Podcasts about workflows

Viele Unternehmen posten regelmäßig auf Social Media – und trotzdem bleiben Reichweite, Anfragen und Wachstum aus. Woran liegt das? Oft entscheidet nicht der Algorithmus über den Erfolg, sondern eine klare Strategie, konsequente Umsetzung und die Bereitschaft, aus den eigenen Daten zu lernen.In dieser Folge spricht Linda Weise von Traumnoten über ihren Weg vom ersten Lern-Content zur erfolgreichen Content Creatorin und Unternehmerin. Sie erklärt, warum Konsistenz wichtiger ist als Perfektion, weshalb man sich zunächst auf eine Plattform konzentrieren sollte und wie Analytics dabei helfen, erfolgreiche Inhalte systematisch zu wiederholen. Außerdem geht es um den Aufbau einer Personal Brand, den Mehrwert von kostenlosem Content, den Verkauf digitaler Produkte sowie praktische Tools und Workflows für die Content-Erstellung – mit vielen konkreten Tipps für Gründer, Unternehmer und Creator.Die Folgen gibt es überall, wo du Podcasts hörst: https://linktr.ee/founderflow https://open.spotify.com/show/1B3JyqXvDP9nWoAwORyRLzhttps://podcasts.apple.com/us/podcast/founderflow-der-gr%C3%BCndungspodcast/id1481847368Folgt uns auf für weitere spannende Einblicke: Instagram / https://www.instagram.com/founderflow.fm/LinkedIn / https://www.linkedin.com/company/founderflow/posts/?feedView=allDas Team wünscht viel Spaß mit der Folge :)

Werbung *** Diese Folge wurde mit freundlicher Unterstützung der Firma  Histoserve produziert ***   Xylolfrei im Labor – Vision oder bereits Realität? Zwei-gegen-Zwei Interview mit Ferdinand Bucerius und André Wilger von Histoserve   Die letzte Folge Patho aufs Ohr im Sommersemester - und wir enden wie wir das Sommersemester begonnen haben mit einer grundlegende Angelegenheit in der Pathologie: die Verwendung von bzw. den Verzicht auf Xylol. Hierüber sprechen wir wieder mit Ferdinand Bucerius und André Wilger von Histoserve, zwei Experten aus der praktischen Laborwelt, die sich seit Jahren intensiv mit histologischen Workflows beschäftigen. Xylol wird vor allem zum Entparaffinieren von Schnitten und zum Klären von Gewebe eingesetzt und ist damit wesentliche Komponente der Präanalytik. Es gehört seit Jahrzehnten zur Standardchemie in der Histologie und ist fester Bestandteil vieler Laborprozesse. Sein Einsatz ist aber auch nicht unproblematisch. Warum das so ist und wie der Verzicht auf Xylol in der Histologie gelingen kann sind u.a. Themen dieses Interviews.   Wir haben mal wieder einiges dazu gelernt… Reinhören lohnt sich also!   Viel Spaß beim Zuhören.   Hier findet ihr Histoserve: https://www.histoserve.de/   Wir freuen uns über euer feedback.   Kontakt: sven.perner@pathopodcast.de linkedin.com/in/prof-dr-med-sven-perner-6a771b48   christiane.kuempers@pathopodcast.de linkedin.com/in/pd-dr-med-christiane-charlotte-kümpers-279a382b8

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

Abel Prieto joins The Next 100 Days podcast to discuss InCruises as a side gig opportunity and its business model.Summary of the PodcastKey TakeawaysUnique Membership Model: InCruises doubles members' monthly contributions (e.g., $100 → $200) into "Reward Points" for travel, forcing savings and guaranteeing a minimum 25% discount on bookings.Strategic Growth Engine: The affiliate model is a key driver for cruise lines, as 75% of InCruises' travelers are first-time cruisers—a market segment the lines struggle to reach directly.Significant Income Potential: The UK market is largely untapped. A mid-level goal of building a team generating ~$250k/month in sales yields a personal income of ~$50k/month (~£38k).Watertight Affiliate Tracking: The referral-link-only sign-up process prevents commission loss, a common problem with cookie-based affiliate programs.The InCruises Business ModelInCruises (corp. name: InGroup) is a travel club founded in 2016 by Frank Codina. Abel was onboarded as a Communications expert.Membership Tiers:Member: Pays a monthly fee (e.g., $100 or $250) which the company matches 100% in "Reward Points."Example: A $2,000 cruise costs the member $1,000 in cash and $1,000 in company-matched points, representing a 50% total value.Guaranteed Savings:Using Points: 25% minimum discount.Using Cash (Insider Pricing): 17% minimum discount.Key Rule: Points are for travel only and are non-refundable, ensuring members use them for vacations. They are transferable upon death.Partner: An affiliate who sells memberships.Partner Member: Both a paying member and an active affiliate.Company Profitability:Wholesale Pricing: InCruises buys travel inventory at wholesale rates.Commissions: Earns commissions from cruise lines on bookings.Unused Points: Profits from points that are never redeemed.Market & OpportunityGrowth:Sales: $350M in 2023.Customers: 750k+ travelers.Inventory: 21k+ cruises, 430k+ hotels, 350k+ tours.Untapped Markets: The UK and Europe are largely unpenetrated, offering significant growth potential.Demographic Shift: Cruising is attracting a younger audience with more diverse offerings (e.g., Virgin Cruises, onboard activities), expanding the market beyond traditional demographics.Building a Business with InCruisesAffiliate Model: Partners sell memberships, not individual travel bookings.Income Potential (UK Context):Mid-Level Goal: Generate ~$250k/month in team sales.Required Team: ~500–1,000 paying members.Resulting Personal Income: ~$50k/month (~£38k).Graham's Initial Strategy:Initial Test: A careers fair booth with a costumed "stewardess" (Karina) and leaflets to gauge interest.Target Audience: Affluent individuals (45+) who can afford the monthly membership.Leverage Existing Assets: Use the "finelyfettled" database of 278k cruise-takers for targeted outreach.Future Innovation: Develop an AI-powered "core agent" to provide personalised travel recommendations, adding value beyond the standard platform. The Next 100 Days Podcast Co-HostsGraham ArrowsmithGraham founded Finely Fettled in 2014 to provide data from The UK High Net Worth Database to marketers targeting affluent and high-net-worth customers. He's the founder of MicroYES, a Partner for MeclabsAI, creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham also provides an Answer Engine Optimisation solution to get your website in shape to be found by LLMs. Through his https://upperdeck.cruises website, Graham introduces people to inCruises. Become a member today by clicking hereKevin ApplebyKevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.com

In this episode, we explore the rapidly changing role of artificial intelligence in the tax and accounting profession. What began as simple AI-generated emails and content creation is quickly evolving into agent-based systems capable of performing real work across firm workflows.Drawing on conversations from industry events and firsthand experiences with firms and clients, we discuss why many believe we're at a major inflection point for AI adoption. Topics include the rise of AI agents, client expectations, the challenges of establishing a reliable "source of truth," and the growing need for governance, testing, and validation as firms automate sensitive processes.The discussion also tackles one of the profession's biggest questions: How do firms preserve professional judgment and develop future talent when more routine work is handled by AI? From structured versus unstructured data to workflow transformation and client service, this episode examines where AI is delivering value today—and where human expertise remains essential.Key topics include:The transition from AI experimentation to AI-powered workflowsWhy agentic AI could transform tax and advisory servicesRisks of overtrusting AI-generated outputsData security, governance, and quality control concernsThe role of professional judgment in an AI-enabled futureHow firms can balance automation with client relationshipsWhat tax season 2027 could look like as AI adoption acceleratesThis conversation offers a practical look at the opportunities, challenges, and realities facing firms as AI moves from novelty to necessity.

Inteligência Artificial, Product Management e marketing se encontram neste episódio do Papo na Arena com Marina Moreira, a “Mother of Agents” e AI Ops Tech Lead na Velora. Ela conta como saiu de uma carreira em marketing para criar agentes e workflows de IA para diferentes áreas da empresa.Entramos em casos reais de IA: qualificação de leads para SDRs, verificação automática de ONGs, integrações com Slack e Salesforce, triagem de demandas no Notion e a construção de um Company OS que compartilha contexto entre os times. Também falamos sobre como profissionais de marketing podem ir além da produção de conteúdo e criar landing pages, dashboards, análises de dados e sistemas completos.Na segunda parte, conversamos sobre a semana de quatro dias, os incentivos para adoção de IA nas empresas e o risco de exaustão de quem passa o dia comandando agentes. Se a IA aumenta tanto a velocidade do trabalho, como proteger a qualidade das decisões e evitar burnout e AI brain rot?

ChatGPT 5.6 ist draußen - funktionieren jetzt alle Workflows noch? Was kann das neue ChatGPT Work? Und warum kann KI kein Schachbrett darstellen?

What if your agency could respond to every lead within seconds and automate follow-ups without hiring more staff? Discover how AI-powered workflow automation helps real estate businesses operate more efficiently and convert more leads. Learn more at https://estateflowapp.vercel.app/#contact Nexoraa Digital City: Coimbatore Address: SF 21, Site No. 26 Website: https://nexoraa.digital Phone: +91 7598470890 Email: nexoraa.hq@gmail.com

Wann baust du einen KI-Agenten — und wann ist eine maßgeschneiderte Applikation die bessere Wahl? TJ erklärt den Unterschied anhand eines konkreten Bewerbungs-Tools und zeigt, warum autonome Agenten oft enttäuschen, während kleine, zweckgebundene Apps überraschend viel leisten. Wer KI vom Anwendungsfall her denkt, gewinnt — und das hörst du in dieser Folge. Vom Anwendungsfall denken — nicht von der Technologie TJ eröffnet mit einer Forderung, die in vielen KI-Kursen fehlt: Fang nicht bei der Technologie an, fang beim Problem an. Was kostet dich gerade Zeit? Wo liegt der echte Schmerzpunkt? Für Holger aus dem Kurs war die Antwort klar: Bewerbungen. Statt lange über das richtige Tool zu diskutieren, stellt sich die konkrete Frage — wie oft durchläufst du diesen Prozess, und lohnt es sich, ihn zu automatisieren? Genau diese Haltung, konsequent vom Intent her zu denken, ist der Ausgangspunkt für alles, was TJ in dieser Episode auseinandernimmt. Warum der Bewerbungsagent enttäuscht hat Im Kurs haben die Teilnehmer die klassische Lernkurve durchlaufen: Prompts, dann Workflows, dann Custom GPTs mit Mentions-Funktion, dann eigene Skills — und schließlich Agenten. Der Bewerbungsagent hat fünf Stellen rausgesucht und eine Entwurfs-Mail geschrieben. Klingt solide, war aber „nicht wirklich ergiebig". Der Höhepunkt kam, als ein Agententeam bei Perplexity — Chef-Agent, Researcher, Autor, Analyst — eine Stunde lang im Hintergrund gearbeitet hat, ohne einen wirklichen Durchbruch zu liefern. Das ist der Moment, an dem TJ den Schalter umlegt: Nicht mehr Agent, sondern Applikation. Drei konkrete Unterschiede: Plattform, Zugang, Fähigkeiten TJ zieht eine klare Linie zwischen beiden Welten. Erstens die Plattform: Agenten laufen in einem „Harness" des jeweiligen Providers — OpenAI, Claude, Perplexity. Sie sind nicht in der freien Wildbahn. Eine App dagegen braucht eine eigene Basis: lokal auf dem Rechner, auf Vercel oder einem eigenen Virtual Private Server. Zweitens der Zugang zur Außenwelt: Agenten nutzen vorgefertigte Konnektoren, die direkt im Provider-Interface konfigurierbar sind — Gmail, HubSpot, fertig. Apps sprechen über APIs und MCPs mit der Außenwelt, was mehr Flexibilität bringt, aber eigene API-Keys und ein bisschen Setup erfordert. Drittens die Fähigkeiten: Im Agenten entwickeln sie sich dynamisch, in der App sind sie definiert und codiert — kontrollierbarer, aber auch bewusster gestaltet. Wegwerf-Software ist kein Makel — sie ist das Ziel Holgers Bewerbungs-App wird in ein paar Wochen in der Schublade verschwinden. Nicht weil sie schlecht ist, sondern weil er dann einen Job hat. TJ nennt das Wegwerf-Applikation — und meint es als Kompliment. Eine App, die einen Menschen präzise durch eine Lebensphase begleitet und danach irrelevant wird, hat ihren Zweck erfüllt. Das ist effizienter als ein generisches Agenten-System, das für alle funktionieren soll und deshalb für niemanden wirklich passt. Die Pointe: Der Prompt, den du für den Agenten geschrieben hättest, funktioniert als Anforderungsbeschreibung für die App genauso gut — der Aufwand ist ähnlich, der Output kontrollierbarer. Fazit: Apps schaffen ein Universum für einen Anwendungsfall TJ fasst es pointiert zusammen: Mit einer eigenen Applikation baust du dir ein Universum für genau einen Zweck — wiederholbar, steuerbar, erweiterbar. Das ist etwas, das ein Agent in dieser Form nicht leisten kann. Der Einstieg ist einfacher als gedacht: einmal klären, wo die App lebt, einmal die nötigen Schnittstellen andocken — und dann läuft ein Werkzeug, das exakt auf deine Bedürfnisse zugeschnitten ist. Wer KI wirklich nutzen will, denkt nicht in Tools, sondern in Problemen. Und baut dann das Kleinstmögliche, das dieses Problem löst. Das nimmst du mit: • Wenn dein Agent lange läuft und trotzdem keine brauchbaren Ergebnisse liefert, ist eine kleine App mit klarem UI die bessere Wahl. • Denk KI immer vom Anwendungsfall her: Welches Problem taucht häufig auf, und lohnt es sich, es zu automatisieren? • Der gleiche Prompt, der einen Agenten beschreibt, taugt direkt als Anforderung für eine App — der Aufwand ist ähnlich, der Output kontrollierbarer. • Agenten leben im Harness des Providers (OpenAI, Claude, Perplexity). Apps brauchen eine eigene Plattform — lokal, auf Vercel oder einem eigenen Server. • Apps verbinden sich über APIs und MCPs mit der Außenwelt, nicht über vorgefertigte Konnektoren — mehr Flexibilität, aber mit eigenem API-Key-Setup. • Wegwerf-Applikationen sind kein Verschwendung: Eine App, die Holger durch die Jobsuche bringt und danach wegkommt, ist ein Erfolg. Kapitel: 00:00 Hook: Die Welt der Wegwerf-Applikationen 00:26 KI vom Anwendungsfall her denken 01:36 Rückblick: Von Prompts über Workflows zu Agenten 02:46 Warum der Bewerbungsagent enttäuscht hat 03:41 Der Shift: Agent vs. Applikation 05:27 Unterschied 1: Plattform und Hosting 06:53 Unterschied 2: Konnektoren vs. API / MCP 08:06 Unterschied 3: Fähigkeiten — Skills vs. Code 08:43 Was das konkret für dich bedeutet Noch mehr von den Koertings ... Das KI-Café ... jede Woche Mittwoch (>350 Teilnehmer) von 08:30 bis 10:00 Uhr ... online via Zoom .. kostenlos und nicht umsonst Jede Woche Mittwoch um 08:30 Uhr öffnet das KI-Café seine Online-Pforten ... wir lösen KI-Anwendungsfälle live auf der Bühne ... moderieren Expertenpanel zu speziellen Themen (bspw. KI im Recruiting ... KI in der Qualitätssicherung ... KI im Projektmanagement ... und vieles mehr) ... ordnen die neuen Entwicklungen in der KI-Welt ein und geben einen Ausblick ... und laden Experten ein für spezielle Themen ... und gehen auch mal in die Tiefe und durchdringen bestimmte Bereiche ganz konkret ... alles für dein Weiterkommen. Melde dich kostenfrei an ... www.koerting-institute.com/ki-cafe/ Mit jedem Prompt ein WOW! ... für Selbstständige und Unternehmer Ein klarer Leitfaden für Unternehmer, Selbstständige und Entscheider, die Künstliche Intelligenz nicht nur verstehen, sondern wirksam einsetzen wollen. Dieses Buch zeigt dir, wie du relevante KI-Anwendungsfälle erkennst und die KI als echten Sparringspartner nutzt, um diese Realität werden zu lassen. Praxisnah, mit echten Beispielen und vollständig umsetzungsorientiert. Das Buch ist ein Geschenk, nur Versandkosten von 9,95 € fallen an. Perfekt für Anfänger und Fortgeschrittene, die mit KI ihr Potenzial ausschöpfen möchten. Das Buch in deinen Briefkasten ... https://koerting-institute.com/shop/buch-mit-jedem-prompt-ein-wow/ Die KI-Lounge ... unsere Community für den Einstieg in die KI (>2800 Mitglieder) Die KI-Lounge ist eine Community für alle, die mehr über generative KI erfahren und anwenden möchten. Mitglieder erhalten exklusive monatliche KI-Updates, Experten-Interviews, Vorträge des KI-Speaker-Slams, KI-Café-Aufzeichnungen und einen 3-stündigen ChatGPT-Kurs. Tausche dich mit über 4.000 KI-Enthusiasten aus, stelle Fragen und starte durch. Initiiert von Torsten & Birgit Koerting, bietet die KI-Lounge Orientierung und Inspiration für den Einstieg in die KI-Revolution. Hier findet der Austausch statt ... www.koerting-institute.com/ki-lounge/ Starte mit uns in die 1:1 Zusammenarbeit Wenn du direkt mit uns arbeiten und KI in deinem Business integrieren möchtest, buche dir einen Termin für ein persönliches Gespräch. Gemeinsam finden wir Antworten auf deine Fragen und finden heraus, wie wir dich unterstützen können. Klicke hier, um einen Termin zu buchen und deine Fragen zu klären. Buche dir jetzt deinen Termin mit uns ... www.koerting-institute.com/termin/ Weitere Impulse im Netflix Stil ... Wenn du auf der Suche nach weiteren spannenden Impulsen für deine Selbstständigkeit bist, dann gehe jetzt auf unsere Impulseseite und lass die zahlreichen spannenden Impulse auf dich wirken. Inspiration pur ... www.koerting-institute.com/impulse/ Koerting Institute auf die Ohren ... Wenn dir diese Podcastfolge gefallen hat, dann höre dir jetzt noch weitere informative und spannende Folgen an ... über 500 Folgen findest du hier ... www.koerting-institute.com/podcast/ Wir freuen uns darauf, dich auf deinem Weg zu begleiten!

Don't get to the end of this year wishing you had taken action to change your business and your life.Click here to schedule a free discovery call for your business: https://geni.us/IFORABEDon't miss an upcoming event with The Institute: https://geni.us/InstituteEvents2026Shop-Ware gives you the tools to provide your shop with everything needed to become optimally profitable.Click here to schedule a free demo: https://geni.us/Shop-Ware-Free-MonthTransform your shop's marketing with the best in the automotive industry, Shop Marketing Pros!Get a free audit of your shop's current marketing by clicking here: https://geni.us/ShopMarketingProsShop owners, are you ready to simplify your business operations? Meet 360 Payments, your one-stop solution for effortless payment processing.Imagine this—no more juggling receipts, staplers, or endless paperwork. With 360 Payments, you get everything integrated into a single, sleek digital platform.Simplify payments. Streamline operations. Check out 360payments.com today!In this episode, Lucas and David are joined by Ash Kaplan, owner of Golden Hour Garage. Ash shares her journey growing up in the automotive industry, how she built her niche helping struggling shops streamline workflow, and why process consistency is essential for shop success. The conversation also explores the impact of AI and automation in automotive repair, emphasizing the ongoing need for human expertise and genuine customer relationships.00:00 Visual calendar time zone issue10:32 Building workflows for struggling shops12:27 Finding a solution for consistency17:50 Explaining the company name25:15 Documenting a car recall dispute30:36 Employee motivation and compensation issues32:59 Overcoming belief in others39:15 Lessons on gullibility from father47:26 Weekly and Quarterly Accountability Meetings52:34 Building internal talent for growth56:56 Discussing AI sustainability issues59:31 AI's Impact on Job Markets01:07:20 AI in automotive service shops

Host Luke McNamara is joined by Jake Nicastro, who leads the AI function for the Frontline Intelligence Operations team within the Google Threat Intelligence Group (GTIG). Jake details how his team is shifting from simple prompt engineering to more advanced agentic workflows, focusing on a model of "human-machine teaming." He shares practical use cases for AI in CTI—including automated script decoding, hunting query creation, and streamlining repetitive metadata-labeling tasks. Jake also discusses the concept of "judge agents" for quality control, the implementation of structured analytic techniques, and how real-time AI assistants can accelerate on-boarding and domain transitions for frontline threat analysts.

What if making great learning videos felt like finishing a slide deck—familiar, fast, and oddly satisfying? Jackie sat down with Pictory co‑founder Vikram Chalana to unpack a clear path from messy tools to simple, repeatable workflows that help educators and L&D teams publish short, accessible videos at scale. No timeline acrobatics. No blank‑screen dread. Just practical steps that turn content you already trust into engaging microlearning.Vikram shares the three design choices that shaped Pictory's approach: keep editing as simple as PowerPoint, start from existing assets to skip the blank page, and integrate best‑of‑breed AI models instead of building everything in‑house. From OpenAI for text to high‑quality voices and licensed visuals, the stack is curated so you don't waste hours evaluating tools.Throughout the conversation, we focus on learning effectiveness. You'll hear concrete guidance on keeping videos two to four minutes, using captions by default, tightening scenes to avoid cognitive overload, and adding quick knowledge checks to boost retention. For busy instructors and training teams, the biggest shift is simple: repurpose first, create second.If you're ready to reduce production time, improve accessibility, and deliver learner‑focused videos without the headache, this one's for you. Subscribe, share with a colleague who wrangles slides every week, and leave a review to tell us which workflow you'll try first.

In diesem Deep Dive spricht Markus mit Wolfgang Weingraber, Gründer von BuyAgain, über den Umgang mit Retourware im E-Commerce. BuyAgain nimmt Retouren an, bereitet sie auf und verkauft sie weiter, ohne die Ware selbst zu kaufen: Die Brand bleibt Eigentümerin bis zum Verkauf, BuyAgain verrechnet eine Fee. KI-gestützte Workflows leiten Prüfung und Bewertung so an, dass auch Mitarbeiter ohne Vorerfahrung sie übernehmen können. Außerdem geht es um den Weg vom eigenen Refurbishment-Marktplatz zum B2B-Infrastrukturmodell und um 700.000 Euro Non-Dilutive Funding vor der ersten Equity-Runde.Production: Hanna MoserMusik (Intro/Outro): www.sebastianegger.com

How can you improve your LLM agent systems through specification enrichment? What are the advantages of having an LLM act as a judge within an agent system? This week on the show, Senior IEEE Member and Quality Engineer Suneet Malhotra joins us to discuss building and evaluating agentic architecture.

Rest Tech innovator Shalece Daniels previously built and operated a seven-figure real estate investment and property management firm, coached multiple seven-figure businesses on scaling, then stepped fully into her zone of genius as an executive rest coach. Shalece created FLOW, an AI rest-tech assistant for organisations, and delivers corporate rest workshops that upgrade meeting norms, cut decision fatigue, and protect recovery time.Summary of PodcastKey Takeaways90-Second Reset: Emotions metabolise in 90 seconds. Shalece's method uses specific tools (e.g., Mind Fold, bubble-blowing) to reset the nervous system from "fight or flight" to "rest and digest," enabling clearer thinking.Data-Driven Proof: A smart ring tracks Key Relaxation Indicators (KRIs) like heart rate and HRV. This anonymised data proves the program's effectiveness to corporate clients, overcoming a key sales hurdle.Referral-Based Growth: The business grew from personal burnout and a Costa Rica sabbatical. Referrals from initial clients and a vocal advisory board were the primary drivers for landing larger corporate contracts.Respectful Disagreement: A discussion on DEI and immigration highlighted how personal experience shapes perspective, reinforcing the need for tools to manage emotional triggers and maintain respectful dialogue.The Problem: Nervous System OverwhelmHigh-pressure work environments keep leaders in a "fight or flight" (sympathetic) state, which impairs decision-making.The goal is to shift into the "rest and digest" (parasympathetic) state, creating the mental space for clear, strategic thinking.The Solution: 90-Second Reset TechniquesThe method is based on Dr. Jill Bolte-Taylor's research: emotions metabolise in 90 seconds.Key Tools & Rationale:Mind Fold: An eye mask that blocks light while eyes remain open. The brain's confusion triggers an automatic reset.Bubble Blowing: Exhaling longer than inhaling activates the vagus nerve, lowering heart rate and inducing calm.Acupressure Wristband: Stimulates the P6 nerve to reduce anxiety.Foot Roller: Activates nerve endings in the feet for discreet, bottom-up grounding.Personalised Approach: The method is tailored to the individual ("N=1"), requiring participants to find the 1–3 tools that work best for their nervous system.Proving Effectiveness with "Rest Tech"Key Relaxation Indicators (KRIs): A smart ring tracks vital signs (e.g., heart rate, HRV) to provide objective data.Corporate Dashboard: Anonymised data from all participants is aggregated into a dashboard.Why it matters: This data-driven proof overcomes the "how do I know it works?" question from corporate clients, enabling the program's sale and adoption.Business Growth & Client AcquisitionOrigin: The method was developed after Shalece experienced burnout and took a sabbatical in Costa Rica.Initial Growth: Spread organically through referrals from a personal network who noticed a positive change.Scaling: A vocal advisory board and speaking engagements helped attract larger corporate clients.Program Delivery: Includes a physical kit, video exercises via QR code, and a tiered service model.Broader Application: Managing Triggers in DialogueA discussion on DEI and immigration highlighted how personal experience shapes perspective.Key Insight: Acknowledging that everyone is "triggered" by different inputs underscores the value of having tools to manage emotional responses and maintain respectful dialogue.The Next 100 Days Podcast Co-HostsGraham ArrowsmithGraham founded Finely Fettled in 2014 to provide data from The UK High Net Worth Database to marketers targeting affluent and high-net-worth customers. He's the founder of MicroYES, a Partner for MeclabsAI, creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham also provides an Answer Engine Optimisation solution to get your website in shape to be found by LLMs.Kevin ApplebyKevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.com

What if the best systems in your business didn't make your client experience feel more automated, but actually made it feel more personal?In this episode, I'm sitting down with Xan Heller, who has gone through both Systems in Session and The Experience Edit, to talk about what happened when she stopped trying to figure out her CRM on her own and finally built systems that supported the custom, high-touch experience she wanted every client to have.What I think you'll find funny about Xan's experience is that she actually told me "no" more times than almost any client I've worked with. No to schedulers her clients wouldn't actually use and no to proposals as a core sales tool. Protecting her client experience mattered more than saving a few minutes—we'll get into those solutions too.If you've ever felt like systems and high-touch, custom client experiences are at odds with each other, this episode is for you!Find It Quickly:00:25 - Meet Xan01:43 - CRM Struggles & Mini Sessions05:02 - One Hour Success08:36 - Systems In Session10:19 - Support Tickets And Coaching13:41 - Knowing But Not Doing16:46 - Offer Math And Profitability23:35 - Automation Pushback Schedulers31:11 - Email Design And Branding34:30 - Proposals And Templates38:43 - Why CRMs Matter40:43 - Mapping Touchpoints46:16 - Workflow Building Mindset48:29 - Notifications and Scheduling Wins52:03 - Done-With-You Philosophy55:39 - New Dubsado Features59:55 - Portal Problems and Alternatives01:03:54 - Accountability and Focus01:07:12 - Experience Edit01:11:08 - Client Experience TakeawaysMentioned in this Episode:The Experience EditSystems in SessionConnect with Xan:Website: xanheller.comInstagram: instagram.com/xan_heller

Have an idea or tip? Send us a text!Your customer starts a photo order on their phone, asks a question in store, pays at the counter, and expects everything to “just work.” Most retailers cannot deliver that simple experience because their POS, e-commerce site, kiosk, and lab workflow are stitched together from different systems, each with its own SKUs, pricing, and payment records. Gary Pageau of The Dead Pixels Society sits down with Pat Hugron, Vice President of Operations and R&D at Dakis, to unpack how unified commerce for photo retailers is meant to fix the daily grind of reconciliation, duplicate inventory, and channel confusion.We trace Dakis's long path from early product recommendation tech to building tools for camera stores, photo labs, and specialty retailers who need a single platform. Hugron shares what has changed in production over the years, why outsourcing the long tail of photo gifts can be healthier than trying to make everything in-house, and why “seamless” matters as much to the lab as it does to the customer. We also dig into the return of film, why the resurgence surprised so many stores, and how better film processing workflow and delivery can keep customers engaged long enough to actually order prints.Then we get practical about what “unified” means: one product catalog across online and in-store, a save-and-send quote that lets staff build a cart and email a payment link, and unified data that can support smarter follow-up and add-on sales. Hugron also previews what is still coming, including purchase orders, receiving, and serial number tracking, plus how retailers can get hands-on training at the IPIC boot camp.Energize your sales with Shareme.chat, the proven texting platform. ShareMe.Chat ShareMe.Chat platform uses chat-to-text on your website to keep your customers connected and buying!MediaclipMediaclip strives to continuously enhance the user experience while dramatically increasing revenue.Independent Photo ImagersIPI is a member + trade association and a cooperative buying group in the photo + print industry.Photo Imaging CONNECTThe Photo Imaging CONNECT conference, March 2027, at the RIO Hotel and Resort in Las Vegas, NBuzzsprout - Let's get your podcast launched!Start for FREEDisclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showSign up for the Dead Pixels Society newsletter at http://bit.ly/DeadPixelsSignUp.Contact us at gary@thedeadpixelssociety.comVisit our LinkedIn group, Photo/Digital Imaging Network, and Facebook group,  The Dead Pixels Society. Leave a review on Apple and Podchaser. Are you interested in being a guest? Click here for details.Hosted and produced by Gary PageauAnnouncer: Erin Manning

Eric Borkowski joins us for a discussion about how he incorporates the iPhone into his sports shooting workflow. Eric's WebsiteEric's InstagramEric's Foggy TimelapseEric's Instagram - PhotosEric's TikTokBuy Me a CoffeeVoicecastGreg's BookThe Podcast WebsiteDave on InstagramDave on ThreadsDave on BlueskyDave on XDave on TikTokDave on VERODave on MastodonDwight on FlickrDwight on VERODwight on GlassDwight on InstagramDwight's Art on InstagramDwight on VSCOGreg's WebsiteGreg on GlassGreg on About.meGreg on InstagramGreg on VEROGreg on FlickrGreg on XThe Podcast YouTube ChannelShayne Mostyn's YouTube ChannelSmartphone Photography TrainingThe iPhoneography Podcast Facebook GroupShayne Mostyn's Bloody Legends Facebook GroupRick Sammon's Smartphone Photo Experience Facebook GroupReeflex's Facebook GroupiPhone 17 Pro & Pro Max PhotographyGet your first year of Glass for $20: https://glass.photo/offer/gregReeflex Lenses - Get 10% off Reeflex lenses with the coupon code MCMILLAN10

We just returned from three inspiring days at AI in the Alps, and in this episode, we dive into the most exciting breakthroughs and honest challenges facing industrial AI today. I share my firsthand impressions of revolutionary tools like TiRex-2, and we unpack what agentic AI really means for engineering, from breaking down data silos to transforming anomaly detection. Alongside Stefan Suwelack from Renumics, we discuss why skepticism has given way to optimism, how data infrastructure is finally catching up, and what the future holds for AI-driven organizations. The conversation is loaded with real-world examples, candid reflections, and a look at the evolving balance between sovereignty, open source, and big tech. Whether you're an engineer, decision-maker, or just curious about the future of AI in industry, this episode offers practical insights and a glimpse into what's next.

dnksaus (Carter) explores the future of Ableton Live, creative sound design workflows, Max for Live tools, and the new Ableton Extensions feature. They dive deep into building custom tools, making better "mud pies," AI-assisted coding, workflow optimization, and how producers can stay inspired while finishing more music. dnksaus is a producer, Max for Live developer, and sound designer known for his innovative Ableton tutorials and custom devices. His Max for Live devices and Ableton racks are used by producers including Marshmello, Skrillex, and RÜFÜS DU SOL. Carter has collaborated with Ableton on educational content and presentations, contributed to Sounds of KSHMR Vol. 5, and has become known for unconventional production techniques that help producers unlock new creative workflows.Sponsored by DistroKid:This episode is supported by DistroKid, which lets you upload once, get your tracks on all major platforms, and keep 100% of your streaming earnings. Their new DistroKid Direct feature also turns your artwork into print‑on‑demand merch like t‑shirts, mugs, and totes with no inventory to manage. Learn more below:⁠https://bit.ly/distrokid-musicFollow dnksaus Below:https://dnksaus.comGrab limited-edition Producer Merch & save 10% with the code "podcast":⁠https://www.abletonpodcast.com/merch⁠Join the newsletter to get free downloads, early episode access, and upcoming events:⁠⁠https://www.abletonpodcast.com/newsletter

This is episode 857. Read the complete transcription on the Sales Game Changers Podcast website. This is an AI and Sales Brief episode, a sub-brand of the Sales Game Changers Podcast. Watch the video of this podcast on YouTube here. The Sales Game Changers Podcast was recognized by YesWare as the top sales podcast. Read the announcement here. FeedSpot named the Sales Game Changers Podcast at a top 20 Sales Podcast and top 8 Sales Leadership Podcast! Subscribe to the Sales Game Changers Podcast now on Apple Podcasts! Purchase Fred Diamond's best-sellers Love, Hope, Lyme: What Family Members, Partners, and Friends Who Love a Chronic Lyme Survivor Need to Know and Insights for Sales Game Changers now! Today's show featured an interview with AI expert Zeev Wexler, CEO at Wexler, and sales expert Tom Snyder, Founder of Funnel Clarity. Find Zeev on LinkedIn. Find Tom on LinkedIn. ZEEV'S TIP: "AI doesn't fix bad sales processes, it exposes them. If you accelerate a broken workflow, you're not improving performance; you're simply making mistakes happen faster. Build the right sales process first, then let AI amplify it." TOM'S TIP: "Begin with a workflow based on sound sales practices. Once that foundation is in place, AI can accelerate it in extraordinary ways. The difference between AI applied to a broken process and AI applied to an optimized workflow is almost impossible to overstate."

This episode was too good to leave in the archive! As part of our Summer Replay Series, we're revisiting my conversation with consultant, SaaS founder, and self-described AI fanatic Andrew Ansley — and honestly, his advice is even more relevant today.Andrew breaks down exactly how he uses ChatGPT and Claude to run multiple businesses, why "clouding up the context window" is killing your AI outputs, and how to train an AI assistant on your business in 30 minutes or less. Then we go deep on SEO: what actually changed after 2022, how Google understands content through entities and embeddings, and what it really takes to build topical authority today.Whether you're an SEO, a small business owner, or just AI-curious, this replay is packed with practical workflows you can put to work this week.What You'll LearnWhy you should delete the messy middle of your AI conversations (and keep only the first prompt + final output)How to set up AI "projects" for each client or task — with business info, SOPs, examples, and your unique perspectiveThe 4 pieces Andrew uploads to train an AI on any workflow in about 30 minutesWhy you must define abstract concepts (like "write an email" or "SEO strategy") instead of letting AI decide what they meanDefinition + example + template: the simple prompt formula that upgrades your outputsClaude vs. ChatGPT: where each tool shines (projects, styles, voice transcription, and more)How SEO changed after 2022: entities, semantics, and user metricsWhat topical authority actually means — going deeper, faster, or wider with your content clustersWhy the real ranking signal is whether searchers end their journey on YOUR siteThe "Could ChatGPT give them this?" test for creating content that still winsEpisode Highlights & Timestamps(00:00) Welcome + meet Andrew Ansley(01:00) Consultant, SaaS founder, community leader: how Andrew wears all the hats(02:00) Why Andrew (and Crystal!) are learning Python(03:30) Inside Andrew's Skool community: automate marketing with AI + n8n(05:30) The one-person, AI-powered business (could you scale to $10M solo?)(07:30) Don't cloud the context window: Andrew's clean-conversation trick(09:45) Building SOPs with AI in an hour instead of a week(10:30) ChatGPT Pro vs. Claude: Andrew's honest comparison(11:00) How to set up an AI project: business info, ICP, SOPs, examples & styles(16:00) The power of templates (stop reinventing the wheel!)(21:30) Screen share: how Andrew organizes projects, prompts & custom styles(28:00) From aspiring pastor to bartender to SEO: Andrew's origin story(36:00) AI as the ultimate learning tool for curious kids (and adults)(45:00) How SEO changed: keyword matching → RankBrain, BERT & semantic search(48:30) User metrics, mobile-first indexing, and ending the search journey(51:30) Topical authority explained: embeddings, content clusters & internal links(54:00) Why sites dip after agencies leave (core algorithm updates + historical metrics)(56:00) The "Could ChatGPT give them this?" content test(56:45) Where to connect with AndrewQuotable Moment"You cannot let the AI decide what abstract concepts mean. You have to define it — and if you give it an example, that's even better." — Andrew AnsleyConnect with Andrew AnsleySkool Community (AI Marketeers)YouTubeAndrew's article on Search Engine LandMentioned in This EpisodeContent Sprout (Andrew's SaaS)n8n (open-source automation alternative to Zapier)GoHighLevelKoray Tuğberk Gübür (topical authority & entity SEO)Bill Slawski (SEO research pioneer)Alex Hormozi & Sam Ovens (Skool)Connect with CrystalWebsiteLinkedInText me your questions or comments!Hey, Shopify store owners! (Especially if you're selling on Etsy, too!)Here's a quick question: Are people actually finding your products on Google?If SEO feels confusing, overwhelming, or like something you'll "get to later", this is for you.I'm hosting a free, seven day Shopify SEO challenge that breaks it down into simple, doable steps.No tech headaches, no fluff. Join us at  Hey, Shopify store owners! (Especially if you're selling on Etsy, too!)Here's a quick question: Are people actually finding your products on Google?If SEO feels confusing, overwhelming, or like something you'll "get to later", this is for you.I'm hosting a free, seven day Shopify SEO challenge that breaks it down into simple, doable steps.No tech headaches, no fluff. Join us atSupport the showFree checklist: Is your Shopify store quietly losing sales? Run the 5-minute self-check →Book a Shopify Store Strategy Call With Crystal!Want to follow up on what you've heard? Search the podcast!AFFILIATE LINKS:Start your Shopify Store!Get SurferSEO!Metricool (to be everywhere online, you NEED a social media scheduler!)Grid and PixelNote: If you make a purchase using some of my links, I make a little money. But I only ever share products, people, & offers I trust & use myself!

Are you struggling to scale your aesthetic practice, or wondering how to actually use artificial intelligence without creating more work for yourself? In this episode of Beauty Bytes, I am joined by Annie Robertson Hockey, the President of Skytale Group and former co-CEO of a nationally chartered infrastructure bank. Annie brings her incredible Silicon Valley experience and Stanford Business School background to the aesthetic industry to share her top strategies for business growth and technological integration. We discuss the critical mental shift doctors must make to become successful CEOs, including how to take yourself out of the equation by building standard operating procedures (SOPs) that can scale your business two, five, or ten times its size.  We also dive deep into the responsible use of AI in your practice. Annie explains why you should treat AI like a fresh, eager college graduate, allowing it to automate rudimentary tasks so your brain can focus on critical problem-solving and high-level strategy. We cover how to prompt tools like Claude to teach you about their own systems, and how integrating platforms like Illume and Corral Data over your EMR can instantly surface powerful revenue analytics without needing a dedicated data team.  Guest Information:Annie Robertson Hockey is the President of Skytale Group and an experienced Silicon Valley entrepreneur and board member.

Do you ever wonder how some travel advisors seem to do it all? In this behind-the-scenes episode, I'm sharing exactly what I automate in my travel business—from client workflows and email templates to AI tools, CRM automations, scheduling, onboarding, and follow-up systems. These are the same processes that help me run a successful travel agency while teaching full-time, creating content, hosting a podcast, and traveling myself. You'll learn how automation creates a better client experience, saves hours every week, and allows you to spend more time building relationships instead of repeating administrative tasks. In this episode, you'll learn: The client emails and workflows I automate from inquiry to post-tripHow I use my CRM to streamline the entire client journeyMy automated consultation reminders and follow-up processThe 30-day onboarding email sequence I use for new travel advisorsHow I use AI inside my CRM and other tools to save timeThe email templates I never write from scratch anymoreWhy automation makes your business more personal—not lessWhere to start if you're new to business automation Whether you're a new travel advisor or looking to scale your travel business, these automation tips will help you create a more organized, profitable, and stress-free business. Connect with Lindsay: Ready to build a travel business with proven systems and support? Learn more about joining At Last I See The World Travel or connect with Lindsay on social media for more travel advisor tips, marketing strategies, and behind-the-scenes business content. https://www.lindsaydollinger.com https://www.facebook.com/lindsay.dollinger https://www.instagram.com/lindsaydollinger

Data is one of the most valuable assets an engineering firm has—but only if it's collected, connected, and put to work. On this episode of Engineering Influence, we sit down with Deb Johnston of Tonic DM to discuss how engineering firms can transform the way they manage project data. Deb explains how Tonic DM helps firms streamline data collection, eliminate redundant processes, improve workflows, and ensure critical information is optimized rather than wasted. From reducing inefficiencies to creating better decision-making across projects, this conversation explores why smarter data management is becoming essential for firms looking to improve productivity, collaboration, and long-term performance.

Eric and Marty speak about how to get things out of your email and into your workflowThe Built-In Tools: Mail RulesFaculty Guide: Managing Outlook Email Effectivelyhttps://helpdesk.lsua.edu/support/solutions/articles/48001276836-faculty-guide-managing-outlook-email-effectivelySet Up Rules in Apple Mailhttps://www.macuncle.com/blog/set-up-rules-in-apple-mail/10 Essential Outlook Rules to Amplify Your Productivityhttps://exclaimer.com/email-signature-handbook/10-essential-outlook-rules-amplify-productivity/Apple Shortcuts: Triggering Tasks from MailCommunication Triggers in Shortcutshttps://support.apple.com/guide/shortcuts/communication-triggers-apdd711f9dff/iosAutomate Tasks in Mail on Machttps://support.apple.com/guide/mail/automate-mail-tasks-mlhlp1120/macZapier: Connecting Email to Everything Else6 Gmail Automation Ideashttps://zapier.com/blog/automate-gmail-with-zapier/Automate Todoist Task Creation from Gmailhttps://www.storylane.io/tutorials/how-to-automate-todoist-task-creation-from-gmail-emails-with-zapierAutomating Task Management: From Gmail to Notionhttps://connex.digital/blog/automating-task-management-from-gmail-to-notion-using-zapier/How to Automate Gmail with Zapier (Filters, Labels, Workflows)https://aiprocesshub.com/automate-gmail-with-zapier/IFTTT: The Simpler AlternativeConnect Email and iOS Shortcuts on IFTTThttps://ifttt.com/connect/email/ios_shortcutsAI in the LoopZapier Automation for Google Workspace Efficiencyhttps://www.automatemy.co/blog/zapier-automation-for-google-workspace-efficiencyFind the ShowEmail: ThePodTalkNetwork@gmail.comWeb: https://ThePodTalk.netYouTube: https://www.youtube.com/@TechSavvyProfessor

SHOW NOTES: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.haileyrowe.com/ai-workflowsJoin my free Facebook community for business support & to connect with other health coaches: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/groups/themarketinghubgroup/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/haileyrowecoach⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/hailey_rowe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/hailey_rowe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Mark Sherwood Edwards is the Sales Side Lawyer. This podcast is all about having a sales-centric approach to legal contracting.Summary of PodcastKey TakeawaysProblem: ~50% of deals die in contracting, often due to lawyers acting as a "Department for Sales Prevention" and triggering buyer anxiety.Solution: Treat legal risk as a price point. Frame buyer requests (e.g., for unlimited liability) as commercial choices that increase the price, shifting the negotiation from legal to business.Tactic: Reduce buyer anxiety by offering easy-out clauses (e.g., monthly after 12 months). This builds trust and forces the seller to focus on service quality to retain customers.Goal: Move all deals toward "no-touch" contracting (e.g., click-through T&Cs) to eliminate negotiation friction and speed up deal closure.The Problem: Contracting Kills DealsLawyers often act as a "Department for Sales Prevention," slowing deals and creating friction.The "Spoon" Analogy: Lawyers often make minor, risk-averse changes that alter the deal's structure, even if the core agreement is sound.Key Driver → Buyer Anxiety: The primary obstacle is a buyer's personal risk ("Fear of Messing Up"), not just corporate risk.Why: A long, complex contracting process increases the risk of the deal failing due to external factors (e.g., strategy changes, competitor bids).The Solution: A Sales-Centric Legal ApproachCore Principle: Treat legal risk as a price point.How: Frame buyer requests for increased risk (e.g., unlimited liability) as commercial choices that will increase the price.Why: This shifts the negotiation from a legal debate to a commercial one, where business stakeholders are more reasonable and budget-conscious.The "Sales Side Setup": Prepare for this by stating upfront in proposals that pricing is based on your standard T&Cs and that changes may affect the price.Easy-Out Clauses: Offer flexible termination terms (e.g., monthly after an initial 12-month period).Why: This reduces buyer anxiety and forces the seller to maintain high service quality to retain the customer.Caveat: This approach may not suit deals with high upfront costs (e.g., outsourcing), where a longer lock-in is needed to reach profitability.Practical Tactics for Small BusinessesContracting Spectrum: Classify deals into three types and aim to move all toward "no-touch."No-Touch: Click-through T&Cs (e.g., Google, HubSpot).Low-Touch: Minor negotiations.High-Touch: Significant negotiation.Simplify Contracts:Use plain English and clear headings.For simple deals, use a signed letter of agreement instead of a formal contract.Payment Terms:Goal: Get paid in advance to eliminate the need for legal recovery clauses.Response to Long Terms: Counter with a price increase (e.g., 5%) or remove scope to meet the budget.The Next 100 Days Podcast Co-HostsGraham ArrowsmithGraham founded Finely Fettled in 2014 to provide data from the UK High Net Worth Database to marketers targeting affluent and high-net-worth customers. He's the founder of MicroYES, a Partner for MeclabsAI, creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham also provides an Answer Engine Optimisation solution to get your website in shape to be found by LLMs.Kevin ApplebyKevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.com

As programmatic advertising moves past traditional cookie tracking, media networks face the challenge of linking early ad exposure with verified digital purchases. At the same time, brands must adapt to new automated workflows changing how consumers assess product value. Dylan Conroy sits down with Matt Sanchez, Chief Operating Officer at Yahoo, to explore how connecting essential communication utilities with massive content properties can optimize open-web attribution pipelines.Key Themes Covered:Aligning a massive digital portfolio to function as a trusted guide across a fragmented web.Testing lab-grade agentic AI features to improve inbox user productivity.Exporting rich behavioral insights out of internal platforms into an open DSP layer.Organizing strategic event timelines to start discussions and close corporate alliances.Integrating independent creator networks alongside traditional syndicated media formats.Matt Sanchez is the Chief Operating Officer at Yahoo, where he directs product execution and global distribution systems across their full-funnel media engine.Connect with Matt Sanchez on LinkedIn: https://www.linkedin.com/in/sanchezmatt/Explore Yahoo's Programmatic Solutions: https://www.yahoo.com/Optimize your enterprise marketing ROI with Strike Social: https://strikesocial.com/guaranteed-paid-social-media-ads-outcomes/

If you've been using ChatGPT or Gemini and quietly wondering whether you can trust everything it tells you, there's a Google tool built to solve exactly that problem. NotebookLM only works from the documents you give it, which means it's grounded in your own content instead of guessing at the entire internet. This week I'm joined by Lisa Monks, an Australian social media strategist and AI educator, who has spent the last few years figuring out how to put that grounded approach to work for real businesses. We talk about everything from repurposing podcasts and building staff training quizzes to creating fully branded slide decks, and by the end of this episode you'll have a clear list of ways to put NotebookLM to work in your own business this week. https://www.theagentsofchange.com/628 Need help with your branding, website, or digital marketing? Reach out to me (Rich Brooks!) today at https://www.takeflyte.com/contact

Natalia Quintero joined Every as head of consulting with a mandate to bring AI into the workflows of executives at hedge funds, private equity firms, and tech companies. She is also a recent Codex convert—someone who spent months resisting the tool before Dan Shipper's daily pestering finally got her to try it.Natalia encountered Codex as a non-technical builder who had learned to navigate file systems and folder structures in Claude Code through sheer effort. She's now used Codex to do everything from automate her CRM setup to build a portal to manage her father's medical care.Dan talked with Natalia for AI & I about what it looks like to go from non-technical to building software with Codex, why Every still uses software-as-a-service products from Attio and Asana instead of vibe coding their own tools, and where she thinks AI agents like Every's internal Claudie employee require human managers.If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperTimestamps:00:01:05 Introduction00:02:35 How Natalia manages Claudie, the consulting team's AI project manager00:04:55 Why the consulting team still pays for SaaS products00:11:47 Codex as a game changer00:14:55 Building personalized learning guides and illustrated explainers with AI00:21:40 Inside Natalia's AI-powered email triage system00:26:44 The shift from knowledge work as sculpting to knowledge work as gardening00:28:57 Using Codex to one-shot a custom CRM00:33:16 Using Codex to build an app that coordinates her father's medical careLinks to resources mentioned in the episode:Natalia Quintero on X: https://x.com/NataliaZarinaAsana (project management): https://asana.comEvery Consulting: https://every.to/consultingGo to attio.com/every and get 15% off your first year.

About This Episode  Artificial intelligence is dominating boardroom conversations, yet many organizations are struggling to move beyond experimentation. In this episode of the Future of Work® Podcast, Frank Cottle sits down with Peter Cappelli, one of the world's leading researchers on management, employment, and workplace strategy, to separate perception from reality.  Drawing on decades of research, Cappelli explains why AI implementation is less about technology and more about organizational design. The conversation explores workflow analysis, job design, hybrid work, remote collaboration, workplace relationships, onboarding, office strategy, and why many executives are approaching AI implementation the wrong way.  The discussion also examines return-to-office policies, the purpose of the office, flexible work models, and why relationships remain the foundation of collaboration. Whether you're implementing AI, leading organizational change, or refining your workplace strategy, this episode provides practical guidance rooted in research rather than speculation. 

In today's episode we speak with Tuna Uskudar about his company Provenance, the company seeking to improve the way large financial institutions use Excel and PowerPoint. We spoke about the vision for the company, fundraising, team, moats, customer obsession, and lots more. I hope you find this conversation exciting.

As B2B technology companies scale, size often comes at the cost of agility. Processes calcify, buying cycles stretch, and marketing teams end up running up and down the stairs faster instead of building the elevator.In this episode of the FINITE Podcast, Jodi Norris sits down with Carol Carpenter, CMO at Cohesity, to unpack what it really takes to market at enterprise scale without slowing down. Fresh from Cohesity's merger with Veritas, Carol shares the realities of integrating two large marketing organisations, building trust across cultures, and holding on to startup-style velocity inside a 5,800‑person business.She explains how her team uses AI to redesign workflows – from translation and brand governance tools, to AI‑powered SDR outreach that doubles lead‑to‑meeting conversion. Along the way, she draws a firm line between what can be automated and what cannot: creativity, taste and strategic judgement.Carol has been in technology marketing for most of her career, starting as a product manager at Apple. She enjoys scaling and transforming companies and has done that in leadership roles at VMware, Google, Apple, Trend Micro and now as CMO of Cohesity. Carol gives back through mentorship programs such as the HBS Women Entrepreneurship program and Monte Jade, an AAPI professional organisation.Inside you'll find…How to merge marketing cultures without losing speed, trust or clarityWhere AI genuinely shortens a six‑month enterprise buying journey – and where humans must stay in the loopA practical framework for lifting teams out of execution and into more strategic, high‑leverage work

What happens when the biggest AI security risk isn't the technology itself, but the people using it? In this episode, I sit down with Cormac Whelan, CEO of Nitro Software, to discuss why organizations need to rethink their approach to AI adoption. With research showing that 68% of C-suite executives are bypassing approved AI tools in favor of their own, we explore how an "ask forgiveness, not permission" culture is creating new security and compliance challenges for businesses around the world. Cormac shares why successful AI adoption begins with business outcomes rather than the latest model or headline-grabbing announcement. Drawing on his experience leading Nitro and previously building an AI company acquired by Apple, he explains why AI should be an enabler rather than the destination, and why organizations that focus on trust, transparency, and practical business value will ultimately pull ahead. Our conversation also looks at why documents, contracts, PDFs, and e-signatures have become some of the most overlooked parts of the enterprise AI conversation. As AI systems increasingly interact with sensitive business information, protecting document workflows is becoming just as important as securing networks and endpoints. We also discuss how European privacy standards are becoming a competitive advantage rather than simply another compliance requirement, how to separate genuine AI innovation from expensive security theater, and why AI should quietly improve the way people work instead of becoming the center of attention. If you're trying to balance AI innovation with security, governance, and business value, this conversation offers practical advice without getting lost in the hype. After listening, I'd love to hear your thoughts. Is your organization focusing on outcomes first, or is it still chasing the latest AI headline?

Kidzplay is a national soft-play membership network that Lisa built from scratch — a wholesale model in which we buy fixed table capacity from partner venues and resell it through memberships.Lisa is an accountant! Kevin will be in heaven. PwC-trained ACA. 18 years in soft play. One MBO (Gameplay, sold to Game Group). Several businesses were built, a couple were buried, and a lot was learned.Flat Cap Friday is a grassroots broker network I co-founded with Nigel Bowers. Regionally rooted, built around genuine peer connection. No pitch decks, no lead-gen noise — just good people showing up on Fridays. Wired is a five-pillar framework for founders who are running on empty: business, financial, body, relationships, restoration. Twelve weeks. Broughton Estate, North Yorkshire. Lisa builds businesses to be cash-generative, mission-driven, and eventually sold or handed on.Summary of PodcastKey TakeawaysKids Play: A membership model solving venue cash-flow volatility by buying fixed-price slots, making family activities affordable and accessible.Wired Framework: A coaching program using 12-week sprints to help founders align business strategy with personal purpose, health, and freedom.Core Purpose: All ventures are unified by a mission to build community and combat loneliness, stemming from a personal experience.Strategic Shift: A new model of delegating operational work enables a strategic focus on high-level thinking and personal freedom.The Problem: A Crisis of ConnectionA crisis of connection stems from modern pressures (cost of living, screen time), causing parents to sacrifice family time and leading to a youth mental health crisis.COVID-19 highlighted the devastating impact of lost social interaction on children's development.Lisa's mission is to bring families together and ensure no one feels lonely.Kidzplay: An Accessible SolutionOrigin: Evolved from a soft play center Lisa bought in 2007.Business Model: A monthly membership for unlimited access to partner venues (soft play, farms, classes).Customer Benefit: Affordable, stress-free family activities.Venue Benefit: Stable, predictable income to offset cash-flow volatility (e.g., a sunny half-term cut revenue from a projected £30k to £4k).Mechanism: Kidzplay buys fixed-price slots from venues, de-risking their economics.Wider Purpose:Soft Play: Provides a safe space for physical activity and teaches sharing.Classes: Develop fine motor skills (e.g., needlework) to counter screen-time effects.Farms: Educate children on food origins.Wired Framework: Coaching for FoundersA coaching program for founders based on Lisa's personal journey from traditional accounting to purpose-driven entrepreneurship.Core Philosophy: Prioritize personal health and purpose first; business success will follow.Structure: 12-week sprints of intense focus, followed by a 1-month consolidation/rest period.Method: Uses the "7 Layers of Why" technique to help founders uncover their true purpose.Delivery: Full-day workshops at Broughton Estate to provide a focused, out-of-environment experience.Lisa's Strategic Model: Delegation for FreedomLisa's model is to delegate operational work to focus on high-level strategy.Rationale: This approach enables personal freedom and prevents burnout, a lesson learned from past overwork.Example: After realizing she was "making herself busy" with pitch decks, Lisa made the direct call to a key venue owner, Nick, securing a meeting.Other Ventures:Flat Cap Fridays: A relaxed, pub-based networking group.Inspiring Women: A goal to show women that career paths are non-linear and self-imposed limits should be challenged.The Next 100 Days Podcast Co-HostsGraham ArrowsmithGraham founded Finely Fettled in 2014 to provide data from the UK High Net Worth Database to marketers targeting affluent and high-net-worth customers. He's the founder of MicroYES, a Partner for MeclabsAI, creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham also provides an Answer Engine Optimisation solution to get your website in shape to be found by LLMs.Kevin ApplebyKevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.com

Send us a text letting us know your thoughts on today's episodeYour podcast doesn't have a consistency problem — it has a workflow problem.If you're recording, editing, creating graphics, and uploading all in the same week on repeat, you're not just tired. You're working against yourself. And as a mom trying to run a business in stolen hours, that cycle is unsustainable by design.In this episode, I'm pulling back the curtain on exactly how I manage my podcast workflow — the mistakes I made early on that wasted so much time, and the batching and systems approach that changed everything for me. We're talking about why podcasting in real time is burning you out, how to reduce friction so you can stay visible without the scramble, and what sustainable visibility actually looks like for moms in business.You'll walk away with a clear picture of how to simplify your podcast workflow so that your show fits into your life instead of consuming it.In this episode:Why most podcaster burnout is a systems problem, not a time problemHow I batch record and edit to maximize momentumThe things I stopped doing that were wasting my timeWhat a streamlined quarterly workflow actually looks likeHow to create consistency without needing a perfect scheduleWant to know exactly where your workflow is breaking down? Snag a Podcast Audit.Need support with your podcast?Book your free Podcast Profit Plan call today! Next Steps: Enjoyed this episode? Let me know over on Instagram and share your favorite takeaway on your Instagram Stories.Ready to hand off your podcast production or experience more podcast growth? Visit our website to learn more about our podcast launch, podcast management and podcast growth services. Book your free Podcast Profit Plan Discovery Call here.Don't forget to hit subscribe/follow so you get notified every time a new episode drops!

Ever wish your agent would just watch you work and copy you?

Enterprise AI initiatives consistently break down in document-heavy environments, not because the underlying models are inadequate, but because fragmented data silos, page-break context loss, and uncoordinated extraction tools erode the semantic layer AI needs to reason accurately. In this episode, Sumedh Chaudhary, CTO US Industry Market at IBM, breaks down why a multi-agent architecture is the operational prerequisite for AI to function reliably in regulated, document-intensive workflows. The conversation covers how governance frameworks with measurable error-rate targets distinguish pilot success from production failure, and how enterprises can structure a phased AI approach that blends automation, fit-for-purpose models, and human oversight. ​ This episode is sponsored by Arango. In this episode, we cover how enterprises can build multi-agent AI architectures to handle document-heavy workflows — and the governance frameworks that determine whether those deployments scale. To go deeper on this topic and learn how to structure landing pages for higher conversion, and how to use self-qualification systems to prioritize high-intent leads, download our free PDF report, "B2B AI Lead Generation Guide," at emerj.com/aig1

Is your team really at capacity, or is your operating model broken? In this episode, Jaime Hunt speaks with Joanne Giles about the common friction points in higher education marketing teams. They discuss why buying project management software isn't a "silver bullet" solution and how to diagnose deep-rooted operating issues before trying to fix the system. Guest Name: Joanne Giles, Founder, All Things Content Guest Social: https://www.linkedin.com/in/joanneaugustingiles/ Guest Bio: Joanne Giles is the Founder and Principal Consultant of All Things Content, a strategic consultancy that helps higher education institutions and complex organizations bring structure to the way work moves across teams. Her work focuses on building workflow foundations for highly cross-functional, multi-stakeholder environments where intake, approvals, handoffs, visibility, and tool adoption often break down. Joanne has supported institutions including Hofstra University, Georgia State University, and Abilene Christian University, helping teams turn operational overwhelm into documented workflows, clearer ownership, and practical systems they can actually use. - - - -Connect With Our Host:Jaime Hunthttps://www.linkedin.com/in/jaimehunt/https://twitter.com/JaimeHuntIMCAbout The Enrollify Podcast Network:Confessions of a Higher Ed CMO 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.

In this episode of The Radiology Report Podcast, Daniel Arnold sits down with Ohad Arazi, incoming CEO of Subtle Medical, to discuss the future of AI in medical imaging, the challenges facing radiology workflows, and the leadership lessons he's learned from more than 20 years in healthcare technology. From the early days of PACS to today's AI-driven imaging ecosystem, Ohad shares why some of the biggest opportunities in radiology may exist before an image ever reaches the radiologist. The conversation explores MRI acceleration, workflow optimization, imaging operations, startup leadership, and how healthcare organizations can unlock greater efficiency, image quality, and patient outcomes through innovation. Whether you're a radiologist, imaging leader, healthcare executive, or technology innovator, this episode offers a unique perspective on where radiology is headed next.

Yaakov Zar is the founder and CEO of Lev, a software platform built to modernize the workflow of commercial real estate professionals. Yaakov started Lev after experiencing firsthand how broken the CRE financing process was, watching a $4 million loan take six months to close. What began as a tech-enabled brokerage has evolved into a purpose-built agentic workflow platform helping lenders, brokers, and investors manage deals, ingest unstructured data, and move faster. Yaakov is based in New York City.(02:26) Bottom Up vs Top Down(04:31) Slack Origin Tangent(05:59) MetaProp Skills Library(09:43) What Is Defensible AI(11:12) MCP & Rapid Change(12:41) Pilots Everywhere & Demo Fatigue(17:34) Same Workflow, Turbocharged(19:34) Real Estate's Move 37 Moment(22:04) Why Winning Is Hard to Define(26:07) Lev Agentic Workflows(29:14) Leapfrogging Past Salesforce(31:43) Data Quality Pushback(33:49) Ingesting Email Into CRM(35:54) Selling Software to CRE(39:06) Overhyped AI and Security Risks(42:50) Collaboration Superpower: Steve Jobs

https://clearmeasure.com/developers/forums/ Tamir Dresher is a Principal Engineer at Microsoft Threat Protection, where he focuses on scaling AI agent systems and distributed architectures, bringing over 15 years of experience building large-scale distributed systems. He is the co-creator of Squad, an open-source multi-agent runtime for GitHub Copilot that orchestrates AI teams directly inside your repository. Tamir is the author of "Rx.NET in Action" (Manning) and "Hands-On Full-Stack Web Development with ASP.NET Core" (Packt), and has been a lecturer in Software Engineering at the Ruppin Academic Center since 2013. A prominent figure in the Israeli and international developer communities, he is a Microsoft MVP alumnus who speaks frequently at global conferences and writes actively on his blog at tamirdresher.com. Website / Blog - https://www.tamirdresher.com/  LinkedIn - https://www.linkedin.com/in/tamirdresher/ GitHub: https - //github.com/tamirdresher Twitter/X - @tamir_dresher Blog Post - https://www.tamirdresher.com/blog/2026/05/24/squad-watch-extensions-customer-success Github - https://github.com/bradygaster/squad Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

Riley Hill and Tim Chaten discuss our experiences using beta 1 of iPadOS 27 and some thoughts about the announcements a week later. Early episodes are available by supporting the podcast at www.patreon.com/ipadpros. Early episodes are also now available in Apple Podcasts!Show notes are available at www.iPadPros.net. Feedback is welcomed at iPadProsPodcast@gmail.com.Links:- https://slatepad.org/2026/06/12/ipados-27-drops-support-for-the-legendary-2018-ipad-pro/- https://slatepad.org/2026/06/10/att-unlimited-day-pass-ipad/ Hosted on Acast. See acast.com/privacy for more information.

The future of land investing isn't coming; it's already here, and it's creating a bigger gap between investors every day.(Show Notes)The land investors pulling ahead today aren't necessarily smarter or working harder. They're using automation, AI agents, CRM workflows, and property data tools to eliminate busywork, respond faster, and make better decisions.I'll walk through the specific capabilities your CRM and operating system should have, including AI call handling, automated follow-up systems, call summaries, direct mail tracking, e-signatures, API integrations, and agentic AI tools like Claude that can actually perform tasks for you.Whether you use Stride CRM, Land Portal, or something else entirely, the goal is the same: give your time and mental bandwidth back while building a more scalable land investing business.

In this episode of Command Control Power, the hosts discuss practical IT uses of AI, including improving client communications, speeding email migration due diligence via AI-generated PowerShell reporting (mailbox size, forwarding rules, aliases, naming pitfalls, licensing limits), and reducing billing friction by summarizing recorded RingCentral calls in Claude to log hours and generate detailed invoices, including for Ubiquiti camera projects. They debate risks such as blindly running AI-suggested commands, clients acting on AI advice, and data leakage when employees paste company information into public AI tools, emphasizing guardrails, policies, and potential local/private AI setups (e.g., Mac mini with Ollama). The conversation broadens to AI's impact on IT business models, automation in ticketing, and Apple's lackluster AI progress, delayed Siri features, privacy positioning, and reliance on partners like Google/Gemini.   00:00 Show Kickoff 00:02 New Studio Tour 00:31 Flag Outage Story 01:35 AI Migration Prep 03:26 PowerShell Due Diligence 05:36 Call Summaries Invoicing 07:31 Automating Call Logs 09:35 AI As Expert Helper 11:51 Safety With Commands 12:58 Clients Using AI 13:54 Data Privacy Guardrails 17:17 Industry Shift Fears 20:07 Auto Reply Ticketing 24:18 Local AI Knowledge Base 26:02 AI Eats Software 27:35 Future Of IT Services 29:04 AI Automation Ethics 30:06 Market Pressure On IT 31:03 Apple Intelligence Doubts 33:10 Privacy And Gemini 35:06 Apple Strategy And Mindshare 37:43 MDM Guardrails Needed 39:39 First Mover Myth 43:32 Ubiquity And AirPods AI 47:21 Beta Plans And Rollout 48:06 AI Policy And Profiles 51:32 Wrap Up And Outro