Podcasts about General Motors

Tesla's former President Jon McNeill reveals the five-step framework behind one of the world's fastest-growing companies— YOU'LL LEARN — 1) What most miss when designing processes2) How to identify outdated requirements that slow things down 3) Why automation should be your LAST step Subscribe or visit AwesomeAtYourJob.com/ep1157 for clickable versions of the links below. — ABOUT JON — Jon McNeill is the CEO and Co-Founder of DVx Ventures. With a track record of founding and scaling companies, Jon has led teams that generated tens of thousands of jobs and delivered multi-billion dollar returns for investors.Previously, Jon served as President at Tesla, where revenue grew from $2B to $20B in under 30 months, and later as COO at Lyft, helping double revenue and take the company public. He currently sits on the boards of General Motors, Lululemon, Asurion, CrossFit, and Stash.• Book: The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX• Website: DVX.ventures— RESOURCES MENTIONED IN THE SHOW — • Book: Sam Walton: Made In America by Sam Walton• Book: The Goal: 40th Anniversary Edition: A Process of Ongoing Improvement by Eliyahu Goldratt• Book: Unreasonable Hospitality: The Remarkable Power of Giving People More Than They Expect by Will Guidara• Past episode: 810: How to Get Stuff Done inside Bureaucracies with Marina Nitze• Research paper: "Attention Is All You Need"— THANK YOU SPONSORS! — • Shopify. Sign up for your $1/month trial at Shopify.com/awesomepodSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

ON TODAYS PROGRAM…   PALOU WINS IN DETROIT!!!…SCHUMACHER P21  IS TOTO HAVING FANTASIES OF A KIMI AND MAX SUPER TEAM FOR THEIR SUPER CAR! WOULD THE TIFOSI WEAR ORANGE TO HELP FERRARI GET MAX? ZACK BROWN TOOK LANDO NORRIS TO THE SPEEDWAY THE DAY AFTER THE 500 LARGEST MOTORSPORTS SPECTACLE IN THE WORLD AND…FERNANDO SAYS: I WILL ATTEMPT THE INDIANAPOLIS 500 ONE MORE TIME!….mention MAX and competition.   THIS WEEK'S NASIR HAMEED CORNER WE HAVE: A MOMENT IN MOTORSPORTS HISTORY WITH CIAO COLLET FROM 2023 WHO CRASHED IN THE INDY 500 WITH 8 LAPS TO GO!! AND A LITTLE HISTORY ON THE MICHELIN TYRE!!  Palou Prevails Amid Chaos, Varying Tire Strategies in Detroit. DETROIT (Sunday, May 31, 2026) – Four-time NTT INDYCAR SERIES champion Alex Palou prevailed in a full-contact race filled with various tire strategies, winning the Chevrolet Detroit Grand Prix presented by Lear on Sunday for his fourth victory in eight races this season. Pole sitter Palou drove his No. 10 HRC Chip Ganassi Racing Honda to a 3.0584-second victory over the No. 27 Sam's Club Honda of Andretti Global's Kyle Kirkwood. It was the 23rd victory of Palou's career in 106 starts, a remarkable strike rate of 21.7 percent, and he has won 12 of the last 25 races (48 percent win rate) dating to the start of the 2025 season. SEE: Race Results “It feels like the first time, honestly” Palou said. “It was a tough one, a very tough one. But the team did an incredible job once again with the strategy. The pit stops were incredible. Incredible run, incredible start of the year, but it was tough.” The victory extended Palou's championship lead to 62 points over Kirkwood, more than a race's worth of margin. The Spaniard is aiming for an INDYCAR SERIES record-tying fourth straight title. Graham Rahal finished third in the No. 15 Fifth Third Bank Honda of Rahal Letterman Lanigan Racing, his third podium finish of the season. Arrow McLaren teammates Pato O'Ward and Christian Lundgaard finished fourth and fifth in the No. 5 and No. 7 Chevrolet-powered cars, respectively, at General Motors' home event. Palou led 71 of the 100 laps, but this wasn't a stroll down Easy Street. He took the lead for good on Lap 69 when Kirkwood pitted from the lead for the last time and stayed out front on restarts on Laps 72, 76, 83 and 93 after full-course yellows bunched the field. The move to the front was paved a few laps earlier when strategist Barry Wanser and Palou decided to make their final pit stop at the end of Lap 63, switching from the faster but less durable Firestone Firehawk alternate tire to the primary tire. Wanser saw a variety of jousts for position unfolding on the tight, nine-turn, 1.645-mile street circuit and wisely didn't want Palou to get caught on track under caution and lose track position. Wanser's decision proved prescient on Lap 66 when Santino Ferrucci's No. 14 Homes For Our Troops Chevrolet of AJ Foyt Racing nudged the rear of Rinus VeeKay's No. 76 Juncos Hollinger Racing Chevrolet into a spin in Turn 5. Kirkwood was leading but still had to make his final stop, which he did under yellow on Lap 69 and was forced to use a set of Firestone Firehawk alternates per INDYCAR rules that require at least two sets of the softer rubber to be used in street-circuit events. Palou rocketed away from Alexander Rossi's No. 20 Java House Chevrolet of ECR on the restart on Lap 72. Rookie Mick Schumacher and David Malukas were engaged in an intense duel for third on the restart, with Schumacher missing the corner in Turn 5 and nosing into the barriers in his No. 47 ENVE Honda of Rahal Letterman Lanigan Racing. Malukas had nowhere to go and ran wide in his No. 12 Verizon Team Penske Chevrolet, with the incident triggering another full-course caution on Lap 73. By this point, Kirkwood had worked his way back to third after his final pit stop and had to make the most of the added early grip of the alternate tire before the increased durability of Palou's primary tires prevailed in the closing laps. Kirkwood passed Rossi and then set sail for Palou, knowing this was his best chance to win. Kirkwood pulled to within two car lengths of Palou on Lap 79 and appeared to be ready to pounce for the lead when Ferrucci's car slowed in Turn 4 with a mechanical problem, triggering the fifth full-course yellow of the race on Lap 80. “We took a little bit of a gamble on tires there, being the only guy on reds (alternates) at the end,” Kirkwood said. “It nearly paid off. It was so, so close. There were two untimely yellows. “We almost covered Palou when we were on primes, which would have been phenomenal, and then we had that other yellow where I had him lined up. I was ready to make a dive on him, and, of course, (the yellow) comes out after I burned 10 seconds of overtake. From there, we just didn't really have another shot at it. I think I just used up my tires too much to make that one pass.” Palou kept the lead on the restart on Lap 83, but Kirkwood continued to push and forced Palou into a flat-spotting tire lockup on Lap 88. But Palou gathered himself and his car and started to pull away, building a lead of 1.8929 seconds by Lap 91. But there was one more restart for Palou to manage after Rossi clipped the rear of the No. 18 BMax Honda driven by Romain Grosjean of Dale Coyne Racing and sent Grosjean into the outside wall approaching Turn 3 on Lap 91. That triggered the last of six full-course yellows, but Palou pulled away from Kirkwood and the field on the Lap 93 restart and was never threatened despite the 173 on-track passes today, a high for a street circuit this season.  “Being able to be up front was key,” Palou said. “On the first stint, I started struggling and kind of put myself in a bad spot and lost two positions with Lundgaard and (Scott) McLaughlin. I lost us positions there, but the team made a great call to be safe with the yellow. It kind of worked out for us.” Fittipaldi Wins Motor City Thriller, Takes Series Lead. DETROIT (Sunday, May 31, 2026) – Enzo Fittipaldi returned his famous last name to Victory Lane in Detroit for the first time in 35 years, winning the INDY NXT by Firestone Detroit Grand Prix despite driving nearly the entire distance with a damaged front wing and nose cone. Series rookie Fittipaldi won the race, originally scheduled for 45 laps but switched to a timed event, under caution in the No. 67 HMD Motorsports car after starting seventh. It was his second victory of the season and vaulted him to the championship lead in the INDYCAR development series, seven points ahead of Nikita Johnson of Cape Motorsports Powered by ECR and eight ahead of HMD teammate Tymek Kucharczyk. SEE: Race Results The victory also was the first by the legendary Fittipaldi name in Detroit since his grandfather and two-time Indy 500 winner Emerson Fittipaldi won INDYCAR SERIES races on a different downtown street circuit in the Motor City in 1989 and 1991. “I just pushed as hard as I could,” Enzo Fittipaldi said. “I found pace. I was really, really fast. Just so happy to get the win. I love to race; I'm a racer.” Series veteran Myles Rowe finished a season-best second in the No. 99 Abel Motorsports with Force Indy machine, with rookie Kucharczyk rounding out the podium finishers in the No. 71 HMD Motorsports entry. Rookie Max Garcia tied his season-best finish by placing fourth in the No. 12 Abel Motorsports machine, with veteran Seb Murray rounding out the top five in the No. 27 Megatron car of Andretti Global. Frenzied action started from the drop of the green flag on Lap 1, as Lochie Hughes made an aggressive move into the Turn 3 hairpin with his No. 26 Andretti Global car, punting pole sitter Alessandro de Tullio into a spin from the lead in the No. 14 AJ Foyt Racing entry. Hughes received a drive-through penalty for avoidable contact. Fittipaldi nudged another car in that chain-reaction melee, which damaged the right side of his front wing and punched a large hole in his nose cone. Kucharczyk took the lead from that point, keeping it on the restart on Lap 8. Kucharczyk built a lead of 3.324 seconds over Fittipaldi by Lap 13, with Rowe climbing to third by Lap 18. Rowe dove under Fittipaldi for second on Lap 20 and started to chase down Kucharczyk. By Lap 21, Rowe pulled to within .5477 of a second of leader Kucharcyzk, slicing 1.6 seconds from the Polish driver's lead in just three laps. But the complexion of the race changed on Lap 26 when the second of four full-course yellow flags in the race were unfurled for debris on the nine-turn, 1.645-mile temporary street circuit. The restart came at the end of Lap 27, with Rowe trying to dive under Kucharczyk for the lead immediately after the green flag, in the Turn 3 hairpin. But the move forced both cars wide, leaving an opening along the inside curb for Fittipaldi. He took it, squeezing past Rowe and Kucharcyzk and never trailing thereafter. Fittipaldi stayed out front on another restart on Lap 34 after Niels Koolen nosed his No. 10 Chip Ganassi Racing machine into the barrier in Turn 8. “I got it done,” Fittipaldi said. “I knew Myles was going to go for a lunge there, and I just prepared my mid-(corner) to exit of Turn 3, and he went on the lunge on Tymek, and I was able to do the crossover and got the lead. I had the pace to stay there, and I was actually pulling away.” The decisive move was one of 141 on-track passes, including 124 for position, in the exciting race – both INDY NXT records for any circuit on which the series has competed in the Motor City. Fittipaldi expanded that gap to nearly six-tenths of a second when Andretti Global's Max Taylor also nosed into the barrier in Turn 1 in his No. 28 Susan G. Komen car with about four minutes, 20 seconds left in what had become a timed race, triggering the final caution. Taylor's car could not be cleared in time to restart the race, with the field finishing under yellow. “I was losing quite a lot of time through (Turns) 6 and 7,” Fittipaldi said of the damage to his car. “It was quite difficult. Down the straight, I could feel the air coming through my legs and I said: ‘Man, this is not good. We're definitely dragging a lot on the straight.' It was hard to keep that lead and keep up with the guys.”

- U.S. Wants More Local Content in Cars - GM Extends Battery Plant Layoffs - Axle Strike Puts GM Trucks at Risk - EU Countries Not Offering Enough Corporate EV Incentives - CATL Sodium Batteries Enter Mass Production - BYD Posts 1st Increase in 8 Months - NIO Abandons PHEVs and EREVs - Nissan Applying Quantum Computing Across Company

- U.S. Wants More Local Content in Cars - GM Extends Battery Plant Layoffs - Axle Strike Puts GM Trucks at Risk - EU Countries Not Offering Enough Corporate EV Incentives - CATL Sodium Batteries Enter Mass Production - BYD Posts 1st Increase in 8 Months - NIO Abandons PHEVs and EREVs - Nissan Applying Quantum Computing Across Company

This week on The Strange Motion Way Podcast, Tim Strange sits down with Dave Gray for a conversation packed with hot rods, horsepower, creativity, and real-life stories from the automotive world. From the passion that fuels the builds to the people and experiences that shape car culture, Dave shares his journey, insights, and the moments that continue to drive him forward.Whether you're into custom cars, classic hot rods, road trips, event culture, or the stories behind the builds, this episode delivers the kind of genuine conversation that automotive enthusiasts love.From running convenience, stores, to restaurants, to working for General Motors and finally finding his rhythm in the Hot Rod world as a full-time Hot Rod shop owner and car builder, Dave Gray has already won a lot of great things in the industry.From the 2026 Goodguys Hot Rod of the Year , to other finalists for Hot Rod of the Year, competing for Americas Most Beautiful Roadster and Wheelhub Live cup.Thanks to @royal_purple and @millerweldersGrab your favorite shop chair, hit play, and ride along with another episode of The Strange Motion Way.

She got everything she worked for. Then she quietly walked away from all of it. Genea Evola dreamed of being a news reporter from the time she was a little girl. She wanted to be seen. She wanted to tell the truth. She wanted to matter. Then she got the job. And the first thing it asked her to do was use her voice to harm people. That moment in a Flint, Michigan newsroom didn't just end a career. It sent a signal straight into her nervous system: being seen is dangerous. And she spent the next decade hiding behind every corporate brand she could find, executing brilliantly for everyone except herself. In this episode of Be the Wolf, Genea Barnes sits down with Jenna Evola, founder of Evolve Systems, for a conversation that starts with AI and personal branding and goes somewhere much more honest. Because you can't talk about building an authentic brand without first asking the question nobody wants to answer: Who are you, actually? And when did you stop letting people see it? Here's what this conversation pulls into the open:

A történetünk főhőse: 2025-ben egy GM EV1-et eladtak egy atlantai (Georgia állambeli) autó árverésen, ami a történelem első és eddig az egyetlen dokumentált EV1-es eladás volt. A General Motors 1990-ben bemutatta az akkumulátoros elektromos Impact prototípust, amely tulajdonképpen a sorozatgyártású EV1 alapjául szolgált. A GM végül 1996-ban kezdte meg az EV1 sorozatgyártását. Nem, nem gurult el a gyógyszerem és a fentebb látható valamennyi dátum helyes! A teljes történetet megismerhetitek ebből az epizódból, a folytatás pedig megtekinthető az alább belinkelt videókban.   Shownotes: https://elektromobilitas.kanadabanda.com/emob023-vilagszenzacio-eladtak-az-elso-gm-ev1-t/ Elérhetőségeink: W: https://Elektromobilitas.info YT: https://www.youtube.com/@ElektromobilitasPodcast/ @: ev@kanadabanda.com P: https://www.patreon.com/KanadaBanda

Podcast del programa Imagen Empresarial transmitido originalmente el 20 de mayo del 2026. Conduce Rodrigo Pacheco Los entrevistados de hoy: Entrevista: Sergio Argüelles, Presidente y director general de FINSA Tema: Cambios en el liderazgo de FINSA Entrevista: Francisco Garza, presidente y director general de General Motors de México Tema: Anuncio de producción en México de 2 modelos

This episode is a re-air of one of our most popular conversations, featuring insights worth revisiting. This week on The Data Stack Show, Brooks and John chat with Andy MacMillan, CEO of Alteryx. Andy discusses the evolving landscape of data and AI, focusing on empowering business users to solve complex problems. He explores the concept of "citizen developers" and how tools like Alteryx can bridge the gap between IT and business teams by democratizing data access. The conversation also emphasizes the importance of creating controlled environments where business users can leverage cloud data platforms and AI technologies to reimagine workflows, without bypassing governance. Key takeaways include the need for organizations to enable innovation through accessible data tools, the potential of AI-driven agents to transform business processes, the critical role of employees who understand their business functions in driving technological transformation, and so much more. Highlights from this week's conversation include: Andy's Background and Journey in Data (0:54) Early Web Development at General Motors (2:23) AI Challenges in the Enterprise (9:03) What is Alteryx and Its Value Proposition (11:25) The Importance of Empowering Business Users (16:10) Bridging the Gap Between Data Platforms and Business Users (20:04) Evolution from Desktop to Data Cloud (25:28) Access and Governance in the Cloud Era (27:57) The Return of Local Data Work and AI Governance (31:24) AI Data Clearinghouse and Governance (34:11) AI-Enabled Workflows and Business Impact (38:13) The Future: Agents, Data Platforms, and Business Logic (41:05) How to Get Started with Alteryx or Learn More (46:54) Product Management Lessons for Leadership and Parting Thoughts (47:56) The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. Each week, we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data. RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Perfect Day enfrenta ‘veto ambiental' de la Semarnat y castigo del mercado, la fiscalización a grandes contribuyentes pierde fuerza recaudatoria y General Motors producirá Aveo y Groove en México como parte de un paquete de inversiones de 1,000 millones de dólares, con Puri Lucena y Selene Ramírez.00:00 Introducción01:10 Perfect Day enfrenta ‘veto ambiental' de la Semarnat y castigo del mercado07:30 La fiscalización a grandes contribuyentes pierde fuerza recaudatoria11:06 Huachicol, el otro desafío de Sheinbaum que deja pérdidas por 1,200 millones de dólares15:19 Voces de celebridades solo podrán usarse bajo consentimiento expreso19:25 GM producirá Aveo y Groove en México como parte de un paquete de inversiones

Muy buenos días, General Motors aumentará la producción de autos en México. Siguiendo el caso de Royal Caribbean, en Mahahual, la Semarnat no autoriza pero surgen más dudas en torno a las procesos de autorización. Hablemos de la fiebre por el reloj de Swatch y Audemars Piguet y los precios de reventa. En España, el hijo del fundador de Mango es detenido por el caso de la muerte de su padre. 

En esta emisión de Autos y Más, arrancamos con las noticias más relevantes del mundo del motor, comentamos el anuncio de la presidenta Claudia Sheinbaum y el secretario de Economía, Marcelo Ebrard, junto a Francisco Garza, director de General Motors México, que la automotriz ensamblará 80,000 vehículos para reducir la dependencia de Asia y fortalecer el suministro nacional, así como proteger el empleo de los mexicanos dentro del “Plan México”. También, platicamos de la alianza entre Kia y y FIFA hasta 2030 parte de la celebración por la Copa Mundial 2026. No dejes de escuchar la transmisión en vivo porque tendremos muchos regalos, recuerda sintonizar de lunes a viernes de 8 a 9 pm y sábados de 10 am a 12 pm por tu estación favorita MVS Noticias en el 102.5 de tu FM.See omnystudio.com/listener for privacy information.

General Motors de México reitera su confianza en México El Edomex presume una baja de 36% en los delitos de alto impactoMás de 17 mil personas deben desalojar sus casas en California por un incendio forestal#grc

La presidenta Claudia Sheinbaum anunció un nuevo proyecto de inversión de General Motors en México por mil millones de dólares, con el que la automotriz iniciará el ensamble local de 80 mil vehículos destinados al mercado nacional a partir de 2027.

For the first time in 26 years of the Working Relations Index, every single North American OEM moved up the chart. Ford, Toyota, Stellantis, Honda, GM, and Nissan all scored higher than the year before. That has never happened. Not once.In this special episode, Jan sits down with Dr. Angela Johnson, principal at Plante Moran responsible for the WRI, along with Sig Huber, Chief Commercial Officer of Elm Analytics and former supplier risk leader at Toyota and Fiat Chrysler. Three sharp voices. One story the industry needs to hear.Tariffs. EV cost recovery. Permacrisis fatigue. Return-to-office mandates. Four undercurrents shaped this year's results, and they all point to the same place. When OEMs can't control the macro, they lean into what they can control. Communication. Accessibility. Buyer responsiveness. Taking the meeting. Listening. Acting. That's what moved the needle, and the suppliers noticed.Ford's 32-point jump is the second-largest gain in WRI history, and Liz Door led that charge from the top. Stellantis is showing the early signs of a real turnaround under Filosa. GM's still working through cultural inertia, but the relationship side keeps moving in the right direction. And Toyota and Honda aren't slowing down.Angela also unpacks her new 6C framework. It's the bridge between transactional and relational. Commercial fairness, consistency, clear expectations, communication, continuity, and collaboration. It's the structure the industry's been missing.But here's the harder truth. The next 18 to 24 months will test every relationship in this industry. Cost of goods sold is climbing. Supplier financial distress is creeping back. Cross-functional alignment inside the OEMs is slipping. The playbook's changing. The question isn't whether we can do this together. It's whether we will.Here's the link to the WRI 2026 StudyThemes Discussed in this EpisodeFirst-time-ever WRI result: all six OEMs scored upPermacrisis fatigue and the shift toward collaborationTariffs, EV cost recovery, and commercial fairnessThe 6C framework: bridging transactional and relationalFord's record-setting jump and Liz Door's leadershipStellantis's rebound under FilosaGM's ongoing culture changeTop 50 suppliers, organizational memory, and cultural inertiaReturn-to-office mandates and buyer performanceCross-functional decline inside the OEMsFrom cost reduction to resilience: the playbook is changing

From designing cars at General Motors to helping shape the future of the Upper Cumberland, Sandy Brecker has never stopped building something meaningful. Sandy joins George Halford to share her journey from Michigan engineer to Algood Planning Commissioner, photographer, and community advocate. She reflects on her passion for creativity, public service, and the impact of organizations like the Cookeville Camera Club. When purpose meets passion, remarkable things develop. Listen To The Local Matters Podcast Today! The UC Now · News Talk 94.1

Muy bien, vamos a por otro resumen trimestral, en cuatro de las secciones habituales (ePrivacy y marco regulatorio; MarTech & AdTech; IA, competencia y mercados digitales; y futuro de los medios).Entre otras cosas, hoy tratamos:* Verificación de edad en sus múltiples variantes y la evolución de la prohibición de medios sociales para menores* Memoria de actividad de la AEPD* Directrices varias del EDPB* Campañas en ChatGPT, píxeles y APIs de conversión* Píxeles de apertura en correos electrónicos (Francia, Italia)* Nuevas directrices ICO para ePrivacy (analytics, A/B testing, etc.)* Multas y juicios en California (Meta, General Motors).Hemos incluido las notas detalladas del episodio y todos los links o referencias en un post específico, como siempre, aquí disponible. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mastersofprivacy.com/subscribe

La industria del automóvil ha experimentado una gran transformación en los últimos años. En lo que durante más de un siglo fue coto cerrado de europeos, estadounidenses, japoneses y coreanos hoy los fabricantes chinos llevan la delantera. En 2023 China superó a Japón como mayor exportador mundial de vehículos y en 2025 envió fuera más de ocho millones de automóviles a casi todos los mercados del mundo. Las marcas chinas suman ya casi un 9% de las matriculaciones europeas, cuando hace sólo cinco o seis años era difícil encontrarse con un turismo chino por la calle. Semejante salto se explica por una apuesta estratégica que hizo el gobierno chino hace 15 años. Decidieron que, en lugar de competir en el campo del motor de combustión interna, un campo en el que Occidente llevaba un siglo de ventaja, se decantaron por concentrarse en la electrificación. Desde entonces han volcado en el sector más de 200.000 millones de dólares en subvenciones, exenciones fiscales y todo tipo ayudas. A eso se sumó el control de la industria de las batería. Adquirieron minas compradas en medio mundo y levantaron sus propias plantas procesadoras. Hoy el 75% de las baterías salen de plantas chinas. Empresas como CATL o BYD son proveedores incluso de sus competidores occidentales. La ventaja china se apoya en cuatro pilares. El primero las baterías, las producen a menor coste y están mucho más avanzados en su desarrollo. El segundo el software, concebido desde cero para vehículos eléctricos. El tercero la velocidad de desarrollo de nuevos modelos. El cuarto el precio, un utilitario eléctrico chino se vende a un precio sensiblemente más bajo que su equivalente europeo. Ni los aranceles europeos aprobados en 2024 son capaces de neutralizar esa diferencia. Los fabricantes chinos están conquistando también el segmento premium, que es el más valioso e interesante para los fabricantes. Los compradores chinos de alto poder adquisitivo se inclinan cada vez más hacia sus propias marcas. En Europa están empezando a competir en el terreno de empresas alemanas especializadas en vehículos de gama alta como BMW o Mercedes. Esa competencia se la están trayendo también en la fabricación ya que son varios los proyectos de apertura de plantas de ensamblaje en territorio europeo. Los occidentales han pasado de desdeñar a los chinos a imitarles. Fabricantes con muchísima historia a sus espaldas como Volkswagen, Mercedes, Toyota, Renault, Ford o General Motors están abriendo centros de investigación en China o han cerrado alianzas con empresas chinas Xpeng, Geely, Huawei y SAIC. Pero estas alianzas podrían ser una trampa. Ceder la fabricación de la batería y el diseño de software a un competidor convierte a la marca occidental en un mero integrador de componentes ajenos. Lo que muchos han dado en llamar invasión china no parece que sea una moda. Es el resultado final de dos décadas en las que en China han planificado mientras Occidente miraba hacia otro lado y fingía que eso nunca iba a suceder. La pregunta es si los fabricantes históricos de Europa, Japón y EEUU sobrevivirán.

The Shred is a weekly roundup of what's making headlines in the world of employment. The Shred is brought to you today by Jobcase.

We are going to reveal 7 new tricks car thieves are using and what you can do to save your car from being stolen. Remember these secret tricks to protect yourself. About one million cars are stolen in the US each year. The biggest targets are Toyota, Honda, and General Motors vehicles. According to the thieves themselves, this is because these cars are a little easier to steal and their spare parts are in high demand. New-generation car thieves are using gadgets to steal vehicles. Criminals are hacking into the latest “keyless” systems to enter the newest models. Criminals copy and reproduce your car's VIN to organize a huge fraud scheme. Another way car thieves get close to your vehicle is by posing as, for example, mechanics. Some thieves will hang out around grocery stores and major supermarkets and stalk their prey, so to say. #cartricks #drivingtips #drivinghacks Learn more about your ad choices. Visit megaphone.fm/adchoices

Today on CarEdge Live, Ray and Zach discuss the latest info on GM's settlement in California. Tune in to learn more! Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

A massive cybersecurity week. On this episode of Cybersecurity Today, David Shipley breaks down the reported breach of Instructure's Canvas learning platform, where attacks linked to the ShinyHunters extortion group may have exposed data tied to up to 275 million user accounts across more than 9,000 educational institutions. The incident disrupted access, delayed exams, and forced Instructure to disable its "Free for Teacher" program after attackers allegedly used it to post extortion messages. Also in this episode: the Gentlemen ransomware group suffers a major internal leak, exposing affiliate chats, tooling, victim data, and operational details — a rare look inside a live ransomware operation. Then, General Motors agrees to a $12.75 million California settlement over allegations involving OnStar-linked driver data collection and sharing, raising fresh questions about privacy in connected vehicles. And finally: security researchers report what appears to be the first documented AI-assisted operational technology (OT) cyberattack attempt targeting a water utility in Monterrey, Mexico. The attempt failed to reach industrial control systems, but combined with confirmed attacks on water infrastructure in Poland, it signals a worrying shift in critical infrastructure threats. If you work in cybersecurity, IT, infrastructure, education, or privacy, this episode matters. Chapters 00:00 Top Headlines Rundown 00:41 Canvas Mega Breach 02:44 ShinyHunters Background 03:26 Ransom Pressure Fallout 04:25 Gentlemen Ransomware Leak 05:18 Inside the Data Dump 06:18 GM OnStar Privacy Settlement 08:17 What Drivers Should Know 09:39 AI Meets OT Attacks 11:52 Monterrey Water Near Miss 13:29 Poland Water Systems Hit 15:07 Defending Critical Infrastructure 16:29 Wrap Up And Thanks #Cybersecurity #Canvas #ShinyHunters #Ransomware #OnStar #GeneralMotors #DataBreach #CriticalInfrastructure #WaterUtility #OperationalTechnology #ICS #CyberAttack #Privacy #DavidShipley #CybersecurityToday

Anthropic says Claude is getting dramatically more capable — effectively unlimited context, multi-agent coordination, interactive self-correction, and even an experimental background learning process it calls "dreaming." At the same time, Anthropic admits Claude Code is growing far faster than expected, raising a bigger question: can the company control what it is creating? In today's Hashtag Trending, Jim Love also looks at a growing backlash against AI infrastructure as residents begin feeling the real costs. In Maryland, ratepayers could be on the hook for roughly US$2 billion in grid upgrades tied to AI data-centre growth happening outside their state. In Georgia, residents discovered water pressure problems that led to the discovery of major untracked data-centre water usage. Also in this episode: General Motors agrees to pay US$12.75 million in a California privacy settlement over allegations it collected and shared connected vehicle driver data without proper disclosure. Canvas restores services after a cyber incident reportedly linked to ShinyHunters, raising serious questions about education-sector data exposure and how much information may actually have been taken. If your car is collecting data, your utilities are paying for AI growth, and AI systems are beginning to "learn" between sessions… this may be one of the most consequential weeks in tech. Chapters 00:00 Headlines and Welcome 00:26 AI Data Centers Backlash 02:50 GM Driver Data Settlement 05:04 Canvas Breach Fallout 07:53 Claude Growth and Dreaming 10:43 Recursive Self Improvement Fears 12:43 Wrap Up and Support #AI #Anthropic #ClaudeAI #ArtificialIntelligence #DataCenters #Cybersecurity #Privacy #GeneralMotors #Canvas #ShinyHunters #TechNews #HashtagTrending #JimLove

In May 1940, President Franklin D. Roosevelt faced the daunting challenge of preparing a technologically lagging America for modern mechanized warfare following Hitler's invasion of France. FDR turned to Bill Knudsen, the Danish-born CEO of General Motors, who had a background as a heavyweight boxer and a veteran of the Fordassembly lines. Knudsen was a master of flexible mass production, a technique he perfected at Chevrolet that allowed for model changes without halting the entire assembly line. Unlike 19th-century methods, Knudsen's approach focused on a continuous flow of production and integrating new technologies into existing workflows. He was tasked with transforming the civilian economy—then focused on cars and refrigerators—into an "Arsenal of Democracy" capable of producing tanks, planes, and artillery at an unprecedented scale. This mobilization was not just about technology, but about Knudsen's belief that American industry could achieve the impossible when directed toward a single, patriotic goal. (1/4)1935

“Send us a Hey Now!”We find ourselves in an off week after Miami and it's a double off week meaning no race to preview either.With the recent race cancellations we had drained our non-race week topics dry until Brian came up with a genius idea for this week.What if we ran through the ABCs of not just F1 but also what each letter means to this here podcast!Yeah, we know you know it's gonna be gold!Episode running order is...1) News & SocialAll the best bits from both the sports news out there as well as what caught our eye on the various social channels 2) Brian's Video Vault              https://www.youtube.com/watch?v=jZVZB369sbU. "It Was Magic!" | Colin Farrell Takes On A Miami Hot Lap! | F1 Pirelli Hot Laps. Formula 1 channel - 4 mins. https://www.youtube.com/watch?v=h8hG4_B-ACY. Max Verstappen vs SuperGT Pro. Red Bull motorsports. https://www.youtube.com/watch?v=lN8mWUFbDjU. We Made a Formula 1 CT5-V Blackwing. General Motors channel. Nearly 10 mins. https://www.youtube.com/watch?v=osjBYEXYiLc. Cadillac Returns to the World Stage: The 2026 F1 Works Car Reveal | Jay Leno's Garage. 29 mins.https://x.com/tirii_f1/status/2051591471736742364. And by popular demand - the Ferrari team dancing the macarena!!!!3) Cadillac CornerThe latest Caddy news we found to stash in the corner4) Formula 1 and Dirty Side ABCsAn entry for all the letters from A-Z for both F1 and also this podcast5) Canada GP preview of the previewWe are one week away from previewing the Canadian GP and we're super excited given we'll actually be there!Expect a very over the top preview edition next week!Support the showWe would love you to join our Discord server so use this invite link to join us https://discord.gg/XCyemDdzGBTo sign up to our newsletter then follow this link https://dirty-side-digest.beehiiv.com/subscribeIf you would like to sign up for the 100 Seconds of DRS then drop us an email stating your time zone to dirtysideofthetrack@gmail.comAlso please like, follow, and share our content on Threads, X, BlueSky, Facebook, & Instagram, links to which can be found on our website.One last call to arms is that if you do listen along and like  us then first of all thanks, but secondly could we ask that you leave a review and a 5 star rating - please & thanks!If you would like to help the Dirty Side promote the show then we are now on Buy me a coffee where 100% of anything we get will get pumped into advertising the show https://www.buymeacoffee.com/dirtysideofthetrackDirty Side of the Track is hosted on Buzzsprout https://www.buzzsprout.com/

In March 1982, General Motors announced a rapid and aggressive conversion to robotics. By 1990, GM wanted 14,000 robots in their factories doing everything from painting to welding to assembly. Nowadays, we dream of robots in the factories, doing everything end to end. In the dark. Lights out. Guess what, GM dreamed the same 40 years ago. And they spent an estimated $60 billion to try to make it reality. In today's video, we look at General Motors and their dreams of the automated, all-robot factory.

In March 1982, General Motors announced a rapid and aggressive conversion to robotics. By 1990, GM wanted 14,000 robots in their factories doing everything from painting to welding to assembly. Nowadays, we dream of robots in the factories, doing everything end to end. In the dark. Lights out. Guess what, GM dreamed the same 40 years ago. And they spent an estimated $60 billion to try to make it reality. In today's video, we look at General Motors and their dreams of the automated, all-robot factory.

WWJ auto analyst John McElroy says General Motors and Ford should work together like they did years ago to develop a ten speed automatic transmission. He says both companies could cut billions in costs.

On this episode, I'm digging into the ins and outs of in-plan Roth conversions. You'll learn what it means to convert pre-tax 401(k) dollars to a Roth 401(k), who is eligible, and why it might make sense for your retirement strategy. I cover the practical steps for making these conversions, and highlight the benefits and drawbacks. I also share a real-life example of how a client navigated her options to maximize her retirement savings.  You will want to hear this episode if you are interested in... [00:00] In-plan Roth conversions [01:51] What is an in-plan Roth conversion? [02:38] Eligibility for in-plan Roth conversions [04:48] Real-life story of after-tax contributions in a client's 401(k) [06:07] Convert after-tax contributions plus gains within the 401(k) plan to Roth 401(k) [08:38] Rolling over after-tax contributions and gains to IRAs outside 401(k) [10:21] Preventing funds from sitting in a money market account   The In-Plan Roth Conversion An in-plan Roth conversion allows participants to transfer funds from the traditional, pre-tax portion of their 401(k) into the after-tax Roth component of the same plan. This means you're taking money that has not yet been taxed and converting it into money that—after the conversion taxes are paid—will grow and can be withdrawn tax-free in retirement. This strategy is different from Roth IRA conversions, which involve moving money from a traditional IRA into a Roth IRA, often at the same financial institution. In-plan conversions, on the other hand, streamline the process by keeping all assets within your employer-sponsored 401(k), offering simplicity and potentially access to preferred investment options.   Who Should Consider an In-Plan Roth Conversion? In-plan Roth conversions can be especially valuable if you anticipate being in a lower tax bracket this year compared to future years, or if you want to build a tax-free income stream for retirement. Additionally, if you already have after-tax contributions in your 401(k), converting those funds can optimize your tax efficiency by ensuring that all future gains are tax-free.   Real-Life Example: Amy's Roth Conversion Journey Let's look at the example of "Amy," who worked with me to create a financial plan. Amy had been contributing after-tax money to her General Motors 401(k), accumulating $63,000 in after-tax contributions and $40,000 in gains.  Here's how her options played out: In-Plan Roth Conversion: Amy could have converted both her after-tax contributions and the gains to the Roth 401(k). However, the $40,000 in gains would be taxable in the year of conversion, amounting to roughly $10,500 in taxes, or 26%. This would put her on track for approximately $200,000 in Roth assets in 10 years, assuming market growth. Rollover to IRAs: Alternatively, Amy chose to roll her after-tax contributions to a Roth IRA and the gains to a traditional IRA. This strategy avoided immediate taxation on the $40,000 in gains. The after-tax funds would grow tax-free in the Roth IRA, and future conversions of the traditional IRA can be planned according to her tax situation. Amy's example highlights the importance of reviewing your plan's rules, weighing tax implications, and considering your long-term retirement goals.   Conversion Best Practices If you have after-tax contributions in your 401(k), now is the time to develop a plan. Consider converting these funds sooner rather than later to maximize the potential for tax-free compounding growth. Some plans allow automated conversions, but others require regular follow-ups with your provider. In-plan Roth conversions can be a powerful tool to improve your retirement outlook. By understanding your plan's rules, analyzing your current and future tax situations, and executing a smart conversion strategy, you can unlock significant tax advantages and peace of mind for your golden years. Resources Mentioned Retirement Readiness Review Subscribe to the Retire with Ryan YouTube Channel Download my entire book for FREE  Fidelity Charles Schwab Vanguard T. Rowe Price Connect With Morrissey Wealth Management  www.MorrisseyWealthManagement.com/contact Subscribe to Retire With Ryan  

Peter Merel: When Telling a Manager "You Don't Have a Role" Backfires — A Lesson in Agile Coaching Humility Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "A failure is not a failure. A failure is just the first step." - Peter Merel   Peter Merel became a Scrum Master by stealth — long before the title existed. Credited in Kent Beck's first XP book and present at the first agile conference, Peter was practicing lightweight processes at Hewlett Packard in the late 1990s. When he took a role at GMAC, the residential finance arm of General Motors, he brought XP practices with him and found early success. After six months of strong results, the project manager, Mike Alakom, sat Peter down and asked the most dangerous management question: "What do I do?" Peter gave what he now calls the stupidest answer possible — "You don't really have a role in this process." The next day, Mike called an all-hands meeting and calmly maneuvered Peter into crediting the entire way of working as Mike's idea. Peter stayed on for another six months, but at arm's length. In hindsight, Peter recognizes Mike did exactly what he should have done. The second failure came at Commonwealth Bank of Australia, where Peter was brought in to coach agile but was actually being set up to fail — a ripcord the organization could pull when it wasn't ready for change. The delivery manager, Des Webster, told Peter directly: "You were set up to fail." Peter walked away, thinking he'd never return. But six years later, every person he had coached had moved up in the organization, and Peter came back as principal coach for 50,000 people. The CIO declared Agile one of the bank's five pillars. Just because you hit the wall doesn't mean it's the end — it might be the beginning.   Self-reflection Question: When was the last time you failed at introducing change, and have you considered that the seeds you planted might still be growing in ways you can't yet see?   [The Scrum Master Toolbox Podcast Recommends]

Despite gas prices hitting a four-year high amid conflict in Iran, GM CEO Mary Barra is staying the course. She joined Bret on Wednesday to discuss navigating economic volatility, a cooling EV market, and the rising competitive threat from China. This interview first aired on Wednesday, 04/29, on Special Report. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode of FOMO Sapiens, Patrick sits down with Jon McNeill, former President of Tesla, former COO of Lyft, and CEO of DVx Ventures, to unpack the operating system behind one of the most extraordinary growth stories in business history. During McNeill's tenure at Tesla, revenue grew from $2 billion to $20 billion in just 30 months. That kind of growth doesn't happen by accident — it follows a system. In his new book, The Algorithm: The Hypergrowth Formula That Transformed Tesla, Lululemon, General Motors, and SpaceX, McNeill lays out the five-step framework Elon Musk built at Tesla: question every requirement, delete every unnecessary step, simplify and optimize, accelerate cycle time, and only then automate. The conversation gets into how established companies like GM used these same principles to build the Hummer EV in roughly half the expected time, why speed is an advantage that shows up most powerfully on the balance sheet, and how the one-way/two-way door framework can help any leader make faster, smarter decisions without second-guessing themselves into paralysis. Learn more about your ad choices. Visit megaphone.fm/adchoices

What happens when a 100-year-old luxury brand decides to think like a startup?In this episode, Jan Griffiths sits down with Joaquin Nuño-Whelan, President of Lincoln, live from Newlab at Michigan Central. This is not a conversation about cars. This is about leadership, culture, and what it really takes to transform a legacy brand from the inside out.Joaquin shares how he's doing exactly that. Building a team-first culture rooted in trust. Reframing Lincoln's identity around “quiet luxury” and Gravitas. And leading in a way that rejects the old command-and-control model in favor of authenticity, clarity, and ownership.But this conversation goes beyond the brand. Joaquin opens up about something deeper, his commitment to developing people. From empowering his teams inside Lincoln to investing his time in education and nonprofit work, he is actively shaping the next generation of talent entering the industry.That belief becomes real when you hear from Diego Vargas, a student at Detroit Cristo Rey. His voice brings a grounded, honest perspective on opportunity, growth, and what young people need from today's leaders. It's a powerful reminder that the future of this industry will be defined by the leaders we develop now.This episode hits at the core of AutoCulture 2.0: leadership, trust, and the courage to do things differently.Themes Discussed in this EpisodeWhy momentum is the most underrated leadership force inside legacy organizationsThe connection between leadership DNA and brand identityWhat “quiet luxury” really means and why it matters nowHow to lead authentically inside a command-and-control cultureThe power of trusting teams to unlock ownership and performanceWhy legacy OEMs must think like startups to stay relevantThe role of education and early talent in shaping the future workforceBridging industry leadership with student opportunity through programs like FIRST

Dickens in Brooklyn is a virtuoso collection of unusual, compelling essays in which critically acclaimed and award-winning author Jay Neugeboren explores experiences that have been central to his life: caring long-term for a brother with mental illness; finding and connecting with long-lost family members; a posthumous lunch with Oliver Sacks; his years as single parent to his three children; his decision as a General Motors executive trainee to violate company policy and hang out with "hourlies;" a thwarted kiss at a teenage summer camp where he was a young Jewish man in exile among Jews.Neugeboren is the author most recently of Whatever Happened to Frankie King and twenty-three other prize-winning works of fiction and nonfiction. His essays have been recently published in The New York Review of Books, The New York Times, The American Scholar, Los Angeles Review of Books, Tablet, and Commonweal, and are here collected for the first time.

OpenAI reportedly missed its own growth and revenue expectations recently, and shares of Oracle and other companies with large deals with the AI giant are trading lower. In this episode, the team discuss the OpenAI news and much more. Tyler Crowe, Matt Frankel, and Lou Whiteman discuss: - OpenAI's disappointing growth and what it means for tech investors - Whether OpenAI and its rivals will be able to scale to profitability anytime soon - General Motors' latest earnings and why Matt is such a big believer - Whether investors should take the time to vote their shares Companies discussed: ORCL, CRWV, GM, F, GOOGL, GOOG Host: Tyler Crowe Guests: Matt Frankel, Lou Whiteman Engineers: Kristi Waterworth, Dan Boyd Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠⁠⁠⁠megaphone.fm/adchoices⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Carl Quintanilla, Jim Cramer and David Faber led off the show with the AI Trade: Semiconductor stocks took a hit and pulled the Nasdaq down from a record high, in reaction to a report that said OpenAI recently missed its revenue and user targets. The Elon Musk-Sam Altman trial enters day two, with opening arguments set to begin. The anchors also explored market reaction to earnings from Coca-Cola, General Motors and UPS — plus what their results are indicating about the state of the economy. Also in focus: Oil prices rise as the White House is skeptical about Iran's latest offer, the United Arab Emirates to leave OPEC, the high-flying stocks in pullback mode, Spotify's stock slump, Starbucks earnings preview.   Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today on CarEdge Live, Ray and Zach discuss the latest info on General Motors. Tune in to learn more! Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

US President Trump is reportedly not satisfied with and is unlikely to accept the Iranian proposal; CNN reports that the US and Iran are not as far apart as they seem.BoJ maintained its policy rate as expected, though subject to a hawkish 6-3 vote split, dissenters highlighted upside risks to inflation. Ueda non-committal on the timing of the next move.European bourses firmer, lifting incrementally after a contained open. US futures are mixed/lower into earnings and after OpenAI missed internal targets.JPY led post-BoJ before retreating and weakening on Ueda, USD firmer to the modest detriment of peers across the board; base & precious metals hit.Energy bolstered by the overnight updates, and as Iran's Foreign Minister is not returning to Pakistan post-Russia.Fixed falters as energy climbs, Bunds hit by the latest ECB surveys, Gilts lag into the Privileges debate regarding PM Starmer.Looking ahead, highlights include US ADP Weekly Employment Change, US House Price Index (Feb), US CB Consumer Confidence (Apr), US Richmond Fed Index (Apr), US Dallas Fed Index (Apr), NBH Policy Announcement (Apr), and speakers include ECB President Lagarde, Supply from the US.Earnings from RobinHood, Bloom Energy, Visa, Booking.com, NXP Semiconductor, UPS, Coca-Cola, Spotify, General Motors, Centene.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

US President Trump is reportedly not satisfied with and is unlikely to accept the Iranian proposal; CNN reports that the US and Iran are not as far apart as they seem.BoJ maintained its policy rate as expected, though subject to a hawkish 6-3 vote split, dissenters highlighted upside risks to inflation.APAC pressured after the reporting around Trump, Nikkei 225 underperformed after the BoJ's hawkish-hold.DXY initially contained but then ticked higher, JPY benefited from the BoJ; JGBs gapped lower, but the move retraced, USTs rangebound.Crude supported by the reporting from the Situation Room, metals hit by the risk tone, hawkish action, and USD gains.Looking ahead, highlights include Spanish Retail Sales (Mar), Italian PPI (Mar), US ADP Weekly Employment Change, US House Price Index (Feb), US CB Consumer Confidence (Apr), US Richmond Fed Index (Apr), US Dallas Fed Index (Apr), NBH Policy Announcement (Apr), Speakers include BoJ Governor Ueda and ECB President Lagarde, Supply from the Netherlands, UK and US.Earnings from RobinHood, Bloom Energy, Visa, Booking.com, NXP Semiconductor, UPS, Coca-Cola, Spotify, General Motors, Centene, Airbus, Air Liquide, BP & Barclays.Click for the Newsquawk Week Ahead.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

It's a good start to the year for General Motors as executives reported a solid first quarter profit. WWJ's Chris Fillar and Jackie Paige have your Tuesday morning news. (Photo by Bill Pugliano/Getty Images)

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

(00:00:00) U.S. Navy Intercepts Iranian Vessel (00:05:38) A Bailout for Spirit Airlines? (00:10:27) Betting on the future when you know the future Craig Collins remains in for Greg Corombus Friday, and Craig and Jim start by noting the U.S. Navy's success in capturing Iranian-flagged ships trying to run the blockade on Iran's ports – while noting that the growing size of the “ghost fleet” in the past decade or so is a scandal.Speaking of ghosts, the Trump administration is attempting to resurrect the bankrupt Spirit Airlines, in a move unpleasantly reminiscent of the federal government's bailout of General Motors.In the crazy martini, Craig and Jim have mixed feelings about the special operations soldier who was involved in the capture of Venezuelan President Nicolas Maduro who allegedly made more than $400,000 by betting on Maduro's removal from office. Is this what they mean when they say to bet on yourself?Finally, a New York Giants fan and a New York Jets fan watch the first round of this year's NFL Draft and come away… happy!Please visit our great sponsors:QuoMake this the season where no opportunity or customer slips away with Quo. Try Quo free and get 20% off your first 6 months at https://Quo.com/3MLPocket HoseFor a limited time, get two free gifts—a 360° rotating pocket pivot and a thumb drive nozzle—when you buy the Pocket Hose Ballistic; just text MARTINI to 64000, message and data rates may apply.Fast Growing TreesBetter plants, better growing, and an extra 20% off with code MARTINI at https://FastGrowingTrees.com/Martini for a limited time; terms and conditions may apply.Noble GoldSchedule a free gold strategy session with Noble Gold. Visit https://NobleGoldInvestments.com/3ML to learn how to build lasting financial security.New episodes every weekday. 

In Episode 20 of the Finish Big Podcast, host Mark Dorman welcomes Laura Bonnet, Founding Director of the Center for Family Business at the Weatherhead School of Management at Case Western Reserve University. Established in 2023, the Center for Family Business was created to connect and equip multi-generational family enterprises to build stronger legacies. Laura brings a powerful blend of strategy, corporate leadership, and advisory experience to the role — having held executive positions at American Greetings and advisory roles at McKinsey & Company, as well as experience with General Motors and Disney. In this episode, Mark and Laura explore the unique dynamics of family enterprises, the challenges of multi-generational transition, governance, strategy, and the importance of intentional legacy building. This is a must-listen for family business owners navigating succession, leadership development, and long-term continuity. Mark and Laura Discuss: The Birth of the Center for Family Business – Why Case Western launched it in 2023. Family Business Complexity – Why governance and relationships matter as much as performance. Multi-Generational Transition – Preparing next-gen leaders early. Strategic Discipline – Lessons from McKinsey applied to family enterprises. Corporate vs Family Culture – Key differences in decision-making. Legacy Thinking – Moving from wealth creation to legacy stewardship. Emotional Capital – The unseen driver of family business success. Board Structures & Governance Models – Professionalising the family enterprise. Education & Community – The role of universities in supporting family firms. Balancing Growth & Harmony – Keeping business and family aligned. Connect with Mark Dorman: Succession Plus US LinkedIn: Mark Dorman LinkedIn: Succession Plus Facebook: Succession Plus (330)-416-9271 mdorman@succession.plus About the Guest: Laura Bonnet is the Founding Director of the Center for Family Business at the Weatherhead School of Management at Case Western Reserve University. Prior to launching the Center in 2023, she served in executive leadership roles at American Greetings and worked at McKinsey & Company advising clients across industries. Her career also includes experience with General Motors and the Walt Disney Company. Laura combines strategic expertise with a deep understanding of multi-generational enterprise to help family businesses build governance, leadership continuity, and sustainable legacies.

In this episode of Manufacturing Unscripted, host Peter Parsons sits down with Congresswoman Haley Stevens, Representative of Michigan's 11th District and one of the nation's leading champions for manufacturing. From her early roots growing up around heavy equipment in Michigan to her pivotal role on the Obama administration's Automotive Rescue Task Force, Congresswoman Stevens shares a first‑hand account of what it takes to protect and grow America's manufacturing economy. The conversation explores the historic auto rescue of General Motors and Chrysler and why saving the broader supply chain, not just the OEMs, was critical to Michigan's economic survival. Stevens offers behind‑the‑scenes insight into the challenges of stabilizing an industry during the Great Recession and how that experience shaped her lifelong commitment to industrial policy. Looking to the present and future, Stevens discusses the legislation she has helped pass to strengthen domestic manufacturing, including workforce development initiatives, STEM education, the CHIPS and Science Act, advanced manufacturing R&D, supply‑chain resiliency, and critical minerals policy. She also shares why bipartisan cooperation is often strongest around manufacturing issues and how policymakers can better partner with job creators on the shop floor. The episode wraps with a forward‑looking discussion on emerging technologies such as AI, quantum computing, advanced materials, sustainability, and recycling, and why manufacturing innovation remains at the heart of economic growth, national security, and opportunity. Above all, Stevens delivers a clear message to manufacturers of all sizes: your work matters, it is valued, and Michigan will continue helping lead the future of American manufacturing. Sponsored by Promess Inc., the leading provider of fully electric servo presses for manufacturing. Watch on Youtube: https://youtu.be/Uq9lf2JfsKE 

Today we learn that the Pentagon approached General Motors, Ford & Stellantis. They urgently need to prepare to switch from automobile design, and manufacture war vehicles and weapons. 00:00 Intro 02:24 Covert Intel 08:11 Russia Being Attacked 11:19 China and Russia 20:42 The Helicopter

#705: Jon McNeill, former president of Tesla and COO of Lyft, starts with a simple problem: his teenage son is about to start driving, and he's worried about texting behind the wheel. Instead of setting rules, he builds a solution. That idea becomes TruMotion, a company that uses smartphone sensors to track driving behavior. You hear how the app figures out whether someone is actually in the driver's seat, and how that technology ends up powering programs used by major insurance companies. From there, we zoom out. McNeill walks us through the systems he uses to build and scale companies. He explains how to question assumptions, including a case where his team reduces a 12-page car loan document down to a few sentences after realizing none of it is legally required. We also talk about speed. At Tesla, he learns to make decisions quickly, even without perfect information. He describes how faster decision-making compounds advantage over time. You hear a story from his early days working with Tesla, when he visits multiple stores, signs up for test drives, and never gets a follow-up. That leads him to identify thousands of missed sales opportunities sitting in the pipeline. The fix comes from focusing on the bottleneck, not adding more leads. McNeill also shares how he approaches negotiations at scale, including working with government officials in China and learning how incentives and systems shape outcomes. Throughout the conversation, he returns to a few core ideas: simplify the problem, identify the constraint, and move quickly once you have enough information to act. McNeill's new book is The Algorithm: The Hypergrowth Formula That Transformed Tesla, Lululemon, General Motors, and SpaceX. Timestamps: Note: Timestamps will vary on individual listening devices based on dynamic advertising segments. The provided timestamps are approximate and may be several minutes off due to changing ad lengths. (00:00) Jon McNeill, former Tesla President and former COO of Lyft (06:50) The "First Principles" Mindset (15:05) Managing Hyper-growth at Tesla Solving for "Pain Points" vs. Chasing Profit Autonomous Driving and Electric Vehicles Working with Visionary Founders Building a Culture of Innovation in any Organization Learn more about your ad choices. Visit podcastchoices.com/adchoices

What's the secret to out-innovating the competition? Former Tesla President Jon McNeill joins the show to discuss his new book, The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX. Motley Fool analyst Rachel Warren talks with McNeill about the five-step formula for achieving hypergrowth, the hidden metric every investor should track, and the AI revolution. Host: Rachel Warren Guest: Jon McNeill  Producer: Bart Shannon, Mac Greer  Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, "TMF") do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices

SEASON 4 EPISODE 70: COUNTDOWN WITH KEITH OLBERMANN A-Block (2:30) SPECIAL COMMENT: We are all looking at it backwards: That was Tulsi Gabbard’s passive-aggressive mini-coup against Trump - about Iran, and the elections. That's what she did in her Senate testimony – and what her deputy Joe Kent did when he resigned, a day earlier (and you think THAT was a coincidence of timing?) In a kind of bizarre code, through omission and not commission, they called Trump a liar about Iran and nukes. Not nobly or bravely. But they did it. They left no other conclusion that Trump was and is lying about Iran. Under oath. And THEN Gabbard passive-aggressively called Trump a LIAR AGAIN about the seized ballots in Georgia. Under oath. She testified that HE sent her. It might be a break in the damn; it might be trivial. It is NOT nothing. Because everybody has a moment in which they realize that they have to protect their own assets - and this might've been theirs. Lord knows all the allies have found theirs. Trump literally has no support from anyone, and the EU just started talking to Iran about a deal to get its ships through the Strait of Hormuz. Trump is neck high in quicksand that he ordered and installed - and he's run out of people to blame. Plus I'll explain what the hell Trump meant when he said “DIG WE MUST" instead of "drill baby drill." (It was his brain defaulting back to 1962 and it's a really bad sign). B-Block (26:00) SPORTSBALL TONIGHT: Are there ANY good feelings left from the US Olympics Men's Hockey Gold? Now the scorer of the winning goal, Jack Hughes, is demanding they give him the puck rather than enshrine it forever at the Hockey Hall of Fame, and an ESPN commentator is demanding we ignore "politics" and admit Russia to the next international tournament (while Russia is aiding Iran, who we are - like it or not - at war with). (34:00) THE WORST PERSONS IN THE WORLD: Newt Gingrich falls for an internet troll who wants to create an instant Trump Canal in the Middle East using nukes. Rachel Maddow becomes the umpteenth commentator to insist HER favorite Trump scandal should be getting more attention than the others. And not only did the Los Angeles Dodgers desecrate Dodger Stadium by slapping a sponsor name on it - but they then lied and said they HADN'T sold the naming rights. C-Block (56:00) THINGS I PROMISED NOT TO TELL: My great grandfather not only turned down stock - circa 1907 - in the company that would become General Motors, but according to family lore he gave the owner the idea for the NAME "General Motors." We are not businessmen.See omnystudio.com/listener for privacy information.

The Boys continue the story of the Du Pont dynasty as they evolve from World War I profiteers into architects of the modern age, embedding themselves in everything from General Motors to the chemicals in your very own bloodstream. From leaded gasoline and the coup to overthrow Franklin D. Roosevelt to their role in the Manhattan Project and napalm, the 20th century becomes a Du Pont production. War, coups, forever chemicals - profit at every step, with no accountability. For Live Shows, Merch, and More Visit: www.LastPodcastOnTheLeft.comKevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/Subscribe to SiriusXM Podcasts+ to listen to new episodes of Last Podcast on the Left ad-free, plus get Friday episodes a whole week early. Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.