Podcasts about Colossus

My guest today is Sarah Guo, founder and managing partner of Conviction, the venture firm she built to back AI-native companies from their earliest days.  Sarah has become one of the most sought-after early-stage investors in AI, often the first check into the companies defining the frontier.  In this conversation, we go inside that frontier: what the small group of people actually building AI believe right now, why some of the field's best researchers are wrestling with their own sense of purpose, and how close we are to robots in the home and a genuine acceleration in scientific discovery.  At the center is Sarah's conviction that no single company will own the future of AI, and what that means for founders, investors, and anyone allocating their time and resources in a world moving this fast. Our managing editor Dom Cooke wrote a profile of Sarah for Colossus, "Sarah's Wager," on how she built the firm closest to the AI frontier and why she's now betting against its biggest companies.  Please enjoy this conversation with Sarah Guo. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:16) Investing Without a Backtest (00:03:31) The AI Wager (00:06:16) Building the Best Investment Firm (00:08:37) Finding Non-Obvious AI Opportunities (00:11:00) The Frontier AI Talent Race (00:13:50) Compute as the Constraint (00:19:10) The Future of Robotics (00:22:15) Making Investment Decisions (00:26:13) How Sarah Spends Her Time (00:28:15) Raising a Venture Fund (00:30:49) Lessons From Her Parents (00:34:38) The Case for Open Source AI (00:39:01) Abundant Intelligence Isn't Inevitable (00:40:55) Compute Independence (00:43:14) Debates Inside Conviction (00:45:27) AI's Opportunity in Biology (00:48:58) Why Conviction (00:50:31) Finding Truth and Taking Risk (00:54:16) What Changes in the Next Year (00:56:46) The Kindest Thing

Strap in and check your thrusters, we're looking back at the animated show Star Wars Resistance! Some of our stand out aspects include the visual style, Tam's journey into the First Order, the found family standing together on the Colossus, and more! If you haven't checked out this hidden gem of a show we hope we can inspire you to give it a chance! Hosted on Acast. See acast.com/privacy for more information.

My guest today is Neil Movva, founder of Sail. Sail is building what Neil calls a token factory, an inference company designed for a specific kind of future, one where AI agents run in the background for hours or days at a time rather than answering a human in real time.  In that world, latency matters less and cost matters more, and Neil has built the whole company around driving the cost of a token as low as it can possibly go. What makes this conversation special is that it is one of the most detailed tours I have ever done through the full stack of intelligence, the software, the chips, and the power, and how all three connect.  Along the way we cover the trade-off between speed and cost that lives inside every GPU, his scavenger strategy for buying the chips and power nobody else wants, his contrarian view on Nvidia, and why the premium the frontier labs charge for being three to six months ahead may not last.  Please enjoy my conversation with Neil Movva. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:20) Neil Movva (00:03:22) Building a Token Factory (00:05:32) The Rise of Long-Running Agents (00:08:47) Deep Research and Cybersecurity (00:15:12) The Full Stack of Intelligence (00:20:03) Throughput Versus Latency (00:24:58) The Future of AI Chips (00:33:19) Why Transformers Work (00:36:43) The Future of Data (00:44:05) The Market for AI Chips (00:47:56) Is the AI Boom Different? (00:51:08) Reinventing the Data Center (00:56:43) Scavenging Power (01:01:04) Where Compute Is Most Inefficient (01:07:02) Open Versus Closed Models (01:10:37) A Trillion Tokens a Day (01:12:42) The Contrarian Case on NVIDIA (01:14:38) Advice for AI Hardware Founders

My guest today is Ben Thompson, the founder and author of Stratechery. Ben is one of my favorite business thinkers and I love talking to him about everything happening in markets and technology.  We go through every important company, including OpenAI, Nvidia, Intel, Apple, Microsoft, Google, and Amazon. We also discuss why he thinks it would be dangerous for the United States to win the AI race outright, what container shipping and the railroads of the 1870s tell us about the buildout, and why the binding constraint on all of this may be capital rather than compute.  Please enjoy my conversation with Ben Thompson. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:16) Winning the AI Race With China (00:08:28) Timing, Capital, and the Railroads (00:11:34) Berkshire, Google, and Absolute Profits (00:14:23) Verifiable and Unverifiable Domains (00:20:20) Aggregation Theory in the AI Era (00:22:06) The Real Cost of Inference (00:25:40) Why Consumer AI Needs Advertising (00:30:08) Compute Shortages and Commodity Markets (00:35:46) Memory Cycles and Boom Bust Dynamics (00:42:08) TSMC, Intel, and Where Risk Goes (00:44:51) The Best Setups in Big Tech (00:52:14) The Frontier Model Contenders (00:54:27) Microsoft's IBM Playbook (01:00:45) Meta, Attention, and Advertising (01:07:29) NVIDIA, Commodities, and Power

X-Men ‘97 Season 2 - Ep. 409 To us, our Normies! The mutants are back as X-Men '97 returns for another action-packed season of Marvel's animated revival! After the shocking events of Season 1, the X-Men find themselves scattered, tested, and facing new threats that could change the future of mutantkind forever. On this episode of Normies Like Us, your hosts are breaking down all of X-Men '97 Season 2! We're talking Cyclops, Jean Grey, Wolverine, Storm, Rogue, Gambit, Magneto, Professor X, and all the mutant madness packed into the new season. Which returning characters stole the show? Which new mutants have us reaching for our old action figures? And can Season 2 possibly top the emotional highs of the incredible first season? Fire up the Blackbird, put on your best yellow spandex, and prepare for spoilers as we dive into another uncanny adventure! Previously… on Normies Like Us! Insta @Normies_Like_Us https://www.instagram.com/normies_like_us/ @_j__a___c___o__b_ https://www.instagram.com/_j__a___c___o__b_/ @Mike_Has_Insta https://www.instagram.com/mike_has_insta/ https://letterboxd.com/BabblingBrooksy/ https://letterboxd.com/hobbes72/ https://letterboxd.com/mikejromans/

“Anthropic will be a $3 trillion company, SpaceX $2 trillion, and OpenAI $1 to $1.5 trillion by Q2 of next year.” — Dave McClure Yesterday, Keith Teare and I debated the circularity of the AI economy. Today, two of Silicon Valley's most experienced investors, Dave McClure and Aman Verjee, not only straighten out this supposed “circularity” but also burst the pessimism bubble that envelops so many conversations about AI. Verjee is not only McClure's partner at Practical Venture Capital, but also the author of the newly published A Brief History of Financial Bubbles. According to him, today's AI-stoked economy is not an unusually large bubble. It may not even be a bubble, given that AI revenue — from Anthropic's $70 billion to OpenAI's $50 billion — is real. The irrational exuberance lives elsewhere — in companies “draping themselves in AI magic sauce” and in the “SaaSpocalypse” that is decimating software-as-a-service companies. They are both bullish about our AI future. McClure predicts that by the first half of next year, Anthropic and OpenAI will have joined SpaceX as public companies. Together, these three AI darlings will be worth $7 trillion. That's seven thousand billion reasons to be optimistic about 2027. Five Takeaways •       Not a Bubble — a Repricing. Both partners reject the bubble call, on the numbers: Anthropic at roughly $70 billion in revenue on under two gigawatts of compute, OpenAI at $40–50 billion, SpaceX guiding to $100 billion with more than half from AI — real revenue, increasingly real profits. The froth is specific: companies “draping themselves in AI magic sauce” without the substance, and the SaaSpocalypse — cloud-software companies whose cash flows are suddenly perceived as far less durable as AI encroaches on design, legal, and medical verticals. Michael Burry's warning gets Aman's definitive treatment (“he's called nine of the last two bubbles”), and the Aschenbrenner blowup was leverage — running four-x in volatile chip stocks — not AI: he kept his Anthropic position, is married to Dario's chief of staff, and “will be just fine.”•       The $2 Trillion Filing. The week's news, baked into the episode: Anthropic has filed to go public, with a very intentionally leaked $2 trillion valuation hinging on a $190–200 billion 2028 revenue forecast — which Dave suspects is conservative. Eight months ago, when these two last visited, the show was about Elon and Sam and Dario was the bit player; then came the weeks when decades happen: Anthropic's bet on coding agents — reportedly inspired by watching Cursor — captured the revenue engine of the entire application layer. Aman's sequencing: SpaceX is absorbing $75–85 billion of IPO capital, Anthropic goes next, and if both trade well, 2026 breaks every record for money raised — leaving 2027 for OpenAI at a $100 billion revenue guide. Google, he reminds us, went fourth after Yahoo, Lycos, and Excite: better to do it right than to do it first.•       The Fastest Pivot in Corporate History. Dave's account of SpaceX's transformation: the $250 billion xAI merger (a largely private transaction Elon approved with himself), the acquisition of Cursor that closed Friday, Colossus data centers scaling from two gigawatts toward ten, and compute deals renting capacity to Anthropic and Google — former competitors — all executed in roughly six months. The S-1, with unprecedented forward projections of $300 billion in annual revenue, mentions artificial intelligence over 1,100 times (“I used AI to count it,” Aman admits). The result is an economy Andrew calls incestuous: SpaceX's valuation now rests on Anthropic's progress. On Elon himself, Dave separates the art from the artist — terrific products, dubious politics — and on OpenAI: more board changes than Spinal Tap had drummers, a team still storming and norming, but Sam is savvy and the IPO lands by Q2 next year at $1–1.5 trillion.•       Circularity as Asset Class. The New York Times sees a vulnerability in tech giants funding their own customers; Aman, a former CFO at eBay and Sonos, sees asset-backed finance. His analogy: buying a Corvette with GMAC financing isn't a conspiracy as long as the terms are commercially reasonable — and NVIDIA's $500 billion backstop, syndicated with Goldman Sachs, Apollo, Brookfield, and KKR, brings third-party money that validates the asset. GPUs, he argues, are cars rather than smartphones: financeable over eight to ten years, not obsolete in three. The red flags to watch are rebates and self-dealing on non-commercial terms; the current evidence looks more like aircraft leasing than Enron. Dave's deeper worry isn't the AI economy at all — it's the national deficit, whose interest payments are now the largest single line item in the federal budget.•       The Luddite Summer Meets the Long Boom. Aman's sharpest historical observation: this may be the first technological revolution whose leaders are the doomers — Sam prophesying idleness, Dario predicting half of entry-level white-collar jobs destroyed within five years (already wrong at eighteen months, with no 10–20 percent unemployment in sight). Against the WSJ's jobless-boom and nation-of-Luddites anxieties, the book offers the long view: of ten historical bubbles, the two positive ones — Britain's 1845 railway mania and America's 1997–2000 internet boom — overbuilt, crashed, and left the world a valuable technology. Buy every stock founded in the boom and hold, and you'd have owned NVIDIA, Amazon, Google, and PayPal. The 1970s wiped out four to six million secretarial jobs in a decade; women's participation rose from 52 to 77 percent. And on China, the free-trader's answer: partners in progress — there's more to gain than lose if we do this right. About the Guests Dave McClure and Aman Verjee are the co-founders and managing partners of Practical Venture Capital, a Silicon Valley firm specializing in venture secondaries. Dave founded 500 Startups, invested at Founders Fund, and ran marketing at PayPal; Aman was COO of 500 Startups, led strategy at PayPal and eBay, served as CFO of Sonos and of eBay's North American marketplace — and wrote the first draft of PayPal's S-1. Aman's new book, A Brief History of Financial Bubbles (out this week), is available at bigbubbletrouble.com. References: •       A Brief History of Financial Bubbles by Aman Verjee — ten manias from the tulips to the subprime crash, out this week at bigbubbletrouble.com.•       Reuters on Anthropic's IPO filing — the $2 trillion valuation and the $190–200 billion 2028 revenue forecast it hinges on.•       “The Summer That America Became a Nation of Luddites” and the “jobless boom” — the Wall Street Journal pieces threading this week's episodes.•       The New York Times on tech giants' circular AI economy — the piece that framed yesterday's TWTW debate and today's rebuttal.•       The SpaceX S-1 — forward projections of $300 billion in ann...

Governments are prioritizing data centers at the expense of communities and the planet, but it doesn't have to be this way. Matt Haugen joins Paris Marx to discuss how national strategies are dismantling environmental protections while enriching a handful of megacorporations, and what an alternative agenda could look like.Matt Haugen is Research and Editorial Manager at the Climate and Community Institute.The podcast is made in partnership with The Nation. Production is by Kyla Hewson. Support the show on Patreon.Also mentioned in this episode:Check out the new report Matt co-wrote AI First.If you missed it, Molly White recently broke down how the tech industry is spending big on elections.Big tech's emissions are continuing to rise.Colossus is still being powered by unpermitted gas turbines.Support the show

Send us a Question!REBROADCAST MOVIE DISCUSSION: Melvin & Dan can only agree on one thing: Black Panther: Wakanda Forever is, in fact, a Marvel movie. To focus their attention, Melvin proposes four ambitions that he feels will make-or-break the film for audience members. (originally released November 16th, 2022)Topics:(PATREON EXCLUSIVE) 44-minutes discussing Kevin Conroy's passing, Video Rental store nostalgia, and media curation (PATREON EXCLUSIVE) Melvin liked that Black Panther: Wakanda Forever had a somewhat self-serious profile as opposed to the more flippant, light sensibilities of other Marvel fare. However, Melvin has four headers - ambitions - that he observed within Black Panther: Wakanda Forever that he felt prevented him from "buying in" to the fiction of the film but may actually be beneficial qualities to certain audience members. His first point covers the film's global stage for a political drama between potentially warring counties. His second point covers the personal character stories within the film. A brief aside from the points, Melvin and Daniel agree: the suits at the end of the movie aren't that good. Melvin's third point has to do with the film's metatextual pairing of T'Challa's fictional death and Chadwick Boseman's real-life death. Melvin's final point covers what he would consider inefficient pacing, such as scenes clearly setting up future movies, or scenes having only one purpose at a time rather than being dynamic in detail. Recommendations: Galatians: An Expositional Commentary by R.C. Sproul (Book) Amy (2015) (Documentary) The Decade-Long Quest For Shadow of the Colossus' Last Secret (YouTube)  Support the showSupport on Patreon for Unique Perks! Early access to uncut episodes Vote on a movie/show we review One-time reward of two Cinematic Doctrine Stickers & PinsSocial Links: ThreadsWebsiteInstagramLetterboxdFacebook Group 

My guest today is Eric Vishria, a General Partner at Benchmark.  Eric has spent his career in software and cloud, and few people know the history of these markets as well as he does. What makes him special is his ability to use that history to make sense of today.  We discuss what the rise of AWS teaches us about AI, what he has learned from investing in Fireworks, Sierra, and Cerebras, and how the criteria for winning have changed for founders and investors.  Please enjoy my conversation with Eric Vishria. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:20) Learning the World Through Fireworks (00:05:42) AWS Was Going to Eat Everything (00:07:40) The Zero-Sum Thinking Trap (00:09:01) Comparing Cloud and AI Adoption (00:11:03) Becoming Enterprise's AI Sherpa (00:13:05) Building Sandcastles (00:14:55) The Return to Being Technical (00:17:13) The Shifting Competitive Frontier (00:22:10) Why the Old Playbook Fails (00:27:53) Energy as the Binding Constraint (00:29:38) The Cerebras Story (00:37:57) The Virtue of Productive Naivete (00:39:19) What Robotics Still Needs (00:45:58) What Makes a Great Board Partner (00:51:13) Raising A Growth Fund (00:55:39) What the Big Winners Taught Him (00:57:37) Hard Work Versus the Hole-in-One (00:58:38) The Best Reasons to Go Public (01:01:09) Debates Inside Benchmark (01:02:16) What If It All Works (01:03:35) What Geoff Hinton Got Wrong

Keith explains why achieving scale rather than simply earning more is the key to long-term financial freedom and how income property uniquely delivers multiple forms of leverage.  He breaks down 25 years of inflation data to reveal which everyday costs have most outpaced wages and what that means for the real purchasing power of the dollar.  Keith also explains why markets like Memphis—combining strong cash flow fundamentals with a massive new AI infrastructure build-out—are positioned as compelling targets for long-term real estate investors. Episode Page: GetRichEducation.com/618 For access to properties or free help with a GRE Investment Coach, start here: GREmarketplace.com GRE Free Investment Coaching: GREinvestmentcoach.com Get mortgage loans for investment property: RidgeLendingGroup.com or call 855-74-RIDGE  or e-mail: info@RidgeLendingGroup.com Invest with Freedom Family Investments.  For predictable 10-12% quarterly returns, visit FreedomFamilyInvestments.com/GRE or text  FAMILY to 66866  Join Mid South Home Buyers' one-time, free live webinar featuring Keith Weinhold on September 30 at GetRichEducation.com/MidSouth to learn how Memphis' economic expansion could create new real estate investment opportunities, and have your questions answered in real time. Will you please leave a review for the show? I'd be grateful. Search "how to leave an Apple Podcasts review"  For advertising inquiries, visit: GetRichEducation.com/ad Best Financial Education: GetRichEducation.com Get our wealth-building newsletter free— GREletter.com  Our YouTube Channel: www.youtube.com/c/GetRichEducation Follow us on Instagram: @getricheducation Complete episode transcript: Keith Weinhold  0:01   Welcome to GRE. I'm your host Keith Weinhold. When I talk to a 25-year-old, it's an epiphany. When I tell them that they need this one thing that they're lacking, then some fascinating takeaways about the 93% inflation we've experienced in the past 25 years, and what you can do about it today on Get Rich Education. What if I told you that one of America's strongest cash flow real estate markets is also becoming the new brains and brawn behind AI? That city is Memphis, believe it or not. In September 30th, we're going to show you why the smart money is paying attention now, along with an investing opportunity you won't want to miss. Join me, Terry Kerr and Matthew Van Horn of Mid South Homebuyers, the largest turnkey company in Memphis with more than 6000 homes under management, for a free live webinar the likes of which I've never done before. We're going to look at what billions in new investment could mean for jobs, housing demand, neighborhood appreciation, and your portfolio. Everyone who attends live will also get exclusive access to the best deal terms Mid South has ever offered. Reserve your free seat at getricheducation.com/midsouth again, that's September 30th. Don't say we didn't tell you. Save your spot at getricheducation.com/midsouth.   Speaker 1  1:33   You're listening to the show that has created more financial freedom than nearly any show in the world. This is Get Rich Education.   Keith Weinhold  1:49   Welcome to GRE from Livonia, Michigan, to Laconia, New Hampshire, and across 188 nations worldwide. You are listening to Get Rich Education. I'm your host, Keith Weinhold, heading up this slackjaw operation for another wealth-building week. But at least I'm just a slackjaw. If this slackjaw gets lockjaw, it would probably end the show. Now I've got to tell you, when I meet a 25-year-old, I soon tend to learn about their job because it takes a lot of their time, even if I don't ask them about it, and I find out that a 25-year-old is usually an employee of some sort. They're working for somebody else, depending on our conversational flow. I ask that person this question: Have you considered adding scale to your life? And they usually don't know what I mean. I ask that question because, sadly, today it's less common to live an economically vibrant life if you have a quote normal job like a teacher, engineer, retail manager, app developer, or other normal jobs like a firefighter, truck driver, physical therapist, or social media manager, that is not going to lead to an economically vibrant life with options and freedom. I mean, you used to be able to raise a family of four in New York City. That opportunity is just gone for anyone under a certain age. Well, what about say doctors, corporate executives, and attorneys, including some people that might be older than 25. I mean, professions like this can still pay exceptionally well. But even white-collar careers now have AI breathing down their necks. AI is drafting briefs, reading scans, and virtually attending meetings without pretending to enjoy them. Okay, well, what about the outcome for a 25-year-old that's gone along with the somewhat more nascent trend of rising AI sheltered trades like plumbing, electrical, HVAC, welding, carpentry, equipment repair, and these other types of jobs where ChatGPT can't crawl beneath your sink. Look, here's the thing: it doesn't matter whether you wear scrubs, a suit, or a tool belt. Employment has one stubborn limitation: even if you grind hard, even if your body holds up, even if promotions help you climb to the top of the corporate ladder, when you stop working, the income stops. That's the big problem, and yet people keep designing their life this way, employees lack scale. Now, what is scale? Scale is your ability to increase your wealth or income without increasing your personal time and effort at the same rate. Now, employees can find just a little scale. 401k contributions can compound for decades, sometimes with an employer match. Some employees receive stock compensation or bonuses, but employees generally sell one unit at a time. That unit is an hour. They're selling their hours for dollars, and here scale is limited, if not impossible. Real estate investors can stack several forms of scale simultaneously, and remarkably, doing it takes zero certification, zero qualification, no license, and no permission slip from the dean.   Keith Weinhold  6:05   The first way real estate investors have scale is through something that you already know so well: real estate pays five ways, leverage appreciation, 10 funded income, loan amortization, tax benefits on the entire asset, and inflation profiting on the bank's loan. Secondly, as a real estate investor, you have scale through operational leverage. Property managers, leasing agents, contractors, lenders, insurers, and software all allow just one investor, you, to control multiple properties. You don't personally collect every rent payment or replace every water heater. I mean, sheesh, that could be a plumbing career with less sleep. And this is all tenant funded. Thirdly, real estate investors have geographic leverage. An individual investor living in Los Angeles can own property in Atlanta, Tulsa, Cleveland, and Belize. Physical location does not limit where your capital works. Your body can only work in one city. Your capital can work the night shift in five. The fourth way real estate investors have scale is with replication. Once you learn how to buy and own one suitable rental, the process can be repeated. You buy, stabilize, finance, rent, and repeat. See, the first property is the hardest, and then your second property does not require learning an entirely new profession. It can be replicated. To review what you've learned so far, those are four dimensions where real estate investors achieve scale through real estate pays five ways: operational leverage, geographic leverage, and replication. Here's the important distinction: employees often mistake earning more with achieving scale.   Keith Weinhold  8:16   A surgeon making $900,000 a year earns a nice income, but see that surgeon has limited scale if the income stops when the surgeon stops working. But an investor earning just $150,000 from a portfolio possesses more scale because dozens of tenants, properties, loans, and operating systems continue functioning without your one-for-one labor. That's the distinction. That's why the $150K investor might or might not be living a better life than the 900K surgeon now, but they are set up to live a better life than the surgeon in the future. Now, your employer, the person who hires you, has scale with their many employees. But if you're an employee, you probably don't have scale. You cannot save your way to scale either. That's just stored labor. Savings become scalable only when you convert them into productive assets. Income is how much money comes in. Scale is how little your personal time needs to increase for more money to come in. You can work 20% more hours, but you cannot sustainably work 10 times more hours. Capital can be deployed across 10 assets without requiring 10 times more personal effort. And you know, once I realized this, at a certain point in my life, I was motivated to obtain loans for rental. This helped me scale and own more, replacing my active income with mostly passive income sooner. All right, so what should you do when you have this epiphany? It doesn't mean you should flip over the stupid copier machine as you storm out of work today and announce that you are now a real estate magnet. Not right away, at least employment that can be your launchpad, just like it was for me when I was a humble construction materials inspector for the state DOT. A job does provide you with some benefits like short-term advantages, seed capital, mortgage qualification.   Keith Weinhold  10:45   I'm talking about health insurance and some steady cash flow, and even some skills. But the mistake, whether you are aged 25 or 55, is allowing employment to remain the only economic engine for your entire life. Your job can fund your future, but having just one single linear income source that should not be your entire future. But you know, some people just stay on lazy cruise control at a slow speed and let their life unfurl that way. Others, you know, they merely haven't been exposed to thinking this way, and fortunately, now you have been. Really, the bottom line here is that labor won't scale; capital does scale; it compounds, and few, if any, investments offer more dimensions of scale than real estate. And you also get all kinds of other ancillary benefits by gradually tilting away from active income and toward passive income. Because increasingly, when it comes to taxes, you're going to pay lower capital gains tax rates instead of the higher ordinary income rates. The sooner you optimize this and get into as many properties as you can, you're also going to gain the ability to borrow against your assets tax-free, and so much more. Scale or fail-that's the lesson here, and most people fear change. It's why they stay stuck in relationships longer than they should, and why they stay stuck in jobs longer than they should. They keep settling for a B plus life. Don't settle for a B plus life. This is something that NYU professor Susie Welsh talks about: If you have a D life, oh, everything is lousy. You don't live where you want to live. You don't have reliable transportation. You don't have friends, and you're so very motivated to change that. If you have an A plus life, you've got it all. You get to do what you want to do, who you want to do it with, and you're tremendously incentivized to keep that. But having a B plus life like so many do, and being stuck in it, that is the most dangerous place to be. You could tread water for years and stay stuck in a life that you know you're not fully satisfied with, but it isn't so terrible that you feel compelled to change it. So the people that grow wealth know it means that sometimes you have to give up the good to have the great, and the K-shaped economic divergence that we've had in the past five years. This is really bringing things to a head, so get scale.   Keith Weinhold  13:43   Scale is the difference between grasping the financial abundance that's available to move you toward that A plus life, or staying on the treadmill, stuck and struggling. Two different people living a B plus life, you know, they have the same starting point, and making a plan is your difference maker. We help you with that here. If you're ready to add real estate scale to your financial life, drop a quick email to GRE Investment Coach Naresh for a complimentary strategy session at Naresh at getricheducation.com. You don't need any qualifications. It can take as little as a 20% down payment on a 200k to 400k rental property, and we have access so that you can buy directly from the builders and get a mortgage rate in the fives. And we are chasing the next hot thing here. Last week we discussed co-living on the show. We waited until that strategy was proven. I like strategies that have had some contact with reality. AI can compose a song, or summarize a meeting, or fabricate a photo of some. Wacky like Abraham Lincoln riding a dolphin, but it still cannot download an affordable bedroom, affordable housing. You're scaling into something sustainable that has a future and can't be easily disrupted by AI. Scale or fail. Stop settling for the B plus life. We can help right now at this moment. Drop a quick email to naresh@getricheducation.com. I should spell that out for you. It's n a r e s h@getricheducation.com.   Keith Weinhold  15:36   More straight ahead. I'm Keith Weinhold. You're listening to Get Rich education. What if you got your mortgage loans the same place I get mine? You sure can at Ridge Lending Group NMLS 42056. They provided GRE listeners with more loans than anyone because Ridge specializes in investment property. They'll help you build a long-term plan for growing your real estate empire with leverage. Start your prequal and even chat directly with President Caeli Ridge. While it's on your mind, start at ridgelendinggroup.com. That's ridgelendinggroup.com.   Keith Weinhold  16:13   Let me ask you something: If you've worked hard to build wealth, is your money positioned to actually support your goals? A lot of accredited investors leave capital sitting in cash because it feels safe, but inflation and missed income opportunities can quietly erode its value. Freedom Family Investments offers freedom notes for investors seeking structured income backed by real estate. It's a straightforward approach built on real assets, not speculation. In full disclosure: I'm an investor myself. What I like is that their team walks you through how it all works, so you can decide if it aligns with your portfolio and income goals. Every investment carries risk, and nothing is guaranteed. But with a track record of consistent, on-time investor payouts, they built real credibility. Go to freedomfamilyinvestments.com to book a clarity call, or text family to 66866. That's family to 66866.   Chris Martenson  17:17   This is Peak Prosperity's Chris Martenson. Listen to Get rich education with Keith Weinhold, and don't quit your daydream.   Keith Weinhold  17:33   Welcome back to Get Rich Education. I'm your host Keith Weinhold. Having residual income from real estate, it can make you more comfortable for sure, but for me, I like to primarily use it to buy back my time. I'll tell you how I just did this. It's a small thing, a small win. It is time for my car's annual routine maintenance. Boring. I really don't want to lose my time dropping it off at the dealership in the morning and then picking it up again. Those two boring round trips don't add anything to my life. But the dealership had the option of, for just 100 bucks, picking it up for me and dropping it off for me at the end of the day. Oh well, that is an opportunity for me to buy some time, so that's why I did that. Now, when it comes to flying, sometimes I fly coach and sometimes first class. I just booked a flight and I refused to pay six times as much for first class. It just wasn't really worth it this time because the experience isn't that much better, and it sure doesn't save me any time. I tend to do that if the price is just 3x more, so I'll pay to save time, but not always to borrow a wider seat for five hours. And you and I both make hundreds of time versus money decisions every day, most of them small.   Keith Weinhold  19:04   With the more residual income you have, you're gonna make better decisions where you can choose the time over the money. One thing's for sure: whatever we're doing with our money, and that is that our dollar does not go as far as it used to. Let's look at inflation during the first 25 years of this century. This is really interesting. We're going to see how the cost of goods and services has changed from 2000 to the end of 2025 on some select categories that you spend on, and then I've got some mind-bending takeaways for you once I describe this chart, and this is the same chart that I sent to you last Thursday. If you are one of my newsletter readers, but I can open up and talk about it more here than I can in the newsletter because I keep that short. Overall inflation is about. 93% during this time period. 93% over these 25 years. Now, here are the items that rose less than that much, meaning that they became then more affordable over this span. What fell the most is the price of televisions down more than 90% in the first 25 years of this century? Toys down 74% Computer software down 73% Cell phones down 44% By the way, this all uses the government's CPI inflation rate, clothing up just one and a half percent, and even though it's up, that's still more affordable because it's up less than the overall 93% CPI inflation rate over this span. Household furnishings up 21% and finally new cars up 26% So all those items became more affordable because they rose less than the general rate of inflation. All right, moving on up. Now we're going to go above the line. Items above the 93% overall inflation rate, food and beverages were up 106% housing up 111% average hourly wages up 131% All right, let's pause. Yes, wages then outpacing 93% inflation. but of course, since that 93% uses the government CPI, well, that's pretty understated. Probably, you know, the true dispersing power of the dollar is probably more than 93% So it's debatable about whether there are real wage gains from 2000 to the end of 2025, medical care services up 147% Next in the category that has become less affordable is childcare, up 159% And as I'm naming these, there are some common threads here where I think you're going to have a few epiphanies when I point them out. College textbooks up 177%. Sheesh, what a scam! College tuition and fees up 197%, and finally the major category that became less affordable here at the top is the worst of all: hospital services. They have soared the most, up over 281% All right, there they are.   Keith Weinhold  22:57   And what takeaways do we have here? The items that became less affordable tend to be where the government either provides subsidies or they heavily regulate and mandate the product or service, like education, child care, and medical care. The categories that have become more affordable-that's where there is little or minimal government intervention, like clothing and technology. The lesson is that free market competition kept prices low, and some of these categories that became more affordable-you know-they would have become even more affordable than that if it weren't for profligate dollar printing, sadly, the items that have become less affordable-and this could really upset you-the items whose price increases exceed the overall rate of inflation, like medical care and housing, these are life's necessities. They are not once the stuff you need most got harder to obtain, healthcare is the ultimate example of this. It's sad to say, but you'll either pay the fee or you'll die, and the price reflects this. With hospital services up 281% outpacing the overall rate of inflation by about 3x. Also, items that have become more affordable, they are then generally the more discretionary purchases like furnishings, toys, and televisions. You can live without that stuff. Items that have become less affordable. They also tend to be more in-sourced activity, while those more affordable are outsourced, like to China. If you've noticed the trend, then anything involving people in the United States will be expensive, like child. Care and medical care. It involves people in the United States, and then it just gets more and more expensive. And this is also why service prices increase more and goods prices increase less. People are expensive.   Keith Weinhold  25:18   Microchips don't ask for dental insurance, and microchips don't file sexual harassment lawsuits. Overall, inflation was just 2.66% per year during this time period. But when it's compounded for this long, that's how it got to 93% cumulatively. But of course, inflation is higher than this 2.66 rate here in the late 2020s, and inflation is poised to rise even more than the level that it's at now. The war in Iran has pushed up energy prices 24% and these costs seep into almost everything, all right. But you're probably aware of this already, so I'm not going to discuss it much more because I discussed that before, like on episode 606, nearly two months ago when I called it our most important message in years, all right. But few seem to understand that this is just one part of a new inflation triple whammy. First, you've got spiking energy prices, like I mentioned. Second, more U.S. tariffs, and third, you've got mushrooming AI spending, and as a result of all this, this new inflation triple whammy that most people aren't aware of, this has pushed up bond yields to their highest point since 2007, and pressure is mounting for the Fed to jack up rates. Mortgage rates are soaring right along with them, and they are now near 7% Could mortgage rates reach 8% This is a real question now. The bottom line here is that inflation made the dollar lose nearly half its purchasing power in the first quarter century. Real asset owners will win, especially leveraged income property owners. This raises the property's replacement costs, spikes rents, and erodes your mortgage's real burden. Nearly everyone else is going to lose, and I don't want to lose a learning moment for you here. Bond yields-they are closely tied to what future mortgage rates are going to be. It's not about what the Fed does, and this is not as esoteric as some people think. This correlation between inflation, bond yields, and mortgage rates. Bonds pay a fixed interest rate long term.   Keith Weinhold  28:01   For example, the 10-year Treasury bond right now pays about 4.7% each year for the next 10 years. That's what that means. Now, would you lock in your investment for 10 years in order to get a 4.7% return? Well, if you were a conservative investor, maybe you would if you knew that inflation was only going to be 2% because then you'd be making about a 2.7% real return on your investment each year risk free. But if you expect inflation was going to be 5% over the next 10 years, oh well, then locking in a return of 4.7% means that you would lose real purchasing power every year. Investors don't want to lose money, so if investors expect that inflation is going to be higher, they will only buy bonds if they're paying higher amounts. And the bond market is telling us that as of today, investors expect at least 4.7% inflation over the next 10 years. If things change and they expect inflation to be higher than that, well, then bond yields will go up. If they expect inflation to decrease, for example, from a recession, bond yields will go down. So therefore, Treasury bonds are a true representation of investor inflation expectations and the movement of that bond yield-that is the number one factor that moves mortgage rates in that same direction. There's your explanation. That wasn't so hard. The market does not believe we're going to escape the Middle East war without substantial inflation or energy supply chain issues. That's what that means. Now, what else is going on in this era is the continuation of a reduction in the volume. Of housing transactions, fewer deals are happening. It had its recent peak of 6 million existing homes changing hands back in 2021. In 2022, it was 5 million, and it's been about 4 million transactions every year since. Now, as far as investor activity, just looking at that, for big investors, activity that's been sideways to a little down these past few years. But let's look at ourselves for smaller investors, mom and pop types, defined as those doing 10 or fewer deals per year, which probably includes you. You know, each of the past three years, activity has been up for smaller investors like you. You have gradually been purchasing more property, and this is as reported by realtor.com. Okay, what are the reasons for this?   Keith Weinhold  30:55   Well, back during the pandemic, you had to compete with owner-occupied buyers, that's when open house lines stretch down the block, and today there are fewer bidders in the room, and small investors are buying because builders are buying down your mortgage rate for you. That's another reason, and the source analysis it found that investors are sticking to affordable Midwest and Sun Belt markets that have strong rental demand. In fact, they're buying at least one out of every five homes in Memphis, Kansas City, St. Louis, Birmingham, and Oklahoma City. Real estate providers know that some prospective owner-occupant homeowners and even some investors-they won't buy anything at today's market mortgage rates, even though you and I know that these rates are historically normal. But providers-they need to stay in business. They need to keep turning things over. They need to sell property. They need to keep their people busy. They're not running museums here, so they're making sure that mortgage rate buydowns happen. And one of the most lucrative sources that I know about for investors is Mid South Homebuyers because they have investment property where the numbers work in Tennessee, Arkansas, and Texas with mortgage rates in the fives and a conventional loan with 25% down. A lot of their income properties cost under 200k, and these are quality homes in decent neighborhoods. I've physically walked inside many of them myself, not by drone, not with a virtual tour, not by AI, and not through some glossy brochure with suspiciously perfect lighting. The reason I'm telling you about this now is that this mortgage rate is one part of their limited triple five program. Here's what else we get as investors: a mortgage rate near 5% like I mentioned, and a 5% property management fee for five years. Though leverage has its benefits, if you decide to pay all cash instead, they provide you with the 5% property management for life, even if you finance later. I think they call that their forever five. Frankly, it's just amazing how many investors rave about the quality of their rehabs and say that their property management never seems to mess up in this industry. I mean, that is about as common as a calm political debate, or perhaps an airline actually improving legroom, and I have helped recommend Mid Health Homebuyers to our listeners for over 11 years. I know some followers that have looked at their available properties and scooped up three properties on one phone call. In fact, where they're based and have a lot of their available properties, Memphis. You know, Memphis has a story where I don't know if any other market in America can tell it right now. Do you know what's happening? Memphis is developing into having both the new brains and the brawn behind AI, and you got more smart money moving there now. Memphis is now home to the world's largest AI supercomputer. It's XAI's Colossus. It's now part of SpaceX. It's the biggest single-site AI facility on the entire planet. Anthropic is paying over a billion dollars a month to run Claude on it. Google just signed a deal worth up to 30 billion starting october 1, and I look forward to announcing that I have got a live event that I am co-hosting for you the day before this happens on september 30.   Keith Weinhold  34:56   So yes, that's the night before Google's money starts flowing. Into Memphis in one year, XAI became the second largest taxpayer in Memphis after FedEx, and the city has committed 25% of the property tax revenue from those sites to infrastructure in the surrounding neighborhoods. And when you add in FedEx, because Memphis already moves more physical goods than anywhere else in the country, you can see how Memphis is increasingly becoming the brains of the digital economy, while it's already been the brawn of the physical one. In every other market, you know they showcase things like their population growth and the rent-to-price ratios, and those attributes certainly matter, but now the fact that perhaps the biggest infrastructure story in America is happening in the most affordable major cash flow market—I mean, this is something that almost nobody has connected the dots on. So join me and my two co-hosts that lead Mid South Home Buyers.   Keith Weinhold  36:01   We're going to discuss market fundamentals, the AI build out, what it means for jobs, rent in neighborhoods over the next decade, and then a heavy live Q and A on Mid South. You're invited to join me. This is happening again on Wednesday, September 30th. It's at 8p.m. Eastern. Yes, you will have me live. Sign up at getricheducation.com/midsouth. It's a special event as Memphis is positioning to become both the brawn and brains of AI and a property provider that already makes a lot of sense for investors. Save your spot at getricheducation.com/midsouth. Until next week, I'm your host Keith Weinhold. Don't quit your daydream.   Speaker 2  36:54   Nothing on this show should be considered specific, personal, or professional advice. Please consult an appropriate tax, legal, real estate, financial, or business professional for individualized advice. Opinions of guests are their own. Information is not guaranteed. All investment strategies have the potential for profit or loss. The host is operating on behalf of Get Rich Education LLC exclusively.    Keith Weinhold  37:22   The pre- program was brought to you by your home for wealth building, getricheducation.com

This week, the White House announced a new framework for regulating A.I. models, but it isn't letting the public read it. We break down what we know about the rules and what the implications are for the industry and A.I. safety as a whole.  Then, yet another report details new incidents in which A.I. agents have gone rogue. Chris Painter, the president of METR, an independent A.I. evaluation organization, joins to discuss how we get these models under control.  And finally, we're hopping on the Hot Mess Express for the very last time. We'll rate the craziest tech headlines from the week, including Google's announcement that Demis Hassabis is stepping into a new role.    Guests: Chris Painter, president of METR.    Additional Reading: White House Readies A.I. Framework to Review Security Risks Inside Trump's AI framework How Do You Measure an A.I. Boom? METR's Frontier Risk Report Google Shakes Up A.I. Leadership Did an A.I. Music App Just Snitch on the Song of the Summer? This AI Assistant Wants to Make Up for Your Boyfriend's Incompetence Google Earth's AI deepfake tool only lasted one day US government map of Africa mislabels every country at global conference Contractor who built Colossus and Colossus II says Elon Musk owes him colossal amount of money   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.

This is a free preview of a paid episode. To hear more, visit andrewsullivan.substack.comRoss is a journalist and novelist. He's the editor-in-chief of The Metropolitan Review, and a columnist for the Nation and UnHerd. He's the author of six books, including his new novel, Colossus. A new non-fiction book, about the rise of Zohran Mamdani, is coming soon. Find much more of his writing on his substack, “Political Currents.”For two clips of the episode — on whether the DSA is truly dangerous, and the US elites' obsession with Israel — head to our YouTube page.Other topics: growing up Jewish in diverse Bay Ridge, BK; parents both federal workers; his dad working at the WTC on 9/11; Ross' cynicism over Obama's rise; coming of age in the Great Recession; the DNC screwing over Bernie in ‘16; Ross running for the state senate with Mamdani as his manager; identifying as “left-populist, woke-skeptical”; engaging and persuading ideological rivals; universal health care; questioning Covid policies; US aid to Israel; the horrors of Oct 7; the war in Gaza and the civilian toll; no pro-Palestine speakers at the ‘24 Dem convention; the decentralized DSA; calls to abolish prisons and the Senate; Jon Chait; AOC's move to the center; border security; Trump's norm-busting; Buckley and the Birchers; trans athletes; the + in LGBTQ+; corporations ditching DEI; the Arday scandal; The Odyssey; Jon Ossoff; the surprising surge of El-Sayed; and debating whether wokeness is dead.Browse the Dishcast archive for an episode you might enjoy. Coming up: John O'Sullivan on conservatism, Emily Eakin on postmodernism, Azam Ahmed on terrifying new drugs, and Arianna Huffington on anything but politics. Please send any guest recs, dissents, and other comments to dish@andrewsullivan.com.

Nico & Kevo explore the most recent episodes of the second amazing season of X-Men ‘97! First up, the debut of Danger, the exploration of Polaris, and the new status quo of Genosha! Then, examine Nightcrawler's family with Graydon Greed, the threat of the Acolytes including Colossus & Exodus, and THE FIRST APPEARANCE OF DOOP! Lastly, followup on Gambit and the ongoing Apocalypse plot – with massive consequences for Rogue and the X-Men as a whole! Watch along with X-Men ‘97 Episodes 6, 7, & 8! – it's all this and more on an all-new X Is For Comics!

Josh Taves maintains a bullish outlook for SpaceX (SPCX) pointing to its AI compute business as a huge factor. He mentions the leasing out of its Colossus servers to companies like Anthropic as one revenue stream to watch. On the potential for a Tesla (TSLA) merger, he goes a step further saying Elon Musk would essentially become "President of Earth" with so many key tech segments under one umbrella. Josh adds that SpaceX could be a bellwether for growth stocks moving forward and "definitely influences" markets as a whole. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

My guest today is Gavin Baker, founding partner and CIO of Atreides Management. This is our seventh conversation, and just two months after Gavin's last appearance. It's about the gap between what the market is doing and what companies are seeing. It's been a tough month or so for public AI names, but there's no sign of a slowdown on the ground in Silicon Valley. We discuss the latest moves, contracted vs. spot GPU prices, the game theory of memory supply agreements, and why Claude has become the Walter Cronkite of the stock market. We close on SpaceX, orbital compute, and what Gavin sees as the single biggest risk to all of it. Please enjoy this conversation, from the famous table at Benchmark, with my friend Gavin Baker. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest.  ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgeline.ai. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:35) First Question: July Was 2022 in a Month (00:04:08) The Private Companies Public Markets Can't See (00:05:06) Old GPUs Repricing Higher (00:06:53) Walking Through the Month (00:08:22) Kimi, GLM 5.2 & the Open Source Freak-Out (00:10:51) Real Yields, Spreads & CDS (00:11:54) Does the Build-Out Need Credit? (00:15:22) A Sell-Off With No Clear Villain (00:17:35) Open Source as Dark Matter (00:18:39) Nvidia's Lowest Forward PE in 10 Years (00:21:35) Claude as Walter Cronkite for the Stock Market (00:23:55) Continual Learning & Sample Efficiency (00:25:19) What Would Actually Scare Him (00:26:38) Routers & the Multi-Model Future (00:30:51) Tokens as a Percent of Comp Spend (00:33:37) The Game Theory of Breaking an LTA (00:36:41) Nvidia's Credit Wrapper & Revenue Share (00:37:45) What He'd Do If He Ran Hynix (00:41:46) Who's More Bullish than Him (00:43:28) China's DUV Machine (00:46:10) Bull Case for Software (00:48:16) The RSI Maximalist View (00:49:31) Inference Clouds Growing Without Burning Cash (00:50:35) The Biggest Risk Is Regulation (00:53:44) Telling the Story Better (00:57:15) Dark Horses (00:58:02) SpaceX in the Public Markets

Slowly at first, then all at once, the First Order is taking over the Colossus.In our first and only triple-header of Season 1 of Resistance, we cover "Dangerous Business," “The Doza Dilemma” and "The First Order Occupation” (Episodes 13–15) so we can parse the speed of the First Order occupation. But do they want the Colossus as a toehold into the wider Republic, or as a weapons manufacturing plant?We also wonder if Captain Doza ever had a plan to combat the pirates or the First Order, and try to understand Synara through the lens of order vs. freedom.Want more Growing Up Skywalker? This is a great time to sign up for our Patreon for bonus audio content!Timestamps:00:00:00 Who Are We?00:02:30 Plot Summary00:11:00 The Speed of First Order Occupation00:24:33 Did Doza Have a Plan?00:40:22 Synara and the Order vs. Freedom Calculus00:50:17 Bae Watch00:57:08 Closing Thoughts

Colossus has entered the chat!   Ian Carlos Crawford, Brett White, Anthony Oliveira, and Rose Dommu talk X-Men 97 S2 Ep 7 'Strange Land, Savage Heart'   CONTACT: slayerfestx98@gmail.com Support us on Patreon: www.patreon.com/slayerfest98 Buy our stuff on etsy: https://www.etsy.com/shop/Slayerfestx98 Like us on Facebook: www.facebook.com/Slayerfestx98 Follow us on Bluesky: https://bsky.app/profile/slayerfestx98.bsky.social Follow us on TikTok: https://www.tiktok.com/@slayerfestx98 Follow us on insta: https://www.instagram.com/slayerfestx98 Follow us on Twitter: https://x.com/slayerfestx98 Follow us on YouTube: https://www.youtube.com/@Slayerfestx98  

This episode is late because I was in the hospital! Don't worry, I'm okay now and I talk all about at the start. But there isn't much time to dilly dally because we have a packed show. Colossus shows up with the terrorist Acolytes, Bishop and Polaris continue their slow burn, and Nightcrawler gets to be a lawyer with the life of a truly awful man on the line. Before that: Marvel and DC are just canceling shit left and right! There are a bunch of trailers, including ones where Michael B Jordan is handsome, Olivia Colman gets railed by a wicker Alexander Skarsgård, Carrie White is still not fat and Spider-Man: Brand New Day is already a blockbuster hit. If you don't care about any of that, skip right to 1:12:32.Speaking of Spidey, we'll be talking about Brand New Day on the next episode of Watch Men, which should be up Sunday. That's available at the $7 and above levels at our patreon - www.patreon.com/cinemasangha -  so this is a good time to come join us! Want your questions answered on the show? Send an email to ask.cinema.sangha@gmail.com and ask away, and ask about pretty much anything at all. Make sure your subject line contains the name of the show on which you want your question answered. One question per email, please, but feel free to send in multiple emails!Spread the word! Tell your friends about us! And go to our YouTube channel and subscribe to our video feed!

As much as we love Colossus and we love Nightcrawler this episode wasn't giving us what we needed this close to the end of the season. We have a great time predicting what is going on with this whole Gambit & Apocalypse thing though. Does anybody care about all the pile of mutants in the background shots? Send us an email: lostlightpod@gmail.com Visit us on the web: https://www.lostlight.stream Listen to all our TAPEDECK podmates at: https://solo.to/tapedeck

Igor Babuschkin, co-founder of River AI and formerly a co-founder of xAI, joins to unpack a career that spans nearly every major AI lab: he led the StarCraft and AlphaCode work at DeepMind, joined OpenAI's reasoning team years before o1 shipped, and co-founded xAI, where he helped stand up the Colossus data center in roughly 120 days and reflects candidly on what it's actually like working with Elon Musk day to day, plus what the Cursor acquisition actually unlocked for Grok's coding models. He also discusses why he left xAI to start River AI, the three bets behind it, and why he's betting on local hardware, not just software, for personal AI. On the enterprise side, he tackles whether companies will actually train their own models or if it's just a cost play, and makes the case that proprietary labs like OpenAI and Anthropic are facing a real business squeeze. He's skeptical that stacking specialized RL domains generalizes the way pre-training scale did, and is candid about the uncomfortable reality that today's frontier open-weight models are almost entirely Chinese. He closes on what's actually needed to push model progress beyond coding into non-verifiable domains, and the broader implications of where AI is headed next.   (0:00) Intro (1:17) Writing Fiction on Where AI Is Headed (4:46) Cracking Agents Beyond Coding (10:29) Why Igor Left to Start River (12:22) River's Three Big Bets (18:06) Weights vs. Memory: The Personalization Debate (22:04) Should Enterprises Train Their Own Models? (25:10) Are Proprietary Labs Losing Their Edge? (32:16) The China Open-Source Problem (44:19) The Elon Call That Started xAI (50:18) Thoughts on Cursor Acquisition (52:16) What's Actually Bottlenecking AI (56:55) Humans, Machines, and Staying Relevant (1:01:29) Igor's Odds This All Goes Well With your host: @jacobeffron - Managing Director at Redpoint

It's the trial of the century as Nightcrawler defends the indefensible, and it's the official debut of Exodus, who's British for some reason. Colossus tries to honor the memory of Illyana by being super judgy. And our favorite blossoming couple, Polaris and Bishop, have the perfect first date: riding bareback on pterodactyls while shooting at everything in sight. Plus, we mad lib some new Rogueisms; they're wilder than thirsty hogs at a broken water fountain.

X-Men '97 Season 2 Episode 7 "Savage Land, Savage Heart" delivers one of the strongest Nightcrawler stories in the series. Logan Janis breaks down the emotional story, and what the shocking ending means for the rest of Season 2.  Timestamps 00:00 Intro 02:10 Spider-Man: Brand New Day Quick Thoughts 04:15 Episode 7 Overview 06:20 Nightcrawler's Family Tree Explained 10:40 Graydon Creed, Mystique & Sabretooth Connections 15:30 Colossus and the Acolytes of Magneto 20:15 Savage Land Explained 26:05 The Trial of Graydon Creed 32:20 Nightcrawler vs. Exodus 35:00 Colossus Returns to the X-Men 36:00 Apocalypse Post-Credits Scene Explained 38:30 Episode 8 Predictions 41:00 Final Thoughts Sources https://www.marvel.com/articles/comics/rogue-nightcrawler-family-tree-explained https://www.laughingplace.com/disney-entertainment/x-men-97-season-2-episode-titles-release-schedule/ Sponsored by Team Jakey Foundation. Your mental health matters. Visit https://www.teamjakey.org/. If you or someone you know is struggling, 988 is available 24/7 by call or text. Watch, listen, subscribe, support at mostlysuperheroes.com/support ©2026 Carrogan Studios

The great Colossus and Nightcrawler step into the spotlight reminding us all what great characters they are while more characters unceremoniously get killed on the latest episode of X-Men (97) TAS! Join us as we discuss...Some belated thoughts on Masters of the Universe and Supergirl movies!Reminiscing about the New Mutants movie mainly because this episode put Anja Taylor Joy's Magik back into our minds!Colossus finally joins the team after all these years???Hoping the X-Men find Gambit totally normal reading a magazine back in his X-Mansion apartment!The X-Men TAS Podcast just opened a SECRET reddit group, join by clicking here! We are also on Twitch sometimes… click here to go to our page and follow and subscribe so you can join in on all the mysterious fun to be had! Also, make sure to subscribe to our podcast via Buzzsprout or iTunes and tell all your friends about it! Follow Willie Simpson on Bluesky and please join our Facebook Group! Last but not least, if you want to support the show, you can Buy Us a Coffee as well! 

Jason and Rosie recap episode 7 of X-Men '97 season two, which sees the X-Men a return to the Savage Land to rescue Graydon Creed from the clutches of Exodus and the Acolytes. Then, they discuss Nightcrawler's role as the moral compass of the X-Men and dive into the comics history of Colossus and Magik. Follow Jason: IG & Bluesky Follow Rosie: IG & Letterboxd Follow X-Ray Vision on Instagram Join the X-Ray Vision DiscordSee omnystudio.com/listener for privacy information.

​Welcome back to the table! In this episode, we are breaking down the latest developments in Season 2, Episode 7 of X-Men '97 ("Strange Land, Savage Heart").​Things get chaotic as the team heads into the hidden depths of the Savage Land for a high-stakes mission. We dive deep into:​The Acolytes and Exodus: How the faction is operating without Magneto, and their tense courtroom standoff involving Graydon Creed.​Nightcrawler's Moral Compass: Kurt Wagner stepping up to fight for what's right in the middle of a brutal ideological trial.​Colossus's Surprising Alliance: Why Piotr Rasputin has aligned with the Acolytes and what it means for the team moving forward.​The Fallout: Where things stand emotionally for Rogue, Polaris, and the rest of the crew as the season builds toward the endgame.

Individually they were just like those guys who like to hang around the comic book shop and talk comics but together they form EMX! Check out Thacher's books a DemonWeaselStudios.com In this eXplicit, uncut and unedited episode of EMX we review Marvel Comics X-Men books of June 2026: Bishop #1 Cyclops #5 Generation X-23 #5 Inglorious X-Force #6 Magik and Colossus #5 Moonstar #4 Storm - Earth's Mightiest Mutant #5 Uncanny X-Men #29-30 Wolverine #21-22 X-Men - Outback #1 X-Men #31-32 X-Men '97 Season 2 #1 X-Men of Apocalypse #4 X-Men United #4 [RSS] Subscribe [RSS] EMX Subscribe [Apple Podcasts] Subscribe [Google Podcast] Subscribe All Podcasts  Email: EMP@EarthsMightiestPodcast.com Website: http://www.EarthsMightiestPodcast.comFacebook Group: http://facebookgroup.earthsmightiestpodcast.com/Viet's Website: http://www.comedianviet.comThacher's Website: http://www.DemonWeasel.com  

My guest today is Sam Altman, CEO of OpenAI. It's a conversation spanning the history, present, and future of OpenAI, from the origin of ChatGPT through Codex, hardware, and their new Jalapeno chip. We discuss the early decision to buy compute at a scale nobody thought was rational, and the plan to build a gigawatt of new capacity every week.  We talk about Kimi and distillation, the Hugging Face incident and what it means for the pace of AI development, and what it's like to raise kids who will grow up never knowing a world without abundant intelligence.  Please enjoy my conversation with Sam Altman. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Intro: Sam Altman, CEO of OpenAI (00:02:35) Refocusing (00:05:43) OpenAI's Compute Bets (00:09:07) Data Centers (00:11:14) Jalapeno Chip (00:11:52) Kimi, Distillation & Open Source (00:14:39) The Hugging Face Incident (00:17:46) OpenAI's Mission & Vision (00:22:14) All the Returns Are at the Frontier (00:22:27) Bottlenecks: Compute, Research, Data (00:23:49) Sam's View on AI & Jobs (00:26:56) Unpopular Bets That Turned Out Right (00:27:45) Model Cycles (00:29:45) How Sam Uses AI (00:32:44) Having Kids (00:34:56) Why Sam Has No Equity in OpenAI (00:35:33) Robotics (00:36:48) The Origin Story of ChatGPT (00:39:22) How to Get AI into More Hands (00:42:20) How Sam Recruited Great AI Researchers (00:43:57) What Sam Learned From Being an Investor (00:45:22) What the Next 6–36 Months Look Like (00:46:31) Codex (00:49:36) Could We Be Oversupplied in Compute in Two Years? (00:50:09) Sam's View on Scaling Laws (00:50:20) Alec Radford (00:51:12) Formative Moments (00:53:50) Kindest Thing

Today, we are breaking down Applied Intuition. Our guests are co-founders Qasar Younis and Peter Ludwig, who started the company in 2017 with a mission to make a billion machines intelligent. The simplest way to understand Applied Intuition is that it builds the brains for machines, and the tools other companies use to build those brains. If a manufacturer wants its tractor, truck, or mining vehicle to drive itself, it can buy the intelligence from Applied Intuition or use its platform to develop its own. The analogy the founders use is Nvidia. Just as Nvidia sells chips into everyone else's machines, Applied Intuition sells intelligence into everyone else's machines, across automotive, defense, mining, agriculture, and robotics, without building any single machine itself. We discuss why the most important companies of the next 25 years will all be physical AI companies, Dana, their new agentic platform for developing and deploying these systems, and how the company raised a billion dollars without spending any of it. Please enjoy this Breakdown of Applied Intuition. For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠⁠⁠ ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- This episode is brought to you by⁠⁠⁠⁠ ⁠Portrait Analytics⁠⁠⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at ⁠⁠⁠⁠⁠portraitresearch.com⁠⁠⁠⁠⁠ ----- Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps (00:00:00) Welcome to Business Breakdowns (00:02:16) Intro: Applied Intuition (00:03:26) State of the Physical AI Market (00:07:19) Why Physical AI Will Be Bigger Than Digital AI (00:09:14) What Applied Intuition Builds & Sells (00:12:36) Staying Flexible Across Technologies & Verticals (00:13:16) Founding Story & Strategic Choices (00:17:10) Evolution of the Business: Tools → OS → Autonomy Stack (00:20:59) Introducing Dana: The Agentic Platform (00:23:40) Building Dana: Customer Demand vs. Vision (00:24:51) Why Applied Intuition Is Uniquely Positioned to Build Dana (00:28:29) The Cross-Vertical Data Flywheel (00:33:25) Rate Limiters to Physical AI Adoption (00:35:41) Business Model & Revenue (00:37:06) Customer Base & Global Reach (00:39:22) Competitive Landscape (00:44:21) Capital Allocation & Financial Strategy (00:47:15) The Future of Physical AI

Today, John, Les, and Special Guest Dinny McMahon—Head of China Markets Research at Trivium—trace the rise and slow-motion collapse of China's old economic model, and the technology and export-led manufacturing behemoth replacing it. For decades, China's economy ran on a simple engine: a high-debt construction and investment boom with real estate at the core and as the primary store of middle-class wealth. As Xi consolidated political power and began deflating the property bubble, he charted a new economic model featuring export driven growth and a high-tech industrial policy fueled by hyper competition—sparking today's high stakes clash with the U.S. and Europe.Can Xi's new economic model actually replace the prosperity that the property collapse destroyed? Why has Beijing continued to suppress consumption and what would it take for Chinese households to start spending? How should Washington be responding to a system that exploits global markets as we try to re-shore industry, de-risk supply chains, and compete for the frontiers of advanced technology? Are the flying cars we were promised finally here?! Check out the answers to these questions and more in this episode of Fault Lines.@lestermunson@johnclipsey@DinnyMcMahonLike what we're doing here? Be sure to rate, review, and subscribe. And don't forget to follow @faultlines_pod and @masonnatsec on Twitter!We are also on YouTube; watch today's episode here: Hosted on Acast. See acast.com/privacy for more information.

My guest today is Matthew Smith. Matthew is the founder and CIO of Chronometer Partners, which invests in energy, industrials, materials, power and utilities, and related infrastructure. For the last 18 months he and his team have modeled nearly every natural gas well, pipeline, and processing asset in the United States. He's reached a conclusion most of the market doesn't share.  Starting in 2028, AI data centers and LNG exports will need more gas than the country can produce and deliver. By his math, the US could exhaust its working natural gas storage by 2030. In his words, the upside risk to prices becomes unbounded and convex. We talk about why this was set in motion long before AI arrived, why the US can't just turn off exports, who wins and loses among producers, nuclear, solar, and the hyperscalers, and what he sees as the only long-term solution. Please enjoy my conversation with Matthew Smith. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- In June, Matthew wrote a letter to a small group of confidants laying out the full case behind his natural gas forecast. He has allowed us to publish it. You can read the full letter here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:02) Episode Intro: Matt Smith (00:03:33) The Conclusion After 18 Months (00:04:56) The Die Was Cast Before AI (00:07:24) Sizing AI's Gas Demand (00:09:33) Why Not Just Stop Exporting? (00:11:38) Is the Gas Even There? (00:13:53) The Timing Problem, Not Supply (00:15:15) Flow Versus Stock (00:19:10) What Slows Gas to Market (00:22:21) If Nothing Changes by 2030 (00:26:11) Could Prices Hit Twenty Dollars? (00:27:00) Gas Producers Poised to Win (00:28:54) Utility-Scale Solar's Windfall (00:30:08) What About Nuclear? (00:32:40) SMRs (00:34:29) The US Consumer Pays (00:36:37) Turbine Makers Building Too Late (00:37:57) Are Hyperscalers Exposed Too? (00:44:25) Kickstarting the Nuclear Build (00:46:20) Put Solar on Every Roof (00:46:52) Implications for the World (00:49:26) No One's Securing Supply (00:52:57) The Challenge for Energy CEOs

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

Today my guest is John Kim. John is one of the world's top and most prolific fundraisers.  He was chief client officer at General Catalyst, where he helped raise many of the firm's flagship funds. He is now chairman and president of corporate development at Lila Sciences, a company building scientific superintelligence, where he has helped raise several hundred million dollars.  He is also the author of The Tao of Fundraising.  This conversation is really a guide on how to raise money from someone who has done it at the highest level. We talk about why persuasion equals desire minus fear, the difference between belief and trust, the laws of fundraising, and how to build the consensus that moves big pools of capital.  Please enjoy my conversation with John Kim. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Introduction of John Kim (00:02:39) Money Moves at the Speed of Trust (00:05:06) How to Start a Fundraising Campaign (00:08:03) Persuasion Equals Desire Minus Fear (00:12:20) How to Raise a Few Billion Dollars (00:15:58) The Benchmark Story (00:18:36) The Law of Differentiation (00:24:13) Law of Tradeoffs and Law of Pipeline (00:27:52) The Karpman Drama Triangle (00:30:42) Oprah Winfrey (00:33:49) Most Common Fundraising Mistakes (00:38:35) Secretary of State (00:45:40) The Inner Game (00:47:38) The Kindest Thing

Individually they were just like those guys who like to hang around the comic book shop and talk comics but together they form EMX! Check out Thacher's books a DemonWeaselStudios.com In this eXplicit, uncut and unedited episode of EMX we review Marvel Comics X-Men books of May 2026: Cyclops #4 Generation X-23 #4 Inglorious X-Force #5 Magik and Colossus #4 Moonstar #3 Psyclocke - Ninja #5 Rogue #5 Storm - Earth's Mightiest Mutant #4 Uncanny X-Men #28 Wolverine #20 X-Men #29-30 X-Men United #3 [RSS] Subscribe [RSS] EMX Subscribe [Apple Podcasts] Subscribe [Google Podcast] Subscribe All Podcasts  Email: EMP@EarthsMightiestPodcast.com Website: http://www.EarthsMightiestPodcast.comFacebook Group: http://facebookgroup.earthsmightiestpodcast.com/Viet's Website: http://www.comedianviet.comThacher's Website: http://www.DemonWeasel.com  

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstBritain's most capable coding model can't be exported, and that ban is the whole reason Cosine set out to build one from scratch. Alistair Pullen, CEO and co-founder of Cosine, sits down with Tim Scarfe to explain how a frontier system he calls Fable, locked behind US export controls, became the founding case for a UK sovereign model trained on the Isambard supercomputer in Bristol.The bet underneath it is economic. Pullen argues that an inference company, rather than a training-first lab, doesn't need billions to compete: millions, a national compute allocation, and a consortium feedback loop can be enough. From there it gets into the machinery, why open-weight models still trail the frontier on size, active parameters and data, the mixture-of-experts versus dense trade-off and why active params dominate how a model actually feels, and the edge that real coding trajectories confer.The back half is about making agents trustworthy. Pullen makes the case for beating "slop" by rewarding the process instead of the final answer, reframes code review as runtime proof (spin the bug up in a VM and force the agent to actually exploit it), and walks through Swarm, Cosine's system running hundreds of sub-agents in one shot. It ends on why memory is still an unsolved hack, how synthetic graders let you run RL on tasks with no built-in test, and why Pullen reads US export controls as an accidental gift, with a supply-chain sting in the tail.---TIMESTAMPS:00:00:00 The sovereign mandate and the Fable ban00:04:02 Millions vs billions: the inference-company model00:07:19 The consortium feedback loop00:07:40 Why open models lag the frontier00:14:59 MoE vs dense, and why active params matter00:16:29 Trajectories: the process-data advantage00:19:48 Beating slop: reward the process, not the answer00:26:06 Reusable abstractions and the epistemic wall00:29:56 Code review becomes runtime proof00:37:32 Do agentic harnesses still matter?00:40:35 Swarm: orchestrating hundreds of sub-agents00:45:14 Why memory is still unsolved00:48:25 Synthetic data and graders for RL00:53:09 The US export gift and supply-chain risk---REFERENCES:organization:[00:01:15] Cosinehttps://cosine.sh[00:04:14] Mistral AIhttps://mistral.ai[00:05:50] Anthropichttps://www.anthropic.com[00:07:42] Coherehttps://cohere.com[00:08:36] DeepSeekhttps://www.deepseek.comtool:[00:02:52] Isambard-AIhttps://isambard.ac.uk[00:05:56] Colossus (xAI)https://en.wikipedia.org/wiki/Colossus_(supercomputer)[00:07:52] GLM (Z.ai)https://z.ai[00:11:52] NVIDIA B300https://www.nvidia.com/en-us/data-center/dgx-b300/[00:15:37] gpt-oss-120bhttps://huggingface.co/openai/gpt-oss-120b[00:15:52] Devstral 2https://mistral.ai/news/devstral[00:16:01] Llama 70bhttps://www.llama.com[00:17:05] Claude Codehttps://www.anthropic.com/claude-code[00:26:23] ARC-AGI (Francois Chollet)https://arcprize.org[00:40:38] Swarm (Cosine)https://cosine.sh[00:40:50] OpenAI Codexhttps://github.com/openai/codex[00:41:16] Lumen Outpost (Cosine)https://cosine.sh[00:41:18] Kimi K2 (Moonshot)https://huggingface.co/moonshotai/Kimi-K2-Instruct[00:49:55] SWE-benchhttps://www.swebench.com[00:52:40] SystemVeriloghttps://en.wikipedia.org/wiki/SystemVerilogperson:[00:23:40] Andrej Karpathyhttps://karpathy.aipaper:[00:27:10] GRPO (DeepSeekMath)https://arxiv.org/abs/2402.03300[00:27:13] GSPOhttps://arxiv.org/abs/2507.18071Incompressible Knowledge Probes, Bojie Lihttps://arxiv.org/pdf/2604.24827Estimating the Size of Claude Opus 4.5/4.6https://unexcitedneurons.substack.com/p/estimating-the-size-of-claude-opus---ReScript:https://app.rescript.info/session/5852d2b884c4ce4b?share=10b9799160845bb11779f8ac6cd3124f

Uncanny X-Men #290 (1992)The immense drama that is the UNCANNY X-MEN continues as Storm takes her time contemplating Forge's marriage proposal, Iceman's date continues to go sideways and Colossus has a day on the town with his newly-resurfaced brother Mikhail.Highlights include:Storm and Bishop go full Natasha BedingfieldJean Grey literally throws in the towelKrang's bodyMystique gets crazy eyesArchangel just went out for croissantsForge needs to cool the heck outAlso, if you're in the Loveland, CO area this Saturday July 11th, come check out Jen & Shawn at the RetroMania event at the Ranch Events Complex. Your hosts will be slinging comic books, action figures and saying hello to y'all.Details can be found right here: https://www.heritageeventcompany.com/loveland-retromania-comiccon-2026.html*** PROPER COMIC BOOK DISCUSSION STARTS AT 00:15:51 ***Promo: BACK TO THE BINS (https://twotruefreaks.com/podcast/qt-series/back-to-the-bins/)Continue the conversation with Shawn (@AngryHeroShawn) and Jen (@JenStansfield) on Twitter / Instagram / Facebook / Threads / Bluesky or email the show at worstcollectionever@gmail.com Also, get hip to all of our episodes on YouTube in its own playlist! https://bit.ly/WorstCollectionEverYTDownload the podcast on Spotify, Apple Podcasts and wherever you get your favorite shows. Please rate, review, subscribe and tell a friend!

Team. Welcome. Back at it. Thanks for your support. Dale's playing Starfield. Chuck is back on that Criterion stuff.LinksCheck out or Ko-fi at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ko-fi.com/batandspider⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠DISCORD⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get your ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider STICKERS here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Steve Barkett Rules t-shirts!!!⁠⁠⁠⁠⁠⁠Get a sweet Bat & Spider ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠t-shirt here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! All sale proceeds go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Movement For Black Lives⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Technical Adviser: Slim of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠70mm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Theme song composed and performed by Tobey Forsman of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Whipsong Music⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Follow ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider on Instagram ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Chuck⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Dale⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ on Letterboxd.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider on Letterboxd⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider Watchlist⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Send us an email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠batandspiderpod@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Leave us a voice message: (315) 544-0966Artwork by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Charles Forsman⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠batandspider.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bat & Spider is a ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TAPEDECK⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ podcast, along with our friends at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠70mm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Letterboxd Show⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Escape Hatch⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Will Run For...⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twin Vipers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Movie Mixtape⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Yeti is Still Broken⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Austin Danger Pod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Lost Light⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. ★ Support this podcast ★

Welcome to Dev Game Club, where this week we continue our series on Psychonauts. We dive into (ha) the lungfish levels and the Milkman Conspiracy and talk about the tension between systems and individually scripted experiences, before turning to user questions. Dev Game Club looks at classic video games and plays through them over several episodes, providing commentary. Sections played: To Milkman Issues covered: a special announcement, turkeys everywhere, long digression into weird Spelunky, setting up the Lungfish, hunting down all the collectibles, an underwater bubble and moving around, consistency in levels, coming in with different player options, involving the empathy of the player, the limits of other genres in engaging empathy mechanically, the difficulty of reading the environment, the unfortunate PS4 port from the PS2, avoiding oysters, a direct lineage of creativity, becoming Godzilla, reuse of abilities when you return, every level being a genre, leading the way creatively, parallel to indie film, re-entering the levels and the tongue-in-cheek, going every direction and the costs, relearning rules each level, the cost of testing boundaries due to other mechanics, jumping between gravity spaces, describing the Milkman level, conspiracy mad-libs, being in an environment where you will fail again and again, memorable levels here and elsewhere, zingers of stingers, lines in movies vs lines in games, reinforcing them, a typical day in the life for a game designer, iterating to solve problems, getting people on board and carrying vision, people who show what's going on with the project, building consensus.  Games, people, and influences mentioned or discussed: Spelunky, Andy Nealen, mysterydip, Beyond Good & Evil, Crash Bandicoot, King's Quest, Costume Quest, Headlander, Keeper, Stacking, Trenched/Iron Brigade, Tim Schafer, Daron Stinnett, LucasArts, Community, Velvet Underground, Four Weddings and a Funeral, Psychodyssey, Nintendo, Majora's Mask, Microsoft, Shadow of the Colossus, Ico, Outer Wilds, Portal, Duke Nuke'em, John Carpenter, Them, Roddy Rowdy Piper, Sasha/scarytiger, Jonno, Starfighter, Kirk Hamilton, Aaron Evers, Mark Garcia.  Next time: Finish the game? Twitch: timlongojr and twinsunscorp YouTube Discord DevGameClub@gmail.com 

My guest today is Jeremy Giffon.  Jeremy has been on the show before as one of our most popular guests, and this conversation is every bit as enjoyable as the first. Over the last 18 months, Jeremy has had hundreds of conversations with founders and with the capital behind their companies. I don't know many investors with such a high rep count in the most interesting corners of private markets, so I asked him what he has learned. We talk about what those lessons mean for founders and investors, why everyone has become subservient to the poster class, the hidden intellectual history behind Silicon Valley and much more. Please enjoy my conversation with my friend, Jeremy Giffon. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Jeremy Giffon (00:02:34) Lessons from 18 Months of Founder Conversations (00:07:01) The Billion-Dollar PDF (00:08:13) The Unifeed & Rise of the Timeline (00:17:02) Power Law & Breakout Content (00:18:48) AI Algorithms Driving Content (00:20:38) Timeline-Native White House (00:21:09) Traits of Great Posters (00:25:27) Peak Guy & the Billionaire Priest Class (00:32:13) Billionaires Now Defer to Posters (00:34:52) Freedom vs. Relevance (00:38:53) AI & White-Collar Job Displacement (00:40:53) Stewarding Your Gifts as Moral Duty (00:43:18) Next Wave of Finance: Equity-First Firms (00:53:26) East Coast vs. West Coast Finance (00:55:34) Beating the Market Is Not That Hard (01:00:40) SPV Feudalism & Allocation (01:02:10) Egregious SPV Fee Structures (01:04:50) Simplicity vs. Complexity in Investing (01:07:15) Hiring: Attracting Differentiated Talent (01:11:00) Silicon Valley's Hidden Intellectual Traditions

Anthropic said Commerce lifted export controls on Fable 5 and Mythos 5, restoring access Wednesday, and launched Sonnet 5. Sony is ending PlayStation game discs in 2028, a 140-company group unveiled Open USD, and Meta's building a cloud business. Anthropic says the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5 and that it will begin restoring access Wednesday (X) Anthropic says the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5 and that it will begin restoring access Wednesday (BleepingComputer) Anthropic launches Claude Sonnet 5, saying it nears Opus 4.8 performance at lower prices and is substantially better than Sonnet 4.6 for agentic work (Anthropic) Anthropic launches Claude Sonnet 5, saying it nears Opus 4.8 performance at lower prices and is substantially better than Sonnet 4.6 for agentic work (The New Stack) Sony says all new PlayStation games from both first- and third-party developers will be sold in digital formats from January 2028, ending physical game discs (Game File) Visa, Mastercard, Stripe, BlackRock, Coinbase, and 140+ companies join Open Standard to launch Open USD, a stablecoin that shares earnings from its reserves (The Block) Sources: Meta is developing plans for a cloud infrastructure business that will sell access to AI computing power and models, to compete with AWS and Azure (Bloomberg) SpaceX cuts monthly Starlink prices in half in the Memphis area, as it endures blowback and legal challenges from opponents of its Colossus data centers (Bloomberg) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

My guests today are Gavin Uberti and Rob Wachen, the founders of Etched.  A few years ago, when they set out to build a better AI chip than the largest companies in the world, almost everyone I called told me it could not be done. They have since done it, taping out a working chip on their first attempt and becoming the first hardware company founded after ChatGPT to do so. They already have more than a billion dollars of customer demand for their first product, and have raised eight hundred million dollars to build it.  Etched builds chips and systems designed to run AI models faster and at lower cost. They started the company in 2023, and that product is a complete rack for inference, the chip along with the boards, the power delivery, the interconnects, and the manufacturing to produce it all. We talk about the technical bets behind their architecture, how they hired industry legends and paired them with elite 22 year-olds, and why they believe inference will become one of the largest markets in the world. I think you will find the story of what they have built hard to forget. Please enjoy my conversation with Gavin and Rob. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:07) Gavin Uberti and Rob Wachen (00:03:54) Two 21-Year-Olds Taking on NVIDIA (00:07:52) The Two Technical Bets Behind Their Architecture (00:14:15) Why Inference Becomes the Biggest Market (00:20:23) Rob and Gavin's Origins Stories (00:28:38) How They Recruit Industry Legends (00:36:30) Moving a Dozen Engineers to Bangalore for Six Months (00:38:01) Speed Wins (00:43:58) Getting More Concurrency Out of Every Megawatt (00:52:44) Vertical Integration (00:57:43) Hardest Obstacles to Overcome (01:01:09) Raising The Largest AI Chip Series A Ever (01:06:29) TSMC (01:13:20) Designing Gen 2 for Gigawatt-Scale Production (01:16:42) Why Machines Don't Think Like People (01:20:03) A Year of Compute Compressed Into a Month (01:23:44) The Trillion-Dollar Data Center (01:26:19) The Kindest Thing

Gerard and Laurent welcome Michel Boutouil, co-founder and CEO of Polarise, a leading European AI infrastructure provider and NVIDIA Cloud Partner based in Berlin. After discussing about what happens outside of a datacenter, it is time to dive inside one.  Polarise is one of the few genuinely European NeoCloud companies — essentially a European counterpart to CoreWeave — specializing in GPU infrastructure for AI inference. Through its partnership with NVIDIA, Polarise designs its datacenters around the GPU rack itself, using liquid cooling from the outset rather than starting with a traditional real estate-first approach. The company has already developed AI factories in Germany, Norway and the UK.  In the conversation, we explore the growing commoditization of large language models and why the real long-term value may lie in AI factories — facilities that are fundamentally different from conventional datacenters. Given Europe's notoriously long grid-connection timelines, Polarise focuses on refurbishing brownfield sites with under 50MW of grid access instead of pursuing massive gigawatt-scale campuses. It's a pragmatic “pod” strategy: adapt to the grid's constraints rather than try to reshape the entire energy system.  We also tackle the thorny issue of digital sovereignty. With the U.S. CLOUD Act allowing U.S. authorities access to data managed by American tech companies, it is fair to ask what hyperscalers are doing with European data — and whether Europe needs its own sovereign AI infrastructure. Polarise has secured €1 billion in backing from Swiss investor SWI Stoneweg Icona, but even that is modest compared with the hyperscalers' spending power. For comparison, SpaceX has reportedly invested around $40 billion in Colossus 1 and 2 alone.  So, what does the future of the European AI ecosystem look like? Michel's answer is clear: Europe should not try to outspend China or the United States head-on. Instead, it should play to its strengths — smart execution, agility, flexibility, and the ability to learn quickly from the mistakes being made elsewhere.    “Today's show is supported by the BMW Foundation Herbert Quandt. The BMW Foundation unites leaders across sectors to develop solutions that foster an innovative economy and a future-proof society. A key focus is "Energy Transition & Climate Change," where the Foundation drives "International collaboration to accelerate the energy transition." With rising energy demands from AI and data centres, new partnerships, effective collaboration, and the exchange of science-based solutions and strategies are essential.”  

Across the country, developers are racing to build huge new buildings to house computers to fuel the AI boom, creating an explosive demand for new energy. While some hyperscalers seek renewable energy, others are turning to fossil fuels. But concerns around high electric bills, air and noise pollution and water depletion have generated widespread community pushback against these giant facilities, and it seems opposing data centers is a bipartisan issue. Many cities and states are working to rapidly update zoning and other local regulations to respond to the dual pressures of developer interest and constituent backlash. Since data center development isn't slowing down, what policies or creative strategies can lessen the impacts for local communities and ratepayers? Guests:  KeShaun Pearson, Executive Director, Memphis Community Against Pollution  Rebecca Egan McCarthy, Freelance Journalist Jason Plautz, Reporter, E&E News and Politico Astrid Atkinson, CEO, Camus Highlights: 00:00 Introduction 3:15 KeShaun Pearson on updates to the Colossus data center pollution 6:18 KeShaun Pearson on state regulators allowing an expansion of gas turbines  8:08 KeShaun Pearson on the effect of the pollution on the community 16:24 KeShaun Pearson on what he hopes the lawsuits can achieve  19:38 Rebecca Egan McCarthy on Archbald and data center development  22:26 Rebecca Egan McCarthy on who has the power to regulate data center projects 28:16 Rebecca Egan McCarthy on data center development outside of Archbald 30:21 Jason Plautz on changing attitudes toward data centers 34:32 Jason Plautz on where there is meaningful regulation happening 39:27 Jason Plautz on state level regulatory changes  41:26 Jason Plautz on the pace of data center development 44:45 Astrid Atkinson on the effects of data center energy load on the grid 46:19 Astrid Atkinson on what flexibility means in the energy world 50:39 Astrid Atkinson on hyperscalers paying for their energy 55:22 Astrid Atkinson on how some policy changes can help communities  For show notes and related links, visit ⁠our episode page⁠ at climateone.org --- Join Climate One for an induction cooking demonstration night on July 21, at 6 p.m. at the Commonwealth Club in San Francisco. Come enjoy delicious food and wine, and learn about why cooking with magnets beats cooking with gas. Tickets available at ⁠climateone.org/events⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

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

My guest today is Vlad Barbalat, the Chief Investment Officer of Liberty Mutual Investments, the $120 billion investment platform that sits within one of the largest insurance companies in the world.  Vlad grew up in Soviet Moldova, came to America in 1990, and built a career that eventually led him to one of the most distinctive capital allocator seats anywhere in finance.  Today we talk about how the mutual insurance structure creates a unique investment platform, what Liberty looks for in a new deal or partner, and what it means to build a career and a life in a country that gave you opportunities you never would have had anywhere else.  Please enjoy my conversation with Vlad Barbalat. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:53) Vlad Barbalat (00:01:28) The Most Interesting Seat in the Market (00:05:53) Breaking Down the $120B (00:10:41) How the Portfolio Is Constructed (00:11:00) The House View (00:13:49) What Liberty Looks for in a GP (00:16:32) Why Not Just Buy Bonds (00:18:30) Benefits of the Mutual Structure (00:23:40) The Luxury of the American Citizen Through Immigrant Eyes (00:30:26) How Immigration Shaped His Worldview (00:32:45) Direct Deals vs. GP Allocations (00:35:23) Branded Capital (00:39:07) Geopolitics & Investing (00:43:48) AI's Impact on Investing (00:46:22) The Valuation Debate (00:50:47) Public vs. Private Markets (00:53:53) Lessons from Goldman (00:54:41) Why Excellence Matters (00:57:30) Managing Permanent Capital (01:03:54) The Kindest Thing 

This week’s Pirate Street Journal episode covered three topics that, on the surface, seem unrelated: the SpaceX IPO and its acquisition of AI coding startup Cursor, the rise of plug-in solar panels for everyday consumers, and KFC’s ambitious brand overhaul. But at the end, each story carries a deeper lesson about how categories are born, how they grow, and what separates winners from everyone else. The Pirate Street Journal is a business show with a simple but provocative premise: the Wall Street Journal does not know how business really works. Not because its journalists are incompetent, but because mainstream business media obsesses over companies, products, and technologies while almost completely ignoring market categories. Hosted by Christopher Lochhead alongside Eddie and Bri, the show takes three major business stories each week and examines them through the category design lens. The result is a sharper, more useful read on what is actually happening in the economy and why it matters. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   SpaceX Did Not Just Buy a Startup, It Bought a Category SpaceX went public last Friday, and by Tuesday it had become one of the five most valuable companies in America, surpassing Amazon with a market cap of roughly $2.5 trillion. Days later, SpaceX agreed to acquire Cursor, an AI coding startup founded by four MIT students in 2022, for $60 billion in stock. Cursor had been valued at around $29 billion just months earlier, so SpaceX effectively paid double almost overnight. Most coverage focused on the eye-popping price tag and the fact that Cursor has roughly 20 employees. But Christopher argues that framing misses the point entirely. SpaceX did not make a consolidation play, where a company in a mature market acquires a competitor to cut costs and grab market share. This was an acceleration play. What SpaceX purchased was the category king position in a brand new and rapidly growing software category: AI tools for building software with AI. Cursor’s founder called it a new type of software, and he meant it. SpaceX, which already owns the bottom of the AI infrastructure stack through its Colossus supercomputer and orbital data center ambitions, just bought its way into the top of that stack through applications.   Plug-In Solar Is Not a Green Hobby, It Is a New Category Forming in Real Time Over a million households in Germany have installed plug-in solar panels that hang from a balcony and connect directly to a wall outlet in under an hour. Each unit is capped at around 800 watts and costs roughly $500. In states like California and Hawaii, where electricity runs 30 to 40 cents per kilowatt-hour, the panels pay for themselves in three years or less. Nine US states have already legalized the technology, with more than 20 others working on similar legislation. Eddie points out that traditional rooftop solar remained a luxury product because of permitting costs and installation complexity. Stripping those barriers away creates a fundamentally different category: distributed, consumer-owned power sold at Costco prices. The real power here is the network effect. One household with solar panels feeding back into the grid is a novelty. One million households doing it is a functioning power plant. Ten million changes the entire economics of the American grid, reduces peak demand costs, and buys the country time while large-scale nuclear and orbital solar infrastructure are developed. As Christopher notes, when a category is designed to produce radical abundance and includes a network effect, the compounding impact becomes truly transformational.   KFC Is Trying a New Look, But the Real Problem Is the Category Model Underneath KFC operates more than 3,600 locations in the United States, which is actually more than Chick-fil-A. And yet Chick-fil-A generates roughly $7.5 million per store each year while KFC pulls in under $2 million, despite being closed every Sunday. KFC’s response is a sweeping rebrand: new sauces, a boba and shakes drink line, immersive restaurant screens, a new logo, and a redesigned loyalty program. Eddie explains that the three things that actually drive success in quick service restaurants are beverages, speed of service, and the drive-through. Some of KFC’s moves make sense on the beverage side, since margins on drinks are far higher than on food. But expanding the menu risks slowing down service, which undermines the entire premise of the category. The deeper issue is structural. KFC is owned by Yum Brands, which for years co-located KFC with Taco Bell, confusing both the consumer and the category. Chick-fil-A, by contrast, is private, has an extraordinarily selective operator model, and charges just $10,000 for a franchise because it is looking for missionaries rather than mercenaries. That ownership clarity and cultural alignment is what produces four times the revenue per store, and no amount of boba or new signage is likely to close that gap without addressing what is happening underneath the brand. To hear more from The Pirate Street Journal, download and listen to this episode. You can also read more Pirate Street Journal entries in the Category Pirates newsletter.   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. 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