Podcasts about 15b

INTRO (00:24): Kathleen opens the show drinking a State Park American Blonde Ale from Tennessee Brew Works. She reviews her Memorial Day weekend in rainy Nashville, painting and making her Grandma Madigan's Pasta Salad for a party with friends.    TOUR NEWS: See Kathleen live on her “Day Drinking Tour.”   TASTING MENU (7:57): Kathleen samples Lay's World Cup French Onion Soup chips, Dill Pickle Hidden Valley Ranch Snack Mix, and Super Stuffed Strawberry Blast Pop-Tarts.    QUEEN NEWS (12:44): Kathleen shares that Taylor Swift attended the Knick's vs Cavs game with fiancé Travis Kelce and NBA coach Steve Kerr snuck her song lyrics as Easter eggs throughout press conferences for an entire season, and Stevie Nicks celebrated her 78th birthday.   HOLLYWOOD HAPPENINGS (17:32): HollyBobby provides the latest news in Hollywood.   SPANISH PHRASE OF THE WEEK (1:29:20): The Spanish phrase to learn this week is “donde esta la playa or “where is the beach” in English.    UPDATES (45:00): Kathleen shares updates on Congressman Pocan's drive to release beagles from the Ridgelan Farm breeding center, Mount Everest sherpas have set new records in 2026, and Shark Tank's Kevin O'Leary maintains that Utah's big data center will create 10,000 jobs.   WHAT ARE WE WATCHING (6:29): Kathleen recommends watching “Feud” on Hulu, and “The Crash” on Netflix.    HOLY SHIT THEY FOUND IT (1:06:16 ): Kathleen reads about rare Andean Mountain cats being captured on a trail camera.   TOURON NEWS (1:08:49): In “moronic tourist” news, Kathleen shares that a tourist stole the skull of the Saint Zdislava from the a Czech church, and a Texas man is arrested after intentionally driving his Cybertruck into a lake to test “wade mode.”    SPORTS NEWS (55:38): Kathleen reports on the outcry in Dallas as World Cup art replaces a beloved whale mural, and UFC Freedom 250 begins construction on the White House lawn.   FRONT PAGE PUB NEWS (1:18:52): Kathleen shares articles on Jaws being shown on the water on Martha's Vineyard this summer, mosquitoes have invaded Iceland for the first time in history, China has loaned 2 giant pandas to the Atlanta Zoo, studies have confirmed the world's best hangover cure, TSA is launching their new “Straight To The Gate” program in Boston, Google announces a $15B data center in mid-Missouri, and London cab drivers put “the Knowledge” test against driverless car programming, a new study shows that Christopher Columbus was actually Spanish, an Irish councilman proves how deep Irish grudges can run, China is hiring “white monkeys” to make businesses appear more global, Sizzler is making a comeback, and Primm Valley Casino Resorts are closing in Nevada.    SAINT OF THE WEEK: Kathleen reads about Saint Bartholomew, the patron saint of Armenia, tanners, book binders leatherworkers, and shoemakers.    FEEL GOOD STORY (1:50:37): Kathleen reads about a grandmother who rolled at a CRAPS table for 4 hours and 18 minutes consecutively at The Borgata in Atlantic City.

May 22, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Strava overhauls strength training with workout logging, auto-generated muscle maps, and 14 partner integrations ahead of IPO after surpassing 500M uploads in 2025 Fresha raises $80M at $1B+ valuation processing $15B+ annual transaction volume across 130K businesses in hair, aesthetics, wellness, and fitness Oura confidentially files for IPO at $11B valuation targeting 5M paid members this quarter with $2B annual sales run rate Today's episode is brought to you by AIIR — a modern communications and experiential agency for health, wellness, fitness, and performance brands. From earned media to events and creator-led campaigns, AIIR helps companies sharpen their story, earn attention, and build trust that compounds. Visit https://aiir.agency to learn more. More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Michael Saylor is the Founder and Chairman of Strategy (formerly MicroStrategy), the world's largest corporate holder of Bitcoin with roughly 818,000 BTC and over $65 billion in capital. After surviving a 99.8% stock collapse that left his company three days from bankruptcy, he rebuilt it into a "reserve bank for Bitcoin" - issuing products like STRC that pay an 11.5% monthly dividend, tax-deferred. In this episode, he breaks down how to build lasting, passive wealth with Bitcoin, why he believes there's no second best in crypto, and why working hard may be the worst financial advice you can follow.THE SHIFT NEWSLETTER

A deep dive into 10 Canadian secondary markets worth serious investor attention in 2026. With Toronto condo sales at a 35-year low and Vancouver projects struggling to hit presale thresholds, capital is flowing into cities where the fundamentals actually pencil.The episode covers Moncton (2.9% population growth, $386K avg price), Halifax (#1 nationally for investor interest, lowest office vacancy in Canada), Quebec City (13% YoY price growth), Ottawa (Ontario's highest industrial rents at $17.33/sq ft, 130K+ federal employees), Hamilton ($2.3B in building permits, LRT in final phases), Kitchener-Waterloo (200K+ tech workers, 46% job growth), Winnipeg (6% multifamily cap rates), Regina (2.9 months of supply, $343K benchmark), Saskatoon (100%+ construction growth, HQ to Nutrien and Cameco), Edmonton (most affordable of Canada's six largest cities), Victoria ($3.15B tech sector), and Kelowna (contrarian buyer's market play).Each market analyzed for population, employers, housing prices, rental data, and the investor thesis. EDMONTON MULTIPLEX EVENT Try it NordVPN risk-free now with a 30-day money-back guarantee! Use our code "realestate" to get 4 extras months from a 2 years plan Exchange-Traded Funds (ETFs) | BMO Global Asset Management LISTEN AD FREESee omnystudio.com/listener for privacy information.

Eddie Travia is the co-founder and CEO of Coinsilium Group, the first blockchain company to complete an IPO, and a pioneering crypto investor since 2013.In this episode, Eddie breaks down how AI agents and crypto are converging into a $15 trillion shift that will reshape investing, fundraising, and entrepreneurship - and explains why millions of jobs won't survive the transition.THE SHIFT NEWSLETTER

Rays on FireThe Rays are 24–12, with 5 sweeps — most in the majors — & have won 9 of their last 1013 consecutive games allowing 3 or fewer runs; starter ERA 1.38, bullpen ERA 0.998–1 in one-run games; 6–0 against AL East opponents; 13–6 at nightTeam ERA (3.68) significantly lower than FIP (4.12) — a sign of elite defensePitching Depth & InjuriesSteven Matz lands on the ILRyan Pepiot likely out for the seasonGriffin Jax & Mason Englert stepping in; Jesse Scholtens expected to piggybackBrody Hopkins - strong Triple-A outing (7 K's in 5 IP)Mat floats Robbie Ray as a trade target to shore up the rotationStandout PlayersNick Martinez – elite command, competitive fire, joyful presence on the moundJonathan Aranda – polished, clutch, consistent; Mat's pick for most exciting RayRyan Vilade – .438 wOBA with men on base, .500 in scoring position vs. leftiesChandler Simpson – speed, contact, pestering baserunner; even opposing announcers are frustratedBen Williamson – .345 avg over last 9 games & versatile defendShane McClanahan – 5⅔ IP, 2 hits, 4 K's vs. Shane; praised for team-first mentalityTaylor Walls & PitchComWalls has earned new respect at the plate and in the fieldUses a PitchCom earpiece to pre-position himself before pitches Compared to the "Jeter school" of anticipating play before it happensMat - "Who will be first to hack or jam PitchCom?"Bullpen BreakdownBrian Baker: 9 saves, 15 K's over 13 IP, only 3 walks — emerged as closer after a rocky 2025Garrett Clevenger returns; Ian Seymour strong from day oneCasey Legumina and Cole Sulser filling unexpected rolesStill missing Manuel Rodriguez, Edwin Uceta & Steven WilsonDespite absences, Pen ranks top 5 in MLBStadium / MOU UpdateRays reportedly willing to cover cost overruns Ask dropped from $1.5B to $1.15B — same as what Orlando has already committedBabby's July deadline looms; officials say documentation hasn't been providedMat and Mark both lean toward Orlando as the likely landing spot for the RaysMat argues the land deal may be the real endgame for ownershipAround MLBNL Central: All five teams at or above .500 — the most competitive division in baseballNL West: Dodgers & Padres in a two-team race, ~ 4 games clear of ArizonaNL East: Braves lead the Phillies by 9 gamesAL West: Only one team above .500 (Athletics, barely); Astros and Angels strugglingAL Central: Every team under .500AL East: Rays and Yankees out in frontFuture of MLB: Promotion & Relegation?Mat proposes a 12-team second league below the majors, with promotion/relegationPotential benefits: fan engagement, gambling markets, new TV deals, expanded rosters/salariesMLBPA would likely support it (more jobs, higher player values)Key hurdle: stadium infrastructure for new citiesBoth agree expansion should come before any realignmentRays Minor League System3 of 4 affiliates leading their divisionsJacob Melton expected back healthy mid-seasonHunter Feduccia finding his groove at catcher; Fortes continues to produceNext EpisodeSpecial guest: Brent Cardy of The Cardy Show to help dissect the Toronto Blue Jays' struggles

Joseph Lubin, co-founder of Ethereum and CEO of Consensys, warns that big tech is racing to build centralized AI systems that could control humans and every system on the planet - and explains why decentralized protocols are the only architecture standing in the way.THE SHIFT NEWSLETTER

Nepal just experienced one of Asia's most dramatic recent political upheavals. A former rapper and Kathmandu mayor, Balen Shah, swept to power in a landslide election, winning 182 of 275 parliamentary seats and wiping out every established political party. With half of Nepal's 30 million people under 25, this “Gen Z Revolution” could signal a trend for young democracies worldwide.In this episode, Sujeev Shakya - Chair of the Nepal Economic Forum and senior advisor for Nepal and Bhutan at BowerGroupAsia - explains what happened, why it matters, and what comes next for this small Himalayan country sandwiched between India and China.We explore:•⁠ ⁠How a youth-led anti-corruption movement toppled the government and formed an interim administration on Discord in just five days•⁠ ⁠Why Nepal's new PM is focused on public service delivery rather than grand promises, and whether he can actually end decades of entrenched corruption•⁠ ⁠Nepal's remarkable economic transformation: GDP growth from $7B to $44B in 20 years, fueled by $15B in annual remittances and a booming IT export sector•⁠ ⁠How Nepal navigates its position between India and China - aiming to be an economic “bridge” rather than a geopolitical buffer•⁠ ⁠The impact of the Iran war and the Strait of Hormuz closure on Nepal's fuel supply and its two million workers in the Gulf•⁠ ⁠Why thousands of Nepali soldiers are fighting for Russia in Ukraine - and the new government's challenge of bringing them home•⁠ ⁠Investment opportunities in hydropower, agriculture, technology, tourism, and infrastructureWhether you follow South Asian politics, India-China competition, or youth-led political movements, Nepal's story offers insights into how small states survive and thrive between great powers.

Hunter Horsley, co-founder and CEO of Bitwise Asset Management, makes the case that this is the most bullish moment in crypto's entire history - with major banks scrambling to catch up and every structural tailwind pointing in the same direction.From the $400 trillion in global assets beginning to shift toward crypto, to institutions going "zero to 500 miles an hour" on adoption, Hunter breaks down why the new era of crypto is already underway and why it's not too late to be early.THE SHIFT NEWSLETTER

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

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Strategy makes a massive $2.54 billion Bitcoin purchase (34,164 BTC), its third-largest ever, as spot Bitcoin ETFs log nearly $1 billion in weekly inflows — the strongest since mid-January. Bitcoin holds near $74K–$75K ahead of today's $7.9 billion April options expiry, which could spark volatility. Global crypto funds attracted $1.4 billion weekly as rebound sentiment grows, while Coinbase expands USDC borrowing to the UK and Polymarket eyes a $400M raise at $15B valuation. Markets show institutional resilience amid macro caution and DeFi liquidity signals—watch the options expiry and ongoing ETF momentum for near-term direction. Hosted on Acast. See acast.com/privacy for more information.

Jeff Park is a macro strategist and Bitwise advisor who believes the financial system is broken beyond repair for young people - from unaffordable housing to AI displacing an entire generation's jobs. He breaks down why real estate is actually a depreciating asset, why Bitcoin is the ultimate escape, and how AI will ignite the biggest wave of Bitcoin adoption the world has ever seen.THE SHIFT NEWSLETTER

Paulo Passoni, Managing Partner at Valor Capital, and Olga Maslikhova break down Claude 4.6, Brex's $5.15B sale to Capital One, and why only 4 US companies can IPO right now. This is the April 2026 edition of TJC Debrief — a monthly show covering tech, venture, and capital markets for Latin American founders and investors.We cover why Claude 4.6 was a bigger “aha moment” than the original ChatGPT for building companies and how it's rewiring CTO roles, org design, and the question of what a moat even is anymore, how Nubank, Revolut, Tether, and Plata are reshaping consumer finance and why Paulo thinks regional US banks are an “aberration” that shouldn't legally exist, the Brex x Capital One deal, Ramp's software multiple, and what the prof stack saving late-stage LPs means for every fintech exit going forward, Brazil's IPO window cracking open and Mexico's sudden flood of Sequoia, Founders Fund, and a16z capital, why Paulo thinks many employees are already “worse than AI” and why every salary should now come with a token budget, how he built a working marketplace in three hours on Perplexity Comet without writing a line of code, and the coming collapse of low-ROI universities and what it means for talent in LATAM.Subscribe to The J Curve Insider newsletter for deeper insights and follow Olga on LinkedIn and Instagram.

Gracy Chen went from a divorced single mom to CEO of Bitget, a top 5 crypto exchange doing $20B in daily volume with 25 million users. THE SHIFT NEWSLETTER

Today's Headlines: Trump held a press conference yesterday that was, by his standards, relatively contained. He threatened to decimate every bridge in Iran by midnight, said he could "take out" the country "in one night," and when asked if bombing civilian infrastructure constitutes a war crime, said he's "not concerned at all." He also threatened to jail reporters who don't reveal sources from the rescued pilot story, and went on his standard NATO rant — adding that his desire to leave NATO "all began with Greenland." France quietly pulled its entire $15 billion gold reserve out of the U.S. and moved it to Paris.  A 45-day ceasefire proposal from Egypt, Pakistan, and Turkey was rejected by Iran, and Trump said he won't extend his 8pm (or was it midnight?) deadline. The Strait remains closed to U.S. ships and the humanitarian cost is mounting: critical medical supplies, food, and aid are piling up at Dubai's port, with clinics across the Middle East, Africa, and Asia facing shortages. The cost of shipping goods now exceeds the value of the goods themselves in some cases. The Supreme Court cleared the way for the DOJ to dismiss Steve Bannon's contempt of Congress conviction — because why the heck not? Justice Samuel Alito was briefly hospitalized in March after getting sick at a Federalist Society dinner held in his honor. The Supreme Court didn't mention it until CNN asked. Artemis II made its historic moon flyby, and the crew named a previously unnamed bright spot "Carroll" after Commander Reid Weisman's late wife. Texas Rep. Tony Gonzalez, already under ethics investigation, now has a second staffer alleging he sent her hundreds of explicit texts she rebuffed. Savannah Guthrie returned to the Today Show after two months — her mother Nancy still has not been found. Kanye's Wireless Festival has now lost Pepsi, Diageo, PayPal, and Rockstar Energy as sponsors. Resources/Articles mentioned in this episode:  WaPo: At White House Easter Egg Roll, Trump tells the children about the war WaPo: Trump threatens to jail reporters if they don't turn over Iran source The Guardian: Trump's homeland security secretary mulls removing customs agents from airports to punish sanctuary cities – as it happened Mining: France pulls last gold held in US for $15B gain - MINING.COM The Hill: US, Iran receive draft ceasefire proposal: Reports NPR: Medical supplies are stuck in Dubai, as clinics around the world face shortages SCOTUS: Court-allows-steve-bannon-to-move-forward-on-dismissal-of-criminal-charges-against-him CNN: Exclusive: Justice Samuel Alito was taken to a hospital last month in previously undisclosed incident BBC: Emotional crew names Moon crater after commander's late wife NBC News:  Second staffer says Rep. Tony Gonzales sent her sexually explicit text messages NPR: Savannah Guthrie returns to the 'Today' show months after her mother's disappearance Rolling Stone: Wireless Festival Loses Diageo and Rockstar Energy Sponsorships Over Kanye West Booking Subscribe to the Betches News Room and join the Morning Announcements group chat. Go to: betchesnews.substack.com Morning Announcements is produced by Sami Sage and edited by Grace Hernandez-Johnson Learn more about your ad choices. Visit megaphone.fm/adchoices

Fresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z's legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc's long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today's moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore's Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc's comparison between today's AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they're free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc's claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet's bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI's “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z's AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What's Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right?Which is like, it's an overnight success ‘cause it's like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today's episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content.We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let's get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I'm joined by s Swix, editor of Lidian Space.swyx: Hello. And we're in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You're moving across the road.Marc: Uh, we're, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We're in actually the original office. We're in the, we're in the, we're, we're in the whole thing.swyx: It's beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I just made friends with Roone and, uh, I wanted to give him something to sort of be spicy about. And I said, uh. Uh, it'll never not be funny. The A 16 Z was constantly going. The future is where the smart people choose to spend their time and then going deep into crypto and not in ai. And that was in October 22nd, 2022.And Ruen says there was an internal meeting in a 16 Z to reorient around Gen ai. Obviously you have, but was there a meeting? What, what was that?Marc: I mean, I don't, look, I've been doing AI since the late eighties.swyx: Yeah.Marc: So I, I don't know, like all that, as far as I'm concerned, this stuff is all Johnny cum lately.Yeah. You, I mean, look, we've been doing ar entire existence. I mean, we've been doing AI machine learning deep, you know, deeply. We've been doing this stuff way from the beginning. Obviously a AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees.Um, you know, we, we both, Ben, Ben and I actually both are world enough to remember the actual AI boom in the 1980s. Yeah. There was like a, there was a big AI boom at the time. Um, and there was a, was names like expert systems. Um, and they of like lisp and lisp machines. Uh, I, I coded in lisp. I was coding a lisp in 1989.When that was the, the language of the AI future. Um, yeah. So this is something that we're like completely, you completely comfortable with. I've been doing the whole time and are very enthusiastic aboutswyx: is there a strong, like this time is different because, uh, my closest analog was 20 16 17. It was an AI boom.Mm-hmm. And it petered out very, very quickly. Um, we, it just, it just in terms of investingMarc: sort of, sort of,swyx: yeah. Investment, investment excitement.Marc: Although that's really when the, the, the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at, at the time it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point.Alessio: Yeah.Marc: Well, and as you guys, you guys have talked about this at length on, on your thing, but, you know, if you really track what happened, I think the real story is, it was, it was the Alex net, uh, basically breakthrough in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in 17.Alessio: Yeah.Marc: Um, and then everything that followed. But, but, you know, look, machine learning, you know, there were, you know, look, uh, I mean look, I've been working, you know, I've been working with, uh, one of my, you know, kind of projects working with Facebook since 2004. Um, and on the board since 2007, and of course, you know, they, they started using machine learning very early, um, and, you know, have used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization.And obviously many, you know, financial services. You know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things, it's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? Yeah. Um, and, and the layers arrive at different paces and, but they kind of build up.swyx: Yeah.Marc: Uh, they kind of build up over time and then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the trans transformer and then. And then as you guys know, there was this really weird like four year period where it's like the, the transformer existed and then it was just like,swyx: let's go.Yeah.Marc: Well, but, but it was just, but, but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chat Botts, but they weren't letting anybody use them.swyx: Yeah.Marc: Right. And then, you know, and then OpenAI developed Chat GT or GPT two, and then they told everybody, this is way too dangerous to deploy.Right. Yeah. You know, we can't possibly let normal people, normal people use this thing. And then you, you guys, I'm sure remember AI Dungeon, um mm-hmm. So the o for, there was like a year where like the only way for a normal person to use GP T three was in, in AI dungeon.Alessio: Yeah.Marc: And so you, you, we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.In reality, you're just trying to talk to talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big, big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took open ai. You know, they, they, they talk about this, it took open AI time to actually adjust, you know, kind of re redirect their researchswyx: path.I, I think, uh, let say Rosewood, right? Uh, the, the dinner that founded OpenAI was right there.Marc: Right, right. But that, that dinner would've taken place in 20swyx: 18Marc: 19. The formation of OpenAI Uhhuh as late as 2018.swyx: Uh, uh, sorry. Uh, no, I'm, I'm, I'm, I'm wrong. Probably It should be 20. Yeah. They just celebrated a 10 year anniversary, so it it is 2025.Yeah, so, so 2015?Marc: Yeah. 2015. Yeah. 2015. But then, uh, um, Alec Radford did G PT one in what, probablyswyx: mm-hmm. 17, 18,Marc: yeah. 17, 18. So it, yeah. For, and then, and then they didn't really, and then GPT three was what? 2020? 2020.swyx: 2020.Marc: Because that became copilot immediately. Even open ai, which has been, you know, the leader of, of this thing in the last decade, you know, e even they had to adapt and, and, and lean into the new thing.And so. Um, yeah, I, I think it's just this process of basically sort of wave after wave layer after layer, you know, building on itself. And then you kind of get these catalytic moments where, where the whole thing pops and, and obviously that's what's happening now.swyx: Is it useful to think about will there be any ai, winter?‘cause there's always these patterns. Like, is this, in the summer is something I constantly think about because do I get, do I just like. Just get endlessly hyped and just trust that I will only be early and never wrong or right. Well, are we, will there be a winter?Marc: So there's something about, say the following.There's something about AI that has led to this repeated pattern. Um, and, and, and you guys know this,swyx: it's summer, winter, summer,Marc: winter, summer, winter, summer, winter. And it goes back 80 years. Yeah. 80 years. Uh, so the original neural network paper was 1943. Right. Which is, which is amazing. Uh, that it was, it was far back that long.And then there was you, if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh, there was an a GI conference at Dartmouth University in 1950. 55. 55, yeah. And they got a NSF grant to, uh, for the, all the AI experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get a GI, uh, at the other end.And they got their, by the way, they got the grant, they got the 10 weeks and then, you know, 1955, you know. No, no. A GI. And like I said, I, I lived through the eighties version of this where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.Um, and, and it's probably on both sides of like the, the, the boom bus cycle. You, you kind of see that play out. Having said that, I think what's actually happened is like just, and you know, and we now know in retrospect like an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years or that was controversial. And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that like, these, these guys are right.They, they, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever, but like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.And, and the, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success.‘cause it's like bam, you know, chat, GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.Um, and thinking, and look, there were AI researchers who spent their entire lives. They got their PhD. They, they worked for, they've researched for 40 years. They retired in a lot of cases, they passed away and they never actually saw it work.swyx: Yeah. It's all sad.Marc: It is. It is sad. It's sad. Knewswyx: Jeff Hinton was like the last guy.Marc: Yeah. Yeah. Well, there were the guys, uh, was a guy, Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventors in the field. He's one of the guys who organized the Dartmouth Conference and you know, he taught at Stanford for 40 years. Wow. And passed, you know, passed away, I don't know, whatever, 10, 10 years ago or something.Never, never actually go. Got to see it happen. But like, it is amazing in retrospect, like, these guys were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another, like, you know, basically boom buzz cycle.And I, I will tell you, like, let, like in a sense, like yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timelessness to that. Having said that, there's just no question. Um, so the form, the foremost dangerous words in investing this time are, this time is different.Do you know the 12 most dangerous words investing? No. The four most d foremost dangerous words in investing are this time is different. Yeah. Um, the 12 most dangerous words. And so like, I'll tell you what's different. Like now it's working like, like there's just no, I mean, look, there's just no question.And by the way, I, I'll just give you guys my take. Like L LLMs, like from, from basically the Chad G PT moment through to spring of 25. I think you could still, I think well intention, well, and of. Form skeptics could still say, oh, this is just pattern completion. And oh, these things don't really understand what they're doing.And you know, the hall hallucination rates are way too high. And, you know, this is gonna be great for creative writing and creating, you know, Shakespeare and so sonnets and, you know, as, as rap lyrics or whatever, like, it's gonna be great and all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of feels that, you know, kind of really, really matter.And I think basically it was the reasoning breakthrough. It, it was oh one and then R one that basically answered that question basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world. And, and then obviously the coding breakthrough over the, over basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.Mm-hmm. Where you're just like, alright, if, if, you know, if Linus Tova is saying that the AI coding is no better than he is like. Like, that's, that's never happened before. That's theswyx: benchmark.Marc: Yeah. That's never happened before. And so now we know that it's, it's gonna sweep through coding and, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else.Right. It's just then, because that's, that's like, that's like, that's like the hardest in many ways. That's the hardest example. And how everything else is gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with Open Claw, which is fantastic. Which is amazing and incredibly powerful.And then we just got the, the, um, the auto research, uh, you know, the, the self-improvement. You know, we're now into the self-improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, l OMS reasoning, uh, agents, um, and then, uh, and, and then now RSI, um, and, and they're all actually working.Um, and so I'm, I'm just, as you like, you can tell I'm jumping outta my shoes. Like, like this is, like this is it like this, this is the culmination of 80 years worth of worth of work, and this is the time it's becoming real.Alessio: Yeah.Marc: I, I'm completely convinced.Alessio: I think the anxiety that people feel is like during the transistor era, yet Mors law, and it's like, all right, we understand why these things are getting better.We understand the physics of it. Yeah. With ai, it's. It's so jagged in like the jumps where like, like you said, it's like in three months you have like this huge jump like, and people are like, well this can keep happening. Right? But then it keeps happening,Marc: it'll keep happening.Alessio: And so like how do you think about also timelines of like what's we're building?I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead space. Right. And how does that inform, like how you think about the shape of the technology? You know, you talk about how it's a new computing platform.If you have a computing platform, then like every six months it like drastically changes in what it looks like. It's hard to build companies on top of it.Marc: Yeah. So, so a couple things. So one is like, look, the, the Moore's law was what we now call a scaling law. Like Moore's Law was a scaling law and for your younger viewers, more Moore's Law was every chip chip chips either get twice as powerful or twice as cheap every, every 18 months.And that, and that and that, you know, that it's gotten more complicated in the last few years. But like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that.Like that, you know, for, for 500 bucks. And so that, that was a scaling law. And then, and then, and then key to any scaling law, including Moore's Law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions, but when they work, they become self-fulfilling predictions because they, they, they, they, they set a benchmark and, and then the entire industry, right?All the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and in and in chips, that was a 50 year, that was a 50 year run. Right. And it, it was amazing. And it's still happening in, in some areas of, of chips.I think the same thing is happening with the, the core scaling laws. The core scaling laws. In, in, in ai, you know, they're, they're not really laws, but like they, they are basically. There are predictions and then they're motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going and, and look, it, it is, it's gonna be complicated and it's gonna be variable and they're, you know, there're gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.And obviously that's, you know, that's been happening a lot, you know, and then look, there's gonna be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're gonna, they're gonna pick up again and surge and then, and then, and then it, it appears what's happening to the eyes is there's not multiple, you know, multiple scaling laws.Um, there's multiple areas of improvement. And, and I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet. You know, they, like, for example, there's probably some scaling law around, um, world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.So that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so, yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah. The, the pace of improvement will continue to move really fast.Um. To your question on like what to build. So, uh, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, leaps and bounds. Uh, the part where I kind of part ways a little bit with how, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are.In many ways, the smartest people in the field, but also the people who spend their entire life, like at a lab, um, and have, have, I would say, have very little experience in the outside world. Um, the, the, the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated.Um, and, um, and doesn't, you know, it it 8 billion people making collective decisions on planet Earth is not a simple process of like, just like you see this happening now. It's like a bunch of AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's these, these obvious set of things that so society to do.Alessio: Mm-hmm.Marc: And then they're like, society's not doing any of those things. Right. And it's like, how can society not, you know, what, whatever their theory is, how can society not see x, y, Z? Mm-hmm. And the answer is, well, society is number one. There's no single society, it's like 8 billion people. And they like all have a voice, and they all have a vote, like at the end of the day of how they, they react to change.And then, you know, it just like, it's just human reality is just really complicated and messy. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, pe there's no question people are gonna, like, there's no question they're gonna be companies.It's already happening. There are companies that think that they're building value on top of the models and then they're just gonna get blissed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is, is just going to be messy and complicated.It's, it's not going to be simple and straightforward. It's gonna be messy and complicated. And there are gonna be a lot of companies and a lot of products, um, uh, and in, in fact entire industries that are gonna get built to, to, to basically actually help all of this technology actually reach real people.Alessio: The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast and Door Cash was like, why don't you just buy 10 x more GPUs? And he is like, because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level. How do you think about that?Also as a risk on, you know, you guys are investors, open AI and thinking machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate, right? Like how comfortable, I guess, do you feel with the downside scenario, like, and say like things Peter out, you think you can kind of like restructure uh, these build outs and uh, you know, capital investments.Marc: Yeah. So should start by saying, so I live through the.com crash, um, and I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic by the way. The, a lot of the.com crash was actually at the time, it was actually a telecom crash. It was a bandwidth crash.Like the, the thing that actually crashed, that wiped out all the money with the tele, the telecom companies.swyx: GlobalMarc: crossing. Global, global, yeah.swyx: I'm from Singapore and they, they laid so much cable o over over our oceans.Marc: Actually there was a scaling law in the.com. Era. And it was literally the, the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter.Um, and, and actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is gonna keep doubling every quarter.Doubling every quarter though is like, you know, grains of chess and the chessboard, like at some point the numbers become extremely large. Right. And, and, and it really, and really what happened was the internet. The internet by the way, continuously kept growing basically since inception. And it's, you know, it's, it's continuously grown.It's never shrunk. And it's grown really fast compared to anything else. Mm-hmm. You know, in, in, in human history. But it wasn't doubling every quarter as of 19 98, 19 99. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the.com crash, which was the, it they, they way over companies like global crossing way overbuilt fiber, which is sort of the, and by the way, fiber, telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data centers, like that was the beginning of the, of the, of the data center build and then, and the data center overbuild.And so you had that, but it was, it was literally, I think it was like $2 trillion got wiped out, right? It was like Jesus, it was like a big, it was. And by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt. ‘cause tech, tech companies generally don't run on debt, but the telecom companies run on debt.Physical infrastructure companies run on debt. And so the companies like Global Crossing not just raise a lot of equity, they also raise a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're, you're just over, you're overbuilding capacity.Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And, and then it, and then it's like they say about the hotel industry, which is, it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state.And then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today. Yeah. But 25 years later. But it, it, it took, and actually the elapsed time was, it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity.The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital, it's like, wow. It's just, I, I don't know how to invest in these crazy software things. For sure I can put build data centers and for sure I can buy GPUs that I can deploy, you know, compute grids and, and all these things.Um, and so, you know, if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000. Obviously that would be bad. The counter argument, which is the one I I agree with, which is the counter on, on the other side is a couple things.One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the do, like Global Crossing was like a, it was like an entrepreneur. It was like a, a new venture, but like the money that's being deployed now at scale is Microsoft, and, you know, and Amazon and Google, Facebook and Facebook and Nvidia and, you know, these, these, these, and, and now you know, by the way, open ai philanthropic, which are now at like, you know, really serious size, um, you know, as companies with, you know, very serious revenue.These are very large scale companies with like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th this is institutional in a way that, that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.Like so, and you guys know this, like everybody's starved for capacity, everybody's starved for compute capacity and then, you know, all the associated things, memory and, and, and interconnected and everything else. Um, data center space. And so e every dollar right now that's being put into the ground is turning into revenue.And, and it, and in fact, I actually think there's an interesting thing happening, which is because everybody starve for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints. That's true. Um, if Right pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful mm-hmm.The models would be much better. ‘cause you would just allocate a lot more money to training and you'd just build better models and they would be better. Um, and so we're, we're actually getting the sandbag version of the technology.swyx: Yeah. No. Everything we use is quantized because the, the labs have to keep the, the full versions,Marc: right?swyx: LikeMarc: we're not even getting the good stuff.swyx: Yeah.Marc: But, but getting the good stuff, it's, it's just, even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years.Once it happens, even the current technology is gonna get, gonna get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Mm-hmm. Like, it, it, you know, this isn't just sending packets across a, a thing, whatever, and hoping that people find something to do with it.This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Yeah. Um. Here's what I know, here's what I know. Um, in the next three or four year, it's like somewhere between three or four years out, basically everything is selling out. So like the, the entire supply chain is, is, is, is sold out or, or, or selling out.And so there, there's no, like, we're just gonna have like chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now. An enormous flood of investment in a new fab capacity and ev you know, every, everything else to be able to do that, at some point the supply chain constraints will unlock, you know, at least to some degree that will be another accelerant to industry growth when that happens.‘cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like, I know they're just gonna get like much, much better from here.And so I, I, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It. It, it is excel. It is, is accelerating. Like the, the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes.But I can't even imagine betting it that this is gonna like somehow disappoint and, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Yeah. Um, it was that Michael Burry, uh, uh, that'sswyx: anMarc: interesting guy, huh? We'll pick on a guy. We'll pick, let's pick on one guy.We'll pick. Well ‘cause he did, he he came out with, it was, it was the, heswyx: doesn't mind.Marc: It was the Nvidia short. Right. He came with the Nvidia short. And then if you guys probably talked about this, which is the, the analysis now that like the current models are getting better faster at such a rate that if you are running an Nvidia, if you're running an Nvidia inference chip today, that's three years old, you're making more money on it today than you did three years ago because the pace of improvement of the software is, is faster than the, the, the depreciation cycle, the chip.And then my understanding is Google is running. I don't if they've, I don't know exactly what, uh, these are rumors that I've heard or maybe it's public, but, um, I think Google's running very old TPUs, very profitably. Ference. Yeah. And very profit and very profitably. Yeah. Um, and so, so it actually turns out, as far as I can tell, it's actually the opposite of the Beery thesis is actually.He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And that, and again, that's an expression of the just ferocious pace of software progress.Ferocious pace of capability payoff. Yeah. Uh, that you're getting on the other side of this. And so I just, the idea of betting against that, like.swyx: Yeah. Yeah. Well, one ofMarc: my, it seems like an invitation to get your face ripped up.swyx: One of my early hits was like modeling the lifespan of the H 100 and h two hundreds and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically haircut cut it down to two to three.Yeah. But actually it's going up and not down. Yeah. And, and uh, that's, I mean that's, I think that's the dream. Uh, we are finding utilization and I think utilization solves all problems. Like, you can, you can find use, use cases for even like the poor, like even memory, we're having a shortage. Right. And, and even like the, the shittier versions of, of memory that we do have, we are finding use cases for it.So like That's great.Marc: Yeah.Alessio: How, how important is open source AI and kinda like edge inference in a world in which you have three years of supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like how do you think about inference, uh, in the data center versus at the edge?Marc: Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental construc, supply crunches for the next, I mean, you, you guys know if you just project forward demand over the next three years, right?Yeah. Relative to supply, one of the, its main predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years? And like, it may rise dramatically, right? Like, so, so what is, and then is, is, you know, like the, the, the big model competition are subsidizing heavily right now.Right? Right. And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do. And I, I don't know, it's gonna. I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw.Right? And so, okay. $30,000 a month. Right? And, and by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do, right? Yeah. And so you, you could imagine there, there's like latent demand of up to, I don't know, five or $10,000 a day of, of, of tokens for a fully deployed, you know, per personal agent.Uh, and obviously consumers can't pay that, right? And so, so, but it gives you a sense of the fu of the fu of the future scope of demand, right? And so, so even, even if there's a 10 x improvement in price performance, that still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay.Mm-hmm. So there's just gonna be like. Ferocious to me, by the way. The agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GGPU constraints, I think the agent thing now also translates into CPU constraints. Mm-hmm. Right?swyx: CPU memory.Marc: Yes. CPU memory, right?And so, like the entire chip ecosystem is just gonna get wait,swyx: wait for network constraints, that that will be the killer.Marc: It's all bottleneck potentially for years. And so, so I, I think that Brad, and, and I think it's actually possible, I mean, generally inference costs are gonna keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.And then at some point, maybe the lab stops subsidizing so much and that, that, that again, will be, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied. Um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean just the dramatic innovations that have happened in the Apple silicon to be able to do, uh, inferences, it's quite amazing the level of effort being put.Like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a pc, and then six months later mm-hmm. Oh, it runs in a pc, right? It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know.There's also like other, there's other motivators. There's other motivators which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for, in certain cases with some people, for certain use cases, people being like, well, I'm not willing to just like, turn everything over.So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a a, a smart local model. There's also performance issues where you want, you know, you want, you know, you're gonna want your doorknob to have an AI model in it.Right. You know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is gonna have an AI model in it. Mm-hmm. And it, a lot of those are gonna be local. Um, and so, yeah. No, like I think, I think you're gonna have ti and then you're gonna, by the way, also wearable devices, you know, you don't wanna do a complete round trip.You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah.swyx: The question, do we care who makes it? Yeah. One of the biggest news this week was the collapse of AI two, the Allen Institute. Mm-hmm. One of the actual American open source model labs. Yeah. Um, and, uh, I'm not that optimistic on, on American open source.Yeah. Like you, you guys invested in MIS trial and MIS trial's doing extremely well outside of China. That's about it.Marc: Yeah. We'll see. We'll see. I look, I, number one, I do think we care. Uh, I do think we, I do think we care who makes it. Um, I would say this, the, the, the, the previous presidential administration wanted to kill it in the us Oh yeah.They wanted to drown in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually like, actually wants it wants it to happen. And youswyx: earned to councilMarc: and Yeah. And the new and the P pcast. Yeah. So the, the, you know, this admin for whatever other political issues people have, which are many, you know, this administration has, I think a very enlightened view and in particular an enlightened view on AI and in particular on open source ai.Uh, and so they're very supportive. Um, my read is the Chi. The Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is, they, they, they don't fundamentally, they don't think they can sell commercial, uh, AI outside of China right now. And or at least specifically not, not in the US for a combination of reasons.And so they, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, uh, you know, paid, paid services. And then kind of an, you know, kind of an ancillary products. You know, they're, they're very excited about it, by the way. I think it's great. I think it's great that they're doing it.Um, you know, I think Deeps seek was like a gift to the world. Um, I think. The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like the paper, the paper, the paper and, and the code, right?And the code. And so, like, for example, I thought this was amazing. So open comes out with L one and it's an amazing technical breakthrough, and it's just like, absolutely fantastic. But of course they don't explain how it works in detail. And then of course they hide the, they hide the reasoning traces, right?And, and then, and then, and then everybody's like, okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is their secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper, and now the whole world knows how to do it.And then, you know, three months later, every other AI model is, is adding reasoning. And so, so you get this kind of double, like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.So that happens and then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, ai, uh, model companies. I mean, look, there's gonna be tremendous, you know, there already is. There's, you know, there's gonna be tre there's tremendous competition, uh, among the primary model companies.You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo Bo both X and then MetAware involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway.And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're backing, that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know how, how many, how many, like main line foundation model companies are there in China at this point?It's probably six. It'sswyx: five Tigers is what they call it. Yeah. Uh, Quinn is in questionable because there's change in leadership,Marc: right?swyx: Yeah.Marc: But that, does that include, that includes like Moonshot,swyx: yes. Can deep seek, uh, uh, ZI, um, Quinn oh one is in there.Marc: Right. And then, um, and by dance and, and then you see,swyx: ance would be like the next tier ance.They weren't as prominent. They weren't, didn't haveMarc: a leading. Yeah. But they, you at least, you know, ance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and, and so forth. And so, so, so like, look, here, here would be a thing you can anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale, at, at some level of a critical mass.It's not gonna be a dozen in three years, right? Like, it just because these industries don't bear a dozen, it's, it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.Um, and I think like open source is one of those strategies. And so I, I think you could see like a whole, i, I, I think the questions like, who's gonna do open source? I think that could change really fast. I, I think that, that, that's a very dynamic thing. I think it's very hard to predict what happens. And, and I think it's very important.swyx: NVIDIA's doing a lot.Marc: Well, I was gonna say. Well, exactly. And then you're got Nvidia and then, and then, you know, just to, again, indu, there's an old thing in business strategy, which is called, uh, commoditize Compliments. Commoditize the compliment. That's right. And so if your Jensen is just kind of obvious, of course, you wanna commoditize the software.Yeah. And he's, and to his enormous credit, he's putting enormous resources behind that. And so maybe it, maybe it's literally Nvidia and I think that would be great.Alessio: Yeah. Uh, narrative violation to European projects, uh, in the, uh, damn.swyx: I'm hosting my, uh, Europe, uh, conference soon. And I got both of them.Alessio: They got us.They got us. MarkMarc: finished. They got us, us. Well, wait a minute. Where was Peter? So where was Steinberger when he did? In AustriaAlessio: was, yeah, yeah, yeah.Marc: He was in what? He was in Vienna. Oh, he was in Vienna. And then where is he now?swyx: Uh, he's moving to sf.Marc: Okay. Okay. Alright. Okay, there we go. And then, yeah, the PI guy, right?The PI guys are European.swyx: Yeah, they're also, they're buddies inAlessio: Australia. Mario's also there. Yeah.Marc: Right. And are they, yeah, they haven't announced yet. Any sort of change changed or have theyAlessio: No, they're, they have a company there.Marc: Okay. Got, okay. Good.Alessio: Good, good,good.Alessio: Um,Marc: yeah, good.swyx: Anyways, I think pie and open cloud very important software things and, and I just wanted you to just go off on what you think.Marc: Yeah. So I think in co the, the combination of the two of them I think is one of the 10 most important softwares. Openswyx: Claw got all the attention, but Right. Talk about pie,Marc: pi pie's, kind of the Yeah. PI's, PI's kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to, I don't know, it still is very important, but like 19, from 1973 to like basically the creation of Linux, which is basically this, this thing used to call like the Unix mindset.Like so, so, ‘cause there were all these different, you know, theories. There are all these different operating systems and mainframes and, and then you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these, like, like in the old days, like, like the operating system that like made the computer industry really work, like in the 1960s mm-hmm.Was this thing called o os 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle. Um, of software. And, and by the way, it worked really well and they were very successful with it.But like, it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is like, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect, how the system worked. And then the, the Unix sky is originally out of at and t and then out out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.And the way architecture's gonna work is we're gonna have, we're gonna have a, a prompt and, and a, and a shell. And then, and then we're gonna, all, all the functionality is gonna be in the form of these discreet modules, and then you're gonna be able to chain the modules together. Mm-hmm. Yeah. And so like the, the, the op, it's almost like the operating, operating system itself is gonna be a programming language.Um, and then that led led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, basically chaining together Unix tools. And then that led to the emergence of these, these scripting languages like Pearl, where you, you could basically kind of very easily do this, and then the shells got more sophisticated and then, and then, and then look like, you know, that, that, that number one, that worked and that, that was the world I grew up in.Like I was, I was a Unix guy. You know, sort of from, call it 1988 to, you know, kind of all, all the way through my work and it worked really well. It, it's in the background, um, you know, nor normal people don't need to, didn't need to necessarily know about it, but like, if you were doing like system architecture, application development, you, you, you knew all about it.Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that.But, um, you know, but look, the inter, the internet runs on Unix, um, and that smartphones, actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, and then we just started taking that for granted. And then, and then so, so basically the, the way I think about what happened with Pie and then with Open Claw is basically what those guys figured out is, I always say the, the great breakthroughs are obvious in retrospect, right?Which is the best kind, the best kind. They weren't obvious at the time or somebody else would've done them already. Um, and so there is a, like a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, oh, of course. Mm-hmm. Like the, the, to me those are always the best breakthroughs.Well, actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course.swyx: Yeah. What other objective mattered?Marc: Yeah, exactly. But, but like it, right. But she's even saying it wasn't obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and, and very important.And so the way I think about pie and olaw is it's basically marrying the, the language model mindset to the un to the Unix, basically shell prompt mindset. And so it's, it's basically this idea that what, what, so what is an agent, right? And as, as, and as you know, like many smart people who have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents and the whole thing.And it turns out what is an agent. So it turns out what we now know is an agent is the following. It's, so it's a language model. And then above that, it's a ba, it's a bash shell. Um, so it's a, it's a Unix shell, and then it's, and then the agent has access, uh, has access to, to the shell. And, you know, hopeful, hopefully in a sandbox, maybe in, maybe in a sandbox.So it's, it's the model. Um, it's the shell. Um, and then it's a fi, it's a file system. Um, and then the state is stored in files. And then, you know, there's the markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called Aron job. There's a loop and then there's a heartbeat for the, there's heartbeat and, and the thing basically Wake Wakes up.Wakes up. So it's basically LLM plus shell, plus file system, plus markdown, plus kron. And it turns out that's an agent. And, and, and every part of that, other than the model is something that we already completely know and understand. And in fact, it turns out that like the latent power of the Unix shell is like extraordinary because basically like all, like, there's just like an, there's just enormous latent power in the shell.There's enormous numbers of Unix commands, there's enormous number of command line interfaces into all kinds of things already in the, you know, your entire, I mean your entire, just to start with, your computer runs on a shell. If you're running a Mac or a, or, or a phone, your computer, your computer's running on a shell, uh, already.And so like the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so like this whole idea where we need like MCP and these like product mm-hmm. Fancy protocols, whatever, it's like, no, we don't, we just need like a command, command line thing.So that's the architecture. And then it turns out what is your agent? Your agent has a bunch of files starting a file system. And then there's the thing that just like completely blew my mind when I write my head around it as a result of this, which is like, okay. This means your agent is now actually independent of the model that it's running on.Because you can actually swap out a different LLM underneath your agent and your, your agent will change personality somewhat. ‘cause the model is different, but all of the state stored in the files will be retained.swyx: Yeah. Different instruction set, but you just compiledit.Marc: Right, exactly. And it's all right.It's like right. Swapping out a ship and recompiling, but it's, it's still, it's still your agent with all of its memories. Um, and with all of its capabilities. And then by the way, you can also swap out the shell, uh, so you can move it to a different execution environment that is also, is also a b shell, by the way, you can also switch out the file system, right.Uh, and you can, and you can, and you can swap out the, the, the heartbeat for the, the crown framework, the, the loop that the agent framework itself. And so your agent basically is ba basically at the end of the day, it's just. It's just, its files. Um, and then, and then there's of course it a openswyx: call.Marc: Yeah, it's, it's basically, it's, it's just the files.Um, and then by the way, as a consequence of that, the agent and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so you're, you can instruct your agent, migrate yourself to a different, uh, runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, swap out the language model.Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually, it actually knows about its own files and it could rewrite its own files. Right. Which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.Like that, that, I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability and then that leads you to the capability. That just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself and it can do that.Extend yourself. Yeah. Right? Extend, extend yourself. Like extend yourself. Give yourself a new capability. Right? And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eat, sleep bed, and it gives me better advice and sleep.And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself. And your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to claw code or whatever.It'll write whatever it needs. And then the next thing you know, it has this new capability. And so you don't even have to, like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.Like if I, if I were, if I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. Yeah. And again, pe people are gonna look at it and they already get this response. People are gonna look at it and they're gonna say, oh, well, where's the breakthrough?‘cause these, the, all of these components were already known before. Mm-hmm. But, but this is the key, the key to the breakthrough was by using all these components that were known before, you get all of the underlying capability of that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls, trivi, trivial.Of course it's gonna be able to use your computer. It has full access to the shell. Right. And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser and, and often away it goes. And, and then you've got all the abilities of the browser also. Um, yeah.And so, and so the capability unlock here is profound. My friends who are, you know, deepest into this, are having their claw do like a, like, literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and, you know, these are, you know, these are prototypes and there are, you know, as you guys know, there's security issues.Yeah. And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability is just incredible.swyx: Yeah.Marc: And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents. And w

Armani Ferrante is the Founder and CEO of Backpack, a regulated global crypto exchange with over $420 billion in trading volume and one of the leading builders in the Solana ecosystem.THE SHIFT NEWSLETTER

"Companies designing for agents, not humans, are going to get a lot of lift."ClickHouse started as an internal tool at Yandex. Today it's the database Anthropic, OpenAI, Meta and Tesla all run on.In this episode, CEO Aaron Katz joins Lukas Biewald to talk about how he turned an open source project into a $15B company, why he acquired LangFuse knowing it could cost him customers, and what he's actually building for the agent era.Snowflake, Datadog and Databricks all come up. He doesn't shy away.Connect with us here:Aaron Katz: https://www.linkedin.com/in/aaron-katz-5762094ClickHouse: https://www.linkedin.com/company/clickhouseinc/Lukas Biewald: https://www.linkedin.com/in/lbiewald/Weights and Biases: https://www.linkedin.com/company/wandb/00:00 Trailer00:57 The Origin Story: From Yandex to ClickHouse Inc.04:43 Building ClickHouse Cloud & Raising $300M10:36 Growing Up Around Xerox PARC12:51 Salesforce, Mark Benioff & the Dot-Com Bust15:32 Cloud Skeptics vs. AI Skeptics | History Repeating18:05 Building a Modern Go-To-Market Playbook21:57 The SaaS Crash, Agents & the Future of Infrastructure27:09 The Datadog Love-Hate Story35:21 Hardest Moments: Russia, SVB & Sleepless Nights43:16 Outro

Welcome back to Upside, where Dan Bowyer of SuperSeed and Lomax Ward of Outsized Ventures, joined by Harry Destecroix, MBE, of SCVC unpack the forces shaping European venture, deep tech and capital.This week's conversation reflects a system shifting: Europe is writing bigger checks, physical AI is moving into focus, and the economics of AI are starting to change.The question is no longer where innovation happens.It is where value accrues.The stack isn't just scaling. It is being contested.What's covered:00:00 Intro and the week's themes02:00 Europe's €15B fund-of-funds and the capital gap08:00 Seed vs growth: where Europe is actually underfunded14:00 Bezos' $100B physical AI strategy20:00 Roll-ups vs rebuilds in the industrial economy25:00 China's token model and the cost collapse of AI31:00 Security, sovereignty and model choice36:00 Innovate UK and founder-led policy41:00 Capex vs revenue: the emerging imbalance47:00 Predictions and market direction52:00 Deals of the week

Xin is the Group CEO of GSR Markets, one of crypto's oldest market-making firms - bootstrapped with $20K over twelve years ago and still independently funded today. In this episode, he breaks down why crypto's market structure is fundamentally broken, how misaligned incentives are holding the industry back, and why GSR is transforming into crypto's first full-service investment bank modeled after Goldman Sachs. THE SHIFT NEWSLETTER

What if the work you think is growing your business is actually just making you feel like it is?After disappearing for 8 years, Travis Kalanick—former Uber founder—quietly built a $15B robotics company, operating in 30 countries with thousands of employees… without PR, branding, or even LinkedIn visibility.This forces a harder question: if you removed validation, visibility, and noise—would your business still move forward?In this episode, Ray breaks down the hidden difference between building and performing—and why the question you're asking might be capping your entire trajectory.What You'll Learn In This Episode:Why most founders are stuck in a “visibility loop” that feels productive—but isn't actually moving the business forwardThe mindset shift from “Can I do this?” to “What would it take?”—and how it changes the scale of problems you pursueHow removing yourself as the constraint fundamentally alters the opportunities, talent, and outcomes available to you//Welcome to The Ray J. Green Show, your destination for tips on sales, strategy, and self-mastery from an operator, not a guru.About Ray:→ Former Managing Director of National Small & Midsize Business at the U.S. Chamber of Commerce, where he doubled revenue per sale in fundraising, led the first increase in SMB membership, co-built a national Mid-Market sales channel, and more.→ Former CEO operator for several investor groups where he led turnarounds of recently acquired small businesses.→ Current founder of MSP Sales Partners, where we currently help IT companies scale sales: www.MSPSalesPartners.com→ Current Sales & Sales Management Expert in Residence at the world's largest IT business mastermind.→ Current Managing Partner of Repeatable Revenue Ventures, where we scale B2B companies we have equity in: www.RayJGreen.com//Follow Ray on:YouTube | LinkedIn | Facebook | Twitter | Instagram

Xiao-Xiao is the President of Jupiter, the leading DeFi super app on Solana, and former digital assets lead at KKR, one of the world's top private equity firms. In this episode, he reveals why he left Wall Street to go all-in on crypto - and how Jupiter is building zero-fee global payments that could disrupt a $5 trillion market. THE SHIFT NEWSLETTER

✔️ Bitcoin isn't acting like a risk asset anymore!✔️ Bitcoin ETFs record 5 consecutive days of inflows for the first time in 2026.✔️ Bitcoin is doing what no other asset in history has ✔️ Senator urges lawmakers to advance Bitcoin & crypto market structure legislation by Easter✔️ FATF guidance on Stablecoins and unhosted wallets✔️ Mastercard to acquire stablecoin infrastructure company ✔️ US Treasury set to buy back $15B in debt today✔️ The CFTC just told self-custodial wallet developers they don't need to register as brokers✔️ SEC Clarifies the Application of Federal Securities Laws to Crypto Assets✔️ Sources:► https://x.com/rhinobitcoin/status/2033923921301639660?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/bitcoinnewscom/status/2033928091744034824?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/bitcoinmagazine/status/2033928536411799755?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/onrampbitcoin/status/2033621476084003032?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/coindesk/status/2033883474755551595?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/bitcoinnewscom/status/2033906131932684372?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/theragetech/status/2033860903498891386?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/cointelegraph/status/2033859695455109341?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://x.com/tftc21/status/2033942582200607112?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► https://www.sec.gov/newsroom/press-releases/2026-30-sec-clarifies-application-federal-securities-laws-crypto-assets► https://x.com/coinbureau/status/2034052955650068840?s=52&t=CKH2brGypO5fEYTgQ-EFhQ► DONATE TO HELP KEONNE AND BILL https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools✔️ Check out Our Bitcoin Only Sponsors!► https://archemp.co/Discover the pinnacle of precision engineering. Our very first product, the bitcoin logo wall clock, is meticulously machined in Maine from a solid block of aerospace-grade aluminum, ensuring unparalleled durability and performance. We don't compromise on quality – no castings, just solid, high-grade material. Our state-of-the-art CNC machining center achieves tolerances of 1/1000th of an inch, guaranteeing a perfect fit and finish every time. Invest in a product built to last, with the exacting standards you deserve.► Join Our telegram: https://t.me/theplebunderground#Bitcoin #crypto #cryptocurrency #dailybitcoinnews #memecoinsThe information provided by Pleb Underground ("we," "us," or "our") on Youtube.com (the "Site") our show is for general informational purposes only. All information on the show is provided in good faith, however we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the Site. UNDER NO CIRCUMSTANCE SHALL WE HAVE ANY LIABILITY TO YOU FOR ANY LOSS OR DAMAGE OF ANY KIND INCURRED AS A RESULT OF THE USE OF THE SHOW OR RELIANCE ON ANY INFORMATION PROVIDED ON THE SHOW. YOUR USE OF THE SHOW AND YOUR RELIANCE ON ANY INFORMATION ON THE SHOW IS SOLELY AT YOUR OWN RISK.

Our 237th episode with a summary and discussion of last week's big AI news!Recorded on 03/13/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:* Perplexity announced “Personal Computer,” a local Mac-based AI agent positioned as a safer alternative to OpenAI's computer-use agents, while Anthropic added GitHub PR code review pricing reviews at $15–$25 and Cursor launched trigger-based “Automations” for always-on coding agents.* ChatGPT introduced interactive math/science visuals and Anthropic added in-chat interactive charts/diagrams; Nvidia released open weights for its 120B-parameter Natron Free Super hybrid Transformer–Mamba latent-MoE model trained natively at 4-bit for Blackwell GPUs.* Nvidia halted H200 production for China amid customs blocks and domestic chip pressure; xAI saw major co-founder departures; Anthropic previewed a Claude Marketplace for enterprise procurement; Yann LeCun's aMI raised $1.3B; humanoid robot maker Sanctuary reached a $1.15B valuation.* Anthropic sued the Pentagon over a “supply chain risk” designation as memos ordered removal within 180 days; research covered models resisting activation steering, limits of chain-of-thought control, inference-scaling boosting cyber-task success, low-probability risky actions, weaknesses in SWE-bench, multimodal pretraining, long-context RNN memory caching, context-parallel training efficiency, RL for CUDA kernel optimization, and latent introspection detecting concept injection.A thank you to our current sponsors:Box - visit Box.com/AI to learn moreODSC AI - go to odsc.ai/east and use promo code LWAI for an additional 15% off your pass to ODSC AI East 2026.Factor - head to factormeals.com/lwai50off and use code lwai50off to get 50 percent off and free breakfast for a yearTimestamps:(00:00:10) Intro / Banter(00:01:23) Response to listener commentsTools & Apps(00:02:06) Perplexity's Personal Computer turns your spare Mac into an AI agent | The Verge(00:04:22) Anthropic launches code review tool to check flood of AI-generated code | TechCrunch(00:08:08 ) Cursor is rolling out a new kind of agentic coding tool | TechCrunch(00:11:14) ChatGPT can now create interactive visuals to help you understand math and science concepts | TechCrunch(00:11:56) Anthropic's Claude AI can respond with charts, diagrams, and other visuals now | The VergeProjects & Open Source(00:13:54) Introducing Nemotron 3 Super: An Open Hybrid Mamba-Transformer MoE for Agentic Reasoning | NVIDIA Technical BlogApplications & Business(00:21:22) Nvidia halts H200 production as China backs Huawei AI chips(00:28:33) Another XAI Cofounder Has Left, and Another Says He's Leaving. - Business Insider(00:34:04) Anthropic's Claude Marketplace allows customers to buy third-party cloud services | TechRadar(00:37:57) Yann LeCun's AMI Labs raises $1.03 billion to build world models | TechCrunch(00:44:52) Humanoid robotics maker Sunday reaches $1.15B valuation to build household robots | TechCrunchPolicy & Safety(00:46:09) Anthropic Sues Department of Defense Over ‘Supply Chain Risk' Label - The New York Times + Google and OpenAI Just Filed a Legal Brief in Support of Anthropic (00:53:24) Internal Pentagon memo orders military commanders to remove Anthropic AI technology from key systems - CBS News(00:58:15) Endogenous Resistance to Activation Steering in Language Models(01:06:27) Reasoning Models Struggle to Control their Chains of Thought(01:09:52) ‘It means missile defence on datacentres': drone strikes raise doubts over Gulf as AI superpower(01:14:57) Evidence for inference scaling in AI cyber tasks: Increased evaluation budgets reveal higher success rates(01:18:24) Frontier Models Can Take Actions at Low ProbabilitiesResearch & Advancements(01:24:20) Research note: Many SWE-bench-Passing PRs Would Not Be Merged into Main(01:28:26) [2603.03276] Beyond Language Modeling: An Exploration of Multimodal Pretraining(01:40:09) Memory Caching: RNNs with Growing Memory(01:48:47) Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking(01:58:41) CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation(02:08:57) Latent Introspection: Models Can Detect Prior Concept Injections(02:16:45) Physics of RL: Toy scaling laws for the emergence of reward-seekingSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Bobby Ong is the co-founder and CEO of CoinGecko, the crypto data platform he bootstrapped with $200 over 12 years - no VC money, no shortcuts.In this episode, Bobby reveals why most crypto founders are destined to fail, how he caught employees taking bribes for listings, and what it really takes to survive in an industry designed to destroy you.THE SHIFT NEWSLETTER

(00:00)Intro (10:13)Memories (17:10)Bantam AA (28:38)Bantam A (37:04)Bantam B (43:50)15A (48:59)15B (54:32)12A (1:03:05)Breakaway Academy (1:11:05)12B (1:21:29)Peewee AA (1:36:01)Peewee A (1:46:26)Peewee B

Episode 502 features Stacey's conversation with Brian Machut (Alliant Health) on how widespread Medicare fee-for-service fraud is inflating costs and undermining ACO shared savings in MSSP and ACO REACH. ACOs uncovered major urinary catheter fraud in 2023 tied to codes A4352/A4353, totaling about $3.5B, with some beneficiaries billed for items never received (including a case shared by Dr. Tara Lagu).  CMS created a "SAHS" (significant, anomalous, highly suspect) process to remove certain suspect costs, but benchmark effects can unevenly impact ACOs; catheter fraud is still projected at $3–$3.5B in 2025. The episode also highlights rapidly growing "skin substitute" spending projected at $13–$15B in 2025; CMS did not classify 2024 skin substitute costs as SAHS, leaving them in ACO performance calculations.  Machut explains this fraud and missed CMS trend projections can reduce provider earnings, discourage participation in value-based care, and potentially drive cost shifting into higher commercial rates—affecting plan sponsors such as self-insured employers. === LINKS ===

In "Is Your ERP a Data Graveyard: How to Unlock Millions with Nauta's Valentina Jordan", Joe Lynch and Valentina Jordan, Co-Founder and CEO of Nauta, discuss how structuring fragmented data turns supply chain silos into actionable revenue. About Valentina Jordan Valentina Jordan is the Co-Founder and CEO of Nauta, where she is re-engineering supply chains through clean AI data infrastructure. Previously, Valentina led product for Rappi's largest business segment, helping build and scale the core product stack behind Latin America's largest delivery platform, before bringing that same operational rigor to leadership roles at Amazon. At Nauta, Valentina brings a product-first, systems-level perspective to rethinking how supply chains operate, tackling the industry's most foundational challenge: building clean, structured data infrastructure that enables smarter decision-making. About Nauta Nauta is the AI-native operating system that connects your inventory, logistics, and procurement data into one intelligent layer. By acting as an intelligent membrane over existing ERP, TMS, and WMS systems, Nauta eliminates "data graveyards" by unifying fragmented data from emails, documents, and spreadsheets into a single source of truth. The platform moves beyond simple visibility, providing SKU-level insights and automated workflows that allow shippers to proactively manage exception handling and cash flow. Trusted by multinational leaders in the food, beverage, and retail sectors including distributors for brands like New Balance, Modelo, and L'Oreal, Nauta manages data for enterprises representing over $15B in annual sales. SOC 2 Type II certified, the platform empowers manufacturers and retailers to reduce container lifecycle times, prevent stockouts, and eliminate costly penalties like detention fees. Nauta's mission is to provide the standardized "rails of data infrastructure" necessary for truly autonomous and resilient global supply chains.  Key Takeaways: Is Your ERP a Data Graveyard: How to Unlock Millions In "Is Your ERP a Data Graveyard: How to Unlock Millions with Nauta's Valentina Jordan", Joe Lynch and Valentina Jordan, Co-Founder and CEO of Nauta, discuss how structuring fragmented data turns supply chain silos into actionable revenue. The "Data Fragmentation" Mess: Global shippers are stuck with data trapped in emails, PDFs, and clunky legacy systems. This chaos forces teams to waste 75% of their day babysitting spreadsheets instead of making moves that actually scale the business. One Single Source of Truth: Nauta fixes this as an AI-native engine that pulls those messy data streams into one place. From finance to procurement, everyone works off the same live data—killing "tribal knowledge" for good. The Real Cost of Stockouts: For brands like Modelo or L'Oreal, a stockout isn't just a missed sale; it's a hit to your reputation and a massive financial penalty. Nauta shifts you from reactive "firefighting" to proactive prevention. Saving Millions in Revenue: Using predictive analytics, Nauta's inventory engine flags risks weeks in advance. One customer even saved $1.2M in a single quarter by dodging retail penalties and lost sales. Killing "Dry Runs" and Fees: Shippers pay for empty trucks because they can't see what's happening at the port. Nauta's predictive tech and automated communication can slash detention costs by up to 80%. SKU-Level Control: Most platforms track the box; Nauta tracks the product. We map data down to the individual item, so you know exactly which vessel is carrying your high-priority promotional stock. Smarter Procurement: With SKU-level insights, your team can make surgical decisions—like rerouting high-demand items before they even dock—ensuring the right product hits the right shelf every time. Learn More About Is Your ERP a Data Graveyard: How to Unlock Millions Valentina Jordan | Linkedin Nauta | Linkedin Nauta The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

Evan Cheng co-founded SUI after leading Facebook's Libra project -  then threw away everything they built because it wasn't good enough. THE SHIFT NEWSLETTER

Pharma ads, biotech IPOs, $1M longevity programs, oh my!This month's Digital Health Download skews towards biotech, which is having a moment. Tune in to hear Halle and Michael cover the latest headlines.We cover:Why pharma ads are surging and the growing push for restrictions on D2C drug advertisingHims & Hers' $1.15B acquisition of Eucalyptus, its global expansion strategy, and the FDA crackdown on compounded GLP‑1 drugsThe return of biotech IPOs, with Eikon Therapeutics and Generate Biomedicines signaling investor interest in platform‑based drug discoveryVaccine makers scaling back research amid policy uncertainty, declining uptake, and tighter fundingTrumpRx's “most favored nation” drug pricing approach, and what one STAT analysis foundBryan Johnson's $1M per year “Immortals” longevity program—Show notes:Should drug companies be advertising to consumers? (The New York Times) Hims & Hers Enters $1.15 Billion Agreement to Acquire Eucalyptus (PharmExec.com)A sign biotech is back? Four drugmakers go public, raising nearly $1 billion in all (STAT)Vaccine Makers Curtail Research and Cut Jobs (The New York Times) TrumpRx claims to offer the lowest prices. But many drugs have cheaper generics (STAT)Bryan Johnson's Immortals: $1M to try longevity regimen (Axios) —"Halle Tecco wanted to see tech used for better medical services and getting people engaged in their own health. Now, she's written a book on how she went about it." - The WSJMassively Better Healthcare is out now!—Rock Health's annual CEO Summit is returning to the New York Stock Exchange on March 27th! Learn more and nominate a CEO to join this invite-only event here. —

Retrouvez l'épisode en version française ici : https://www.gdiy.fr/podcast/alice-bentinck-vf/ She might be the most underrated founder in Europe.Alice Bentinck has no massive press coverage.Just 15 billion dollars worth of companies built quietly over 10 years.Alice is the co-founder of Entrepreneurs First, the talent investor that finds founders before they have a company, before they have an idea, sometimes before they even know they want to be a founder.The model sounds crazy.VCs have told her it would never work.But Entrepreneurs First has now produced 500 seed-funded companies, counts Reid Hoffman and Patrick Collison among its backers.In this episode, Alice breaks down everything she has learned about co-founders: why breakups kill more startups than bad ideas, how to know in 48 hours if someone is the right partner, why three co-founders is the most expensive mistake you'll make, and why megalomania is not a flaw but a necessity in every great founder.If you've ever struggled to find the right co-founder, or wondered whether the one you have is actually the right one, this episode is for you.You can contact Alice on LinkedIn.If you want to apply to Entrepreneurs First, you can reach Julia and Anastasia at: gdiy@joinef.comTIMELINE:00:00:00 Finding founders before they have a company00:11:37 The co-founder mistake that kills startups00:17:42 The 3-founder trap: The most expensive mistake00:26:22 How to know when to have that hard conversation00:33:23 The Human Algorithm: How Alice spots potential before the idea00:44:26 How to access American capital without losing your European soul00:52:11 Scaling the Unscalable: How EF went from 10 to 100 companies a year01:03:47 The Customer Secret: Why your location defines your speed01:12:05 The 5-Attempt Rule: Why your first company doesn't need to work01:19:53 High Personal Exceptionalism: You must believe you are different to succeed01:35:46 The 996 Reality: Startups are the ultimate negative lifestyle choice01:53:07 Methodical is Slow: Why European founders are focusing on the wrong things02:01:12 The AI Performance Hack: How to manage your health & a $15B portfolio02:08:20 The $1,000-An-Hour Secret: How coaching builds a high-performing teamWe referred to previous GDIY episodes : #487 - VO - Anton Osika - Lovable - Internet, Business, and AI: Nothing Will Ever Be the Same Again#500 - VO - Reid Hoffman - LinkedIn, Paypal - How to master humanity's most powerful invention#429 - Nicolas Dessaigne - Y Combinator - Le berceau des futurs géants de la tech#483 - Carlos Ghosn - Out of the box : masterclass business de l'évadé du siècle#158 Edgar Grospiron - Athlète et conférencier - Avance, fais-toi confiance.A few recent episodes in English : #513 - VO - Jesper Brodin - IKEA - 40 billion in revenue empire with no bank loan#500 - Reid Hoffman - LinkedIn, Paypal - How to master humanity's most powerful invention#487 - VO - Anton Osika - Lovable - Internet, Business, and AI: Nothing Will Ever Be the Same Again#475 - VO - Shane Parrish - Farnam Street - Clear Thinking: The Decision-Making Expert#473 - VO - Brian Chesky - Airbnb - « We're just getting started »#452 - VO - Reid Hoffman - LinkedIn, Paypal - L'humanité 2.0 : Homo technicus plus qu'Homo sapiens#437 - James Dyson - Dyson - “Failure is more exciting than success”#431 - Sean Rad - Tinder - How the swipe fever took over the worldWe spoke about :DuolingoEntrepreneurs first's portfolioY CombinatorOur documentary to understand the American DreamAu Royaume-Uni, l'impopularité du Brexit relance le débat sur les liens avec l'UEOpenAI to remove non-profit control and give Sam Altman equityAztecPolyAIThe 996 working hour systemReading Recommendations :Fierce Conversations, by Susan ScottSuper Founders, by Ali TamasebThe Road Less Travelled, by M.Scott PeckHow to Be a Founder, by Alice BentinckA work in progress, by René RedzepiInterested in sponsoring Generation Do It Yourself or proposing a partnership ? Contact my label Orso Media through this form.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Anand Gomes, CEO of Paradex, exposes how most crypto token launches really work, reveals the red flags that separate serious builders from teams that cash out and disappear, and explains why Paradex locked 80% of its own team tokens to public performance milestones.THE SHIFT NEWSLETTER

February 20, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Ergatta joins Interactive Strength portfolio alongside Wattbike, CLMBR, and FORME, continuing independent operations under CEO Tom Aulet D1 Training hires Harris Williams to explore sale, operating 170+ locations serving 100K+ scholastic athletes in $154B youth sports market Hims & Hers acquires Australian telehealth platform Eucalyptus for $1.15B, expanding globally with brands serving 775K patients across Australia, UK, and Japan I'm heading to LA this week for the Connected Health & Fitness Summit to host a fireside chat with Fritz Lanman, CEO of Playlist (parent company of Mindbody and ClassPass), on AI in fitness and the anticipated $7.5B EGYM merger. If you're attending or based in LA and want to meet up, email team@fitt.co. More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Jeff Yan, founder of Hyperliquid Labs, returns after one of crypto's most extraordinary moments - a $1 billion community airdrop that grew to $10 billion, built by a team of just 11 people who didn't stop to celebrate.THE SHIFT NEWSLETTER

In this episode of the Model FA podcast, host David DeCelle spoke with Jennifer des Groseilliers, CEO of The Mather Group, to discuss her career journey, the firm's growth strategy, and its distinct business philosophy. Jennifer detailed her transition from practicing law to becoming a financial advisor, moving through the broker-dealer world to ultimately embrace the Registered Investment Advisor (RIA) structure. The conversation explored The Mather Group's holistic service platform, which includes in-house tax and investment construction, its two-part M&A integration approach, and how their core value of "Unreasonable Hospitality" drives success metrics like high client retention and referrals.In this episode: • The RIA Model is a Growth Area: Jennifer des Groseilliers' move from the broker-dealer world highlights the perceived stability and future of the Registered Investment Advisor (RIA) model. • Embrace Holistic Wealth Management: The Mather Group's success with $15B in AUM/A is built on a comprehensive platform that integrates financial planning, in-house tax services, investment management, and a family office approach. This "all-in-one" model offers greater efficiency and coordination for clients. • M&A Success Hinges on Culture: For firms pursuing growth via mergers and acquisitions, the primary focus ("heavy lifting") should be on ensuring cultural alignment, not just operational synergy. • Define Success by "Unreasonable Hospitality": High client retention and referral rates can be achieved by moving beyond basic service to align a client's financial goals with their personal values, creating a truly exceptional experience. • Strategic Growth is Dual-Track: The firm demonstrates that expansion can be effectively achieved through both organic client acquisition and strategic M&A. • Team Structure and Governance Matter: Utilizing a team-based service model and having an advisory council composed of equity owners are effective mechanisms for ensuring service consistency and internal goal alignment. • Advice for Women in Finance: Aspiring women professionals should seek out firms that demonstrably align with their personal values and provide a genuinely supportive working environment.  #RIAMergers #FinancialAdvisor #WealthManagement #TheMatherGroup #ModelFA #RIAAcquisitions #FinancialPlanning #UnreasonableHospitality #BusinessStrategy #WomenInFinance #SuccessionPlanning #OrganicGrowth #TaxServices Connect with Jennifer des Groseilliers and The Mather Group: Website: TheMatherGroup.com Email: info@TheMatherGroup.com --- About the Model FA Podcast The Model FA podcast is a show for fiduciary financial advisors. In each episode, our host David DeCelle sits down with industry experts, strategic thinkers, and advisors to explore what it takes  to build a successful practice — and have an abundant life in the process. We believe in continuous learning, tactical advice, and strategies that work — no "gotchas" or BS. Join us to hear stories from successful financial advisors, get actionable ideas from experts, and re-discover your drive to build the practice of your dreams.  Did you like this conversation? Then leave us a rating and a review in whatever podcast player you use. We would love your feedback, and your ratings help us reach more advisors with ideas for growing their practices, attracting great clients, and achieving a better quality of life. While you are there, feel free to share your ideas about future podcast guests or topics you'd love to see covered.  Our Team President of Model FA, David DeCelle If you like this podcast, you will love our community! Join the Model FA Community on Facebook to connect with like-minded advisors and share the day-to-day challenges and wins of running a growing financial services firm.

Joe's Premium Subscription: https://standardgrain.com/Apple Podcasts https://podcasts.apple.com/us/podcast/grain-markets-and-other-stuff/id1494161095Spotify https://open.spotify.com/show/4NJ9AZcSQBrLXFLCcPrGGG

Danica Mason is an ENR Northwest 2024 Top Young Professional and Principal of Red Team Go. She has over 19 years leading A/E/C clients in winning proposals, DBE/inclusion strategies, and civil rights management for 15B dollar plus projects. Top 3 Value Bombs 1. Scaling as a woman -or BIPOC- owned AEC firm is hard but specializing and delivering exceptional work creates real paths to the top. 2. True equity isn't a box to check, it shows up when small and diverse firms gain cash flow, capacity, and repeat work. 3. The fastest-growing firms aren't afraid to outgrow "small business" status, they embrace competing with the big players. Check out Danica's website to learn more about her work in AEC, equity, and major infrastructure projects - Red Team Go Sponsors HighLevel - The ultimate all-in-one platform for entrepreneurs, marketers, coaches, and agencies. Learn more at HighLevelFire.com. Cape - Cape is a privacy-first mobile carrier, built from the ground up with security as the priority. Visit Cape.co/fire to sign up today.

Send us a textBrex was once valued at $12.3B. Capital One just bought it for $5.15B.In today's episode of Market Outsiders, Jenny Rae and Namaan break down why Capital One was willing to buy Brex at a $7B discount – and what the deal actually tells us about fintech valuations, banking strategy, and the future of credit cards.We unpack:Why the 50% cash / 50% stock structure reveals who really had leverageWhat Capital One is actually buyingWhether this is a smart buy vs. build move or a risky integration betThe bigger question: Is this how banks future-proof growth in financial services – or an example of catching a falling knife?Episode Links:Capital One is buying startup Brex for $5.15 billion in credit card firm's latest deal (CNBC)Partner Links:Learn more about NordStellar's Threat Exposure Management Program; unlock 10% off with code SIMPLIFIED-10Chapters:The $7B Brex DiscountWhat Brex Actually DoesWhy the $12B Valuation BrokeWhat Capital One Is BuyingCash vs. Stock LeverageRevenue Synergies vs. RiskFit with Capital One's Card StrategyMarket Reaction ExplainedSmart Bet or Falling KnifeConnect With Management Consulted Schedule free 15min consultation with the MC Team. Watch the video version of the podcast on YouTube! Follow us on LinkedIn, Instagram, and TikTok for the latest updates and industry insights! Join an upcoming live event - case interviews demos, expert panels, and more. Email us (team@managementconsulted.com) with questions or feedback.

Market update for January 23, 2026Follow us on Instagram (@TheRundownDaily) for bonus content and instant reactions.In today's episode:Global investors quietly diversify away from U.S. assets, fueling gains in gold and emerging marketsIntel shares slide after the chipmaker admits it can't keep up with surging AI data-center demandTikTok finalizes a deal to stay in the U.S., reshaping ownership but keeping its algorithm in placeNatural gas prices surge as a major winter storm threatens nearly half the countryCapital One stock falls after announcing a $5.15B acquisition of fintech BrexCollege football and the NFL deliver massive ratings, proving live sports still dominate TV

Joe's Premium Subscription: www.standardgrain.comGrain Markets and Other Stuff Links —Apple PodcastsSpotifyTikTokYouTubeFutures and options trading involves risk of loss and is not suitable for everyone.Farm Aid & Policy UpdateAdditional farm aid was not included in the current government funding package, catching many lawmakers and farm groups off guard. Senate Republicans had pushed just last week to add up to $15B in aid, but that language was left out.With aid excluded for now, supporters say relief may need to come via supplemental appropriations or future legislation.Congress must pass the funding package by January 30 to avoid another government shutdown.It was also reported yesterday that nationwide E15 language was again dropped, though GOP leaders are discussing a possible supplemental bill that could allow year-round E15 sales.Major Winter Storm RiskA major winter storm is forecast to impact the Southern US this weekend. While the exact track remains uncertain, over 70 million people from Dallas to Little Rock to Nashville are currently under a winter storm watch.Snow, ice, dangerous travel conditions, and power outages are possible. Snow cover remains limited across key HRW wheat areas in the Southern Plains. Temperatures in western Kansas and surrounding regions could fall into the single digits, raising the risk of winter kill depending on snow totals and duration. Livestock stress is also a concern.US–China Trade WatchUS and Chinese officials may soon hold another round of trade talks ahead of the planned April meeting between Donald Trump and Xi Jinping.According to the US Trade Representative, negotiations would focus on common goods and services, avoiding sensitive areas like tech and national security.China has already fulfilled its commitment to purchase 12 mmt of US soybeans. Treasury Secretary Scott Bessent said both sides are now looking ahead to China's pledge to buy 25 mmt annually through 2028, though Trump continues to push for larger volumes.Europe, Greenland & TariffsPresident Trump announced he will refrain from imposing new tariffs on European countries, following meetings at the World Economic Forum.He stated that a framework for a future deal involving Greenland has been reached, marking a major shift from prior tariff threats. While details remain limited, Denmark continues to oppose any US takeover.Grain Market RecapSoybean futures rebounded Wednesday, with the most-active Mar26 contract gaining roughly 12 cents, settling near $10.65.Support came from a slow start to Brazil's soybean harvest due to rainfall in northern regions, along with comments from Treasury Secretary Bessent pointing to ongoing Chinese demand for US soybeans.Global Protein TradeChina has reopened its market to Canadian beef imports, ending a ban that had been in place since 2021. Initial shipments are expected to be small, but the move is a positive long-term opportunity for Canada's cattle industry.Meanwhile, US beef exports to China have declined sharply over the past year amid ongoing trade tensions.India & Wheat ExportsIndia has approved the export of 500,000 tons of wheat flour and related products. Wheat exports had been restricted since May 2022, but a strong monsoon is expected to boost domestic supplies.India is typically self-sufficient in wheat and is sometimes a net exporter. Its re-entry into the export market is considered a bearish factor for global wheat prices.

Allen covers court victories allowing Empire Wind and Revolution Wind construction to resume, while Vineyard Wind joins the legal fight. In the UK, EnBW walks away from Mona and Morgan with a $1.4B write-off, even as KKR and RWE announce a $15B partnership for Norfolk Vanguard. Plus Ørsted’s leaked “Project Dragon” reveals the offshore giant is considering Chinese turbines, and Fortescue breaks ground on Australia’s Nullagine Wind Project using Nabrawind’s self-erecting tower technology. Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTube, Linkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us! Last week I told you about Equinor’s ultimatum. Resume construction by January sixteenth… or cancel Empire Wind forever. Well… the courts have spoken. Last Thursday, Judge Carl Nichols issued his ruling. Empire Wind can resume construction. The harm from stopping, he said, outweighs the government’s concerns. One day earlier, Ørsted won the same relief for Revolution Wind. And now Vineyard Wind has joined the fight in Massachusetts. Three projects. Three courtrooms. Two victories and one victory yet to come. Meanwhile in Britain… a different kind of drama. German utility EnBW announced Thursday it is walking away from two major UK projects. Mona and Morgan. Three gigawatts of potential capacity. The cost of leaving? One point four billion dollars in write-offs. Eight hundred forty million pounds already paid… gone. Rising costs. Lower electricity prices. Higher interest rates. Their partner, Jera Nex BP, says they still see good pathways forward. But EnBW has had enough. Yet in the very same week… Investment giant KKR and German utility RWE announced a fifteen billion dollar partnership. Norfolk Vanguard East and West. Three gigawatts. One hundred eighty-four turbines. Power for three million British homes. Big winners and losers. In the same market. In the same week. Danish media outlet Berlingske obtained a confidential report from Ørsted’s procurement department. The world’s largest offshore wind developer… is exploring whether to buy turbines from China. They call it Project Dragon. The plan covers twenty-twenty-six through twenty-twenty-eight. CEO Rasmus Errboe told reporters they continuously evaluate all technologies and suppliers. Quality. Technical capabilities. Commercial conditions. He did not deny the report. For years, European developers have resisted Chinese turbines. Fear of losing their industry to China… just like they lost solar manufacturing a decade ago. But Ørsted is under pressure. In Australia, Fortescue has broken ground on its first wind project in the Pilbara. The Nullagine Wind Project. One hundred thirty-three megawatts. Seventeen turbines. But here is what makes it special. Nabrawind’s self-erecting tower technology. Hub height of one hundred eighty-eight meters. A new global benchmark for onshore wind. No giant cranes required. Fortescue plans two to three gigawatts of renewable energy across the Pilbara by twenty-thirty. Wind. Solar. Batteries. To power their mining trucks. Their drills. Their processing plants. Last week we talked about Equinor’s deadline. About Ørsted losing one and a half million euros every single day. About billions in limbo. This week… the courts stepped in. Empire Wind resumes. Revolution Wind continues. Vineyard Wind fights on. All while the North Sea quietly crossed a milestone. One hundred one operational wind farms. Thirty gigawatts of clean power. More than any body of water on Earth. Some companies are walking away. Others are doubling down with fifteen billion dollar bets. The wind industry is evolving very quickly. And that’s the state of the wind industry for the 19th of January 2026. Join us tomorrow for the Uptime Wind Energy Podcast.

CleanSpark CEO Matt Schultz joins us to discuss CleanSpark's fiscal year 2025 earnings. Subscribe to the Blockspace newsletter for market-making news as it hits the wire! Welcome back to The Mining Pod! Today, Matthew Schultz, CEO of CleanSpark, joins us to talk about CleanSpark'sfiscal year 2025 earnings. CleanSpark raked in $766M in revenue for the year, hit the 50 EH/s milestone, and has begun expanding into AI loads. Schultz shares insights on CleanSpark's partnerships with Submer for cooling solutions, the company's capital strategy, the potential for hybrid mining-HPC loads at CleanSpark sites, and what the AI revolution means for bitcoin mining in the United States. Subscribe to the newsletter! https://newsletter.blockspacemedia.com **Notes:** • $766M in FY 2025 revenue  • LEadership change refocused company • Austin Texas site underway • AI load feasibility being explored for Sandersville, other sites • $1.15B convertible note almost over subscribed Timestamps: 00:00 Start 03:15 Overview of fiscal year 07:16 Leadership change 09:43 Texas site development 13:41 Submer cooling solutions partner 18:30 Containerized solutions 21:39 Green field vs retrofit 27:23 Blended AI & mining sites 30:50 Two business lines 36:11 $1.15B note 45:19 Evaluating operators

Get the cheat sheet: Jamie's 5 steps to build a $1B product from a $200 idea: https://clickhubspot.com/ajn Episode 768: Sam Parr ( ⁠https://x.com/theSamParr⁠ ) and Shaan Puri ( ⁠https://x.com/ShaanVP⁠ ) talk to Ring founder Jamie Siminoff ( https://x.com/JamieSiminoff ) about the wild story of building and selling Ring–plus business ideas he thinks someone should go after.  — Show Notes: (0:00) Intro (2:47) Selling Ring for $1.15B (6:48) Getting sued by ADT (17:18) Working with Jeff Bezos (19:29) $400M to $4B (24:02) Getting the wire (26:44) Money v freedom (28:29) Rule 1: Start with the problem (30:02) Rule 2: Little solution, massive market (33:31) Idea: Modern bug control ($5-10B idea) (40:35) Rule 3: The snowball approach (43:54) Ding Dong and other must reads (46:32) The Tom Brady philosophy on hiring (51:33) The too hard pile (54:25) Stickwithitness (56:14) Last mile marketing (58:08) Rebuilding a town — Links: • Ding Dong - https://tinyurl.com/3zrsjete  • Ring - https://ring.com/  — Check Out Shaan's Stuff: • Shaan's weekly email - https://www.shaanpuri.com  • Visit https://www.somewhere.com/mfm to hire worldwide talent like Shaan and get $500 off for being an MFM listener. Hire developers, assistants, marketing pros, sales teams and more for 80% less than US equivalents. • Mercury - Need a bank for your company? Go check out Mercury (mercury.com). Shaan uses it for all of his companies! Mercury is a financial technology company, not an FDIC-insured bank. Banking services provided by Choice Financial Group, Column, N.A., and Evolve Bank & Trust, Members FDIC — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano //

In this podcast episode, Dr. Jonathan H. Westover talks with Francoise Brougher about rethinking the workforce of the future. Francoise Brougher is a pioneering technology leader with more than 25 years of experience scaling category-defining companies and driving AI-first business transformation. She currently serves as the Chief Executive Officer and Board Member at Pebl (formerly Velocity Global), where she is leading the company's reinvention as an AI-first global workforce platform. Under her leadership, Pebl is reshaping the Employer of Record industry by combining 10+ years of compliance precision with AI-driven simplicity, speed, and transparency, empowering companies to hire and manage talent across 185+ countries. Francoise has a proven track record of building and scaling global organizations responsible for multi-billion-dollar revenue growth. She took both Square (2015) and Pinterest (2019) public as the executive leader of GTM strategy. Earlier at Google, she scaled SMB Global Sales and Operations into a 15B+ business, pioneering the application of machine learning to customer engagement. She currently serves on the boards of Qonto (Chair, Compensation Committee), Too Good To Go, and as a Board Observer at Alan. She started her career in Japan, working for L'Oreal in a manufacturing plant for three years, where she installed a Computer-Assisted Manufacturing System. After her MBA, she joined Booz Allen and Hamilton in Paris and San Francisco. Check out all of the podcasts in the HCI Podcast Network!

In this episode of Unchained, Laura is joined by Rob Hadick, General Partner at Dragonfly, and Sam MacPherson, Co-founder and CEO of Phoenix Labs, to break down the fast-moving world of stablecoins and stablechains. They discuss Ethena's USDe $5 billion drop, the rise of “stablecoin-as-a-service” models, the emergence of payment-focused blockchains like Tempo and Codex, and the return of TradFi heavyweights like Visa, Mastercard, and Western Union to the digital dollar race. From liquidity challenges to regulatory shakeups and tokenized deposits, the conversation explores what it really takes to win the stablecoin wars, and, importantly, whether any of these players can even make a scratch to king Tether. Thank you to our sponsors! Binance Guests: Rob Hadick, General Partner at Dragonfly Sam MacPherson, Co-founder and CEO of Phoenix Labs Links: Previous coverage on Unchained: Stablecoins Are Popping Up Everywhere. What's the End Game? Why Every Chain Suddenly Wants Its OWN Stablecoin - The Chopping Block Timestamps: