Podcasts about zoox

This week, hosts Chad Sowash, Joel Cheesman, and Emi Beredugo sling zingers at the tech and policy chaos of today's work of work. First up, they cackle over OpenAI's Sam Altman throwing shade at Meta, claiming Zuck's crew dangled $100 million bonuses to poach his AI wizards. Altman, smirking on his brother's podcast, scoffed, “Meta's not exactly an innovation powerhouse,” betting OpenAI's culture will outshine cash as they chase superintelligence—AI that'll make humans look like dial-up modems. Chad quips, “Zuck's throwing cash like confetti, but Altman's holding the AGI trump card.” Next, the hosts tackle Trump's immigration whiplash. Last week, he hit pause on ICE raids targeting farms and hotels—where 42% of crop workers and 7.6% of hospitality staff are undocumented—after farmers cried foul. But days later, he flipped, doubling down on mass deportations, especially in blue states, risking $315 billion in economic fallout. Tech gets weirder with Amazon's Andy Jassy predicting AI will shrink corporate jobs, leaning on generative AI and Zoox's 10,000 robotaxis to replace drivers. Meanwhile, Zoom's Eric Yuan shrugs off work-life balance, saying leaders live for work and family, but sees AI pushing Gen Z toward three-day workweeks. Klarna's CEO, Sebastian Siemiatkowski, not to be outdone, launches an AI hotline starring a digital him. Surely, AI Sebastian will be running interviews at Klarna soon, right? Tune in for insight. Chapters 00:00 Introduction and Summer Vibes 01:49 Current Events: Juneteenth and Global Chaos 03:21 TikTok's Staying Power 05:10 Browser Dating: Privacy or Romance? 08:08 Indeed's New Market Squeeze 08:25 Meta vs. OpenAI: The Poaching Wars 24:32 Trump's Economic Tightrope 29:35 Immigration vs. Market Needs 35:26 AI's Job Displacement Threat 45:33 Culture and Burnout 50:23 The Infinite Workday Free stuff at http://www.chadcheese.com/free

- Cars Hold Up U.S. and Japan Trade Deal - Brazil Wants Higher Tariffs on China Now - Most China Plants Underutilized - U.S. Tariffs Will Raise Car Prices $1,760 - Is GM Trying to Torpedo Ford Battery Plant? - Buick Electra E5 Has 500K Mile Battery - Zoox Starts Building AVs in California - Helm.ai Uses Vision-Only for L4

Get ready for a game-changing episode of Truck Tech! Thomas Wasson sits down with Ali Javidan, Founder & CTO of Range Energy, to reveal groundbreaking electrification technology that's not focused on the tractor. Range Energy is putting the trailer to work with an innovative e-axle and battery system that promises to revolutionize fuel efficiency, safety, and operational transparency for fleets. In this deep dive, learn about: The "Ignored" Trailer: Why focusing on the trailer, not just the tractor, is key to decarbonizing commercial transportation. E-Axle Innovation: How a drive motor and battery pack on the trailer can reduce fuel consumption (up to 70%!), provide regenerative braking, and integrate seamlessly with any tractor. Smart Braking & Stability: Witness incredible safety improvements with faster reaction times than traditional ABS, significantly reducing stopping distances and preventing jackknifing, even on ice! Reefer Electrification: Discover how Range Energy's system can power refrigeration units, eliminating the need for diesel gensets and capturing enough regenerative energy to run reefers for an entire shift. Operational Simplicity: Learn why Range Energy's system requires zero modifications to the tractor, using standard air and light connections, and how it can be easily bolted onto existing trailers. Future Applications: Explore the potential for powering auxiliary components, APUs, and even defense applications. Ali, with his extensive background from Tesla, Google, and Zoox, shares the vision behind Range Energy and how they're empowering fleets to achieve significant decarbonization without major operational disruptions. Don't miss this look at the smart, sustainable future of trucking! #TruckTech #RangeEnergy #ElectricTrailer #EAxle #FleetElectrification #EVTrucking #ReeferTrailer #RegenerativeBraking #SmartTrailer #CommercialTrucking #SupplyChain #Logistics #Decarbonization #CleanTransportation #ACTExpo #FutureOfTrucking #Innovation #TruckingIndustry #Freight #ColdChain #SustainableLogistics Learn more about your ad choices. Visit megaphone.fm/adchoices

Description:In this episode, I explore the emerging landscape of RoboTaxis, focusing on Tesla's driverless Model Y and its implications for the autonomous vehicle market. I discuss Tesla's vision-based technology, the planned unmodified taxi fleet, and the transparency concerns that arise with Tesla's claims. We compare Tesla's strategy with competitors like Waymo and Zoox, while also addressing Elon Musk's ambitious scaling goals and the regulatory hurdles ahead. This conversation highlights the critical balance between innovation and public safety in the RoboTaxi sector, urging listeners to remain engaged with these evolving developments.Support the Show:PatreonAcast+Other Podcasts:Beyond the Post YouTubeBeyond the Post PodcastShuffle Playlist918Digital WebsiteNews Links: AV in Austin WebsiteRobotaxi VideoElon Musk gives us some Robotaxi detailsElon says Tesla Robotaxi launch will force companies to license Full Self-DrivingTesla starts Robotaxi testingTesla moves to block city of Austin releasing Robotaxi infoTesla admits it would 'suffer financial harm' if FSD data becomes publicTesla Robotaxi what we knowTesla sets tentative date for RobotaxiRobotaxi launch info in Austin Robotaxi update*Show Art Created By Dall-eSupport this show http://supporter.acast.com/kilowatt. Hosted on Acast. See acast.com/privacy for more information.

Chris and Josh share stories from their overlapping (but separate) Vegas trips—featuring video poker luck, suite mix-ups, and the ever-popular Ultimate X dilemmas. Chris scored an $8,000 royal flush on Super Times Pay; Josh hit 4 Aces on Ultimate X. Josh's Palazzo stay included a room mix-up (no elliptical!) and a “non-ESS” experience. Chris's smoky suite The "accidental kicker" mistake Venetian/Palazzo's new sundry store opened; Josh shares comp tips (hint: no comped undies). Listener Amy praised the Vegas community; Robin shared Ultimate X tips; Jenna asked about comps for smaller bankrolls. The Big Wheel featured Amazon's Zoox robo-taxis and a notary conference at Green Valley Ranch.   Follow the show:
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In this episode of The Straight Shift, The Car Chick explores the world of autonomous vehicles and robotaxis, discussing their origins in science fiction, the major players in the industry, the technology that powers them, and the challenges they face. The conversation delves into the safety concerns surrounding robotaxis, highlighting both humorous incidents and serious implications of their operation on public roads. LeeAnn also addresses the public's perception of robotaxis and the future of autonomous driving technology. Takeaways:·       Robotaxis are inspired by sci-fi concepts.·       Waymo is the leading company in the robotaxi space.·       Motional operates robotaxis in Las Vegas through Lyft.·       Zoox is building purpose-built autonomous vehicles with no driver controls.·       The technology behind robotaxis combines LIDAR, radar, cameras, ultrasonic sensors and artificial intelligence.·       Public perception of robotaxis is mixed, with safety concerns.·       Hilarious incidents highlight the learning curve of robotaxis.·       The future of autonomous vehicles is still uncertain, but evolving.You can view a full list of resources and episode transcripts here. Connect with LeeAnn: Website Instagram Facebook YouTube Work with LeeAnn: Course: The No BS Guide to Buying a Car Car Buying Service Copyright ©2024 Women's Automotive Solutions Inc., dba The Car Chick. All rights reserved.

Want more MTM Vegas? Check out our Patreon for access to our exclusive weekly aftershow! patreon.com/mtmvegas Episode Description This week the two newest casinos on the Vegas Strip were in the news. Resorts World partnered with Zoox to bring more robotaxis to the Strip while eliminating paid parking...sort of. Fontainebleau went ahead with their dealer layoffs and announced the opening date and prices for their Tom Brady driven sports museum named Hall of Excellence. What will the Summer have in store for these properties? In other news the April visitor numbers looked very mixed, but what is the overall picture and is it really bad? With Vegas struggling a number of deals are beginning to surface including an amazing Guy Fieri AYCE special, free parking at Resorts World and a Summer all-inclusive package at Plaza. We also discuss: Brian Christopher out at Plaza, taking a million dollar jackpot in cash, Primm is full, Flamingo continues to mess up check-in and we remember when Las Vegas was advertised as "The World's Best Vacation Value". Episode Guide 0:29 April visitor stats & airport numbers - Is it really bad? 2:13 Will the Southwest changes cause problems for Vegas? 4:03 Las Vegas “The World's Best Vacation Value” 5:39 Flamingo's crazy early check-in fee & the repercussions 8:30 An amazing Vegas AYCE deal! 9:59 Plaza's summer all-inclusive deal 11:56 Brian Christopher out at Plaza 13:19 Fixing Plaza's non smoking casino & Sand Dollar 15:26 Fontainebleau's Hall of Excellence opening - $$$ 16:55 Million dollar Vegas jackpot paid in all cash 17:55 $250 spins - Is a million dollars enough? 19:48 Fontainebleau's dealer layoffs 20:25 Resorts World partners with Zoox 21:32 Resorts World brings free parking back…sort of 23:13 Which garage to park in at Resorts World Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

Spring has sprung, and the AV sector is full of happenings. From Sterling Anderson's surprise move from Aurora to GM, to a series of recalls at Waymo and Zoox, the gang discusses the AV sector's busy spring.

Apple is rumored to unveil Solarim, a visionOS-inspired software interface at WWDC, Zoox has issued its second software recall in a month, and Android Auto will receive several updates beyond the integration of Google’s Gemini assistant. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you,Continue reading "Apple Will Likely Unveil A New Software Interface Inspired By visionOS at WWDC – DTH"

  Today Razib talks to Tim Lee, a previous guest on Unsupervised Learning. Lee hosts Understanding AI. Lee covered tech more generally for a decade for Washington Post, Ars Technica, and Vox.com. He has a master's degree in computer science from Princeton. Lee writes extensively about general AI issues, from Deep Research's capabilities to the state of large language models. But one of the major areas he has focused on is self-driving cars. With expansion of Waymo to Austin, and this June's debut of Tesla's robotaxis, Razib wanted to talk to Lee about the state of the industry. They discuss the controversies relating to safety and self-driving cars. Is it true, as some research suggests, that Waymo and self-driving cars are safer than human-driven cars? What about the accidents Waymos have been implicated in? Is it true that they were actually due to human error and recklessness, rather than the self-driving cars themselves? Lee also contrasts the different companies' strategies in the sector, from Waymo to Zoox to Tesla. Razib also asks him about the fact that self-driving cars' imminent arrival seems to have been overhyped five years ago, with Andrew Yang predicting trucker mass unemployment, to the reality that Waymo has now surpassed Lyft in ride volume in San Francisco. They also discuss the limitations of self-driving cars in terms of their ability to navigate cities and regions where snow might be a major impediment, and why there has been a delay in their expansion to freeway routes.

Join Eric, @EnglishNick67, @WesMoss365, @TimAndrewsHere, @Autopritts, @JaredYamamoto, Greg, and George in their newly extended timeslot from 3pm-7pm as they chat about Trump's visible hand, Zoox, World Bee Day, and so much more! *New episodes of our sister shows: The Popcast with Tim Andrews and The Nightcap with Jared Yamamoto are available as well!

Les notícies que et mouen. - Elèctric: Evolució de les vendes. - Autònom: Waymo escala producció i acord amb Toyota. Uber integra vehicles amb molts actors de tecnologia autònoma. Aurora llança operacions de camió autònom sense conductor. Zoox fa un recall dels seus vehicles degut a un accident a Las Vegas. - Aire: Custom Cells, empresa per bateries d' EVTol que havia de proveïr a Lillium, presenta bancarrota. - Espai: Kosmos 482 torna 52 anys després a la Terra. Hypatia liderada per Ariadna Farrés farà la tercera missió anàloga a Mart al desert de Utah aquest mes de Juny.

En el episodio 663 del podcast Diario en Movimiento hacemos un resumen de las principales noticias tecnológicas de los últimos 7 días. 05/05 Google irrumpe en la producción cinematográfica y televisiva con la iniciativa “100 Zeros” para impulsar sus tecnologías. 05/05 Adiós a Skype: el pionero de las videollamadas cierra tras más de dos décadas de servicio. 06/05 Apple revela cómo influye el ciclo menstrual en los hábitos de ejercicio, según un estudio con más de 110.000 participantes. 06/05 Nvidia planta cara a las restricciones: rediseña chips para seguir operando en China. 07/05 Software de la semana: Monday.com. Gestión de proyectos visual y flexible. E 07/05 Alibaba refuerza su estrategia de comercio electrónico con una alianza clave con RedNote. 07/05 Zoox detiene las pruebas y retira su robotaxi tras un accidente en Las Vegas. 08/05 Meta quiere automatizar toda la publicidad con IA: miles de anuncios de prueba inundarán Facebook, Instagram y Threads. 08/05 Apple desafía a Google con planes para integrar opciones de búsqueda con IA en Safari. 09/05 Estrenos de plataformas de streaming de la primera mitad de mayo. 09/05 Innovaciones en la agricultura para un futuro sostenible. 09/05 Netflix rediseña su app de TV y lanza la búsqueda por IA conversacional en iOS. 10/05 Intento de extorsión a distritos escolares de EE. UU. tras un ciberataque a PowerSchool: la compañía admite haber pagado un rescate. 11/05 Fidji Simo abandona Instacart y se incorpora a OpenAI como nueva directora ejecutiva de Aplicaciones.

Netflix a revu sa page d’accueil et son appli mobile pour ressembler un peu plus à TikTok… et pour intégrer l’IA d’OpenAI. Une bonne affaire pour les amateurs de vidéo? Pascal et Alain s’interrogent. Promo C2 MTL: 100$ de rabais sur le prix d’entrée avec le code-promo C2aimeUneTasseDeTech https://c2.eventnroll.com/fr/billetterie/achat-de-billet/2048/9874?PROMO=C2aimeUneTasseDeTech Testés: la musique sans perte sur les AirPods Max USB-C d’Apple vaut-elle le prix? Le thermomètre Combustion fera de vous un chef du gril! Promo PlanetHoster: La souveraineté de vos données vous inquiète? La solution Code promo : PHA-UTDT The World N0C - Hébergement mutualisé - https://bit.ly/phutdtm HybridCloud N0C - Hébergement dédié - https://bit.ly/phutdt Aussi: EB Games Canada de retour Spotify écoute les balados OpenAI demeurera à but non lucratif L’AQUOPS contre l’interdiction des téléphones à l’école La Vache qui rit veut son émoji Waymo et Zoox: le taxi autonome prend son envol Instant de silence pour la fin de Skype Et plus! Voir https://www.cogecomedia.com/vie-privee pour notre politique de vie privée

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss the abrupt resignation of Aurora Co-Founder Sterling Anderson, why Waymo needs more vehicles and the continued fragmentation of the robotaxi market. Mr. Anderson's sudden departure sent shockwaves through the industry, raising critical questions about his next move and the potential impact on Aurora's partnership with Volvo, along with Uber's reaction. Yet, curiously, none of these questions were raised by analysts on the company's earnings call, leaving investors and industry insiders to speculate.Meanwhile, Waymo appears to be scaling faster than its fleet can support. With 1,500 vehicles on the road today and another 2,000 expected by next year, the company has yet to activate highway operations, likely due to the increased wait times it would cause in already constrained markets.As Uber doubles down on global autonomous vehicle partnerships, Waymo appears to have cooled on Lyft, potently leaving them at a competitive disadvantage. As Uber aggressively ramps up its global autonomous vehicle partnerships, Waymo appears to have cooled on Lyft, potentially putting Lyft at a growing competitive disadvantage in the evolving autonomy economy.Episode Chapters0:00 On The Road2:03 Sterling Anderson Resigns from Aurora7:55 Autonomous Trucking Revenue Metrics of Success9:39 Waymo's New Factory12:38 How Many Cars Does Waymo Need in a Market?15:41 Autonomy Markets On The Road in D.C.20:30 Growth of Robotaxis on Uber & Lyft25:48 Is Uber Accelerating the Growth of Chineses AVs in Europe?30:13 What's Going on at Zoox?34:25 Rivian's Autonomy Ambitions37:42 Next WeekRecorded on Friday, May 9, 2025--------About The Road to AutonomyThe Road to Autonomy provides market intelligence and strategic advisory services to institutional investors and companies, delivering insights needed to stay ahead of emerging trends in the autonomy economy™. To learn more, say hello (at) roadtoautonomy.com.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/autonomy-economy/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

After the Sphere issued Beyoncé a cease and desist order (!) for using an image of the Sphere in her concert visuals, she's now replaced it with Allegiant Stadium. Co-hosts Dayvid Figler and Sarah Lohman are joined by Andrew Kiraly, creator of TheList.Vegas, to discuss whether or not the Sphere messed up — or if there's more to it. The trio also tackles the recent recall of Zoox robotaxis after an accident with a passenger vehicle, and what the gloomy outlook of the Legislature's Economic Forum could mean for Las Vegans. Learn more about the sponsors of this May 9th  episode: Aura Frames - Get $35-off plus free shipping on the Carver Mat frame with Promo Code CITYCAST The Neon Museum Get tickets to our live episode recording at the RDA Museum on 5/22 HERE Want to get in touch? Follow us @CityCastVegas on Instagram, or email us at lasvegas@citycast.fm. You can also call or text us at 702-514-0719. For more Las Vegas news, make sure to sign up for our morning newsletter, Hey Las Vegas. Looking to advertise on City Cast Las Vegas? Check out our options for podcast and newsletter ads at citycast.fm/advertise. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's show: Jason, Lon & Alex discuss the day's hottest tech and startup news. Zoox's robotaxi crash and voluntary recall, COATUE's innovative open-ended venture fund that could democratize private tech investing, the NSO Group's massive $167M fine for spyware abuse, Uber's record-setting earnings and in-person work push, and a candid AI wake-up call from Fiverr's CEO urging employees to embrace automation or fall behind. From regulatory shakeups to funding innovation and the rise of AI productivity, this episode captures the chaos and opportunity of tech in 2025.Timestamps:(1:09) Show Intro(2:43) Will Coatue's new low buy-in fund replace or supplement traditional VC investments?(9:55) OpenPhone - Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at www.openphone.com/⁠twist(13:38) Zoox issues recall: why this might be the best case scenario for self-driving cars.(20:18) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(22:39) Meta wins settlement against NSO: do spyware companies even need to exist?(25:45) Uber bests expectations in Q1! Why their future is looking bright.(29:48) Notion - Try it for free today at https://notion.com/twist(33:10) How scared should we be of AI replacing us? Fiverr's CEO says VERY.(40:57) In Office Hours, Evan from FusionAds wants to know: how to make clients feel more confident in with AI-generated marketing?(50:03) The Founder Friday Tournament's Final Four is now... a FINAL FIVE?!Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpLinks from episode:Do Things That Don't Scale: https://paulgraham.com/ds.htmlFusionAds: https://www.fusionos.ai/ai-generative-advertisingFollow Evan:X: https://x.com/EG_FusionLinkedIn: https://www.linkedin.com/in/evangraj/Follow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(9:55) OpenPhone - Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at www.openphone.co(20:18) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(29:48) Notion - Try it for free today at https://notion.com/twistGreat TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

ICYMI: Hour Three of ‘Later, with Mo'Kelly' Presents – Thoughts on Amazon's decision to recall 270 of its new ‘Zoox' robotaxis after one of the self-driving vehicles crashed in Las Vegas…PLUS – Chad Stahelski & Keanu Reeves are wading back into the world of the Baba Yaga with ‘John Wick 5' AND a factory camera caught a humanoid robot violently attacking a factory workers - on KFI AM 640…Live everywhere on the iHeartRadio app & YouTube @MrMoKelly

The Trump administration announced plans to shut down the Energy Star program, Zoox issued a software recall for robotaxis after a Las Vegas collision, and a Second GTA 6 trailer shed a little more light on the story. It's Wednesday May 7th and this is your morning tech news roundup from Engadget.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Ahold Delhaize is heel erg afhankelijk van de Amerikaanse markt en toch doen ze daar iets bijzonders. Ze hebben de prijzen verlaagd, zonder dat hun winstmarge er aan moet geloven. En ook de handelsoorlog raakt ze niet echt. Let je op, Elon? Deze aflevering hebben we het namelijk over de tactiek van de topman waar Musk wat van kan leren: Frans Muller. Hoe komt het dat Ahold geen echte last heeft van de importtarieven van president Trump? Over Trump en tarieven gesproken: China en de VS gaan met elkaar om de tafel over de handelsoorlog! In (toepasselijk) Zwitserland gaan de kemphanen met elkaar in gesprek. China zegt nu al dat je er niet teveel van moet verwachten, maar wij kijken of er tóch gestunt gaat worden. Stunten doet Disney ook. Met de streamingtak bijvoorbeeld. Netflix wil géén abonneecijfers delen, Disney doet dat wél. Er komen miljoenen klanten meer bij dan verwacht. En topman Bob Iger werkt nog even aan zijn erfenis. Er komt een themapark bij. De zevende van Disney op de wereld en die wordt gebouwd in een golfstaat. Ook hebben we het over Oprah Winfrey, de kwartaalcijfers van Uber, over een goedkoop model van Tesla en over twee landen die elkaar bestoken met raketten. See omnystudio.com/listener for privacy information.

Ahold Delhaize is heel erg afhankelijk van de Amerikaanse markt en toch doen ze daar iets bijzonders. Ze hebben de prijzen verlaagd, zonder dat hun winstmarge er aan moet geloven. En ook de handelsoorlog raakt ze niet echt. Let je op, Elon? Deze aflevering hebben we het namelijk over de tactiek van de topman waar Musk wat van kan leren: Frans Muller. Hoe komt het dat Ahold geen echte last heeft van de importtarieven van president Trump? Over Trump en tarieven gesproken: China en de VS gaan met elkaar om de tafel over de handelsoorlog! In (toepasselijk) Zwitserland gaan de kemphanen met elkaar in gesprek. China zegt nu al dat je er niet teveel van moet verwachten, maar wij kijken of er tóch gestunt gaat worden. Stunten doet Disney ook. Met de streamingtak bijvoorbeeld. Netflix wil géén abonneecijfers delen, Disney doet dat wél. Er komen miljoenen klanten meer bij dan verwacht. En topman Bob Iger werkt nog even aan zijn erfenis. Er komt een themapark bij. De zevende van Disney op de wereld en die wordt gebouwd in een golfstaat. Ook hebben we het over Oprah Winfrey, de kwartaalcijfers van Uber, over een goedkoop model van Tesla en over twee landen die elkaar bestoken met raketten. See omnystudio.com/listener for privacy information.

The line between technological innovation and dehumanization grows increasingly blurred as companies embrace AI solutions that fundamentally alter human interaction. At the forefront of this shift is Otter AI founder Sam Liang, who has developed an AI-powered avatar that attends 90% of his business meetings, complete with voice synthesis mimicking his speech patterns and the ability to make decisions based on his past behaviors. This represents a troubling evolution where leadership presence becomes optional, raising profound questions about authenticity, trust, and what we sacrifice when algorithms replace human connection.Meanwhile, scammers continue finding sophisticated ways to exploit our trust through technology. A particularly alarming trend involves fake banking apps designed to mimic legitimate banking platforms, allowing fraudsters to display convincing "payment successful" screens while walking away with sellers' goods. The face-to-face nature of these scams blends old-fashioned confidence tricks with digital deception, leaving victims thousands of dollars out of pocket with little recourse.The tech landscape continues to be shaped by major corporate competition and legal challenges. Google faces its second antitrust loss in a year as a US judge ruled the company maintains an illegal monopoly in online advertising. Simultaneously, Amazon expands its technological empire across multiple fronts—from entertainment franchises to autonomous vehicles with Zoox robotaxis, to challenging SpaceX's Starlink with its Project Kuiper satellite internet service.These developments highlight the growing consolidation of power among tech giants while raising important questions about innovation, competition, and how technology serves humanity. As AI systems increasingly stand in for human judgment and interaction, we must critically examine whether these advances truly enhance our lives or merely replace authentic connection with algorithmic approximation.Join us as we navigate this complex technological landscape with humor, insight, and a commitment to understanding how these developments affect our daily lives and future prospects.Support the show

How Sama is Improving ML Models to Make AVs Safer // MLOps Podcast #307 with Duncan Curtis, SVP of Product and Technology at Sama.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractBetween Uber's partnership with NVIDIA and speculation around the U.S.'s President Donald Trump enacting policies that allow fully autonomous vehicles, it's more important than ever to ensure the accuracy of machine learning models. Yet, the public's confidence in AVs is shaky due to scary accidents caused by gaps in the tech that Sama is looking to fill.As one of the industry's top leaders, Duncan Curtis, SVP of Product and Technology at Sama, would be delighted to share how we can improve the accuracy, speed, and cost-efficiency of ML algorithms for ​A​Vs. Sama's machine learning technologies minimize the risk of model failure and lower the total cost of ownership for car manufacturers including Ford, BMW, and GM, as well as four of the five top OEMs and their Tier 1 suppliers. This is especially timely as Tesla is under investigation for crashes due to its Smart Summon feature and Waymo recently had a passenger trapped in one of its driverless taxis.// BioDuncan Curtis is the SVP of Product at Sama, a leader in de-risking ML models, delivering best-in-class data annotation solutions with our enterprise-strength, experience & expertise, and ethical AI approach. To this leadership role, he brings 4 years of Autonomous Vehicle experience as the Head of Product at Zoox (now part of Amazon) and VP of Product at Aptiv, and 4 years of AI experience as a product manager at Google where he delighted the +1B daily active users of the Play Store and Play Games. // Related LinksWebsite: https://www.sama.com/Tesla is under investigation: https://www.cnn.com/2025/01/07/business/nhtsa-tesla-smart-summon-probe/index.htmlWaymo recently had a passenger trapped: https://www.cbsnews.com/losangeles/news/la-man-nearly-misses-flight-as-self-driving-waymo-taxi-drives-around-parking-lot-in-circles/https://coruzant.com/profiles/duncan-curtis/https://builtin.com/articles/remove-bias-from-machine-learning-algorithms~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Luca on LinkedIn: /duncan-curtis

Jeudi 10 avril, François Sorel a reçu Clément David, président de Theodo Cloud, Cédric Ingrand, directeur général de Heavyweight Studio, et Frédéric Simottel, journaliste BFM Business. Ils sont revenus sur le lancement de la nouvelle box Internet de Free et d'Orange, l'ambition de l'Allemagne de rivaliser avec Starlink, et les tests des robotaxis de Zoox d'Amazon à Los Angeles, dans l'émission Tech & Co, la quotidienne, sur BFM Business. Retrouvez l'émission du lundi au jeudi et réécoutez la en podcast.

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss their road trip to Forward Forth Worth, Waymo's expansion to Washington, D.C. and the emerging opportunity in autonomous trucking.Texas is thriving capital of autonomous trucking with Aurora, Bot Auto, Kodiak, Torc and Waabi all having a presence in the state. Now autonomous vehicle companies are flocking to the state with Avride, May Mobility, Waymo and Zoox all having a presence in the state.While Texas emerges as the capital of autonomy, Waymo continues to solidify its lead as the world leader in robotaxis. With their lead in tact and the demand for their service growing, Waymo announced this week that they are expanding service to Washington, D.C. in 2026. As both Waymo continues to grow and expand, the economic impact of autonomy is only just beginning to be felt.Episode Chapters0:00 Waymo Subreddit01:17 Forward Fort Worth03:10 Insurance06:55 Investing in Autonomous Trucking11:56 May Mobility Arlington Deployment13:56 Autonomy Markets on the Road17:28 Waymo Announces Service is Coming to D.C. in 202621:41 Waymo / Uber Relationship26:50 Waymo Testing Zeekr Robotaxis on I-85 in Atlanta27:44 Waymo, The World Leader in Robotaxis30:46 London31:56 New York33:34 Autonomy Markets Confessions34:34 Waymo Scheduled Rides38:05 Waymo Pricing41:08 Tesla's June Robotaxi Launch48:03 Next WeekRecorded on Tuesday, March 25, 2025--------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and commentary on autonomous vehicles/trucks and the emerging autonomy economy™.Autonomy is transforming industries and creating an entirely new economy that we call the autonomy economy™. The Road to Autonomy provides advisory and market intelligence services that helps you better understand the market and stay ahead of what's coming next. To learn more, say hello (at) roadtoautonomy.com.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/autonomy-economy/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Old-fashioned bribery and high-tech software was used to snatch gadgets from doorsteps across the U.S., federal authorities say. Plus, Amazon-backed Zoox is getting ready to launch a commercial service for its driverless vehicle. We speak with WSJ columnist Tim Higgins about the CEO's vision for its toaster on wheels. Victoria Craig hosts.  Sign up for the WSJ's free Technology newsletter.  Learn more about your ad choices. Visit megaphone.fm/adchoices

YouTuber, writer, and coder Sophia Tung recaps the week's most important AV news, including Waymo's big Bay Area expansion, multiple Tesla controversies, Travis Kalanick's robotaxi regrets, Rivian's hands-free driving debut, and more. This week's episode is shorter than usual, but hopefully it serves as a good introduction to Sophia and her point of view on AI mobility. She'll be podcasting more with us in the future. As a reminder, the first Ride AI summit is taking place on April 2 at Neuehouse in Hollywood, California. We already have an amazing group of speakers lined up, including Amnon Shashua of Mobileye, Gill Pratt of TRI, and other top decision makers from Waymo, Zoox, Wayve, Apollo Go, Nuro, and more. There's a ton of excitement around the fact that this will be the first event of its kind that people will be able to take a fully driverless Waymo robotaxi to, making it the perfect opportunity to inaugurate the second chapter of this technological space. The on-stage conversations will be focused on this shift, from experiments and ideas to delivering real-world realities, and how to reboot conversations with stakeholders in the public sector, capital markets, media, and beyond.Tickets are currently on sale ⁠here⁠: https://ti.to/rideai/ride-ai-2025

CES 2025: HERE Automotive Navigation Solutions Create a Virtual Display on Your Mac with DeskPad CES 2025: Zoox Robotaxi Funded by Amazon CES 2025: j5create Matter-Enabled Smart Plugs and Thunderbolt 5 Dock Support the Show Security Bits — 16 March 2025 ☘️ Transcript of NC_2025_03_16 Join the Conversation: allison@podfeet.com podfeet.com/slack Support the Show: Patreon Donation Apple Pay or Credit Card one-time donation PayPal one-time donation Podfeet Podcasts Mugs at Zazzle Podfeet 15-Year Anniversary Shirts Referral Links: Parallels Toolbox - 3 months free for you and me Learn through MacSparky Field Guides - 15% off for you and me Backblaze - One free month for me and you Eufy - $40 for me if you spend $200. Sadly nothing in it for you. PIA VPN - One month added to Paid Accounts for both of us CleanShot X - Earns me $25%, sorry nothing in it for you but my gratitude

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss Waymo's Bay Area expansion and what it means for the great highway and airport unlocks, our visit to May Mobility in Ann Arbor and Amazon's struggles with Zoox. Waymo has expanded its service area in the Bay Area by 50%, adding 27 square miles in Silicon Valley, for residents only at this time. Is this restriction due to a vehicle shortage? It very well could be, as Waymo continues to scale at a rapid pace in multiple markets. Is airport pick-up and drop-off at San Francisco International Airport (SFO) next? With Waymo's growing service area in the Bay Area, it seems to only be a matter of time. Demand for Waymo at SFO is there as in December 2024, there were approximately 13,366 searches for “SFO” on the Waymo app, and 718 people installed the app while physically at the airport.Once Waymo is allowed to operate at SFO, the economic impact is projected to be nearly $100 million. The positive economic impact is immense and California's economy needs Waymo to succeed. When Waymo succeeds, California's economy succeeds.Episode Chapters0:00 Autonomy Markets Visits May Mobility7:28 Waymo's Silicon Valley Expansion11:25 Waymo's Fleet Challenges14:52 Waymo's Eventual Expansion to SFO24:20 Waymo LAX Service29:20 Wayve‘s ChatGPT Moment34:41 What Does Amazon do with Zoox?37:22 Could Autonomy Reaccelerate Growth at Apple?40:36 Did Uber Make a Mistake Selling ATG?43:33 Unforced Error of The Week44:33 Next WeekRecorded on Friday, March 14, 2025--------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and commentary on autonomous vehicles/trucks and the emerging autonomy economy™.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/autonomy-economy/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

When Aicha Evans took over robotaxi startup Zoox from its founders in 2019, she made two big moves: selling the company to Amazon for over $1.2 billion, and keeping Zoox's radical design for a driverless car that looks like a lounge on wheels, with no steering wheel or brake pedal. Now, as the robotaxi industry drives toward a pivotal moment in public acceptance, Zoox is preparing to launch its commercial service later this year. How does the company fit in alongside rivals like Google's Waymo and Elon Musk 's Tesla? And why does Evans take inspiration from the early days of aviation? She speaks to WSJ's Christopher Mims and Tim Higgins in the latest episode of our interview series Bold Names. Check Out Past Episodes: Palmer Luckey's 'I Told You So' Tour: AI Weapons and Vindication  Humanoid Robot Startups Are Hot. This AI Expert Cuts Through the Hype.  Reid Hoffman Says AI Isn't an ‘Arms Race,' but America Needs to Win  Why Bilt's CEO Wants You To Pay Your Mortgage With a Credit Card  Let us know what you think of the show. Email us at BoldNames@wsj.com Sign up for the WSJ's free Technology newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

The first Ride AI summit, an intimate gathering of top leaders in driving automation technology and related AI-empowered hardtech, is taking place on April 2 at Neuehouse in Hollywood, California. In this episode, our hosts, Edward Niedermeyer and Timothy B. Lee, preview the aspects of the event program they are most excited about. We already have an amazing group of speakers lined up, including Amnon Shashua of Mobileye, Gill Pratt of TRI, and other top decision makers from Waymo, Zoox, Wayve, Apollo Go, Nuro, and more. There's a ton of excitement around the fact that this will be the first event of its kind that people will be able to take a fully driverless Waymo robotaxi to, making it the perfect opportunity to inaugurate the second chapter of this technological space. The on-stage conversations will be focused on this shift, from experiments and ideas to delivering real-world realities, and how to reboot conversations with stakeholders in the public sector, capital markets, media, and beyond.Tickets are currently on sale here. Space is limited.

- Tesla Aiming for Robotaxis In California - Waymo Hits 200,000 Rides/Week - Stella CEO Tavares Walks Away with Only $36.4 Million - Trump Promises Car Tariffs on Tuesday - Mercedes Says AI to Replace Car Designers - Xiaomi 1500 HP Porsche-Killer Priced At $73,000  - BMW Going with Dedicated EV Plants - Ford Scraps EV Inventory Plan - Lightweight Speaker for EVs - Autoline Poll on Paddle Shifters

- Tesla Aiming for Robotaxis In California - Waymo Hits 200,000 Rides/Week - Stella CEO Tavares Walks Away with Only $36.4 Million - Trump Promises Car Tariffs on Tuesday - Mercedes Says AI to Replace Car Designers - Xiaomi 1500 HP Porsche-Killer Priced At $73,000  - BMW Going with Dedicated EV Plants - Ford Scraps EV Inventory Plan - Lightweight Speaker for EVs - Autoline Poll on Paddle Shifters

Zoox Chief Product Officer Mike White sets the stage for the Zoox Explorer Program, which will allow users to sign up for robotaxi test rides in Las Vegas later this year. White also emphasizes the importance of experience, which led Zoox to build an in-house ride-hailing app and a robotaxi from the ground up. The content of the video is for general and informational purposes only. All views presented in this show reflect the opinions of the guest and the host. You should not take a mention of any asset, be it cryptocurrency or a publicly traded security as a recommendation to buy, sell or hold that cryptocurrency or security. Guests and hosts are not affiliated with or endorsed by Public Holdings or its subsidiaries. You should make your own financial and investment decisions or consult respective professionals. Full disclosures are in the channel description. Learn more at Public.com/disclosures.Past performance is not a guarantee of future results. There is a possibility of loss with any investment. Historical or hypothetical performance results, if mentioned, are presented for illustrative purposes only. Do not infer or assume that any securities, sectors or markets described in the videos were or will be profitable. Any statements of future expectations and other forward-looking statements are strictly based on the current views, opinion, or assumptions of the person presenting them, and should not be taken as an indicator of performance nor should be relied upon as an investment advice.

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss Walt's recent trip to San Francisco, where he observed more aggressive driving from Waymo vehicles and spotted a FasTrak transponder—hinting that the great highway unlock is imminent.In the markets, Aurora and Lyft reported earnings, with Aurora's market cap soaring to $17 billion—triple Lyft's $5 billion. On its earnings call, Aurora emphasized its driver-out readiness and their hardware kit approach, mirroring Kodiak's SensorPods. If hardware kits are the future of autonomy, power could shift from OEMs to autonomous driving developers, opening new market opportunities and validating Kodiak's OEM-agnostic strategy.And then there's NVIDIA. What if they acquired an autonomous driving developer and vertically integrated? How would the market respond? What would it mean for chip sales? As fragmentation grows in the autonomous vehicle market, so does Uber's advantage. Opportunity abounds.Episode Chapters0:00 Walt's San Francisco Trip1:54 Hesai LiDAR6:19 Waymo's More Assertive Driving7:37 Should we be Concerned about Zoox?13:08 Aurora Earnings16:43 Autonomous Driving Hardware Kits21:43 Should NVIDIA Vertically Integrate an Autonomous Driving Stack?26:24 Nuro's Licensing Opportunity30:29 Uber's Next Autonomy Partner—May Mobility?33:50 Autonomous Vehicle Fleet Management & Financing38:00 DoorDash's Autonomous Delivery Ambitions40:57 Next WeekRecorded on Saturday, February 15, 2025--------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and commentary on autonomous vehicles/trucks and the emerging autonomy economy™.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/autonomy-economy/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Bridget Carey and Scott Stein go on a journey together as Bridget recalls her first time in a driverless vehicle with the Zoox.

本集節目由【克立淨】贊助 你是否正在尋找一款能同時淨化空氣與除濕的全能家電？克立淨推出的【CS101 Max 超能雷神除濕旗艦款】就是市面上最頂的選項之一，而且還提供獨家的終身售後服務，值得每一個追求好空氣的你。 ✨︎ 一機搞定：結合電漿空氣清淨與智能除濕功能，讓室內空氣潔淨乾爽。 ✨︎ 業界最高標準：機身密合＋濾網密封技術＋層層分工，出風口PM0.3=0。 ✨︎ 超能電漿滅菌艙：滅絕病菌、分解甲醛等有害氣體、吸附 PM0.0001 奈米微粒。 ✨︎ 雙濾淨風道：清淨除濕強強合一，智慧對應各種環境問題，不妥協任何一邊。 ✨︎ 終身服務：完整的售前/售中/售後服務，且永久到府服務。 創下 4,100 萬集資紀錄後好評再登場，限時 65 折賣場：https://cleanstation.tw/dtlzq/ 結帳輸入專屬優惠碼「Manny500」（注意大寫）可以再折 $500 元！ -- (00:00) 在地好生意、曼報百萬年薪徵才 (20:45) Zoox：創業故事 (34:44) Zoox：商業模式 (53:17) 無人計程車服務的價值訴求 -- 曼報首度徵才，百萬年薪邀情你加入成為訂閱產品線主編 超詳細招募說明：https://mannyyhl.notion.site/18f1f2fbdfe48082afa7e4f2d4f5b5cf -- 我們唯一使用的筆記軟體：Heptabase 立刻試用：https://get.heptabase.com/0riyv036mxrp -- 商業合作報價：https://manny-li.com/sponsor/ 訂閱電子報：https://manny-li.com 追蹤 IG：@manny_li 追蹤 FB：manny yh li Powered by Firstory Hosting

Bridget Carey and Scott Stein go on a journey together as Bridget recalls her first time in a driverless vehicle with the Zoox.

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discussed Tesla's Q4 earnings, Waymo's road trip and Kodiak's historical announcement. It was Tesla this week that captured investors attention as Elon Musk spoke at length about Tesla's robotaxi plans, licensing FSD and their plans to launch an Airbnb like service for autonomy. Will Tesla really hit the June 2025 timeline for unsupervised autonomy in Austin, Texas? Anything is possible, but the precedent is that the launch will be delayed, along with a potential nationwide expansion in 2026. Then there is the potential to license FSD (Full Self-Driving), will Tesla really do it and what will the impact be on the global auto industry? As Tesla works towards full autonomy (unsupervised), Waymo continues to expand. This week Waymo announced that they are taking roadtrips to 10+ markets beginning with San Diego and Las Vegas this year, along with opening the Atlanta market to employees. Additionally, Waymo began offering highway service to employees in Los Angeles.Then there is Kodiak, which made history this week, becoming the first company to sell an autonomous truck to a third party—Atlas Energy. The episode concludes with discussion of Zoox's challenges in the competitive autonomous vehicle market, particularly in Las Vegas and San Francisco.Episode Chapters0:00 Secretary Sean Duffy2:59 Washington Post: If Self-Driving Cars are Safer than the Alternative, What's to Lose?7:02 Tesla Q4 2024 Earnings 9:28 FSD Unsupervised Launch13:58 Capital Markets & Analysts 16:48 LiDAR Narrative 21:30 Licensing Tesla FSD26:20 FSD is Coming to Semi28:51 Kodiak Makes History 31:28 Tesla Cortex Training Cluster32:32 Waymo Goes Fully Autonomous on LA Freeways (Employees Only)35:21 Waymo Opens Atlanta to Employees37:29 Waymo's 10 City Road Trip41:06 Zoox45:06 Super Bowl46:09 Next WeekRecorded on Thursday, January 30, 2025--------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and commentary on autonomous vehicles/trucks and the emerging autonomy economy™.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/autonomy-economy/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

MotorTrend's Ed Loh & Jonny Lieberman sit down with Founder & CEO of Range Energy, Ali Javidan! The guys chat with Ali about his incredible resume (Ground Control Suspension, Tesla, Google, Zoox, Dinan), Tesla before & after Elon Musk, bringing the original Tesla Roadster & Model S to life, AND they talk all about his new company, Range Energy - Electric Powered Trailer Systems! Learn more at Range.Energy!0:28 - About our guest...2:07 - Ali's professional history & background7:22 - Aging Infrastructure and Heavy EVs. 9:52 - Ali's Path to Working on the First Tesla Roadster.14:25 - Early Days of Elon Musk's Takeover at Tesla.16:36 - Building the First Tesla Model S.22:14 - Inspiration for Tesla's Turbine Wheels.25:05 - Initial Reactions to "CLS" Model S Rollout.27:18 - History of Tesla's Integrated System. 30:14 - Elon's First Principles Thinking.36:36 - Internal Response to MotorTrend Naming Tesla Model S the 2013 Car of the Year.44:32 - BTS - Tesla Model S Reveal Party.48:05 - What is Range Energy? Innovations in Trailer Technology!51:45 - Software Defined Towing: Safety and Control Systems.53:10 - Fuel Efficiency and Emission Reduction.01:02:32 - Refrigeration Trailer Technology.01:07:22 - Driver Experience and Confidence.01:08:28 - Charging Flexibility.01:13:38 - Cost Analysis and ROI.01:17:34 - Complementing Existing Technologies.

The future of wireless technology is unfolding, are you ready for what's next? How can Intel regain market dominance? How will AI and IOT shape the next generation of wireless? What are the challenges in transitioning to 5G, NextG, and beyond? How will academia and the startup world intersect in the 21st century economy?  We explore these questions and more in our latest episode of the TechSurge Deep Tech VC Podcast, as we sit down with Dr. Andrea Goldsmith, Dean of Princeton University's School of Engineering and Applied Science and a pioneer in wireless communication. Dr. Goldsmith shares insights from her groundbreaking research in multi-antenna systems, the evolution of wireless networks, and the future of cellular technology. We explore her journey as a successful entrepreneur behind Quantenna and Plume WiFi, and her current leadership role as Dean working to build a vibrant engineering and startup ecosystem around Princeton. Dr. Goldsmith also shares her thoughts on the future of Intel, the strategic choices that lie ahead, and its important role within the U.S. tech economy, as well as the broader geopolitical landscape.Enjoyed this conversation? Subscribe now and leave a review to help us grow! Join our newsletter for exclusive insights and upcoming TechSurge Live Summits at techsurgepodcast.com. Links: Check out our video episodes on YouTube Follow Celesta Capital on LinkedIn and X Learn more about Dr. Goldsmith's pioneering research at Princeton: https://ece.princeton.edu/people/andrea-goldsmith Discover Intel's latest innovations: https://www.intel.com/ Explore how Plume WiFi is redefining smart home connectivity: https://www.plume.com/  See how Medtronic is shaping the future of healthcare innovation: https://www.medtronic.com/ Experience the future of autonomous mobility with Zoox: https://zoox.com/

Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM

After years of intense effort and turmoil, Zoox is nearing the finish line in its pioneering ground-up robotaxi, possibly the most ambitiously novel vehicle of our time. Ed and Kirsten join Zoox CTO Jesse Levinson for a ride along the famous Las Vegas strip, taking in and discussing this unique new autonomous experience.

Today on Equity, we're taking you on stage at TechCrunch Disrupt for Kirsten Korosec's conversation with Zoox co-founder and CEO Jesse Levinson. The pair discuss building custom robotaxis, how Zoox's approach compares to that of Tesla, and the 'current and future landscape' of AI on wheels. It's also worth noting that Amazon-owned Zoox recently scooped up some of Tesla's top talent, bringing on Zheng Gao late last month to lead hardware engineering. Equity will be back on Friday with a full CES recap, so don't miss it! Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes here. Credits: Equity is produced by Theresa Loconsolo with editing by Kell. We'd also like to thank our illustrator, Bryce Durbin, and the TechCrunch audience development team.

Waymo, the self-driving car startup owned by Google parent Alphabet, may be the front-runner in the race to lead the driverless car industry, but it's got competition. Elon Musk's Tesla and Amazon's Zoox are also building out robotaxi technology and services to get riders in self-driving cars. On the second episode of our special series on the growing driverless car industry, host Danny Lewis looks at these companies' efforts to catch up and where Waymo's success could take it and its tech into the future. What do you think about the show? Let us know on Apple Podcasts or Spotify, or email us: FOEPodcast@wsj.com  Sign up for the WSJ's free The Future of Everything newsletter.  Further reading: General Motors Scraps Cruise Robotaxi Program  Musk Shows Off Driverless Robotaxi to Be Priced Under $30,000  Waymo, Uber, Lyft Are Biggest Winners From Tesla's Robotaxi Flop   Elon Musk Plays a Familiar Song: Robot Cars Are Coming   Learn more about your ad choices. Visit megaphone.fm/adchoices

Waymo, the self-driving car startup owned by Google parent Alphabet, may be the front-runner in the race to lead the driverless car industry, but it's got competition. Elon Musk's Tesla and Amazon's Zoox are also building out robotaxi technology and services to get riders in self-driving cars. On the second episode of our special series on the growing driverless car industry, host Danny Lewis looks at these companies' efforts to catch up and where Waymo's success could take it and its tech into the future. What do you think about the show? Let us know on Apple Podcasts or Spotify, or email us: FOEPodcast@wsj.com Sign up for the WSJ's free The Future of Everything newsletter. Further reading: General Motors Scraps Cruise Robotaxi Program  Musk Shows Off Driverless Robotaxi to Be Priced Under $30,000  Waymo, Uber, Lyft Are Biggest Winners From Tesla's Robotaxi Flop  Elon Musk Plays a Familiar Song: Robot Cars Are Coming  Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss the dramatic turn of events that led GM to abruptly shut down Cruise, just days before a planned driver-out launch in Houston. The decision made by CEO Mary Barra, reportedly stems from fears of another incident.With the decision to pull the plug on Cruise, GM burnt $9 billion of investment dollars with nothing to show for it. The Cruise shutdown highlights the ongoing challenges of how traditional automakers are approaching autonomy. As traditional automakers falter, Waymo and Tesla continue to accelerate as the market is beginning to consolidate around them as the leaders in robotaxis.As we look to 2025, more consolidation is likely on the way, with Daimler Truck potentially scaling back their in-house autonomous trucking program (Torc Robotics). Then there is Uber which is struggling to define their autonomy narrative to the market. What moves do they make to calm market jitters?Episode Chapters0:00 GM Pulls the Plug on Cruise7:57 Uber & Lyft in the Era of Autonomy16:47 Zoox and Amazon. What is the Path Forward?22:45 Could Daimler Truck Follow GM and Shutter Torc Robotics?29:25 Amazon and Autonomy31:06 Walmart32:15 Cybercab in 2026?33:10 Aurora33:51 Next WeekRecorded on Thursday, December 12, 2024--------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and commentary on autonomous vehicles/trucks and the emerging autonomy economy™.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/autonomy-economy/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Want more MTM Vegas? Check out our Patreon for access to our exclusive weekly aftershow! patreon.com/mtmvegas Episode Description: As a reminder you can watch this show as well at: http://www.YouTube.com/milestomemories This week as F1 continues its slow tear down in Las Vegas we learned that a popular mall along the course will be expanding. Harmon Corner is not only building new space out over the sidewalk, but it is adding kiosks and other areas to maximize every square foot. Is it too much or is this maximization just the new normal in Las Vegas? In other news Holstein's has announced its return to Vegas as 6 Chinese eateries make the Yelp top 100. We also discuss: an accident at the Wynn flower beds, an explanation for the Strip gaming declines, stuff stolen from The Orleans, a crazy 25 leg parlay, announcing casino wins, the huge expansion of gaming apps and why the Mirage Atrium is definitely gone for good. Episode Guide: 0:00 Attack of the Wynn flower beds 0:47 Update on Bellagio's post-F1 teardown 2:00 Confirmation on Mirage Atrium removal 3:19 Harmon Corner's expansion - Squeezing in even more 4:45 Explaining the decline in Vegas gaming revenue 8:08 6 Las Vegas Chinese restaurants on Yelp Top 100 9:40 Holstein's coming to the Arts District 10:13 Zoox publicly launching driverless taxis on the Vegas Strip 11:43 Stuff stolen from a room at The Orleans 13:00 Interesting way to announce casino jackpots 14:22 Crazy 25 leg parlay win 15:49 Apps and problem gambling 18:10 Booming app industry & barrier to entry     Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

In this episode of Munro Live Podcast, Chris Fox, Munro's Low Voltage Systems Lead Engineer and expert in automotive wiring and ADAS, sits down with Chris Stoffel, Director of Industrial and Creative Design, and Ryan McMichael, Director of Sensors & Systems for Advanced Hardware at Zoox. The discussion dives deep into Zoox's journey as a trailblazer in autonomous mobility, exploring the company's history, the innovative design philosophy behind their vehicle, and the cutting-edge sensor technology that powers its autonomy. Join us for an insightful conversation on how Zoox is shaping the future of transportation! Munro Live is a YouTube channel that features Sandy Munro and other engineers from Munro & Associates. Munro is an engineering consulting firm and a world leader in reverse engineering, costing and teardown benchmarking. Munro Home of Lean Design https://leandesign.com/

Emily Forlini is filling in for Mikah Sargent this week! Lyft is entering the robotaxi space as it partners with a robotaxi company. Some of the latest self-driving tech is making its way into vehicles. Is ChatGPT's search feature a worthy rival to Google Search? And what does the feature hold for technology as the US enters a new administration in the coming months? Abrar Al-Heeti talks about autonomous vehicles as Lyft partners with robotaxi companies to explore deploying autonomous vehicles within its network and how a company called Zoox seeks to deploy robotaxis in San Francisco and Las Vegas in the coming weeks. Emily Forlini continues the autonomous vehicle discussions about new self-driving systems being utilized in GM and Ford vehicles. Lance Ulanoff of TechRadar stops by to talk about his experience using ChatGPT Search and how it can rival the dominant Google search engine. And Dan Patterson from Blackbird AI chats with Emily about how the upcoming Trump administration could shape the tech industry's future. Hosts: Emily Forlini and Abrar Al-Heeti Guests: Lance Ulanoff and Dan Patterson Download or subscribe to this show at https://twit.tv/shows/tech-news-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: 1password.com/twit Melissa.com/twit e-e.com/twit uscloud.com