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Free guide + skill: build social carousels in claude cowork: https://clickhubspot.com/dkhc Ep. 438 How do you differentiate your marketing when everyone else is using the same AI tools?  Kipp and Kieran dive into building next-level marketing systems with AI that actually set you apart, featuring digital growth consultant and educator Grace Leung. Discover why context is king in AI marketing, how to build simple but powerful systems that scale, and the secret to making your brand voice and strategy shine through every asset. Learn more on designing reusable AI workflows, structuring context-rich file systems, and maximizing team collaboration while building a future-proof marketing stack. Mentions Loop: Outlearn. Outmarket. Outgrow https://a.co/d/08By5k2w Grace Leung https://www.youtube.com/@graceleungyl Claude Cowork https://support.claude.com/en/articles/13345190-get-started-with-claude-cowork ChatGPT https://chatgpt.com/ Codex https://openai.com/codex/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

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

In this episode of Business Brain, we get into when to stop using chat and move to Cowork. Chat is a great place to start — it’s just not where we should live. Dave’s signals: more than five back-and-forths, or constantly pasting in screenshots, log files, and PDFs. That’s the tell. Point Cowork at the folder instead and stop copying and pasting. His trick is to ask the chat directly whether it’s time to move, and to have it write the handoff prompt for the Cowork session, since Cowork doesn’t inherit the full context. Flip the default: assume you’re going to Cowork, then convince yourself why you should stay in chat. We also untangle chats vs. projects vs. Cowork vs. Claude Code — and the one real reason to stay put, which is cloud sync across devices. Then Dave walks us through a wild experiment: handing $10K of found money to Claude to run as a 90-day trading portfolio. He planned it in chat with Fable, executed in Cowork with Opus, and let it pick platforms with API and MCP access — Kraken for crypto, Alpaca for securities. It insisted on a seven-day paper trading run first, keys live in a 1Password vault instead of the session, and there’s a kill switch on his phone. No options, so the floor is zero. Whatever happens, it’s tuition. Real story: Claude didn’t earn the money — it just got him far enough through the process to actually collect it. Get out of the chat, and keep living that Charmed Life. 00:00:00 Business Brain – The Entrepreneurs' Podcast #771 for Casual FridAI, July 17, 2026 00:00:15 July 17th: National Tattoo Day 00:01:26 Defaulting to Claude Cowork instead of Claude Chat 00:10:26 SPONSOR: FanVue. Are you ready to start your own creator journey and make it big? Visit https://www.fanvue.com/ today and launch your career! 00:11:43 SPONSOR: Shopify: Own your customer relationships. Own your revenue. Start with a free trial at Shopify.com/BusinessBrain. 00:12:58 Letting Claude invest the money it earned 00:20:06 Business Brain 771 Outtro This Episode's Big Takeway: Get out of the chat! Check out Business Brain Blueprints Tell Your Friends! Business Blueprints Review Business Brain Subscribe to the show feedback@businessbrain.show Call/Text: (567) 274-6977 X/Twitter: @ShannonJean & @DaveHamilton, & @BizBrainShow LinkedIn: Shannon Jean, Dave Hamilton, & Business Brain Facebook: Dave Hamilton, Shannon Jean, & Business Brain The post FridAI – Cowork vs Chat and $10k to Claude – Business Brain 771 appeared first on Business Brain - The Entrepreneurs' Podcast.

Pour en savoir plus sur helloDarwin : https://go.hellodarwin.com/hypercroissance?utm_source=helloDarwin&utm_medium=podcast&utm_campaign=grants-hypercroissance"J'ai branché mon IA à mes systèmes" : 18 questions clients réglées en 30 minutes

The episode highlights a shift from technology selection to operational risk management in the AI landscape for MSPs. Service providers are being forced to navigate the fast-changing interplay between AI models, the harness software that mediates their deployment, and the financial realities of consumption-based billing. The rapid proliferation of open-source and open-weight AI models, alongside market behaviors from closed vendors and regulatory interventions, is introducing volatility and uncertainty in both cost structures and client offerings. This dynamic creates structural challenges related to margin maintenance, vendor dependency, and responsibility for AI-driven decisions. The discussion cites the release of GLM 5.2, an open-weight model from Z AI, which now rivals expensive closed models on key benchmarks at a fraction of the cost. At the same time, large-scale investments by commercial AI vendors have yet to deliver returns on expectations, with reports indicating businesses that adopted AI are not seeing projected value. Specific attention is given to operational constraints such as compute scarcity, token consumption variability, and export policy restrictions impacting AI availability. The episode notes that these pressures are driving both vendors and MSPs to reconsider the viability of reliance on expensive, closed offerings versus investigating open alternatives. Supportive examples include the proliferation of AI “harnesses” (middleware layers like Perplexity, Claude Code, and Cowork) that sit between service providers and underlying AI models, increasing both choice and complexity. Token billing models are highlighted as a source of unpredictability for MSPs, with vendors like Atera and ConnectWise experimenting with different abstractions to shield or pass through token risk to service providers. The potential for on-premises AI deployments using smaller language models is discussed as a cost-mitigation strategy, though this raises further questions about data privacy, infrastructure burden, and long-term vendor roles. Additionally, uncertainty is flagged around sustainability of leading vendors, with projections that at least one major AI player may exit or be acquired within a year due to financial vulnerability. For MSPs and IT service leaders, these structural and supporting developments translate into increased operational and financial complexity. There is a pressing need to evaluate not just which AI technologies to adopt, but how to architect solutions that can withstand rapid vendor movement, cost swings, and evolving regulatory requirements. Practical safeguards include testing open-source AI models alongside commercial offerings, exercising caution in vendor selection, and closely monitoring evolving consumption billing models. Preparing staff and clients for adaptive, process-oriented approaches—rather than fixed solutions—is positioned as a necessary step to maintain resilience as the AI adoption cycle continues to correct course. Supported by:Pax8CometBackupGuardz

Mike Switzer interviews Peter Marsh, founding partner at Flywheel in Greenville, SC.

(Disclaimer: erstellt mit ChatGPT)Hallo liebe Community,

Ich zeige dir, warum ich Agent Funktionen in Claude Cowork bewusst https://www.alexhurschler.ch/ki-community selbst baue statt sie von anderen Systemen zu kopieren, und wie ich mein Kontextfenster klein halte, obwohl mein Agent nichts vergisst.

It's the start of Microsoft's new financial year and there's already a lot to unpack. In this month's update we cover the new Microsoft Frontier company, Cowork going GA (and the new consumption cost model), a wave of Copilot and model updates, plus a huge run of Teams calling, meetings and devices news from InfoComm - including the big Teams Phone Agent announcement.Huge thanks to this month's benefactor, Neat, for their continued support of the community. Neat have just announced an MCP server for their management platform (a first for OEM tooling) and their intelligent framing beta. Details on the Neat blog — and check out Graham's video on the Neat MCP server.

You've probably seen Cowork mentioned inside Claude, or perhaps you clocked the headlines when Microsoft folded it into Copilot a couple of weeks ago. Either way, the question people keep asking is simple: how is this actually different from just chatting with Claude? The answer comes down to files, time and money - three things that change quite dramatically once you move from chat to Cowork. In this How I AI episode, Neo and I unpack exactly what Cowork can do that regular chat can't, how to know which one to reach for, and what the new Microsoft version means if you're a Copilot user. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: What actually changes when Claude can touch files on your own computer The simple test for deciding between Chat and Cowork for any task How Cowork differs from Claude Code, and who each one is really built for Three lesser known Cowork features worth exploring What Microsoft's version of Cowork does differently, and what it costs Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

Les vacances en freelance, c'est souvent une illusion.Dans cet épisode, je te montre les 3 goulots qui t'empêchent de vraiment décrocher. Je te raconte comment la préparation de 15 jours de stage de surf intensif m'a forcée à regarder en face les 3 blocages qui empêchent les solopreneurs de vraiment couper.Et ce que j'ai mis en place pour les régler avant de partir :

Fala galera, nesse episódio eu falo sobre as coisas que eu aprendi ao criar o curso de Claude. Eu falo sobre a diferença dos 4 harnesses da Anthropic, o Claude Chat, Cowork, Code e Design. Falo sobre alguns projetos que fiz e sobre a tentativa da Anthropic de nos prender do ecossistema deles, uma estratégia similar a da Microsoft com o Word e o Excel.Link do curso Vida com Claude: https://vidacomclaude.com/Link do curso completo: https://www.cursovidacomia.com.br/Cupom de 10% de desconto à vista e parcelado: claude10Link do grupo do wpp: ⁠https://chat.whatsapp.com/KJBSOV4IbHKIWmKudYiCeh⁠lnstagram do podcast: ⁠https://www.instagram.com/podcast.vidacomia⁠Meu Linkedin: ⁠https://www.linkedin.com/in/filipe-lauar/⁠

Chinese AI models grabbed over 30% of US token use as Beijing weighed curbing access. Anthropic expanded Cowork to mobile and web, Meta launched its first AI image generator on Instagram and WhatsApp, and Xbox's $80B Game Pass bet failed. OpenRouter: Chinese AI models have drawn 30%+ of token use by US companies each week since February 8, peaking at 46%, up from 11% over the previous 12 months (CNBC) Chinese authorities have held meetings with top tech firms about potentially restricting overseas access to China's most advanced AI models, sources say (Reuters) Anthropic is bringing Claude Cowork to mobile and web, letting tasks run in the cloud and continue working even when no device is online (9to5Mac) Anthropic is bringing Claude Cowork to mobile and web, letting tasks run in the cloud and continue working even when no device is online (ZDNet) Meta launches Muse Image in Meta AI, Instagram, and WhatsApp, and previews Muse Video, the first media generation models from its Superintelligence Labs (Meta) Meta's new Muse Image tool lets users generate AI photos similar to what they'd normally post, including vacation selfies and photo-booth style shots (NYT) Public Instagram profiles are automatically opted into being used as material for others' AI image generations via Meta AI, unless users adjust their settings (Wired) Xbox spent nearly $80B over a decade on content deals betting gamers would flock to Game Pass, but most gamers prefer sticking to a handful of favorite games (Bloomberg) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this solo episode, Danny Gavin reveals how he automated his Google Ads Search Query Report process using Claude Cowork and Windsor.ai, eliminating the 3-4 hours per account it previously took to complete manually. The system pulls live metrics, merges them by keyword and ad group, and outputs a structured Excel file with an ad group summary, KPI-focused observations, and pre-populated negative keywords. It's a must-listen for those looking to systematize time-consuming account analyses and free up time to tackle other tasks. Episode Highlights:Search query reports are one of the highest-leverage tasks in Google Ads management, and doing this job manually can eat up three to four hours of work per account per cycle.Danny shares how he connected Claude Cowork to live Google Ads data through Windsor.ai and turned Optidge's entire SQR methodology into a repeatable automation.He shares what the six-tab Excel output actually contains, including auto-generated observations, pre-populated negatives, and an ad group summary that flags structural problems before anyone looks at individual search terms.The 80/20 split: See what Claude handles automatically versus where human judgment still makes the final call.This episode gives four practical lessons for building AI automations in an agency context, from documenting your process first to understanding why the ROI compounds over time.Episode Links: Digital Marketing Mentor PodcastDanny Gavin on LinkedInOptidgeClaude Cowork Windsor.ai Send us Fan MailFollow The Digital Marketing Mentor:Website and Blog: thedmmentor.comInstagram: @thedmmentorLinkedin: @thedmmentorYouTube: @thedmmentorInterested in Digital Marketing Services, Careers, or Courses? Check out more from the TDMM Family:Optidge.com - Full Service Digital Marketing Agency specializing in SEO, PPC, Paid Social, and Lead Generation efforts for established B2C and B2B businesses and organizations.ODEOacademy.com - Digital Marketing online education and course platform. ODEO gives you solid digital marketing knowledge to launch/boost your career or understand your business's digital marketing strategy.

Anthropic has expanded its AI ecosystem with Claude Cowork and Claude Code, two agentic tools designed for autonomous task execution across desktop, web, and mobile platforms. While Claude Code serves as a specialized terminal-based assistant for developers managing complex codebases, Cowork provides a user-friendly interface for general knowledge workers to automate document and data workflows. Access to these features is primarily distributed through tiered subscription plans, with the Max plan offering the highest usage limits for power users. Recent updates include integration with Microsoft 365 write tools and specialized versions for government agencies requiring high-security environments. Additionally, the high-performance Fable 5 model has been transitioned to a pay-per-use credit system due to its significant processing demands and advanced capabilities. Together, these sources outline a shift from simple chat interactions toward a comprehensive suite of autonomous digital agents tailored for diverse professional needs.

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In this episode, we discuss the exciting launch of Claude Co-Work on mobile devices, enhancing accessibility for users. Additionally, we explore AI advancements from companies like DeepSeek, SK Hynix's massive IPO, and innovative tech like Solos' camera-less smart glasses and Vercel's strategy in AI model deployment.Chapters00:00 Introduction to Claude Co-Work00:10 DeepSeek's Inference Chips00:21 SK Hynix's $28 Billion IPO00:35 Launch of Solos Smart Glasses00:45 Vercel's AI Model Strategy00:59 Conclusion and Personal Update Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

#980 What if the fastest way to get ahead of your competitors was already sitting in an app on your phone? In part 2 of this 2-part episode hosted by Brogan Williams, Jacques Hopkins breaks down exactly how everyday entrepreneurs can start using AI agents today, no Mac Mini or technical setup required. He walks through the three tabs of the Claude app, and makes the case that Cowork is the easiest on-ramp for non-coders who want to connect tools like Gmail and their calendar and start delegating real tasks. Jacques shares the origin story of his AI Operator Bootcamp, born from a retreat where fellow course creators were blown away by what he'd built with Rocky, and reflects on rebranding his business around AI as information becomes commoditized and transformation becomes the real product. He closes with candid advice on what he'd do differently starting out today (hint: audience first, always) and how he stays current in a space that changes by the week! What we discuss with Jacques: + Claude app's three tabs: Chat, Cowork, Code + Cowork as the easy on-ramp for non-coders + Connecting Gmail and calendar via connectors + Natural language works even in Claude Code + VS Code vs. using Claude Code directly + Origin story of the AI Operator Bootcamp + Migrating clients from OpenClaw to Hermes + Why transformation beats information now + Audience-first advice for starting over + Using X lists to stay current on AI Thank you, Jacques! Check out Part 1 of this episode. Check out Piano In 21 Days at ⁠PianoIn21Days.com⁠. Check out The Online Course Guy at ⁠TheOnlineCourseGuy.com⁠. Check out AI Operator Bootcamp at ⁠AIOperatorBootcamp.com⁠. Watch the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠video podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ of this episode! To get access to our FREE Business Training course go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/training⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠millionaireuniversity.com/sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

In today's Cloud Wars Minute, I explore Microsoft's shift to usage-based Copilot Cowork pricing and what it reveals about the changing economics of enterprise AI. Highlights 00:10 — Microsoft is moving Copilot Cowork from a fixed-price subscription model to usage-based pricing, and this is really reflecting the fact that heavy users are racking up massive compute costs compared to others. 00:55 — More and more, the focus is shifting to how organizations can scale those (AI) capabilities in a way that's financially stable, but beyond that, Microsoft has also said that it's considering a Microsoft-hosted version of DeepSeek as a lower-cost model alternative. 01:16 — Right now, at the moment, Copilot Cowork workloads are powered by models from OpenAI and Anthropic. We should expect to hear from Microsoft regarding DeepSeek, or another low-cost model choice, within the coming weeks. 01:32 — So, what are we really seeing here? Well, Microsoft's AI strategy is evolving beyond simply offering access to the most powerful models. Increasingly, it's about giving customers the right balance of performance, economics, and choice. 01:49 — This is also highlighting, for me, a big divide between how governments and businesses view the AI race. Governments often frame this AI race as a competition between nations, but enterprises are more likely to focus on which models deliver the best outcomes at the lowest cost for their customers. Visit Cloud Wars for more.

Special Guest : Deb Ashby TIKTOK QOTW:  What is Cowork and Credits? Plus, we go off on tangents (as usual)

Fresh out of the studio, Benedict Evans, independent technology analyst and author of AI Eats the World, returns to explore whether the AI model layer is becoming commodity infrastructure. Benedict argues there is no winner-takes-all effect in models yet, drawing parallels to telecoms, cloud, chips and the fiber bubble to ask where durable value actually accrues when everyone runs similar infrastructure on similar tokens. He unpacks why the chatbot remains a poor interface, introduces the "blank screen" and "jagged frontier" problems that keep software companies alive, and explains why large language models inherently give you "the average." Closing the conversation, Benedict reflects on the indicators that would show AI has truly eaten the world — and why the answer is better products, not better models."When you automate away work, you can always see the jobs that are going away because they're right there. And you don't know what the new jobs are going to be. Human needs are infinite. How many people are earning a living from making podcasts now? Imagine predicting that 10 years ago. There's a stage in the evolution of the market where like if you're still arguing about that, you're an idiot. But there's a stage at the beginning where you might have opinions about some of these questions, you're probably not even asking the right questions. That, I think, is where we are with this stuff today." — Benedict EvansEpisode Highlights: [00:00] Quote of the Day by Benedict Evans from AI Eats the World[01:16] The public market test: what are investors buying?[04:21] How far up the stack can models go?[05:30] Models can't build all the apps themselves[06:00] The thesis: models as commodity infrastructure[07:52] "All the value went up the stack"[08:24] Chips and Rock's Law: down to three players[11:23] The 1999 reseller story: one-time sales[13:28] The S-curve framing of technology[16:38] You're probably not asking the right questions on AI[18:02] "If this works, we're competing with a Mac"[20:25] Incumbents make it a feature[22:14] Big tech "killing startups" is overstated[24:39] Cowork as the new spreadsheet[26:01] The blank-screen and jagged-frontier problems[29:00] The hard part isn't writing the code[31:25] "What a good answer would probably look like"[33:38] The job displacement debate[37:38] Jevons paradox and the lump-of-labour fallacy[40:30] LLMs inherently give you the average[42:36] Why you really hire McKinsey[45:33] Punk versus prog rock: outside the training data[49:00] Automating ever-higher human functions[49:55] Why this is unanswerable: no theory of scaling[51:30] Indicators that AI has eaten the world[54:53] The solution isn't a better model[56:39] Where to find Benedict EvansProfile: Benedict Evans, Independent Technology AnalystLinkedIn: https://www.linkedin.com/in/benedictevans/Website: https://www.ben-evans.com/newsletterPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.Here are the links to watch or listen to our podcast.Analyse Podcast Main Site: https://analysepodcast.comAnalyse Podcast Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Podcast Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Podcast LinkedIn: https://www.linkedin.com/company/analyse-podcast/Sign Up for Our This Week in Asia Newsletter: https://www.analysepodcast.com/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the release of Microsoft Copilot Cowork and its hidden financial implications for your business. You’ll learn how to calculate potential costs by categorizing your daily tasks into light, medium, and heavy workloads. You’ll discover how to apply the 5P framework to prevent runaway AI spending in your organization. You’ll identify specific strategies to optimize your workflows by separating planning from execution. You’ll explore how command-line tools can help you maintain efficiency without burning through expensive credits. 00:00 – Introduction 03:15 – Categorizing AI tasks 08:45 – The shock of the credit-based bill 14:20 – Applying the 5P framework for cost control 19:10 – Using planning to save money 25:30 – Call to action Watch this episode now to learn how to keep your enterprise AI costs under control before you start using Microsoft Copilot Cowork. Use the free Trust Insights Microsoft Copilot Cowork Cost Calculator! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-how-to-manage-microsoft-copilot-cowork-costs.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about the newly generally available Microsoft Copilot Cowork, which is a licensed version of Claude Cowork. So Katie, you have spent a lot of time with Claude Cowork. You teach for Smarter X for their AI Academy on all the different uses of Claude Cowork. You’ll be doing an entire workshop at the Marketing AI conference on the Claude ecosystem and stuff like that. So when you hear that now Microsoft, the largest enterprise AI deployment system, has made effectively a copy of Claude Cowork available, what comes to mind? Katie Robbert: Endless opportunities. I have never met someone who is like, “Yay, Microsoft.” And we’ve talked about why a lot of companies are tied into Microsoft and a lot of it comes down to security and privacy. Chris, you have a whole series on enterprise AI, so enterprise AI not being the size of the company, but really more of the security and governance requirements needed. Microsoft as a workforce software, Microsoft 365, tends to check the most of those boxes, which is why so many large companies or companies in general tend to be tied into Microsoft. Which also means what we hear is, “Well, I can’t use Claude or I can’t use OpenAI, I can only use Copilot. I want all the bells and whistles that I’m seeing you guys talking about.” Very quick anecdote. My husband, who I’ve mentioned numerous times, is not a technology person—that is not the nature of his job—was lamenting that the new version of Microsoft is hiding all the replies to his emails from the entry-level user to the expert user. I don’t know anyone who enjoys using Microsoft, but I’m hoping now that this little bell and whistle is something that could bring people around on the users. Because Claude Cowork has been such a literal game changer for the way that I operate. The amount of things that I can get done that I couldn’t get done before because I’m just one person is infinite. Just the other day, I’ve always done the company financial projections—it’s very laborious. I have a spreadsheet, I have to check numbers from four or five different places. That’s something that Cowork can now not only help me with, but build an interactive dashboard for. And it’s like, “Yeah, you got multiple data sets, I got this, I can build that for you.” The amount of time it saves me is immense because it unlocks my time to do things like, “Hey, what’s a new target market we need to go after? What does that look like?” I didn’t have the brain space to do that before because I was so bogged down. So when I hear that Microsoft now has their version of Cowork, I’m like, “Wow, people are going to get so much done if they want to, if they see the opportunities within the software, if they’re curious.” Christopher S. Penn: If they can afford it. So that’s what I want to talk about on today’s show because Microsoft has released an Excel spreadsheet, of course, a calculator for how much Cowork will cost you because it is pay-as-you-go, it is not flat rate. So let’s talk about some of the tasks that you do, Katie. They define tasks in three categories: light, medium, or heavy. A light task is basically prompt and chat, no tool calls, one deliverable. And they classify this by the four different categories: corporate knowledge workers, customer-facing knowledge workers, technical workers, and managers and senior leaders. Now I would say that you are a manager and senior leader—I think that’s who you are, what you do. I am a technical worker. We have Kelsey who is a customer-facing knowledge worker—she’s our account manager—and we have John who is our corporate knowledge worker. John is our head of business development. So we actually check the box on each of these Cowork types of people. Now on a daily basis, Katie, you for sure have at least one Cowork process that calls more than one tool because you send out a daily update. So you have at least one of those that’s a medium-level task that sends up our daily sales report. What other daily tasks do you have Coworks have to do? Katie Robbert: I have Cowork Daily set up to send me a daily writing prompt. All it’s doing is writing to a Word document. I would imagine that’s a lightweight task. Basically, one of the things that I’m doing for my own professional development is I’m trying to make sure I don’t lose that writing muscle. As AI makes it so easy to replicate our voices, I want to make sure I don’t lose it. So I spend a few minutes every morning writing to a randomly generated prompt. So I would imagine that’s a lightweight thing. You mentioned the update that I send to the team. This is calling on our CRM data, and that I would imagine is sort of a medium because that’s only one piece of software. But once a month, I’m calling on our CRM and our financial data and a couple of other sources, so that would be a heavy task. So on a day-to-day basis, the scheduled tasks that I have are fairly lightweight. But then when I get into the real thinking, that’s when—so I was working on something this morning on behalf of the team. I was engaging a plug-in, I was engaging the Google Drive connector, I was engaging the Google Search connector, I was engaging that deep thinking of “put all this information together,” and all of the skills that are involved: the skill of building a Word doc, the skill of building a PDF, the skill of building an HTML interactive page, the skill of building a PowerPoint—all of those in one specific task. So I would say that is a heavy task, even though it looks at the surface like a lightweight task. Christopher S. Penn: I would say, and I think this is a fair characterization, you probably do two heavy projects a day in Cowork because you’re constantly doing deep strategy and things. So I’m going to put two a day—this is a monthly calculus—put down 60 there. Now for Kelsey, I would say Kelsey at least does at least one light and one medium task in Claude per day. I think it’s actually more than that, but I’m going to put that down as a starting point. What do you think? Katie Robbert: I think that’s a fair starting point. Christopher S. Penn: Okay. For me, I work in Claude code, which is slightly different, but since we’re just trying to get a sense of what Cowork will cost, I’m going to do the equivalent. On a day-to-day basis, I probably do five tasks that are light, so that’s going to be 150 of those a month. I probably do 10 tasks that are medium, so that’s going to be 300 a month. And I probably—actually, I know I do over 10 tasks a day that are heavy, that are like pure heavy code lifting. So that’s going to be another 300 there for John. John really doesn’t use Claude much at all, I don’t think. So maybe like 30 at most. Katie Robbert: Yeah, I think so. We have a skill that was built specifically with his role in mind, and he runs it maybe once every couple of weeks. When I look at the weekly tasks—so this is looking at a month at a glance—I would actually bump up the medium tasks for me because I have weekly reports that are run that engagement, the Claude Chrome extension, the connections to our CRM, connections to our project management software. I have eight of those weekly. Christopher S. Penn: Okay, so you’re basically running two mediums a day. Effectively. Katie Robbert: Yeah. Christopher S. Penn: Claude or Microsoft Copilot Cowork bills on what are called credits because why make this easy? Light tasks bill 125 credits, medium tasks bill 500 credits, and heavy tasks bill 1,200 credits. The cost is a penny per credit. So our Microsoft Copilot Cowork cost—are you ready for this, Katie? $1,600 a month. Katie Robbert: Get out. We’re going back to candlelight and whittling pencils. Christopher S. Penn: That is because it’s a penny per credit, which they do to make it sound cheap, not realizing that a single heavy task is 1,200 credits. So a single task is $12. So for me to do one QA run on a piece of software is swipe the credit card for $12. On a monthly basis, we are consuming effectively 657,000 credits, which is $6,570 total, all in. It’s $1,600 per user. So Katie, our Trust Insights Copilot Cowork bill is $6,570. Katie Robbert: I have no words. That is insane. And to be fair, so you and I, Chris, I would say are power users. We are turning to these tools to do all kinds of things all day long. Even with trying to do things and schedule them off-hours to not be during peak usage, we’re still using up usage. And yeah, we are a small team. If we take out the work that Kelsey does just for the sake of this example, you and I are still eating up the majority of the cost. If we take out you, I’m still eating up a majority of the cost. I don’t know how a company or team is supposed to be able to afford to use this. It’s a real bait and switch. Shame on Microsoft. Christopher S. Penn: Well, this is enterprise. They can do this. Katie Robbert: Yeah, they can. It doesn’t mean they should. Christopher S. Penn: So your usage, because a credit is a penny, your usage of Copilot Cowork a month would be $1,057.50. That is how much you consume in equivalent credits in the system. Now granted, we pay for the four of us to share a Claude Max 20 account; we pay $200 a month for it. This at the enterprise level, you’re talking four people, $1,600 for four people, one of whom barely will use it. Realistically, like you said, we’re probably going to average $3,000 an employee is what it will cost to use Cowork. Katie Robbert: Which is an insane amount. For some companies that don’t even blink at that, but that’s a very small handful of companies who would feel that way about $3,000 a month. One of the things that we’re doing with a lot of our clients right now is trying to help them find cost savings in their tech stack—like how many tools can they reduce or licenses they can let go of and replace with things like Claude Code or Claude Cowork. But if they’re like, “Yeah, I want to do that exercise,” and what I have is Microsoft Cowork, I would say, “Cool, we’re not doing that exercise until Microsoft changes the billing,” because it’s going to cost you 10x more than it’s costing you now. It’s not worth it. Which is a real shame because Microsoft users have been waiting for this kind of functionality. Christopher S. Penn: And so what I wanted to talk about on today’s podcast episode, now that we’ve worked out that this thing is going to cost you three grand a month—because one of the things that people have pointed out on LinkedIn is, “Oh great, you fired all these people so you can switch to AI; now AI is going to cost you more than the people did”—is how do we reduce AI costs? How do we use AI more efficiently? Because this is clearly a lot of money. Katie Robbert: If only we had a few things to start with. I’m going to shock and dazzle everyone and say, “Guess what? Start with the 5P framework by Trust Insights.” You can learn more about it at TrustInsights.ai/5P-framework. At a high level, the five Ps are: Purpose—what the heck are you doing? People—who the heck’s involved? Process—how do you do the thing? (These are your SOPs). Platform—what tools are you using? (Not just the AI, but also your external data sources). And Performance—did you do the thing? It sounds really straightforward because it is. However, a lot of people go straight to pushing the buttons and “vibe coding” and, “Hey, build a thing.” “What do you want it to be?” “I don’t know, you pick.” Without doing this work up front, yeah, you’re going to find yourself at $650,000 a month very quickly. There is no tool that allows you to skip over good planning upfront, good governance up front. Microsoft Cowork is no different from any other large language model in that you still need to have good requirements, you still need to have good prompting, you still need to have good governance, even if you’re just using it internally on your own systems. Enterprise companies, any company, has sensitive data somewhere within their SharePoint stack, within their databases, their document repositories. You don’t want to accidentally or carelessly give a large language model access to that because you didn’t plan ahead. So that’s my soapbox. I’m coming down off of it. Chris, what would you add to how to make AI efficient? Christopher S. Penn: So planning, yes, 100% is going to make the most of the tools you have. The other question is, given these outlandish costs, is Microsoft the right system for you to use? Because Claude in Anthropic’s enterprise level is just as expensive. Companies have recently seen their burn through their entire Claude usage for the year, their budget in weeks. I think it’s Uber that burned their 12-month budget in a month and a half in terms of their token budget. So when we look at these prices, Katie, you remember a while back I had said, “Hey, Nvidia’s got this cool little desktop box. It’s $5,000.” You’re like, “You’re not buying $5,000 worth of hardware.” Absolutely not. Now if Microsoft or Anthropic said, “Hey Katie, you need to pay us $6,500 a month,” you’d be like, “You know what, Chris, go and buy one of those boxes; let’s buy one for each of the team and we’re going to drop Anthropic because we are not paying $6,500 a month for AI.” Right? Katie Robbert: You know, and so it’s an interesting question because where we started the conversation was saying there’s a reason why people are wedded to using Microsoft because of the security and privacy. I don’t know that introducing an Nvidia box would comply with the regulations set forth by that company. I mean, that’s a big question. It’s an interesting workaround, but it’s not going to work for everybody, especially the more regulated the industry gets. It just might not be an option. Christopher S. Penn: Yeah, it’s going to very heavily depend on IT. However, because it lives literally in your infrastructure, you do have a lot more governance over it because it’s literally a box that sits on your desk that you control. But more importantly, today’s top local models match a lot of the cloud foundation models and capabilities. GPU AI’s new GLM 5.2 matches Claude Opus 4.8 capabilities. Now you’re going to need a few of those Nvidia boxes to be able to load and run it well for a small cluster of employees. But for the lighter models like Qwen 3.6 or Google’s Gemma 4 if you have to, or Nvidia’s Neotron Ultra if you have to use a US-based model because of regulatory reasons—like you’re not allowed to use anything Chinese, regardless of the fact that it’s on your infrastructure—those are options that you would then use a tool like Open Cowork to handle the inference for it. So my suggestion is that to Katie’s point, use the 5Ps and then drill down and say, “What are the things that we absolutely positively have to use Cowork for?” Or can we make that task as deterministic as possible using command-line tools and stuff that do not require AI? So for example, Katie, when you query HubSpot every day with Claude Cowork, that is using the MCP connector that uses a ton of tokens back and forth. Now we don’t see it because we’re on an individual plan. The moment we’re forced to switch to a team or an enterprise plan, we will say, “Okay, we’re going to use the HubSpot command-line tool which can fetch data in and out.” And then the AI just says, “Hey tool, give me the thing,” and it goes off and does the back and forth and brings the data back and hands it to the AI. That will dramatically cut the amount of AI usage you have because a non-AI tool is getting data for you. Katie Robbert: As you’re describing it, I want to sort of make sure I understand because you’re making it sound like it’s an easy switch from the process that I currently have built in Cowork to, “Okay, just use a command-line tool.” I’m not someone who’s well-versed in command-line tools. You’re someone who is. However, you have your own set of things to do right now. So it’s time. It’s internal resources to make those switches to make the cost savings. I just want to be clear about that; it’s not a, “Oh well, in order to save money, let me just go ahead and use a command-line tool.” Like you still have to set it up. Christopher S. Penn: Yes, and corporate IT will be very busy doing that. However, corporate IT also likes us because they can then govern it. They can say, “Okay, we will ensure that this suite of 10 command-line tools is installed on every computer in the company, and there’s a joint service key that we can maintain programmatically and rotate every 30 days and stuff like that.” So that infrastructure, which corporate IT is very well-versed in, is going to be much happier with that than kind of like the whole shadow IT where people are like, “Oh, I’ll just have Claude make me this thing.” No, they would much rather say, “I would like to have control over the command-line tools that are installed on every machine in the company.” Katie Robbert: So work that out. You’ve worked with IT teams before. How likely is it that they’re going to—if you say, “Hey, I would like to have control over the command-line tools on every machine in the company,” they’re like, “Yeah, sure, Chris, no problem. Let me bump you to the top of the list. You’re a priority now.” I think you’re going to have a hard time. Like, we see the value in it, we know that it’s a useful thing. I just want to be realistic, and I’m trying not to derail the conversation too much, but I just want to be realistic that, like, yes, that’s the thing. If you have the skills to do it and if you don’t have to go through your IT team to do it, absolutely do it. If you have to go through your IT team and they have to set it up, get comfy, get in line; you’re not a top priority right now. Christopher S. Penn: Yeah, well, my perspective is IT would want to do that. It would be like, “We would love to have more control over this to stop the shadow IT that’s happening all over the place because of AI.” So IT in its MDM config would say, “Okay, these are the 10 tools that we’re going to drop on every machine, and we’re going to also programmatically alter your Claude MD files and stuff to tell Claude this is what’s installed. You must use it so that it cuts those costs.” And IT can then say, “We certify these 10 command-line applications are safe to use.” Katie Robbert: Provided it has the time to get skilled up to do that. So yeah, I like to make sure that we’re very clear about caveats because in the 25 to 30 minutes we have for a podcast, we go through things like “do this, do this,” and then it’s, “Well, what do you mean? It said no.” So let’s get back to—Microsoft has started to release Cowork, their version of Cowork, and we’re talking about AI efficiencies. When you think about starting places for someone who’s using Microsoft, someone who’s using their Cowork version, what is the first thing you think somebody should do before they start burning tokens or usage or spending pennies? Christopher S. Penn: The five Ps, the planning, and build all of your prompts and all of your infrastructure for Cowork in regular Copilot, because regular Copilot is very smart. Now in regular Copilot, if you go in the upper right-hand side, there’s a little menu, a little drop-down saying “models,” and you should choose for planning. Choose GPT 5.5, soon to be 5.6—”think deeper,” that’s the smartest model that’s available. And say—and that’s where you have your conversation like, “Oh, I want to do this in Cowork. I don’t want to do this, I want to do this. Help me figure this out. Ask me questions. Let’s plan this out. Here’s the Trust Insights 5P framework. Help me use this to come up with these plans.” So you do all of your planning and all that heavy token usage in regular Copilot to build the skills and the pieces that you can then drop into Cowork, so you don’t have to use Cowork to plan because Cowork is going to chew up your usage. That way, if you can use regular Copilot, it should be a little bit lighter on your budget. Katie Robbert: And I think one of the questions that you should add into your planning is, “Can I do this in Copilot or do I need Cowork for this?” And you know, I want you to use your human judgment, but it would be a good idea to ask the large language model like, “Do you have the capabilities to do this within Copilot or do I need to bring this into Cowork to actually execute it?” Because you may be surprised. You know, to Chris’s point, the models are getting smarter every day. And so you may not need to execute what you think you need to execute in Cowork; you may be fine with using Copilot. Yes, I get it’s not as shiny and as exciting, but you know what’s also not exciting? Being told you owe the company $60,000. That’s not exciting. Christopher S. Penn: Exactly. Even for something like scheduled tasks—Microsoft Copilot tasks are scheduled tasks—so if it’s not something that needs Cowork’s horsepower, that will obviously keep you from chewing up those extra credits over there. Katie Robbert: Yeah, and I think that’s a good best practice for a lot of these tools, you know? So can you do your planning in Claude Chat before bringing it into Cowork? Can you do your planning in Gemini before bringing it into their version of whatever that is? And that’s just a good best practice for efficiency in general. Christopher S. Penn: Yeah, I mean, when I do my planning for even software builds and stuff like that, the first thing I do is I have a master planning prompt. It’s actually a skill that incorporates the 5P framework by Trust Insights. And so I have the model ask me questions from the 5P framework: “What are you doing? Who’s it for? How should it work? What are the additional command-line tools that we should be using? What is the definition of done?” And all of that is stuff that if I don’t dictate it out loud, it knows to ask me for it. So I can plan first, and then the language model rebuilds the prompt into something that meets all of those conditions and produces a really solid output that I can then go use to build requirements documents and all the stuff. You will save so much time and money by investing more heavily in planning up front, and you can then hand off the execution of the plan to a very small, fast model. Katie Robbert: And I think that’s a really good pro tip. And I just want to give a small plug—you can actually download, we have for sale in our academy at Academy.TrustInsights.ai, a “prompt-to-skill.” So basically, as Chris was just describing, he has a specific process for building those requirements. This prompt-to-skill will help you do that and get more efficient at building those requirements. And then what you may find that you have is a reusable template, and it makes that even more efficient. So start with that. Go to Academy.TrustInsights.ai, purchase the prompt-to-skill—it’s very awkward to say that—and then start building out those requirements before you bring it into something like Cowork. And you’re going to save yourself a lot of time and money, and people are going to be like, “Wow, you did that really fast. How did you do that?” And you’ll be like, “I don’t know, I’m just that good.” But in the back of your mind you’re like, “I use the 5P framework by Trust Insights. It got me there faster.” Christopher S. Penn: Exactly. So Copilot Cowork from Microsoft is now generally available. Before you type one character into it, please take the time to use the 5P framework by Trust Insights. Take the time to understand what your company has budgeted. Take the time to understand what tasks fall in each category, and as best as you can, try to reserve it for the things that truly need Cowork’s capabilities. And don’t just make it the default. If you’ve got some thoughts about the new Microsoft Copilot Cowork that you want to share, pop by our free Slack group. Go to TrustInsights.ai/analytics-for-marketers where you and over 4,700 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TI-Podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Daniel's on vacation, so Darrell delivers a demo-heavy 365 Message Center Show episode to distract you. Tasks from Teams meeting notes can soon be connected to existing Planner plans. Copilot Cowork introduces cost in it's usage-based billing. And you can publish your prompts to the organisation. 0:00 Welcome - without Daniel 2:19 Microsoft 365 Copilot: Outlook emails in Copilot Notebooks - MC1392569 6:24 Microsoft 365 Copilot: Copilot surfaces a summarized answer above your search results - MC1392570 10:17 Connect Teams meeting to existing Planner Plans - MC1392571 14:22 Cowork in Frontier: New Value + Usage-based Billing - MC1393468 21:39 Outlook for iOS and Android: Send Availability feature to be retired - MC1393802 24:57 Microsoft 365 Copilot: Publish organization prompts to Copilot Chat - MC1396361

Fiona Fung leads the teams behind Claude Code and Cowork at Anthropic (overseeing Boris Cherny and the entire engineering and PM team). Before Anthropic, she spent 11 years at Microsoft building Visual Studio and TypeScript and then moved to Meta, where she started Facebook Marketplace (now generating over $100 billion in GMV annually), worked on Meta's first smart glasses and AR glasses, and led infrastructure, growth, integrity, and safety teams at Instagram. She's been an engineer for over 25 years and has a unique perspective on how the role of building software is changing.In our in-depth conversation, we discuss:1. What she's learned about running a team that's shipping 8x more code than before2. Which roles AI will transform next3. Specific ways her team uses AI4. How Claude “routines” have changed how she operates as a manager5. The context-switching problem no one has solved yet6. The biggest unsolved problem in AI7. What keeps her up at night—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyMercury—Radically different banking, now with Command: https://mercury.com/—Where to find Fiona Fung:• LinkedIn: linkedin.com/in/fionafung—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Fiona Fung(02:31) How the engineering role has transformed over 25 years(09:28) What an AI-pilled software team looks like in 2026(12:26) Using Claude to manage and review team output(14:40) The evolution of code review and verification(16:55) Who to hire: creative builders and deep systems experts(18:18) The shift to ambitious thinking(19:40) The growth mindset required to thrive in AI-native teams(25:52) Helping small businesses adopt AI tools(31:46) How Anthropic spots latent demand and builds for it(35:08) The next frontier: asynchronous work with AI routines(38:06) Agency and accountability in AI-native teams(39:40) The vibe shift from token-maxing to ROI measurement(44:24) The “bad vs. sad” quality framework(49:34) Why all managers start as ICs at Anthropic(55:24) Preventing skill atrophy(58:43) Managing context switching with 20 AI agents running(1:00:08) How PM and data science roles are transforming(1:03:40) The importance of dogfooding and using your own product(1:08:36) Outstanding questions(1:12:48) The future of engineering jobs and education(1:17:59) What keeps Fiona up at night: team culture at scale(1:22:53) From six-month roadmaps to JIT (just-in-time) monthly planning(1:27:03) Lightning round—References: https://www.lennysnewsletter.com/p/building-the-most-ai-pilled-engineering—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Can a company reach 1 billion users before figuring out how to make money—and still dominate the future of AI?This week's AI news cycle delivered a fascinating mix of milestones, competitive shakeups, enterprise AI breakthroughs, security concerns, and agentic innovation. OpenAI crossed the historic 1-billion-user mark, Microsoft opened Copilot CoWork to the masses, SpaceX made a massive move with its $60 billion Cursor acquisition, and new open-source challengers emerged to challenge the industry's biggest players. For business leaders, the message is becoming increasingly clear: AI capabilities are no longer the bottleneck. Adoption, governance, employee enablement, and operational execution are now the real competitive advantages. Organizations that successfully train their teams and embed AI into daily workflows are already seeing dramatic productivity gains and measurable business outcomes. In this session, you'll discover: Why OpenAI's 1-billion-user milestone may be more complicated than the headlines suggest  How ChatGPT's market share slipped below 50% while Gemini and Claude continue gaining ground  OpenAI's new $150 million partner network and what it means for enterprise AI adoption  Why Microsoft Copilot CoWork could become a game changer for organizations already invested in Microsoft 365  The strategic implications of SpaceX acquiring Cursor for $60 billion  How new open-source coding models are challenging leading closed-source AI systems  Why AI governance and international cooperation became a major focus at the G7 Summit  The growing scrutiny facing OpenAI ahead of its anticipated IPO  New developments in agentic AI platforms from Databricks and Vercel  How leading companies are using AI agents to transform productivity and operations  What business leaders need to know about AI's growing impact on jobs, hiring, and workforce planning  Why employees who openly use AI may still face workplace stigma despite widespread adoptionAbout Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Sign up for Practi, a new platform that helps law firms use subscription billing.Here are the top 5 takeaways from this episode:* Lawyers need an AI strategy and policy first. Before adopting any tools, firms must have a written AI policy, even if it simply says no tools are approved yet. Without one, a staff member using an unapproved (non-enterprise) AI tool can cause an ethical breach if client data ends up in model training.* Stick to two AI tools, not a dozen. Jennifer recommends picking one AI within your existing workspace (Copilot if on Microsoft, Gemini if on Google) plus one secondary tool for drafting or checking work. Chasing every new model is counterproductive. Depth beats breadth.* Document infrastructure is the real foundation. Before AI can be useful, a firm's documents need to be organized, accessible, and OCR'd where necessary. Getting documents into a state where an AI can actually “talk” to them is the unglamorous but critical first step.* Claude (especially via Claude Code/Cowork) is the top recommendation for legal writing. For transactional work requiring a long context window, Jennifer sees Claude as unmatched. She's actively installing Claude's Cowork integration for clients, who are amazed at its ability to handle contract redlines directly in their workflow.* AI increases productivity but also workload. Jennifer invokes Jevons' Paradox: AI tools make lawyers faster, but that extra time tends to get filled with more work. The real win is choosing intentionally: take on more clients, deepen client relationships, or bill at a higher rate, rather than just working more hours.__________________________Want your question to be answered on a future show? Fill out this short survey.Have subscription model question? Check out this free resource to ask all of your questions at notebook.practi.ai.Check out Law Tech AI.Sign up for Paxton, my all-in-one AI legal assistant, helping me with legal research, analysis, drafting, and enhancing existing legal work product.Get Connected with SixFifty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a business and employment legal document automation tool.Sign up for ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Gavel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, an automation platform for law firms.Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Law Subscribed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to subscribe to the weekly newsletter to listen from your web browser.Prefer monthly updates? Sign up for the Law Subscribed Monthly Digest on LinkedIn.Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mathew Kerbis'⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ law firm ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscription Attorney LLC⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Want to use the subscription model for your law firm? Click here to sign up for a new platform that helps law firms use subscription billing. Get full access to Law Subscribed at www.lawsubscribed.com/subscribe

Episode 223: Automate Your Lead Generation with our FREE online course: https://go.digitaltrailblazer.com/auto-leads-course-freeMost online business owners are barely scratching the surface with AI — copy-pasting prompts and hoping for magic — while their competitors are building automated workflows that do the heavy lifting. Without a real AI system behind your business, you're stuck in the weeds doing repetitive, time-consuming tasks instead of the high-value strategic work that actually grows your revenue.In this episode, Rocky Pedden teaches us how to build AI-powered workflows that run your business operations, including how to map your deliverable process into trainable agents, structure prompts for specific outputs, use tools like Claude Cowork and Zapier to automate multi-step tasks, and repurpose content at scale — all so you can spend 80% of your time on strategy instead of button-pushing.Connect with Rocky:https://revenuezen.com/ https://www.linkedin.com/in/rockypedden/ https://www.linkedin.com/company/revenuezen/ https://www.instagram.com/revenuezen/ https://www.youtube.com/channel/UCgRWKt7IPwh3_rH66XlwuEgWant to SCALE your online business bigger and faster without the endless hustle of networking, referrals, and pumping out content that nobody sees?Grab our Ultimate Ad Script for Coaches, Agencies, and Course Creators.Learn the exact 5-step script we teach our clients that allows them to generate targeted, high-quality leads at ultra-low cost, so you can land paying customers and clients without breaking the bank on ad spend.Grab the Ultimate Ad Script right HERE - https://join.digitaltrailblazer.com/ultimate-ad-script✅ Connect With Us:Website - https://DigitalTrailblazer.comFacebook - https://www.facebook.com/digitaltrailblazerTikTok: https://www.tiktok.com/@digitaltrailblazerX (Twitter): https://x.com/DgtlTrailblazerInstagram: https://www.instagram.com/DigitalTrailblazer

He handed AI a full marketing project… and it came back finished in 30 minutes.What used to take 4 days of analysis, reporting, and presentation building was done in a single sitting with better insights included.In this episode, Andrew Bruce Smith breaks down how tools like Claude Co‑Work are changing the way marketers actually work. This is not about faster copy or better prompts. It is about delegating entire workflows to AI and shifting your role from execution to management.You will hear how AI can analyse raw marketing data, build reports, create presentations, and even improve on your thinking without being explicitly asked. The implications for agencies, in-house teams, and professional services are immediate.There is also a clear warning. Most organisations are still treating AI like a tool rather than a system. That gap is where both risk and opportunity now sit.If you work in marketing, this is already affecting your output, your value, and your role.Send us Fan Mail Is your strategy still right in 2026? Book a free 15-min no obligation discovery call with our host:

Today I want to walk through the difference between an AI tool and an AI system, because the gap between those two things is where most of the money is. We're working with Claude and Claude Cowork today, nothing else. By the end of this you'll understand why one-off prompts don't compound no matter how good they are, you'll be able to spot where a system pays for itself within the first month, and you'll know how to map the processes already running in your business onto AI workflows. Before any of that makes sense I should explain what these two tools actually are.Claude is an AI assistant. Same general category as ChatGPT, made by a different company called Anthropic. You can use it free at claude dot ai in your browser, and there's a paid plan at twenty dollars a month that gives you more room to work. Inside Claude there's a feature called Projects, which is basically a folder with a memory, and that's going to matter a lot today.Claude Cowork is a desktop app from the same company. If regular Claude is a conversation in a browser tab, Cowork is closer to a coworker with access to your computer. You point it at a folder, it can read the files in there, create new ones, run jobs on a schedule, and keep working through multi-step tasks while you do something else. It comes included with the paid Claude plan.

As America approaches its 250th anniversary of independence, powerful forces including Rockefellers and others are working on a comprehensive plan to fundamentally transform America and “Refound” it, explained researcher and writer Lisa Logan in this interview on Conversations That Matter with The New American magazine’s Alex Newman. This refounding agenda involves a “color revolution” organized ... The post At 250, Rockefellers & Co. Work to “Refound” America With “Color Revolution” appeared first on The New American.

Send us Fan MailI am sitting down with Austin Armstrong, author of Virality, keynote speaker, founder of Syllaby AI, and someone who has generated billions of views across social media through short-form video strategy. We talked about what's actually working on Instagram in 2026  including how Austin uses trial reels to test content, his strategy for repurposing videos across platforms, and why collaborations are still one of the fastest ways to grow your audience organically. But we also went deep into AI. Austin shared how he's using ChatGPT's newest image generation update to create Instagram carousel posts in a fraction of the time, how Claude Co-Work is changing the way creators and business owners work with AI beyond just prompting, and how entrepreneurs can use these tools to simplify content creation without losing authenticity or personality. WHAT YOU'LL LEARN IN THIS EPISODE02:11 How Austin went from MySpace at 14 years old to becoming one of the leading voices in AI marketing07:31 Why every CEO and founder should be building a personal brand and how it lets you pivot11:24 Why consumers increasingly buy from people who share their belief systems13:20 What's actually working on Instagram right now: Austin's top strategies for 202613:48 How Austin reposts top-performing content and why one repost just hit 600,000 views15:13 The tool Austin uses to download Instagram videos without a watermark16:13 Why collaborations are still one of the most underrated growth tools on Instagram (and how to add up to 5 collaborators per post)19:24 How Austin uses trial reels to A/B test hooks before posting to his main feed25:32 How Austin is posting 3 times a day on Instagram — and how AI makes that possible25:59 The exact process for using ChatGPT Image 2.0 to create Instagram carousel posts in minutes31:11 What Claude Co-Work actually is — a plain-English breakdown for beginners39:46 All about Syllaby — the AI video creation and scheduling tool Austin foundedLinks Mentioned:Hot Reels — my 12-month Instagram content lab. DM me the word HOT on @elizabethmarberryFree DM Automation Guide + 1 Month of ManyChat Free — DM me the word LEADS on @elizabethmarberryFree Monetize Your IG GuideSyllaby - Austin's AI video creation and scheduling platformAustin Armstrong's WebsiteFollow Austin on Instagram SnapInsta - free tool to download your Instagram videos without watermarkPrevious episode — Ep119: How I Use AI to Reverse Engineer Viral HooksWORK WITH ELIZABETH MARBERRYApply for your FREE Instagram Breakthrough Session with ElizabethFree guide to Monetize Your IG: Seven Simple and Proven Ways to Finally Make Money on InstagramFollow Elizabeth Marberry on Instagram, TikTok, Facebook Please be sure to rate, review and follow the show on Apple podcasts (or wherever you find your podcasts) so we can get this free value to other people who need it.

Agentic AI is being misread as a series of separate battles - e.g. Snowflake vs. Databricks, copilots vs. agents, model makers vs. app vendors, etc. We think the real story is that the biggest opportunity in software is converging around who owns the new intelligent client and the AI back end that makes it useful. The new client is the agent-based system of engagement - Snowflake's CoWork & CoCo, Databricks Genie, Microsoft Copilot, Google Gemini Enterprise, ChatGPT/Codex, Claude/Cowork and others. But that client cannot deliver business outcomes without a new back end - what we call a System of Intelligence - that represents a model of the enterprise in terms of its business rules and tacit knowledge. You can't build one without the other. We frame this premise using Clay Christensen's integrated innovation and Jensen's extreme co-design as applied to enterprise software.That is why Snowflake is the focal point for this Breaking Analysis, but not the whole story. Snowflake is not just competing with Databricks anymore. It is now in the same strategic arena as Microsoft, Google, OpenAI, Anthropic, Salesforce, SAP, ServiceNow, Celonis and others - all trying to define where business users, builders and agents get work done, and where the enterprise context that powers that work gets built.

The Information's San Francisco Bureau Chief Jason Dean talks with TITV Host Akash Pasricha about Meta's internal plans to charge up to $200 a month for its premium AI agent, Hatch. We also talk with Helion Energy Founder and CEO David Kirtley about the nuclear fusion company's new $465 million funding round at a $15.5 billion valuation, Netskope CEO Sanjay Beri about the cybersecurity market's growth deceleration and using Anthropic's Mythos model to spot code vulnerabilities, and Snowflake Chief Data and AI Officer Anahita Tafvizi about the enterprise launch of its newly rebranded CoWork and CoCo tools. Finally, we get into the systemic shift from open academic research to closed frontier AI laboratories with our Applied AI reporter Laura Bratton.Articles discussed on this episode: https://www.theinformation.com/newsletters/ai-agenda/billionaire-databricks-perplexity-co-founder-pitches-ai-researchers-work-big-techhttps://www.theinformation.com/articles/fusion-startup-helion-nearly-triples-valuation-15-5-billion-thrive-led-roundhttps://www.theinformation.com/articles/meta-looks-charge-200-month-planned-hatch-ai-agentSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Meta's $200/Month AI Agent Hatch08:26 - Helion Energy Raises $465M for Fusion16:10 - Netskope CEO on AI Growth & Anthropic Mythos27:36 - Snowflake Launches CoWork and CoCo AI Tools34:26 - Databricks Co-Founder on Open AI Research

Anthropic just closed a $65 billion Series H round at a valuation approaching one trillion dollars — and has crossed $30 billion in annualized revenue, driven largely by enterprise demand. Claude Code alone became generally available in May 2025 and reached $2.5 billion in annualized revenue in February 2026, with that figure more than doubling since the beginning of 2026. Meaghan Choi, Head of Design for Claude Code and Cowork at Anthropic, was in that room. This conversation goes inside the operating model behind that growth.What you'll learn:Claude Code's evolution from an internal feature into one of the fastest-growing revenue products in historyAnthropic's secret sauce to shipping products at an incredibly high cadence while ensuring qualityHow product teams get structured into small pods of 5 AI Builders and a fleet of agents, where non-engineers ship code into productionDriving enterprise adoption through PLG from technical teamsHow organizations can measure AI ROI beyond AI adoption and token usageDesigning user interfaces for agentic capabilities, including CLIKey takeaways:Titles and role boundaries matter less than contribution. At Anthropic, designers ship code and engineers design, and the pod owns the output collectively.Quality gates have moved downstream. The richest product learnings come from working software, not from reviewing mocks or PRDs.Managing a team now means managing both people and a fleet of AI agents. The skills are more similar than they appear.Credits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Meaghan ChoiSocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

In this Marketing Over Coffee: CEO and Co-Founder of Trust Insights talks with us about AI Tactics, Site Management, Unlocking CRM Data, and more!! Direct Link to File Claude Desktop changed everything Using Cowork to unlock data in other platforms Automating website updates 9:25 – 10:53 Incubeta: the “old way” of marketing – with creative, […] The post Katie Robbert on Putting Claude Cowork to Work appeared first on Marketing Over Coffee Marketing Podcast.

Claude is not just another AI tool lawyers can chat with. It may be a preview of where legal work is heading. In episode 619 of the Lawyerist Podcast, Zack Glaser talks with Sam Harden about Claude, Claude for legal, and the growing role of AI in law firm workflows.  Sam breaks down how Claude can work with documents, folders, PDFs, Word files, and connected legal tools in ways that go far beyond simple prompting. They discuss the difference between Claude Chat, Claude Cowork, connectors, and skills, and why those distinctions matter for lawyers trying to understand what AI can actually do.  They also explore why law firms should not rush into automation without first building better systems. From deposition summaries to document creation to legal research support, this episode explains how AI can become more useful when it is guided by strong processes, clear instructions, and thoughtful implementation.  Listen to our previous episodes on Artificial Intelligence and the Future of Legal Practice.  #612: AI for Lawyers: What You Need to Know Before Your Clients Do, with Cat Casey  Apple | Spotify | LTN  #601: Beyond Chatbots: Using Agentic AI in Law Firm Intake, with Matt Spiegel Apple | Spotify | LTN  #590: Innovating Without Overwhelm: Practical AI Tips for Lawyers, with Graydon Trusler Apple | Spotify | LTN   #587: Future-Proofing Your Firm in the Age of AI, with Jack Newton Apple | Spotify | LTN   #577: Rethinking Law Firm Growth in the Age of AI, with Sam Harden Apple | Spotify | LTN     Have thoughts about today's episode? Join the conversation on LinkedIn, Facebook, Instagram, and X!   If today's podcast resonates with you and you haven't read The Small Firm Roadmap Revisited yet, get the first chapter right now for free! Looking for help beyond the book? See if our coaching community is right for you.   Access more resources from Lawyerist at lawyerist.com.   Chapters / Timestamps:  00:00 – Introduction  00:34 – Why Claude for Legal Matters  01:28 – Live Crabs and Work from Home Chaos  07:04 – Setting Up the Conversation  07:29 – Meet Sam Harden  08:04 – What Lawyers Should Watch with Claude  10:14 – What Claude Actually Is  11:16 – How AI Moved Beyond Chat  12:07 – From Claude Code to Claude Cowork  13:22 – How Claude Works with Documents  15:20 – Why Claude Cowork Is a Big Shift  15:45 – Creating Documents and Presentations  16:52 – Claude Chat vs. Cowork vs. Code  18:12 – Legal Plugins and Connectors  19:30 – Reducing Context Switching with AI  20:49 – Connecting Claude to Legal Tools  22:24 – What Legal Connectors Can Do  24:47 – MCP, Tools, and Connector Limits  26:37 – What Claude Skills Are  28:44 – Why SOPs Come Before AI Skills  29:43 – Using Skills for Legal Documents  30:35 – AI Skills for Deposition Summaries  31:14 – Combining Connectors and Skills  32:15 – Teaching Claude Like a Team Member  33:11 – Choosing the Right Skill  34:00 – How Bad Instructions Create AI Risk  35:49 – Building Better Skills and Plugins  37:13 – What Comes Next for Claude for Legal 

This week, we welcome Adam Youngblood, AI strategist, to discuss how AI and agentic AI are becoming pervasive, why early “super Google” use is giving way to assistants that perform work, and how non-technical users can start by asking AI questions when they don't know where to begin. The conversation covers Claude (including Cowork) for research, costing, and spreadsheet creation; the lack of effective onboarding and growing privacy concerns; and frustration with AI bots conducting first-round job interviews. Adam describes agentic tools like OpenClaw and emerging offerings from Google, Amazon, and others, plus practical business opportunities (reducing waste, after-hours call handling, predictive maintenance, and camera-based visual inspection), while also addressing job displacement, data center power/water demands, and calls for ethics and guardrails.

How is AI transforming accessibility for indie authors — and why should you care even if you consider yourself able-bodied? What happens when the tools designed to help people with disabilities end up making everyone's creative business better? Jeff Adams, accessibility expert and romance author, explores how AI is opening doors that were previously closed. In the intro, Spotify Audiobook Innovations; The Economics of Convention Life [The Indy Author]; Friction in your Author Business [Self-Publishing with ALLi]. Today's show is sponsored by Draft2Digital, self-publishing with support, where you can get free formatting, free distribution to multiple stores, and a host of other benefits. Just go to www.draft2digital.com to get started. This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Jeff Adams is the author of YA thrillers and gay romance, and the co-author of Content for Everyone, a practical guide for creative entrepreneurs to produce accessible and usable web content. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How ending a long-running podcast made space for more writing — and how to know when it's time to let go of a good thing What accessibility really means for indie authors and why your digital content might be excluding part of your audience How AI agents like Claude Cowork are removing physical and cognitive barriers for authors with disabilities, chronic pain, or limited energy The culture of shame around AI use in the writing community and why blanket anti-AI statements can be ableist Practical tools including NotebookLM, ElevenReader, and ChatGPT for marketing copy, metadata management, and multimodal research Exciting futures in personalised reading, real-time translation, and AI browser agents that could change how everyone interacts online You can find Jeff at JeffAdamsWrites.com. Jeff also now has a SubStack at contentforeveryone.substack.com Transcript of the interview with Jeff Adams Jo: Jeff Adams is the author of YA thrillers and gay romance, and the co-author of Content for Everyone, a practical guide for creative entrepreneurs to produce accessible and usable web content. Welcome back to the show, Jeff. Jeff: Thanks so much, Jo. It's good to be back. Jo: It is. You were last on the show in March 2023, so over three years ago now. Give us a bit of an update on your writing and publishing business and what it looks like at the moment. Jeff: Sure. I think the biggest thing that happened is that my husband Will, who is also a writer, we ended the Big Gay Fiction Podcast at the end of 2024, after 470-something episodes. It was basically time to do that. So we both focused on writing from that point. In 2025 we had some of our biggest successes in getting writing out into the world. I refound my groove—my difficulty in writing went away finally. We talked a little bit about that back in 2023 too. Will started a new pen name and started producing again, and it was really good to be able to move in that direction. Jo: Was this the hockey romance that really hit at the right time? Jeff: You know, I wish I could have capitalised more on Heated Rivalry when it came out, but I did get hockey books out, and I think I did get to ride that wave a little bit there too. Jo: Yes, and if people don't know about that, that was a super popular streaming series. Was that based on a book? Jeff: It was, yes. Rachel Reid was the author of that book and that series that then Jacob Tierney optioned and made into what fairly turned into a global phenomenon at the end of 2025. Jo: Yes, absolutely. Although I particularly liked Red, White and Royal Blue. That was the one I liked. Not so much into hockey. But anyway, I just wanted to ask you about the Big Gay Fiction Podcast. As you say, you did hundreds of episodes over many years. You and I met over podcasting. You've had lots of connections with people. You ended it, and I know you struggled with ending it, but it sounds like it went really well for you. So maybe you could talk a bit about— How do you know when it's time to end something—a good thing rather than something bad? Does that make more space for writing, essentially? Jeff: It absolutely did make more space for writing for both of us, in particular for me because I have a day job. I balance everything on the creative side with the day job. Will and I had been talking about it for over a year. It just was like, it's really time. After nine years, getting to that 470 mark, we thought about trying to get to 10 years and we thought about, if not 10, then getting to 500 and ending on a milestone. As we looked at everything in our creative business, it was like, this is fun, we enjoy it, but we're not getting as much out of it as we might be if we were actually also writing books, which we also really want to do. It became a time thing and what was the best use of the time. We absolutely miss it occasionally. The whole Heated Rivalry thing, I would've loved to have had episodes to talk about that on, but in the long run, it was worth it. Jo: I mean, one of the things with a podcast, particularly around fiction, was that it was a marketing angle for your fiction. This show is a marketing angle mainly for my nonfiction. So what did you replace the podcast with, in terms of book marketing? Jeff: It was really stepped-up email marketing. I'd always had a list. Will started a list, of course, as he started his new pen name. So it was really turning on that, focusing on that, getting some email marketing with a Bargain Booksy and a Fussy Librarian and a BookBub occasionally to do that work. To be honest, even though we covered things in our genre that if you like what we're talking about, you should like our books, there was never as much of a connection there as you'd want there to be. Even from that book marketing angle, these other things that we can do, it's also a better spend of the money to get those types of promos than it was to continue running the show. Jo: Yes, that is interesting. I mean, obviously I think about podcasting a lot since I have this one, and I put Books and Travel on a hiatus and that was meant to help my fiction and definitely didn't help my fiction sales. But I want to bring it back again because I love doing it. Do you have this hankering sometimes? Do you think you'd ever do the podcast again? Because you are also quite into all the technical stuff and all that. Jeff: It's possible. I've toyed with the idea of doing a short accessibility podcast geared towards creatives, tilting to the same audience that Content for Everyone does. Then I come back and look at the time—is my time better served writing new fiction or perhaps starting a Substack, which I also toy with the idea of, for accessibility stuff? So it bounces around in my head to do another show, but I haven't really decided to jump on that yet. Jo: Yes, and I think that waiting is really good. As you say, you quit a big thing and you don't have to rush to fill it again. I love that you guys are writing more books. So I wanted us to talk about that up front because I know people who listen to this show—I encourage people to start podcasts if you want to, but equally it can take a lot of time. So that's fantastic. Now, you mentioned accessibility, and I feel like the word can be quite difficult for people. So let's just start with a definition. What is accessibility? Why do you care and why should we care? Jeff: So accessibility is really about making sure that whatever the thing is, whether it's something out in the physical world or in the online world, that everybody has access to it. Access to the information, access to getting into a building or being able to cross the street appropriately, whatever that is—that the accessibility of the thing is high. So that regardless of who is approaching it, they can interact with whatever the thing is. If we put that into the digital world, it's about making sure that text on a screen can be perceived by anybody, whether they're trying to read it visually or if they're trying to read it through a screen reader or through a braille monitor. Whatever that is, they need to be able to interact with it, get the information they need, do all the functions of whatever it is on the screen. Check out on Amazon, check out at their favourite e-commerce place, be able to get the products in their cart, check out, et cetera. For creatives, it's about the things that we do: the websites that we build for ourselves, the e-commerce platforms that we use, our email marketing, our social media posts. Making all of that as accessible as we can so that we're not perhaps missing a part of our audience or our prospective audience from being able to engage with our work and in turn, hopefully, buy our books and enjoy our books and become a fan. This became important to me because of my day job. I hadn't really considered this—like, I think most people don't—until I started working at UsableNet. It's going to be 15 years I've been at that company come this autumn, and I really started to see the impacts because UsableNet is all about accessibility on the digital front. I really started to learn, being a project manager for them, what all of that meant and how it impacted people who couldn't buy something online, couldn't book a hotel room, couldn't book an airline ticket. It just really became something I got passionate about. I ended up writing the book because I realised that nobody talks to creatives about this. Nobody tells the independent author what they should do to help make their digital stuff accessible so that they don't miss people. I never expected my day job to interact with my creative side so much, but this certainly has over the last few years. Jo: I mean, has it got better? Like we said, you were on here three years ago. We did talk about some of the things around EPUB formats and taking off DRM and what we need to do on our websites—labelling images, for example, and that kind of thing. Do you think accessibility has gotten better? Jeff: I think the awareness of it has improved, both within the creative community and in the broader web ecosphere, that the awareness is better. There's so much knowledge that needs to go into creating something that is accessible. Sometimes there's so much that you have to think about with colours and alt tags on images and all the little bits and pieces, if it doesn't really come to muscle memory, it's easy for it to fall off. There's a survey that's done by WebAIM every year about the top one million homepages out in the universe, and they surveyed those for just the things that an automated scan can detect, which is a small portion of overall accessibility, and the number of errors across that top million actually ticked up this year. Even though there's all these laws around the world—people get sued all the time in the US—the number of errors ticked up for the first time in a few years. So I think the awareness is up, but I think being able to take action on it and make the time to take action on it isn't where it needs to be. Jo: So last time you gave us all those tips. I'll refer people back to that and also to your book Content for Everyone, which has got loads of great stuff in. I wanted to talk to you for this show because I was sitting watching Claude Cowork—now I use Claude Code a lot more—but updating 140 titles on IngramSpark, where me clicking things and there's like 15 clicks per record on IngramSpark updates for pricing, is an absolute nightmare. I was watching the AI do the work and I realised this isn't just saving me time, it's actually saving my wrist and my arm from repetitive strain injury. That's when I thought about this accessibility thing. As you mentioned, for example being physically accessible into a building, say someone's in a wheelchair, they can't necessarily get into a building if there's no ramp. I was thinking that for many years, being an indie author, being a writer online, there's also been these physical barriers because there's a lot of plumbing and clicking for us. So I wondered, starting with an attitude around a shift in who this is opening up to— How is AI starting to help people with these accessibility issues? Jeff: Yes, there's so much opportunity around this. We should note, just to timestamp this, that we're talking on 14th April 2026, because who knows what will change, even in an hour from now. I think Cowork was one of the first things that we saw, and that's only been out since the very top of this year. Being able to do actual agentic tasks. Other things have sort of gotten there, but Cowork really opened it up. You mentioned the repetitive stress that you would've had clicking all of those forms on IngramSpark across 140 books. But there's that type of stress, chronic pain, cognitive drain for somebody who may have some cognitive disability and trying to work through that form. The cognitive energy just might drain out and maybe knock them out for several days after trying to get through that, or the tasks take them multiple days to do. Someone who has lower vision, someone who's trying to work through that form with a screen reader—all of that draws energy, draws focus. Now we've got something where, with plain language, we could say something like: here's all my pricing information, I've logged into IngramSpark, go update these books. Obviously the prompt's going to be a little more than that, but in broad terms, that's what we're going to tell it. Jo: Hmm. Jeff: And being able to have it go through and do the thing. If it gets stuck, have it come back and say, “Hey, I've got trouble with this. Please help me.” That can just free up so much of the drains that people can have—the things that can take them out of doing the part of the work that they need to do for an author business. They can go write the book through whatever process you're going to use to do that, rather than getting caught up in something like having to update all those books on IngramSpark. Jo: You mentioned writing the book there. I have this real sense of being an able-bodied indie author in terms of my computer use and my ability to write a whole book, a 70,000-word thriller that I write regularly. We're all special in some way, but I do have a reasonably normal brain where I can do this work without too much strain. It's hard work, but I can do it. I meet people who are now using AI to help them write, to help them organise their work—maybe someone has dyslexia or ADHD or cognitive issues or pain—there's just so many things that I take for granted that don't affect me. I hear from people who, at this point in time in the community, are almost shamed for using AI to write. So I wanted to bring this up to discuss it under the terms of accessibility. Do you have any thoughts on that? Jeff: I have real difficulty with people who will say anything in the broad range of, “I don't need to use this thing, and therefore you should not either.” Which is adjacent to indie anti-AI speak that there is out there. Certainly we're living right now at probably the highest point that it's ever been, where more and more there's a sentiment towards not using AI for whatever the reason is. I totally respect that people can have concerns about the environment and about energy use and water use, et cetera. Not to mention all the other things that are on the more difficult side of AI. To shame someone who may not be able to put their story out there without the use of that AI, whichever one they're using, or to shame them because they're using AI to run part of their business—updating IngramSpark, doing other things like that—I think it can come down to there being some ableism there. Ther is some privilege behind that too, where they're just like, “I don't need this, and you shouldn't have it either.” I want to give people just a sliver of an idea of what this can mean for someone who is disabled and what AI can unlock for them. There is a person on LinkedIn that I follow whose name is Hannah Desmond. She's an ADHD coach and a former software developer, and very recently she posted this on LinkedIn. This is a paraphrase of what she said, but: having something that can meet you where you are and help you bridge that gap is what I think I have found so helpful about using AI. Here's what I keep coming back to. Without that support, I wasn't more motivated or more capable. I was just stuck. That's the bit that gets lost. We've been taught that struggling is how you know you're doing it properly. So when something reduces the struggle, it can feel wrong—even when it's the thing that actually makes the work possible. Because there's a difference between avoiding thinking and being able to think at all. I think that rounds it up. She's talking about her time as a software developer, but you can apply that to any realm of AI when we're thinking about trying to shame someone for why they may be using it. We may not know that they have a disability because we don't always share that part of ourselves. So I really feel strongly about that and how we are in this culture of shame. Jo: Yes. It drives me up the wall, actually. But I will also say: you don't have to have a disability or accessibility issues in order to use AI in whatever way you personally decide is okay—talking to the listeners now. I think Orna Ross from the Alliance of Independent Authors says it well, which is you should have your own AI policy. So you personally decide where your lines are, how it helps you, what you want to keep for you, and what you want help with. I was also thinking in terms of accessibility around money. Again, for many of us, professional cover design, professional editing, professional human-level translation, these are things that are pretty pricey for many people. So again, this makes it more accessible. One of the reasons we got into the indie way and being indie authors was to try and remove the barriers to entry to people who have been excluded from the environment of publishing. So, yes, it is really hard to talk about this, and yet that's why I wanted to talk about it, because— There's so many variables for each individual and there's no situation that's the same, really, is there? Jeff: No, not at all. The things that I may need to do my work in the most efficient way possible is different from the way that you're going to work, is different than the way my husband's going to work, is different than every other person and the way that they're going to work. Which is why any kind of blanket statement about “I don't need something and therefore you shouldn't need it either” can just be so problematic, because we have no idea what someone else is going through. Either it's a permanent part of their lives or maybe it's something that is happening temporarily with them where they might need to leverage other tools. Jo: Yes. Talking about that temporary, I think I really got the first sense of this when I had COVID the first time, which was really bad. I remember I was so sick, the only thing I could do was listen to an audiobook. I couldn't think, I couldn't read. It was really probably months of not having my brain back. Then the other thing that's happened as I age, as women age, is menopause kicks in and the brain fog is a real thing. I've heard from other people too who've said having Claude or whoever, an AI tool, to help with the brain fog is so important because otherwise I just wouldn't be able to gather my thoughts. Again, as you said— Even if we don't need these things now, it's quite likely we're going to need them at some point, given ageing, given the potential for injury and disease. I mean, we don't escape this alive, do we? Jeff: Yes, that's a great point because unless we're extremely lucky as individuals, we're all likely to have some sort of a disability in our lives at some point. I know for me, as I age and my eyes get more and more tired after being in front of a screen all day for work, and then whatever creative stuff I do in the afternoon on a book—when it comes near bedtime and I do want to read, I probably want to do that with an audiobook, much more audio, especially for any long reading project. That can also be like, if I have a long document or a long article to read, I am likely to give it to ElevenReader, let it load itself up, and then listen to it, because I take the information in better than trying to follow words across a screen. Jo: Yes. Jonathan, my husband, now also listens to a lot of academic papers on ElevenReader. Most of us will know it as where we publish some audiobooks from ElevenLabs, or you can also publish other things there. So it is super useful to think about what we can do with ElevenReader. Another thing that I found really useful recently is NotebookLM. On NotebookLM, there is a free tier. You can put various things in there and then create a custom audio. So this is something I've been doing as part of research. You can put in, say, 10 YouTube videos or some PDFs or your book or whatever, and then you can create a custom audio. Then I'll go for a walk and I'll listen to the custom audio, and then I'll go back and look at the detail of what it was. It gives me the framework of whatever I'm thinking about on a broader level, and then I can come back to the details. So again, it's this multimodal approach that can help us manage our energy, I guess. Jeff: And it's all about the managing of the energy, I think, too. That is a great way to think about the accessibility of it all. You mentioned a great use there for NotebookLM. That could also be putting your book in there and having it help you build a world bible or something like that. Or building marketing materials off of that. There's a lot of things now that NotebookLM can do in terms of helping you create FAQs maybe for a newsletter or for your website, and building video stuff off of the material that it has. So there's a lot of options there, and ever-growing options that can be useful for someone to manage any number of the things that they may need in their creative business. Jo: Yes. In fact, talking about Claude, there are a lot of Claude plugins now, skills and integrations. Shopify just released a Claude plugin and many of us now have Shopify stores. I have a lot of products with a lot of different variations and the metadata. There's so much metadata. And again, I'm just so pleased now that I can work with Cowork and get it to actually update directly into Shopify. In fact, coming back, you mentioned updating alt tags earlier. That's something again that AI could help you update—the back list of your alt tags on a website. I've now got my Cowork doing EPUBs so I could finally update all my EPUBs with back matter and all of this kind of thing. So I feel like perhaps we could go beyond accessibility to talk about amplification. All the things that we didn't do because it was too tiring and we just couldn't be bothered, or it would just be way too much work, that now it's opened up as a possibility because of these tools. Jeff: Absolutely. I mean, you look at a backlist as large as yours and the things that you're now able to do. I didn't know that Claude had a Shopify plugin. So the abilities that we have now to maybe do things in the business that we hadn't before. One of the things I've been working with Claude on is rewriting my website and creating a more proper website for Will. I'm really making sure that it is not only SEO prepared but also GEO prepared, with all the metadata and all the backend code schema that it needs so that LLMs can find me, can understand what I do, can understand the books, branch out to the other areas that it needs to. Doing that through WordPress would've been so much more difficult, even with Claude, that to be able to rewrite the site in a way that is going to let me manage it better so that I will do it on a more consistent basis. Whatever that thing is, we're now able to do these things. That could be updating keywords in Amazon or making sure we're aligned across all of the sales platforms that we might be on and things like that, that Claude can do and do well. Jo: Yes, I think marketing is just the killer app really for people, isn't it? I think most authors do not enjoy marketing. I find Claude better for creative work, for strategic work, for doing work through Cowork or Code, but— ChatGPT with marketing copy is very, very good. So I've actually been using that as we record this. I've got a Kickstarter launching next week, so I've been getting it to do ad copy and social media copy and all that kind of thing. This is stuff when you have to produce—give me 20 taglines, give me 20 hooks, give me another 20 and another 20. I mean, we just cannot do it as humans, right? Jeff: Yes, I have found GPT wildly helpful. I mentioned trying to get Bargain Booksy and Fussy Librarian promos. Jo: Mm. Jeff: And you have to give it the marketing hook, and it can't just be the blurb that's on Amazon—it's got to be something fresh, and they each have slightly different requirements. Having GPT—here's the blurb, give me a dozen different options—and then I may take pieces of all of them and create one of my own. But it reworks that much faster than my brain was ever going to try to find the right thing I want to give to Bargain Booksy. Jo: Yes, you are right. Or it says write this in 300 characters or less. Jeff: Yes. Jo: I do exactly the same. That kind of transformative work can be really good. In fact, there was somebody I know who has been rampantly anti-AI for years and then said, “Would this help me? I have to do a synopsis for an agent, so I've got this 100,000-word book and it needs to be a 10-page synopsis. How would I do that with AI?” So I was encouraging her to take each chapter and ask it to summarise the chapter, and of course read through it and everything. But I mean, doing a synopsis once you've actually written a book—that can be super useful. So I think what we're saying is— There are levels of need in terms of both the author and the audience. Then there are levels of your personal use from one end of the spectrum to the other in terms of how far you want to go in every area of the business. And in that way, it's just different for everyone. Jeff: Yes, and I think getting to that mindset shift that we were talking about a little bit—it can be so easy to dip your toes in. That one author came to you and said, “Do you think it could do this?” And I think that's the beginning exploratory area for perhaps anyone. People are going to hear us talk about this and it might inspire them to go try something that we've talked about. But these things, whether it's Claude or GPT or Gemini or whichever one it is, you can come to it and say, “I'm an author, I have X, Y, Z going on in my life”—whether that's a disability, whether that's a time constraint because you have a day job and maybe you have kids and a family that need your attention—”I have these time constraints, I want to do X, Y, and Z in my business. How can you help me with that?” It's going to tell you what it can do to help you with that. I would even say, if you have the ability to have multiples of these, you could ask the same question to GPT and Claude, and they're going to give you similar answers in some instances, but they may also have different ones because of the abilities that the different platforms have around these things as well. That can help you make that mindset shift of, “Well, now I see that it can do that. Could it also do this?” And then ask it if it could do that. Because I know for me, Jo, I've taken so much from you and your journey with Cowork that it's like, “Oh, she did that. I wonder if I could do this.” And all of that piles on top of itself. Then eventually I think your brain starts to think on its own, “Oh, I have to do this task. Can Claude maybe do this for me? Let's go find out.” Jo: Yes, and if it couldn't do it for you yesterday, you never know, it might be able to do it tomorrow. Jeff: Right? Because I haven't tested yet its new ability to actually use your computer. Jo: Mm. Jeff: And I'm curious what that might open up. Because one of the things that I've seen that I wish it would do is be able to take the EPUB that's on my drive and actually put it into a platform I'm trying to upload to. Cowork on its own hasn't been able to cross that barrier, but I wonder if with computer use added to that, if it could. Like, “here's the EPUB, upload that over there,” be able to pick it from the file picker, essentially. Jo: Yes. I think, well, a little tip for everyone: I wouldn't give access to your entire file system to the AI. Jeff: That's a good point too. Jo: Yes. I have a Claude folder in my drive and it only has access there. So if you put files in that drive, it might be able to do that. But I know what you mean. I have been using it to help me publish things in German on KDP. Now I can use the browser, so you can actually do that. In terms of uploading the actual file, I know what you mean. These things will change. As we record this, again middle of April, we are almost about to get the next models being Mythos, which might be Claude 4.7 Opus, or also ChatGPT has a new model coming, and these models are getting very powerful. With every shift they can do more things. So as you say, the very first thing to do is ask it, “I want to do this—what are my options?” And some of them, for example, doing an AI-narrated audiobook, ChatGPT and Claude don't do that. You want ElevenLabs or one of the other services for that, but they can tell you what your options are. So that's one thing, but I wondered if you have any thoughts on the gaps that you are seeing. You mentioned one there around file uploads, but— What do you hope might come and some of the things that might be exciting if they arrive? Because you never know, they might be here already. Jeff: There's certainly some movement in some areas. One of the things I'll share is, in March I was at the 2026 CSUN Assistive Technology Conference—CSUN is California State University, Northridge—and they've run this conference for some 40 years now. One of the sessions I went to was from Tara Maisel—I hope I'm pronouncing her last name right. She's a senior project manager in books accessibility at Amazon, and she was doing a session specifically on readability. She had all kinds of statistics and information about what goes into making something readable. One of the things she talked about with AI was the future of personalised reading. If you think about the Kindle app, for example, there's a lot of settings you can make there—font size, colours, brightness, text spacing. There's a lot of tools in there. She was pointing out that potentially readers don't even know what they actually need for the optimised visual reading experience. She sees a world where AI can perhaps do an analysis of your reading behaviour and then help you find the optimal settings. Maybe even multiple optimal settings for, say, if you were reading in a room that had daylight versus at bedtime, and the ways you might shift it. I was almost thinking of this like when you're at the optometrist and they're like, “Which lens is better—this one or that one?” Jo: Oh, sometimes that is very hard. Jeff: Yes. It's that AI could step you through that a little bit to help you find that optimal reading experience in that moment. And then it might even notice, potentially, if you're changing something in the way that you're moving through a page, that it might flag to say, “Hey, do we need to adjust something?” Some other areas that I think are really exciting, for everyone and perhaps particularly for people who are disabled and needing the support of some assistive technology, is what we're seeing in the browsers. OpenAI's Operator has been out for quite a while now, since sometime I think autumn of last year. Perplexity Comet has been around even longer. Then we've got browser extensions from Gemini and Claude that are available, that can let you just type natural language. You know, “Please go find for me jeans in this size that are on sale on this website. Find me the best price for blue jeans on this site and this size,” and it'll just go do it. Which can certainly speed things up for people in the disabled community to find things quickly, to spend time navigating less, and maybe ending up with the AI coming back and saying, “I found these five things. Which one would you like me to buy for you?” Or, “I found this one thing that you do need and it's waiting for you in your shopping cart.” The ability for that on the horizon is an amazing jump from an accessibility point of view. But really it's one of those things that accessibility will then help everyone because we can all just shop that way, if we choose to. These are early days for these browsers and these extensions. The other side of it comes back to basic web accessibility too, because I've seen these types of activities not work so well on a site that may not actually be accessible on its own. A great example is something I ran into with Claude Cowork about a month ago. I was testing to see if it could help me navigate and get things uploaded together for a site where I wanted to upload books, knowing again that it's not going to upload the actual file, but it could fill in the metadata from my master database of metadata stuff. There were areas on the site that it actually couldn't hit the button, because the site itself was also not functional to a screen reader. So there are gaps there. It's early days, but I really see that as an interesting future that'll really help people with disabilities—but again, help everybody too, just manage time better. Jo: I know exactly what you mean there. I've done some collaborative work with Claude Code when it's like, “I can't click the button,” and I'm like, well, I'll click the button—you fill in everything else. Jeff: Exactly. Jo: It's actually quite a funny situation. But goodness, coming back to IngramSpark again—these things need APIs. We need better functions. It's funny because I think a lot of traditional publishers have these APIs or backend upload things that you can do. I'm like, well, we need to get to that with these systems. But I think things will change. Another thing that I think has also shifted is the use of voice. Voice for dictation—it used to be with dictation that you would have to say “comma,” “open quote,” “new line,” and all of that. And you'd also have to make sense. Whereas now I feel like you can just dictate a whole load of things to these AIs and then say, “Tidy that up,” and they will do a lot more than the old situation. So I think voice will also help. Also automatic translation. I don't know if you know this about X, and if you're on X anymore, but just this week they've made it multi-language. So I can read tweets by people who've posted in another language in English. I can read something from Korean or read something that someone French has posted and it gets translated. It has made a huge difference to the content I'm seeing, which is fascinating because I don't think we've ever had this kind of automatic “everything is translated into your language” situation. It's really got me thinking about how [automatic translation] might work for eBooks or other things if the rights are there. I don't know. Have you seen stuff like that? Jeff: There's so much available now with voice and the ability to not have to speak all the other stuff that went with it—comma, full stop, next line. It was a little mind-bending sometimes, trying to think about quote marks and all that stuff. And now it's so good. Different platforms do it to different degrees of ability. Even being able to speak your prompts into the very platforms themselves without having to type all of it. Chronic pain comes to mind, any kind of mobility thing—all the typing would be a drain or maybe even impossible. So the voice ability is so powerful there and unlocks more things. At the same time, those translation abilities—I believe AirPods now have the ability, if you've got the right stuff on your phone, that you could be talking to somebody, they may speak back to you in a language you don't speak, but your AirPods will give it to you in your language. Jo: Hmm. Jeff: Google has, I believe, a live captioning app that you can use. I think there's even a split screen—I don't know if that's available now or something in their future—where you could put the phone on the table and tell it who's looking at what side of the screen, and it'll put the language that I need on my side and the language the other person needs on the other. So there continues to be such a shift in how we're being able to translate stuff that really opens up communication and can open up our books to so many more people. I'm very interested to see—I haven't pulled the trigger on this yet—but how Amazon's auto-translation rolls out and how that's received in terms of the accessibility around our books and being able to put it in someone's hands who doesn't speak—I think it's only English to other languages right now—but who doesn't speak the language it was written in but wants to read that book. We could never, as indies, or really even big five publishers, wouldn't have the money to create custom translations everywhere. But if the AI can help do that and spread those books around so that everybody could have the story they want to read, I think that's such a win for the reading audience. Jo: Yes, I think it's so exciting to think what might be coming, and that's what I want to stay on the side of on the AI discussion. There's enough negativity out there and you can get that information somewhere else, but for me I want us to stay on the positive side of how this helps both the author and the reader. And hopefully the community, to create more and read more and enjoy being human more. Right? Because I find that I do get out more and listen to stuff, or I'm out walking instead of at my desk, and I mean, that's what it's about. I'm pretty excited about the future. How about you? Jeff: I am. I think there are, quite honestly, some scary things that could be out there in the future. I mean, there's been a lot of talk about what Mythos is capable of. But on the other side of it, there are all these advances. I also look back at Google and AlphaFold and what DeepMind was able to do there for science. There's more of that stuff out there, and individually for each of us, spending a little bit of time—and I do have to say, I think you need to spend time on a paid plan because the free stuff doesn't give you the idea of what these platforms are actually capable of. So if you only drop in, even briefly, to experiment on one of the $20-a-month plans and give it your situation, ask it what it can do for you, I think you'll see where, on a personal level, AI will help you unlock some things. It can help you move some things to the next level in your business that for whatever reason you haven't been able to do. You don't have to use it for everything. You may decide that it's still not for you for whatever reason, and that's fine. But I think there's so much to explore here and to let your curiosity run for a little bit to see what's possible and what you might unlock with it. Jo: Brilliant. So where can people find you and your books and everything you do online? Jeff: So pretty much everything lives at JeffAdamsWrites.com. Jo: Well, thanks so much for your time, Jeff. That was great. Jeff: I loved it, Jo. Thanks for having me..The post Accessibility And AI: How New Tools Are Opening Doors For Indie Authors With Jeff Adams first appeared on The Creative Penn.

Learn how Claude can help Amazon sellers organize workflows, automate tasks, improve keyword research, build smarter systems, and save time in business and life. AI is no longer just a tool for writing quick prompts or brainstorming ideas. In this episode of the AM/PM Podcast, Bradley Sutton sits down with Andrew Bell and Zoe Lu to explore how Amazon sellers can use Claude to organize their business, simplify daily tasks, and create repeatable systems that save serious time. Bradley opens by sharing his own failed attempt at using Claude for a presentation, proving an important point: AI is only as good as the context, instructions, and structure you give it.   Andrew breaks down what makes Claude different from a basic chatbot experience. He explains how Claude Chat, Claude Co-Work, Claude Code, skills, sub-agents, and scheduled tasks can help sellers move from simple conversations to actual workflows. For Amazon sellers, this can mean pulling keyword data, organizing it into Excel, mapping search intent, building product truth cards, judging keyword relevance, and even using Claude to support listing optimization, PPC planning, and product research.   Zoe then brings the conversation back to a beginner-friendly level by explaining how sellers can get started. Her advice is to install the desktop app, use Claude Co-Work, create a “brain” folder with personal and business context, connect the tools you already use, and turn repeated tasks into projects, schedules, dashboards, or skills. From managing calendars and emails to organizing files, creating daily briefs, analyzing customer feedback, or preparing for meetings, Claude becomes more powerful when it understands your goals and connects with your workflow.   The biggest takeaway is that Amazon sellers do not need to become AI experts overnight, but they do need to start building AI into their operations. Claude can help reduce busywork, organize messy information, create smarter processes, and uncover insights faster when used correctly. For sellers who want to stay competitive, the opportunity is not just using AI once in a while, but building repeatable systems that help them work faster, think more clearly, and grow smarter. In episode 524 of the AM/PM Podcast, Bradley, Andrew, and Zoe discuss: 00:00 - Introduction 02:58 - Claude Vs. ChatGPT: What Amazon Sellers Should Know 04:33 - Claude Chat, Co-Work, And Code Explained 08:31 - Using Claude Skills Like Repeatable SOPs 12:49 - Turning Keyword Research Into Excel Workflows 16:18 - Product Truth Cards And Keyword Relevance Checks 19:02 - Helium 10 MCP And Faster AI Workflows 20:12 - Simple Life Automation Ideas With Claude 25:59 - Zoe's Beginner Guide To Getting Started 29:02 - Building A “Brain” Folder For Better Outputs 35:23 - Tasks, Projects, Scheduled Agents, And Dashboards 42:31 - Q&A: Helium 10 Claude Connector, Agents, And Claude Workflows

Steve P. Young just got back from MAU Vegas 2026, slightly jet-lagged, heavily caffeinated, and loaded with fresh insights on app growth, AI, monetization, onboarding, and the strategies mobile leaders are quietly using right now.In this special debrief episode, Steve breaks down the biggest trends, smartest tactics, and most interesting conversations from MAU Vegas, including the app growth strategies that work… until everyone starts copying them.From AI-powered creatives and onboarding psychology to monetization experiments and distribution tactics, this session is packed with practical takeaways for app founders, marketers, and growth teams looking to stay ahead in 2026.Couldn't make it to MAU this year? Don't worry, this episode brings the best insights, lessons, and “hallway conversations” straight to you.

You’ll sharpen your daily tech game this week: add names directly to Mail recipient fields, kill those sneaky iOS nickname pop-ups before they embarrass you, and stay alert to Low Power Mode. Long-press your steering wheel button to summon Siri faster, welcome ChatGPT and Perplexity to CarPlay, untangle Apple’s App Entitlements, and stream HLS video right inside the updated MGG iOS app. Don’t Get Caught treating your LLM like a glorified search bar—re-task it as a brainstorming partner, let agents check each other’s work, troubleshoot stubborn email issues, and have it build its own skills using Claude Code and CoWork. Your questions and tips drive the back half: disconnect AirPods from your Mac in one tap with ToothFairy or Control Center, dial in rock-solid remote screen sharing using Jump Desktop, Zoom, and Tailscale, stop your iPhone ringer from accidentally flipping, and plan your escape from Comcast email by grabbing a real domain through Cloudflare, Namecheap, or GoDaddy. Then it’s Cool Stuff Found season—Bartender 6 reclaims your menu bar, the Syntech case protects your Apple Vision Pro, and the Mila Air3 and Honeywell HEPA purifiers clean up your air. Plus a heap of love for Eufy lawnmowers, vacuums, and doorbells, all wired together with Homebridge and Home Assistant. 00:00:00 Mac Geek Gab 1142 for Monday, May 18th, 2026 May 18th: Send an Electronic Greeting Card Day MGG Monthly Giveaway – Enter to win a Function101 Apple TV Button Remote The MGG Merch Store is Live! Quick Tips 00:00:01 Ben-QT-Add a name to the Mail recipient field 00:03:43 Beware of Nicknames showing on iOS You can disable this! 00:08:08 The lessons we learn about our tech when traveling 00:08:49 QT-Be aware of Low Power Mode. Also App Tamer 00:13:56 Larry-QT-Long Press Steering Wheel Voice Command to activate Siri 00:16:14 ChatGPT and Perplexity are allowed to use CarPlay now 00:18:00 Apple's App Entitlements 00:19:26 Mac Geek Gab iOS App adds HLS video 00:22:35 David-QT-Use an LLM to troubleshoot your email 00:24:33 Re-assign your LLM, re-task it. Treat your LLM like a brainstorming assistant. Claude CoWork (and Claude Code) 00:29:45 Let your agents check one another 00:33:16 Have your LLM create skills for you Reviews 00:36:26 Jamcycler-MGG Review-My Favorite Podcast Sponsors 00:38:02 SPONSOR: Keeper. Right now, Keeper is offering our listeners 60% off personal and family plans at https://Keepersecurity.com/MGG. This offer is only for podcast listeners! 00:39:41 SPONSOR: Shopify. In 2026, stop waiting and start selling with Shopify. Sign up for your one-dollar-per-month trial and start selling today at https://Shopify.com/MGG 00:41:28 SPONSOR: Gusto. Get three months free when you run your first payroll when you start at https://gusto.com/MGG Your Questions Answered and Tips Shared! 00:43:07 Gino CO-How can I easily disconnect my AirPods from my Mac? ToothFairy Or Control Center Or Sound Menu Opt-plus-Mute/Volume keys will bring you to System Settings Sound Pane 00:49:09 Paul-Best Method for Screen Sharing? Jump Desktop Tailscale 00:55:04 Barb-How can I stop from accidentally toggling my iPhone ringer on and off? 00:57:13 Roger-What to do about Comcast email going away? Cloudflare Registrar Namecheap GoDaddy Cool Stuff Found 01:02:21 DLH-CSF-Bartender 6 / Pro / Mega 01:04:53 ATC/PP-CSF-Syntech Apple Vision Pro Case 01:09:25 CSF-Mila Air3 Purifier 01:11:37 n-Greg-CSF-Honeywell Allergen Plus HEPA Large Room Air Purifier 01:12:41 Some love for Eufy Eufy Lawnmower Eufy Vacuums Eufy Doorbells Homebridge Home Assistant 01:24:36 MGG 1142 Outtro MGG Monthly Giveaway Bandwidth Provided by CacheFly Pilot Pete's Aviation Podcast: So There I Was (for Aviation Enthusiasts) The Debut Film Podcast – Adam's new podcast! Dave's Business Brain (for Entrepreneurs) and Gig Gab (for Working Musicians) Podcasts MGG Merch is Available! Mac Geek Gab iOS app Mac Geek Gab YouTube Page Mac Geek Gab Live Calendar This Week's MGG Premium Contributors MGG Apple Podcasts Reviews feedback@macgeekgab.com 224-888-GEEK Active MGG Sponsors and Coupon Codes List BackBeat Media Podcast Network

How can you supercharge your creativity in an age when AI is reshaping everything — including how we write, edit, and market our books? What does it look like to use AI as a genuine creative partner rather than a shortcut? And could professional speaking become an income stream that complements your writing career? With James Taylor. In the intro, Audible's new royalty model; New royalty model details [ACX; Kindlepreneur]; Public Speaking for Authors, Creatives and other Introverts; Why Indie Authors Should Ignore the Market's Mood and Focus on their Mission [Self-Publishing with ALLi]; Lichfield Cathedral; This podcast is sponsored by Kobo Writing Life, which helps authors self-publish and reach readers in global markets through the Kobo eco-system. You can also subscribe to the Kobo Writing Life podcast for interviews with successful indie authors. This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn James Taylor is a nonfiction author, professional speaker, podcaster, and entrepreneur who helps people unlock their creative potential. He hosts the SuperCreativity Podcast and his latest book is SuperCreativity: Augmenting Human Creativity in the Age of Artificial Intelligence. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How to define creativity and why it's becoming the most valuable skill in the age of AI The five stages of the creative process — and the stage most people skip Three types of creative purpose: play, self-expression, and legacy How James used multiple AI tools alongside human collaborators to write, edit, and market SuperCreativity Bulk book sales, industry-specific editions, and revenue models for nonfiction author-speakers Practical tips for authors who want to break into professional keynote speaking You can find James at JamesTaylor.me. Transcript of the interview with James Taylor Jo: James Taylor is a nonfiction author, professional speaker, podcaster, and entrepreneur who helps people unlock their creative potential. He hosts the SuperCreativity Podcast and his latest book is SuperCreativity: Augmenting Human Creativity in the Age of Artificial Intelligence. Welcome to the show, James. James: Well, thank you for having me as a guest. I'm looking forward to this conversation today. Jo: It's going to be really good. First up— Tell us a bit more about you and how you got into writing and publishing. James: Well, today I'm a professional keynote speaker, so I deliver about fifty to a hundred keynotes per year in twenty-five-plus countries. Primarily I speak on creativity, innovation, and artificial intelligence. Go back into my deepest, darkest history—I actually used to manage rock stars. That was my old job. I used to be in the music industry for many, many years. I worked with members of The Rolling Stones, and for our listeners in the UK, I managed bands like Deacon Blue. Then I went to the dark side. In 2010, I moved to California to work in Silicon Valley, to work in the world of tech. That got me involved in artificial intelligence. Right about 2017, I was speaking at an event in San Francisco and someone came up to me and said, “You realise you could probably speak for a living, you could do this for a living.” So I thought, well, how does that work? And he told me. Then I embarked on the career that I have today, which is primarily as a speaker, with writing now coming a bit more to the fore. Jo: Wow, I remember Deacon Blue. James: Yes. Jo: “Dignity.” That's crazy. Very, very cool backstory there, but we'll come back to the career side of things. Let's get into super creativity, because my listeners are certainly creatives. Most of the listeners will have a book either on the way or they might even have lots of books. So we all do want to be super creative. How do you define creativity, and why is it important to keep focusing on this even if we do identify that way? James: For me, creativity is about bringing new ideas to the mind. Innovation is about bringing new ideas to the world, but without creativity, there is no innovation. So creativity is really the engine of innovation. Whether that is designing new products, new services, or creating new works of art and new books. The reason that creativity is becoming more important is because of what we're seeing right now in terms of artificial intelligence. AI is going to replace a lot of the non-creative tasks that we currently do in our jobs. If you look at things like the World Economic Forum, there was recently a study with a thousand global business leaders, and work from companies like LinkedIn—they all highlight that creativity is going to be one of the foremost important soft skills for this new future. So creativity, strangely, will actually become more important, not less important, as we go ahead. That's the creativity side. Probably for many of the listeners here, they'll consider themselves to be creative. That is not the norm. As I mentioned, I speak in about twenty-five countries a year, and if I ask the audiences—primarily corporate audiences—to put their hands up if they consider themselves to be creative, only between ten to forty per cent of the audience will raise their hands. So part of my job is to show them why they are more creative than they think they are and why we're all born with this creative potential. Then moving into the super creativity side, it's really to show them how they can augment that creativity by collaborating more deeply with other people or machines—things like artificial intelligence. So SuperCreativity, the book that I've written and the speeches I give on it, is really about how we can augment our individual creativity by collaborating more deeply with other people or artificial intelligence. For me, that's been the thing I've been fascinated by for the past few years, and probably for many of our listeners who are now using AI in their writing, their researching, and their marketing of their books, they're probably getting into this space as well. I really wanted to dive into that—both the collaboration with other people and with machines and AI. Jo: In terms of the super creativity then, do you have any practices or ideas? Before we get into collaboration, many of us authors work alone—and of course we can come back to the AI stuff in a minute—but in terms of super creativity, are there ways that we can even supercharge what we do already? Then, of course there are people listening who might not feel creative. So give us a few tips on how we can potentially change our mindset or become even more creative. James: In the book I talk about what I call the eight Ps of super creativity, which are purpose, personality, practice, people, process, place, product, and persuasion. Persuasion is really the marketing piece at the end. Probably the one that could be most useful to many listeners today is the practice piece—the practice or the process side of things. For many of us, what that usually consists of is just having some type of daily creative practice. Different people do it in different ways. Many of your listeners will know the works of people like Julia Cameron—the morning pages style of having some type of daily practice. Other people do it in slightly different ways. The process bit is really interesting. I talk about this creative process that we all have, and I talk about these five stages of the creative process. The first stage, let's say if we're writing a book, is really that preparation stage. That is usually the stage where we are trying to absorb as much information as possible about the thing that we're going to be writing about. The topic, if it's nonfiction, or going to the places, visiting the scenes that we're going to set certain things within for the book. So that preparation stage is really about absorbing as much information as possible from the outside. It's not going to look very creative. We're just absorbing at that stage. Now the mistake that a lot of people tend to make is they immediately try to jump from that preparation stage to looking to generate ideas. But what all the studies show us is we should spend a little bit of time in what we call the incubation stage. This is where it's often very useful if we've done some research, that we put things to one side for a little while, maybe a few weeks, move on to another project, think about something completely different. Your brain will continue to work in the background. Your unconscious brain will work on that content you've been absorbing. Then what often happens as a result of that is we come to this third stage, which is that insight stage—that aha moment. That happens for various different reasons and you can seed that in slightly different ways so you're more likely to get inspiration in your day-to-day work. Then as we know—as you are a writer of many, many books—many people think, “Well, that's it. I've done it. The idea for that book or that chapter has come to me.” That is really just the first five per cent of the process. The next stage is where we look at all the different ideas we have and decide which ones we want to pursue, which ones are going to make the grade. This is what we call the evaluation stage. Once we've done that, we move to that final stage, which is the elaboration stage. If it's a startup, this is when you're building your minimum viable product. As a writer, this is where you're actually doing the work, putting those words out onto the page. It's a very iterative process, so it's not necessarily linear. You'll go back and forth. Even as you're getting input from readers and audiences in that last stage, that is then giving you the material to move back to the preparation stage and think, “Oh, I wonder if this next book in this series, maybe I go in a slightly different direction with this character.” So each of those different stages, you can do different things to increase your levels of creativity. Jo: I love all of that, but can we go back to purpose? Because you mentioned that as one of the Ps and I think this is something that a lot of us need. As we are recording this in April 2026, the world is an interesting place. There are lots of things going on that have people worried. Well, we are not talking about politics, but I think one of the things that people struggle with is, what's the point in writing this story, for example, or what's the point in trying to get my words out there when things are difficult? I feel like coming back to purpose is perhaps the thing that helps people even take it into the process as you were talking about. And then of course, just from a practical angle— Is purpose about making money or reaching people? So maybe you could talk about the purpose side of things. James: Yes. So I talk about three different purposes, and it's not that there's just one that predominates, but usually there's one that maybe predominates on different projects. The first one is creativity as play. It's what we're basically, as humans, hardwired to do—this instinctive joy that we get just for creating for its own sake. There's nothing that really sits beyond that. We just have fun. We find pleasure in creating something. That could be a musician creating a piece of music, a sculptor creating a sculpture, an entrepreneur creating a new business or product or service. There's just this sense of play. One of the things I talk about in the book is this idea of being childlike, not childish. If you look at children, you see this very instinctively. If you see a three-year-old or a five-year-old, you give them some crayons and they will just naturally create. That's part of who they are and it's pretty abstract. Then what happens is they go to school and they're taught useful conventions—”this is how you should do it.” You even see their work start to change. You start to see them move from abstract paintings to more formal structures. Then you get your peer group, then you go to college or university and the world of work, and you're taught all these useful conventions. That's fine, but as adults, it is our responsibility to become what we call post-conventional, where we see these conventions as a useful signpost but we're willing to challenge them. We're willing to have a playfulness in what we do. So the first one is just this hardwired thing—creativity as play. The second one, and this is maybe for a lot of your listeners the reason that they are writers, is self-expression. It's a way of placing something out into the world. I was actually just in France recently, and I was talking to a young visual artist, a painter from Hungary, and she had to go up and give a speech. She really hated doing it. She was having to talk about her work and she was really uncomfortable. I could see the discomfort and my heart went out for her, because that is not the way she primarily expresses herself. She expresses herself through her art form, which is painting. For many of us, we might struggle to get on a stage, but we can express ourselves in the written word. We have something we want to say, a position we want to have, and we want to express that and get that out into the world. The final one is just this idea of legacy. That is not going to be for everyone. I can tell you, for me personally, legacy is not the reason that I write and do a lot of the stuff that I do. Maybe that changes—maybe as we get a bit older, we want to leave a body of work. So those are the three main purposes that we tend to see. Then you mentioned the financial side of what we do as well. This starts to come into that self-expression, because we need to be able to get people to buy our books or download our books and read our books in order to give us the ability to write new works and create new things. The financial side is an important component of it, but it is not the only one. I think there's a great question any writer should ask themselves. One of the first questions that I asked myself as a relatively new nonfiction writer is: why am I writing this book? What is the purpose of this book? For me, primarily it is a form of self-expression, and then you have to go, “Well, that's fine, but I also need it to have some type of financial basis for it.” It doesn't need to be the main driver of my income, but I need to have some type of revenue model. I'm happy to talk about revenue models, because probably the type of revenue model that I have as a writer is going to be different from other listeners. I tend to focus more on bulk selling of books rather than individual selling of books. Jo: Yes, I definitely want to come back to revenue models and business, but a few other things first. I want to circle back to collaboration, because I've certainly co-written with some humans, and I know a lot of listeners either have co-written or collaborated with other humans—and some of it works and some of it doesn't. You have some great information on human-plus-human creativity and collaboration. So maybe you could give us some tips on how we can be more effective collaborators with other humans. James: So there's a whole section about this idea of creative pairs. Often if you look at great creative work or innovative companies, very often when you strip it all back, you'll find at the core lots and lots of creative pairings. That is usually two different but complementary personalities who are willing to develop and challenge and improve each other's ideas. We think of Jobs and Wozniak in the world of business, or Warren Buffett and Charlie Munger. For authors, often that relationship is the work with their editor. There was a documentary I saw—I think it was a New Yorker documentary that came out a while ago—talking with a writer of history books about his relationship with his editor. It was a really beautiful relationship. These were two very different personalities, but what worked was the fact that they were different. A core component of having these creative pairings is a sense of trust—or what some people today would call psychological safety—that you are willing to challenge someone's ideas, but in a space of trust. The Germans have a great phrase for it. In English it translates as “someone to steal horses with,” which I love. Hopefully our listeners have that person where you can go to them and say, “I had this idea for a book or a chapter or a character,” and that person is a “yes, and.” Like, “Yes, and have you thought about doing it this way?” or “What would happen if you did this?” They stress test your ideas. They make your ideas better. For many of us, maybe it's our husbands or wives, our partners. Some of us are lucky enough to have editors. When I started rewriting this latest book, I actually had someone like that—a human, not an AI—that I worked with, especially on taking all these random thoughts and ideas I've been expressing in keynotes and putting them into more of a book form. The format and the structures that we use for telling stories in a speech are quite different from the structure that we would use for a nonfiction book. I didn't have as much experience there, so I wanted someone who could say, “Have you thought about structuring it this way?” or “This is a great story arc you might want to think about.” So I don't know, for you, who is your creative pairing? Who is your “someone to steal horses with”? Jo: Well, it's funny. I really think since the arrival of Claude Opus 4.6, it is absolutely Claude. James: Yes, yes. Jo: All the way. I mean, so we could come onto that next in terms of how AI has changed, because I do still work with a professional editor for both fiction and nonfiction, but it is very much in the “make my finished work better” stage. It is not in the exploratory phase. I find particularly the latest reasoning models to just be fantastic at this. And my Claude is not sycophantic. The Opus 4.6—I'm sure you've been using it too—it just doesn't behave in the way that a lot of people think these AIs did. They did behave like that, and now it's changed. So let's talk about that. What are your thoughts on collaborating more effectively with AI tools, especially as they become more and more powerful? As we record this, Claude Mythos has not come out, but it's certainly rumoured to arrive. I'm pretty excited. James: So because I've been doing this AI thing for a little while, it's given me the ability to experiment with things—the early versions of what many people are using today. I'll give you an example. Even before I started writing the book, I decided to write a book proposal. Even though I could pretty much sense I wanted to independently publish this book through my own publishing company, I thought it's a good practice to put it down into a proposal form, even though I don't go to a traditional publisher or a hybrid publisher. One of the things I did within that was get a sense of who my ideal readers are. I used a very early version—this was a few years ago—of an IBM AI tool, creating what we call a psychometric map of my ideal reader. This basically tells me, over about seventy-two different factors, how this person thinks, how they feel, what their value system is, very broadly for my ideal reader. I pulled in different sources. I knew the kind of magazines and books they were reading and what their general worldview was. So I created this—going one step beyond just creating your ideal reader to really understanding their psychometrics. I do this in my keynotes too. Before I ever give a keynote or an important pitch or a presentation, I use AI to analyse the psychometrics of the audience I'm going to be speaking to. This might tell me, for example, this audience values humour a little bit more, or this audience values a bit more practicality so they want actionable next steps, or this audience is going to be a little bit authority-challenging so they're going to push back. So even in those very early stages, just starting to think about the book—who was I writing this book for, what was the purpose of the book—I was using AI to understand the psychometrics of my absolutely perfect, ideal reader. I gave her a name. It was a female reader. There was someone similar to her that I already knew. Probably for some of your listeners, they do this instinctively anyway. They maybe have a person or a few different people they think of in their head. Then from that stage, because I've been delivering lots and lots of keynotes—and this may be an important distinction in the way that I have decided to write books as opposed to how other people write books—my family were all jazz musicians. The difference between a rock musician or a pop musician and a jazz musician is this: a rock or pop musician will go into the studio, create this opus, this work, and then tour that for the next two years. A jazz musician, on the other hand, goes out and performs the songs and the things from the album that they're eventually going to create hundreds of times, thousands of times, to find out what works with audiences, and then they go into the studio and record the stuff that works best. So I created a book more like a jazz musician. I'd delivered keynote versions of the book hundreds of times before I ever decided to actually write the book. So it had been stress-tested with real people to a certain extent. Then, getting into it, I thought—well, what works as a keynote is not necessarily going to work as a structure for a book. So what I did was start using ChatGPT models at that point to think about the structural edit of the book. What was the structure going to be? What was great is you can basically feed it every single keynote you've given over the years, all the notes, everything you've done, and it could start to give me something to riff with and really get into thinking about how I was going to create this. I was using it a little like that creative pairing we spoke about earlier. Then once I'd done that—so I've now got an idea of a structural edit essentially—I then go back and speak to some humans about it. “What do you think about this?” “What do you think about that?” And try some things out over dinner conversations. “I'm thinking about doing this—what do you think?” Then once I did that, I just did the thing that I really didn't want to do, but I guess you absolutely have to do: sit in a seat for multiple weeks and just get that crappy first draft done. That was just me writing, from my voice, in my way of doing things. Every so often I would use an AI to research a particular thing, but I didn't want to slow down the pace too much. I was focused on getting that word count done. Once I had the first draft, I then brought the AI back in. In this case, I was still using OpenAI at this stage, to act more like an editor. To tell me what was weak about the book. At this point I was starting to give it the overall framing. What was weak, what chapters needed to be improved. I then went back, started reworking each of the chapters, and worked chapter by chapter using that AI as a sparring partner. But once again, the AI is not really writing my words for me. It's maybe saying, “This part could be said better. You might want to think about doing it this way,” or “You are missing a really powerful case study or example here,” or at the very end of each chapter, I have actionable next steps, and “You're missing some things here.” So I've gone through that entire process of writing, and now I'm essentially at the second draft. At this point, what I'm doing is using another AI tool—Claude, in this case—to have a different perspective on it. I gave it the work. I mentioned a couple of editors that I really respect and different writers I respect and said, “I'm going to create a virtual beta readers group. Give me feedback on this now.” For someone that's listening to this, and we're recording this in April 2026, here's some good news for you. There are now a bunch of tools out there that use AI swarms, as we call them. You can basically feed it your book and it will create synthetic readers—thousands and thousands of synthetic readers that read your kind of style of book—and it will then give you feedback from these synthetic readers. Essentially, I was just doing an early version of that. So I got the feedback from the synthetic readers, the AI readers, and then reworked a little bit. Some of the stuff I just decided not to do because it didn't align with what I was trying to say in the book. Then the next stage was I had a beta reader group of about thirty human beta readers—my ideal readers. I sent the book to them, they gave me feedback. I then used AI to give me an overview report of all their feedback, and then I was able to go back into reworking the book. That's still really just draft three of the book, not the final book at this stage. But just to give everyone a sense of opening up the process: you could see how the human and machine were working together. Jo: Yes, I love that. I also often say to people who are speakers first that you can, if you have recordings of your talks or if you use your slide decks to record them as MP3s and then just use that transcript as the basis of a draft. Obviously it's not the book or a chapter, but it can actually preserve your voice—your speaking voice—which I think can be really effective for speakers. I like your multi-step process there. And then of course, if you have audience avatars in AI, that can help you design your book marketing. So take this into book marketing and how you're doing that. James: So I still decided to go old school with a human editor—a book editor that someone had recommended to me. I used that human book editor just to go through the book. At that point we're talking about style, some stylistic things that we wanted to do, and they can pick up other things as well. So I've got that book, and then I'm obviously starting to use AI to understand what tags, what kind of copy do I want to have in terms of putting it onto Amazon, putting it onto IngramSpark, and all these other platforms I want to put it out into. I'm using Claude here in particular—and with Claude, you have something called Cowork. It wasn't quite fully happening at that point, but there were early versions of it and Claude Code—to almost start working with and creating a virtual marketing team. I give it the book and then they could start thinking about: what is the marketing strategy for this book? What does the campaign look like? What are the things that we need to do? That was then starting to break it down. We're now three months out or so before the book is due to get released, and I'm starting to deploy that particular campaign. So for example, I'm on a podcast right now, and we try different versions. We have a human going out and reaching out to potential shows for me to be a guest on, but I also have an agent. There's also one going out and finding and researching podcasts and reaching out to those podcast hosts to have me as a potential guest. So they're doing some of the tactical work there at the same time. One mistake I made—and I don't know if you've experienced this as well—if I was to go back, one thing I would do differently is this: I decided to record the audiobook version after the physical book was already committed and ready to go out. Jo: Mm-hmm. James: And I noticed so many small errors or things I would change after having spent two days in a studio recording the voice for the entire book—changes I would have made. This is something other people did ask me: why are you not using ElevenLabs or an AI clone of your voice to read the script? There are some things I feel quite personal about, and my voice is one of those things. As a professional keynote speaker, I decided I wanted to keep that and have it in there. So it's going to be different for everyone which things they decide to offload to AI, which things they decide to give to a human member of their team, and what they decide to keep to themselves. Jo: Yes, I mean, I human-record my nonfiction, but I have an AI voice clone with ElevenLabs for my fiction now. But obviously, for people listening, you can't put an ElevenLabs voice-cloned audiobook on Audible, and a lot of your sales will be on Audible, especially for a book like this. So I think that's also important. I agree with you on doing the audio edit. There's always things you want to change. But as you mentioned, you're self-publishing this, so you can just go in and change your files. James: Yes, and that was the other reason, and this was part of the marketing—now we're moving into the marketing and the business model behind the book. For me, the book doesn't have to be a financial driver in its own sense. The way that I sell books, and usually people like myself—professional speakers—is we bulk sell books to our clients. Let's say I'm speaking at four different events this month. Each has about a thousand people at them. Those organisers will buy, say, a thousand copies of the book. So at the end of that month, you might have sold four thousand copies—not individual copies. Anything that sells on Amazon or in other places is almost like a positioning piece. Obviously you want people to buy the book and learn things from the book, but in terms of the distribution model, it's slightly different because I'm primarily selling through bulk sales. Now, here's a little twist you can do on this, and this is a decision I made even before we released this version of the book. I speak to lots of different industries. There was a speaker and author—I've forgotten his name now, I think he was from Florida—and what he decided to do was to write a slightly different version of his main book every year, but for a different industry. So what this allows him to do is, let's say in my case, I'm doing a version of the SuperCreativity book just for legal professionals because I speak to a lot of law firms and legal groups. I've already started working on a version of the book which is a little bit more attuned to that audience. As a speaker, it allows me to go to all these law firms and legal associations and bar associations and say, “Hey, I've just written the book on creativity and artificial intelligence for the legal industry.” That makes you a very bookable proposition for a client. And then obviously you can sell books from that as well. And that's before we get into the foreign language versions. That's just a model that happens to work pretty well for my part of the industry, but obviously it's going to be very different for other types of authors. Jo: No, I think that's great. For nonfiction authors, as you say, there are different revenue models. Your income, I guess, would be what, eighty, ninety per cent speaking revenue? Or do you have other things as well? James: Yes, primarily it's the keynote speaking, and anything that comes from the back of that. Sometimes it's boardroom advisory work that I do as well. But primarily it's the speaking side. So really the book is just the simplest form to get my ideas out and the most affordable form. Jo: Mm-hmm. James: Because the other thing is, you want as many people getting your ideas as possible, and there is no better, more affordable way of getting someone's ideas out there than in the form of a book. I think it's just the most unbelievable transmitter of knowledge—a book. That's why I love to write the book as well. A lot of my friends say, “Listen, books are old hat. You don't need to do a book any more. You can do these other things, other forms, online courses.” I've done lots of online courses in the past and membership sites and all those things, but there's just something that is great about a book—to be able to summarise your ideas at a particular point in time. It's also a great transmitter of value to other people. And it is affordable. Any book, someone can download a book on Audible or wherever they want—that's just an affordable way of absorbing that content. Jo: Yes. Well, of course we are all fans of books here. I do speak—I don't tend to do keynote speaking. I do more content speaking at conferences. For people listening, keynote speaking is where you tend to get the higher revenue. So if people listening have books already—let's say they have nonfiction books or even fiction books that could be turned somehow into different topics—if people want to get booked for speaking gigs, preferably ones that pay— How would you recommend authors think about moving into speaking if that's something they want to do? James: So obviously it's much easier for nonfiction authors to do that. I mean, I'll give you an example. I was speaking at an event last week in New York for L'Oréal, the hair care and cosmetics company. They had six different speakers. One of them was a speaker on macroeconomics and geopolitics. Another was an expert on communications. Another was an expert on AI. Another was an expert on storytelling. So you have to think: does my topic have value for that type of audience—that corporate audience? An easy way of finding that is if you just go onto any of the speaker bureau websites, type in “speaker bureaus,” look for the speaker bureaus, and then type in your topic area—emotional intelligence or whatever the topic area is—and look at the other speakers. See if there is obviously a number of speakers talking on this area. Importantly, look at how busy they are and look at their fee levels as well. I did an online summit a few years ago called the International Speakers Summit, where I interviewed a hundred and fifty of the world's best professional keynote speakers. I interviewed Sally Hogshead, who's an author and a speaker, and she said to me, “James, you're going out speaking about creativity, but if you just twisted it a little bit and spoke more in terms of innovation rather than creativity, you would earn an extra five thousand dollars per keynote.” So creativity and innovation—an extra five thousand dollars. That's just a simple thing that, as you get to understand the industry, you learn. Then once you do that, it's like any business—you have to treat it like a business, obviously. What makes someone a great storyteller on stages is not the same as what makes a great storyteller on the written word. So depending on where you're at, you might need certain training and skills development. If you are listening to this from America, there are things like the National Speakers Association, the NSA. If you're living in the UK, the Professional Speakers Association. These are great ways just to develop your skill set and learn from other professional speakers. Here's the good news, I didn't know anything about professional speaking until 2017–18, and it was only from having a conversation with someone who said, “Listen, you have some original thoughts. You can get paid to speak about this on stage.” Then I spent the next year really researching and understanding and looking at how to do it and creating a minimum viable product—a speech—that was a very short period of time, a year. Most of the listeners here have gone through that process of writing a book, which takes many, many months. So you have the stamina to do this type of work. You just need to find out where you fit. I thought I was going to be a speaker in marketing. I thought that was going to be my thing. And it turns out that's not what the market wanted from me. They wanted me to talk about creativity and artificial intelligence. So you have to listen to the market, like you have to listen to your readers. Jo: Yes, I think that's really interesting. I was also a member of the PSA here, and I learned in Australia with the NSAA as it was. James: Yes. Jo: And that thing about who you speak to—I mainly speak to author conferences, who, I just want to be frank, don't pay very well, if at all. So exactly what you said there— If you want to be a highly paid speaker, you have to pick the audience who's going to pay, as well as a topic that works with them. It is a very different thing to writing a book, I think. James: It is a different model. This is what was interesting when I interviewed those hundred and fifty professional speakers—the thing that came back loud and clear is there is a model to suit everyone. Jo: Mm. James: So the model that works for me—getting paid high fees to go and travel around the world, speaking on stages to primarily corporate audiences—that is not the only model. There is another model, which is called the “sell from the stage” model, where you maybe don't get paid anything to go and speak on the stage, or very little, but what you're doing is you're selling your consulting, your online course, your books, your other products from the back of the stage. That's another model as well. I have friends who have young families and they are writers and they don't want to schlep on planes like I do. I know one speaker in particular who never leaves his own city. He is a very successful professional speaker. He happens to live in Orlando, Florida, which is one of the busiest cities for conferences. So literally, he's home with his kids every night. He gets to do all this cool stuff he wants. He never has to step on a plane if he doesn't want to. That just shows you the range. I remember I once interviewed a person whose title was a Buddhist monk, French speaker, and author. He figured out he could live very affordably by living in Thailand. So he lives in Thailand for part of the year and he's very into meditation, mindfulness, yoga, and writing. He figured out he only had to give two keynotes per year to pay for his entire lifestyle. That was it. So that gives him a lot of freedom. He does those two corporate keynotes a year and for the rest of the year he's doing his yoga, his meditation, his writing, and surfboarding, whatever he's into as well. So you can see there's a whole range of different ways you can design that life. Jo: Yes, we talk a lot about definition of success and it's great to hear those different examples. So before we finish up, I just want to come back to your journey into the writing side, into books and self-publishing. We all understand, me and the listeners, how hard it is to write a book and also to market a book, but we've got the bug. So we wonder: how much have you got the bug? Do you plan on doing more writing, more books, or do you still want to lean more heavily into speaking? James: Primarily the income for me will still come from speaking. I remember listening to Elizabeth Gilbert once when she talked about her writing. She said she always wanted to have other things, so she never had to push onto her writing that it had to be the income stream for her. If it was successful, great, that's fantastic. So I have a little bit of a similar view to that. In terms of my own writing, I've got about five different nonfiction book ideas I'm now looking at. Some of them relate to speeches that I already do. Some don't. I'm looking at different versions of the SuperCreativity book, so there'll be other versions coming out—different industries, different languages. That gives you a few years of work. The other side that I want to develop is the fiction writing side. I'm already starting to work on a fiction book at the moment—a little bit like this idea of one for them, one for me. Jo: Mm-hmm. James: So one for them is for the corporate audience, that world that I live in, and the other one is for me, for my own creativity. My hope—and I don't know, maybe we need to speak in a year's time when I've written and published it—is that by doing the fiction side, it will make me a better storyteller on stages as well for my corporate audience. It will help me understand story arcs, slightly different ways of expressing stories, building emotion, building the anti-hero characters within a book, for example. So I'm hoping that they both feed off each other. But we will see. Jo: Yes, we will. All the best with that. So where can people find you and your books and everything you do online? James: The easiest place to go is JamesTaylor.me, and you can find the book, which is called SuperCreativity, there. Or just go to wherever you buy your books—your local independent bookstore—and get a copy of SuperCreativity. The audiobook may already be out by the time you're listening to this as well. If you want to learn a little bit more, we also have a podcast called the SuperCreativity Podcast, where I interview lots of wonderful guests talking about this area of super creativity. Jo: Well, thanks so much for your time, James. That was brilliant. James: Thank you, Joanna. Thanks for having me as a guest on the show.The post SuperCreativity And KeyNote Speaking With A Non-Fiction Book With James Taylor first appeared on The Creative Penn.

In this week's pep talk, I am sharing the simple three-layer website strategy we're using to organically add 20+ new email subscribers every single week without relying on ads, social media, or constantly creating new content. We're talking about sustainable audience growth, smarter website conversions, and how to turn your existing traffic into real leads that actually want to hear from you.  In today's episode, I share:02:58 – The behind-the-scenes website strategy currently bringing in 20+ organic email subscribers every single week04:03 – Why these subscribers convert differently than people coming from social media or paid ads05:01 – The real reason small weekly subscriber growth compounds faster than most entrepreneurs realize05:45 – Who this strategy works best for and why website traffic matters more than follower count06:23 – The biggest mistake entrepreneurs make when people visit their website but never join their email list07:41 – How pop-ups have evolved since the early blogging days and why they still work when used strategically09:12 – The first layer of the system: rotating podcast pre-rolls that direct listeners to specific lead magnets11:08 – The second layer: smart website pop-ups that capture attention without feeling intrusive or spammy13:26 – The third layer: blog-specific pop-ups tailored to exactly what someone is already searching for15:44 – Why sustainable business growth comes from optimizing what you already have instead of constantly creating more content 

#910 If your business is growing but you are the bottleneck, this episode will change the way you work forever! In Part 1 of this 2-part episode, host Brien Gearin sits down with Corey Ganim — host of the "Build with AI" podcast and owner of Return My Time — to break down his AI Operating System, a practical framework for installing AI into every part of your business so you can scale without hiring and without learning to code. Corey walks through the three-tool foundation (Claude, Claude Cowork, and Skills) and dives deep into the first two of five business pillars: Sales and Marketing. You'll learn why speed to lead is the single highest-ROI opportunity for most small businesses — backed by an MIT study showing you're 21x more likely to close a deal if you respond in under five minutes — and how to build an AI-powered system that sends personalized responses to new leads around the clock. Corey also shares a simple but powerful marketing skill that turns a 30-second voice memo into a polished LinkedIn post, saving business owners hours every week! What we discuss with Corey: + AI Operating System: scale without hiring or coding + 3 tools: Claude (brain), Cowork (hands), Skills (playbooks) + Skills = AI-executable SOPs + Speed to lead is your biggest revenue opportunity + Average B2B response time: 42 hours + MIT study: 21x more likely to close in under 5 minutes + AI sends personalized lead responses around the clock + Voice memo → polished LinkedIn post on autopilot + 5 skills = ~250 hours saved per year + AI handles sales & marketing so you stop being the bottleneck Thank you, Corey! Check out Return My Time at ReturnMyTime.com. Listen to The Build With AI Podcast. Work with Corey. To get access to our FREE Business Training course go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/training⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠millionaireuniversity.com/sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Episode Overview Burnout is pushing executives to rethink their careers. But most make one critical mistake: they try to escape too fast. In this episode, Michael D. Levitt speaks with Matt Raad, digital investor and co-founder of eBusiness Institute, about how corporate professionals can transition into digital assets and online businesses without risking their income. This is not about quitting your job. It is about building a second engine of income and optionality. Why Burnout Is Driving the Shift to Digital Assets Burnout is no longer isolated. It is systemic. Key pattern: Mid to senior leaders in large organizations are experiencing sustained overload Pandemic-era changes accelerated fatigue and disengagement High earners are seeking control, not just income The result: Leaders are looking for exit options that do not create financial instability. The Core Strategy: Build Before You Exit Matt outlines a disciplined transition model: Maintain your corporate income Build a digital asset over 2 to 3 years Replace income gradually Exit only when the asset is stable This avoids: Financial pressure Poor decision-making Reactive career moves This is a structured transition, not an escape plan. What Is a Digital Asset Business? A digital asset is a business that can operate with minimal physical infrastructure. Examples: Content-based websites Online courses Affiliate and SEO-driven platforms Acquired online businesses Key characteristics: Scalable Transferable Lower operating costs Location independent This aligns directly with a leadership operating system: build systems that run without constant intervention. The Financial Advantage: Low-Cost Entry, High Leverage Traditional businesses require: Large capital investments Physical locations Staffing overhead Digital businesses: Can start under $10K to $20K Require fewer fixed costs Allow testing before scaling This reduces risk and increases strategic flexibility. The Critical Mistake: Skipping Foundations AI is accelerating business creation. But it is also creating a false sense of competence. Matt emphasizes: AI tools can build faster But they cannot replace business fundamentals Without understanding: Market demand Customer acquisition Conversion systems …AI amplifies bad strategy. AI as a Force Multiplier, Not a Shortcut Tools like CoWork are changing the game: Faster business setup Automated workflows Scalable content creation But the advantage goes to those who: Understand business models Apply AI strategically Build systems, not hacks AI reduces friction. It does not replace leadership. New Opportunity: Digital Advisors for Traditional Businesses One overlooked opportunity: Corporate professionals can become: Digital transformation advisors Online growth strategists AI integration consultants For: Brick-and-mortar businesses Local service providers Traditional industries This creates: Immediate income potential Skill development Entry into digital business ecosystems The Leadership Shift: From Operator to Asset Builder This conversation highlights a deeper shift: Traditional career path: Climb the ladder Increase compensation Increase dependency New model: Build assets Create optionality Reduce dependency This is not entrepreneurship for its own sake. It is control over time, income, and direction. Key Takeaways Do not quit your job to escape burnout Build a digital asset while maintaining income Focus on fundamentals before leveraging AI Use low-cost business models to test and learn Think like an asset builder, not just an employee Action Steps Assess your burnout level Is it role-based or system-based? Identify a digital asset model Content, course, acquisition, or advisory Allocate weekly build time Consistency over intensity Learn core business fundamentals Traffic, conversion, monetization Use AI to accelerate execution Not to replace thinking Guest Links Website: https://ebusinessinstitute.com.au Podcast: Digital Investors LinkedIn: https://www.linkedin.com/in/matt-raad/

Today's show:Cerebras just jacked its IPO range to $150–$160 a share, OpenAI bought a consulting firm to seed its $4 billion private-equity joint venture, and a startup in Oakland is electrolyzing magnesium out of seawater for one-third the going price. Alex Wilhelm and Jason Calacanis go deep with AI21 co-CEO Ori Goshen on why model orchestration, not bigger LLMs, will decide who wins enterprise AI.The crew also covered the decline of OpenClaw, TikTok's new £3.99 ad-free tier, more entries in the live-show sidebar bounty, and had time for a little Off Duty before signing off.Guest Links:Ori Goshen on LinkedInAI21Alex Grant on LinkedInMagrathea MetalsTimestamps:0:00 Ori Goshen, CEO of AI21 joins the show1:20 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!5:14 Why enterprises care about token cost optimization6:01 Jamba as open-weight; Maestro as proprietary orchestration10:08 LinkedIn Jobs - Hire right, the first time. Post your first job and get $100 off towards your job post at https://LinkedIn.com/twist12:56 AI21 customer roster: FNAC, US tech giants, Israeli companies19:01 Alex Grant, CEO of Magrathea Metals joins to discuss pulling magnesium from seawater20:03 Live video of the Oakland pilot electrolyzer20:10 Deel - Founders scale faster on Deel. Set up payroll for any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit https://deel.com/twist to learn more.22:01 Magnesium as a "gateway metal" for aluminum, defense, aerospace23:20 TETRA joint venture & the Evergreen Project in Arkansas23:38 Series A close, JV economics: $3,000/ton vs. $7,000/ton market29:10 Sidebar bounty update: Glass Sidebar (Oliver Choy) demo30:47 Netsuite - Get the free business guide Demystifying AI at https://netsuite.com/twist31:47 Sidebar bounty update: Sidecast (Patrick Hughes) demo36:09 Reducing scope to "real-time fact checker only" for final round37:24 Ro.co: Ro's insurance checker will let you know if your coverage includes GLP-1s for FREE. Go to https://Ro.co/Twist for your free insurance check.39:07 Cerebras IPO: $115–$125 → $150–$160 per share44:19 Will OpenAI's compute commitments to Cerebras actually get funded?49:19 Fervo Energy IPO — venture-backed geothermal company going public49:40 OpenAI Deployment Company + Tomoro acquisition explained53:51 Anthropic's parallel $1.5B PE joint venture with Blackstone & Goldman56:05 Why Jason thinks these PE spinouts are convoluted financial engineering56:40 OpenClaw's decline + competition from Cowork, Perplexity, Grok1:04:22 TikTok's £3.99 ad-free subscription launches in the UK1:09:09 Knicks sweep 76ers — Jason's playoff predictions1:11:24 Off-duty: "There Is No Antimemetics Division" by qntm1:13:25 Off-duty: Fall of Civilizations podcast by Paul Cooper1:16:37 States' rights, federalism, housing supply & closing thoughtsSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisCheck out all our partner offers: https://partners.launch.co/Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

Everything got a bit more powerful this week.