Podcast appearances and mentions of Satya Nadella

What's Steve D doing with his tights? Find out on this week's PlayingFTSE Show!The Steves are back to winning ways in the stock market. But Steve W's had a disaster on one name in particular…The FTSE 100 made new highs, but Rentokil shares fell 20% in a day. Steve W owns this one and he's been having a closer look. Results for Q2 don't look too bad. But lower organic growth, operating margins, and declining market share isn't a good combination.Greggs shares might be at the start of a comeback. The stock surged after results this week, despite some good – but not great – results.Is it a short squeeze, or is there more going on? Steve D has the latest news on the number of sausage rolls people are ordering when they don't have to ask for them in person.Microsoft shares surged this week after the company's Q4 (as they call it) results. Revenue growth is strong and capex looks under control, but Steve W is looking at something else.Satya Nadella outlined a really interesting plan. Instead of competing with OpenAI and Anthropic, why not just let them compete with each other and cash in?We've had a user question from a viewer! How do we screen for stocks and what do we use?The short answer is that we don't really. But the reasons could be more involved and complicated than you might think…Only on this week's PlayingFTSE Podcast!► Free Share + Exclusive Deals — Start Here:

This week: Microsoft and Amazon both reported quarterly numbers, and both stocks rose on cloud results that beat expectations. Is AI spending is paying off in real business? And in related news, Microsoft sees a rare annual headcount decline, hitting product R&D hardest. Plus: Satya Nadella builds a Power BI dashboard out of an analyst's research report, and touts it on the earnings call to make a bigger point. Jeff Bezos names Amazon's chips business as the long-awaited fourth pillar. And AI House managing director Jacob Colker delivers a much-needed pep talk for Seattle tech, calling on the region to recognize and build on its strengths. With GeekWire co-founders Todd Bishop and John Cook. Related stories and links: Microsoft and Amazon earnings Microsoft Azure tops $100B in annual revenue as record AI spending cuts into cash flow AWS is 'booming,' but Amazon's free cash flow turns negative on record AI spending Microsoft R&D jobs drop for second straight year as total headcount falls for first time in a decade Which Microsoft businesses are growing and shrinking, according to obscure table in regulatory filing Amazon's fourth pillar Jeff Bezos says this business is becoming Amazon's next 'pillar' A rallying cry for Seattle tech Watch: A venture capitalist's passionate speech, a rallying cry, really, about Seattle Seattle's AI2 Incubator rebrands as AI House, and adds key investor as managing director 'I'm tired of that narrative': Seattle VC pushes back on tech exodus talk The Washington tech ecosystem New map traces Washington state's tech 'universe' to a few key hubs, and shows what's at risk After hiring AWS exec and raising $107M seed round, Virginia startup plants flag in Seattle area GeekWire's Seattle engineering centers list See omnystudio.com/listener for privacy information.

No Bom Dia, Minha Vida de hoje, Isadora Basile comenta o crossover completo de Dungeons & Dragons com World of Warcraft revelado na Gen Con, o apagão de mais de vinte horas na Xbox e a promessa de Satya Nadella de uma volta ao crescimento em 2027, o novo modo ranqueado de Deadlock, a curiosidade sobre como Wind Waker 2 virou Twilight Princess, e o crossover de Helldivers 2 com Warhammer 40K.Apoie o Bom dia, minha vida: apoia.se/bomdiaminhavida❤️___

The AI industry spent the week arguing with itself, which is honestly the healthiest thing it's done in years. One coalition led by Nvidia begged regulators not to slow down open AI models, while more than 1,100 AI workers signed another letter asking regulators to do exactly that. Anthropic warned about bioweapons, OpenAI admitted the four-hour workweek isn't happening after all, and Microsoft's Satya Nadella reminded everyone that building your entire business on someone else's AI is a fantastic way to outsource your own brain. In other words, the people selling the future still can't agree on what they're selling, except that you'd better buy it fast.Meanwhile, the AI bubble keeps demanding sacrifices. Patreon, BuzzFeed, and Uber all swung the layoff axe while insisting AI isn't replacing humans... it's just reorganizing them out of a paycheck. Nvidia is floating another three-quarters of a trillion dollars in infrastructure deals that have people muttering "AI subprime mortgage crisis," China has discovered there's money in renting your face to AI companies, and the U.S. is carving data centers out of pollution rules because apparently acid rain is a small price to pay for chatbot dominance. Add in ChatGPT suddenly refusing to imitate famous authors, researchers discovering AI coding assistants are happily installing malware if you ask nicely, and Meta launching an ad campaign about the wonders of AI that somehow forgets to mention what any of those wonders actually are.Elsewhere in Tech Hell™, eBay finally paid the price for its executives' unhinged harassment campaign involving pig masks and live insects, Elon is shopping The Boring Company while promising money won't matter in the AI utopia of 2036, and a traveler is headed to court after allegedly triggering GrapheneOS's "duress PIN" during a border search. We also hit Media Candy with Star Trek: Strange New Worlds, Silo, Blade Runner 2099 and Neuromancer, celebrate Dave Bittner's birthday, marvel at Jason's ever-growing Boneyard of dead websites, and somehow end up discussing thermal cameras, piano repair, and old-man bladder supplements. Because that's what happens when Gen X gets older - we just add more tabs instead of closing the old ones.Sponsors:DeleteMe - Get 20% off your DeleteMe plan when you go to JoinDeleteMe.com/GOG and use promo code GOG at checkout.StoryBlocks - For a limited time, they're offering 15% off any annual plan at storyblocks.com/gogPrivate Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/757Watch on YouTube at https://youtu.be/Sii_nodESkcSHOW NOTESEBay and Former Execs Will Pay $56 Million to Couple They Tormented With Bloody Pig MasksAs US weighs response to Chinese AI, industry urges against broad open-weight restrictionsAI researchers call for new tools that can slow automated model developmentOver 1,100 AI workers sign letter asking US to support tools that 'pace the frontier of automated AI development'Anthropic's Dario Amodei responds: doesn't oppose open-weight models, but fears Chinese AIAmodei Denies Anthropic Supports Open-Weight AI Ban, Warns of China RisksSam Altman said AI hasn't led to 4-hour workweeks because people are competitiveSam Altman Shuts Down Hopes of an AI-Led 4-Day Workweek Anytime SoonOpenAI Proposes 32-Hour Pilot Incentives for EmployersSatya Nadella says companies that trust one AI for everything may not survive Microsoft CEO Satya Nadella warns companies relying on one AI model may not surviveOpenAI's rogue hacking incident was a warning shot. Will it be a wake-up call to finally create AI safety regulation?Behind the Curtain: AI godfathers converge on regulationsPatreon is laying off 20 percent of its staffAI Mania Is Eviscerating Global Decision-MakingBuzzFeed Lays Off 33 Percent of Remaining Staff After Bizarre Pivot to AIUber lays off 10 percent of its customer service team in favor of using AIIn China, people are renting out their faces to AIAs SpaceX Sheds a Tesla's Worth of Value, Elon Musk Is Trying to Sell Investors on Another CompanyHow Much Charity Would It Take for People to Like Elon Musk?ChatGPT is now refusing requests to write in famous authors' stylesNvidia's $750 Billion Deals Revive Fear of AI Circular FinancingThe Trump administration is exempting data centers from pollution laws intended to prevent acid rainUS accuses American of allegedly wiping his phone using a 'duress' password during border searchMeta launches new Facebook Verified badge for actual, real humansMeta launches a storefront platform through Facebook MarketplaceMeta's pro-AI ad campaign is conspicuously light on AIIf You AI-Generate Code, Hackers Just Found a Devious Method to Install Malware Directly on Your ComputerMinions & MonstersStar Trek: Strange New WorldsBroadchurch S2SiloTed Lasso Season 4 TrailerNeuromancer — Official Teaser | Apple TVPrime Video Finally Lifts the Lid on ‘Blade Runner 2099'Star Trek: Section 31Bad Deeds by Andrew Hunter MurrayDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingJason DeFillippoJurassic Park computers in excruciating detailWho are the Mandalorians? | Star Wars: Galaxy GuideHF96V Thermal Camera with Visual Camera & Laser Pointer, Intelligent Scene Detection, 240 * 240 Super Resolution Thermal Imaging Camera,25 Hz, 50° FOV, -4°F to 1022°F, IP54 Infrared CameraProject FarmSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

P.M. Edition for July 30. The U.S. economy grew just 1.5% last quarter, lower than the previous quarter and falling short of economists' expectations. WSJ economics reporter Harriet Torry explains why the details in the report, particularly around consumer spending, suggest things aren't as bad as the headline number makes it seem. Plus, the buzzy AI-focused hedge fund Situational Awareness, founded by AI whiz kid Leopold Aschenbrenner, sold most of its stock portfolio to investment firm Citadel. We hear from WSJ special writer Greg Zuckerman about why this happened and where the company goes from here. And a big rally in tech companies sent U.S. stocks soaring today. Alex Ossola hosts. See the new fronts in the Iran war.  Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A.M. Edition for July 30. Meta shares fall and Microsoft rallies after the hyperscalers sent very different signals on how they're monetizing their massive AI outlays. Plus, as some people are allowed to return to their homes in southwestern France, WSJ's Ed Ballard says a debate over aging water bombers and the country's readiness ahead of a summer of wildfires is picking up. And DoorDash secures a key certification from the FAA to use drones for deliveries. Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Plus: Microsoft sees jump in profits, beating Wall Street expectations. And Russia charges Telegram founder with aiding terrorism. Julie Chang hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Businesses that rely wholly on the major AI labs ultimately won't survive, Microsoft CEO Satya Nadella predicts. Also, Apple is facing a lawsuit from three users who say they collectively lost more than $1.8 million after downloading a fraudulent crypto wallet from the App Store, challenging the company's longstanding claims that its app review process keeps users safe from scams. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Today on the show, Fareed speaks with Satya Nadella, CEO of Microsoft, about some of the dangers posed to humans and companies by artificial intelligence, as well as some of its profound benefits, in an exclusive interview. Then, After US forces captured Venezuelan president Nicolás Maduro earlier this year, many expected President Donald Trump to install Maduro's main political opponent—María Corina Machado—as the country's new leader. That has not happened, however, and she remains in exile. Fareed speaks with Machado about when she expects to go back to her country and what the future might hold for Venezuela. GUESTS: Satya Nadella (@satyanadella); Marina Corina Machado (@MariaCorinaYA) Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this recent episode of Possible, Reid Hoffman sits down with Microsoft CEO Satya Nadella fresh off Microsoft Build 2026. The conversation goes wide: how AI is reshaping work, business, and society—and why the transformation sweeping through software development today is only a preview of what's coming for all knowledge work. Satya makes the case that human capital and "token capital" are now deeply intertwined, that companies—not just countries—must build their own AI capabilities, and that the organizations best positioned to thrive are those that can leverage their unique expertise inside intelligent systems. Reid and Satya also explore Microsoft's enterprise AI vision, Reid's work with Manas on AI-powered scientific discovery, lessons from past technological revolutions, and why demonstrating real, tangible benefits may be the most important thing the industry can do to earn—and keep—the public's trust.You can catch and subscribe to more Possible here: https://www.possible.fm/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Grab a cup of tea, stay hydrated, and join host Tawnie Wolf on this rainy Wednesday edition of The Second Act Executive! On today's show, Tawnie blends mindset, real world tech vision, high level macroeconomic analysis, and hands on podcast studio instruction into an actionable episode for leaders navigating their second act.In this episode, we cover:Mindset & Grounded Living: Why real credentials, true education, and intentional living always win over internet trends and surface level “aesthetic” coaching, featuring insights from Jay Shetty's Think Like a Monk and Radhi Devlukia Shetty's clinical approach to wellness.Finding Your Voice & Tech Visionaries: An inspiring look at Tawnie's son's book, Asher, the Chief Ranger, detailing his journey through speech therapy and his vision to leverage technology, inspired by figures like Bill Gates, Satya Nadella, Sundar Pichai, and Mark Zuckerberg, to give every child a voice.Macroeconomics & Digital Safety: How social media impacts the U.S. dollar, the mechanics of digital attention risk, and why institutional leaders must prioritize real time data defense and platform integrity to protect our schools and communities.Podcast Workshop (Day 16 Focus): Essential studio setup strategies using Riverside.fm, showing you how to balance high fidelity local recording, audio gain staging, and professional visual framing to build an authoritative broadcast presence.How to Listen & Learn:Hit play to tune into the full audio breakdown! Whether you are listening along with our live Podcast Workshop cohort, a corporate executive transitioning into private practice, or a leader building your digital media platform, this episode provides the strategic framework you need to record and lead with authority.Ordering the Book:Listen to the episode to learn how you can place your preorder for Asher, the Chief Ranger while our primary brand websites are undergoing updates.The Second Act Executive is available on Apple Podcasts, Spotify, iHeartRadio, and everywhere else major podcasts are streaming.

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Welcome to Week 3 of The Second Act Executive: Podcast Workshop!In tonight's power packed episode, host Tawnie Wolf officially launches Week 3: Recording & Live Streaming. We bridge the gap between real world executive leadership, parenting in a digital age, macroeconomics, and the studio setups needed to build an independent media platform you own 100%.Whether you are currently navigating corporate America or transitioning out to launch your own private firm, this session is your blueprint for turning your expertise into sovereign media authority. What We Cover in This Episode:Monday Vibe Check & Kitchen Lessons: Why oat milk is non negotiable for crisp onion rings...Wolf Vibrations,LLC YouTube Announcement: A special shoutout for the grown and extremely sexy (ages 55–110)! Discover how Wolf Vibrations, LLC supports corporate leaders and transitioning executives in stepping into their power, asserting their authority, and staying on top.Market Insights: Palantir ($PLTR) & AI Patriotism: Why backing foundational U.S. defense AI infrastructure in 2026 is an act of patriotism, and why long term investors don't sweat short term price fluctuations around $136.Parenting, Social Media Literacy, & Real Value:Reminding our children that social media is a business, not real life.Unpacking the post 2017 foreign capital pullback, desperate monetization models, and why online trolling is just broken business models in disguise.Focusing on big economic success, global competition (BRICS), and real innovation over performative “costume party” clout.Asher the Chief Ranger: A personal story of Tawnie's son finding his voice despite a speech delay, inspired by tech “wizards” like Mark Zuckerberg, Bill Gates, Sundar Pichai, and Satya Nadella.Week 2 Recap (Days 12 & 13): Reviewing the 30 Second Hook Formula (Problem → Promise → Identity) and the non negotiable rule of keeping PDF music licensing certificates stored safely in Google Drive.Day 14 Audit & Showcase: Evaluating cover art.Day 15 Intro: Riverside.fm:Local High Quality Recording: Why local WAV audio and 4K video recording on Riverside eliminates glitchy Zoom streams permanently.Leveraging Riverside's direct partnership with Spotify for Creators and AI generated short form clips.“Our freedom of speech is not for sale. When the public square is compromised by trolls and broken algorithms, you must own your microphone.” Tonight's Homework Assignment (Next 24 Hours) and so much more! Links & Resources:YouTube: Subscribe to Wolf Vibrations for corporate leadership strategies and executive transition guidance.Workshop Schedule: Join us live every night at 8:30 PM EST. Be sure to turn on your notifications so you never miss an episode!

Open source AI models just hit 41% of Hugging Face downloads — and the real cost gap is 90% or more. Here's what that means for your business.The numbers moved fast this week. Chinese open-weight models now dominate downloads, the quality gap versus closed models has shrunk to 3.3%, and a new repo opens on Hugging Face every seven seconds. Half of the Fortune 500 is already running open source models in production.Isar walks through the new frontier open models Kimi K3 and DeepSeek V4, Thinking Machines Lab's first release, Satya Nadella's Token Capital essay, the new state-level AI laws in New York and Illinois, BCG's AI at Work report, and the Apple lawsuit hanging over OpenAI's hardware plans.In this session, you'll discover:Why Chinese open-weight models now account for 41% of Hugging Face downloadsHow Kimi K3 and DeepSeek V4 price against top US closed models ($15 vs $50 per million output tokens, down to 87 cents)What Satya Nadella's "Token Capital" and reverse information paradox mean for your company's dataWhat New York's data center moratorium and Illinois Senate Bill 315 change for AI companiesWhy BCG found that AI strategy beats tool access — and 72% of CEOs now own the AI decisionBCG "AI at Work: Strategy Matters More Than Tools" — the 12,000-person study covered in this episode — https://www.bcg.com/publications/2026/ai-at-work-why-strategy-matters-more-than-toolsAI 2040 "Plan A" paper — the 90-page proposal to delay superintelligence until 2040 discussed in the rapid fire — https://ai-2040.com/About 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!

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

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

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Kimi K3's benchmark breaking results 2) How Kimi K3 fits alongside MuseSpark 1.1 and Grok 4.5 3) What are OpenAI and Anthropic's advantages today? 4) Is the price of frontier intelligence about to drop? 5) It's all about the product now 6) Satya Nadella's Reverse Information Paradox 7) What is happening at Google? 8) Is Google too focused on 'Flash' models 9) Apple's lawsuit vs. OpenAI 10) OpenAI's boneheaded espionage 11) Why does OpenAI struggle to maintain good relationships with partners? --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

AI headlines have already moved on to the deep end. Most businesses haven't. Every week brings another headline about what's next for AI. Build your own model. Train your own LLM. Customize everything. It's exciting, unless you're one of the thousands of companies still trying to answer a much simpler question: where does AI actually fit into the work we do every day? Here's the thing. AI has become everyone's priority and almost nobody's job. Leadership knows it matters but the real work still lives inside thousands of everyday workflows, where tribal knowledge, context, and experience drive the decisions. That's the gap, and it's a very different problem than the one the headlines are chasing. That's the conversation Rob and Justin have this week. Sparked by Satya Nadella's comment that every company should eventually have its own LLM, they make the case that the industry is getting ahead of itself. As Rob puts it, "Everyone's sitting poolside and Satya's talking about the deep end." Most organizations don't need a custom model. They need AI that understands their business, their data, and their workflows. That's where the biggest wins are happening today, and it's exactly where companies should be focused before they start worrying about building their own LLM. If AI has started to feel like an arms race you somehow missed, this episode is a welcome reminder that the biggest opportunities are still waiting in the shallow end.

Welcome to episode 363 of The Cloud Pod, where the weather is always cloudy! Justin, Matt, and Ryan are in the studio this week to bring you all the latest in cloud and AI news, including Amazon SQS turning 20, Grok solving a Rubik's cube, and Cloudflare's new “spot the bot” tool, which harnesses *checks notes* monitoring mouse movements? Ok… It's been a busy week in the cloud, so let's get started!  Titles we almost went with this week AI Speed Dating: Grok Wins, Cube Loses Grok, GPT, and Claude Walk Into a Rubik’s Cube One Gateway to Rule All the Claude Credentials AWS Puts a Bouncer on the Claude Code Party Claude Solves the Cube, GPT Just Sees Dark Faces Cloudflare Catches Bots by Their Shaky Hands GuardDuty Sniffs Out Bedrock Bandits at Last A big thanks to this week's sponsors: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our Slack channel for more info. General News  01:36 Former GitHub CEO Unveils Distributed Git Network Built for AI Coding Agents Former GitHub.com CEO Thomas Dohmke has launched Entire, a startup building a distributed Git network aimed at reducing load on centralized hosting caused by AI coding agents.  The company raised a $60 million seed round at a $300 million valuation. The core idea is to mirror GitHub repos across regions (US, Europe, and Australia currently) so AI agents pull from nearby mirrors instead of hitting a single central repo, which addresses rate-limiting and latency issues that come with high-volume automated cloning and pushing. Reported internal benchmarks include about 570,000 clones per hour on a single repo and 586 pushes per second, though these are self-reported and not yet independently verified; Entire says it plans to open source the Git backend and benchmarking tools for third-party validation. Beyond distribution, Entire is building a semantic layer on top of Git history, capturing agent prompts, reasoning, and tool calls, with features like Entire Blame tracing AI-generated code back to originating prompts, and Entire Review supporting multi-agent code review. Worth discussing: this treats AI agent traffic as a distinct infrastructure problem separate from human developer workflows, and the long-term roadmap includes data sovereignty features letting companies keep code within specific regions while staying connected to a global network. 03:31 Justin – “Get fired for having all these issues, and then solve the problem anyway. 06:03 Satya Nadella on X: “https://t.co/xv6csf1SbV”  The problem: Microsoft CEO Satya Nadella coined the “Reverse Information Paradox”: AI flips Kenneth Arrow’s classic info paradox. Instead of sellers giving away value before being paid, buyers now have to feed proprietary knowledge into a model just to make it useful, paying twice: once in dollars, once in IP. The mechanism: Na

Xbox just laid off thousands, spun off four studios, and cancelled games it had defended a day earlier. Greg Posner and games-industry analyst Colan Neese take apart Microsoft's restructuring memo and land on an uncomfortable read: this wasn't a bad week, it was the bill coming due for a decade of running gaming as a lifestyle business.They dig into the losses (64 cents gone on every dollar spent), the decision to starve Minecraft to prop up everything else, the Game Pass numbers that never made the memo, and the blame pie: how much lands on Phil Spencer, and how much on CEO Satya Nadella. Then they split hard on the future. Colan makes the case that the fix is an ad-funded, one-to-one discovery engine. Greg goes down swinging for a different read of why players won't leave the games they already know.Two live games along the way: ranking Microsoft's biggest IP by revenue (and where the AI got it wrong), and an invest-or-sell round on Xbox's dormant franchises.In this episode(0:00) Cold open, and the week that broke Xbox(9:00) The layoffs and studio spin-offs: Avowed cancelled, a new Fallout greenlit(11:54) The memo: losing 64 cents on every dollar, and starving Minecraft(18:15) Phil Spencer, the "wayward son with a credit card"(19:07) Restructuring, and the Game Pass numbers that never got named(23:49) Colan's "script for Asha Sharma," and why Xbox is a distant fifth platform(29:24) The pitch: ad rev-share, the way YouTube pays creators(32:00) Game: rank Microsoft's biggest IP by revenue(37:35) Game: invest or sell Xbox's dormant franchises(44:49) The blame pie: how much is on Satya Nadella(49:56) The debate: is discovery broken, or are players just comfortable?(1:09:00) Games for Change NYC, and what's nextQuotablesColan Neese: "You lost 64 cents on a dollar, and you took from the profitable engine that was Minecraft."Colan Neese: "Ads are just content. It's just a different kind of content."Links and mentionsPlayer Driven: https://playerdriven.ioThe Operator's Briefing, our weekly newsletter: https://playerdriven.io/level-upJoin the community on Discord: https://discord.gg/GC5PkbKFHGames for Change, NYC, July 21AboutPlayer Driven Live breaks down the business behind the games every week, for the people who build, run, and rebuild them. Hosted by Greg Posner with analyst Colan Neese. If this is your kind of conversation, subscribe, and come argue with us in the Discord.

Connect with Early Riders — https://www.earlyriders.com/contactConnect with Onramp — https://onrampbitcoin.com/contact-us/Presented collaboratively by Early Riders & Onramp Media…Final Settlement is a weekly podcast covering capital markets, dealmaking, early-stage venture, bitcoin applications and protocol development.This week Michael, Liam, and Brian break down the banks' accelerating tokenization push: BlackRock, Goldman Sachs, and JPMorgan joining a 54-firm UK tokenization task force, Swift's blockchain ledger pilot with 17 banks, and what it means for Bitcoin's role in the emerging plumbing. They dig into Satya Nadella's AI sovereignty thesis, Microsoft's Frontier Company data play, Apple's lawsuit against OpenAI, and the Fed's new AI task force under Kevin Warsh. The guys run through a stack of deals: TerraWolf's $19 billion Anthropic infrastructure agreement, OLAMA's $65 million raise, Paradigm's $1.2 billion fourth fund expanding into AI and robotics, and Tether's $20 million investment in Mercado Bitcoin. They close on TradFi's crypto expansion via SBI Holdings and Russia's Alpha Bank, Kraken's $22 million lawsuit win, where the Clarity Act stands ahead of the midterms, and Spiral's new open source push to merge Bitcoin and AI.Chapters00:00 - Introduction and Show Overview00:18 - Market Sentiment and Show Focus01:15 - Banks and Traditional Enterprises in Digital Asset Space02:40 - UK Tokenization Task Force and Use Cases03:40 - Swift Blockchain Ledger Pilot with 17 Banks04:34 - Global Sprint in Tokenized Securities and Deposits07:23 - Bitcoin's Role in the Future of Digital Infrastructure09:39 - The Bullish Case for Bitcoin Amidst Tokenization12:28 - AI Tools and Sovereignty in Data Ownership15:37 - Microsoft's Investment in AI and Data Sovereignty22:16 - Microsoft Frontier Company and Proprietary Data Platforms25:07 - The Importance of Human Agency in AI Adoption28:40 - AI and the Disruption of Traditional Business Models29:08 - Apple Suing OpenAI Over Trade Secrets32:36 - The Long-Term Impact of AI and Open Source Models36:50 - DoorDash's AI Code Reviewer and Internal AI Adoption38:19 - Fed's AI Task Force and Economic Policy43:32 - Russia's Strategic Use of Bitcoin and Digital Assets50:07 - Paradigm's New Fund and Broader Tech Investment55:13 - Traditional Finance's Entry into Crypto and Digital Assets01:02:41 - Regulatory Developments and the Future of Crypto Legislation01:09:18 - Bitcoin's Role in the Future of Digital Infrastructure01:11:21 - Open Source AI and Bitcoin IntegrationIf you found this valuable, please subscribe to Early Riders Insights for access to the best content in the ecosystem weekly: https://www.earlyriders.com/researchKeep up with Michael:https://x.com/MTangumaKeep up with Liam:https://x.com/Lnelson_21Keep up with Brian:https://x.com/BackslashBTC

Apple verklagt OpenAI wegen Diebstahls von Geschäftsgeheimnissen und nennt die OpenAI-Kultur "rotten to the core". Satya Nadella warnt vor der KI-Nutzung durch Unternehmen: Ihr bezahlt nicht nur mit Geld, sondern mit eurem proprietären Wissen. Elon Musk und Sam Altman liefern sich auf X einen Schlagabtausch. Bei OpenAI konsolidiert Greg Brockman die Macht nach Fiji Simos Rücktritt, während die Anthropic-Bewertung am Secondary-Markt in Richtung 1,2 Billionen läuft. 200 Ökonomen und 16 Nobelpreisträger unterschreiben ein "We Must Act Now"-Statement zur KI-Job-Verdrängung. Meta committet weitere 40 Milliarden für sein Louisiana-Data-Center, Gesamtinvestition Richtung 250 Mrd. Shein bekommt grünes Licht für den Hongkong-IPO auf 40 Mrd. Bewertung. Counterpoint zeigt: Smartphone-Markt bricht 11% ein wegen Memory-Crunch.  Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Apple verklagt OpenAI (00:15:09) Nadella-Warnung (00:24:53) Altman kontert Musk (00:26:49) Codex 7 Mio. (00:29:18) Fable 5 verlängert (00:31:37) Brockman übernimmt (00:32:19) Anthropic $1,2 Bio. (00:34:01) Cursor baut SAND (00:35:53) We Must Act Now: KI Jobverlust ohne Belege (01:00:36) Meta Louisiana (01:02:43) Shein Hongkong (01:03:46) Smartphone-Einbruch (01:08:11) Pioneer 3 Shownotes Apple verklagt OpenAI wegen Trade-Secret-Diebstahl - techcrunch.com Apple: OpenAIs Hardware-Business rotten to the core - uk.pcmag.com Nadella warnt vor KI-Daten-Risiko für Unternehmen - techcrunch.com Sam Altman kontert Elon Musk - xcancel.com OpenAI: Brockman konsolidiert Macht vor dem IPO - cnbc.com Anthropic verlängert Fable-5-Zugang bis 19. Juli - economictimes.indiatimes.com Codex knackt 6-7 Mio. aktive Nutzer - latent.space Anthropic auf $1,2 Bio. Bewertung am Secondary-Markt - businessinsider.com Cursor baut SAND als Claude-Cowork-Konkurrent - theinformation.com We Must Act Now: 200 Ökonomen zum KI-Risiko - wemustactnow.ai NYT: Ökonomen warnen vor KI-Job-Verlust - nytimes.com Indeed: Codex-Launch treibt Entwickler-Hiring +15% - hiringlab.org Meta Louisiana Data Center wird $250 Mrd. - bloomberg.com WSJ: Shein bekommt grünes Licht für Hongkong-IPO - wsj.com Counterpoint: Smartphone-Markt bricht 11% ein - counterpointresearch.com Bild: Thelen sagt Brandmauer schadet Deutschland - bild.de Bunte: Thelen verliert Millionen wegen Politik-Aussagen - bunte.de Pioneer 3 launcht - instagram.com Anne Schwedt: Optionen-Websession bei Ikonista - montagshappen-verlag.de Übermedien: Grat zwischen Journalismus und Anlage-Tipps - uebermedien.de NYT: Terrorgruppen wie Boko Haram nutzen KI - nytimes.com

El programa 2902 de Radiogeek repasa las novedades tecnológicas más importantes del día: X acaba de modificar su algoritmo para hacerlo más amigable y menos conflictivo; Samsung Health eliminará tus datos a menos que los entregues para el entrenamiento de la IA; Satya Nadella ha lanzado una alarmante advertencia a las empresas que utilizan IA; Un nuevo malware para Android puede vaciar tus cuentas bancarias en secreto; Meta acaba de eliminar la polémica función de IA de Instagram tras la reacción negativa de los usuarios; Ciberseguridad en 2 minutos: qué hacer si perdés o te roban el celular y por último Apple demanda a OpenAI por robo de secretos comerciales. Toda esta información la pueden encontrar desde nuestra web www.infosertec.com.ar o bien desde el canal de Telegram/Whastapp, o Instagram. Esperamos sus comentarios.

In this recent episode of Possible, Reid Hoffman sits down with Microsoft CEO Satya Nadella fresh off Microsoft Build 2026. The conversation goes wide: how AI is reshaping work, business, and society—and why the transformation sweeping through software development today is only a preview of what's coming for all knowledge work. Satya makes the case that human capital and "token capital" are now deeply intertwined, that companies—not just countries—must build their own AI capabilities, and that the organizations best positioned to thrive are those that can leverage their unique expertise inside intelligent systems. Reid and Satya also explore Microsoft's enterprise AI vision, Reid's work with Manas on AI-powered scientific discovery, lessons from past technological revolutions, and why demonstrating real, tangible benefits may be the most important thing the industry can do to earn—and keep—the public's trust.You can catch and subscribe to more Possible here: https://www.possible.fm/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Patrick Moorhead and Daniel Newman unpack OpenAI's reported equity offer to the U.S. government, Anthropic's phased return following export controls, and why AWS and Microsoft are betting billions on the engineers who deploy AI rather than the labs that train it. They also dig into NVIDIA's response to its roadmap critics, Meta's expanding cloud ambitions, and the week's biggest market developments, from SpaceX to Samsung. Catch the full analysis on Episode 311 of The Six Five Pod. The handpicked topics for this week are: OpenAI's Equity Offer to Washington: OpenAI is reportedly considering offering the federal government a 5-10% stake in exchange for a two-year runway on Department of Defense contracts. Pat and Daniel read it as a distribution and IPO hedge rather than a governance concession, especially with a trillion-dollar valuation still the target. (The Decode) Anthropic's Phased Return After Export Controls: Fable 5 is back online, and Mythos remains partially restored to a limited set of vetted institutions following Commerce Department approval, a rollout Pat treats as the first real test case for how future frontier models clear review. Daniel adds that Alibaba's claimed block on Claude in China carries more optics than substance given how easily a VPN routes around it. (The Decode) NVIDIA and Palantir's Open Source Enterprise Play: Alex Karp's viral CNBC interview argued that enterprises pairing Palantir with NVIDIA's Nemotron models can match frontier-level output without a frontier lab in the loop, a clip that pulled nearly 200,000 views once Pat posted it. Both hosts agree the priority for open source models has moved from leading capability benchmarks to closing that gap fast enough for slower-moving enterprises to adopt them. (The Decode) Forward-Deployed Engineering Becomes a Billion-Dollar Line Item: AWS invested $1 billion, followed just two days later by Microsoft's $2.5 billion commitment to forward-deployed engineering teams, signaling a shift in where enterprise AI value is being created. Daniel argues the biggest opportunity now lies in implementation talent rather than model development, while Patrick sees the investments as a strategic hedge, noting that frontier AI labs have already been building their own forward-deployed engineering organizations to help customers put AI into production. (The Decode) Meta's Cloud Ambitions Meet a Trust Problem: Meta Cloud can likely match neocloud-level GPU pricing and performance, but Pat argues the company's record outside advertising makes enterprise-grade services a much harder sell. He expects the offering to function as tactical overflow capacity for labs needing compute, with a shelf life tied to demand rather than a durable AWS rival. (The Decode) The Flip: Has Enterprise AI Value Already Left the Model Layer? Daniel argues that Microsoft and AWS writing nine- and ten-figure checks for forward-deployed engineering in the same week, alongside an essay from Microsoft's Satya Nadella declaring models replaceable, confirms that implementation talent now captures value model providers used to keep. Pat counters that today's models remain far from AGI, and that once frontier labs get there they will spin off cheap narrow models fast enough to keep pricing power on their side. SpaceX Joins the Nasdaq 100 Within Weeks of Its IPO: SpaceX entered the index just 15 days after going public, the fastest addition on record, which Pat says will pull in passive buying regardless of the underlying valuation. Both hosts flag the speed itself as a signal of broader market froth. SK Hynix and Samsung Signal Memory's Return to the Center of the AI Trade: SK Hynix is pursuing a $28 billion US listing that could value the company near a trillion dollars, timed just ahead of Samsung earnings both hosts expect to show a sharp profit jump. Daniel ties the moves directly to NVIDIA's memory demand, and Pat notes most investors had barely heard of SK Hynix before this year. NVIDIA and Microsoft Split Paths in the Magnificent Seven: NVIDIA gained 7% for the half while Microsoft shed 23%, a divergence Pat ties to Microsoft's exposure to OpenAI's fading momentum and its perception as a bundled SaaS play rather than a leading infrastructure provider. Daniel calls the selloff overdone and floats it as a generational buying opportunity. NVIDIA's Roadmap Rebuttal Tests the Limits of Corporate Denial: NVIDIA issued a statement calling its roadmap intact after a SemiAnalysis report raised delay concerns tied to its Kyber rack architecture, and Pat reads the denial as legally meaningful given the liability regulated companies take on when refuting analyst claims. Both hosts land on the same read, that first customer shipment may hold while full volume ramp slips. Palo Alto Networks and CrowdStrike Undercut the SaaSpocalypse Narrative: CrowdStrike climbed 95% and Palo Alto Networks gained 113% from April to June, each posting record quarters that Daniel says quiet the theory that frontier models would simply replace dedicated cyber tools. Pat adds that security was always the wrong category for that narrative, since it ranks among AI's biggest risks rather than one of its casual replacement targets. New episodes of the Six Five Pod land every week. Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode.   The Decode The American AI Champions Doctrine Comes Into Focus — OpenAI 5% Gov Stake, GPT-5.6 Gated to 20 Orgs, and MGX Closes $49B https://www.cnbc.com/2026/07/02/openai-proposes-us-government-own-5percent-stake-to-address-political-blowback.html Anthropic's Super-Week — Mythos + Fable Restored (with California in Tow), and Alibaba Fires Back https://www.axios.com/2026/06/27/commerce-anthropic-mythos-restrictions-lift Palantir–NVIDIA Sovereign AI Alliance — Karp Attacks Token Pricing, Stock +8–9% https://blogs.nvidia.com/blog/palantir-secure-ai-us-agencies-nemotron-open-models/ The Embedded AI Engineering Arms Race — AWS $1B FDE Meets Microsoft Frontier Company ($2.5B) https://www.reuters.com/business/retail-consumer/amazons-aws-commits-1-billion-toward-new-unit-embedded-ai-engineers-2026-06-30/ Pat's Anthropic Forbes' Article https://www.forbes.com/sites/patrickmoorhead/2026/05/05/enterprises-need-to-be-careful-before-they-go-all-in-on-anthropic/ Meta Compute — The World's Biggest AI Capex Spender Becomes a Cloud Provider https://www.cnbc.com/2026/07/01/metas-plan-to-launch-a-cloud-business-eases-the-biggest-overhang-on-the-stock.html  The Flip: Enterprise AI value has shifted from model providers to implementation partners — the buyer's next big check is for who builds it, not who trained it. FOR: The AI stack has commoditized fast enough that integration is the new moat. https://www.reuters.com/business/retail-consumer/microsoft-launches-firm-help-companies-adopt-ai-with-25-billion-2026-07-02/ AGAINST: Frontier-model access is still the scarcity, and enterprise IP is the real moat. https://www.cnn.com/2026/06/26/tech/anthropic-mythos-release Bulls & Bears SpaceX Joins the Nasdaq-100 Effective July 7 — First-Ever Pre-IPO-Type Index Add of Its Kind https://finance.yahoo.com/markets/stocks/articles/spacex-joins-nasdaq-100-july-133000124.html SK Hynix Launches ~$28B Nasdaq ADR Listing Today — Potentially the Biggest-Ever First-Time Sale by a Foreign Company https://www.bloomberg.com/news/articles/2026-07-05/sk-hynix-seeks-access-to-ai-investors-in-29-billion-us-listing H1 Mag-7 Divergence — NVDA +7.3% While MSFT Shed 22.9% Over the Same Six Months https://finance.yahoo.com/markets/article/tech-stocks-post-best-6-months-since-2023--even-with-much-of-the-magnificent-7-in-the-penalty-box-chart-of-the-day-100000120.html Palo Alto Networks and CrowdStrike Log Best Cyber Quarters Ever — +113% and +95% Between April and June https://www.cnbc.com/2026/06/30/palo-alto-crowdstrike-stock-ai-mythos.html Enterprises Need To Be Careful Before They Go All-In On Anthropic https://www.forbes.com/sites/patrickmoorhead/2026/05/05/enterprises-need-to-be-careful-before-they-go-all-in-on-anthropic/  

En el Radar Empresarial de hoy ponemos el foco en Microsoft y en la nueva reducción de personal que acaba de comunicar, una decisión que refleja una tendencia cada vez más frecuente entre las grandes compañías tecnológicas. Estas empresas están reorganizando sus estrategias para concentrar recursos en el principal motor de crecimiento actual: la inteligencia artificial. En este caso, Microsoft prescindirá de 4.800 empleados, una cifra que representa más del 2% de su plantilla mundial, y además contempla eliminar otros 1.600 puestos durante el próximo ejercicio fiscal. La mayor parte de estos recortes afectará al área de videojuegos, especialmente a la división de Xbox, donde se concentrarán alrededor de 3.200 despidos. Como consecuencia de esta reorganización, la compañía también prevé desprenderse de cuatro estudios de desarrollo. La situación del negocio gaming se ha deteriorado en los últimos meses: las ventas de consolas cayeron un 33% durante el tercer trimestre fiscal y la división de videojuegos registró una bajada del 7%. Desde la pandemia y tras las adquisiciones de ZeniMax Media y Activision Blizzard, que inicialmente impulsaron los ingresos del sector, el rendimiento de esta área ha ido perdiendo fuerza. A esta complicada evolución se suma el aumento del precio de las consolas Xbox, influido por la escasez de componentes de memoria causada por la creciente demanda relacionada con la inteligencia artificial. La diferencia de precios resulta evidente: en 2020 una Xbox de 500 GB tenía un coste inicial de 500 dólares, mientras que actualmente la versión de un terabyte alcanza los 800 dólares. El director ejecutivo de Microsoft, Satya Nadella, señaló en el podcast Hard Fork que uno de los grandes retos de la compañía es conseguir innovar tanto en hardware como en videojuegos. También generó preocupación que mencionara que YouTube obtiene más ingresos que Xbox. El problema no afecta únicamente a Microsoft. Sony ha incrementado los precios de sus consolas PlayStation y estudia abandonar progresivamente los videojuegos físicos, mientras que Nintendo también ha encarecido su Switch. Este nuevo ajuste se suma a otros recortes realizados por Microsoft recientemente. A principios del año pasado anunció una primera reducción de plantilla, seguida en mayo por otra que afectó a 6.000 trabajadores de áreas como ingeniería, producto y LinkedIn. Además, en abril de 2026 puso en marcha un programa de bajas voluntarias para cerca de 9.000 empleados en Estados Unidos que cumplían determinados requisitos.

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Zuck says AI agent progress isn't going to plan 2) Meta explores selling excess compute 3) Why can't anyone build an AI agent? 4) Are Anthropic and OpenAI becoming the point of failure in the AI trade 5) Is Google hedging? 6) What is Satya Nadella up to? 7) Should Microsoft bring back bad Sydney 8) Palantir CEO Alex Karp challenges the frontier labs 9) Everyone vs. OpenAI and Anthropic? 10) Should OpenAI give the U.S. government 5% of its equity? 11) Taylor Swift & Travis Kelce wedding trutherism --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan MailGreg McKeown, author of Essentialism, reveals the 7 words that make people open up instantly while breaking down communication skills, active listening, emotional intelligence, conflict resolution, social media distraction, the AI age, nonviolent communication, and the psychology of being misunderstood. In this conversation, Greg explains why modern life has moved beyond distraction into disorientation — and why the real bottleneck in relationships, leadership, family, and business is “confident misunderstanding.”Ryan and Greg explore Carl Rogers' forgotten 1952 communication rule, the phrase “Let me see if I understand you,” and why making someone feel deeply understood can defuse arguments, build trust, and unlock better conversations. They also discuss how Satya Nadella helped shift Microsoft's culture, how listening played a role in locating Saddam Hussein, and why removing noise is often more powerful than adding more signal.Watch this episode if you want to become a better listener, communicator, leader, partner, friend, or thinker. Subscribe for more conversations on business, psychology, philosophy, health, wealth, and self-mastery.#GregMcKeown #CommunicationSkills #EssentialismTIMESTAMPS00:00 – Why Important Work Rarely Gets Done01:55 – How Social Media Steals Your Attention06:36 – Why AI Makes Us Feel Disoriented09:27 – The Psychology of Confident Misunderstanding13:20 – How the Internet Distorts Reality17:22 – Why Polarization Breaks Families Apart21:26 – Carl Rogers' Forgotten Communication Rule25:33 – The 7 Words That Make People Open Up27:25 – How to Make Someone Feel Understood33:14 – Why Listening Changes Every Relationship38:37 – How Communication Helped Rebuild Microsoft48:16 – The Signalist Method for Better Conversations54:17 – How Listening Helped Find Saddam Hussein1:00:58 – The Hidden Power of Removing NoiseConnect with Us!https://www.instagram.com/alchemists.library/https://twitter.com/RyanJAyala

Microsoft's CEO Satya Nadella recently wrote a blistering essay criticizing how the race for AI supremacy has played out, and specifically called out tech leaders' dire prophecies about job losses. Nadella says the industry needs to figure out a path forward that is more beneficial to everyone, not just the biggest AI companies. WSJ's Bradley Olson, who spoke with Nadella in an exclusive interview, says that there might be a business calculus behind his message. Ryan Knutson hosts. Further Listening: - The Era of AI Layoffs Has Begun - How AI Is Being Trained to Do Your Job - The ‘Class of AI' Enters the Workforce Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan MailJoin hosts Alex Sarlin and special guest co-host Gemma Lenowitz Picot of Samvid Ventures as they break down the latest developments in AI, workforce development, edtech adoption, and education policy.✨ Episode Highlights:[00:03:26] OpenAI, Google, and Anthropic expand AI workforce training initiatives[00:10:30] Data center investments create new opportunities for workforce development[00:15:59] Anthropic launches Claude Core to help nonprofits adopt AI[00:21:00] AI safety concerns raise questions about education-specific AI models[00:27:23] Khanmigo tops the new EdTech AI Visibility Index[00:33:40] Teacher requests for AI tools on DonorsChoose have tripled[00:38:26] AI adoption may accelerate growth in microschools and school choice[00:41:22] UK proposes banning social media access for children under 16[00:42:37] UK expands AI tutoring access for low-income students[00:50:18] Satya Nadella introduces the concept of AI-powered organizational learning loopsPlus, special guests:[00:54:02] Tom ap Simon, President of Higher Education and Virtual Learning at Pearson, on AI readiness, career pathways, and workforce outcomes [01:16:03] France Hoang, Founder and CEO of BoodleBox, on AI collaboration, financial literacy, and preparing learners for an AI-powered future 

Satya Nadella talked about cognitive coverage in the age of AI, about being able to understand and manage AI agents to get work done as a software developer. The interview from Hard Fork Live covers the future of work and comfort in this new age. This reminds me of a book that the CEO of Redgate recommended, Reshuffle. I love the book, but it's slow reading as I constantly stop and think. Work is changing; it's becoming unbundled and re-bundled in different ways, and many of us will have to learn to work in new ways. Not all of us, but many of us. Some might see their day-to-day efforts change little; some will not recognize their job a year from now. As with anything, lots of us will be in the middle with some changes, some status quo. That's certainly where I am with AI assistance. Read the rest of Cognitive Coverage

On Episode 310 of The Six Five Pod, Patrick Moorhead and Daniel Newman unpack the biggest stories from the week, including insights from Qualcomm Investor Day 2026, OpenAI and Broadcom's Jalapeño AI chip, Anthropic's Micron partnership, SpaceX's massive Reflection AI compute deal, Sakana AI's new Fugu orchestrator, and why memory is emerging as a critical layer of AI infrastructure. Plus, Bulls & Bears covers NVIDIA's $25B bond offering, Apple's MacBook price increases, Micron's record quarter, and Cerebras' first earnings as a public company. The handpicked topics for this week are: Qualcomm Investor Day 2026 — The Data Center Debut: Pat and Dan break down Qualcomm's push into the data center after the company took the stage with Microsoft's Satya Nadella and Meta's Mark Zuckerberg as named customers. They unpack the new Dragonfly platform, including the C1000 250-core data center CPU with PCIe Gen 7 and CXL, the AI200 and AI250 inference accelerators, and a novel High Bandwidth Compute (HBC) architecture that stacks compute under LPDDR memory at dramatically lower cost than HBM. They highlight Qualcomm's ambitious growth targets: $15B data center revenue target for FY 2029, an increased total non-handset revenue goal from $22B to  $40B, and a shortened timeline for automotive revenue by two years. They also debate the identity of Qualcomm's unnamed hyperscaler customer and why its robotics opportunity may be flying under the radar. (The Decode) OpenAI and Broadcom Unveil Jalapeño, OpenAI's First Custom Chip: A photo of Sam Altman and Hock Tan holding a wafer and packaged die kicked off OpenAI's reveal of Jalapeño, a custom inference chip built with Broadcom and slated for late-2026 deployment. The chip reached tape-out in roughly nine months, which is an aggressive cycle for an ASIC of this size, and uses HBM3E memory. Pat takes a victory lap on his long-standing heterogeneous compute thesis: every hyperscaler and now every model lab is building accelerators, and the XPU efficiency argument has played out as predicted. Dan frames OpenAI's broader move as existential: they cannot serve frontier models at premium margins if compute remains constrained. He flags that OpenAI is trying to do everything from chips and fabs to social networks and browsers, and that its IPO is now delayed. (The Decode)   Anthropic and Micron Sign a Strategic Multi-Year Memory Agreement: Anthropic and Micron announced a multi-year supply agreement for HBM, DRAM, and SSDs, including co-designed next-generation memory for AI workloads, along with a strategic investment by Anthropic in Micron. The pattern mirrors Samsung and SK Hynix's pre-funding Anthropic in May, and follows OpenAI's Jalapeño as another frontier lab moving to lock in supply chain control. Dan frames it as the same circular financing playbook NVIDIA ran two to three years ago, but with the ball now in the memory triopoly's court. Pricing-floor agreements with no ceilings, customized rather than commoditized memory architecture, and demand running well past the previously assumed 2027-2028 horizon. Pat notes that the rumored 14% free cash flow margin at Anthropic makes the strategic investment math work cleanly for both sides. (The Decode)   SpaceX Signs $6.3B Compute Deal with Reflection AI: SpaceX inked a $6.3B compute lease with open-source AI lab Reflection AI, at $150M per month from July 2026 through 2029, giving Reflection access to NVIDIA GB300 chips inside the Colossus infrastructure. Combined with the $920M-per-month Google compute contract and existing xAI commitments, SpaceX now has a contracted backlog larger than most public AI startups' entire revenue base, with some calling it the largest commercial AI infrastructure provider at $80B in contracted revenue. Pat reads it as XAI failing to land with developers, consumers, or enterprises, leaving SpaceX with a pot of gold worth far more as wholesale capacity than as XAI's own training compute. Dan flags that Google owning 7% of SpaceX ahead of an IPO is not accidental, and the open question is whether this becomes a Nebius-style infrastructure trade or a full-stack Google-equivalent platform. (The Decode)   Japan's Agentic Orchestrator Sakana AI Ships Fugu Plus and Fugu Ultra: Japan's Sakana AI released Fugu Plus and Fugu Ultra, an agentic orchestrator built on a multi-agent MOE approach that routes workloads across multiple underlying models rather than training a new frontier base model. Sakana claims agentic capabilities on par with or better than top frontier models at significantly lower input/output token costs, similar to the DeepSeek and GLM cost-undercut narrative. Pat compares the architecture to OpenRouter and notes the developer-facing parallel to Perplexity Computer's model-routing approach. Both agree that models themselves are no longer moats, and suggests the real moat is the harness, tooling, connectivity, looping, agentic stack, and total compute availability. Expect more sovereign agentic plays from Japan, the Middle East, and elsewhere on the same template. (The Decode)   The Flip — Is the Era of Memory as a Commodity Over? Daniel takes the FOR side: memory has moved from commodity to strategic AI infrastructure, citing 16 multi-year agreements covering $22B in committed volume booked through 2027, 84.9% gross margins higher than NVIDIA's, the technology barriers of HBM yield/stacking/packaging that only three companies can clear, and demand drivers tied to HBM as the binding constraint on every AI accelerator rather than to elastic consumer cycles. Patrick takes the AGAINST side: long-term agreements and SCAs signal a commodity in a strong cycle, not a structural rerating; nearly every relevant memory standard — DDR5, MRDIMM, HBM3/3E/4, LPDDR5X/6, GDDR6/7, LPCAM2 — is JEDEC-standard and therefore commodity at the pin; and CXMT's China DDR5 production ramps in 2H 2026 with Lenovo already shipping and HP and Dell qualifying. Custom HBM4 and Qualcomm-style HBC are where strategic memory genuinely lives. (The Flip)   NVIDIA's $25B Investment-Grade Bond Offering: NVIDIA priced a $25B multi-tranche bond offering on June 15, its first investment-grade debt sale since 2021, with seven tranches maturing between 2028 and 2056 and $85B in orders against an initial $20B target. Dan reads it as raising when capital is cheap, and oversubscription is real. NVIDIA doesn't need the money, it has a gold balance sheet, and is establishing a credit benchmark rather than funding CapEx. Pat agrees the optics are clean, but flags the irony of NVIDIA, with negative debt, borrowing while the stock trades like dead money at a sub-20x forward P/E. Both note that NVIDIA's underperformance reflects the market's skepticism on memory-as-strategic and on NVIDIA's own capex pace relative to the buildout opportunity ahead. (Bulls & Bears)   Tim Cook Calls Apple's Memory Crunch Price Raises on MacBook and iPad "Unsustainable": Apple announced MacBook and iPad price increases of up to $300, with Tim Cook telling the WSJ the memory cost environment is unsustainable. AAPL fell ~5% on the news, the broader rally was momentarily wiped out before Micron held the gains by close. Dan frames it as a moment when the market saw who is going to pay for the AI buildout: the consumer. He notes Apple's pricing power and inelasticity test is now live. Pat traces the backstory to Apple's negative-margin pricing pressure on Micron during the 2022-2023 memory downturn. The question is whether consumer-price blowback will eventually flow back to the memory vendors. (Bulls & Bears)   Micron Blows the Doors Off Fiscal Q3 — $41.46B Revenue, 84.9% Gross Margin: The memory story continues as Micron reported its largest beat in company history with fiscal Q3 revenue of $41.46B versus a $35.69B consensus, EPS of $25.11, year-over-year growth of more than 340%, and a record 84.9% gross margin that is roughly 10 points above NVIDIA's. Q4 guidance came in at a $50B midpoint against a $43B consensus. The 16 multi-year strategic customer agreements add up to $22B in committed volume, with most contracts containing pricing floors but no ceilings on most of the volume — a structurally asymmetric setup. Pat notes 95% of the beat came from price, not units, which reinforces his commodity argument; Dan flips it as the early innings of an NVIDIA-style run that puts Micron's 2027 profit on par with Google. (Bulls & Bears)   Cerebras' First Earnings Report Since IPO — Revenue Doubles, Margins Compress: Cerebras (CBRS) reported its first earnings as a public company, doubling year-over-year revenue and beating the top line while missing EPS, but the stock sold off hard amid gross margin deterioration. Core revenue came in at $191M, up 12% sequentially, with a $194M Q2 guide that is essentially flat, core gross margins at 47% guiding to 36-38% and 38-41% for the year, and operating margins flipping from positive 2% to a guided -30% to -32%. Customer concentration is shifting from Core42 and G42 (86% of FY25 revenue) to OpenAI, which loaned Cerebras $1B and gets paid quarterly in warrants. Pat flags that Cerebras' uncontested speed claim is no longer uncontested with Groq, TPU v8i, and Tenstorrent putting up real numbers. Cathie Wood is down 52% on her position. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode Qualcomm Investor Day Lands the Data Center Pivot — Microsoft Deploying Qualcomm HBC XPUs in Azure (Per Satya Nadella) + Meta MOU on Three New Qualcomm Datacenter CPUs (Per Zuckerberg); $3.9B Modular Acquisition; Dragonfly Brand + AI200/AI250 Roadmap; HUMAIN 200MW Ramp; Qualcomm to Become Largest Automotive Silicon Company; Targets $3B Datacenter Revenue FY27, $35B by FY31 https://finance.yahoo.com/markets/stocks/articles/qualcomm-investor-day-detail-data-163247063.html  OpenAI Begins Vertical Integration — First Custom Inference Chip "Jalapeño" Unveiled With Broadcom June 24 (Hock Tan: As Good as Blackwell + TPU; ~50% Cost Savings; Late-2026 Microsoft Deployment, 10GW Multi-Gen Roadmap); Daybreak Cyber Stack (June 22) Confirms the Platform Shift https://x.com/OpenAI/status/2069770172802773292  Frontier AI Labs Are Now Financing Their Own Supply Chains — Anthropic Locks In Multi-Year Micron HBM/DRAM/SSD Supply + Micron Becomes Series H Investor; Same Pattern as Samsung + SK hynix Pre-Funded Anthropic in May; $965B Post-Money, $47B Revenue Run-Rate, October IPO Target https://investors.micron.com/news-releases/news-release-details/micron-and-anthropic-announce-strategic-agreement-scale-next  SpaceX Signs $6.3B Compute Deal With Reflection AI — $150M/Month July 2026 → End of 2029; NVIDIA GB300 + Colossus 2 Capacity; SpaceX Now Largest Commercial AI Infrastructure Provider With $80B+ Committed Compute Revenue Through 2029 https://finance.yahoo.com/technology/ai/articles/spacex-reportedly-grant-reflection-ai-162749237.html  The Sovereign AI Stack Lands — Japan's Sakana Ships Fugu + Fugu Ultra Multi-Agent System (June 22) That Beats Opus 4.8, GPT-5.5, and Gemini 3.1 Pro on 10 of 11 Benchmarks; Designed Around US Export-Control Risk; Completes the Three-Bloc Sovereign-AI Map With Mistral Compute (Europe) + DeepSeek $7.4B (China) https://www.datacamp.com/blog/sakana-fugu  The Flip Is the Era of Memory as a Commodity Over? FOR: Memory is now strategic AI infrastructure with multi-year supply lock-ins. The cycle dynamics that defined the last 30 years no longer apply. https://www.benzinga.com/markets/tech/26/06/60062500/micron-earnings-could-echo-nvidias-2023-moment-says-futurum-ceo  AGAINST: Memory is cyclical and priced for perfection. This print is either step change or top of the cycle, and the second one is more likely. https://www.cnbc.com/2026/06/25/apple-macbook-ipad-price-hike-memory.html Bulls & Bears NVIDIA (NVDA) $25B Bond Sale Anchors the AI Debt-Finance Boom — First Bond Offering Since 2021; Joins Alphabet $80B, Amazon $27.5B, Meta $30B, Oracle Stack; Dan: "Locking In Cheap Capital While It Can" https://finance.yahoo.com/technology/ai/articles/nvidia-record-us-25-billion-131039687.html  Apple (AAPL) Falls −5%+ Thursday June 25 on Confirmed MacBook + iPad Price Hikes — Tim Cook RAM "Unsustainable" Comment Lands as Real Price Action; Apple Hikes Erase Micron-Driven Tech Rally Mid-Session; Memory Beneficiaries (SanDisk, Micron) Surge; Analysts "Mostly Nonplussed" https://tickerspark.ai/market/apple-inc-aapl-drops-5-3-as-price-hikes-spook-investors-1782399950638  Micron (MU) Q3 FY26 ACTUALS — Largest Beat in Company History; Revenue $41.46B (+346% YoY) Crushes $35.69B Consensus; Non-GAAP EPS $25.11 (+1,215% YoY) Beats $20.49; Record 84.9% Gross Margin (Higher Than NVIDIA); Q4 Guide $50B Midpoint vs $43B Consensus; Stock +18-19% Overnight to $1,242 https://www.nasdaq.com/articles/nvda-who-micron-blows-doors-q3-earnings-revs  Cerebras Systems (CBRS) Q1 ACTUALS — First Earnings Post-IPO; Revenue $193.4M Nearly Doubled YoY; 2026 Guide $855-$865M Beats $824M; BUT Gross Margins Forecast 38-41% (Down From 45% Q1, Half of NVIDIA + Micron); Stock −20% AH on Margin Compression; Sets Up Inference-Tier Margin Debate https://investors.cerebras.ai/news-releases/news-release-details/cerebras-systems-announces-strong-first-quarter-2026-results  

Announcing the CTP for SpaceX. MahJong Craze gone wild. Goodbye to Alan Greenspan – The Maestro. Have you seen RAM prices? PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - Announcing the CTP for SpaceX - MahJong Craze - Goodbye to Alan Greenspan - The Maestro - Have you seen RAM prices? Markets - Economic Collapse Imminent? - Breathe is narrowing again - chips chips chips are the only play - Spacex coming back down to earth? What is that sucking sound? -- Markets getting weird..... 3% down for NASDAQ 100 today - 8% for SMH and 14% for Memory ETF - Just announced - Alphabet (Google) will replace Verizon in DJIA DEDICATION: Alan Greenspan - Died Monday at age 100 Google Enters DJIA - High priced shares - Moves tech to 22% of DJIA from 17% or so - very meaningful move - Every $1 move for Google = $7 move on DJIA - Tech:  S&P 500 (~30%+), Nasdaq (~50%+) Computer Pricing - What as $2,000 a year ago for a nice desktop is not like $4,000 - Dell not holding pricing quotes - and even if they do, back ordered so prices could go up after order - Will IPOs put more money in the pocket of tech companies to buy gear at any price? Endless - SpaceX recently finalized two massive, multibillion-dollar artificial intelligence contracts: a $6.3 billion computing power agreement with Reflection AI and a $60 billion acquisition of the AI coding startup Cursor. - AI Compute Deal with Reflection AI - - - - The Terms: Reflection AI agreed to pay SpaceXAI $150 million per month from July 2026 through the end of 2029. - - -- - - The Infrastructure: The startup will tap into hardware and GB300 chips housed at SpaceX's Colossus 2 data center in Memphis, Tennessee. More SpaceX - SpaceX shares were as high as $220 post IPO. - Sharea ahve been down over the past 3 days. - Most that got in POST IPO probably bought in at about $162-$165 - Newsline: SpaceX shares slipped for a third straight day, shedding hundreds of billions of dollars in market value, after the company said it is selling investment-grade bonds for the first time. - The stock fell 16% Monday to close at $154.60, the lowest level since the company's first day of trading, pushing its three-day loss to 23% and erasing over $600 billion in value over that period. - SpaceX is seeking to raise at least $20 billion from the first bond offering to fund its artificial-intelligence ambitions. Missed Opportunity - Short the Mattress companies he said...... ----- Got squeezed out....Never to return Swing and a Miss Maybe Because this can happen... - Shares of Getty Images Holdings Inc. soared as much as 145% on Monday after it announced a licensing deal with OpenAI. - Getty said that images from its library will appear in the search and discovery features of ChatGPT, marking a key reversal for the firm. - The partnership with OpenAI could improve “licensing optics” and shift the narrative on the stock, according to analyst Mark Zgutowicz. - Getty shares were up 118% to $1.32 as of 12:44 p.m. in New York, putting them on track for the best session since July 2022. The stock had fallen about 55% this year to close at 61 cents on Thursday before the Juneteenth holiday weekend began. KOREA - SK Hynix - New #1 in South Korea: SK Hynix surpassed Samsung Electronics on Monday to become the country's most valuable listed company. - Remarkable turnaround: A striking reversal for a chipmaker that nearly collapsed under heavy debt roughly two decades ago. (CYCLES) - AI memory leader: Now the dominant supplier of high-bandwidth memory (HBM) chips powering AI systems. - Marquee customers: Key buyers include Nvidia (NVDA) and Alphabet's Google (GOOGL). - Massive 2026 rally: Shares are up more than 340% year-to-date, fueled by the global AI boom. - Market cap milestone: Valuation now exceeds both Samsung and Micron (MU). Markets Get Chopped - Questions being asked about if AI spend boom producing fast enough return - Back to earth on valuation scare - (all of a sudden?) - KOSPI down 11% - Chips getting hit - 12% for Memory ETF - MU down 9%, Intel 4%, ASML 7% RAM Prices... - Looking at some additional RAM today for some office computers .... --- ARE THEY KIDDING? RAM Prices Imminent Collapse???? - President Donald Trump said the prospect of global economic collapse was a big reason he signed an interim peace deal with Iran. - According to sources, the deal reopened the Strait of Hormuz and set in motion waivers for sanctions on Iran's oil sales to the international market, with the effect being an immediate drop in oil prices and a rise in US stocks. - The agreement has been seen as skewed in Iran's favor, giving the country broad gains before the next round of talks, and has prompted pushback and anger from Republican lawmakers. - MOU signed lat Wednesday - also now more waivers of sanctions on sale of Iranian oil - 60 day reprieve. China - Weak economic conditions - H Shares about to enter bear market - Hong Kong - Close to a technical bear market, dragged down by weak domestic consumption, a struggling property sector, and an exodus of funds fleeing "old tech" for AI plays elsewhere in Asia. - A-shares are listed in mainland China (Shanghai/Shenzhen) and primarily target domestic investors. H-shares are listed in Hong Kong and are freely available to international investors More China - Retail sales declined for the first time since December 2022, dropping 0.6% from a year earlier. - China's urban fixed-asset investment contracted 4.1% as of end-May, dragged by real estate and manufacturing. - Manufacturing fixed-asset investment contracted for the first time since December 2020. - Industrial output was the lone bright spot, rebounding from April's near three-year low. - The national unemployment rate fell to 5.1% in May, compared with 5.2% in April. Marrrr Jonggg - Mahjong can be highly addictive due to its rewarding blend of strategy, luck, and social interaction. The rapid tile-drawing, need for pattern recognition, and "just one more round" mentality trigger dopamine releases. If compulsive play disrupts your finances or daily life, it can become a behavioral addiction requiring intervention. - Tactile and Auditory Appeal: Many users on community forums like Reddit agree that the physical weight, texture, and distinct clinking sound of shuffling tiles provide soothing, sensory satisfaction. - There has been a 70% surge in mahjong content on TikTok in the past year - Yelp recently named the Chinese tile game a top trend of 2026, noting that searches for mahjong clubs surged 4,467% year over year for the period from September 2024 to August 2025 and that searches for mahjong lessons rose 819%. Alphabet - WHAT>????*&*^ - Alphabet shares slid 7%, on track for the search giant's worst day in a year. - Alphabet's Google has seen consecutive high-profile researchers leave in the last several days. - The company also has exposure to the market's concerns around commoditized AI and ballooning capital expenditures. - The share slide also came on the heels of a Sunday Wall Street Journal interview with Microsoft CEO Satya Nadella, who called for less dependence on “AI Giants” and said the AI market was commoditized. Back to Oracle - Oracle reduced workforce by 21,000 employees over past twelve months. - Cuts broader than previously disclosed, driven by artificial intelligence adoption. - Global headcount fell from 162,000 to 141,000 full-time employees year-over-year. - Workforce reductions generated $1.8 billion in restructuring costs, company reported. - Company warned AI deployment may continue resulting in workforce reductions. NVDA - Underperforming - Nvidia shares slipping recently despite remaining up about 12% in 2026. - Stock down roughly 3% past month, underperforming semiconductor peers. - SMH ETF surged 84% year-to-date, gaining 15% last month. - Traders predict Nvidia chip pricing power is beginning to decline. - Wall Street focus shifting toward memory and infrastructure AI buildout. - Micron and Sandisk shares jumped nearly 60% over past month. Gloom and Doom - JCD sent interesting take from Chris Bloomstran - Traditionally asset light companies with all sorts of revenue, high margins now.... ---- Converting into asset heavy with no real understanding of what the profitability or even revue will be in the future ----- Here are the highlights of his commentary we can explre: ------------AI buildout shifting markets from asset-light toward capital-intensive infrastructure cycle - Hyperscaler capex surge reflects move into heavy, long-duration asset base - Massive capital requirements challenge economics versus prior asset-light models - Depreciation burden rising sharply as infrastructure scales across AI ecosystem - Returns depend on utilization of expensive, long-lived physical compute assets - Asset-heavy cycles historically lead to overbuild, weak returns, eventual consolidation - Infrastructure spending absorbing nearly all operating cash flow for hyperscalers - Off-balance-sheet financing masking true scale of capital intensity shift - AI economics hinge more on physical capacity than software-driven scalability - Echoes of past asset-heavy booms with eventual oversupply and value destruction Amazon Day - Today - June 26th - US consumers will spend $26.3 billion online at Amazon and other retailers during the four-day sale, up 9% from last year's event in July, according to Adobe Inc. - About 201 million Amazon shoppers in the US were Prime subscribers as of March, up about 3% from a year earlier - Amazon will capture about 60% of all US online spending during Prime Day, its highest market share since 2019, according to estimates from EMarketer Inc. Chevron and Microsoft - Chevron Corp signed 20-year deal with Microsoft for data center power. - Agreement supplies natural-gas fired generation for massive West Texas facility. - Project Kilby expected online 2028, ramping to 2.67 gigawatts. - Full output enough to power more than 530,000 Texas homes. - Chevron partnering Engine No. 1, final investment decision planned later. - Deal follows prior reports of exclusive long-term power negotiations. More Oil News - Drill baby Drill - Interior Department cutting federal drilling bonds by 95% to spur exploration. - Required bond drops from $500,000 to $25,000 for leases. - Bonds ensure cleanup costs don't fall on taxpayers if wells abandoned. - Policy change aims to encourage more oil and gas development. - Proposal subject to 60-day public comment after Federal Register publication. FedEx Earnings - FedEx posted strong fiscal fourth-quarter earnings on Tuesday in the company's last quarter that included the freight business before its spin off. - FedEx Freight spun off into a separate publicly traded company on June 1. - The company said it saw a 3% year-over-year increase in domestic volume. - Stock down 6% A/H   Love the Show? Then how about a Donation? PayPal.Donation.Button({ env:'production', hosted_button_id:'JJJHP2GDEJC7J', image: { src:'https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif', alt:'Donate with PayPal button', title:'PayPal - The safer, easier way to pay online!', } }).render('#donate-button'); ANNOUNCING the THE CLOSEST TO THE PIN for SpaceX (SPCX) Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

In this episode of Tank Talks, recorded live at the Global Startups Conference, Matt Cohen and John Ruffolo take the stage for a wide-ranging conversation on the future of Canada's innovation economy, the AI infrastructure race, and why this moment feels different, even if John still refuses to fully believe “this time is different.” The conversation opens with SpaceX's historic IPO, massive valuation hype, and the question of whether public market demand can support a new wave of AI and frontier tech giants like OpenAI and Anthropic.Matt and John then dig into one of the biggest strategic questions facing founders today: should startups build on top of frontier AI models, or will those platforms eventually come for their margins? John draws a sharp comparison to Hootsuite's dependence on social media APIs, warning founders not to build businesses where a monopolistic platform can eventually “come calling.” From LLM unit economics and inference costs to local models, edge compute, AI sovereignty, and Canada's weak position in the full AI stack, this episode breaks down why real moats may come from deep tech, defense, energy, chips, space infrastructure, and hard-to-build businesses.The discussion also tackles Canada's AI strategy, the tension between innovation and regulation, the rise of dual-use defense startups, the shortage of domestic growth capital, and whether Canada is becoming a farm team for U.S. acquirers. John and Matt close with a candid look at family offices, immigrant founders, Canadian ambition, and what actually separates fundable founders from the noise: purpose, focus, and the ability to build something hard when everyone else is chasing the latest shiny object.SpaceX's IPO and the return of the hype machine (02:48)Matt and John open with the massive SpaceX IPO, its soaring valuation, and whether the market is being driven by fundamentals or pure scarcity-fueled hype. John argues that discounted cash flows still matter, even when investors are caught up in the next great frontier tech story.Satya Nadella's warning to AI founders (05:56)Matt brings up Satya Nadella's warning about relying too heavily on frontier models. The discussion explores why businesses built on top of OpenAI, Anthropic, or other LLM platforms may eventually face direct competition from the very infrastructure they depend on.Canada's AI strategy: long overdue, but too unfocused? (16:14)Matt and John assess the government's AI strategy and the promise that Canadian AI adoption could add massive GDP growth. John says the strategy contains useful objectives, but risks becoming a laundry list without a clear answer to the question: which pedal are we actually pressing?Building trust in AI without creating regulatory capture (21:46)The audience asks how Canada can build trust in AI adoption. John argues for clear guardrails, but warns that large AI players may eventually welcome heavy regulation because it protects incumbents and locks out smaller competitors.Defense tech is hot again, but not every startup is real (25:19)Matt and John discuss the surge of interest in dual-use defense technology. John warns that when government money appears, everyone suddenly claims to be a defense company, making it harder to separate serious builders from PowerPoint tourists.Is building in Canada patriotic or financially irrational? (33:19)Matt asks the blunt question: in 2026, is staying in Canada a patriotic endeavor or a financial mistake? John argues Canada has the talent, ecosystem, and raw materials, but lacks confidence and ambition at the capital layer.Why Canada needs real growth capital, not just early-stage funding (37:34)John explains why he created Mavericks to address the gap in Canadian growth equity. The issue is not founder ambition, but the lack of domestic capital willing to write meaningful checks once companies need to scale past the early stage.Family offices, education gaps, and Canada's missing innovation capital (43:56)Matt explains why many Canadian family offices are still learning how venture and startup investing work. Unlike real estate or private equity, venture requires patience, a tolerance for the J curve, and a different understanding of risk and return.Canada's AI edge may be hiding in resources, minerals, and chip substrates (49:43)The episode closes with a discussion of Canada's possible edge in AI infrastructure through natural gas, rare earth materials, zinc byproducts, indium phosphide, and semiconductor supply chains. Matt and John argue that Canada's issue is not a lack of resources, but a lack of permission, capital, and long-term conviction to build around them.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Anthropic's most powerful models are still offline, and the U.S. government now wants a guarantee no lab can give. Paul Roetzer and Mike Kaput unpack the ongoing export-control standoff, the Lutnick letter, and what it means for the models expected this week, then turn to Satya Nadella's "future of the firm" essay, the unsolved mess of AI pricing and usage limits, a wave of lab talent shakeups including Noam Shazeer's move to OpenAI, the G7 AI summit, Midjourney's leap into medical scanning, and research showing AI can out-persuade expert humans. Show Notes: Access the show notes and show links here AI-Pulse Survey: Fill out this week's AI-Pulse Survey here. Timestamps: 00:00:00 — Intro 00:04:44 — Anthropic vs. the White House 00:22:37 — Microsoft CEO on the Future of the Firm 00:34:37 — AI Pricing and Usage Strategy 00:56:04 — Noam Shazeer Joins OpenAI (and Other Major AI Hiring Updates) 01:04:57 — Trump's G7 AI Push 01:09:33 — Midjourney Launches a Medical Division 01:12:59 — AI Can Now Out-Persuade Expert Humans 01:17:00 — AI Use Case Spotlight 01:22:12 — AI Product and Funding Updates This week's episode is brought to you by SiteImprove. AI search is changing what it means to be discoverable. Siteimprove is the Agentic Content Intelligence Platform marketing teams use to track, optimize, and prove performance across both traditional and AI-driven search. From AEO visibility to content quality, Siteimprove helps you stay ahead of the shift.  Start with a free AEO check at siteimprove.com/aipod. Visit our website Receive our weekly newsletter Join our community: Slack Community LinkedIn Twitter Instagram Facebook YouTube Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

The west's big security agencies issue a sincere warning on AI risk acceleration, plus Microsoft's Satya Nadella wants to change the tone on AI.Starring Tom Merritt and Robb Dunewood.Show notes can be found here. Hosted on Acast. See acast.com/privacy for more information.

Carl Quintanilla, David Faber and Sara Eisen led off the show with market reaction to positive comments by Vice President Vance about the U.S.-Iran negotiations. AI in the spotlight: Chevron announced it signed a 20-year agreement with Microsoft to provide power for the tech giant's massive West Texas data center project. The anchors remembered the influential former Fed chairman Alan Greenspan, who died Monday at the age of 100. Also in focus: President Trump's comments on Anthropic and national security, Microsoft CEO Satya Nadella's message for companies leading the AI boom, SpaceX shares on track for a three-day losing streak, AbbVie agrees to buy immunology drugmaker Apogee Therapeutics for $10.9 billion in cash. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The 7 Principles of Successful Partnering in the Age of AI Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/Check Out UPX:https://theultimatepartner.com/experience/ In this engaging session, Vince Menzione reflects on his extensive career transitioning from direct enterprise sales to building massive channel ecosystems, while unveiling the seven core operating principles essential for modern partnering. Highlighting tectonic industry shifts—from the PC and Cloud eras to the current AI revolution—Vince explains how traditional playbooks are becoming obsolete and why adopting a growth mindset, modeled by leaders like Satya Nadella, is critical for survival. He delves into the rising importance of hyperscaler marketplaces and co-selling, urging leaders to cultivate adaptability (AQ), emotional intelligence (EQ), and mutual trust to thrive in this rapidly changing tech landscape. https://youtu.be/5n8dqiamnmE Key Takeaways Traditional industry playbooks are outdated almost immediately due to the rapid acceleration of AI and market changes. Implementing a “growth mindset” is a foundational operating principle that can transform corporate culture and drive massive valuation increases. Executive commitment and clarity of vision are mandatory for aligning an entire organization around successful partnering. Building a strong brand story and maintaining a maniacal focus on OKRs turns strategic vision into executed results. The technology landscape has experienced massive tectonic shifts from the PC era to the Cloud, Mobile, and now AI, requiring high adaptability (AQ). Mutual trust remains the non-negotiable foundation for any successful professional relationship or partnership. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Vince Menzione, growth mindset, Satya Nadella, channel building, tech ecosystem, tectonic shifts, AI revolution, co-selling strategies, hyperscaler marketplaces, organizational alignment, executive commitment, OKRs execution, AQ strategy, mutual trust, B2B technology Transcript [00:00:00] Vince Menzione: Because I think we’re all paralyzed by AI and all the changes that are going on in our world, and playbooks are no longer good because they’re outdated the week after they come out. [00:00:12] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. What a list. Oh my gosh. I gotta tell you, I was just going back this morning and, and looking to see first of all the number, the sheer number is incredible. [00:00:36] Vince Menzione: But look at, look at all these top executives. These are, these are like market movers. The game changers. These are people that are doing more in our world, in our ecosystem than most others. And we are very fortunate to have the representation from these organizations. From these leaders in the room, and we try to curate an event that is more than a, a sales pitch. [00:01:00] Vince Menzione: We’re, in fact, we, we’re not a sales pitch. We’re all about, you know, helping you achieve more. And we try to frame that around operating principles. So, uh, a little bit of a roadmap lately. I mean, this started out like how did we get here in like, maybe five spots along the way. But, uh, for those of you who don’t know me and my background, and I’ve had an incredible career, I’ve been very blessed. [00:01:20] Vince Menzione: I did a startup that we grew from 6 million to 125 million. Went public on the Toronto Exchange. I’m still friends with the CEO, by the way. Helped, helped him grow and exit that company. Uh, I then followed one of the leaders there to go do a turnaround with Golden Gate Capital, and we took that and that’s where I built my first channel. [00:01:37] Vince Menzione: I went from doing enterprise sales as a direct seller, direct sales leader, VP to then going to building a channel. During nine 11, uh, this company was selling rugged notebook computers. Our biggest competitor was not a US company, and I spent a lot of time on Capitol Hill. I met with several congressmen and senators at a time when people did that, and they talked to each other. [00:01:58] Vince Menzione: And, uh, I built a channel. I got its a GSA schedule, and I understood. So I understood intuitively, even from that point in my career, how to move, how to shift from direct selling to building a channel, building a business around that. We became the growth engine of the company. One of my partners was one of the largest defense contractors, general Dynamics. [00:02:19] Vince Menzione: They had the big contract if you were selling to the US Army. And I knocked down the door basically and said, you got a partner with us. And that’s how we got the relationship established. And they wound up buying us for like 10 x what Golden Gate Capital had had spun us out for. And then Microsoft recruited me. [00:02:36] Vince Menzione: And for almost 10 years I was the GM of public sector partner strategy. And so I was, I was there and we’ll talk about Satya and other things, but I was there when we started the cloud. I was there when we pivoted the business from the old model and working with OEMs and trying to, to do things a different way to the cloud and co-selling and things like that. [00:02:56] Vince Menzione: And, uh, had a great experience. And then when I left I was like, oh, I’m just gonna go work for another big tech company. I started a podcast. I had a friend who said, you should do a podcast on partnering. You know a lot about this more than you probably think you do. And almost 10 years ago, I started a podcast in a spare bedroom. [00:03:13] Vince Menzione: And you know, it, it was, it built a following and there’s a lot of work, by the way, people, a lot of people do podcasts today. It was a lot of work for those of you. I congratulate anybody doing that. Uh, I went back inside for two years because I felt like I needed to go back into a big corporate environment. [00:03:29] Vince Menzione: And then I left during COVID and I learned a lot being at a big corporation about how hard it was to partner. Like it’s still hard. I don’t know how many people in the room feel this way. I know, I know the numbers are much better and Jay will talk through the numbers, but it’s not easy and a lot of organizations don’t understand it. [00:03:47] Vince Menzione: And that’s what we talk about here and we try to help people to achieve more and how to, how to get that mindset in the right place. But anyway, so. We started, we started doing the podcast after COVID, it took off. We did an event. Uh, there’s actually four of the five people that did partner. We called it Partner Mastermind. [00:04:06] Vince Menzione: We did an event about four years ago, uh, separately. And that led to Ultimate Partner. And it’s a long, the long history in the last four years of 10 events, like it’s been an incredible blast. And I want to thank each of you for being along this, this incredible ride with us as we continue to grow and expand. [00:04:24] Vince Menzione: We’ve been doubling every year for the last four years and um, I feel very blessed to be part of this. So I did wanna spend a minute with you on this. I don’t like the drain this slide, but I do wanna identify what I believe are seven operating principles of what makes successful partnering. And you know, you might say there’s eight, you might say there are other things I think about principles as opposed to tactics. [00:04:50] Vince Menzione: Tactics are transactional. They’re temporary and a point in time, and it’s how you respond and react to a situation. Principles are things you take with you, and that’s what we hope to do at Ultimate Partner. Take those things with you and then, then apply some of the things to the tactics that we need to have. [00:05:06] Vince Menzione: And so we talk about growth mindset. Uh, you know, depending on where you stand about Microsoft, these days, when this guy came in, stock was $36 a share. Okay. It’s in the four hundreds now. It was up to over 500 not long ago. He applied a different mindset. The first three things he did, Le got a copy of Carol Dweck’s book about mindset. [00:05:28] Vince Menzione: Growth mindset versus fixed mindset. Uh, he brought in Dr. Michael Vet, who’s a leading sports psychologist, like in, in the industry, who was the Seattle Seahawks sports psychologist. Mike’s been a podcast guest of mine. I’ve been to his studio. Um, and then he, we, he, he changed, he, he brought down, he took down the walls of the way Microsoft operated because leaders fought with each other. [00:05:51] Vince Menzione: They competed with each other for resources, for monetization, for everything. And he changed the mindset. Nobody’s a perfect CEO, but if I was to say to you who I think the best CEO of the last 10 years were, I’d give it to Saja Nadella, but it’s about mindset. It’s about changing or having the right mindset and applying that growth mindset to a successful partner. [00:06:12] Vince Menzione: Executive commitment, I talked about that. Other organizational will go nameless, but if you don’t, you can have the CEO down to the selling floor. Everyone needs to speak partnering, like in order to get it right in an organization. The whole company, the resources, the investments, the alignment, all has to align around partnering. [00:06:32] Vince Menzione: Executive commitment is incredible. Tony Saan took a small MSP to a half a billion dollar exit, took them to go, uh, Google Partner of the Year, seven straight years in a row. I think they’re eight this year. Uh, but Tony’s a good friend of mine. He is also been a guest on the podcast and, uh, somebody I’ve admired and worked with. [00:06:50] Vince Menzione: This is Dr. Michael Dravet. We talk about clarity, like once you get your mindset, once you get executive commitment, you then need to determine like how, what’s the vision? How do we drive success together? You need to turn, you need to know internally how to go do that. Then you lock arms with another organization and then you apply it to that partnership. [00:07:10] Vince Menzione: So that’s incredibly critical. Then, then you gotta do everything right? Like I always kid around about my days at Microsoft, we’d have these incredible meetings with leaders. They’d come meet with us at partner conference. I would literally go back to back for several days in the room. Slide deck after slide deck. [00:07:27] Vince Menzione: We’re high fiving at the end. [00:07:29] Vince Menzione: We’re gonna go do it [00:07:31] Vince Menzione: six months later. Crickets. Nothing happens, right? This happens a lot in partnering. Unfortunately, like we, we set up the right situation. We line everybody. We’re gonna go execute, we’re gonna drive results. You have to apply maniacal, focus, OKRs, everything to everything you do. [00:07:48] Vince Menzione: You need to apply. And by the way, you’re gonna hear from a lot of leaders here that do this type of work. So this is incredibly, uh, critical to success, brand and story. Like I wanna work with Microsoft. There’s gonna be probably 40 plus Microsoft leaders in the room, some of ’em sitting here and around the room. [00:08:06] Vince Menzione: How do you do that? Right? This is Ducks Raymond S. Good friend of mine at Point. I knew at point when they were just starting out. Scott Sackett is here. He’ll be up on stage. Uh, this man was expert on brand and story. Learn from people that are successful, how to be successful yourself, if you wanna be a top partner, if you wanna grow your business, whether you’re working with Microsoft, Google, Amazon, or any of the other partners in this room. [00:08:30] Vince Menzione: You need to be very clear about your brand, articulate it well, and drive a story against that. And that’s really super critical for success. And then once we do all those things, we start driving a flywheel of success. Aaron Feiger and some of the other people in the room, Reese Barry, are gonna be talking about how they do that. [00:08:47] Vince Menzione: They will help these organizations be successful. Pick putting that stake in the ground and driving it. And then what happens is after you drive this incredible success, what does my partner do? My tech giant, the company I’ve been working with, they go change everything. The market changes, the dynamics change. [00:09:05] Vince Menzione: This thing in November of 2022 called AI Happens, Chad, GBT hits the market. How do I respond and react to that? I need to be adaptable. I need to drive an AQ strategy on top of my EQ and iq, and we’ll talk more about that. So these are the operating principles, and we lay it out as a, as a diagram. And by the way, you see mutual trust. [00:09:26] Vince Menzione: Trust has to be in every room without trust, you have no partnerships, without trust, you have no business success. Like you can get buy in business, you can get buy in life, but trust is foundational. And I was very blessed to have that like grain ingrained in me as a young boy. Uh, so that’s our, that’s our operating principles. [00:09:48] Vince Menzione: Um, I’m working on a book right now. It’s almost done though. We’re, we’re talk, we’ll talk about that more, but that’s, that’ll be in the book. Um, and then we’ve been talking about tectonic shifts and I don’t know who said it first, Jay or, or me, but I know who you said it in the studio several years ago. [00:10:04] Vince Menzione: Jay’s been in our, our Boca studio many, many times. But we’ve been talking about tectonic shifts and Oh my gosh, right? So think about, I want everybody to think about this for a second. If you’ve been around tech for a while. We’ve gone through several, like these 10 year phases, the PC era, the cloud era, the well, the cloud. [00:10:23] Vince Menzione: We had client server, pc, client server, we had cloud, we had mobile, and now we hit ai. Those eras all took a period of time, right? They didn’t happen overnight. Like there was a trend like five, six years, seven years, maybe eight years, and then COVID happened, and I believe that COVID was the acceleration point because. [00:10:44] Vince Menzione: We were all forced to do things we didn’t do before. People went out and bought PCs that didn’t have them. Kids had to learn from home. Healthcare was administered tele telehealth, we didn’t do telehealth before. We had like 5% of the population to telehealth before that, uh, our work environment changed, right? [00:11:02] Vince Menzione: We were doing Zoom calls or teams calls back when I was at Microsoft Days, but the world started doing it. Our life started to change. That’s why being in the room places like this is so important. And so that really has accelerated everything. And this, you know, all these things have been accelerating over time and these are significant shifts. [00:11:22] Vince Menzione: We have the three leaders of the three marketplace organizations coming on stage here. Uh, the three hyperscalers, because marketplace went from, we were talking about it like, this is really cool. You need to go do it. A few years ago. So Microsoft lowering the rates on it, and then everything changed and then everybody started accelerating and it became the fungible token. [00:11:43] Vince Menzione: ’cause we used to, we used to partner, we used to take spreadsheets and put ’em up against each other and try to figure out deals and fax copies of deals that came in and say, we want credit for this one. And then Marketplace became a way to create a fun non fungible token. And really drive your success. [00:11:59] Vince Menzione: And so we have all the leaders that are running marketplaces in this room, by the way. So this is gonna be like the most incredible rich conversation. Co-selling. Co-selling is a, you know, a non-starter day. You have to co-sell it. People, we used to do vendor channel, which means I had somebody selling my stuff that’s not happening anymore. [00:12:19] Vince Menzione: And Jay, we’ll talk about the seven seats at the table. But this is all, these are all the things that have been changing. And of course, ai. I think that we are sitting here and I, I, I’ll share, and I’m stressing this, like this is, you need to be in this room because you’re gonna hear from leaders about what the next steps are. [00:12:35] Vince Menzione: ’cause I think we’re all paralyzed by AI and all the changes that are going on in our world and playbooks are no longer good because they’re outdated the week after they come out. So I need to, I need to follow this in real time. I think this is super important that you do, and it’s why we exist and it’s why this time is like no other. [00:12:53] Vince Menzione: I think, you know, we said maybe a generation, maybe it’s a lifetime in terms of the shifts that we’re seeing. So I, I kind of started here and I wanted to end here, uh, just because the light doesn’t go out. That’s what it’s all about. And this is it. This is it for me, right? This is my, my last run. I’m not gonna go work for a company after this. [00:13:16] Vince Menzione: I’m not gonna go into become a consultant. And I want this truly to be like special. And I want you to all feel like you’re part, you are part of it, and however much you wanna lean in and be part of it in the future, we want to grow this in the right way. I, I feel that we have an a unique opportunity. [00:13:34] Vince Menzione: Because we’re not a vendor, we’re not selling anything. I feel like we’re a platform. We’re that we’re that lighthouse and others can come in that are experts and I feel like more and more of ’em are showing up. And you know, the PDG guys did a great job today and others in the room and people that have been friends and supporting us for for years as on that sponsor slide. [00:13:56] Vince Menzione: And so we just want to continued this journey with each of you. Um, and so I want your feedback on what we’re doing. I want, I love your support. I love your passion. I love the fact that you’re still here in the room talking with, with or being here, listening to me today. Um, this is, that lighthouse is, you can see these pictures. [00:14:15] Vince Menzione: These are all family photos. Um, we go to that lighthouse, not because it’s a lighthouse, but uh, it happens to be like a landmark in our town. And, uh, it’s kind of cool. And actually the re Joe Namath has owns the restaurant across from the lighthouse, so we, we’ve got to see him a couple of times, which is kind of cool. [00:14:34] Vince Menzione: But I, I, I, I was posting this lighthouse when I started the podcast. And I was, yeah. ’cause that’s where I live and it’s my hometown. And I think about Dakota Rings and I think about other things. But, um, this is what matters. This is what matters is helping others. And we all are gonna need each other in this world because AI is gonna change our lives. [00:15:00] Vince Menzione: And dramatically it’s, I I think this is a once in a lifetime thing. But I think having people that you trust and being in the room with others where you can learn and grow and adapt, adaptability is so important. So, um, analog is the new digital as my, my good friend Gary V now says. And I think there’s this huge opportunity around what we do as ultimate partner to help everybody reach their pinnacle to everybody. [00:15:26] Vince Menzione: Be the ultimate partner. And I want to thank you for coming. I want your, thank you for your support, friendship, love. And, uh, you’re just an incredible group. Thank you. [00:15:41] Vince Menzione: Until next time, we’ll see you in person. Hopefully at our next event.

Oil prices are falling as the U.S. and Iran continue talks, but the Strait of Hormuz remains the key pressure point for energy markets, gas prices, and the broader economy.Mike Armstrong and Paul Lane break down the latest signs of tanker traffic returning through the Strait of Hormuz, why Iran has a strong economic incentive to keep oil moving, and why energy markets remain difficult to predict even as crude prices fall. They also discuss the legacy of former Fed Chair Alan Greenspan, the risks facing a highly concentrated stock market, why the AI spending boom could become a warning sign for investors, Satya Nadella's comments about AI giants, and how rising memory chip costs are starting to push up prices for consumer technology.

This week, John, Niki, and Lotus go very long on this week's seismic Xbox news, including the numerous studio closures on the table, key departures, and Microsoft CEO's Satya Nadella's curious take on why their gaming arm is struggling.00:00:00 Intro & Lotus' Big Saturday00:11:10 South of Midnight developer Compulsion Games is the unsurprising yet disappointing first studio to confirm possible closure or spinoff00:17:00 A long, great conversation about the games media's role in reporting on these layoffs and closures00:28:00 How Xbox got here (mostly)00:48:52 Satya Nadella seems to be confused about how Xbox got into this jam00:54:00 That Senua sequel was only announced to help fuel a possible sale of Ninja Theory01:02:14 The weight of working in corporate-owned game development right now01:06:34 Key personnel leaving Xbox01:07:56 What's the plan moving forward for Xbox?01:16:10 EA introducing new in-game advertising platform01:25:57 UK government is banning teens from social media; plan will not work01:33:10 Fuck's Quest 2 lacks consistent humor and mechanical clarity01:37:07 Niki played the first two Sonic games on Genesis (skip this part, Sonic fans!!!)01:45:10 John decided to give Ocarina of Time another try01:49:10 Our latest (possibly final) thoughts on 007 First Light02:02:47 Hive Questions02:18:12 OutroThanks for listening!Please leave us a review! We'll read it on the show and it helps us out a lot.VGBees is ad-free, AI-free, and completely supported by you! https://vgbees.com/joinVGBees is a weekly games media podcast hosted by Niki, John, and Lotus.

Xbox is going broke. Microsoft CEO Satya Nadella complains that Xbox is basically subsidized by the rest of Microsoft, and that this will be ending. This includes reports of more studio closures and layoffs. What he didn't say was what the END of Xbox actually looks like. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Xbox #VideoGames #Gaming #Microsoft #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Group Chat crew breaks down the biggest SpaceX IPO on Wall Street, the AI shutdown scare gripping the tech world, and the World Cup mania taking over America. Business news, markets, and culture from one of the longest-running business podcasts. This week on Group Chat News: SpaceX goes public at a $2.1 trillion valuation: We break down the IPO frenzy, the OG Starbase investors, and Elon's path to $1 trillion by 2030 The El Segundo wealth boom: how space money is about to reshape the South Bay World Cup mania hits America, Team USA run, and the electric atmosphere in LA A Middle East peace deal looks imminent: what it means for summer gas prices and inflation The Stanford walkout: graduates, free speech, and real-world consequences in tech and finance The Knicks' historic win, Wembanyama, and how you're supposed to carry a loss The Fable 5 AI shutdown scare, government involvement, and Satya Nadella's warning to the industry Should the US government invest in AI companies? The guys debate the trillion dollar question Group Chat News covers business, markets, tech, sports, and culture every week. If you like All-In, My First Million, and business news that actually keeps up with the week, follow and subscribe.   ⭐ Enjoying the show? Leave us a rating, it helps more than you'd think. Hosted by Dee Murthy, Anand Murthy, and Chris "Drama" Pfaff 

This week and next, we're bringing you recordings from our second-ever live taping in San Francisco. First, we sit down with Microsoft's chief executive, Satya Nadella, to hear what he's maxing out his A.I. tokens on, why he's skeptical that software developers will ever be fully replaced, and how he's hoping to create a new business model for Xbox. Then, Phil Mohun tells us what it has been like to watch people in the Bay Area interact with two robot dogs that wear the faces of Elon Musk and Mark Zuckerberg. And finally, we talk with the longtime privacy defender Cindy Cohn about where things stand in the fight to protect internet users from digital surveillance by Big Tech and the government. Guests: Satya Nadella, chairman and chief executive of Microsoft. Phil Mohun, executive director of Node. Cindy Cohn, former executive director of the Electronic Frontier Foundation and author of “Privacy's Defender: My Thirty-Year Fight Against Digital Surveillance.” Additional Reading: Microsoft C.E.O. Satya Nadella Says, ‘Everyone Is a Stakeholder' in A.I. Node presents “Beeple: /Infinite_Loop” We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

When you don't know the answer to a problem at work, what is your immediate reflex? Do you search for a quick answer from an AI chatbot, or do you pick up the phone to ask a colleague? In this episode of Truth, Lies & Work, we dive deep into the hidden cultural cost of digital convenience. While artificial intelligence is incredible for cutting through administrative noise and streamlining corporate tasks, it is quietly automating away the most critical asset your business has: human connection. Our guest today is Sean O'Shea, the brilliant mind behind Craft Your Culture and Locon. Sean spent a fascinating decade working at Microsoft, sandwiched perfectly between the leadership of Steve Ballmer and Satya Nadella. He witnessed firsthand how a radical shift in corporate mindset and the intentional removal of rigid, performance-stifling systems could skyrocket a company's share price from $30 to over $540. Now, through his data-driven work at Locon, Sean is on a mission to measure the "relationship gap" between team members. He breaks down the phenomenon of "messy moments"—those vulnerable, slightly awkward, but entirely essential human interactions that act as the true engine for workplace psychological safety, team learning, and high performance. If you are a business leader, founder, or manager trying to navigate remote-first challenges, return-to-office mandates, or AI integration, this conversation will completely change how you design your team interactions tomorrow morning. Key Takeaways From the Episode The Trap of the Frictionless Workplace: AI onboarding bots and agents are fast, non-judgmental, and always available. However, by relying on them exclusively, employees bypass the vulnerable moments of asking a peer for help—the exact moments where corporate trust is built. The High Cost of the "Relationship Gap": High performance isn't just an aggregate of individual talent. It is directly limited by how well your people actually know each other. Loneliness and disconnection don't just hurt morale; they actively cost businesses billable hours. The 3 Pillars of Accelerated Trust: How do you build genuine, bulletproof workplace relationships when everyone is short on time? Sean reveals the three non-negotiable ingredients: vulnerability, shared emotionally significant experiences, and active, empathetic listening. Overcoming the "Eye of Sauron" Management Style: Reflecting on his time under Steve Ballmer's mid-year review process, Sean highlights how defensive corporate cultures destroy innovation. True leadership requires getting your ego out of the way and letting your team collaborate without you always being in the loop. The 6 Pillars of Team Effectiveness: Sean breaks down the core framework measured by Locon: psychological safety, accountability, connection, learning, clarity, and adaptability. Episode Timestamps 00:00 – Cold Open: Are chatbots silently killing your team's natural human connection? 01:15 – Meet Sean O'Shea: The mission to turn people potential into business performance. 04:20 – What is a "messy moment" and why are modern teams hiding from healthy conflict? 07:30 – The 3 critical elements needed to fast-track real human relationships at work. 10:45 – The AI paradox: Why a small business champion is incredibly worried about the rise of perfect tech. 15:10 – Designing connection: How leaders can architect micro-moments of collaboration instead of boring, packed agendas. 23:15 – The Train Experiment: The fascinating behavioral science proving we are terrible at predicting social interactions. 30:30 – Lessons from Microsoft: The real story behind Satya Nadella's growth mindset revolution. 39:45 – Quantifying wasted time: The data showing how many hours your team loses each month by not collaborating. 45:10 – The story of Locon: Using six-week experimental sprints to give teams true agency over their culture. Connect with Our Guest To learn more about Sean's work, access his data diagnostics, or follow his daily insights on corporate culture, use the links below: Craft Your Culture Website: www.craftyourculture.co.uk Locon Website: www.locon.co.uk Sean O'Shea LinkedIn: https://www.linkedin.com/in/soshea7/ About Truth, Lies & Work Truth, Lies & Work is the award-winning podcast where behavioural science meets workplace culture. Hosted by Al Elliott and Leanne Elliott, a chartered occupational psychologist, we are here to help you simplify the science of work, boost employee engagement, and build high-performing teams. Truth, Lies & Work is a proud part of the HubSpot Podcast Network, the ultimate audio destination for business professionals.

Satya Nadella walked into Microsoft in 2014 when the company was a slow-moving dinosaur. He stopped reacting and started acting. Cloud-first, AI before anyone cared, open source. Microsoft's stock climbed over 1,000%. In this episode of DarrenDaily On-Demand, Darren Hardy uses that turnaround to make a harder point: most leaders are stuck managing the present while someone else is creating tomorrow's advantage. Darren introduces the pace setter framework and the three rules top performers follow to protect time for what actually moves the needle. Get more personal mentoring from Darren each day. Go to DarrenDaily at http://darrendaily.com/join to learn more.

Did you miss everything Microsoft announced at its Build conference?

In this episode, Scott Becker discusses Microsoft's recent rebound, highlighting its recovery from a steep decline, strong AI and Azure performance, and the continued leadership of CEO Satya Nadella.

Gary Rivlin details the dramatic November 2023 firing of Sam Altman by OpenAI's nonprofit board in AI Valley. The board alleged Altman gave "short shrift" to the company's original trust and safety mission in favor of rapid growth. This decision nearly destroyed the $90 billion startup when 700 employees threatened to resign in protest. Microsoft CEO Satya Nadella intervened, offering to hire the entire team to stabilize the company's future. Within five days, Altman was reinstated as CEO, signaling a definitive shift from OpenAI's idealistic roots to a competitive corporate structure. This melodrama highlights the internal tension between safety-focused researchers and executives pushing for market dominance. (7/8)1906 LA L FIESTA DE LOS ANGELES