the first half of this Rough Cut on why that announcement landed like a betrayal anyway, then pull the same thread through the Paramount Skydance and Warner Bros. Discovery merger, Mark Ruffalo's New York Times op-ed on quiet industry retaliation, and what it means that four studios now control what used to take six. From there the panel turns to Christopher Nolan's press tour for The Odyssey and the practical-versus-VFX marketing dance that VFX artists roll their eyes at every single time it resurfaces. They close with a Monstrous Moonshine state of the union, walking through the June July shoot and a quick, lighthearted aside on why the show still uses AI tools like Suno for its own theme song. Mentioned this episode: A24, Google DeepMind Paramount Skydance, Warner Bros. Discovery, Mark Ruffalo, Matt Stoller Christopher Nolan, The Odyssey, D-Neg, Weta Workshop Monstrous Moonshine June July Suno Special thanks to our sponsor: Center Grid Virtual Studio: https://cgvirtualstudio.com/

Hello and welcome to the ZimmCast. In this episode I'm going to share some comments and a question with an IFAJ Webinar. The topic was "Artificial Intelligence's Impact on R&D for Agriculture." Moderating was Steve Werblow, IFAJ President and Martin Clough, Syngenta Crop Protection's R&D Head of Digital Collaboration and Sustainability and Andre Piza, Syngenta's Group's Global Head of Digital AgTech. My question was, are there concerns about the cost of data centers and including issues like the climate and sustainability. Each of them will provide their comments and then the answer of my questions. If you are not a member of IFAJ consider joining your local agricultural journalist guild. Find out more on the IFAJ website. That's the ZimmCast for now. I hope you enjoyed it and thank for listening.

Molecular biology is more than a set of techniques. It is a toolkit for asking better questions. In this special crossover episode of Speaking of Mol Bio, Steve Lewis welcomes Jordan Ruggieri, Senior Marketing Manager at Thermo Fisher Scientific and co-host of the Absolute Gene-ius podcast. Jordan shares how Absolute Gene-ius began with a focus on digital PCR and evolved into a broader celebration of molecular genetics, genomics, and the scientists using diverse tools across multiple research areas. From endpoint PCR and qPCR to digital PCR, sequencing, sample prep, and downstream analysis, the conversation highlights how different technologies work together to help researchers move from sample to answer. Jordan also reflects on memorable podcast stories, including Antarctic microbial research, transplant science, lab mishaps, and the ways PCR-enabled discoveries can have real-world human impact. The episode also explores themes that run through many scientific careers: curiosity, grit, mentorship, failure, and openness to unexpected opportunities. Jordan shares his own path from aspiring dentist to R&D scientist, marketer, and podcast host, while offering early-career scientists practical advice: stay open, keep learning, and make time for hobbies and life outside the lab. Subscribe to get future episodes as they drop and if you like what you're hearing we hope you'll share a review or recommend the series to a colleague.  Visit the Invitrogen School of Molecular Biology to access helpful molecular biology resources and educational content, and please share this resource with anyone you know working in molecular biology. For Research Use Only. Not for use in diagnostic procedures.

Dr. Z and Dr. C sit down with Dr. Sarah Gheuens, Chief Medical Officer and Head of R&D at Agios Pharmaceuticals, to break down one of the most frequently heard but least understood terms in the treatment conversation: FDA approval. From the phases of clinical development and the role of regulators, to the difference between traditional and accelerated approval pathways, Dr. Gheuens demystifies the science and the process in plain language. They also discuss why asking questions of physicians about their care is one of the most important things a patient can do. SHOW DESCRIPTION Cheat Codes is intended for patients, caregivers, providers, and the greater community of people who are impacted by Sickle Cell Disease.  Each episode, Cheat Codes strives to provide listeners with critical education, the latest scientific updates, and voices from the Sickle Cell community.   Join an inclusive community and build connections with other hemolytic anemia allies by following @AllyVoicesRising on Instagram. TRANSPARENCY STATEMENT  Cheat Codes: A Sickle Cell Podcast is made possible by Agios Pharmaceuticals Inc. Visit Agios.com to learn more. The following Agios-supported programs are intended for informational and educational purposes only and are not intended as medical advice. Please speak with your healthcare professional before making any treatment decisions. Hosts Drs. Ahmar Zaidi and Mike Callaghan are Agios employees.  

In this episode of The Capital Raiser Show, Richard C. Wilson sits down with Marc Halpern, co-founder of the Deep Due Diligence Investors Club, for a fireside chat on screening private placements at scale, managing risk in alternative investments, team-based due diligence, and what separates disciplined investors from everyone else. Marc shares lessons from five decades of breakthrough R&D and private investing, including the "jockey, horse, and track" framework, how his club uses AI to screen 400+ deals a year in seconds, and why reading every word of a PPM is still non-negotiable. The conversation dives into deal screening discipline, portfolio strategy, diversification, stress testing sponsors through market cycles, and how a team of 58 investors from wildly different professional backgrounds covers each other's blind spots better than any single expert could. Topics covered include: The "jockey, horse, and track" framework for evaluating any deal How AI deal screening tools cut through hundreds of opportunities in seconds Deep due diligence in teams and why diverse professional backgrounds matter Minimizing risk vs. eliminating it - and why you can only do one Portfolio strategy before individual investment selection Diversification vs. de-worsification and finding the right balance Stress testing sponsors through the Fed rate cycle of 2022-2023 When to walk away quickly and take no for an answer The biggest misconception private investors have about succeeding in alternatives The Capital Raiser Show brings together billionaire investors, family offices, elite entrepreneurs, and capital allocators to discuss investing, scaling, strategic growth, and wealth creation. Subscribe for more interviews with top investors, founders, family offices, and industry leaders.

Grain markets found more support on Wednesday as we digest all of this week's news and data ahead of the 4th of July holiday. Mike Zuzolo with Global Commodity Analytics joins us to put some puzzle pieces together with technical analysis of the grain markets, the weather outlook and some perspective on this week's cattle trade action. More at https://www.globalcommresearch.com. At the start of the show in Segment One, Susan Littlefield joins us as she talked with StoneX Senior Commodities Economist Mike Castle about the grain market action, weather and possible USMCA news having an impact on the market trade. And in Segment Four, we have a conversation about AI use in agriculture and how it is being incorporated into agriculture workflows. Ryan Wolf, Director of Innovation Experience, R&D at WinField United, joins the show to talk about the use of AI and the launch of Oz, an AI-powered tool from WinField United that helps make near-instant agronomic recommendations and more.

Sam, Dylan, and Dark Smith are back to break down: Dark Smith's job interview to become an escort and the loofah-color swinger code of Florida's villages, the Boyle Heights fire spilling Freon and ammonia while firefighters spray a blaze they can't reach (and the owner being a Karen Bass donor), a documentary trying to abolish the Electoral College, the deep origins of the Vatican built on top of Vatican Hill, the pagan goddess Cybele and the Anatolian mother god Magna Mater brought to Rome via the prophetic Sibylline Books after Hannibal's slaughter at Cannae, the blood-baptism bull-sacrifice rituals later dug up by archaeologists, H.P. Lovecraft's "The Rats in the Walls" and its cannibal cult, the serpent-shaped Vatican auditorium and the Pope's fish-head mitre tied to the Babylonian fish god Dagon (who turns out to be Godzilla), Sam's pitch for roller derby as the next great pro sport, the Iran war being "over" again with IAEA inspectors as the "milestone" achieved by undoing the milestone, Israel allegedly trying to assassinate the negotiators before Trump called them off, Nicki Minaj's distraction-death theory, JD Vance's Thiel-engineered glow-up, the Albanians tearing their country apart over Jared and Ivanka's island and Sam's theory it's about controlling the Strait of Otranto, the nu-metal psyop, military stock buybacks instead of R&D, Bezos prioritizing AI water over "baseline human comfort," and a woman who lost ten years of memory from straining too hard on the toilet. Subscribe and give us that sweet brown hype.   Grab Tickets To Sam Tripoli's Live Shows At: https://samtripoli.com/events/   Miami, Fl: 7/31-8/1 Lawerence, KS: 9/17-9/19 Tulsa, OK: 10/9-10/10 Dallas, TX: 11/07 New Orleans, LA: 11/13 - 15 Austin, TX: DEC 11th-13th:   Buy Our Merch or Sam Will Fight You: https://conspiracy-social-club-aka-deep-waters.myshopify.com/   Subscribe to the Patreon: https://www.patreon.com/AkaDeepWaters   Check out Dylan's instagram - @dylanpetewrenn   Check out Deep Waters Instagram: @akadeepwaters   Check out Bad Tv podcast: https://bit.ly/3RYuTG0   THANK YOU TO OUR SPONSORS:   LUCY.CO/CSC Promo Code "CSC" to get 20% off your first order   HIMS.COM/CSC HIMS.COM/CSC for your FREE Online Visit

Après les taxes sur les véhicules électriques chinois, l'Union européenne envisage de nouvelles barrières douanières pour contrer cette fois l'arrivée massive des automobiles hybrides rechargeables. La Chine a acquis, ces dernières années dans ce segment, une expertise et une compétitivité auxquelles les constructeurs européens ont bien du mal à répondre. C'est le segment qui attire les regards ces derniers mois : les hybrides rechargeables grignotent à toute vitesse des parts de marché. Elles représentent désormais près de 10% des ventes de véhicules neufs en Europe, avec des progressions très rapides en Italie, +85% ou en Espagne, +45%, depuis le début de l'année. Or ce succès est une nouvelle fois porté avant tout par les modèles chinois qui se sont engouffrés dans un secteur épargné par les droits de douane supplémentaires, contrairement aux véhicules purement électriques. « Les véhicules hybrides rechargeables chinois sont ultra-compétitifs, d'autant plus qu'il s'agit de "super-hybrides", des modèles à propulsion essentiellement électrique avec un complément thermique, ce qui n'est pas le cas de leurs concurrents européens », explique Gianmarco Guadalupi, consultant automobile pour le cabinet Efeso, « en 2024, personne n'avait entendu parler des hybrides rechargeables chinois, ils sont arrivés sur le marché européen en 2025 et cette année, BYD est devenu leader sur ce segment ». Protection ou simple respiration pour les constructeurs européens ? Ces nouvelles taxes douanières sont accueillies favorablement par les géants européens de l'automobile, notamment allemands, pourtant pas très fans des droits de douane habituellement. Mais les Mercedes et BMW misent beaucoup sur les hybrides rechargeables pour relancer le marché premium. Le problème, c'est qu'il est peut-être déjà trop tard. Les véhicules chinois restent en effet compétitifs même avec des taxes supplémentaires, ce qui a déjà été constaté pour les voitures 100% électriques. « L'Europe manque d'une vision stratégique sur l'industrie automobile, note Gianmarco Guadalupi. Pour l'instant, l'objectif est de ralentir l'arrivée des véhicules chinois en espérant que les constructeurs européens rattrapent leur retard d'ici trois ou quatre ans, mais la vague venue de Chine ne pourra pas être arrêtée. » Des barrières douanières pour préserver les actionnaires ? Une vague d'autant plus difficile à ralentir que la Chine semble avoir déjà anticipé le problème. Des constructeurs chinois ont d'ores et déjà noué des alliances industrielles pour contourner les barrières douanières. Dongfeng va ainsi faire produire dans l'usine Stellantis de Rennes des modèles de sa marque premium Voyah, dont pourraient faire partie des hybrides rechargeables. La mise en place de nouvelles taxes ne paraît donc pas en mesure d'inverser la donne, et au-delà du répit potentiel accordé aux constructeurs européens, le délégué CGT Renault, Florent Grimaldi, y voit surtout une stratégie financière : « Les barrières douanières, ça protège les actionnaires qui peuvent obtenir un délai supplémentaire pour vendre leurs parts ou encaisser les dividendes, mais ça ne protège absolument pas l'outil industriel et les salariés. » Car la grande crainte des syndicats des constructeurs européens, c'est que ce bras de fer aboutisse surtout à des négociations entre constructeurs européens et chinois pour intégrer la technologie ou des pièces venues de Chine dans les véhicules français, allemands ou italiens. Les conséquences pourraient être sévères pour l'emploi, en particulier dans la recherche et le développement. Renault a ainsi confirmé jeudi la suppression de 600 postes d'ingénieurs en France, dans le cadre d'un plan mondial de restructuration de son département R&D. Au total, 2 400 postes vont être supprimés, particulièrement en Roumanie, au Brésil ou au Maroc.

One half of R+D in the NC, former city council member Larken Egleston, joins Bo and Beth for their usual Thursday chat. We talk about why R+D works well together, Charlotte's new mayor, and the LaMelo Ball trade breaks during the segment, so we cover that too!See omnystudio.com/listener for privacy information.

If your career looks successful on paper but feels off inside, you're not alone. Many mid-career professionals reach a point where the work that used to fit no longer does. Breanne Gjurich-Wozniak's story is one path forward: a portfolio career that combined her biotech expertise with her animal welfare interests. Breanne had a career that looked solid from the outside. Biotech R&D, then regulatory work submitting new technologies to the FDA. Director-level. Real success. Inside, the fit was changing. The work was drifting.She started looking at R&D and regulatory roles she was qualified for. Nothing gave her any excitement. That's when she found HTYC's free 8-day mini course and joined the Career Change Bootcamp. She built one 5-year career plan with her manager and a separate one with her career coach. The two plans were so different. The energy in each was so different. She knew it was time to leave. What followed wasn't a traditional job search. It was 20 conversations with strangers, a series of career experiments, and surprising feedback from people who'd only met her for an hour. Her coach introduced the idea of a portfolio career: multiple types of work, multiple interests, all under one umbrella. Breanne built a consulting business that combined her biotech expertise with the animal welfare work she'd always wanted to do. What you'll learn: The 5-year career plan exercise that reveals when your current path no longer fits How to use Social Goldilocks conversations to get strangers to see what you can't about yourself What a portfolio career actually looks like (and why one full-time job isn't always the right structure) How to translate decades of industry experience into consulting work Why "successful on paper" is the hardest career trap to leave Our book, Happen To Your Career: An Unconventional Approach To Career Change and Meaningful Work, is now available on audiobook! Visit  happentoyourcareer.com/audible to order it now! Visit happentoyourcareer.com/book for more information or buy the print or ebook here! Want to chat with our team about your unique situation? Schedule a conversation Free Resources What career fits you? Join our free 8 Day Mini Course to figure it out! Career Change Guide - Learn how high-performers discover their ideal career and find meaningful, well-paid work without starting over. Related Episodes Changing Careers (When You Don't Know Your Next Job Title) (Spotify / Apple Podcasts) How to Figure Out What You Really Want (Spotify / Apple Podcasts)  

This Day in Legal History: Title IXOn June 23, 1972, President Richard Nixon signed the Education Amendments of 1972, a sweeping federal education law that included what became one of the most consequential civil rights provisions in American history: Title IX. Title IX stated that no person in the United States, on the basis of sex, could be excluded from participation in, denied the benefits of, or subjected to discrimination under any education program or activity receiving federal financial assistance. The language was brief, but its legal effect was enormous because it tied sex-equality obligations to the federal funding received by schools, colleges, and universities. That structure gave the federal government a powerful enforcement tool: institutions that accepted federal education money also had to comply with anti-discrimination rules.Although Title IX is often remembered for transforming women's and girls' athletics, the law was never limited to sports. It also affected admissions, scholarships, hiring, classroom access, pregnancy discrimination, and later legal debates over sexual harassment and institutional responsibility. Before Title IX, many educational institutions openly limited opportunities for women, including through quotas, unequal athletic resources, and restricted access to professional programs. The statute helped turn those practices into legal liabilities rather than accepted traditions. In later decades, courts and federal agencies would shape Title IX's meaning through regulations, enforcement actions, and major cases interpreting what counts as sex discrimination in education. Its influence reached far beyond individual lawsuits because schools had to rethink policies, reporting systems, athletic budgets, and equal-access obligations.Title IX also became a model for how civil rights law can operate through spending power, using federal money as the hook for national anti-discrimination standards. Its passage showed that a single sentence in a larger statute could become a foundation for generations of legal, political, and cultural change. On June 23, 1972, the federal government did more than amend education law; it created a durable legal framework for challenging sex discrimination wherever public money supported educational opportunity.A federal judge in California dismissed the Trump administration's lawsuit challenging Los Angeles's limits on cooperation with federal immigration enforcement. The administration had argued that the city's ordinance was unconstitutional because it restricted the use of city resources to support federal immigration operations and limited the collection of citizenship-status information. U.S. District Judge Fernando Olguin rejected that argument, finding that Los Angeles was regulating the conduct of its own employees and agencies rather than trying to control the federal government. The dismissal was not necessarily the end of the case, because the judge allowed the administration to file an amended complaint. Los Angeles City Attorney Hydee Feldstein Soto praised the ruling, saying it confirmed that local governments can decide how to use their own personnel and resources. The lawsuit was filed after immigration-related protests in Los Angeles and after Trump sent troops to the city in response to unrest over deportation operations. The case is part of a broader Trump administration effort to challenge local “sanctuary” policies in Democratic-led jurisdictions. Similar administration lawsuits against Boston and Chicago have also been dismissed by federal judges. The White House did not immediately comment on the ruling. The decision leaves Los Angeles's ordinance intact for now while giving the federal government another chance to revise its legal claims.US court dismisses Trump administration lawsuit over Los Angeles immigration policy | ReutersA federal judge in Washington, D.C., blocked the Trump administration from using a revised immigration database to help states check voter rolls. The database, known as SAVE, is used by the Department of Homeland Security to verify citizenship and immigration status, but the administration had changed it to make bulk searches easier for state and local officials reviewing voter eligibility. U.S. District Judge Sparkle Sooknanan sided with voting-rights and privacy groups that argued the changes made the system less reliable and could wrongly remove eligible voters from registration lists. The challengers said the database can be outdated, especially when naturalized citizens are still incorrectly listed as noncitizens. The judge also found that the revamped system raised serious privacy concerns because it gave users access to sensitive information, including Social Security numbers. DHS criticized the ruling and framed the case as part of its effort to prevent noncitizen voting. The ruling comes as the Trump administration has tried to expand the federal government's role in election administration before the November 2026 midterm elections. Courts have already blocked several related efforts, including parts of executive orders involving proof-of-citizenship requirements and mail-ballot restrictions. The administration has also faced setbacks in lawsuits seeking full voter-roll data from states. For now, the decision limits how the federal government can use immigration records in voter-roll checks.Judge blocks Trump's use of revamped immigration database for voter checks | ReutersIn my Bloomberg column this week, I wrote about OpenAI's request that Treasury update an outdated R&D tax credit rule for computer-related research expenses. My argument is that OpenAI's position should not be dismissed as just another technology company asking for a more generous tax benefit. The problem is that the existing rule was designed for an older world of identifiable physical computers, not modern cloud computing, data centers, GPUs, and reserved compute capacity. Section 41 allows a research credit for certain amounts paid to another person for computer use in qualified research, but Treasury regulations narrow that benefit by requiring that the computer be owned and operated by someone else, located off the taxpayer's premises, and not be a computer for which the taxpayer is the “primary user.” That “primary user” test made more sense when a taxpayer could point to a discrete machine, but it becomes unstable when a company is buying access to capacity inside a provider-owned cloud or data center.I argue that reserved or exclusive use of computing capacity should not automatically be treated as ownership or abuse, because modern AI research may require dedicated capacity for security, speed, and performance reasons. The real question should be whether the taxpayer is buying a third-party service or has effectively acquired, operated, or taken control of the infrastructure. Treasury can still protect against abuse without treating ordinary commercial cloud arrangements as disguised ownership. I suggest that a practical safe harbor could presume service treatment where the provider owns, operates, maintains, and houses the equipment off the taxpayer's premises while bearing the incidents of ownership. That presumption should remain rebuttable where the taxpayer bears ownership-like risks or is simply routing its own equipment through another entity to claim the credit.The broader point is that modernizing the rule would not need to turn the R&D credit into an AI subsidy machine, but it would prevent an old regulatory framework from excluding a major category of modern research. The column closes with the idea that tax rules meant to police fake outsourcing should not end up penalizing real outsourcing just because the computing world no longer looks like it did when the rule was written.OpenAI's Call for Modernized R&D Credit Rule Makes Perfect Sense This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

The Ethereum Foundation cuts 54 employees. Ethlabs introduces its independent R&D lab. Bastian outlines the EF's execution roadmap. Aztec achieves Stage 2 rollup status. And an ENS treasury delegation proposal sparks controversy. Read more: https://ethdaily.io/974 ETH Daily sponsorships are now open. Reach over 10,000 Ethereum-native subscribers every weekday. Learn more at ethdaily.io/ads Disclaimer: Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

This content has been funded by Angelini Pharma. In this episode, Isabel sits down with Rafal Kaminski, Chief Scientific Officer, Angelini Pharma, to explore how new science is reshaping brain health and what that means for patients. In the episode, Rafal reflects on his path into neuroscience and explains how Angelini is building its brain health pipeline, with a focus on epilepsy. He discusses the promise of new treatment approaches, the role of partnerships in driving progress and why better care depends on more than medicine alone. Guest bio Rafal Kaminski is Chief Scientific Officer at Angelini Pharma, where he leads the company's research and development strategy across small molecules, biologics and RNA-based therapies. He joined Angelini Pharma in 2022 after more than 20 years in senior R&D roles at UCB Pharma, Roche and Molecure Therapeutics, leading drug discovery programmes from early research into clinical development. A medical doctor with a PhD in Neuropharmacology, Rafal has authored more than 100 peer-reviewed publications and is an inventor on several patents. Outside of work, he is an avid music listener and vinyl collector and is married with one adult son.

JDK 26 optimise la JVM dans ses moindres recoins, le SDK Java d'Agent2Agent passe en 1.0, Micronaut 5 est là. Côté terrain, un retour d'expérience après 40 jours à coder avec 100 % d'IA : génie ou junior, Alzheimer numérique et dette technique invisible. Pendant ce temps, GitLab restructure, Microsoft suspend ses licences Claude Code, et un développeur injecte un prompt destructeur dans sa lib JUnit. La révolution IA a un coût et les boites commencent à s'en rendre compte. Enregistré le 12 juin 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-341.mp3 ou en vidéo sur YouTube. News Langages Les améliorations de performance dans le JDK 26 https://inside.java/2026/06/09/jdk-26-performance-improvements/ Côté bibliothèques, l'API LazyConstant (anciennement StableValue) fait son entrée en prévisualisation pour permettre une initialisation paresseuse, sécurisée pour les threads et optimisée par le mécanisme de constant-folding de la JVM. L'extraction de chaînes de caractères via MemorySegment::getString a été revue pour réduire considérablement les allocations intermédiaires et les copies en mémoire off-heap, accélérant fortement les traitements sur les chemins critiques (hot paths). La méthode générée automatiquement hashCode() pour les classes de type record a été optimisée par la JVM pour atteindre un niveau de performance équivalent à une implémentation écrite manuellement. Le ramasse-miettes G1 bénéficie du JEP 522 qui redessine sa table de cartes (card-table) afin de réduire les coûts de synchronisation des barrières d'écriture, offrant un gain de débit de 5 % à 15 % sur les applications manipulant énormément de références d'objets. Grâce au JEP 516 (Project Leyden), le cache d'objets Ahead-of-Time (AOT) adopte un format de flux agnostique, ce qui lui permet d'être compatible avec n'importe quel Garbage Collector, y compris le ramasse-miettes à très faible latence ZGC. Le démarrage de la JVM s'accélère par défaut lorsqu'aucune taille de tas n'est configurée, car HotSpot n'applique plus de pourcentage initial (InitialRAMPercentage) mais démarre directement avec la taille minimale (MinHeapSize) pour éviter d'allouer des métadonnées inutiles. Les threads virtuels gagnent en robustesse en étant désormais capables de céder la main (yield) pendant les phases d'initialisation des classes, éliminant ainsi le risque de famine des threads porteurs (carrier threads). Le compilateur C2 JIT améliore son modèle de coût pour la vectorisation des boucles (SIMD) et se montre maintenant capable de compiler et d'optimiser des méthodes dotées de listes de paramètres extrêmement longues. Librairies Release candidate du A2A Java SDK supportant versions 0.3 et 1.0 en même temps https://medium.com/google-cloud/a2a-java-sdk-1-0-0-cr1-released-f0c651ec9139 Dernière étape avant la GA : Toutes les fonctionnalités prévues pour la version 1.0 sont finalisées. Migration simplifiée depuis la Beta1. Compatibilité v0.3 : Ajout d'une couche de compatibilité permettant aux agents v1.0 de communiquer avec les systèmes v0.3 (via JSON-RPC, gRPC ou REST). Support natif pour Android (nouvel AndroidHttpClient). Uniformisation des clients HTTP pour garantir une cohérence entre les versions. Nouveau parseur SSE (Server-Sent Events) conforme aux spécifications. Ça y est, le SDK Java de l'Agent 2 Agent Protocol est sorti en version 1.0 finale ! (avec compatibilité v0.3 et v1.0) https://medium.com/google-cloud/a2a-java-sdk-1-0-0-final-released-10c05b6aee34 Lancement officiel : Sortie de A2A Java SDK 1.0.0.Final, la première version stable (GA) du protocole Agent2Agent. Objectif du protocole : Standard ouvert (Linux Foundation) permettant aux agents IA de communiquer, déléguer des tâches et collaborer, indépendamment du langage ou du framework. Interopérabilité : Introduction de l'Integration Test Kit (ITK) pour valider la compatibilité entre les SDK (Java, Python, TypeScript, etc.). Transports supportés : Support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Alignement total avec la spécification A2A 1.0.0. Passage aux Java records pour l'immutabilité et moins de code répétitif. Architecture interne basée sur un MainEventBus pour garantir la persistance et éviter les conditions de concurrence. Intégration d'OpenTelemetry pour le suivi et la surveillance. Support d'Android et compatibilité descendante avec la version 0.3. Installation : Gestion des dépendances via Maven BOM (org.a2aproject.sdk). Sortie de Micronaut 5.0 https://micronaut.io/2026/05/20/micronaut-framework-5-0-0-released/ Lancement majeur : Disponibilité générale de Micronaut 5, incluant une refonte de plus de 70 modules et la plateforme BOM. Baselines techniques : Support de Java 25, Groovy 5, Kotlin 2.3 et GraalVM 25.0.3. Optimisations internes : Amélioration significative des performances au démarrage et réduction de la surcharge à l'exécution via une refonte du conteneur IoC et du traitement à la compilation. Architecture HTTP : Support stable de HTTP/3, nouvelle API de formulaires (multipart) et annotations de nullabilité (JSpecify) pour une meilleure interopérabilité Kotlin/IDE. Configuration : Nouveau système d'importation de configuration (remplaçant le Bootstrap Configuration) et validateur de schéma JSON intégré. Fiabilité : Nouvelles API programmatiques pour les politiques de retry et circuit breaker. Sécurité & Outils : Mise à jour majeure des dépendances (Jackson 3, Ktor 3), rafraîchissement du Panneau de contrôle et diagnostics AOT améliorés. Écosystème : Mises à jour complètes pour les bases de données (Data, SQL, R2DBC, MongoDB, Redis), le cloud (AWS, Azure, GCP, OCI) et les tests (JUnit 6, Testcontainers 2.0). Évolutions notables : Intégration HTMX dans Micronaut Views, retrait du support RxJava 2 et migration de divers processeurs d'annotations vers des modules dédiés. Comment rajouter un agent IA dans une app Android, avec le tout nouveau framework ADK pour Kotlin https://glaforge.dev/posts/2026/05/21/wiring-adk-kotlin-agents-in-an-android-application/ Guillaume a participé au développement et au lancement du nouveau runtime ADK pour Kotlin et Android https://developers.googleblog.com/adk-kotlin-android-building-ai-agents/ Tutoriel sur comment intégrer un agent ADK dans une app Dépendances : Ajout du noyau ADK (google-adk-kotlin-core) et du processeur KSP dans build.gradle.kts. Sécurité API : Utilisation de local.properties pour stocker la clé API Gemini et l'exposer via BuildConfig afin d'éviter le hardcoding. Définition de l'agent : Création d'un objet LlmAgent configuré avec le modèle Gemini, des instructions spécifiques et des outils (ex: GoogleSearchTool). Utilisation de InMemoryRunner pour gérer automatiquement le contexte et l'historique de la session. Implémentation de runAsync avec StreamingMode.SSE pour un retour en temps réel dans l'interface. Threading : Exécution des requêtes réseau sur Dispatchers.IO et mise à jour de l'état de l'interface utilisateur sur Dispatchers.Main. Comment développer et hoster des agents IA sur la plateforme d'agents managés de DeepMind https://glaforge.dev/posts/2026/05/21/managed-agents-with-the-gemini-interactions-java-sdk/ L'équipe DeepMind de Google a lancé une plateforme d'agents managés sur son API Gemini Interactions https://blog.google/innovation-and-ai/technology/developers-tools/managed-agents-gemini-api/ Guillaume a implémenté un SDK Java pour utiliser cette API Gemini Interactions, qui donne entre autre accès à tous les modèles mais aussi à cette plateforme managée d'agents IA Agents managés : Permet d'exécuter des agents autonomes qui raisonnent, planifient et exécutent du code dans des environnements isolés (sandboxes), sans gestion d'infrastructure par le développeur. Environnement distant : Utilise des espaces de travail Linux éphémères dans le cloud via le paramètre remote, permettant l'accès réseau et la persistance des fichiers sur plusieurs appels. Agents prédéfinis : Accès immédiat à des agents spécialisés comme deep-research-pro (recherche multi-étapes) ou antigravity (tâches de codage généralistes). Agents personnalisés : Possibilité de configurer ses propres agents avec des instructions système dédiées, des outils spécifiques (exécution de code, recherche Google) et des règles réseau (egress) personnalisées. Architecture basée sur les étapes (Steps) : Utilise une structure de données typée (Step, Content) pour suivre le raisonnement de l'agent, ses appels de fonctions et ses résultats en temps réel. Outils et Schémas : Inclut des utilitaires pour générer des schémas JSON complexes via une interface fluide (DSL), par réflexion Java ou par parsing JSON. Streaming réactif : Support natif des événements en temps réel (SSE) pour suivre la progression de l'agent et recevoir les deltas de contenu au fur et à mesure de la génération. Flexibilité : Fournit un gestionnaire de routage (InteractionsHandler) pour créer facilement des serveurs proxy ou des backends intermédiaires traitant les interactions Gemini. Spring Boot 4.1 https://github.com/spring-projects/spring-boot/wiki/Spring-Boot-4.1-Release-Notes Support natif pour Spring gRPC permettant de créer et tester facilement des applications clientes et serveurs basées sur Netty ou des Servlets via HTTP/2 Introduction du lazy fetching pour les connexions JDBC via la propriété spring.datasource.connection-fetch=lazy afin de ne prendre une connexion du pool que lorsqu'un Statement est réellement exécuté Amélioration de l'auto-configuration de Jackson permettant de définir globalement les contraintes de lecture/écriture pour les formats JSON, XML et CBOR via des propriétés de configuration Sécurisation des clients HTTP bloquants et réactifs face aux attaques SSRF grâce à l'introduction d'un InetAddressFilter bloquant les requêtes sortantes vers des adresses spécifiques Améliorations majeures autour d'OpenTelemetry avec le support complet des variables d'environnement OTel, la possibilité de désactiver le SDK via une propriété globale et l'ajout du support SSL sur les exporters OTLP Ajout de l'auto-configuration pour l'utilisation de Spring Batch avec MongoDB incluant un nouveau starter dédié spring-boot-batch-data-mongo Auto-configuration des endpoints @RedisListener sans nécessiter la déclaration manuelle d'un RedisMessageListenerContainer Dépréciation du support de Apache Derby (projet arrêté), suppression définitive du mode layertools du JAR et réintroduction du support de Spock 2.4 (avec Groovy 5) Upgrade des dépendances majeures de l'écosystème avec notamment Spring Framework 7.0.8, Spring Security 7.1.0 et Micrometer 1.17.0 Outillage Vous êtes plutôt endive ou chicorée ? La librairie Chicory qui permet d'exécuter du code WASM à partir de son application Java est forkée et rejointe la Bytecode Alliance pour continuer son développement https://bytecodealliance.org/articles/endive-and-the-next-chapter-of-webassembly-on-the-jvm Annonce d'Endive : Nouveau projet hébergé par la Bytecode Alliance ; fork de Chicory (moteur WebAssembly pur Java, sans dépendance native). ​Objectif principal : Permettre aux développeurs Java d'intégrer, charger et déployer des modules Wasm nativement via les workflows Java habituels. ​Compilateur "Redline" : Intégration à venir de Redline (basé sur Cranelift) pour compiler le Wasm en code machine natif ; performances comparables à Rust/Wasmtime. ​Zéro dépendance (Java 25+) : Grâce à l'API standard Foreign Function & Memory (Project Panama), l'exécution à vitesse native se fait sans composants externes. ​Modèle de Composants (Component Model) : Support futur prévu pour consommer des composants (Rust, Go, JS, etc.) via des interfaces typées et sécurisées directement dans la JVM. ​Prochaines étapes : Fusion de Redline, conformité stricte aux specs Wasm (dont WasmGC) et amélioration du support WASI. Un visualisateur de sessions de travail avec Antigravity https://glaforge.dev/posts/2026/06/11/antigravity-brain-visualizer/ Un projet open source construit avec Micronaut, LangChain4j et GraalVM pour analyser les sessions de travail avec l'outil de développement agentique Antigravity (de Google) Analyse toutes les étapes, les requêtes utilisateur, les outils utilisés, les erreurs rencontrées, les réponses du modèle Gemini fait une analyse pour comprendre les moments clés de cette session de travail Outil buildé avec l'aide d'Antigravity lui-même SBX-Kits : des environnements de développement simplifiés pour les débutants (et les autres) https://k33g.org/20260501-sbx-kits.html Philippe Charrière (:whale: ) présente SBX-Kits (Sandbox Kits), une initiative personnelle visant à simplifier radicalement la mise en place d'environnements de développement pour les débutants, en éliminant la complexité d'installation des outils traditionnels. Chaque "kit" est une archive prête à l'emploi contenant un outil de développement spécifique (comme un langage, un framework ou une base de données) configuré pour s'exécuter de manière isolée et portable. La philosophie du projet repose sur le principe de "zéro configuration" et "zéro dépendance globale", permettant de tester une technologie ou de commencer à coder immédiatement sans polluer son système d'exploitation. L'approche technique s'appuie sur des scripts légers et des binaires portables pré-packagés, offrant une alternative plus simple et moins gourmande en ressources que les conteneurs Docker ou les configurations d'IDE complexes pour l'apprentissage. L'objectif à terme est de proposer un catalogue de kits couvrant les technologies courantes (JavaScript, Python, petites bases de données) pour faciliter les ateliers de programmation et le prototypage rapide. De nombreux kits sont disponibles sur https://github.com/docker/sbx-kits-contrib ghui: une interface utilisateur en ligne de commande (TUI) interactive pour GitHub https://github.com/kitlangton/ghui ghui est un outil en ligne de commande (TUI) écrit en Rust qui fournit une interface visuelle, interactive et rapide directement dans le terminal pour interagir avec GitHub. Il permet de gérer ses pull requests, ses issues et ses notifications sans avoir à ouvrir son navigateur web ou à taper de longues commandes avec la CLI officielle de GitHub. L'outil propose une navigation fluide au clavier, des raccourcis efficaces, et permet de réaliser des actions courantes comme valider une PR, ajouter des commentaires, attribuer des reviewers ou inspecter les logs des GitHub Actions. Conçu pour être extrêmement réactif, ghui s'intègre naturellement dans le flux de travail des développeurs adeptes du terminal et du mode "sans souris". Sortie de Homebrew 6.0.0 https://brew.sh/2026/06/11/homebrew-6.0.0/ Introduction du mécanisme de sécurité Tap Trust : comme les dépôts tiers (taps) peuvent exécuter du code Ruby arbitraire non sandboxé sur la machine, Homebrew demande désormais une confiance explicite de l'utilisateur avant d'évaluer ou d'exécuter leur code. L'API JSON interne devient le choix par défaut, offrant un système plus léger et beaucoup plus rapide pour les développeurs. Sécurisation renforcée de l'environnement avec l'implémentation du sandboxing sur Linux. Évolution des comportements par défaut basés sur un sondage utilisateur : le mode "ask" est activé par défaut pour les développeurs, affichant un résumé des dépendances et une demande de confirmation avant toute action de brew install ou brew upgrade. Améliorations notables des performances globales, notamment un boost de ~30 % sur la vitesse de la commande brew leaves et la parallélisation de la récupération des bottles (binaires) lors des mises à jour. Ajout du support initial pour la prochaine version d'Apple, macOS 27 (Golden Gate). Multiples optimisations pour brew bundle, incluant une gestion plus sécurisée des installations de paquets npm. Méthodologies Retour d'expérience très détaillé et 100% humain sur 40 jours avec une équipe 100% AI hormis le superviseur https://www.linkedin.com/pulse/jai-vir%C3%A9-mon-%C3%A9quipe-de-dev-pour-une-100-ia-pendant-40-luc-bonnin-jlgjf/ Voici le résumé en bullet points : Expérimentation de 40 jours : remplacer une équipe de dev par 100% IA agentique (Cursor) sur un vrai projet en production (playthatsheet.com, 200k lignes de code legacy) Chiffres bruts : 2,3 milliards de tokens consommés, 1 477 prompts, 260 564 lignes ajoutées (+145%), 59% du code final produit par l'IA ROI vertigineux à court terme : 9 mois de travail humain livrés en 40 jours, coût total 260$ d'abonnement + 15 jours de supervision, ROI x18 Profil psy de l'IA : Alzheimer (oublis de contexte), schizophrène (change de méthodo), ado de 12 ans (refait les mêmes erreurs), oscille entre génie et junior sans prévenir Effet iceberg : la dette technique ne disparaît pas, elle se camoufle et s'accélère ; hallucinations = bombes à retardement détectables uniquement par relecture humaine ligne par ligne Paradoxe du bateau de Thésée : perte de paternité et de maîtrise fine du code, baisse de l'autonomie du dev humain qui valide sans avoir construit Arnaque du "monkey money" : consommation de tokens opaque, non corrélée à la complexité (écart de 350% sur des prompts identiques), facturation imprévisible donc impossible à budgéter Syndrome du bazooka : les devs utilisent l'IA même pour changer une couleur CSS, atrophie progressive des compétences et coût écologique délirant Risque stratégique : dépendance irréversible aux vendeurs de tokens (Nvidia, Anthropic, OpenAI), business non rentable qui devra augmenter ses prix Conseil final : approche Pareto, garder 20% du temps en code "fait main", nommer un responsable stratégie IA, l'humain senior reste irremplaçable pour superviser Une libraries de test JUnit cache un prompt qui demande aux coding agents d'effacer les tests https://arstechnica.com/security/2026/05/fed-up-with-vibe-coders-dev-sneaks-data-nuking-prompt-injection-into-their-code/ Agacé par les « vibe coders », un développeur introduit une injection de prompt destructrice dans son code Le développeur de jqwik (un moteur de tests pour JUnit 5) a volontairement inséré une injection de prompt dans la version 1.10.0 de sa bibliothèque Java pour saboter le travail des agents d'IA. L'instruction injectée via la sortie standard (stdout) ordonne textuellement aux LLM d'ignorer les consignes précédentes et de supprimer l'intégralité du code et des tests jqwik du projet. Pour dissimuler cette action aux yeux des développeurs humains, le mainteneur a utilisé des séquences d'échappement ANSI qui effacent la ligne d'injection dans les émulateurs de terminaux interactifs. La modification a été découverte par un utilisateur qui a pointé du doigt les risques majeurs et disproportionnés pour les machines des utilisateurs, bien que certains outils comme Claude d'Anthropic aient détecté et bloqué la consigne malveillante. Face aux critiques de la communauté et aux accusations de comportement infantile ou potentiellement illégal, le développeur a mis à jour ses notes de version pour documenter explicitement son opposition à l'usage de son outil par des IA, avant de refuser tout commentaire supplémentaire sur conseil de son avocat. La réalité du rôle de Principal Engineer https://leaddev.com/career-development/reality-being-principal-engineer Le passage au rôle de Principal Engineer marque une transition majeure où les compétences techniques ne suffisent plus, l'impact se mesurant désormais à travers l'influence, la stratégie et la capacité à aligner la technique avec les objectifs business. Contrairement aux attentes, le quotidien est souvent marqué par une forme d'isolement, car le poste se situe à l'intersection de la direction (qui attend des solutions) et des équipes techniques (qui attendent des directives), sans appartenance directe à un groupe précis. Le rôle exige d'accepter une grande part d'ambiguïté et l'absence de retours immédiats, les projets et les décisions stratégiques mettant parfois des mois ou des années à porter leurs fruits. La gestion du temps devient un défi critique, nécessitant de savoir naviguer entre les sollicitations constantes, la présence en réunion et le besoin de préserver des moments de réflexion approfondie pour concevoir des visions à long terme. La réussite à ce niveau repose sur le développement de compétences humaines pointues (soft skills), notamment la négociation, la communication vulgarisée auprès des profils non techniques, et la capacité à faire grandir les autres ingénieurs par le mentorat. Sécurité Une attaque de la chaîne d'approvisionnement npm utilise binding.gyp pour compromettre des dizaines de paquets https://cybersecuritynews.com/binding-gyp-supply-chain-attack-compromises-dozens-of-npm-packages/ Une nouvelle variante du ver auto-propageable "Shai-Hulud", baptisée "Miasma", cible l'écosystème npm (et PyPI sous le nom de "Hades") en dissimulant son exécution dans le fichier binding.gyp au lieu des scripts classiques preinstall ou postinstall. La technique, surnommée "Phantom Gyp", exploite le fait que npm lance automatiquement node-gyp rebuild dès qu'un fichier binding.gyp est présent à la racine d'un paquet pour compiler des modules natifs C/C++, exécutant ainsi le code malveillant dès la commande npm install. L'attaque contourne la plupart des outils de sécurité traditionnels car l'injection s'appuie sur l'évaluation récursive de commandes (via la syntaxe ) ou directement sur la fonction eval() de Python sous-jacente à GYP, cachée sous n'importe quelle clé du fichier. Le script malveillant télécharge un runtime alternatif (Bun) pour échapper aux détections comportementales de Node.js, puis moissonne les identifiants et secrets des développeurs et des environnements CI/CD (npm, GitHub, AWS, GCP, Azure, Kubernetes, HashiCorp Vault). Plus de 57 paquets npm (dont le SDK serveur de Vapi ou des outils liés à l'IA) et des dizaines de paquets PyPI ont été infectés via des comptes de mainteneurs compromis, le ver republiant automatiquement de nouvelles versions vérolées en utilisant les jetons volés. Loi, société et organisation Restructuration chez Gitlab https://about.gitlab.com/blog/gitlab-act-2/ GitLab entame une restructuration majeure pour s'adapter à l'ère de l'intelligence artificielle agentique, incluant une réduction d'effectifs planifiée de manière transparente et ouverte. L'entreprise prévoit de réduire de 30 % le nombre de pays où elle maintient de petites équipes, d'aplatir sa hiérarchie en supprimant jusqu'à trois niveaux de gestion, et de réorganiser la R&D en une soixantaine d'équipes plus petites et autonomes. Les processus internes vont être revus en intégrant des agents d'IA pour automatiser les revues, les approbations et les passages de relais afin d'accélérer le rythme de travail. La stratégie repose sur la conviction que le logiciel sera bientôt écrit par des machines et dirigé par des humains, ce qui va multiplier la demande de logiciels et transformer le rôle des ingénieurs vers la résolution de problèmes complexes. Sur le plan technique, GitLab reconstruit son infrastructure sous-jacente (notamment Git) pour supporter la charge massive générée par les agents d'IA, tout en misant sur l'orchestration du cycle de vie, la centralisation du contexte des données et une gouvernance intégrée. Le modèle économique évolue vers un système hybride combinant les abonnements classiques et une tarification à la consommation pour le travail effectué par les agents d'IA. Un LLM local sur un mac pourrait coûter plus cher en électricité qu'un modèle hébergé sur OpenRouter dans le cloud https://www.williamangel.net/blog/2026/05/17/offline-llm-energy-use.html Conclusion : L'inférence locale sur Mac M5 Max est 3x plus chère et 2x plus lente que le cloud (OpenRouter). Électricité : Négligeable (~0,02 $/heure pour 50-100W). Matériel (Le vrai coût) : Achat du Mac à 4 299 $; l'amortissement sur 3 à 5 ans plombe la rentabilité horaire. Coût au million de tokens (Gemma 4 31b) : Mac M5 Max : 0,40 à4, 79 (pour 10-40 tokens/s). OpenRouter : 0,38 à0, 50 (pour 60-70 tokens/s). Verdict pro : Le temps humain perdu à cause de la lenteur locale coûte infiniment plus cher que les tokens cloud. Privilégier les API (Anthropic, OpenRouter). Ai didn't kill your junior pipeline https://andrewmurphy.io/blog/ai-didnt-kill-your-junior-pipeline-you-did L'IA n'a pas tué le recrutement des juniors, les entreprises l'ont fait elles-mêmes, par effet de mode. Sans juniors, pas de futurs seniors : on retire l'échelle qui nous a tous fait monter. Tout le monde pêche dans le même bassin de seniors sans le réapprovisionner, pénurie garantie dans 3-5 ans. Une équipe 100% senior + IA est fragile : un départ et tout le savoir tacite s'évapore. Les juniors posent les "pourquoi ?" qui révèlent les bugs et processus absurdes ; l'IA, elle, exécute sans questionner. Les seniors s'atrophient aussi en déléguant leur réflexion à l'IA, pince à double effet sur les compétences. Dépendre des outils IA, c'est sous-traiter sa stratégie talents à des fournisseurs dont les prix vont tripler. Solution : redéfinir le rôle junior (revue de code IA + mentorat), pas le supprimer. Les rapports internes de Microsoft révèlent la crise des coûts de l'IA : les agents coûtent plus cher que les employés humains https://fortune.com/2026/05/22/microsoft-ai-cost-problem-tokens-agents/ Des données et rapports internes chez Microsoft et d'autres géants de la tech ébranlent la promesse de rentabilité de l'IA, révélant que le déploiement d'agents autonomes à l'échelle de l'entreprise revient souvent plus cher que de payer des humains pour le même travail. Le modèle de tarification à l'usage (basé sur les tokens) se heurte à la nature même des architectures agentiques : contrairement à un simple chatbot, un agent boucle, enchaîne les appels d'outils, crée des sous-agents et auto-évalue son code, ce qui multiplie la consommation de tokens par un facteur de 5 à 30, voire jusqu'à 1 000 fois pour des tâches de programmation complexes. L'impact financier sur les budgets de calcul cloud est immédiat ; par exemple, Uber a entièrement épuisé l'intégralité de son budget annuel 2026 dédié au codage par IA en l'espace de seulement quatre mois. Face à cette explosion des coûts, des retours en arrière drastiques sont observés : Microsoft a ainsi commencé à suspendre une grande partie de ses licences internes Claude Code pour rediriger d'urgence ses milliers de développeurs vers sa propre solution moins onéreuse, GitHub Copilot CLI. Les directeurs techniques (CTO) et acheteurs de solutions logicielles qui ont signé des contrats pluriannuels basés sur des projections de réduction de masse salariale se retrouvent pris au piège, les gains réels de productivité ne parvenant pas à compenser les factures d'infrastructure exorbitantes. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 26-27 juin 2026 : LeHACK - Paris (France) 27 juin 2026 : Asynconf - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2 juillet 2026 : MCP Connect Travel Edition - Paris (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 25 septembre 2026 : SAP Inside Track Paris 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 8-9 octobre 2026 : Forum PHP 2026 - Marne-la-Vallée (France) 12 octobre 2026 : Dev With AI - Paris (France) 22-23 octobre 2026 : Agile Tour Bordeaux 2026 - Bordeaux (France) 26 octobre 2026 : Agile Tour Montpellier - Montpellier (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 29-30 octobre 2026 : Agile Tour Nantais 2026 - Nantes (France) 29 octobre 2026-1 novembre 2026 : Pycon FR - Biarritz (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 19 novembre 2026 : Agile Laval 2026 - Laval (France) 19 novembre 2026 : OVHcloud Summit - Paris (France) 19 novembre 2026 : Codeurs en Seine - Rouen (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 2-3 décembre 2026 : Cloud Native AI Summit Europe - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 3 juin 2027 : Cloud Native Days France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

The Space Show Presents Dr. Ryan Dowdy re ISS & Artemis Astronaut Food, May 15, 2026, FridayQuick Summary:The Space Show featured Dr. Ryan Dowdy, a former NASA ISS Food System Manager, discussing food in space and his commercial food company ReadyBar. Dowdy explained how NASA's 20-person team in Houston produces all space food, with 60+ signatures required for each item due to government regulations. He detailed the challenges of providing nutrition for astronauts, including the need for 500-calorie meal replacement bars and the importance of fresh food for psychological well-being. The discussion covered topics like packaging limitations, radiation effects on food, and the potential for growing food in space. Dowdy also shared information about his company ReadyBar, which makes meal-replacement bars designed to replace an entire meal with 30 grams of protein and 15 grams of fiber for $5.99, currently sold online with plans to expand to retailers and Amazon. The conversation explored how commercial space companies are developing their own food systems and the need for continued R&D investment in space food technology.Detailed Summary:Ryan discussed the challenges and innovations in providing food for astronauts, particularly focusing on NASA's ISS and Artemis missions. He explained how meal replacement bars became popular due to cost and packaging efficiency considerations, noting that 60% of food weight on the ISS was packaging that was discarded. Ryan described his work at NASA from 2016, where he helped update the food menu by adding more fish, fruits, and vegetables to address astronauts' nutritional needs for omega-3 fatty acids and antioxidants. The discussion concluded with Ryan sharing how he initially became interested in space food science through a professor's suggestion to directly ask NASA about their challenges rather than proposing solutions without understanding their needs.Ryan also discussed his experience as the ISS Food System Manager at NASA's Space Food Systems Laboratory, explaining how food scientists create meals designed to last up to two years in space. He described the process of working with astronauts to taste and rank approximately 200 menu items, noting that while shrimp cocktail was a popular choice, the Russian crew preferred more soup in their meals, leading to food exchanges between the two teams. When asked about future food options for space missions, Ryan clarified that while no insects were consumed on the ISS during his tenure, there is potential for growing food in space, though current plant growth experiments like the Veggie system didn't provide sufficient daily calories. He concluded that while space food systems aren't ready for long-duration missions like Mars yet, continued research through initiatives like the Deep Space Food Challenge is necessary.Special thanks to our sponsors:American Institute of Aeronautics and Astronautics, Helix Space in Luxembourg, Celestis Memorial Spaceflights, Astrox Corporation, Dr. Haym Benaroya of Rutgers University, The Space Settlement Progress Blog by John Jossy, The Atlantis Project, and Artless EntertainmentWe use Zoom phone numbers for program participation.For real time program participation, email Dr. Space at: drspace@thespaceshow.com for instructions and access.The Space Show is a non-profit 501C3 through its parent, One Giant Leap Foundation, Inc. To donate via Pay Pal, use:To donate with Zelle, use the email address: david@onegiantleapfoundation.org.If you prefer donating with a check, please make the check payable to One Giant Leap Foundation and mail to:One Giant Leap Foundation, 11035 Lavender Hill Drive Ste. 160-306 Las Vegas, NV 89135Upcoming Programs:Sunday, June 21: No Program For Father's Day | Sunday 21 Jun 2026 1200PM PTGuests: Dr. David LivingstonNo program due to Father's Day Get full access to The Space Show-One Giant Leap Foundation at doctorspace.substack.com/subscribe

Giving People a Second Life! We are thrilled to welcome back Rishi Krishna, the visionary Founder and CEO of Symbionic, for his first appearance since November 2021! A lot has changed since our last conversation.  After losing his right arm in a tragic bus accident in 2018, Rishi channeled his personal adversity into founding Symbionic, an assistive tech startup dedicated to building affordable, life-changing prosthetic devices.  In this episode, we dive deep into the massive pivots the business has undergone, what it really takes to build and scale in India, and how Symbionic is quite literally giving people a second life. Rishi also pulls back the curtain on his recent experiences pitching on Shark Tank India Season 4 and Startup Singham, offering invaluable advice for early-stage founders on navigating the noise, securing finance, AI and finally An inspiring story of delivering a prosthetic to a user just before her wedding, allowing her to celebrate with confidence and without self-consciousness.

Turning activewear into technology that actually supports how you move — that's the magic

A version of this essay has been published by Open Magazine at https://openthemagazine.com/world/india-will-collapse-without-digital-sovereignty-and-pax-indica-lessons-from-hormuzBy now it is clear that the Iran War (or West Asia War) has been a disaster to all concerned, including the principals as well as assorted passersby. The massive amounts spent by the US (at last count $25 billion) are at least articulated; the bill for the enormous infrastructural and human suffering inflicted on Gulf states, in the theater of war, must be greater, by definition.The collateral damages suffered by the rest of the world from the cessation of trade through the Straits of Hormuz will presumably run into the trillions of dollars. As one of the worst affected, India, which imports 90% of its hydrocarbons from the Gulf, not to mention other essential items such as urea (for fertilizer), sulfuric acid, helium, etc., is on track to take a massive hit. As an article in The Economic Times said, “India must brace for broad-based economic shock”.Indian exports of up to $50 billion are also affected, especially agricultural products including perishable foodstuffs, but also gems and jewellery, electronics, textiles and garments. Some of this can be diverted via Oman and the UAE's Fujairah port, but much of it passes through the Straits of Hormuz and is potentially blocked and/or stranded at sea.The Hormuz closure is a body blow to India's economy. What can and will India do about it? The Indian State has a habit of rising to the challenge only when there is a crisis, while vegetating otherwise. The 1991 economic crisis is a case in point; the sanctions following “The Buddha is smiling”, and the denial of cryogenic rocket engines and supercomputers are other examples where the nation rallied. So were covid vaccines. Necessity, they say, is the mother of invention.Turning a threat into an opportunityIf I were to be an optimist, I could say that the current crisis is actually an opportunity. In fact, a major opportunity. My reading of the Iran War is that it is President Trump's strategic tit-for-tat against China for denying him rare earths and cutting off soybean purchases. In return Trump decided to deny China access to oil by closing access to Venezuela and Iran. Whether this will work, or whether the G2 condominium (read ‘surrender') will prevail, is unclear.But that is, in a sense, background noise that needs to be managed. India needs to focus on its own issues, of which I see several as critical, and the solution in general is to become Atmanirbhar, self-reliant, and from that, to create an Anti-Fragile nation:* National security/defense* Food security* Energy security* Digital security/narrative control* Trade securityThe first three do not need an explanation: they are obvious. Internal and external security are pre-requisites for any successful society. If India's hard-won food security can be threatened by external threats, then there needs to be some deep introspection. Energy security means diversification, both of hydrocarbon sources, and of types of energy, including renewables, nuclear, biomass, coal-based, and so on.Malign narratives and digital sovereigntyNarrative control is something that the Indian State has failed at so far; it is laughably easy to create hate speech against Indians and India (as has been demonstrated freely by any number of players, starting from the MAGA crowd, to Audrey Truschke to a”Cockroach Janata Party” and some nitwit Norwegian journalist in just the last fortnight) and there are no consequences to the culprits. It's enough to make me pine for Lee Kuan Yew's aggressive legal battles against the media.It's one thing if it were only a problem with foreigners, but with the massive spread of social media, and in particular generativeAI, it is becoming a serious domestic issue. Since India is an avid consumer of social media, and because generativeAI is trained on things like Wikipedia, X, Whatsapp and Google content, biased and motivated material becomes ensconced as The Truth. I have written about narrative warfare and manufacturing consent.This used to be a one-way tsunami of (mis)-information by legacy media, but now there is also the opposite: the wholesale and free vacuuming-up of Indian data (whatever happened to “data is the new oil”?). The “Great Firewall of China” both kept out foreign BIg Tech applications and prevented their plundering Chinese data: is that the way to go?Manufactured narratives are intended for regime change: all the color revolutions today are hatched with massive bot-farms funded by some combination of Deep State, CCP, ISI, Qatar etc. (for example the alleged Gen-Z uprisings that rocked Nepal, drove Sheikh Hasina out of Bangladesh). Thus muzzling malign narratives, and ensuring data security, are imperative.Even Singapore is not immune: it had to block anti-India narratives that likely originated from Chinese sources.A particularly striking example of narrative warfare is the virtual hate speech inducted into Wikipedia by deeply prejudiced anonymous editors. Ashley Rindsberg, who exposed the mighty New York Times' biases in his book The Gray Lady Winked, provides many examples of this.Of note to Indians and Hindus is his recent substack titled “Wikipedia's India War” where he identifies just four editors as having created most of the content condemning the Hindu American Foundation (HAF) in ‘Wikivoice', i.e. the allegedly neutral perspective of Wikipedia. They are, on the contrary, shown to be highly one-sided.As Rindsberg mentions, Wikipedia being central to generativeAI, the damage is baked into the world-view of all AI applications. Truly Orwellian. Says Rindsberg: “four… anonymous accounts can have an enormous impact on what millions of people believe to be the truth.” “Over four years (2021-2025), editors systematically erased HAF's identity as an American civil rights group, transforming its Wikipedia page into a heavily curated dossier of accusations.”Trade, and how the Spice Route was far superior to the Silk RoadFinally, something that is becoming increasingly important: ensuring freedom of trade. This is more than just freedom of navigation, although I find it instructive that Emperor Rajendra Chola sent a huge fleet 1,001 years ago simply to open up the Straits of Malacca. India can make an active attempt to regain primacy in Indian Ocean trade, the whole Pax indica idea.Here is another example of the power of narrative: we have been led to believe that the Silk Road to China was some major highway of commerce between ancient Rome and ancient China, but it was a term coined only in 1877 by the German Ferdinand von Richthofen. There was no highway. A large caravan might take six months, and with 500 camels traversing treacherous deserts and braving bandits, it might carry a maximum of 100 tons. That is puny.In comparison, on the Spice Route, a single stitched ship from Muziris could carry 400 tons of ivory, pepper, silk, tigers and elephants; and the historian Strabo around 1 CE talks about fleets of 250 ships going from Alexandria to India on a six-week monsoon-powered journey. That is 100,000 tons of merchandise. No wonder Pliny the Elder complained that Rome's treasuries were being emptied of gold by India.Simple question: where are hoards of ancient Roman coins found in Asia? Answer: not along the Silk Road. The hoards are in Kerala, Tamil Nadu and Sri Lanka.Today, it is possible for India to aspire to port-led development of trade, especially with the major ports at Trivandrum (Vizhinjam), Maharashtra (Vadhavan), and Great Nicobar (Galathea Bay). The underlying ‘software' of India's millennia-old trade competency was a ‘multi-protocol switch' as I pointed out, and today's India Stack can replicate that. Then there is the need for a blue-water navy: muscle to provide security on the Hormuz to Malacca sea-lanes.So there is a vision. How can India get there? This is where policy matters, as I discussed with policy expert Anuj Gupta. Policy, especially industrial policy, has had a bad reputation in certain circles because it was deemed to violate the virginal purity of classical capitalism. However, in a recent U-turn, even the World Bank admitted that industrial policy may not be all that bad, after all: the success of Japan, the Asian Tigers, and China can't be ignored.That leads to the question of why policy in India has produced mediocre outcomes, what is different now, and where the best use of policy might be.Industrial Policy: What went wrong in the past?There are many problems here. To begin with, the Soviet model, which Nehruvians swore by, was, in hindsight, a dead end. Second, there is the problem of governance: post-Independence bureaucrats have awkwardly borne the legacy of imperial hauteur and the needs of a developing society. Third, until recently, the bare necessities (food, electricity, road access) were not available to many citizens, and GDP growth was not their priority.There is also the culture of jugaad: of clever ways in which you overcome constraints through frugal improvisation and seat-of-the-pants making-do. This is fine for one-off things (e.g. converting a tractor trailer into a makeshift transport vehicle because your truck broke down), but it does not make for efficient and replicable industrial products. As The Economic Times said recently, it is time to junk jugaad. Quality has to become ingrained in people's minds.The issue of governance is significant: the bureaucracy and the judiciary have both under-performed, politicians, as everywhere, have been venal. It is said that China's growth can be attributed to the fact that its babus are engineers, and therefore with engineering ruthlessness move in straight lines. The US' babus are lawyers, and India's are humanities graduates. Well, engineers are not very good at second-order effects (eg. China's lurch from one-child policy to demographic collapse), but a little bit of ruthlessness is probably good.What is going reasonably well?There are a few modest success stories: for example, in electronics manufacturing or assembly. The PLIs (and DLIs) have produced the desired effort, with clusters of excellence where global suppliers have also set up shop (as they did earlier for the automobile industry in, say, Sriperumpudur). The fact that a lot of iPhones in the US are now imported from India is laudable, even though it may be derided as “screwdriver jobs”. That's where one starts the move up the value chain.The current semiconductor policy is a big hope, especially after the landmark agreement by the Dutch firm ASML with Tata Electronics in Dholera, Gujarat. Given that ASML has a near-monopoly position in Deep Ultraviolet Lithography (DUV) this is a major boost to India's chip ambitions. My recent conversation with AMD CTO Suraj Rengarajan went into India's chances to realize its ambitions.A recent announcement from Trivandrum-based fabless startup NetraSemi (a recipient of DLI) of the commercial availability of its edge AI chips is a landmark.Next is the newly announced plan for energy security revolving around both coal gasification and intensive offshore exploration. These fall squarely into the Atmanirbhar category: India simply cannot afford to have its energy held hostage by distant nations. It also needs distinctly Indian innovation.The Samudra Manthan initiative is also showing some promise. At least one out of three deep-water wells in the Andaman Sea (SriVijaya Puram-3) are reported to be showing the availability of natural gas, although it will take 5-10 years for this to be commercially available.What should the future look like for India's Industrial Policies?This of course is the hard question. Here is my personal perspective, and I accept that reasonable people may disagree. I think three areas need to be focused on, and will pay large dividends.* Drones and swarming software* Social media and AI stack* Maritime Trade and Blue-Water NavyI admit that these are not the only worthwhile industrial policies. Another is for copper, which would reverse the catastrophic effects of the closure of the Sterlite plant in Thoothukkudi, as the metal is an increasingly important component in electronics, data centers, etc., and far from being self-sufficient earlier, India now imports 50% of its needs. Another area of interest in quantum computing.There are also failures from which the right lessons need to be learned. The policy for EV batteries has apparently failed: according to Swarajya magazine, India has not been able to escape from near-total dependence on imported Chinese batteries.Drone swarmsI wrote recently that drones may well herald a step-change in warfare. For the moment, though, they are searching for their niche in offensive/defensive warfare. Drone hardware is already a well-trodden path with Chinese and other nations dominating it, although with IdeaForge, Paras, Garuda, IoTechworld Avigation etc., India is also making progress there. And India is indeed buying the hardware, $2 billion-worth, according to the Economic Times.But I believe the real game is in drone swarms. AI-based control software (similar to HiveMind) that would allow an entire swarm to act autonomously, just like a murmuration of starlings, would be the gold standard to aim for. Such a self-managing swarm would be virtually impossible to defend against, and I think India should put in place a PLI to support it, leveraging software capability in the country.Of course, drones are not just for military purposes, but also for commercial uses including things like logistics and agricultural use, such as precision delivery of fertilizer and pesticide to crops (as Garuda demonstrates). An Indian initiative that supports both drone hardware, and especially drone software, would be a potential winner.Digital Sovereignty: Social media and AI stackThere is a raging battle over which part of the AI stack India needs to invest in. As an old Unix hand, I believe the foundational model is not where the differentiation is. In analogy with Linux (the open-source Unix variant that was popularized by Linus Torvalds and an army of volunteers), there is little value in re-writing the operating system, but one can differentiate by building on top of it, or by judiciously choosing certain modules of it.Besides, the cost of building an entirely new foundational model would be astronomical and would consume the entire budget of IndiaAI Mission.Thus, my personal opinion is that the foundational model (especially when, it is believed, there are more or less open-source models available for free, e.g. Llama, DeepSeek) is not where India should expend its precious R&D resources, but on the layers of the stack above it. It is the data that matters, as Larry Ellison apparently suggests too.But there is the interesting counter-example of Sarvam AI which is producing its own sovereign model: multi-lingual and presumably otherwise tuned to Indian needs. The question is whether this can survive when hundreds of billions worth of capital investment are going to the US Big Tech companies and their Chinese rivals. The sad history of Koo, a Twitter rival, comes to mind. So does Arattai, a Whatsapp rival, whose popularity has waned. .A well-thought-through industrial policy on generativeAI is therefore essential. The status quo ante is unsustainable; given the fact that Sarvam has also found it difficult to raise funds in the US, it is worth pondering whether a China-style massive subsidy is the answer. And where should it go, into foundational models or into the layers of the stack above it? The answer is “both”, but with priority to the latter.Here is where I would prioritize investments, in order:* Vertical applications in specific domains: e.g. defense, healthcare, agriculture, governance (particularly in the judiciary and in ease of doing business in the bureaucracy)* Fine-tuning and customization: for the needs of the Indian context, e.g. multi-linguality under Bhashini* Compute infrastructure: GPUs, sovereign and protected indian datasets* Sovereign Small-Language Models such as Sarvam AIAs mentioned above, at the moment India's data is being sucked up for free by US Big Tech. In addition, there is the real danger that Indic Knowledge Systems will be mined and digested, as has happened to yoga, pranayama, etc., which have been given Western analogs and nomenclature, as in Pilates, ‘coherent breathing' etc.These two problems are connected, and both need to be tackled in parallel. Social media is being weaponized against India, and this is magnified by the legacy media in a positive feedback loop. Three examples: one was the rage against Adani based on the dubious research of Hindenburg, which then went under; the second is Bloomberg's reckless accusation about gold reserves being sold by the RBI, which they were forced to retract, but social media and Wikipedia will remember it; the third is the meteoric (media) rise of the Cockroach Janata Party.Trade using major ports, Digital Public Infrastructure and a blue water navyUsing trade for competitive advantage is an age-old tactic. The trade tiffs between the US and China are examples of this: we are witnessing war by other means. Many nations are getting into this act, and India does have some advantages, partly based on geography. Maritime trade is likely to continue to be the key, which makes naval chokepoints the big story, but not the only story to watch out for.The major aspects of maritime trade include infrastructure, the digital “multi-protocol switch”, and security. On the one hand, India is developing not only major container ports, and the road/rail links to get to them, and the industrial goods to ship out through them, but also a serious shipbuilding industry, which was one of India's historical strengths. Then it used to be stitched wooden ships (teak beams lashed together with coconut rope). Now it's modern steel ships.There are the big, efficient new ports, which can now turn ships around with Singapore-like efficiency; the proposed third aircraft carrier group which will make it possible to patrol the Arabian Sea and the Bay of Bengal at the time; the Air-Independent Propulsion diesel submarines and nuclear submarines that can monitor (and if necessary, deny) narrow straits; the sale of supersonic Brahmos cruise missiles to the Philippines, Vietnam and Indonesia (and Cyprus) that create ship-denial zones: all this is muscle.And the final piece, the ‘software' for trade, the “multi-protocol switch”. This last is complicated. Its value is underestimated by many. But this is what enables friction-less transactions between various unrelated parties. The India Stack and the Digital Public Infrastructure can be utilized to provide such a facility. But it is complex enough to need significant study as to what is possible, and how to roll it out.Second-order effectsIn closing, it is worth considering some of what the (unintended) consequences of these proposals may be. Let us note that the G2 has no interest in allowing India to grow and make it a G3. They will do everything in their power to kneecap India, by all means possible.There is also a certain derision for India in some circles. Here is a generic western opinion on why China got rich, and India didn't. Well, the author doesn't consider the second-order effects of the wholesale destruction of Chinese civilization: that is a tradeoff Indians may not prefer for themselves. We all know how China's well-intentioned One Child Policy turned into demographic collapse within a few years. Besides, as The Economist asks, “China is innovative. Its economy is a mess. Which will win out?”This is why I think planning for these second-order effects is important. We tend to ignore them because they seem counterintuitive or unlikely, but Nassim Taleb has sensitized us to how low-probability Black Swan events can have grave consequences.As an example, attempting digital sovereignty may have unwelcome side-effects: Big Tech have the first-mover advantage and network effects and there are increasing returns to scale. They will surely make it hard for a new player to break in. Besides, the large investments in data centers and GCCs that they are making in India would make it very difficult for them to be ejected with a “Great Indian Firewall”.Even taxing their capture of Indian data will be complicated; not to mention that they have demonstrated that they can happily violate copyright laws with no consequence; therefore they will find ways to chew up and spit out Indian Knowledge Systems, and essentially re-colonize India. Digital colonialism is not a threat, it is a reality today, and it is a consequence of the relatively open Indian system.In addition, there is a malign group, the “barbarians within” as Arnold Toynbee once put it, who are ready to sacrifice Indian sovereignty for a pittance.Given all this, it will be very difficult to put in place serious measures to gain digital independence; and the narrative-peddling is likely to gain further momentum: just consider the caste allegations that have haunted BAPS in the US (despite the cases being dismissed by the US DoJ), the Cisco Systems case where, again, the case was dismissed, but the narrative continues, and the persistent efforts in various US states to turn caste into a weapon to bludgeon Indians.Another sensitive issue is that of the multi-protocol switch for trade. While from an Indian point of view, it eases trade and harks back to a Golden Age of Indic maritime commerce, but that will be viewed elsewhere very differently, for instance by the US as an attempt to de-dollarize. The US has jealousy guarded – with very good reasons that we will not go into here – the dollar's reserve currency status.We have also seen what happened to those who attempt to hurt the dollar's primacy: in 1985, the Plaza Accord devalued the dollar, and that was a body blow to Japan's economy, which has not recovered its mojo to this day. Later, Iraq's Saddam Hussein and Libya's Muammar Gaddafi both had ideas about replacing the petro-dollar with, respectively, the Euro and a new pan-African gold-backed currency. We know what happened to them.If the India Stack multi-protocol switch is perceived as an alternative to the US dollar, there may be grave consequences. Therefore, it should be conceived and deployed only as an adjunct to it and to the almighty SWIFT settlement system.ConclusionIndia is at a crossroads now. Even though the Hormuz closure is a serious problem, if it plays its cards right, adversity can be turned into opportunity across a variety of perspectives. The key is Atmanirbhar, self-reliance. If India can now implement a crash program of industrial policy, and at the same time overcome an ingrained Third-World tendency to cut corners, it can finally break free of the years of underperformance, what I called the Nehruvian Penalty in 2004.It is possible, but there are caveats: unforeseen consequences. Hic sunt dracones. Here be dragons. Be afraid. Be very afraid.3700 words, 7 June 2026This is episode 192 of the Shadow Warrior podcast. Here is a companion AI-generated slideshow. (Note that the borders of India are not necessarily depicted correctly here, because it is generated by an AI, notebookLM.google.com) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit rajeevsrinivasan.substack.com/subscribe

In this episode, Howard Farran is joined by Ellen Bösl, Vice President of Research and Development at Solventum, where she leads the strategy and execution of the Global Dental Solutions R&D organization. A chemical engineer by training with a 25-year career spanning Solventum and 3M — including R&D leadership across MedSurg, Transportation, and Electronics — Ellen brings a systems-level lens to one of the profession's most stubborn problems: oral health inequity. The conversation confronts why oral health remains one of the most unequal corners of healthcare despite unprecedented advances in technology, materials, and knowledge. Ellen and Howard dig into the root causes that rarely get discussed, whether inequity is best understood as a clinical, business, or policy challenge, and which innovations genuinely broaden access versus those that merely sound impressive. They also explore the gaps between urban, rural, private, public, and corporate dentistry, what frontline providers actually need, and what realistic progress could look like decades from now.   Episode #1708 : Dentistry Uncensored with Howard Farran, Howard welcomes Ellen Bösl — Vice President of R&D at Solventum — to tackle a question the industry doesn't ask often enough: with more technology and knowledge than ever, why is oral health still so deeply unequal? Drawing on 25 years of innovation leadership, Ellen separates the technologies that truly expand access from the ones that just make good headlines.

In this episode of Tank Talks, Matt Cohen sits down with Michael Arbus, CEO of moomoo Canada, for a wide-ranging conversation on Wall Street trading floors, Canadian fintech, crypto regulation, AI-powered investing, and what it really takes to build and scale financial technology companies. Michael's career has taken him from the chaos of Bay Street and Wall Street desks at firms like RBC Capital Markets, TD, UBS, and Merrill Lynch to operating industrial businesses, helping scale Bitbuy into a regulated Canadian crypto marketplace, and now leading moomoo Canada as it pushes into self-directed retail trading, AI tools, options education, and investor communities.Michael shares the lessons he learned from covering major hedge funds, why one trade during the financial crisis made him rethink his role as an advisor, how spreadsheet-driven decision-making helped him turn around operating businesses, and why retail investors in Canada are ready for better tools than the legacy banking platforms have historically offered. They also dive into moomoo's AI-native product strategy, agentic investing, algorithmic trading in natural language, Canada's loyalty to big banks, the rise of retail options trading, and the founder advice Michael wishes more entrepreneurs would hear before wasting years on a business that cannot support them.Whether you're a fintech founder, startup operator, retail investor, trader, or Canadian tech ecosystem builder, this episode is packed with sharp, practical insights on the future of investing, AI, and financial platforms.From Trading Floor Chaos to Wall Street Scar Tissue (03:30)Michael describes life on massive institutional trading floors as a “casino with pumped oxygen and insanity.” The early mornings, morning meetings, nonstop client calls, and constant pressure to be relevant. Why the trading desk taught him how to process information quickly and turn noise into action.Investing in Businesses vs. Investing in Stocks (12:12)Matt and Michael break down the difference between knowing a ticker and understanding the actual company underneath it. Why some good businesses can be bad stocks, and some bad businesses can still become great trades.Scaling Bitbuy and Learning the Reality of Regulation (22:33)Michael shares how he joined Bitbuy when the business was growing but still needed operational structure. The shift from institutional finance to B2C financial services. Why serving retail customers creates a much deeper sense of accountability, especially when people are trusting a platform with their hard-earned money.What moomoo Actually Is and Why Canada Matters (27:41)Michael explains moomoo's global footprint across markets like Hong Kong, Singapore, Australia, the U.S., and Canada. The scale of Futu Holdings, the role of product and R&D, and why joining a 4,000-person global fintech machine was very different from building Bitbuy from a smaller startup team.Canada's Loyalty Problem With Big Banks (29:44)Why Canadian retail investors are deeply loyal to legacy banking brands. Michael explains how trust, safety, and brand recognition are baked into Canadian financial behavior, and why moomoo believes a regulated challenger brand can win by offering a product Canadian investors have not seen before.AI Trading Inside the moomoo App (35:55)How moomoo became one of Canada's first AI brokerage platforms. Why its AI tools are different from generic public LLMs because they are connected to paid, live market data behind the platform's firewall. How this changes the quality of answers retail investors can access.Can moomoo Challenge Canada's Banking Giants? (41:24)Michael explains who moomoo is built for and who it is not built for. Why the next generation of investors may care more about control, education, and outperformance than branch loyalty. How AI, job market uncertainty, and personal financial pressure could make self-directed investing more important.Canada's Top Trader and the Nasdaq Partnership (43:20)Michael shares moomoo Canada's trading competition, including real prize money, a $100,000 top prize, and partnership with Nasdaq. Why the initiative is designed to bring retail investors into the market in a more systematic, educated way.Can Your Startup Actually Pay for Your Life? (47:42)Michael explains why founders need to calculate their personal take-home pay before committing years to a venture. The danger of getting caught in a self-fulfilling story that feels exciting but cannot support the builder behind it.The Leadership Lesson That Still Matters Most (51:32)Michael closes with a simple but powerful lesson: kindness goes a long way. After scaling multiple teams from small groups to dozens or more than 100 people, he explains why intelligence is common, but kindness is what compounds in leadership, hiring, and company building.About Michael ArbusMichael Arbus is the CEO of moomoo Canada, a global self-directed investing and trading platform under Futu Holdings focused on retail investors, trading tools, market data, options education, and AI-powered investing experiences. Before joining moomoo, Michael built a diverse career across institutional finance, entrepreneurship, industrial operations, crypto, and fintech. He spent years on Bay Street and Wall Street trading desks at firms including RBC Capital Markets, TD, UBS, and Merrill Lynch, working with global hedge funds and covering M&A event-driven situations, mining, and energy stocks. He later moved into operating businesses, including oil, scrap metal, industrial recycling, and crypto mining, before helping scale Bitbuy into one of Canada's leading regulated crypto marketplaces. Today, Michael is focused on expanding moomoo Canada, bringing institutional-grade tools to retail traders, and helping Canadian investors understand the future of AI, options trading, and self-directed financial platforms.Connect with Michael Arbus on LinkedIn: https://www.linkedin.com/in/michael-arbus-cfa-mba-299a49/Visit the moomoo Canada website: https://www.moomoo.com/caConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

This week we talk about LDL, HDL, and cardiovascular issues.We also discuss one-time therapies, statins, and pharmaceutical economics.Recommended Book: Blood by Dr. Jen GunterTranscriptCholesterol is the most common type of what's called a sterol, which is a type of steroid, but also structurally technically an alcohol. But functionally, and classified by scientists, cholesterol is a lipid, which in this case is similar to a fat in all but how the body uses it. Cholesterol is the type of sterol most commonly found in animals—other types are found in plants and fungi—and its function, and this is where it varies from fats, which are used to store energy, is to basically help hold the cell membrane together, and it also serves as an intracellular messenger.Cholesterol is especially prevalent in the brain and spinal cord of animals, but it's found throughout their bodily tissues, as well, and again, it's vital for holding everything together and helping things communicate, in addition to being a precursor for vitamin D, steroid hormones, and bile.You want to have cholesterol, then, as without it you would be dead.Too much cholesterol in the blood, however, can also make you dead, especially when it's bound to what's called low-density lipoprotein, or LDL, as that contributes to cardiovascular disease like heart attacks and aneurysms, which can massively impact one's overall wellness and quality of life, and at extremes lead to the whole system shutting down as a consequence of heart attack, stroke, and the like.A lot of things can contribute to the development of cardiovascular disease, including habits like smoking, genetic predisposition, and the enthusiastic consumption of alcohol and unhealthy foods. But high blood cholesterol, of the LDL variety, is one of the top contributors, as these low-density clusters of lipoprotein can clog the pathways that blood takes throughout our bodies. Other, denser types of lipoproteins, HDLs, can clear it, like a heavier, denser substance pushing through clogs of less-dense materials that are gumming up a pipe, but LDL is at times accumulated as a result of consuming delicious but unhealthy foods, which are hard to avoid, and for some people the only consistently available and affordable foods; and for other people LDL accumulates as a result of their genetic predispositions—two things that are devilishly difficult to change.What I'd like to talk about today is a new type of therapy that may be very good news for people who struggle with the accumulation of LDL, and why this is being seen as very good news more broadly, at the scale of entire nations, as well.—Pharmaceutical company Eli Lilly is testing a new, experimental drug called VERVE-102 which is a one-time infusion that is currently administered over the course of about four hours, and once completed, it turns off a gene called PCSK9, which is responsible for making a protein that regulates cholesterol levels in humans.As I said, this drug is still being tested, so these are early results. But in a study of 35 people with high cholesterol levels, high levels of LDL or LDL-C, which is short for lipoprotein cholesterol, they found that this infusion, which again, is a one-time treatment, so get it once and then theoretically at least you never have to get anything done ever again, it reduced those LDL and LDL-C levels by as much as 62%, and that reduction was maintained a year and a half after the infusion; that's how far out they're retested so far, and the hope is that each retest will continue to show the same.On the strength of those very promising results, a Phase 2 study has been planned by the end of 2026, and the US Food and Drug Administration, the FDA, previously fast-tracked this existing study, because of the promise and potential this drug already demonstrated in early studies; all of which is considered to be very significant progress and possibility.To understand that significance, though, it's useful to know some health stats. And I'm going to focus on the US here, as that's where this drug is being developed, but many wealthy countries have similar stats, at least in terms of cardiovascular disease struggles.As of 2024, which is the last year we had good, cohesive data on this in the US, it was estimated that about 11-12% of the US adult population has high cholesterol levels. This typically doesn't come with any symptoms, but it can contribute a higher risk for all those cardiovascular diseases, including heart attack and stroke. A further 86 million US adults have borderline or elevated cholesterol levels, which can easily tip higher, but also, even in that existing, elevated state, contribute to negative cardiovascular outcomes.There are treatments for high cholesterol, the most common of category of which are called statins, which reduce the production of LDL by inhibiting an enzyme that produces cholesterol in the body.Unfortunately, these drugs do come with some usually minor side effects, which can cause patients to stop using them, and they have to be taken daily, ideally at the same time each day. That necessity for consistency leads to a lot of incorrect or incomplete usage, which reduces the effectiveness of these drugs. But it's also estimated that only about 54.5% of US adults who would benefit from statins are currently taking one—so that's people who could benefit and who have it prescribed, and then within that number are all the people who are taking this drug incorrectly or incompletely, reducing the effectiveness. So a relatively small number of people who should probably be on these things are getting the full benefit they offer because of the nature of the drug.And that's not great, because in the US alone, heart disease is the leading cause of death for pretty much every adult demographic; men, women, people of most racial and ethnic and economic groups, you name it, heart disease is the biggest threat to their lives.One US citizen dies every 34 seconds of some kind of cardiovascular condition, and as of 2023, 1 in every 3 deaths in the US was caused by the same, adding up to just over 919,000 people that year.Between 2021 and 2022, alone, the cost of services and medications related to heart disease added up to more than $168 billion; again, that's just in that period, and just in the US.And once more, these are ailments that are caused or heavily influenced by high levels of cholesterol, which are themselves amplified by common lifestyle choices, environmental factors that are hard for many people to avoid, and just by raw, dumb luck because of genetics.This treatment category, then, is being seen as a pretty big deal because a one-time infusion means those who receive it don't have to remember to take a pill every day at the same time, and won't experience those statin-based side-effects.It also means that people who are currently costing the medical system a bunch of money each year, because they need treatments for all the issues they suffer as a result of high cholesterol, will suddenly cost the system a lot less money, for treatments and medications. Not for nothing, their health and quality of life will likely improve as well. So in addition to having better, healthier outcomes personally, their cost to healthcare systems will drop.Eli Lilly's drug isn't the only one currently working its way through clinical trials, either.Amgen is working on a similar treatment, and Novartis and Ionis Pharmaceuticals have drugs that are even further along in the process, their medicines that cut heart attacks, strokes, and cardiovascular deaths could be approved by the FDA as soon as next year.There are a lot of caveats worth noting here, including that the science is still out as to whether this approach, silencing proteins that lead to the creation of more LDL and a similar substance called Lp(a)—which is more dangerous because it's stickier and thus more likely to get stuck in important blood pathways, and it's also more likely to be caused by genetics than lifestyle—the word is still out on whether reducing these things in the body actually reduces hearth attacks and stroke.Some people have had this particular risk variable dramatically reduced, but have still suffered from cardiovascular events, which raises the question of whether this path is the right one to take in trying to reduce this category of health issues; the correlation between LDL and heart attacks and strokes might not be a clear-cut as long assumed.There's also the issue of price. Drug-makers are economically incentivized to sell treatments over cures, because that means they can continue selling their product over time, potentially for the life of the patient, and a cure, in contrast, is a one-time hit that in theory should alleviate the need for future treatment.There's a chance, then, that the drug-makers will decide they need to make these one-hit treatments really, really expensive in order to make their R&D dollars back and to make the kinds of profits their investors expect from them. That could then reduce the potential audience for these treatments, even if they are effective, and could further slow their deployment and future research in this space.If these trials continue to go well, though, there's a good chance that this combination of similar but distinct treatment types will provide a more sustainable alternative to current options, and that, like the recent bogglingly rapid and widespread deployment of GLP-1 treatments for all sorts of issues, could lead to a new paradigm in this facet of the medical world.Show Noteshttps://en.wikipedia.org/wiki/Cholesterolhttps://en.wikipedia.org/wiki/Cardiovascular_diseasehttps://en.wikipedia.org/wiki/High_cholesterolhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10982736/https://www.cdc.gov/heart-disease/data-research/facts-stats/index.htmlhttps://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1https://www.ama-assn.org/public-health/chronic-diseases/what-doctors-want-patients-know-about-high-cholesterolhttps://en.wikipedia.org/wiki/Statinhttps://pubmed.ncbi.nlm.nih.gov/42187087/https://abcnews.com/GMA/Wellness/new-drug-game-changer-people-high-cholesterol/story This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

Fresh roti is simple to eat and hard to automate. Komal Kashiramka is building Poshan, a robotics company developing next-generation kitchen appliances, starting with an automatic roti maker designed to create soft roti that lasts for hours. Komal shares how she moved from Microsoft and Meta into entrepreneurship. Why hardware requires a different level of patience than software and what founders can learn from customer discovery, AI, parenting, and building something people will trust in their kitchens.  We also discuss:  • Why soft roti is the real product challenge  • Building a hardware company in the R&D phase • What AI changes for junior engineers • Why fundamentals still matter in software • Leaving corporate life without having everything figured out • Startup 425 and Seattle Public Library business resources • Raising kids while building a company • RotiTales and making roti easier to understand  Komal  links Poshan Website: https://poshan.us/ Poshan LinkedIn: https://linkedin.com/company/poshan-inc RotiTales Website: https://rotitales.com/ RotiTales Instagram: https://instagram.com/rotitales RotiTales LinkedIn: https://www.linkedin.com/showcase/rotitales/ RotiTales Facebook: https://www.facebook.com/rotitales RotiTales YouTube: https://www.youtube.com/@rotitales Jason links Jason's Linktree: https://linktr.ee/admin Jason's LinkedIn: https://www.linkedin.com/in/jasoncavness/ YouTube Channel: https://www.youtube.com/channel/UCGsw6kzZE40sSUZgoStVaJw?sub_confirmation=1  

What does great leadership actually look like? Can you make a difference even if you're in the middle of the hierarchy? "If you think you're too small, you've not spent the night under a bedsheet with a mosquito." In this episode, educator and Deming practitioner Balaji Reddie explains why W. Edwards Deming was far more practical about leadership than many people realize. Drawing on both The New Economics and Out of the Crisis, Balaji shares stories and examples that bring Deming's 17 principles of leadership to life. From creating trust and joy in work to understanding variation, coaching people, and improving systems, this conversation challenges conventional management thinking and offers a clear path toward transformation. TRANSCRIPT 0:00:02.2 Andrew Stotz: My name is Andrew Stotz and I'll be your host as we continue our journey into the teachings of Dr. W. Edwards Deming. Today I'm continuing my discussion with Balaji Reddie, who is an educator and trainer in the teachings of Dr. Deming and quality management generally. And the topic for today is Principles of Leadership. Balaji, take it away.   0:00:27.9 Balaji Reddie: Good morning. Thank you so much, Andrew. We had left our last session with that, we'd be dealing with this. And of course, Dr. Deming gave us the outline of Profound Knowledge and he gave us 14 points. He also gave us the deadly diseases and the 16 Obstacles. So people often talk about the diseases, but very often they forget the obstacles. And there are 16 of them which he highlighted for us. And if you think that they're outdated, they're as relevant as they ever were. So you need to keep revisiting those. I think if you start working on removing the obstacles, it's like you're taking your foot off the brake rather than pressing on the accelerator.   0:01:11.3 Balaji Reddie: So you're removing the things that actually stop you before you actually take things forward. But nevertheless, we start with point number 14 where he says, take action to complete, to make the transformation. And he says that there should be a critical mass of people that you need to educate and train and get them on the same page as you are. I'm gonna quote Hazel Cannon here, who is current president of the British Deming Forum. And she talks about the time when she was very young and she attended the Deming four-day seminar, I think in Birmingham. And at the end of those four days, she was overwhelmed as you normally are when you hear how the man speak. And he spoke... He wanted you to make drastic changes. It's not just tinkering here and there.   0:02:08.2 Balaji Reddie: And so she went up to him and she said, "I'm really taken up by what you just said." And then she made a statement, "I'm too small to make these changes in my organization." I believe she worked as a lab assistant in a chemical manufacturing company. They used to make chemicals for cosmetics. So she said, "I'm too small." And Deming just interrupted her and said, "Never think you're too small. If you think you're too small, you've not spent the night under a bedsheet with a mosquito." So make a change where you are and take it from there. So I would like to now quote Dr. Deming from Out of the Crisis. This is Plan for Action: Take action to accomplish the transformation. So he writes there, there are three points and then I'll come to what he writes below that.   0:03:01.8 Balaji Reddie: So he says, "Management in authority will struggle over every one of the above 13 points, the deadly diseases, and the obstacles. They will agree on their meaning and on the direction to take. They will agree to carry out the new philosophy. Management in authority will take pride in their adoption of the new philosophy and in their new responsibilities. They will have courage to break with tradition, even to the point of exile among their peers." So he talks about courage. He talks about courage of conviction. And then he says, "Management in authority will explain by seminars and other means." So I think he leaves it to people of the ways and means. And now today there are a lot of means of doing that. DemingNEXT is one of them. And he says, "To the critical mass of people in the company why change is necessary and that the change will involve everybody."   0:04:00.9 Balaji Reddie: Now he writes something very interesting. He says, "This whole movement may be instituted and carried out by middle management speaking with one voice." So he gave instructions. Why are people saying that he did not tell us what to do? It is just that he expected maybe a lot. And now let's get to that middle management and what he expected. He says here... Let's see here. I'm coming to chapter four now in The New Economics where he says, "A System of Profound Knowledge. The aim of this chapter: the prevailing style of management must undergo transformation." So we just heard that, that what we need to do. And he says, "A system cannot understand itself. The transformation requires a view from the outside. The aim of this chapter is to provide an outside view, a lens that I call a System of Profound Knowledge.   0:04:59.7 Balaji Reddie: It provides a map of theory by which to understand the organizations that we work in." Then he says, "The first step is transformation of the individual. This transformation is discontinuous. It comes from understanding the System of Profound Knowledge." Then he says that "the individual, once transformed, will set an example." So setting an example, I believe, is doing the right thing under adverse circumstances, when you stick to your principles despite the fact that there is an easier way out. As they say, choosing a path between good and bad is easy, you choose good. But good and better, you need to make the right choice. And that needs profound knowledge. "So be a good listener," he says, "but will not compromise. Continually teach other people and help people pull away from their current practice and beliefs and move to the new philosophy without a feeling of guilt about the past."   0:06:02.7 Balaji Reddie: So he explains to us what was needed here, right? And he says this is what we actually need to do. Now I'd like to, I mean, I'll be referring to a document. I don't know how we're gonna get this to people, but for the Principles of Leadership. All right, I think I'll have to send this over to you later, but we will do that. So in the Principles of Leadership, just come to them. I am quoting again from both Out of the Crisis and The New Economics. So you will find this there when he speaks about what needs to be done. Modern Principles of Leadership. And he says, "The modern principles of leadership will replace the annual performance review. The first step in a company will be to provide education in leadership." So that would be introducing people to profound knowledge from what we just heard. Then he said, "The annual performance review may then be abolished." Of course, that will take time. "Leadership will take its place, and this is what Western management should have been doing all along."   0:07:12.6 Balaji Reddie: So he says, "The annual performance review sneaked in and became popular because it does not require anyone to face the problems of people. It is easier to rate them, focus on the outcome. What Western industry needs is methods that will improve the outcome." And he says, "Suggestions follow." So first, institute... The first principle. "Institute education in leadership: the obligations, the principles, and methods." And so I think introduction to the System of Profound Knowledge will help. And then after profound knowledge has been sort of brought to the notice of... Of bringing to the notice of the people then you get into perhaps teaching them about 14 Points, et cetera.   0:07:57.8 Balaji Reddie: Comes the second principle. He says, "Ensure more careful selection of people in the first place." So choosing the people, he says again, now here's where it requires you to understand the purpose of what you're doing, purpose of your organization, purpose of the people you're looking out for and making this change. Because when you know your purpose, you know the aim, then you can choose people in the right way. And I believe he said this somewhere, it's a combination of education, training, skills, and experience. So we need to combine these four factors in choosing the right people. Then he says, after selection of the people, ensure better training and education. So we fine-tune all of their... He says a complete background. He said their aspirations, their goals.   0:08:54.2 Balaji Reddie: I kind of borrowed this idea from a company here in India where they had this thing called roles, responsibilities, and objectives. And they used to meet once in a month, but once in a year they used to decide. So the top management, the HR, would sit down with each and every employee and say that, "In this calendar year, this is what we intend to do and this is what we expect from you." And in turn, they used to ask the employee, "What do you expect from us? Because this is what we want from you." And then the employee had a chance of putting forth what he or she wanted, the management, what help they needed. And I think this is where we have to be... It's a give and take. And they didn't just meet once a year; every month they would meet and the question was, "How are we doing?" not "What have you done?"   0:09:51.1 Balaji Reddie: So I think it wasn't a traditional appraisal. If there was any appraisal, it was appraising what top management were doing or intended to do and not so much the employee. I thought that was a good move. So that's what we need to do here: better training and education. Principle number four states: "A manager understands and conveys to his people the meaning of a system. He explains the aims of the system. He teaches his people to understand how the work of the group supports these aims." Now, here's where, you know, when you talk about, say, hiring people in the first place, when you bring in new employees, I believe that there should be a special session by people inside the company who have stayed the longest, who served the company the longest, especially during their bad days. Because the employees need to know what really happened and how the company survived and how we were resilient, we came back despite all the problems that we had.   0:11:00.7 Balaji Reddie: And the historical perspective, especially if there's someone who's in touch with the founding members, that would be a great boon. I know nowadays we talk about the older companies, obviously none of the founders are there, but if there is such a person, exchanging those ideas with the young employees would definitely make a difference. So they would then understand the purpose, the aims, and how your work supports these aims. I think it's the best way to do that. But what I see right now in companies and I'm being very specific about this, because today when new employees join the company, they have an orientation, they have onboarding, as they call it, but that's done by a rookie, someone who's just joined the company and is just making...   0:11:46.8 Andrew Stotz: [0:11:46.8] Following a checklist?   0:11:48.1 Balaji Reddie: Exactly. Like a PowerPoint presentation. They don't talk about the history of the company. And I think there has to be an emotional connect before there is a logical or an intellectual connect. That emotional connect, I think, then makes you feel that pride and you feel good about coming to work and you say, "Oh, I did not know." So I believe this fourth principle is important in that sense, in the way to do that. Now, he says that... Principle five says he helps...   0:12:19.7 Andrew Stotz: By the way, do you know what chapter are you in?   0:12:23.9 Balaji Reddie: Oh, I have combined.   0:12:27.9 Andrew Stotz: Okay.   0:12:29.4 Balaji Reddie: I took some of the text... Okay. If you want to see here, this is management of people, all right? In that chapter. So I've taken... There are 14 principles there, management of people. In the new edition of The New Economics. It appears...   0:12:48.2 Andrew Stotz: So chapter six.   0:12:50.2 Balaji Reddie: Chapter six, yeah. That's chapter six...   0:12:51.8 Andrew Stotz: Yep.   0:12:52.6 Balaji Reddie: All right. And he talks about pictorial effect of transformation, and then he talks about management of people, role of a manager of people. So there were 14 there, but in Out of the Crisis, the first three which were there, he did not include here.   0:13:10.0 Andrew Stotz: Okay. I just just asked...   0:13:11.0 Balaji Reddie: So I just included those. Yeah. No, so that when people read the book, they could read it clearly, right? So, yeah. So he says now principle number five, which in Economics is principle number two or three, right? He says "he helps his people to see themselves as components in a system, to work in cooperation with preceding stages and following stages toward optimization of the efforts of all stages towards achievement of the aim." So we want optimization, not compromise. So you need to sit together. Just if I were to ask a simple question to you, Andrew, and without thinking, if I were to try to answer this question... Okay. I presume you know how to make a cup of tea.   0:13:58.7 Andrew Stotz: Yes.   0:14:00.1 Balaji Reddie: So what is the first step?   0:14:02.7 Andrew Stotz: For me, boil water.   0:14:04.6 Balaji Reddie: Boil water. And what if I say that's not the first step?   0:14:12.0 Andrew Stotz: Well, first of all, I think you probably have more experience with tea than I do, but I have more experience with espresso, probably. But anyways, go ahead and tell me.   0:14:20.9 Balaji Reddie: Okay. The first question is, whom am I making a cup of tea for? So what I just tried to convey is it's not natural to think about the customer. And so the first step is, for whom is the cup of tea? If it's the person...   0:14:30.8 Andrew Stotz: Grandma.   0:14:40.7 Balaji Reddie: That's right. If she's diabetic, then you would not need sugar. So you gather the ingredients accordingly. If he wants black tea, you don't take milk, right? And that's the point he's trying to say here. When you look at different stages, every every person has a customer. So the first question is, who is my customer?   0:15:07.1 Andrew Stotz: Right.   0:15:07.4 Balaji Reddie: And that part of profound knowledge, understanding psychology, I mentioned this last time, is empathy. The word empathy captures this. So you go to the next process as, "Whom am I doing this work for?" and sit down with that person and say, "What do you expect from me? How may I help you?" And that's what decides what you're gonna do. So this this fifth principle here, that he helps his people see themselves as components, I think this is important. The next process is your immediate customer, and the rest of them are customers in a very oblique sense. But what you do is critical to the next person in line, right? So you always spend extra time with that person and of course the other people down the line who your work is gonna be impacting over a period of time, right? But these are the... This is the first step you find out. So who's my customer? So that's principle five.   0:16:09.0 Balaji Reddie: Principle number six: now this comes under psychology again, that a manager of people understands that people are different from each other. He tries to create for everybody interest and challenge and joy in work. Now, if you look at the theory of knowledge, what exactly did he give us when he brought that component of profound knowledge into play? He says that theory is a statement that conveys knowledge by relating cause to effect. So I repeat, theory is a statement which conveys knowledge by relating some cause to some effect. It fits without fail all the observations of the past and helps us predict the future with the risk of being wrong.   0:17:04.7 Balaji Reddie: So I'm gonna repeat this whole statement again. Theory is a statement which conveys knowledge. How? By relating some cause to some effect. It fits without fail all the observations of the past and helps us predict the future with the risk of being wrong. So no amount of examples can establish a theory, and even one example can lead to either abandonment of the theory or modification of the theory. That's what he kept saying. Now, how does this work? So he says it's a system of learning, and all of us have this built in, right? Now, he came from the school of Clarence Irving Lewis, Mind and the World-Order. And if you read that book, Lewis says all knowledge is a priori, it's based on what you already know.   0:18:00.9 Balaji Reddie: For example, let me take this example here. Now, suppose I were to start describing the road to my house. Now, you've not been here, but if I start saying that the road bends towards the left and then there is a command you get to see, now you start constructing a picture in your head based on what you have already seen. It's not the same. That's your theory, right? And then when you actually visit, you say, "Oh, it's the difference between theory and what I actually saw," and then you change your theory. So theory is... It's natural. All of us think naturally like this. And that's why he says here that people are different from one another and we need to celebrate those differences. All of us are born with the system of learning, but not all of us learn the same way.   0:18:49.8 Balaji Reddie: There are some who learn by watching, there are some who learn by doing, there's some who learn by reading, there's some who learn by writing. For some people, one word is enough. You utter a word and they say, "I got it." And for some people, you have to repeat the statement maybe 10 times, 11 times, and then the 12th time you repeat it, they say, "Okay, I got it." Now, is that wrong? We're just different, right? And that's why he says here that we need to understand the learning process of people. And when you understand the learning process of a person and then put that person in the right job, you'll have to stop that person from working. That was his definition of joy in work. People enjoy their work when they realize it resonates with them.   0:19:40.4 Balaji Reddie: And how does that resonance come in? When you under... And because this is so difficult to do, we just throw the responsibility on them by saying, "Here's the target." So the target actually distracts them when actually you should be working on understanding their learning process. So it's a lot of hard work. And sometimes people are motivated enough to discover it themselves, which is great, but we need to create that atmosphere for them to enjoy their work. So interest, challenge, et cetera, he tries to optimize. Now, here's the key. This is beautiful. He tries to optimize family background, education, skills, hopes, and abilities of everyone.   0:20:21.7 Balaji Reddie: So this is not ranking people, very clear. It is instead recognition of differences between people and an attempt to put everybody in a position for development. I think this is one of the most important principles in getting things done. When I teach this to the HR students in my college, I keep saying that I don't think you should call this science as human resource management, because the definition of a resource is obtain it, shape it, use it, and throw it away. We don't wanna do that. I think we should change the title of that department to Department of Learning, because that's what exactly this is all about, and it's learning in both ways where you are trying to understand their process of learning and in effect, you're trying to understand how the company is going to be learning.   0:21:17.0 Balaji Reddie: So you put this in... So this principle, he says, combine all of these things: family background, education, hopes, I love that word. Because if you see one of the things that people talk about, customer satisfaction, I think Deming was the only person who said customers should be happy. Not just satisfied, happier, right? Now comes the next principle. "He is an unceasing learner." So you can never say, "I know it all." Unceasing learner, he encourages his people to study. And I think this fits Dr. Deming himself. He made no excuses to learn. "May I not learn," he would keep repeating that. And I remember Bill Cooper getting irritated and said, "The last time I met you, you said this, and now you're saying this. I got that on tape." He said, "Well, you got this on tape now." He said that, "I do, I learn. And as I learn," he said, "that could have been under different circumstances that I said that, but I'm saying this."   0:22:22.4 Balaji Reddie: And so you keep learning. And he encourages his people to study. The word is study. And he provides, when possible and feasible, seminars and courses for advancement of learning, encourages continued education in college or university for people that are so inclined. So I think this bit is in many places getting to be a part of the systems in most companies. I've seen that happen now, which is a good sign. But it doesn't end there, there are a lot of other things to do. This was the Principle 7 in the list of 17. Now comes Principle 8, and this is so difficult to look at. He says "he's a coach and a counsel, not a judge." You judge people, they shut up.   0:23:15.4 Balaji Reddie: So he says coach and counsel. When they need help, guide them, show them the path. Sometimes maybe you need some help in doing that, well, go ahead. So that was principle number eight. Principle number nine says "he understands a stable system. He understands the interaction between people and the circumstances that they work in. He understands that the performance of anyone that can learn a skill will come to a stable state." Now, this is amazing. He said this way back in the 1950s when he was in Japan teaching them the control chart, where he took one example where he says that further training to the worker and the process was still in control. And he says, "I think he's reached the limit of his learning. He perhaps needs to be taken to another process or maybe given something more challenging so that we can develop the learning process."   0:24:17.6 Balaji Reddie: So he was speaking about this way back in the 1950s, which today you can say comes under understanding psychology through variation. And he says, upon which furthest the lessons will not bring improvement of performance, and a manager of people knows that in this stable state, it is distracting to tell the worker about a mistake, because he says you'll actually then demotivate someone. So these three principles...   0:24:44.1 Andrew Stotz: Because a mistake may be just normal variation, or are you saying... Okay. Yep. Okay.   0:24:51.0 Balaji Reddie: Yeah. I mean, it could be anything, right? But if you are highlighting that when he's already reached a stable state, it could just work in a detrimental way, the opposite direction.   0:25:05.4 Andrew Stotz: Ultimately you've reached your goal. A steady state is fantastic.   0:25:07.4 Balaji Reddie: A steady state. And then now you say if you want him to... Anything better here, I think you need to move him out from there, since maybe he needs to be given something either more challenging or whatever it is. But use of psychology and variation together. If people are saying that he spoke about this in the 1990s, he actually spoke about this in the 1950s in Japan. And I have proof. If you go and check Elementary Principles of the Statistical Control of Quality, the series of lectures that he gave in Japan, you will see this in one of the chapters, very clearly stating what needs to be done.   0:25:47.9 Balaji Reddie: Now we come to the next principle, which is... I don't know how to explain this, but it's amazing. He says that "the leader has three sources of power: authority of office, knowledge, and personality and persuasive power, tact." So authority, that's your title, knowledge, and personality. Now, personality, persuasive power, and tact is more of a personal thing. It is something that is an attribute. Authority is the title you're given. I think the only thing that you can really work on is your knowledge. And he says that a successful manager of people develops knowledge and personality and persuasive power, does not rely on authority of office. He nevertheless has obligation to use his authority, a source of power, for him to bring changes. He says that maybe some drastic changes to equipment, to materials, to methods, and to reduce variation.   0:26:55.0 Balaji Reddie: So he attributes this to a gentleman, Dr. Robert Klekamp, or Klekamp, I don't know how to pronounce that. So he says, "He in authority, but lacking knowledge or personality, must depend on his formal power. He unconsciously fills a void in his qualifications by making it clear to everybody that he's in position of authority, his will be done." So I think he said if things needed to be done and if he's being guided the right way, then he has to bring his authority into power. I think this brings me to one of the interactions he had with... Was it James McDonald at Ford? When he made him stand up and asked him, "What is your job?" And he said, "I'm vice president, manufacturing," and he sat down. Deming said, "Stand up. That's your title, not your job." And then for the next half an hour, he grilled him on what his job was. And after half an hour, he still didn't get an answer. He said, "You don't know what your job is. Do you think other people in the company know what their jobs are? I think you're running a mess here."   0:28:02.2 Balaji Reddie: So Jim McDonald, instead of feeling insulted, took it in a very different way. Though he said, "I did feel that I wanted to resign and just walk out of there," but he said, "I knew this man was onto something." And that kind of thing of authority of office, I think he did not like if people used it for the wrong reason, but he wanted them to develop knowledge, personality. Personality, well, I think again, on the soft side, persuasive power tact. Not all of us have that, but I think we are living in a knowledge economy, so knowledge would be the key here. And he also says that if you're in a position of authority, use this to get the right work done.   0:28:47.3 Balaji Reddie: Then next he says "he will study the results with the aim to improve his performance as a manager of people." So when the system is not getting what it's supposed to do, then he does not put the blame on the people. He says, "I have... I may be going wrong somewhere." I'd like to share an example of my father in Japan. My father was in Japan in 1964, I said this last time. And he was on this Asian Overseas Technical Scholarship, AOTS. And they run these courses even today. They have three-month, six-month, nine-month, and one-year courses. And from what I remember my father telling me, it's integrated in the sense, I think he was there for six months. So during the morning sessions, they used to have classroom training, sitting in a classroom. And in the afternoon, post-lunch, they would go and work in a company, and that was like their intern. And so it was a combination of theory and practice taking place almost every day.   0:30:02.4 Balaji Reddie: Now, what happened there was on the first day... And that's where he started working with Showa Electric, and said they were called the interns. So on the first day, he was taken to the company and was introduced to his supervisor. The supervisor took him on the shop floor and introduced him to the team that he would be working with. And then, while he was leaving, that supervisor said, "I just need to tell you this, that we also form what is called as a quality circle." And this was... The quality circle movement started in 1962, so '64, the quality circle. And so my father said, "I don't know what you're talking about." And he said, "Well, this is something new. So would you like to be a part of it?" Because quality circle is voluntary, not mandatory. They make you a part of the quality, so if you want to be a part of the quality circle. It's not imposed on you.   0:31:05.0 Balaji Reddie: So my father said, "I need to talk to my teacher, my sensei, at the class." He said, "Yeah. You can talk to him." So he went back to the class the next day in the morning, he asked the teacher, the sensei, that this is what they said. He said, "Oh, it's a very good system. You can become a member of the quality circle." So on the second day, he said, "Yes, I'll be a member of the quality circle." "Great," he said. Now, on the third day, his actual work started. Now, they used to make television screens, CRO, et cetera. And one of the steps there was soldering. They had to solder. And the soldering was the dip soldering. You had to take the printed circuit board and dip it into the solder bath and take it out. Of course you were to... There was a technique.   0:31:52.8 Balaji Reddie: And so his job was that. His first job that he was assigned is to do soldering on these PCBs. And so the supervisor himself sat with my father and demonstrated 10 to 15 times how to do it. Then he told my father, "Now you do it." And then he was guiding him, and he made him make around 10 pieces until he said, "Okay. Now you're getting it right." Okay. Now he said the ground rules. If by any chance you press it down too hard or you keep it too long because of the extreme heat, there will be a superficial crack on the PCB. And that would not be something that affects the customer right away, but over a period of time, it can result in the board cracking and the radio not working. So when you see a superficial crack, you're supposed to pull the cord. There was a cord there. And when you pull the cord, the supervisor will come and help you. Fine.   0:32:56.1 Balaji Reddie: Now my father started doing his work, and his fifth or sixth piece developed a crack. Now, he said, I don't want to sound derogatory, but the Indian in me caught up. Should I report this? What would he think? I hardly left this man alone, and his fifth piece is a rejected piece. And he said, I did not want to pull that cord. But then... He said that, he told me, "Please pull the cord," I decided, let me go ahead and pull it. So when he pulled the cord, a red lamp went on there, and there's a big siren that went on. And the supervisor came running and turned off the siren and turned off that lamp and said, "What happened?" My father showed him the crack. So he said, "Okay, no problem." He put it aside. He demonstrated to my father 10 times again how to do it. And then he made him do it 10 times till he said, "Ah, see, you did this." And he got it right. Now he said, "Let's continue production."   0:33:58.8 Balaji Reddie: Now they went away and now my father got it right. After an hour or so, or maybe two hours, they had their tea break. And they were sitting around a table. Now, this was the quality circle. So the supervisor got up and started speaking in Japanese. Now, this was my father's third day there, so obviously he did not understand what was going on. The only thing he knew that they were referring to him because they could not pronounce his name properly. So instead of Reddie, he was being called Leddie. So Leddie-san, Leddie-san, Leddie-san. So my father said, "I knew he was talking about me." And he said, "I felt so ashamed, I was looking down at my cup of tea rather than looking up." And then when I looked up, he said, all of them were looking at him in admiration and the thumbs up sign. And he was wondering what the hell just happened.   0:34:51.0 Balaji Reddie: And at the end of it, when that supervisor stopped speaking, they all clapped. They clapped. And as they dispersed, each one came and held his hand and they went away. And now my father told the supervisor, "What did you tell them? Did you tell them I made a mistake?" He says, "Yes, yes, I did tell them that." He said, "Then why are they complimenting me? Why are they... Why did they clap? Why did they clap for me? Why are they shaking my hands?" He says, "They're shaking your hand, they're clapping, and they're complimenting because you pulled the cord." So he said, "What do you mean?" He says, "Well, we have a saying here, here in Japan, if after explaining to a person 10 times how to do something, if the person still makes a mistake, then there's something wrong in the way I explained it." So this bit over here is he will study results with the aim to improve his performance as a manager. Don't blame the other guy. What am I doing wrong?   0:35:54.0 Andrew Stotz: You hired him, you train him.   0:35:56.4 Balaji Reddie: Yep. So when Jack Welch used to say, "Sack the bottom 10% of the people every year," and he called them dead wood, well, I would say when you hired them, they weren't dead. You killed them. So that was principle number 11. Now principle number 12 is where he combined both variation and psychology together. He said "he will try to discover who, if anybody, is outside the system, in need of special help." So he draws a normal curve. I'll pass on this document to you so you could share it along with the podcast. And he says here that people belong to the system. These are people who need not be ranked. But a person outside the system on the lower side needs special help. People outside the system on the higher side, well, we need to take the system to that level to improve the system.   0:37:08.4 Balaji Reddie: So he talks about that. He says this can be accomplished with some simple calculations. If there be an individual with figures on production or on failures, special help may be only simple rearrangement of work. It might be more complicated. He in need of special help is not in the bottom 5%. He's clean outside that distribution. So he's trying to use the understanding of variation in a very different sense to understanding people. And he says that we try to reduce that variation in performance between people. That's the job of the system. So this is principle 11 and 12.   0:37:51.0 Balaji Reddie: Now you come to principle 13: "he creates trust." And that creates trust, I would believe, it's a two-way process. And he creates an environment that encourages freedom and innovation. That is the environment where people are unafraid to make mistakes. Because we learned that theory is not the opposite of practice; it's a guide to better practice. And we need all of us working together. And that trust, I think, has got a very funny meaning in my country. I keep joking about this. In India, trust is we will lie a little less to each other. But that's not what this is. We need to be straight honest with each other. And honest is you can only do that by example. Like what happened in my case. I remember when we had installed the ERP system in our company, and there are interlocks. And I remember there was a backlogged order. And I knew that because when we did not deliver the order on time, I negotiated with the customer and I got the delivery date postponed.   0:39:08.0 Balaji Reddie: Now I was trying to test the ERP that month. So I said, let me see if the ERP can capture this because it should show it as a backlogged order. But it showed it as an order that was to be delivered on the new adjusted date. And I said, "How did that happen?" Because that should not have changed. And so I called my assistant. I said, "This should be in backlog. Why is it showing me as a spillover order?" And he said, "No, I changed the date." I said, "Why did you do that?" And he said, "No, because the finance guy will get angry with me." And I said, "That is my problem." I said, "When I told you you're not supposed to change that date..." And I removed his administrative powers in changing the date so that he could not change the date in the system.   0:40:01.7 Balaji Reddie: I removed his powers. And he apologized profusely and said, "Please let me." I said, "No." So till the day I resigned, I kept it. I said, "You're not gonna be doing this because it's not a question..." I said... If I had succumbed to that Andrew, they would have lost my trust. They would have thought that, "Oh, Balaji just talks. He doesn't walk the talk." I said, "No, you're not supposed to do this. We are trying to go by a system. Let's go by the system." So I think you can only create trust through example, through demonstration, if I may say so, and especially under adverse circumstances that you need to demonstrate this.   0:40:46.1 Balaji Reddie: Principle number 14: he says "he does not expect perfection." I think that even he said it in principle of variation. Principle 15: he says "he listens and learns without passing judgment on him that he listens to." This is an extension of the previous points. Principle number 16: he will hold an informal, unhurried conversation with every one of his people at least once a year, not for judgment, merely to listen. The purpose would be development of understanding of his people, their aims, their hopes, and their fears. This meeting will be spontaneous and not planned ahead. So there should be no bias, like an audit.   0:41:41.5 Andrew Stotz: Right.   0:41:42.2 Balaji Reddie: And lastly, principle number 17: "he understands the benefits of cooperation and the losses from competition between people and between groups." So these were the 17 principles of leadership, the beginning of transformation. I think there can be nothing more to do than this. He was so clear in what he wanted us to do. I wonder why people say that there was no method.   0:42:16.5 Andrew Stotz: Yeah. He definitely outlined a lot of stuff there. One of the questions I had for you on that list is, what do you say to people that say that he's kind of a dreamer? The idea that you can sit down with your employees and have this time and everybody's so busy and just talk about your fears and your goals and all that stuff where we live in this age of, we've gotta get the result, we've gotta be focused. How do you respond to that?   0:42:51.1 Balaji Reddie: Well, I say give this a try. All right? You've done it your way, right? You've done it... Let's just forget about it, and you're seeing what's happening. You want a change, you gotta do something different. So why don't you go by what this man is saying? And if you say that, you know, a dreamer or whatever, well, I'd like to quote John Lennon here: "You may say I'm a dreamer, but I'm not the only one."   0:43:16.8 Andrew Stotz: Yep. Yep. Yep. And what do you say for people that feel that you gotta have these targets and goals and KPIs to get the most out of people? And when we think about what Deming's talking about, we're talking about this intrinsic motivation. But it's scary for people to think. It's a lot more comfortable to have these goals and structures than what you could argue is a little bit more unstructured. And how do we balance that? And obviously Deming wasn't saying don't have goals.   0:44:02.1 Balaji Reddie: Yeah, yeah. I think Henry addresses this very well in his 12-day course where he has a specific section on goals, et cetera. And he talks about how Deming said that there are some things called facts of life. Facts of life is, okay, we need to turn out, we need to generate so much of revenue this year because we need to pay for all our salaries and blah, blah, blah, blah, blah, and then we need to have some money for the future. So we need to make so much of money this year. Now that's not a goal, that's a fact of life. But when you are bringing that number out and showing that to everyone, please also indicate to them how we intend to achieve that. Don't just leave it to them and say we need to do this.   0:44:54.4 Balaji Reddie: Okay. I'll give an example here. I don't want to sound... It may sound a little self-serving, but okay, take it in the right spirit. I remember when we had our first strategic meeting at my company, and my boss... Okay, was... He said... I think 20 of us sitting in the room and he said, "Last year, our target was 30 million and we're getting there and we're doing a great job. So this year we're gonna aim for 45 million." Now when he said that, I just put my hand up and he said, "Yes." So I said, "Why 45 million?" And he just stared me down and he looked up at everyone and said, "That's it. Meeting dismissed." He just walked out. These are those days when you had... You know the OHP? You know the overhead transparencies, the projector?   0:45:56.9 Andrew Stotz: Oh, yeah. Overhead transparencies, yep.   0:45:58.8 Balaji Reddie: Yeah. So he had the transparencies, and he just took them and walked out. And all the guys came to me, "Are you mad? You're questioning the owner of the company? Are you nuts?" And I was thinking, "God, what did I say wrong?" And then we started going back to our cabins, and when I sat down at my desk, the phone rang, and it was boss. And he just uttered one word, "Come." So when I was walking towards his cabin, I was thinking to myself, "Nice company, nice friends." And then I knocked on the door, and he said, "Yeah, yeah. Come in." He said, "Sit down." And then he said, "Shut the door." He said, "What the hell were you trying to do today? Are you trying to mock me?" I said, "Please, why would I want to mock you, boss? I wouldn't want to mock you. I just wanted to know why 45 million."   0:46:52.9 Balaji Reddie: He says, "All right." And so he took out what is called the blue book, where we have the yearbook, what happened in our country in the last one year. We have these books that get written, right? So he said, "Look, this is growth in our country in industry. This is our... Sector that we are in, and we are in the organized sector in this industry. And the year-on-year growth for the last five years has been this, and this year the expected growth is so much. And can I expect at least 3 or 4% of that growth?" I said, "Of course, why not?" He said, "That, son, is 45 million." So I said, "Why didn't you tell me this? That's all I wanted to know." He said, "You think these asses..." He was referring to my other colleagues... "Would understand?" I said, "Boss, if I can understand, they can understand. It's one and the same." "Okay. Let's meet tomorrow."   0:47:52.1 Balaji Reddie: So the next day we met again. And he said, "Yesterday, when I uttered 45 million, this genius asked me why, and so I'm gonna tell you why." And he went on to explain. After he finished explaining, my sales guy... Sorry, my marketing guy got up and he said, "I have something to share." "Okay, please come forward." He put the transparency. And he had listed there the top 10 selling items in my company based on revenue, based on profits, and based on quantities. Top 10 for each. There were three products that were common to all the three. So obviously he was sending a message to us, that we had to attain our targets, at least by focusing.   0:48:44.8 Balaji Reddie: The moment he showed that, he underlined these three, the sales guy put his hand up and said, "Yes." "That second product you underlined, our competitor is selling it as a package with another product, but we don't seem to have that on our list." So the R&D guy got up and said, "Could you tell me what the part number..." And he says, "It's part number so-and-so." He said, "Hang on, I've already developed that." You know what was happening, Andrew? We were talking to each other. And that meeting went on for three and a half hours. And at the end of the three and a half hours, all of us knew how to attain 45 million.   0:49:23.8 Andrew Stotz: I thought you were gonna ask a question on the second day, "Hey, boss, so 45 million, why is there no market share gain of our business that we're growing faster than the industry?"   [laughter]   0:49:41.4 Balaji Reddie: So anyway, but this was... This is what I think goals should be transparent in this sense, that why are we giving you this number? And more importantly is the discussion that happens is how are we gonna do this? It just doesn't happen by itself, right? And if you leave it to people, they start distorting numbers, right?   0:50:03.8 Andrew Stotz: Yeah.   0:50:04.2 Balaji Reddie: As Brian Joiner said, "Distort the data, distort the system, or distort both."   0:50:12.2 Andrew Stotz: Yeah. And we're working on a growth plan for my coffee business.   0:50:19.0 Balaji Reddie: A growth.   0:50:19.6 Andrew Stotz: And really what it comes down to is three things. Number one, are we as the owners gonna hire more salespeople? Because salespeople bring in revenue.   0:50:36.3 Balaji Reddie: Right.   0:50:37.0 Andrew Stotz: Number two, are we as the owners going to develop together with the rest of the team a higher value-added offering...   0:50:50.6 Balaji Reddie: Wow.   0:50:50.8 Andrew Stotz: That we can bring more value than what we're bringing right now, which would bring potential customers to us and allow us to sell more easily. Or are we as the owners going to buy another company?   0:51:07.8 Balaji Reddie: Oh, okay.   0:51:09.2 Andrew Stotz: So those are the three things. And Dale and I have been discussing each one of those in a lot of detail, testing out and debating and discussing. But those are the type that... When it comes to growth, that's just... We know the growth we can produce with no change. And that's in line with the inflation rate or whatever the economic growth, for sure. But as long as we don't lose people on our team or something like that. But to go to our team and say, "How are we gonna grow faster?" Well, that whole point is we can see. Also the other thing is that we can see bigger about the industry sometimes. Sometimes they see something at a small level that they bring back to us and think, "Whoa, wait a minute, that's something valuable." And yeah, so we're getting ready for our final decisions on where we're gonna go with that. But yeah, without that type of change, we're not gonna reach the type of growth that we want to get. And really our idea is 5x growth in five years.   0:52:19.9 Balaji Reddie: Okay.   0:52:20.5 Andrew Stotz: And in order to do that, we have to have a completely different level of quality, service, product, thinking. And so, yeah, it's fun... It's challenging. Anyways...   0:52:32.9 Balaji Reddie: Right.   0:52:33.2 Andrew Stotz: So how do we wrap this up? What is it you want people to take away? You've shared a lot of different stuff. What would you like them to take away from it?   0:52:42.0 Balaji Reddie: Yeah. One, I'm trying to shatter that myth that Deming did not tell us what was to be done. I think he was very clear and we need to reread and reread. And we have to take these as guidelines. You may come up with your own method, but see these as a guideline by and large to put you on the right path. And once you do that, you may develop something which works for you, and that's what he wanted. But let us not just say that he only philosophized about things. I think he was very clear in his head. He just wanted us to do things our own way because nobody understood our problems better than we ourselves. And he was just showing us how to understand things around.   0:53:32.6 Balaji Reddie: He wanted us to know, to understand what we do not know. Through these principles, we can address some of the gaps. Perhaps we were getting a few things wrong. So point number 14, take action to accomplish the transformation. I think it begins with leadership. So point number seven comes into the picture. It begins with training and education. Point number six comes into the picture and it also brings in point number 13, which is learning and development. And education and training is different from learning and development. Training can be very company specific and you can measure the outcomes of training, but you cannot measure the outcomes of development because that takes time.   0:54:19.8 Balaji Reddie: So you need to have some things going in your favor. And for that you need to choose, and he told us how to do that. And yes, he wanted top management to be a part of this because he said those in authority need to do this. But that one sentence that middle management can commence, it can commence there, is a telling statement. So he knew it was possible.   0:54:45.0 Andrew Stotz: That's great. And I like that. Commence. That there's... It's not necessarily gonna be completed by middle management, but middle management can start right now, right where you are. So that's a great way, that's a great way to end with the start. So, Balaji, I want to thank you on behalf of everyone at the Deming Institute. And it's an interesting discussion and I'm enjoying it very much. And for listeners out there, remember to go to deming.org and also there, jump on DemingNEXT to continue your journey. This is your host, Andrew Stotz, and I'll leave you with one of my favorite quotes from Dr. Deming, and that is: "People are entitled to joy in work."   0:55:32.1 Balaji Reddie: Oh, yeah. Andrew, I think saying thank you on behalf of the institute, I am also a part of the institute.   0:55:38.5 Andrew Stotz: Of course. Of course. You are. I appreciate it. Okay.

What does it take to walk away from a decade in product, and a job most people would envy, to bet on yourself?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Peter Yang, who just left his product lead role at Roblox to go full-time on his newsletter and podcast, Behind the Craft and build his own projects. Peter talks through the trade-offs of solopreneur life, why his calendar is suddenly empty, and how he uses an AI personal advisor with three principles to decide what to say no to.They explore his day-to-day AI builder stack, from running Codex as a daily driver to using Hermes for his recurring scheduled tasks, his working definition of slop and why he guards against it, and what he's actually measuring as success now that nobody is handing him a promotion.If you're a PM weighing whether to leave a stable job to build on your own, a creator trying to scale output without sliding into slop, or anyone wiring AI agents into their daily work, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Harry Massey is a Bioenergetics Pioneer, bestselling author, award-winning filmmaker, and Quantum Wellness Visionary whose life's work was forged through one of the most unlikely paths to healing imaginable. After a catastrophic mountaineering accident left him bedridden for nearly a decade, Harry discovered firsthand what happens when every health system fails you — Western, holistic, and functional alike. Determined to survive, he turned to physics. Through his research, Harry co-founded one of the world's first R&D companies in bioenergetics and spent two decades building technology that could measure, manage, and master the human energy field. Today, Harry is the Founder & CEO of Energy4Life and the visionary behind some of the most groundbreaking bioenergetic technologies in the world — including the miHealth Device, the Bioenergetic Voice Scan, and the GEM Wearable, an AI-powered device that detects and corrects emotional energy imbalances in real time.  Beyond his work as an entrepreneur and inventor, Harry is a bestselling author and the writer, director, and producer of several documentaries, including the award-winning film The Living Matrix. His latest film is set to release Summer 2026 — a timely and powerful exploration of what it means to heal at the energetic level. In this episode, Tara and Harry Massey dive into bioenergetics, infoceuticals, and wearable tech that aim to restore "energy for life" by imprinting corrective information into the body's energy field.   RESOURCES: Learn more about Harry Massey here: https://e4l.com/ Instagram: @official.energy4life Get 10% off Peluva minimalist shoe with coupon code COACHTARA here: http://peluva.com/coachtara   CHAPTERS: 00:00 Bioenergetics, infoceuticals and Harry intro 00:51 Why Tara is open but skeptical about energy tech 03:53 Sponsor: Peluva minimalist barefoot shoes 07:00 Interview begins: What is Energy for Life? 09:17 Physics over chemistry and the body's energy control system 12:21 Vitality equation: information × voltage ÷ resistance 14:19 Trauma, ACE score and emotional energy drain 18:44 Inside the app: tongue, face, voice, labs and AI coaching 21:55 FIELD model: functional, integrative, energetic, longevity, direction 24:45 Reading tongue, face and voice for organ stress 28:18 Infoceuticals as optimal blueprints for cells and tissues 33:40 Harry's Addisons story and meeting Peter Fraser 37:43 Mapping the body field via resonance and photon exchange 40:48 Imprinting information into water with lasers and devices 42:18 Homeopathy history, water memory and modern infoceuticals 45:13 Quantum view of reality: information structuring energy 47:40 Films, books, Energy for Life and where to learn more    WORK WITH TARA: Are You Looking for Help on Your Wellness Journey? Here's how Tara can help you: TRY TARA'S APP FOR FREE: http://taragarrison.com/app INDIVIDUAL ONLINE COACHING: https://www.taragarrison.com/work-with-me CHECK OUT HIGHER RETREATS: https://www.taragarrison.com/retreats   SOCIAL MEDIA:  Instagram @coachtaragarrison TikTok @coachtaragarrison Facebook @coachtaragarrison Pinterest @coachtaragarrison   INSIDE OUT HEALTH PODCAST SPECIAL OFFERS: ☑️ Upgraded Formulas Hair Test Kit Special Offer: https://bit.ly/3YdMn4Z ☑️ Upgraded Formulas - Get 15% OFF Everything with Coupon Code INSIDEOUT15: https://upgradedformulas.com/INSIDEOUT15 ☑️ Rep Provisions: Vote for the future of food with your dollar! And enjoy a 15% discount while you're at it with Coupon Code COACHTARA: https://bit.ly/3dD4ZSv   If you loved this episode, please leave a review! Here's how to do it on Apple Podcasts: Go to Inside Out Health Podcast page: https://podcasts.apple.com/us/podcast/inside-out-health-with-coach-tara-garrison/id1468368093 Scroll down to the 'Ratings & Reviews' section. Tap 'Write a Review' (you may be prompted to log in with your Apple ID). Thank you!

In this episode, we welcome Alex Boekelheide from Northville, South Dakota, a fifth-generation farmer passionate about stewardship, continuous improvement, and preparing his operation for future generations. Alex shares the story of his family farm, the responsibility that comes with carrying on a legacy, and the lessons he's learned working alongside his father while transitioning leadership responsibilities to the next generation. The conversation dives into: Growing up on a fifth-generation farm Leadership lessons learned from family and mentors Why succession planning should start earlier than most farms think The value of advisory teams and outside expertise Building a resilient operation through crop diversity Incorporating oats and cover crops into a corn-soybean rotation Soil stewardship and conservation-focused farming Drainage tile, salinity management, and improving productivity Farm marketing strategies and working with trusted advisors Technology adoption and equipment decisions The importance of transparency when preparing the next generation to farm Alex also shares how Farm4Profit episodes featuring Onshore Advisors and BOA Safra inspired him to explore opportunities that ultimately generated substantial value for his operation through R&D tax credits and fertilizer tax programs. He walks through his experience, the process, and why surrounding yourself with knowledgeable experts can help uncover opportunities many farmers overlook. Most importantly, this episode is a reminder that successful farms aren't built by knowing everything—they're built by continuously learning, asking questions, and surrounding yourself with great people. Whether you're focused on succession planning, conservation, profitability, or simply becoming a better operator, this conversation is packed with practical insights and real-world experiences from a farmer who is intentionally building for the next generation. Want Farm4Profit Merch? Custom order your favorite items today!https://farmfocused.com/farm-4profit/ Don't forget to like the podcast on all platforms and leave a review where ever you listen! Website: www.Farm4Profit.comShareable episode link: https://intro-to-farm4profit.simplecast.comEmail address: Farm4profitllc@gmail.comCall/Text: 515.207.9640Subscribe to YouTube: https://www.youtube.com/channel/UCSR8c1BrCjNDDI_Acku5XqwFollow us on TikTok: https://www.tiktok.com/@farm4profitllc Connect with us on Facebook: https://www.facebook.com/Farm4ProfitLLC/Farm4Profit Media is not a financial, legal, or tax advisor. Content is provided for informational purposes only, and we serve solely as a platform for third-party opinions. Any actions taken based on this content are at your own risk. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this conversation, host Tad Hussey (KIS Organics) and Dr. Deron Caplan (CannaCribs Horticulture Consulting) discuss the intersection of agricultural science and commercial cannabis cultivation, focusing heavily on irrigation, lighting, and research methodologies. Drought Stress vs. Drybacks: Dr. Caplan clarifies a major industry misconception by distinguishing standard irrigation "drybacks" from actual "drought stress." He explains that drybacks work primarily by pulling oxygen into the root zone (which cannabis loves), whereas true drought stress requires pushing the plant past the point of water availability. The Nuance of Cannabinoid Bumps: While Dr. Caplan's landmark PhD research found that controlled drought stress in week seven of flowering could boost THC/CBD content by 30% to 40% without losing yield, he notes that repeating this in smaller pots with faster drybacks yielded no positive results. This highlights just how incredibly nuanced and difficult it is to trigger beneficial plant stress without harming the crop. Organics vs. Mineral Salts at Scale: The two debate the logistics of commercial cultivation. While mineral salts offer strict baseline consistency and easier pathogen sterilization for medical export markets, Dr. Caplan notes that data-driven, evidence-based living soil systems have come a long way and are proving to be increasingly scalable. Debunking the Leaf-Tip Cloning Myth: Dr. Caplan shares that his early research on propagation disproved a ubiquitous legacy market myth: cutting the tips off clone leaves actually reduces rooting success, unless leaves are so large that they are actively shading adjacent clones. Advanced Canopy Management: They discuss the massive yield and quality benefits (15% to 20% increases) of under-canopy lighting (UCL). Dr. Caplan details how commercial facilities can use a PAR meter to calculate the Leaf Area Index (LAI) to mathematically standardize how much foliage to prune, rather than relying on visual guesswork. The Future of Research: Dr. Caplan highlights his new commercial R&D facility in Canada designed to test applied science practices. They close by discussing the validity of peer-reviewed data versus private white papers, concluding that regardless of a grower's background, scientific data is the ultimate tool for bridging the gap between organic and conventional cultivation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Edward Shenderovich is the Founder and CEO of Roebling, a software platform that helps industrial companies evaluate, design, and finance manufacturing projects before breaking ground. After initially setting out to solve bottlenecks in biomanufacturing, Shenderovich and his team uncovered a broader challenge: the economics of scaling physical infrastructure are often poorly understood until it's too late. In this episode of Inevitable, Cody and Edward explore whether the US is making the same mistake with domestic manufacturing that climate tech once made with the “green premium.” If consumers were unwilling to pay more for cleaner products, will they pay more for American-made ones? The conversation examines China's long-term manufacturing strategy, the gap between scientific breakthroughs and industrial scale-up, and why engineering—not invention—is often the missing link in commercial success. Edward argues that national security, data sovereignty, and AI infrastructure may become the forces that justify renewed domestic investment in manufacturing and energy systems. They also discuss the lessons learned from the recent biomanufacturing boom and bust, why many bioindustrial companies struggled to achieve economic viability, and how AI can help bridge the gap between R&D and large-scale industrial deployment. Finally, Edward shares how Roebling is using AI-powered techno-economic analysis to help companies build factories that can actually compete on cost and performance. Episode recorded on May 28, 2026 (Published on June 9, 2026). In this episode, we cover:  (0:00) An overview of Roebling  (3:37) Why consumers rarely pay more for domestic or sustainable products  (6:12) How the US can compete with China's manufacturing strategy  (7:25) The gap between R&D innovation and industrial scale-up  (9:13) Why engineering is often the bottleneck  (11:25) AI data centers as a catalyst for industrial and energy infrastructure  (14:30) National security, data sovereignty, and domestic manufacturing  (17:31) Roebling's origins in biomanufacturing  (20:03) Why AI may finally help unlock biology at scale  (23:25) Building products that are better, not just greener  (26:11) How Roebling helps companies plan and finance factories  (31:08) Lessons from the biomanufacturing boom and bio-winter  (34:33) The opportunities of nuclear energy and industrial growth  Enjoyed this episode? Please leave us a review! Share feedback or suggest future topics and guests at info@mcj.vc.Connect with MCJ:Cody Simms on LinkedInVisit mcj.vcSubscribe to the MCJ Newsletter*Editing and post-production work for this episode was provided by The Podcast Consultant

This week we speak with Auryan Ratliff of Arizona State University about how AI—especially agentic AI—is transforming course design, assessment, and the broader student experience. They explore how moving beyond simple question-and-answer tools to more autonomous, action-oriented systems enables institutions to build more dynamic, personalized, and scalable learning environments. The conversation also tackles the challenges AI introduces—particularly around academic integrity—and how institutions can respond by rethinking assessment itself. Through examples like AI-powered conversational language practice, they highlight a shift toward more authentic, interactive, and human-centered learning experiences. Ultimately, it's a call for institutions to embrace experimentation, invest in culture and collaboration, and actively engage with AI rather than waiting for the “right” moment. Guest Name: Auryan Ratliff - Director of Technology Innovation and R&D at EdPlus at Arizona State University Guest Social: LinkedIn Guest Bio: Auryan Ratliff is the Director of Technology Innovation and R&D at EdPlus at Arizona State University, where he has worked for over a decade across AI, XR and digital learning. He manages a portfolio of emerging technology projects aimed at supporting the student experience. He founded EdPlus' AI product team and led the development of DegreeMe, an AI-powered tool that helps prospective students find the right degree through a personalized quiz. He is also the founder of SPLIT Studio, the student-powered lab focused on creating immersive experiences for ASU Online. SPLIT has also produced work for Dreamscape Learn and ASU partners, including the Hall of Teachers exhibit at the Bishop Museum, created in partnership with the Polynesian Voyaging Society. - - - -Connect With Our Host:Dustin Ramsdellhttps://www.linkedin.com/in/dustinramsdell/About The Enrollify Podcast Network:The Higher Ed Geek is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too!Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Adeniyi Abiodun has been in crypto since 2012, built trading and risk systems at investment banks, and led R&D on Facebook's Project Libra at Meta before co-founding Mysten Labs. So when he says every other L1 has a "skill issue" baked in at architecture time, it's worth listening.David Sencil sits down with Adeniyi at Consensus 2026 to walk through how Sui solved horizontal-scale consensus, why a famous L1 founder said it was impossible, and what comes next — native stablecoins, private payments by default, Walrus storage, and the agentic payment rails Stripe is pricing at a billion TPS.We cover:- Why every other L1 is capped by a single CPU and Moore's Law- The Project Libra story — "way too early" and what survived into Sui- 300ms finality vs Solana's 12 seconds- SuiUSD: $63M in a month and a half, free stablecoin transfers- Protocol-level private stablecoin transactions launching this year- Walrus storage outgrowing Arweave in a year- Why "AI doesn't care about your tribe"Filmed at Consensus 2026.Host: David Sencil

Dr. Katie Gold and Dave Combs from Cornell University join Fritz to unpack one of the most confusing topics facing winegrape growers today: how biological fungicides truly fit into a spray program. Katie explains how the Cornell grape pathology program has expanded rapidly, moving from strictly conventional efficacy trials into extensive work with biologicals as more of these products come out of R&D pipelines. She outlines why growers can't rely on "the next new conventional" anymore and how biologicals are becoming essential tools for sustainable disease management and resistance stewardship. Dave brings decades of field experience and shares how his initial skepticism about biologicals shifted after seeing modern products perform on highly disease‑prone varieties in one of the toughest vineyards for powdery mildew, downy mildew, black rot, and bunch rots. Together, they walk through what "compatibility" really means and why water pH, formulation, and whether an organism is alive or dead matter far more than many growers realize. Listeners learn why mixing biologicals and conventionals in the same tank often provides no added control (and may waste money), where negative interactions can show up, and why tightening intervals and understanding infection periods is critical when working with protectant biologicals.  In this episode, you will hear: Why more new products in the pipeline are biologicals rather than conventional chemistries How Cornell's high‑pressure pathology vineyard reveals the real-world limits and strengths of biologicals Why many biologicals are strictly protectants and must be applied before significant disease is present How tank mixing biologicals with conventionals can reduce cost-effectiveness without improving control Why understanding water chemistry, pH, and product formulation is now an essential spray-program strategy Follow and Review: If you enjoyed this episode, be sure to follow the podcast and leave a 5-star review on Apple Podcasts! Your support helps us reach more listeners.

When an AI model discovered a catastrophic vulnerability in Zcash's cryptography, users realized a hard truth: without mathematical proof, code is never truly safe. While everyone was distracted by the latest price pumps and ETF narratives, something massive just happened in the privacy space. A security researcher leveraging the new Opus 4.8 AI model found a “soundness bug” in Zcash's Orchard protocol. We aren't talking about a small glitch. This was a constraint bug that allowed for potential infinite minting since Orchard debuted in 2022. That means, for years, anyone could have printed counterfeit Zcash out of thin air. Now, here is the kicker. While Zcash users were sweating bullets, wondering if their funds were actually real or just digital paper, Pirate Chain users slept like babies. Zero panic. Why? Because Pirate Chain operates on the older, Sapling protocol, which was unaffected. While Zcash was busy rushing to implement new, flashy tech without proper safety checks, Pirate Chain stuck to the guns that work. Watch on: Odysee | YouTube | X | Rumble | Bitchute | Vigilante.tv This brings us to the concept of formal verification. Most crypto projects skip this because it is hard, expensive, and slows down development. But here is the reality: formal verification is essentially proving mathematically that the circuit works and it cannot be exploited. You can't just “trust the devs” or “trust the auditors.” Even the smartest minds in zero-knowledge proofs missed this bug in Orchard. Human error is inevitable. Math, however, does not make mistakes. Pirate Chain's strategy is brilliant in its simplicity. They let Zcash act as an unpaid R&D department. Zcash takes the risks, breaks things, and finds the holes. Pirate Chain watches, waits, and only implements the tech once it is mathematically secured. Zcash tries to fix these supply issues with something called “turnstiles,” which force users to unshield their coins—creating metadata trails and destroying privacy—just to verify the supply. Pirate Chain says no thanks. We don't play games with metadata. We wait for Ironwood, the next formally verified pool, before we even think about upgrading. We are seeing the gap widen between the experimenters and the executioners. The market is full of confusion, which is why we constantly highlight privacy champs like Zano, Monero, and Pirate Chain that actually deliver. While Zcash is still figuring out how to stop infinite minting, Pirate Chain is rolling out the new Unified Light Wallet and preparing for a future where the tech is actually polished. One is a beta test for your financial sovereignty. The other is the finished product. Don't get caught holding the bag when the bugs turn into bank runs. Stay ahead of the curve with the latest crypto news and analysis and remember: in this game, if you aren't private by default, you aren't private at all. The post AI Finds the Hole: Why Formal Verification Saved Pirate Chain From Zcash's Fate appeared first on The Crypto Vigilante.

Have you ever lost the joy in your creative work — that sense of fun you had when you were starting out, before the admin and the algorithms drained it away? How do mid-career creatives get it back, and what can a four-year-old teach us about play? Austin Kleon talks about productive procrastination, silly rituals, the case for paper reference books in an AI world, and how his newsletter went from a marketing cost to the day job that keeps the lights on. In the intro, Does social media still sell books? [Self-Publishing with ALLi]; Trial by algorithm [The Bookseller]; Publishing's AI Hypocrisy Problem [The New Publishing Standard]; ALLi AI survey for authors; Brave New Bookshelf Podcast, and Pics from signing at BookVault. Today's show is sponsored by ProWritingAid, writing and editing software that goes way beyond just grammar and typo checking. With its detailed reports on how to improve your writing and integration with writing software, ProWritingAid will help you improve your book before you send it to an editor, agent or publisher. Check it out for free or get 15% off the premium edition at www.ProWritingAid.com/joanna This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Austin Kleon is the New York Times and international bestselling author of nonfiction books, including Steal Like an Artist, Show Your Work!, and Keep Going, as well as an artist, professional speaker, and poet. His latest book is Don't Call It Art: 10 Ways to Create Like a Kid Again. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes Why Austin wrote Don't Call It Art now, and what his kids taught him about creative joy Productive procrastination, silly rituals, and treating writing like Lego Comedy as a philosophical position, and giving yourself permission to be bad in private Sharing process in the algorithm era, and why your whole life is the process Bibliomancy, paper reference books, and what AI can't give you that a dictionary can Style, the Taco Bell distinctiveness rule, and how Austin's newsletter became his day job You can find Austin at AustinKleon.com. Transcript of the interview with Austin Kleon Jo: Austin Kleon is the New York Times and international bestselling author of nonfiction books, including Steal Like an Artist, Show Your Work!, and Keep Going, as well as an artist, professional speaker, and poet. His latest book is Don't Call It Art: 10 Ways to Create Like a Kid Again. So welcome back to the show, Austin. Austin: Thank you for having me back. It's nice to talk to you again. Jo: You were on the show in March 2020, and at the time, your book was Keep Going, which was prescient considering the pandemic and politics. So I wondered, why this book, Don't Call It Art, now? Was this something you see in the creative community or your own life that made you want to write this book? Austin: Keep Going is a book about what happens when the world goes crazy around you and you're still trying to do your creative work. This is a book about what happens when inside has bottomed out. Keep Going is a book about the world bottoming out, and you're worried that your own creative work is going to bottom out too. How do you keep pushing through and keep making stuff? This book, to me, is about what happens when you bottom out inside—when you've lost that love and feeling for the thing that you wanted to do, and you're just not connecting with it in the way that you used to or the way that you want to. How do you get back? How do you return to that sense of joy and wonder and fun that we have when we're starting out? And for me, it was being around my little kids that taught me how to tap into that. My kids were natural—they didn't have any creative hangups. I would spend all day talking to people who had creative hangups, and then I'd get back in the house, and I'd just be around these beings who didn't have any of them. It was really instructive. I felt like, if I could bottle the energy of my kids when they were about four years old and try to put it in a book, I think it could really help a lot of the people that I run into, and the people with the kinds of problems I hear from. Jo: You mentioned bottoming out. How do people know when they've hit that point? Austin: You just don't want to do it anymore. You're kind of like, “This just isn't giving me back what it used to.” When we start with our creative work, that's the thing that juices us. We come away from it feeling full up. I think you hit a certain point where you start to feel drained after it. Or maybe you don't feel drained by the thing itself that you're doing—maybe it's all the stuff around it, which is more often the case. For example, if you're a mid-career writer like me, who's been publishing books for 16 years now, I still really like writing. I still really like drawing. I still really like cutting and pasting and putting things together. It's the admin around the work—the emails, the meetings, the running-a-business part of it—that's super draining for me, and that stuff can start to bleed over into the creative work. So it's really important for me to make sure that I'm having some playtime, some R&D, some research and development time, to make sure it's not just all business. When you take the thing that you love and you turn it into the thing that you make a living from, you can really run into a lot of problems. Jo: I'm at 20 years, so I know exactly what you're saying, and a lot of listeners are the same. We love writing books, but it's all the stuff that goes around it. So for those of us who do this for money as well as passion, what are some practical ways to have more fun with our creativity? Austin: Something I learned from my kids is that you really are your most creative when you're supposed to be doing something else. So one of the things I use a lot in the studio is productive procrastination. Whatever I'm supposed to be working on, I start another little project, and that's my little naughty fun time. When I first come into the studio, I try to do something that I'm not supposed to be doing—something that I won't have much to show for. That could be making one of my blackout poems. That could be making a collage in my notebook. It could also be sitting here. I have a bass in the studio now, so I can practise my bass guitar. Sometimes I'll do that for the first 15 minutes just to get in that headspace of, “Hey, what's it like to do something just for yourself? Just because you want to do it?” The juice that you get from that little naughty “I'm going to do what I'm not supposed to be doing right now” thing, that carries into the rest of the day. It's like a nice start to things. Jo: Do you think that play could be something different to what we make our money with? For me, writing novels and stories is great fun in one way, but it's also what I then publish and make money on. So writing stories is more serious, I guess, than playing with Lego or something. Austin: Right. So the trick is, how can you make writing your stories like playing with Lego? That's kind of been my whole career. I hate staring at Microsoft Word and that blinking cursor, taunting you like, “Come on, what have you got?” A lot of my creative life has been about trying to make it more playful, trying to make it feel more like a game. That's how I came up with my blackout poems. I take an article from The New York Times and I black it out until it only has a few words left behind. It sort of looks like if the CIA did haiku, for some people listening. That was one little exercise. Then weirdly, that side thing that I thought was just play, just fun—that turned into my first book. So then it's, okay, what else can I mess around with and play with? I do a lot of collage work in the studio, and I rarely actually use that for any of the books. Sometimes I use it for my newsletter to illustrate the newsletter. But it's always about trying to figure out, how can I make writing a game? How can I make it more playful? There are different things that I do to make it feel more playful. One of them's really stupid. I really believe in silly rituals because I think silliness is really powerful. People talk about their daily rituals—Mason Currey has that great book, Daily Rituals: How Artists Work. When I was reading that book, I realised it was really the silly stuff that I really liked. There was, I think it was Balzac counting out coffee beans or something before he got to write. Or Steinbeck sharpening 12 pencils or something goofy like that. So one of the things I like to do before I write is that I have these cigarette pencils. They're pencils that look like cigarettes in the studio. I put one in my mouth before I start writing, and I pretend to be some old '40s writer on a typewriter. I like doing goofy stuff in the studio because I think when you do goofy stuff—stuff that you'd be embarrassed if anyone else saw it—it gets you in that playful state. Jo: It's interesting. In your book, you have a section that says, “Don't take things too seriously.” For many of us, we write memoir for example, and that is very close to us. It's like the deepest expression of what we want to say in the world. It feels very serious. So how can we hold things more lightly and not take things so seriously? Austin: For me, comedy is actually a philosophical position. What I mean by that is, I think a lot of people set out with a tragic model of creative work. They think, “Oh, I have this special gift,” or, “I have this thing that I really need to do, and I need to put it out into the world, and I need to make the world look more like I want it to look.” They have this idea that, “Through blood and sweat and tears, I'm going to see this thing through, and I'm going to push it into the world, and I'm going to have my way.” I think there's another way of working where it's more like, “I'm just a normal person trying to play with my environment, and take my experiences and put them into something interesting. So I'm going to play and use my wits, and we're going to see what we come up with.” Those really are two modes of life. The pandemic taught me that it was really when we were keeping our sense of humour, when we were having a laugh and keeping our egos in check around the house and just acknowledging how goofy we all were and how ridiculous the situation was, that seemed to be when we were really thriving. Versus, “Well, we're in this tough situation. We've got to make it into what we want it to be.” That felt really bad. But when we cruised along and we were just improvisational, when we went at things with a kind of lightness, that worked. There's a great Italo Calvino essay about lightness in Six Memos for the Next Millennium. Lightness is really underrated. Even when we're going about heavy work, having a sense of lightness and play with it just makes the work better. That's a philosophical position of mine. I aspire to comedy. I aspire to a comic outlook on life. I'm just a creature with a body who's going to die, and I'm fundamentally ridiculous. Life is pretty absurd. You just make the best of it. Jo: There's certainly some truth there. Staying on a similar theme, you have a chapter in the book on permission to be bad. Many of the listeners also have your book Show Your Work, and it shaped many of us into sharing our work in progress. It feels quite dangerous now, in a world where judgment is much louder than it maybe was when you wrote Show Your Work. So tell us a bit about permission to be bad versus should we keep some of this private? Austin: Permission to be bad is about the making part of things. It's the private part. It's permission to be bad when you're in private, when you're actually doing the work. Show Your Work is a book about what you do after you've done the work, or while you're doing the work. It was never about putting up a webcam and running a 24/7 feed. It was more like, hey, what are the ways that I can connect with the kind of audience I can build while I'm making the work itself? So the way I see permission to be bad is, you really have to give yourself permission when you're not sharing, when you're off screen, to really be as bad as you want to be. It doesn't necessarily mean quality-wise. I think it also means letting yourself write stuff that you would never say on social media. Letting yourself read stuff that you wouldn't admit you were reading on social media. Letting yourself listen to stuff. Letting yourself really be that unfiltered, unhinged, private person that you want to be. Then when it comes to sharing, you put some time in between that input time, that making time, and the sharing time, and then you share what you think is going to be useful or helpful or interesting to other people. Jo: I think you wrote that book before TikTok, and how fast people are moving. Do you think people need to slow down a bit in what they share, maybe? Austin: I don't know. I obviously had a lot more faith in social media back then. I use all the principles from Show Your Work in my newsletter. Newsletters are very much the new kind of great thing. They're doing a lot of the work that social media used to do, in that you're still able to have this direct connection with the people that you're trying to reach. The big problem with social media now is that it's all algorithmically tuned, where the people that are following you don't see the stuff that you're doing most of the time. What you have to do now, if you want the people who are following you to see your stuff on social media, is you have to make stuff that the algorithm likes. That's a whole different thing. As far as the Show Your Work principle—which is share your process as much as your product—that carries over to any platform. In my newsletter every Friday, I share a list of 10 things that were going on behind the scenes here. It might have been what I was watching on TV, what I listened to, a new pen I was trying out, or something like that. The Friday newsletter is almost always process stuff. When I talk about process, my definition is actually very broad. For a lot of people, it's drafting, editing, whatever. For me, the process is the whole life. The process is almost everything except the finished thing. A writer's life is 24/7. My friends who have real jobs really are like, “What do you do all day?” And I'm like, “Well, what do you mean?” They're like, “Well, I see you out on your bike ride.” I'm like, “Yes, when you see me out on a bike ride, I'm thinking through something half the time.” If I'm watching TV, I'm thinking, “Hey, would this be good in the newsletter?” I'm never off. My whole life—everything is copy, as Nora Ephron said. That's part of the job. It's very hard to turn off. So I see the whole life as process, and the question becomes, what little bits and pieces of that life and that process can you share with people while you're making the things that you hope to sell them later? Right now, I'm in a cycle where I'm selling this book, but all these people have showed up because I've shared my process every week for the past seven years since I put out a book. Jo: It's funny you say that. I was at the dentist yesterday, and— My dentist literally asked me, “So where do you get all your ideas?” This is a common question for all of us, right? And it just becomes so hard to explain that to people who don't walk around in the world just constantly getting ideas. Austin: I can't believe I'm going to tell this story. I was getting my vasectomy after my second kid, and I was talking to this doctor just before the operation. He said, “So what do you do for a living?” I said, “I'm a writer.” He said, “Oh, that must be cool. You get to use your brain.” And I said, “That's everything that you want your doctor to say.” I was going to say, “Please use your brain,” before he's about to cut into you. He said, “Oh, no, no. What I mean is, I know what I'm going to do every day for the next 10 years.” He knew exactly what his day was going to look like. He said, “You have to use your brain. You've got to figure out new stuff.” I was like, “Oh, that's really interesting.” That's the trade-off, right? He's got the job security. He knows what he's going to do. Every writer has a moment where they have to talk to a normal person about what you do. Jo: I was going to say, I'm married to one. Austin: Now, my wife, on the other hand, grew up the daughter of a writer, so she knows exactly what it's like. Nothing ever phases her. She's totally used to it. She's used to me staring off into space, completely checking out of a conversation. She's used to me using lines on her that I'm going to put in a piece later. She's used to the whole rigmarole. It's very handy. I've been very lucky in that sense. Jo: Coming back to the book, you talk about your use of bibliomancy for inspiration. Since we're talking about that, tell us about it. I think all the book people listening will be happy. Austin: I'm a person who still keeps a dictionary nearby—a paper dictionary. I keep a big old American Heritage. It's just a big, thick book. When I really don't have any ideas, I will turn at random to the dictionary, close my eyes, stick my finger down the page, open my eyes, and just see what I come up with. Sometimes just that act will give me an idea. I also do that with books. I'll go around the studio, pick up a book, flip to a random page, and just see what it says there, or read an old piece of marginalia that I've left in a book. I believe deeply in the power of bibliomancy, and I think it's a case for paper books. I'm one of those people that still really believes in reference books. I've started collecting more and more of them. I have an old, big dictionary that's always open on my desk, and I look up words. I learned from John McPhee, the writer, that you should look up words that you think you know. That was the first time I'd ever heard anyone say that. So I look up words that I think I know. Instead of reaching for a thesaurus when I need a different word, I actually just look up the definition of the word that I already have. That's another McPhee tip. The other thing that happened that I thought was really interesting is, I got a Roget's for the first time—a thesaurus. I don't think most people know what an actual thesaurus is. Most people think of a thesaurus as a synonym finder, and that's not actually what a thesaurus is at all. A thesaurus is more like an encyclopaedia, weirdly. You look up things based on big concepts, and then it gives you a bunch of words to look up later. It's a very strange thing. It's not what most people think it is. I have a couple of editions of Roget's in here. I like the really old Roget's from the 1900s because they actually have opposing ideas facing each other on the page. Do you have an old-school Roget's? Have you ever looked through one? Jo: I don't have one now, but I certainly grew up with them. I was literally just thinking, I wonder if there are ones for Americans and ones for British people, because so often we say different things and mean different things. I always hear Americans say, “Oh, that's a doozy,” or something, and it means the complete opposite thing here. Austin: Like if you say “fanny pack” over there. That means something very different than it means here, right? Chips or fries, that kind of stuff. So I wonder if there are different ones for different cultural references. Jo: I don't know. Austin: As people, with ChatGPT and all these LLMs and stuff, people are like, “Why would you ever pick up a paper reference book?” And I'm like, “I actually like the friction.” I like having to move in space and go over to my dictionary. I like flipping the pages. I like having to scan a page for the word I'm looking for, because— This marvellous thing happens when you're looking for the word, where you bump into all these other words. If you're a word nerd, you get to start thinking about the root of the word—oh, why is this word next to this word? Well, it's because they share the same root. Then you're going down all these fun rabbit holes. The thing that I'm trying to do as a writer and a creative person is, I'm trying to get to the thing that I didn't know I was looking for. The thing that people misunderstand about AI, I think personally, is that it's a great tool if you know what you're looking for. If you're like, “Find me this thing. I want exactly this. I want to see a picture of a dog wearing a king's costume,” or some crap like that, then it can spit that picture out for you. Or, “I want to know what happened on this day,” and whatever. It can do that. But that's not actually what I'm doing most of the time when I'm writing or making something. I start with an idea, but what really happens—the magic of writing and the magic of making stuff in general—is when you discover something that you didn't even know you were headed for. That's the real magic for me. Sometimes I have an idea and I want to articulate it for people, but more often than not, there's something that bothers me or something that I want to talk about, and I sit down and write, and I figure out what it is that I actually have to say and what I actually think. Every writer really knows this, and that's why the dictionary, stuff like that, those are ways of training you to get in that discovery mode. “Well, let me—oh, I bumped into this. I went looking for this one thing and then I ran into this other thing.” That's why I love the library. I don't know what system you use over there, but you look for one book in the Dewey Decimal System over here, and then, okay, here's all these other weird books next to it. Then you end up with three other books other than the one that you were looking for. That's the magic. To me, that's the magic of creative work, discovering what you didn't know you were looking for. That was particularly important for me when I was writing this book because we discovered that my wife has a condition called aphantasia. It's very rare in the population, about 2 to 3% of people. There's probably some people listening to this right now who are like, “What is this? Tell me.” Jo: Aphantasia actually more common in the creative industries. Austin: Yes. What it is, is that you don't see—when I say close your eyes and picture an apple, you don't actually see the apple in your head. You can think about an apple and the qualities of an apple, but you don't actually see it. Some people, and it's a matter of degree—some people like me, I can close my eyes, I can tell you what the apple looks like, I can tell you what colour it is, I can tell you where the shading is. Someone like my wife doesn't see the apple. She can tell you what an apple is. It's really interesting because she has a degree in architecture, which is known as a very visual field. But the thing you discover about aphantasia is, it doesn't keep people from becoming artists. In fact, it's the opposite. Someone like Ed Catmull, who co-founded Pixar, writes about it in his book, and so many of the great animators at Pixar are actually aphantasics. The reason is that they learned that they had to draw in order to see things. When you don't have a picture in your head of what you want something to look like, things appear in the drawing, and you find things that you couldn't even picture. A lot of writers actually are aphantasics. John Green discovered recently that he has aphantasia. It turns out that it's a superpower for writers, because if you don't have a picture in your head, then you don't have to translate that picture into words. A lot of writers talk about thinking in radio, like they have a constant narrator. My wife—she's probably going to kill me for talking about her this much—when she describes it to me, she's like, “Oh, it's like a radio in my head. I'm constantly hearing a voice, and it's a narrator.” I was like, “Holy shit, that would be really helpful to me.” I don't have anything like that in my head. I read Mrs Dalloway for the first time, and I gave it to her and I said, “You've got to read this book. I think this must be what it's like in your head.” And she said, “Oh my God, it is.” Part of the thing that I took away from that experience—this is a long-winded way of getting here—is that I take a lot of inspiration from people with this condition. Most of the people I know in the arts or the creative fields, they set out with this grand vision, and then they start working on the thing and it's nothing like what they had in their head, and they get really depressed: “This isn't what I had in mind.” Whereas if you set out without a picture in your head, and you just start manipulating things and you see what appears, that's more of the comic mode I was talking about earlier. What would happen if we just sat down with our materials and we started playing and we saw what appeared on the page? What if we started typing and saw what appeared, and then we played with that? That's the kind of joy. That's more like how kids operate. Kids are better at that. They're better at reacting to what's actually in front of them, instead of having these grandiose visions about what they're trying to achieve. Jo: Just coming back on the longevity of a creative career. Your books are very distinctive. You have a very distinctive visual style, your handwriting and the way the books are done. I wondered if another part of the ennui, perhaps, or the draining of the later career is that we get trapped into doing something that feels like it looks the same. Or we have a voice, and we're happy in that voice, but sometimes we want to do something completely different. For authors, we have different names. I write under two different names, and that helps. But equally— How do you define author voice, and do you ever feel like doing something completely different to your normal style? Austin: Style, in a lot of ways, is self-plagiarism. Style is the repeated things that we notice in people's work. Hitchcock talked about this in films. Wes Anderson is someone like that—Wes Anderson has a style. I'm sure that he gets really sick of it too sometimes, but you also can't help it in some ways. I thought a lot about this because people worry about style so much. A lot of the time, what we call style is what Adrian Tomine one time said: “Style is just the distance between what's in my head and what comes out of my hand.” I really like that definition. With this book, I was trying to think, “Okay, if I do another book in this series, how can I push things a little bit?” And then I was reading this article about Taco Bell. You guys have Taco Bell over there, don't you? Do you have Taco Bell? Jo: No. Austin: So Taco Bell, for people who don't know, is this American Mexican chain, and they have tacos and burritos and stuff like that. They're well known for making these really insane… it's so American, this company. They make a taco with a Doritos as a shell. Doritos are crisps, I guess. Jo: Yes, we have Doritos. Austin: Okay. I spent time in England, I just don't remember if I ate Doritos when I was in England. Anyway, I was reading this article about Taco Bell. It was really funny. They have an innovation kitchen at Taco Bell, and they have a rule about new products. The rule is called the distinctiveness rule, and the rule is: you can change the flavour or you can change the taste, or you can change the form, but you can't change both at the same time. I got really obsessed with this concept because I thought, “Well, this could be kind of interesting.” If you're someone who's had success and you're known for something, this presents an interesting thing. You could do a complete break and do something completely new, or you could try the distinctiveness rule. Okay, well, what if I play with this idea of taste versus form? What if I change the taste and keep the form? So the idea for Don't Call It Art was, what if I do another one of these books, but the taste is more like if my kids made it? It had the texture of kids' art, it had lots of scribbles in it, it was loose and messy. That was kind of the idea. The actual book ended up being more like the other books. It ended up looking like an Austin Kleon book, because I just can't help that. The thing you said about having multiple names that you write under, that's kind of what I do with the newsletter. I think of the newsletter as very different from the books. The newsletter is this twice-weekly thing where I can be a little bit more of myself. In the books, I'm this very helpful, happy version of myself. It's me, but it's me on my best day. I'm really helpful and interesting for you. The newsletter is still a highlight reel in a sense, but it's a little bit more of my weird everything-I'm-into. It's more of the unclipped version of me. The newsletter becomes a place where I can do a lot of the weird stuff that's much different from the books. I have these little projects going all the time. Sometimes I'll make a bunch of prints and put them online. Sometimes I'll make a bunch of zines on a topic I haven't covered in the book. Sometimes I'll do a mixtape. As someone who's interested in a lot of different forms and genres and just different modes of output, having something like a newsletter has been really creatively fruitful for me. It's kept me from getting too bottomed out with the books because the books do a certain thing for the reader, and as much as I'd love to do a book that was radically different, I also think I've been given a real gift with the form of my books, in that I kind of own the way that they feel and look. There aren't a lot of books that look like those books and feel like those books, and so I like playing with that form. It would be hard to get rid of it now. The pseudonym for me is kind of like the newsletter in a sense. The newsletter is a little bit more of where I get to be wild and wacky. Then the books are a little bit more of a chiselled thing. Jo: The books are perfect examples of the form, as you say, but it's interesting about the newsletter. You mentioned at the beginning that we can be drained by the admin around the work. For many people listening, a newsletter becomes admin. So how does the newsletter fit into your business? The books are traditionally published, they're very professional. How do you have your independent side, and how does all of that work together in your business? Austin: Thank you for asking that question. I run the whole show at the newsletter. The newsletter is just me, and then my wife edits it, and no one else is involved. I don't have an assistant. I don't have a team. It is just me, and that's why I love it. I control everything. I pick who gets in there. I pick everything. I love that. I grew up watching David Letterman over here, and Letterman had a nightly show, and I always thought that was killer. I thought, “Man, what a fun job. You have a show every night where you have a new guest, and you have all these wacky things going on.” It was like a variety show. I always thought that would be really fun, so the newsletter is my version of that. I started the newsletter in 2013, and it was just a Friday newsletter. It quickly became a list of 10 things I thought were worth sharing. I had a friend, Hugh MacLeod, who was like, “Hey, I have a newsletter. It's bigger than any conference you've ever gone to.” He was talking about South by Southwest here in Austin. He's like, “I have a newsletter now, and it's bigger than South by Southwest.” Jo: Oh, I remember him. Austin: He would say, “Every time I have a new print, I put it out, and there's a button, and then they buy it.” He was like, “You've got to get it. This newsletter thing is killer.” This was in 2011 or something. Jo: Yes, I still have his books. Blogging in Your Underwear or something. Austin: Totally. So Hugh's a whole different story, but I was just like, “Oh, I should really get a newsletter.” Letterman always had a top 10 list on his show. I just always thought a 10 list was really fun. And of course the books are lists of 10 too. So it just worked to have a weekly list of 10. It felt good, and it felt like an infinitely repeatable format. What I'm looking for as a creative person is an infinitely repeatable format that can go on and on and on and be new every time. So the list of 10 is something that people know the form of. It goes back to the Taco Bell thing. They know the form, but they're not sure what's going to go inside. They know it's going to be a burrito, but they don't know what's going to be in the burrito, and that's the exciting part. The newsletter, business-wise, was always a marketing cost for about the first eight years of its existence. I paid MailChimp to send it out. Then in about 2021, when I hadn't done a book for a while, my agent said, “You know, you should really think about doing a paid tier of your newsletter.” And this is to his credit, because he doesn't make anything off the newsletter. He said, “There's this thing called Substack now that makes that really easy.” So we moved to Substack in 2021 in October, and I started doing a Tuesday edition of the newsletter that was just for paid people. That grew enough that it's gone from a marketing cost to something that's almost—it's not quite as much as I make on my books, but it's close. And to be candid, my books sell pretty well. So suddenly the newsletter has become this really healthy income stream. The newsletter to me is actually the day job now. The newsletter is what really keeps the lights on. It's also the perfect mix. It's the day job, it's the thing that keeps income coming in on a regular basis, but it's also the thing I like to do the most. I'm not like a traditional writer who likes to just get lost in their book and take years and years and go away. I'm someone who loves to be doing a lot of different things. The newsletter is a perfect format for me. I'm talking myself into not quitting, actually. It's funny. It's gone from this thing that was a marketing cost to now it's a significant part of our income. That journey—such a bad word, journey—that trip has been very interesting. It's been really cool. But I'm also just lucky. I've been really lucky, and I think part of my thing is, I'm always just trying not to squander my luck. Jo: Well, the book is fantastic, and I know people are going to love it. And the newsletter, of course. So tell us— Where can people find you and your books and newsletter online? Austin: The easiest thing to do is to just go to AustinKleon.com, and that has links to everything—the books, the newsletter. I do actually keep an old-school blog still. I'm one of the few people that still maintains their blog and keeps it up to date. I'm hedging my bets because I think in the end everything will come back to a self-hosted website. I think in the end everyone's going to just go back to their little websites, or at least I hope so. Jo: Well, that was great, Austin. Thanks so much. Austin: Oh, thank you. The post Don't Call It Art: Rediscovering Creative Joy With Austin Kleon first appeared on The Creative Penn.

On today's show, we dive into the cost structure of audio speakers. We start with an article that asks whether 'audiophile' speaker brands are milking you for $20,000. We also read your emails and cover the week's news. News: Important update to your DIRECTV account SVS Auto EQ Room Correction for R|Evolution Subwoofers YouTube TV adds Fox One, Peacock to Primetime Channels store Other: Monoprice Alpha In-Wall Speaker There's never been a better time to grab a new Google TV launcher Are 'Audiophile' Speaker Brands Are Milking You for $20,000 The listeners keep delivering great ideas for show topics. This week Mike LaBorde sent in an article published at headphonesty.com entitled A Former FTC Economist Quit His Job to Prove 'Audiophile' Speaker Brands Are Milking You for $20,000.  The author talks about how a former FTC economist quit his job to design and build affordable high-performance speakers.  He argued that many premium audiophile brands are significantly overpriced because they use similar OEM drivers from the same factories while charging massive markups for branding, cabinets, and dealer margins. We'll break down this article into five points we felt were interesting. The full article is linked and you may want to read it for more details. Many premium audiophile speaker brands rely on the same small group of OEM driver manufacturers (like Sinar Baja/SB Acoustics, SEAS (Scandinavian Electro Acoustic Systems), Scan-Speak, etc.). The same factories and engineering talent supply drivers to both high-end and mainstream brands, even when the final speakers carry vastly different logos and price tags. "Custom" or "proprietary" drivers are often overstated. Most brands customize only the "soft parts" (cone, surround, voice coil) on top of standard off-the-shelf "hard parts" from OEM suppliers, rather than designing and building drivers entirely from scratch. Pricing of speakers — The actual cost of the drivers is a tiny fraction of the retail price. In the Wilson Audio Yvette example, the three drivers cost roughly $530–$580 total, representing only about 2% of the $25,000+ selling price. The vast majority of the cost comes from cabinetry, finish, dealer margins (40-50%), distribution, marketing, and brand prestige, with a typical 5x markup from manufacturing cost to retail. Only a few brands truly manufacture their own drivers in-house. Companies like Focal, KEF, Dynaudio, Paradigm, and Bowers & Wilkins are exceptions. Most premium brands outsource driver production due to the high cost and complexity of vertical integration. High performance doesn't require extreme prices. Former FTC economist Dennis Murphy's Philharmonic Audio proves this by offering well-engineered speakers (like the $850/pair Ceramic Mini using quality SB Acoustics drivers) with minimal overhead, direct sales, and no lavish dealer/showroom costs — challenging the idea that great sound must come with five-figure price tags. The article essentially argues that much of the ultra-premium speaker market is driven more by branding and distribution economics than by revolutionary driver technology. What is the Cost Breakdown of Thousand Dollar Speakers? After going through the previous article we wondered what the actual cost breakdown of Passive bookshelf speakers retailing at $1,000 per pair? ThinkKEF Q series, ELAC Debut Reference, or similar mid to high end consumer hi-fi brands. They balance good performance with accessible pricing.  What follows is our best estimation based on the data we uncovered. If you are in the industry and have better data, please let us know and we will update this analysis. Sources for this analysis include - Audio Science Review, AVS Forum, WhatHifi, headphonesty.com, hubhifi, and a few others.  1. Design & Development (R&D) – Upfront Investment Typical cost: $50,000–$250,000+ for a new model line. Includes acoustic modeling, driver selection/tuning, crossover design, enclosure simulation, multiple prototypes, listening tests, and anechoic chamber measurements. For this price tier, brands often use a mix of off-the-shelf and mildly customized drivers rather than fully bespoke high-end ones.   Amortization: Spread over production volume and for this exercise we used a production run of 5,000–20,000 pairs. This adds roughly $5–$25 per pair at a reasonable scale. 2. Prototyping & Tooling Prototypes: 5–15 iterations at $300–$1,200 each which include custom cabinets, driver samples, hand-assembled crossovers. Tooling: CNC molds/jigs for cabinets, baffle cutting, or vinyl wrap tooling: $8,000–$40,000 upfront. Amortized to $2–$10 per pair. 3. Bill of Materials (BOM) – The Biggest Per-Unit Cost For a typical 2-way passive bookshelf (6.5" woofer + 1" tweeter) at this price point: Drivers - $80–$180 - 6.5" coated paper woofer (~$30–$70 ea.), soft dome or aluminum tweeter (~$15–$50 ea.). Brands like SEAS, SB Acoustics, or custom OEM. Cabinet -  $60-$130, - Braced MDF (18–25mm), vinyl wrap or basic veneer, internal damping, port tube, terminals. Real wood veneer adds premium. Crossover - $30-$80 - 2nd/3rd order with air-core inductors, film capacitors, resistors. Higher quality parts (Mundorf-level) push toward the upper end. Other (grille, wiring, hardware, terminals) - $20-$50 - Magnetic grilles, internal wiring, binding posts. Total BOM per pair: $190–$440 at volume production (typically in China or Vietnam for most brands). Premium touches (better drivers, thicker bracing, nicer finishes) push BOM toward the higher end. 4. Manufacturing, Assembly & Overhead Labor & Assembly: $25–$60 per pair (cabinet gluing/bracing, driver mounting, crossover soldering, final wiring, testing). Quality Control & Testing: Burn-in, frequency sweeps, distortion checks: $10–$25. Factory Overhead/Utilities: $35 - $50. Total Manufacturing per pair: $70 - $135 5. Full Cost Structure to Retail ($1,000/pair) We will assume a large brand that sells 20,000 units and has already invested in tooling and requires minimal new tooling for each new speaker design.  Design and R&D Amortized - $5 Prototype and Tooling  - $2 Bill of Materials - $315 - We split the $190 - $440 down the middle Manufacturing -  $103 - We split the $40 - $135 down the middle Shipping, duties etc to distributor per pair on average - $50 Total to Manufacture $474. The rest of the thousand dollars covers the distribution chain, branding, and profit. And in reality, depending on the efficiency of the factory and ability to leverage design histories from years of experience, the soft costs can be about a third of $110 we came up with, bringing the total cost to about $400. Key Variables Affecting Cost Volume: Higher production = lower per-unit costs. Driver Quality: Exotic materials (beryllium tweeters, carbon fiber) can double driver costs. Cabinet Finish: Vinyl vs. real walnut veneer = big difference. Brand Positioning: Established names (KEF, ELAC) have higher R&D/marketing allocation than direct-to-consumer brands. For comparison DIY builders can replicate similar performance for $300–$600 per pair in parts using higher quality drivers and crossover components and flat-pack or self-built cabinets, eliminating most of the overhead and markups. And after building over 30 sets of speakers I can say without doubt that what you build will sound as good as speakers costing ten times the amount. Plus you can use material that works best for you as well as customizing the look to match your decor. Even my latest set built from stock off the shelf components bought from Part Express for about $200 sound simply amazing!  

Jessica has had a cool path in biochemistry, with work in both academia and industry, as well as an internship at the National Institutes of Health. She is the Director of New Product Development at LCG Clinical Diagnostics, where she oversees the R&D department. In addition to her work at LCG Clinical Diagnostics, Jessica has been an enthusiastic volunteer for both the Maine Science Festival and the Bioscience Association of Maine's Bioscience Day. This conversation was recorded in May 2026. ~~~~~The Maine Science Podcast is a production of the Maine Discovery Museum. It is recorded at Discovery Studios, at the Maine Discovery Museum, in Bangor, ME. The Maine Science Podcast is hosted and executive produced by Kate Dickerson; edited and produced by Scott Loiselle. The Discover Maine theme was composed and performed by Nick Parker. To support our work: https://www.mainediscoverymuseum.org/donate. Find us online:Maine Discovery MuseumMaine Discovery Museum on social media: Facebook Instagram LinkedIn Bluesky YouTubeMaine Science Podcast on social media: Facebook Instagram YouTubeMaine Science Festival on social media: Facebook Instagram LinkedIn YouTube© 2026 Maine Discovery Museum

Hey friends, Chase here Austin Kleon is back on the show, and this conversation is exactly the kind of reminder every creative person needs. You probably know Austin from Steal Like an Artist, Show Your Work!, and Keep Going, the books that have helped millions of people rethink creativity, sharing, influence, originality, and what it actually means to make things in public. But Austin's new book, Don't Call It Art: 10 Ways to Create Like a Kid Again, goes somewhere even more fundamental. It asks a question that feels especially urgent for creators, entrepreneurs, artists, writers, photographers, parents, and anyone trying to make meaningful work in a world that wants to turn everything into content: What if the way back to your best creative work is not becoming more serious, but becoming more playful? That question matters because most of us have made creativity too heavy. We have wrapped it in identity, pressure, productivity, platforms, metrics, perfectionism, and the fear of being judged. We get stuck asking whether we are real artists, serious writers, successful creators, or legitimate professionals. We worry about the noun before we do the verb. Austin's message is simpler, deeper, and more freeing: "Don't call it art. Don't worry about being an artist. Forget the nouns. Do the verbs. Just make stuff." That idea is the center of this episode. We talk about what kids can teach us about creativity, why play is not frivolous, how to build the conditions for your best work, why attention is your most valuable resource, and why some of the most important ideas in your life might come from goofing off. This conversation is about loosening the grip. It is about getting back to the part of you that makes before it judges, explores before it explains, and follows the energy before it knows exactly where the work is going. Why This Conversation Matters Right Now We are living in a strange moment for creative people. On one hand, there has never been more opportunity. An individual with a laptop, a camera, a newsletter, a sketchbook, a phone, a point of view, or a weird little idea can reach people directly. That is extraordinary. But it also comes with a cost. The pressure to turn every interest into a brand, every hobby into content, every project into a product, and every creative impulse into a strategy has never been stronger. We are constantly being asked to define ourselves: What do you do? What is your niche? What is your platform? What are you building? How are you monetizing it? What is the plan? Those questions can be useful at the right time. But when they show up too early, they can suffocate the very thing they are trying to organize. Austin's work reminds us that creativity begins before identity. Before "artist." Before "writer." Before "photographer." Before "entrepreneur." Before "content creator." Before the nouns, there are verbs. Drawing. Writing. Walking. Noticing. Building. Playing. Collecting. Tinkering. Making. Sharing. Kids understand this instinctively. They do not sit down and ask whether what they are making fits the market. They do not wonder whether they are allowed to call themselves artists. They do not freeze because the thing in front of them might not be good enough. They simply begin. And in that beginning, there is a kind of wisdom most adults have forgotten. What We Explore in This Episode Why kids can be some of the best creativity teachers because they make before they judge, label, or perform. How to reconnect with the feeling you wanted as a kid, not necessarily the exact childhood you had. Why play is not the opposite of serious work, but a form of creative research and development. How to create the conditions for creativity through time, space, materials, and permission. Why tools should feel more like toys if you want to stay curious and experimental. How phones fracture attention and why protecting the edges of your day can change the texture of your life. Why hobbies matter and how bikes, music, golf, drawing, and other forms of play can return us to ourselves. Why "don't call it art" can be liberating for anyone who feels trapped by labels or legitimacy. How to use jealousy, disgust, and frustration as creative information instead of letting them turn into bitterness. Why people pay attention when someone truly believes in what they are doing. The Core Idea: Forget the Nouns. Do the Verbs. The fastest way to get unstuck is often to stop asking what you are and start paying attention to what you do. That sounds simple, but it is one of the biggest traps in creative work. We get obsessed with identity. Am I an artist? Am I a real writer? Am I a serious photographer? Am I a professional? Am I successful enough to call myself this thing? Am I allowed? That kind of thinking can freeze you before you even start. Kids do not have that problem. They are not trying to become "artists." They are drawing. They are building. They are making noise. They are inventing stories. They are throwing materials around and seeing what happens. Austin's point is not that craft does not matter. It is not that ambition does not matter. It is not that we should abandon discipline. It is that the living center of creativity is action. The verb comes first. Make the thing. Move the pencil. Open the notebook. Pick up the guitar. Ride the bike. Take the walk. Make the zine. Shoot the photo. Write the sentence. Start the weird little project that begins with, "Wouldn't it be funny if…" That is where the energy is. Play Is Creative R&D One of the big tensions in this conversation is the voice many of us carry around that says play is not practical. That voice says: You have responsibilities. You need to make money. You need to be serious. You need to have a plan. You need to stop messing around. Austin's response is that play is not the opposite of serious work. Play is often what makes serious work possible. He talks about play as research and development. Any healthy company needs R&D. It needs space to explore, test, wander, fail, and discover things that cannot be found through pure efficiency. The same is true for a creative life. A lot of us start in explore mode. We are curious. We are trying things. We are learning. We are following our taste. We are discovering our voice. Then, if something works, we shift into exploit mode. We repeat the thing. We build a career around it. We systematize it. We professionalize it. We optimize it. That can be useful. But if you stay there forever, you eventually run out of juice. You need space to explore again. That is what play gives you. It returns you to the part of the process where you are not just producing, but discovering. And in creative work, discovery is everything. Create the Conditions, Then Get Out of the Way One of my favorite parts of this conversation is Austin's simple equation: Play = time + space + materials. That may sound almost too simple, but it is profound. When I look back at the most creative seasons of my life, the pattern is obvious. I had uninterrupted time. I had a place to go. I had the right materials around me. I had enough structure to begin and enough freedom to be surprised. That is what we often give kids when we want them to create. We give them a table, some paper, some markers, a chunk of time, and permission to make a mess. Then we grow up and deny ourselves the same basic conditions. We say we are blocked, stuck, confused, or uninspired, but often we have not created an environment where anything could actually emerge. No time. No space. No materials. No quiet. No room to tinker. The lesson is not complicated, but it is easy to forget: Set the conditions. Allow the work to happen. Get out of the way. That is not laziness. That is not indulgence. That is how the good stuff gets a chance to show up. The Best Ideas Often Come From Goofing Off I have said this before, and I mean it: so many of the best ideas in my life have come from goofing off. Not from trying to optimize. Not from grinding. Not from forcing. Not from staring at a blank screen and demanding genius. They came when I was tinkering. Playing. Walking. Talking with friends. Making something that had no obvious point. Trying something because it felt fun, strange, or impossible to explain. Austin and I talk about this because it is one of the hardest things for ambitious people to accept. We want the path to be linear. We want effort to equal outcome. We want the best ideas to come from the most serious hours. But creativity often does not work that way. The mind needs room. The body needs movement. The soul needs a little nonsense. Goofing off is not always avoidance. Sometimes it is how the deeper intelligence gets a chance to speak. Tools Should Be Toys Austin says something in this episode that every creator should sit with: Tools should be toys. That does not mean your tools are unimportant. It means the best tools invite you into a state of play. They make you want to touch them, try them, misuse them, combine them, push them, and see what happens. A sketchbook can be a toy. A camera can be a toy. A guitar pedal can be a toy. A bicycle can be a toy. A cheap notebook, a box of crayons, a microphone, a drum machine, a kitchen table, a phone in airplane mode, a pile of index cards — all of it can become part of the creative playground. The danger is when tools become only professional instruments. When every object in your creative life carries the pressure of output, performance, monetization, or proof, it becomes harder to begin. A toy invites curiosity. And curiosity is one of the most reliable doors back into making. Attention Is the Beginning of Everything Another major theme in this episode is attention. Austin shares a simple practice: start and end the day without your phone. Not as a moral performance. Not as some extreme digital detox. Just as a way to protect the edges of the day from people and companies that do not care about you, but desperately want your attention. That hit me hard. Because attention is not just another resource. In many ways, it is the resource. What you give your attention to shapes your thoughts, your desires, your mood, your relationships, your sense of possibility, and your work. If the first thing you do every morning is hand your mind to the internet, you are letting someone else set the tone for your day. Austin's practice is simple. Coffee. Breakfast. Journal. Kids. Life. Then the phone. At night, the phone charges in the kitchen. Small boundary. Huge impact. Creativity requires attention. And attention has to be protected. Return to Who You Were Before All This There is a beautiful thread in this conversation about returning to the things that made you feel alive before life got complicated. For Austin, that includes riding a bike and playing in a band. For me, golf has become one of those things. Not because it is productive in the traditional sense, but because it gets me outside, off my phone, walking with friends, and fully present for hours. That matters. A lot of people feel lost because they are trying to think their way back into aliveness. But sometimes the way back is physical. Pick up the instrument. Ride the bike. Throw the baseball. Walk the dog. Draw badly. Make noise. Get outside. Do the thing you used to love before you thought it had to mean something. Austin brings up the question: Who were you before all this? Before the career. Before the metrics. Before the audience. Before the obligations. Before the identity got heavy. There may be clues there. Not because you need to go backward, but because some part of you may have been waiting to be invited forward again. Don't Call It Art The title of Austin's book is not a dismissal of art. It is a liberation from the weight we put on the word. For a lot of people, "art" has become intimidating. Sacred. Serious. Something that belongs to museums, geniuses, experts, critics, galleries, and people who have permission. But making is older and deeper than all of that. Kids understand this. They do not call it art. They just do things. And when we stop obsessing over whether something is art, we create more room to actually make. We get less precious. Less frozen. Less performative. Less worried about the label and more connected to the act. That is the invitation: Don't call it art. Don't worry about being an artist. Forget the nouns. Do the verbs. Just make stuff. It sounds almost too simple. That is why it works. Use What Bothers You Austin also offers a surprising creative tactic: pay attention to what you hate. Not publicly. Not performatively. Not as a way to become bitter or cynical. But privately, as information. Disgust can point toward values. Frustration can reveal desire. Jealousy can show you something you want. The things that bother you can become clues, if you are willing to ask what the opposite would look like. Instead of turning your irritation into a rant, turn it into a project. What would you rather see in the world? What is the opposite of the thing you cannot stand? What would it look like to make that? That shift is powerful because it transforms complaint into creation. It turns "I hate this" into "What if we made something different?" People Pay Attention to Belief Near the end of the conversation, Austin shares a line from Kim Gordon that I love: "People will pay to watch other people believe in themselves." That is true in art. It is true in music. It is true in entrepreneurship. It is true in leadership. It is true in life. We are drawn to people who are alive in what they are doing. Not perfect. Not polished beyond recognition. Not optimized into sameness. Alive. When someone believes in what they are making, that belief travels. This does not mean you will always feel confident. It does not mean you will never doubt yourself. It does not mean every idea will work. It means you keep returning to the work. You keep paying attention to what matters to you. You keep making the thing only you can make in the way only you can make it. That is where the signal comes from. About Austin Kleon Austin Kleon is the New York Times bestselling author of a series of illustrated books about creativity in the digital age: Steal Like An Artist, Show Your Work!, Keep Going, and Don't Call It Art. He is also the author of Newspaper Blackout, a collection of poems made by redacting the newspaper with a permanent marker. His books have sold over two million copies and have been translated into more than 30 languages. Austin's work has been featured on NPR's Morning Edition, PBS Newshour, The New York Times, and The Wall Street Journal. New York Magazine called his work "brilliant," The Atlantic called him "positively one of the most interesting people on the Internet," and The New Yorker said his poems "resurrect the newspaper when everybody else is declaring it dead." He has spoken for organizations including Pixar, Google, Netflix, SXSW, TEDx, Dropbox, Adobe, and The Economist. In previous lives, he worked as a librarian, a web designer, and an advertising copywriter. He lives in Austin, Texas, with his wife and sons. Follow Austin Kleon Website Don't Call It Art Newsletter Instagram X YouTube Timecodes 04:24 – Austin returns to the show and talks about the new book 06:17 – How Austin's kids became his best creativity teachers 07:04 – What it means to take care of a creative person 10:43 – The childhood question that reveals what makes time disappear 18:34 – Why play is creative research and development 21:43 – Finding what you were not looking for 23:06 – How a fixed vision can blind you to what is actually in front of you 28:13 – Chase reflects on creating the right conditions for creative work 31:37 – Austin's equation: play equals time plus space plus materials 32:48 – Why tools should feel more like toys 35:25 – Reconnecting with the activities that made you feel alive as a kid 38:53 – Who were you before all this? 43:08 – Protecting attention from companies that want to take it 44:17 – Starting and ending the day without your phone 47:08 – Why friendship, hobbies, and shared activities matter 57:17 – Where the title Don't Call It Art came from 58:32 – Forget the nouns, do the verbs, just make stuff 01:00:01 – Why "wouldn't it be funny if…" is a clue worth following 01:03:15 – Finding your creative family tree 01:06:36 – How to use frustration and disgust as creative information 01:08:31 – Why people pay attention when you believe in what you are doing 01:09:44 – Austin's newsletter, book tour, and where to find his work Questions to Ask Yourself If you want to turn this episode into action, take a few minutes with these questions: What did I do as a kid that made hours pass like minutes? Where am I making creativity heavier than it needs to be? What noun am I clinging to that might be keeping me from doing the verb? What conditions do I need in order to make more freely? Do I have time, space, and materials available on a regular basis? What tool in my life could become more like a toy? Where is my attention being stolen before I have a chance to choose? What hobby, activity, or form of play would help me return to myself? What bothers me enough that it might contain a creative clue? What would I make this week if I stopped worrying whether it counted as art? A Simple Practice for Making Like a Kid Again Here's something practical you can do this week. Set aside one uninterrupted hour. No phone. No audience. No outcome. No need to make something good. Choose a space. Put a few materials in front of you. Paper and markers. A camera. A guitar. A notebook. Clay. Index cards. A laptop with the internet off. Whatever feels inviting. Then begin with this prompt: Wouldn't it be funny if… Follow whatever comes next. Do not evaluate it too early. Do not ask what it is for. Do not decide whether it is art. Do not turn it into a brand, a strategy, or a pitch deck. Just make stuff. Then notice how you feel. Notice what surprised you. Notice whether something small wants to keep going. That is enough. Final Thought The longer I do this work, the more I believe that creativity is not something we need to earn. It is something we need to return to. It was there before the labels. Before the pressure. Before the metrics. Before the platforms. Before the fear of being judged. Before we learned to ask whether we were allowed. Austin's invitation in this conversation is simple, generous, and quietly radical: Stop making creativity so precious that you cannot touch it. Give yourself time. Give yourself space. Give yourself materials. Protect your attention. Find your friends. Pick up the toy. Follow the weird little idea. Let yourself begin before you know what it means. Until next time: forget the nouns, do the verbs, and just make stuff.

JONDOSPORT USA is a golf-focused sunglasses brand that specializes in performance eyewear designed to help golfers see the course more clearly. Their sunglasses use what they call "KRISP" lens technology, which is designed to enhance contrast, reduce glare, improve depth perception, and make it easier to track the golf ball and read greens. On this episode of the Wednesday Match Play Podcast, brought to you by Eden Mill St Andrews, Morné talks about where JONDO Sport is based, shares how the company got started, details their approach to R&D and where he sees the future of eyewear going, discusses prescription lenses, and explains how he first met Ernie Els. He also breaks down why 17% light transmission matters, talks about their warranty and repair process, and shares how they stay competitive in such a crowded market. This was such a cool conversation and an honor having Morné on the show. Let's tee off.

Breaking into any industry is difficult, but it is especially challenging when going against the large food manufacturers. Shekhar Patel, CEO of Mushroom Squared, discusses how he has benefitted from his time spent as Director of R&D at PepsiCo's Advanced Research Group when bringing a burger alternative, made with mushrooms, to market. He shares the importance of taste, nutrition, profitability, and customer satisfaction when developing a new product.  Learn more at mushroomsquared.com and follow on Instagram @mushroomsqrd Visit ConfidenceThroughHealth.com to find discounts to some of our favorite products.Follow me via All In Health and Wellness on Facebook or Instagram.Find my books on Amazon: No More Sugar Coating: Finding Your Happiness in a Crowded World and Confidence Through Health: Live the Healthy Lifestyle God DesignedProduction credit: Social Media Cowboys

Most people get their hormone levels tested once a year — if they think about it at all. Marina Pavlovic Rivas, Co-Founder & CEO of Eli Health, is on a mission to change that.  Ramon Vela sits down with Marina for a fascinating conversation about the technology she spent nearly seven years building: a saliva-based, at-home hormone monitoring platform that delivers cortisol, testosterone, and progesterone results to your phone in minutes, giving people real-time visibility into one of the most overlooked dimensions of their health. * The gap nobody was solving. After searching online for a way to track her own hormonal data, Marina realized the product didn't exist. So she built it, spending more than six years on R&D, regulatory approval, and venture capital before bringing it to market. * Hormones affect everything, not just fertility. From energy and mood to sleep, libido, bone health, cardiac health, and cognitive performance, Marina breaks down why hormonal data is one of the most important and most underused signals in personal wellness. * The wearable parallel that puts it all in context. Checking your hormones once a year is like measuring your heartbeat once a year: technically useful, but dangerously incomplete. Eli Health gives users the same continuous feedback loop that smartwatches brought to sleep and heart rate. * A six-second saliva test. Results in 20 minutes. Users collect saliva, wait 20 minutes, then photograph the test to receive results directly in the app. Repeat over time and you build a hormonal picture that no annual blood draw could ever provide. * Cortisol first, for good reason. Eli launched with cortisol because it sits at the top of the hormonal cascade. When cortisol is off, everything else follows. Testosterone and progesterone, including their interactions, are coming next. Join us in listening to this episode for a genuinely eye-opening conversation about hormonal health, the future of at-home diagnostics, and what it means to build a category that didn't exist before you created it.  Whether you're a health-conscious consumer, a founder, or someone who just wants to understand what their body is actually telling them, this one is for you.  Visit: https://eli.health/ Try their Instant Cortisol Test: https://eli.health/products/cortisol?view=sl-44128C61 If you enjoyed this episode, please leave The Story of a Brand Show a rating and review.  Plus, don't forget to follow us on Apple and Spotify.  Your support helps us bring you more content like this! * Today's Sponsors: Saral - The Influencer OS: https://www.getsaral.com/demo SARAL is the all-in-one influencer platform that finds brand-aligned creators, automates outreach, and manages everything in one place. Request a live demo today. Let the SARAL team know you're a The Story of a Brand Show podcast listener to get an extended free trial! Visit the link above. 

Eric Ries is an author, podcaster, and founder of The Lean Startup. He hosts The Eric Ries Show and his notable books Incorruptible: Why Good Companies Go Bad... and How Great Companies Stay Great, The Lean Startup, Farther, Faster, and Far Less Drama, The Leader's Guide, and The Startup Way. Greg and Eric discuss why startups and corporations lose their mission through shifts from founder-to investor-control, changing from long-term focus to short-term focus, and purpose-driven to profit-driven behavior. Eric argues governance is “organizational soul craft” and critiques shareholder primacy as a recent, judge-and-academic-driven ideology that creates unaccountable short-term pressure, metric surrogation, and value destruction, even for shareholders.  Eric also explains how markets reward short-term cost-cutting (e.g. reduced R&D), and why mission-driven companies can outperform. He outlines practical protections such as writing mission primacy into charters, converting to Public Benefit Corporations, and stronger structures like foundation ownership (e.g. Novo Nordisk and Patagonia). *unSILOed Podcast is produced by University FM.* Episode Quotes: Mission-driven or mission-hopeful? 12:39: So I think for companies, we're seeing this world now where we have a divergence between the mission statement and the actual mission or purpose of the organization. So the mission statement is lofty. I tell the story in the book of Silicon Valley Bank before it collapsed. Its mission statement was something like, “To advance the innovation economy,” or whatever. But its actual legal purpose was just maximize shareholder value. So this divergence caused the collapse of the bank. And so, first of all, if you have a mission statement, but your purpose says “any lawful act or activity,” you're lying. Just so you know, you are lying to your customers. You are lying to your employees. You're lying to everyone you say that mission to because, according to current legal theory, you could be replaced at a moment's notice by your investors, who will then can change the mission to whatever they want. I call that not being mission-driven. You are mission-hopeful. You're hoping nobody will do this to you in the future. Governance is organizational soul craft Governance sounds really boring, but it's really the art of organizational soul craft. It's actually really interesting. And if we can get leaders and founders to pay more attention to it, they can have a much higher probability of their organization enduring. The age of temporary organizations 27:46: I say we've entered an era of temporary managers running temporary organizations for the benefit of temporary owners because executive tenure, company lifespan, and average holding period of stocks have all collapsed in the last, especially the last twenty-five years, let alone the last forty years. So, I don't think it's possible to really have—it's very difficult to build a value-creating organization in that span, and the markets will punish you for doing so. Show Links: Recommended Resources: Governance Shareholder Primacy Overlapping Consensus Silicon Valley Bank Andy Rachleff Environmental, Social, and Governance Mark Zuckerberg Guest Profile: LinkedIn Profile Wikipedia Page The Lean Startup Social Profile on X Guest Work: The Eric Ries Show YouTube Channel Amazon Author Page Incorruptible: Why Good Companies Go Bad... and How Great Companies Stay Great The Lean Startup by Eric Ries – How Today's Entrepreneurs Use Continuous Innovation to Build Successful Businesses The Leader's Guide The Startup Way: How Modern Companies Use Entrepreneurial Management to Transform Culture and Drive Long-Term Growth Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Group Function, Alan connects with fellow Michiganders Austin and Lindsay Ritter, the husband-and-wife team behind Ascentcare Dental Products. Austin shares his unique journey from an HR background to dental product innovation, inspired by casual, after-hours brainstorming sessions with a dentist neighbor. They dive deep into the engineering behind their flagship Vacu-Lux Isolation System, explaining how they designed it as a true high-volume evacuation (HVE) system to turn the oral cavity into a negative pressure environment. The discussion covers the critical role of user feedback in small-business R&D, material selection for patient comfort and optical clarity, and their ultra-lightweight Feather Flex Vacuum Line which helps prevent wrist fatigue for hygienists and dentists alike. Some links from the show: Ascentcare website Vacu-lux HVE Isolation Systems VacuVUE Evacuation Mirror Featherflex HVE Vacuum Line Join the Very Dental Facebook Group using one of these passwords: Timmerman, Paul, Bioclear, Hornbrook, Gary, McWethy, Papa Randy, Frank or Lipscomb!  The Very Dental Podcast network is and will remain free to download. If you'd like to support the shows you love at Very Dental then show a little love to the people that support us! We're proud to be supported by the folks at Net32! I'm a big fan of the Bioclear Method! I think you should give it a try and I've got a great offer to help you get on board! Use the exclusive Very Dental Podcast code VERYDENTAL8TON for 15% OFF your total Bioclear purchase, including Core Anterior and Posterior Four day courses, Black Triangle Certification, and all Bioclear products. Are you a practice owner who feels like the bottleneck in your own business? If you're tired of being the hardest-working person in your office, I've got something you need to hear. Dr. Paul Etchison, is hosting a virtual event that is a total game-changer. Paul is honestly one of the most brilliant minds in dental leadership today, and he's hosting the 3-Day Freedom Practice Workshop from February 19th through the 21st. He's going to show you exactly how to break through that two-million-dollar revenue ceiling while actually compressing your clinical week. It's about building a leadership team that takes ownership so you can finally step into the CEO role you deserve. Head over to DentalPracticeHeroes.com/freedom to grab your spot. And do me a favor—mention the Very Dental podcast when you sign up. It's 100% guaranteed, so you've got nothing to lose but the stress. Crazy Dental has everything you need from cotton rolls to equipment and everything in between and the best prices you'll find anywhere! If you head over to verydentalpodcast.com/crazy and use coupon code "VERYSHIP" you'll get free shipping on your order! Go save yourself some money and support the show all at the same time! The Wonderist Agency is basically a one stop shop for marketing your practice and your brand. From logo redesign to a full service marketing plan, the folks at Wonderist have you covered! Go check them out at verydentalpodcast.com/wonderist! Enova Illumination makes the very best in loupes and headlights, including their new ergonomic angled prism loupes! They also distribute loupe mounted cameras and even the amazing line of Zumax microscopes! If you want to help out the podcast while upping your magnification and headlight game, you need to head over to verydentalpodcast.com/enova to see their whole line of products! CAD-Ray offers the best service on a wide variety of digital scanners, printers, mills and even  their very own browser based design software, Clinux! CAD-Ray has been a huge supporter of the Very Dental Podcast Network and I can tell you that you'll get no better service on everything digital dentistry than the folks from CAD-Ray. Go check them out at verydentalpodcast.com/CADRay!

On this week's episode of Tying it Together, host Tim Boyum is joined by former Charlotte city council members Tariq Bokhari and Larken Egleston to talk about potentially one of the most consequential months of Charlotte politics in at least a decade. The council recently reversed course and canceled a major transportation plan. It is poised to potentially ban data centers in the coming weeks, and this all comes after the mayor announced she was stepping down at the end of June, long before her term expires. Bokhari, a Republican, and Egleston, a Democrat, work at The Southern Group and host their own podcast, R&D in the N.C.

We're about one simple thing. We help the owners of companies between $2 million and $20 million in size achieve large-scale growth of their business. We don't believe in growth for growth's sake. As far as we're concerned, a company is just a tool to support the goals of its owners and its people. We help people to grow their businesses in support of whatever those goals are. We help with marketing, sales, operations, human resources, underlying structures, management, leadership development – whatever it takes to grow. That's what we do. https://walshbusinessgrowth.com/    About Walsh Business Growth Institute. Founded in 1995 by Michael Walsh, Walsh Business Growth Institute is an international business consulting organization: We support business owners across North America and Europe to take their companies to the next level of growth. Our goal is simple – to help people get what they want, using their business as the primary tool to get them there. Whether the company wants to grow to $10 million or $100 million, what most business owners ultimately want is more profit and more freedom. We're committed to synthesizing the best practices for business growth and leadership development. We continue to invest in R&D to continually evolve best practices and strategies that help our clients meet their goals. We help you grow consistent with your goals. The biggest challenge that business owners face "The reason we look at business growth is because, at any given size, a company has strengths and limitations. If you can grow it a little bit, you can actually morph it to get what you want."

Running is already punishment with better shoes. In ROI Podcast® #509, Law Smith and Eric Readinger talk with Josh Sprague, owner/operator and founder of Orange Mud, the endurance gear company making hydration packs, running vests, biking packs, transition wraps, and outdoor gear for runners, cyclists, triathletes, mountain bikers, gravel riders, ultra runners, and other people who hear "24-hour race" and somehow do not immediately fake a hamstring injury. Josh built Orange Mud because the hydration packs he used were too tight, bounced around, caused chafing, and made running feel like a backpack had filed a workplace grievance against his torso. So he made his own. Fourteen years later, Orange Mud is still solving that same core problem: helping athletes carry fluid, gear, phones, calories, and post-race beer-garden money without turning their upper body into a friction crime scene. This one starts with practical product design, then takes the scenic route through SEC football smuggling economics, premium floppy flasks, Raiders fan brawls, secret backpack wine compartments, fake casts full of booze, organic street nuts, nicotine pouches, and the kind of R&D conversation that should probably be notarized. Then Josh drops the business stuff that actually matters. Founders confuse activity with ROI. Busy is not a metric. Busy is a haunted LinkedIn carousel with a calendar invite. Josh's cleanest example: the best account he ever landed took five years of follow-up, then turned into $5 million in the first year. Most people quit after two polite emails and a sad CRM note. Josh kept showing up. The bigger lesson is marketing infrastructure. Josh explains why companies chase platforms like Facebook ads before they understand the full pipeline. If your website is bad, your tracking is broken, your CRM is decorative, your follow-up is asleep, and your customer problem is defined with the clarity of a gas station bathroom mirror, the ad platform is not the problem. You are just paying Meta to make the fire look warm. Listen if you are a founder, ecommerce operator, product builder, endurance athlete, marketer, salesperson, agency owner, or anyone who has ever said "we need Facebook ads" before knowing who the customer is, what problem they have, and what happens after the lead comes in. Hosted by Law Smith, @LawSmithWorks, LawSmithWorks.com, and Eric Readinger, @EricReadinger. Powered by Tocobaga Consulting, Tocoba.ga. ROI Podcast® is the business show for people who want the math, the mess, and the occasional product idea that belongs in a stadium parking lot. Topics: Josh Sprague, Orange Mud, hydration packs, running gear, cycling gear, endurance racing, 24-hour races, trail running, gravel cycling, product design, customer pain, marketing ROI, sales follow-up, CRM, Facebook ads, Meta attribution, ecommerce marketing, founder story