Podcasts about scale ai

The federal government's human capital arm added several new industry partners to its Tech Force hiring effort Monday as the program begins to take root in agencies. The new batch of companies is Cisco, Scale AI, Wiz, Arista Networks, Armada, Cognition AI, Cognizant, Payward, and Moveworks, per a release from the Office of Personnel Management. They join a cohort of a couple dozen companies that are already part of the program's industry support, including OpenAI, Google Public Sector, xAI, and Palantir. “These partnerships bring world-class engineering expertise into public service and create a stronger pipeline between industry and government at a moment when modernizing federal technology has never been more important,” OPM Director Scott Kupor said in a written statement Monday. According to the release, the companies will provide training resources and programming, as well as their own employees who they'll nominate for temporary federal service. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

Greg is CIO at Man Group, Head of the Americas, and lead PM for the firm's flagship multi‑strategy fund, overseeing $228B in AUM (as of 3/31/26). He shares how Man Group pursues alpha at scale by fostering collaboration across systematic and discretionary teams, using AI as a connective tissue between human judgment and quantitative rigor, and designing a culture that preserves independent thinking.-This podcast/webcast is provided for informational purposes only and should not be considered legal, tax, investment, or business advice. It is not a solicitation, recommendation, or endorsement. All opinions expressed by participants are their own and do not necessarily reflect the views of the Evoke Advisors Division of MAI Capital Management, LLC ("Evoke”), its affiliates, or any companies mentioned. Information shared has not been independently verified by MAI or its affiliates. MAI Capital Management, LLC (“MAI”) is registered with the U.S. Securities and Exchange Commission ("SEC"), which does not imply any particular level of skill or training.Certain information contained herein has been obtained from third party sources and such information has not been independently verified. No representation, warranty, or undertaking, expressed or implied, is given to the accuracy or completeness of such information by any person.While such sources are believed to be reliable, Evoke does not assume any responsibility for the accuracy or completeness of such information. Evoke does not undertake any obligation to update the information contained herein as of any future date.The content is intended for a general audience and does not constitute a recommendation to buy or sell securities or adopt any investment strategy. Any examples or scenarios discussed are illustrative only, involve risks and uncertainties, and do not guarantee future results. Non-traditional assets carry significant risks and may not be suitable for all investors. Decisions should be based on individual objectives, risk tolerance, and circumstances.Statements herein are general and may not reflect an individual's or entity's specific circumstances or applicable laws, which vary by jurisdiction. Further, speakers' views are personal and may differ from Evoke and MAI recommendations and are not specific investment advice; and do not consider client objectives, risk tolerance, and diversification. Guests may have current or past relationships with Evoke and MAI, its affiliates, or the host, including as clients, service providers, or business partners. Participation does not constitute an endorsement or testimonial. No compensation has been paid or received for guest participation unless disclosed. MAI and its affiliates may have business relationships with entities mentioned in this podcast, which could create potential conflicts of interest. These relationships may include advisory services, investment management, or other arrangements. MAI seeks to manage such conflicts consistent with its fiduciary obligations and policies.(As of December 22, 2025)

The conversation around AI in education is changing fast, and the latest GSV Learning and Earning Forecast now identifies trust as the factor that will determine the near-term future of AI in the classroom. In this episode, we explore the “AI trust gap” forming between the people racing to expand AI in schools and the educators, parents, and students who are starting to push back. Through discussions with educators, school leaders, learning science researchers, analysts, ed tech developers, AI vendors, and non-profits across the community, we zoom in on the hard questions surrounding AI's future in education. What happens when innovation starts moving faster than trust? What is required to bridge the gap? Who is working on solutions? What's working? Sources: Forecast for Learning & Earning in 2025-2026 | Digital Promise | Learning Commons | Surgeon General's Office Advises Schools to Limit Screen Time | Teachers and parents weigh benefits and risks of artificial intelligence in schools | Do AI's risks outweigh the benefits for students and schools? | Fostering Trust in the Age of AI | GSVtv | The Next AI Maturity Curve – Orchestration, Trust, and Scale | AI is Air: Ambient AI in Every Breath, Step, and Swipe | GSVtv | Lincoln High students swap screen time for study time after phone ban | How to Choose Safe and Effective Classroom Technology | More Students Boo AI at Commencement Nick Melvoin, a Los Angeles Unified School District (LAUSD) board member who recently drafted a resolution to restrict student screen time in classrooms. Sandra Liu Huang, Head of Education & Product at CZI and president of Learning Commons. Jean Claude Brizard– President and CEO of Digital Promise. Jeremy Roschelle– Executive Director of Digital Promise's Learning Sciences Research team. Melissa Loble, Chief Academic Officer, Instructure. Patrick Gittisriboongul, Ed.D., Superintendent of Lynwood Unified School District. Justin Reich, Director of Teaching Systems Lab at MIT. Jennifer Lee Partner GSV Ventures. Muktha Ananda– Google's Director of Engineering. Robert Wong, Google's Director of Product Management. Brian Carslon, CEO, Storytime AI.Tim Sanders, Chief Innovation Officer at G2 and Executive Fellow at Harvard. Chris Hamatake, parent. Rebecca Winthrop, Senior Fellow and Director of the Center for Universal Education at Brookings. Dr. Eugene Kim, Professor of Education at Concordia University.

Two former Benchmark investors are pitching something you don't usually see this soon: a joint, $800 million AI fund—less than a year after each left to raise smaller, founder-led vehicles on their own. Victor Lazarte left the Silicon Valley fund in July 2025 after backing companies like Mercor, Heygen and Applied Compute in his two-year stretch as a partner. After exiting Benchmark, he quickly raised $200 million for his own fund, VL. Now, Lazarte is pitching a much bigger fund to make bets on early and growth stage startups. He's telling prospective limited partners he plans to raise a new fund called Diffusion and co-manage it with Kris Fredrickson, according to several investors who say they were pitched on the effort. The target is roughly $800 million, one of the larger first-time venture raises of the year. Fredrickson started his investing career at Benchmark before moving to hedge fund Coatue, where he backed companies like Instacart, Chime and Scale AI. Forbes reported last July that Fredrickson had raised $175 million for his own fund, Verified, to back growth stage AI startups like legal platform Harvey and search engine Perplexity. By Iain Martin, Forbes Staff Learn more about your ad choices. Visit megaphone.fm/adchoices

My podcast guest this week is Andy Hock, Chief Strategy Officer at Cerebras.  Andy and I are discuss the the revolutionary details of the Cerebras Wafer-Scale Engine (WSE). We also explore where the next major competitive frontier lies for AI hardware, how the latest WSE addresses the critical latency and energy efficiency challenges of deploying massive AI models for inference and Cerebras' role in democratizing access to large-scale AI research and deployment.

In this episode of What's Next, Aki sits down with Morné, Chief Technology Officer at Logicalis, to unpack how artificial intelligence is rapidly reshaping modern business. The discussion explores why many organizations are struggling to keep pace with AI adoption, despite the enormous pressure to innovate and remain competitive. Drawing from recent CIO research, Morné explains that while businesses are eager to embrace AI, many are failing to implement it strategically, leading to stalled pilot projects, wasted investment, and a lack of measurable business value. The conversation also dives into the growing risks and leadership challenges tied to AI transformation, including cybersecurity threats, governance, data management, and the ongoing shortage of digital skills. Morné emphasizes that AI success is no longer just a technology issue — it is a leadership responsibility that requires collaboration, clear governance, responsible scaling, and continuous learning. The episode concludes with practical advice for CIOs and executives on how to embed AI securely and effectively into everyday business operations while preparing organizations for the rapidly evolving digital future. Logicalis CIO Report - https://www.za.logicalis.com/cio-report-za

Waterfowl science is entering a new era — and Ducks Unlimited is right in the middle of it.In this episode, host Dr. Mike Brasher is joined by co‑host Dr. Jerad Henson and guest Dr. Patrick Donnelly, Research Scientist with Ducks Unlimited's Western Region, for a deep dive into how emerging technologies are transforming the way we understand ducks, wetlands, and flyways.Patrick brings decades of experience from the U.S. Fish and Wildlife Service, joint ventures, and academia, and now applies cutting‑edge tools like AI, cloud computing, GPS telemetry, remote sensing, and environmental DNA to answer some of the most important conservation questions at continental scales.In this episode, listeners will hear about:Patrick Donnelly's journey from the Fish & Wildlife Service to Ducks UnlimitedMovement ecology and why scale matters for migratory birdsHow GPS transmitters revolutionized waterfowl researchUsing satellite imagery to map wetlands across 40+ years“Functional wetland loss” and why water matters as much as land protectionThe role of snowpack, hydrology, and climate in western wetlandsDisease risk, botulism, and crowding during molting periodsLinking bird movements, habitat conditions, and timeThe Western Mallard Project and tracking 800 birds across the Pacific FlywaySentinel and Landsat satellites explained in plain languageCloud computing and why conservation can now run at scaleUsing citizen‑science data (eBird) alongside satellite dataNew applications of environmental DNA (yes — duck poop)How AI helps identify patterns humans can't seeTraining the next generation of conservation scientistsWhy this moment feels like a “second revolution” in waterfowl scienceThis episode pulls back the curtain on how Ducks Unlimited is using modern science to maximize conservation return on investment, ensuring that every dollar delivers the greatest benefit for waterfowl now and into the future.Listen now: www.ducks.org/DUPodcastSend feedback: DUPodcast@ducks.orgSPONSORS:Purina Pro Plan: The official performance dog food of Ducks UnlimitedWhether you're a seasoned hunter or just getting started, this episode is packed with valuable insights into the world of waterfowl hunting and conservation.Bird Dog Whiskey and Cocktails:Whether you're winding down with your best friend, or celebrating with your favorite crew, Bird Dog brings award-winning flavor to every moment. Enjoy responsibly.

What if the thing you feared most became your greatest asset?In this episode, I'm chatting with Kathleen, an Online Business Manager and AI consultant who had a complete breakdown when AI emerged—convinced it would end her career. Now? She's the expert helping business owners like us streamline operations and embrace automation.Kathleen shares her honest journey from corporate burnout to building a thriving business, starting with photography, pivoting to VA work during COVID, and eventually finding her zone of genius in systems and AI consulting.If you've ever felt overwhelmed by tech tools or unsure where to start with AI, this conversation is your permission slip to start small.What we cover:Why Kathleen thought AI would take her job—and how she turned fear into expertiseThe truth: AI won't replace you, but not learning it might hold you backHow to start with AI without the overwhelm (one thing at a time)ChatGPT vs. Claude vs. Casti AI—which to use and whenThe most common mistakes people make with AI in businessEssential systems every business needs: lead management, response process, and onboardingThree actionable steps you can take todayConnect with Kathleen: https://linktr.ee/thevirtualspellConnect with me, Hayleigh Hayhurst:Steal my Podcast Launch Checklist for free: http://espressopodcastproduction.com/checklistWebsite: https://www.espressopodcastproduction.com/YouTube: https://www.youtube.com/@EspressoPodcastProductionInstagram: https://www.instagram.com/espressopodcastproduction/TikTok: https://www.tiktok.com/@espressopodproductionMusic: John Kiernan. www.johnkiernanmusic.comProduced by Espresso Podcast Production: https://www.espressopodcastproduction.com/Join the Conversation: What did you think of this episode? Share your thoughts and key takeaways with me on social media using the hashtag #EmployeeToBoss. If you enjoyed this episode, please leave a review and share it with your network.

Join Yogi Goel, Co-founder, CEO, and CFO of Maxima, for an unvarnished conversation on breaking the legacy architecture of corporate finance. After a 20-year career spanning auditing at EY, tech IPOs at Citi and Barclays, and scaling Rubrik from $5M to $900M in ARR, Yogi was firmly on the venture-backed CFO track. Instead, he realized that despite decades of enterprise software, accounting teams were still trapped in a monthly cycle of manual data wrangling and spreadsheet anguish. In this episode, we explore how Maxima secured $41M in funding from Kleiner Perkins and Redpoint, why the "semi-annual close" debate misses the mark, and why the future of finance relies on AI acting as a horizontal system of work layered directly over existing ERPs.

Hi, welcome, I've used Notebook LLM to create this video based on an article I've written about scaling AI projects from pilots to production ready. Read the full article HERE.https://futurestrong.org/2026/04/23/beyond-genai-pilots-how-enterprises-build-scalable-ai-with-governance-and-trust/I'm Rachana, and I write short stories, poetry and essays on our enduring humanity. For 15+ years I've been helping people unlock their highest potential and build lives of purpose, resilience and unstoppable momentum.My big dream? To consciously create a better future where everyone is excited about their own potential – and yes, I'm aiming to win the Nobel in December 2044 for contributions to human development. Crazy? Maybe. But will you join me on this journey of growth and transformation?

AI in the supply chain has moved well beyond the pilot stage, but for many organizations, it still hasn't moved far enough.In this episode of Supply Chain Now, Scott W. Luton, together with co-host Jorge Morales, Global COO of ISCEA, is joined by Prabhat Pinnaka, Lead Product Manager for Supply Chain at Lowe's Companies, Inc., and Advisory Board Member at ISCEA for a practical, been-there-done-that deep dive on AI-driven supply chain transformation. From what separates organizations that actually scale AI from those stuck in endless experimentation, to the guardrails every enterprise needs before letting AI execute autonomously, Prabhat brings a hard-won perspective from working with Fortune 50 companies across fulfillment, warehouse operations, and enterprise decision support. Jorge adds his lens on what it means to be AI-competent, not just AI-aware, and why that distinction will define the next generation of supply chain professionals.The conversation spans the full arc: change management, data integrity, bonded autonomy, and what it looks like when AI shifts a DC manager's role from operational execution to strategic governance. If you're navigating your own AI journey or building the next generation of supply chain talent, this one's for you.Jump into the conversation:(00:00) Intro(06:03) Prabhat's career: lessons in adoption(08:29) AI competence: finding the sweet spot(11:26) Building AI foundations for success(18:21) Moving AI from pilots to scale(23:50) Recommendations vs. execution in AI use(28:04) Establishing guardrails for autonomous AI(33:44) Computer vision example in supply chain(47:06) Getting AI competent in six monthsAdditional Links & Resources:Connect with Prabhat Pinnaka: https://www.linkedin.com/in/prabhat-pinnaka-a9549421/Connect with Jorge Morales: https://www.linkedin.com/in/jorgeamorales/Learn more about Lowe's Companies, Inc.: https://www.lowes.com/Learn more about ISCEA: https://www.iscea.org/Learn more about SCTechShow: https://www.sctechshow.com/Learn more about our hosts: https://supplychainnow.com/aboutLearn more about Supply Chain Now: https://supplychainnow.comWatch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-nowSubscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/joinWork with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/WEBINAR- How “Almost Right” Shipping Decisions Turn Into Six-Figure Losses: https://bit.ly/4mMov2TWEBINAR- There's No Finish Line in Leadership: Tips to Optimize Your Strategy & Execution: https://bit.ly/4tHOWJAWEBINAR- Delivering Flawless Field Service with Predictive Insights and AI: https://bit.ly/4sXVZfVWEBINAR- From AI Pilots to Performance: How Supply Chain Leaders Are Scaling Agentic AI: https://bit.ly/49hCqIqWEBINAR- Amazon Supply Chain 101: Enabling efficiency and growth for businesses everywhere–and everywhere they sell: https://bit.ly/49r8N7DThis episode was hosted by Scott Luton and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/secret-sauce-tips-leaders-want-scale-ai-effectively-1583

C'est un marché inattendu, né dans les coulisses de l'économie des startups. Depuis avril 2026, la société SimpleClosure propose une nouvelle activité : revendre les archives numériques d'entreprises en liquidation. Code source, échanges Slack, e-mails internes… tout peut être cédé sous licence. Pour son PDG Dori Yona, il s'agit d'une véritable « ruée vers l'or ».En un an, près d'une centaine de transactions auraient déjà été conclues, pour plus d'un million de dollars redistribués aux fondateurs. Et la concurrence s'organise. La plateforme Sunset, par exemple, valorise particulièrement les données issues de secteurs sensibles comme la santé ou la finance, où les historiques sont riches et interconnectés.Pourquoi un tel engouement ? Parce que les données sont devenues la matière première essentielle de l'intelligence artificielle. Or, comme l'a souligné Ilya Sutskever, les grandes bases publiques, Wikipédia, Reddit ou les livres numérisés, sont aujourd'hui largement exploitées. Les nouveaux systèmes d'IA ont besoin d'exemples concrets de travail réel : des échanges imparfaits, des erreurs, des processus humains. Résultat : un nouveau secteur émerge, celui des environnements d'entraînement simulés. Des entreprises comme AfterQuery vendent des univers professionnels reconstitués, « Finance World » ou « Tax World », où des agents IA apprennent à évoluer comme dans une entreprise. Des acteurs majeurs comme Anthropic ou Scale AI investissent déjà massivement dans ce domaine.Mais cette économie soulève des questions sensibles. Juridiquement, les entreprises détiennent généralement les données produites par leurs salariés, y compris sur des outils comme Slack. Pourtant, pour des experts comme Marc Rotenberg, l'enjeu dépasse la simple propriété intellectuelle : il s'agit aussi de données personnelles. L'anonymisation, souvent mise en avant, reste imparfaite. Des études menées par OpenAI et Google ont montré que certains modèles peuvent mémoriser et restituer des données sensibles. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Send us Fan MailPeaches is back for the May 7 Daily Drop—and yeah… this one goes from special operations gambling scandals to America's obesity problem real damn fast.A Green Beret allegedly used classified intel, dropped $30K on a prediction market… and walked away with $400K. And honestly? Peaches has thoughts. Spoiler: he's not exactly mad about it. Meanwhile, the Pentagon keeps throwing hundreds of millions at AI, cyber warfare, and data analytics, the Air Force is resurrecting B-1 bombers from the boneyard, and the Coast Guard is building its own special missions command.Oh—and if you think America's doing great physically… Peaches spent one night on the Vegas strip and came back with some uncomfortable observations.Bottom line: the future is getting faster, warfare is getting smarter, and if we don't fix ourselves physically… none of it matters.⏱️ Timestamps: 00:00 Yeah… I'm Half Retarded 01:00 Tasty Gains & Pennsylvania OTS 03:00 Green Berets Leaving Stuttgart 05:00 Baumholder—Win or Loss? 07:00 Green Beret Wins $400K Betting on Missions?! 10:00 “Good for Him” 13:00 Army Wants $2.1B More for R&D 16:00 Navy Changes Amphib Command Structure 18:00 Iran Tanker Gets Disabled 20:00 F-22s Land in Japan 22:00 AI Is Taking Over Air Operations 24:00 B-1 Bomber Resurrected from the Dead 26:00 AFRL Shake-Up 28:00 32,000 Gallons of Jet Fuel… Gone 30:00 Coast Guard Builds Its Own SOF Command 33:00 Autonomous Sail Drones Hit the Lakes 35:00 $500M More for Scale AI 38:00 Cyber Training Gets Overhauled 40:00 Fitness Test Is BACK 43:00 America Has a Serious Problem 46:00 Vegas Strip Reality Check 49:00 Final Thought—Fix Yourself First

Join us Thursday as we break down OpenAI's new Workspace Agents and what they mean for the future of work.We'll cover:⚙️ What workspace agents are

Go try Agent Opus and start making your own videos today! Get 600 free credits using our link: https://agent.opus.pro/home?special_credit=SOHK Lucy Guo is the youngest self-made female billionaire in the world and the co-founder of Scale AI, one of the most important infrastructure companies behind the artificial intelligence boom. After dropping out of college through the Thiel Fellowship, she helped build Scale AI into a company valued around $26 billion.In this episode, Lucy shares how she handled family pressure, why she believes founders must pivot quickly, how Scale AI found product-market fit, and what young entrepreneurs need to understand about AI, talent, risk, and proximity.Hosted on Ausha. See ausha.co/privacy-policy for more information.

Today on the Salesforce Admins Podcast, we talk to Avi Shah, Senior Director of Product Management for Salesforce AI. Join us as we chat about Agentforce Grid, a new way to coordinate data, automation, and AI agents. You should subscribe for the full episode, but here are a few takeaways from our conversation with Avi […] The post Agentforce Grid Enables Next-Gen Admins To Scale AI Workflows appeared first on Salesforce Admins.

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

Rajiv sits down with Neil Sheperd (formerly Cohere, Scale AI, BCG, McKinsey) and Amit Malhotra (formerly buybuy Baby, 1-800-Contacts) to get brutally specific about how humans and Agentic AI will coexist in the future workplace.  They discuss how AI changes tasks first and why the shock may hit high-skill jobs sooner than most people expect. We debate agent guardrails, attention economics in B2B marketing, and the leadership skills that still matter when execution gets automated.• AI replacing tasks before whole jobs• Why high-paid cognitive work can be disrupted fast• What makes agentic AI different from expert systems• Enterprise mistakes like boxing work into factory tasks• B2B marketing when content gets commoditized• Brand trust as a shortcut for scarce attention• Guardrails to prevent KPI chasing and hidden technical debt• Using tight use cases and human-in-the-loop verification• American Dream Index and AI as an inequality accelerant• Lessons from imperial governance for decentralized autonomy• How org charts tighten while individuals become “IC++” with agents• Clear intent-driven orders as the new management skillAI isn't waiting politely at the edges of the org chart. It's already taking tasks, and the uncomfortable twist is that high-skill, high-wage work may feel the impact sooner because the cost arbitrage is impossible to ignore. From Park City during our Growth Marketing Summit, we gather for a roundtable on the future of human work, enterprise adoption of AI, and what this acceleration means for leaders trying to stay useful and humane at the same time.We talk agentic AI beyond the demo: where it actually lands in real companies, why “optimize the KPI” can become a Trojan horse that piles up technical debt, and how to design guardrails when agents move faster than any human can audit. We also dig into B2B marketing strategy as the cost of content trends toward zero, making attention, trust, and brand credibility the real battleground for growth.Neil shares what he's building with the American Dream Index and why affordability data matters when AI can widen inequality. Amit brings lessons from past tech cycles plus a surprising angle from imperial history on decentralized governance, autonomy, and scope creep. We end with what leadership looks like in 2026: clear intent, better judgment, and teams that include both people and AI agents.Neil Shepherd: https://www.linkedin.com/in/neilshep/Neil Shepherd, Neil is the Founder of the American Dream Index.  A seasoned growth executive with 25 years of experience in Silicon Valley, he most recently served as the VP of Growth at Cohere, and has led marketing and digital strategy at organizations like BCG, ScaleAI, PayPal, and McKinsey. Neil is an expert in product-led growth, data science, and leveraging generative AI, where he helps companies scale their revenue and user acquisition.Amit Malhotra: https://www.linkedin.com/in/amitx/Amit Malhotra is a Private Equity Operating Advisor and technology builder. With over two decades of experience at the intersection of AI, digital transformation, and business growth, he has led massive turnarounds and rebuilt technology stacks from scratch for major brands like buybuyBABY and 1-800 Contacts.Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Sandeep Parikh: https://www.instagram.com/sandeepparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

We're Going To Do An OpenClaw Install (Live Demo)   Meet OpenClaw - the open-source AI that's been the sensation of the year so far. Imagine an actual 24/7 AI Agents that does the work for your automatically. Can it work for real world recruiting?   We're not just talking about it - we're showing you.   In this live demo session, watch us tackle:   • How fast can you spin up a custom application workflow from scratch? • Native integrations: does it play nice with your existing HRIS and assessment tools? • Compliance and data privacy: where does OpenClaw stand on GDPR and security? • Custom reporting: can you build the dashboard your executives actually want? • API flexibility: how deep can technical teams customise the backend? • Cost reality check: where are the hidden expenses vs. proprietary platforms?   We're pressure-testing Openclaw in real time—no slides, no sales pitch, just raw functionality and honest verdicts.   Recruiters, HR tech leads, and TA innovators—this demo is for you. Follow the channel here (recommended) and register for the show by clicking on the green button 'Save My Spot'   Episode 369 is sponsored by Juicebox   PeopleGPT is the leading outbound recruiting platform built on Generative AI. Find talent across 30+ data sources, get interactive Talent Insights, and reach out with personalized AI email campaigns. Key features include: PeopleGPT (Search): the world's first people search engine that uses natural language. Describe who you're searching for and find the perfect match from over 30 data sources. Talent Insights: Visualize your talent pool with 15+ interactive charts. Refine your search and dive deep into stats based on location, employers, job titles, skills, and more. Email Outreach: Maximize candidate engagement with AI-powered email campaigns. Personalize messaging at scale using AI commands, and boost response rates by 40%. Get 15% off your Juicebox subscription with code: BRAINFOOD15   Trusted by 5000+ companies including Scale AI, Mindbloom, and Patreon. Free trial available. Try Juicebox today

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

Arrancamos con la noticia más absurda y a la vez más reveladora del momento: Allbirds, la famosa marca de zapatillas sustentables que era el calzado favorito de los VCs de San Francisco, anunció que vende toda su línea de calzado y se convierte en una empresa de infraestructura de AI con GPU as a Service. La bolsa lo celebró con una suba de 500% en un solo día. No importa si tiene sentido. Importa que dice AI.Después Cristóbal nos trae su experiencia del LatAm Tech Week en Silicon Valley, organizado junto a Colombia Tech Week y True Hora. El gran takeaway: los fondos americanos no tienen una tesis Latam, no necesitan llenar ningún cajón regional, pero sí están convencidos de que hay founders excepcionales en la región. Invierten en personas, no en geografías.De ahí saltamos a dos historias de founders que vuelan bajo el radar pero mueven cifras increíbles. Primero, Víctor Cárdenas, venezolano que dejó Stanford en el tercer semestre para fundar Slash.com, un neobanco por verticales que hoy vale 1.6 billones de dólares. Segundo, Jeffrey Yan y Hyperliquid: un exchange descentralizado de cripto, fundado en 2023, con 11 empleados, que en los últimos 12 meses generó 900 millones de dólares de profit neto. No revenue. Ganancia.Luego viene uno de los temas más fascinantes del episodio: la mafia de los campeones de matemáticas olímpicas. Scott Wu, Johnny Ho, Alexander Wang, Jesse Zhang y varios más tienen dos cosas en común: ganaron medallas de oro en olimpiadas de matemáticas de adolescentes y todos pasaron por el fondo de high frequency trading HRT. Hoy lideran algunas de las compañías más importantes del AI y el cripto, incluyendo Perplexity, Scale AI, Cognition y Hyperliquid. La venganza de los nerds llegó y es total.También hablamos del próximo IPO de Vercel, liderado por el argentino Guillermo Rauch, que podría catapultarlo al top 5 de los argentinos más ricos. Y del nuevo modelo de Anthropic que la propia compañía considera tan peligroso que no quiere lanzar todavía. ¿Marketing o realidad?Cerramos con dos reflexiones que van a cambiar cómo ves el mundo tech. Primera: ¿puede alguien sin conocimientos de programación crear un SaaS viable usando Claude Code? La respuesta es sí, y el gran diferencial ya no es el código sino la distribución. Segunda y más importante: la era del SaaS está terminando. Los agentes que cobran por uso de tokens van a reemplazar el modelo de suscripción mensual por empleado que dominó los últimos 20 años. Las compañías SaaS en bolsa ya lo están sintiendo, muchas cayeron más del 50% desde sus máximos históricos.

Audrey AI, the Dublin-based startup building AI purpose-built for financial auditors, has closed a $1.8 million pre-seed funding round. The round is led by SVV (Sure Valley Ventures) and Delta Partners, with participation from Enterprise Ireland, Donnchadh Casey (ex-CEO Calypso), Conor Jones (ex-CBO Wayflyer) alongside former Big 4 auditors. Financial auditing is a global market worth more than $100 billion, yet it remains one of the most manual professions in financial services. Qualified professionals spend the majority of their time in spreadsheets, chasing documents and testing evidence – manual work that drives up costs, squeezes time for higher value work and pushes talented professionals out of the industry. General-purpose AI tools have struggled with the messy tabular data and complex evidence workflows that define the profession. Audrey AI was founded in 2025 by Ryan Loughran and David Burke, who met on the Founders programme at Dogpatch Labs.Loughran, who holds a degree in Accounting and an MBA from Stanford GSB, previously worked at McKinsey & Company and Qualtrics. Burke, formerly VP of Engineering at Inscribe, built document and data automation technology for regulated financial institutions. The platform automates the most time-intensive, manual parts of an audit engagement; evidence gathering and testing. Purpose-built as an agentic system, it orchestrates messy client data and applies audit procedures end-to-end, eliminating context switching. Crucially, it learns how each firm audits, compounding in value the more it is used. Audrey has piloted at top-10 and top-20 audit firms, delivering 85%+ time savings on client data collection, validation, and tests of detail, alongside measurable improvements in audit quality. The investment will fund growth across engineering and audit specialists as the company expands with firms across Ireland, the UK and beyond. "Developers have Copilot, lawyers have Harvey, but auditors still primarily work in Excel," said Loughran. "We're building AI that understands auditing deeply enough to raise the bar on quality, not just speed, freeing auditors to focus on the judgment and oversight that matters most." Liam Te-Wierik, Partner and Head of Digital Assurance at Grant Thornton Australia, echoed the focus on quality: "The value of Audrey lies in how it enables a step change in audit quality by redesigning the execution of manual procedures – not by changing our methodology, but by strengthening how it's applied in practice. That's the kind of innovation we believe will define the next generation of audit." Barry Downes, Managing Partner at SVV, described the investment as addressing "a critical pressure point for audit firms – chronic talent shortages and margin pressure in a heavily regulated industry," adding that Audrey's ability to "save 80-90% of time on repetitive work and tailor to each firm's methodology gives it the unique ability to scale capacity and dramatically improve margins." Dermot Berkery, Partner at Delta Partners and a former auditor himself, added: "Ryan and David have built something that doesn't just automate tasks but raises the bar on audit quality across the profession. We're excited to back them." About Audrey AI Audrey AI is building agentic AI purpose-built for financial auditors. Its platform automates the manual workflows that dominate the audit process, from intelligent data requests and evidence gathering to automated review and transaction testing, allowing auditors to focus on judgment and client relationships rather than spreadsheets and document chasing. Audrey AI generates smart, context-aware requests for client data, reviews submissions in real time and provides instant feedback, eliminating the back-and-forth that consumes a disproportionate share of audit hours. The platform adapts to each firm's methodology, compounding in value the more it is used. Audrey AI is headquartered in Dublin. For more information, visit tryaudrey.ai. See mo...

Meta released Muse Spark, previously named Avocado, on Wednesday, the much-anticipated—and delayed—first large language mode under AI chief Alexandr Wang, sending Meta shares soaring as the company seeks to catch up to industry AI giants OpenAI, Google and Anthropic. KEY FACTS The AI model is available on Meta's AI website and its app, with the company claiming it can carry out the same actions as its previous model, Llama 4 Maverick, with less computing power. Muse Spark is Meta's first AI model under Wang, a billionaire tech entrepreneur who Meta brought on as its chief AI officer after investing $14.3 billion into his company, Scale AI. Meta shares jumped as high as 9% on Wednesday following the announcement, erasing a string of losses recorded in late March. The release of Muse Spark comes after a delay reportedly caused after the AI model failed to outperform rival models developed by Google, OpenAI and Anthropic in benchmark tests. A comparison table in Meta's announcement claims Muse Spark can compete with or outperform rival AI models in various benchmarks. BIG NUMBER $135 billion. That is how much money Meta expects to spend on AI this year, nearly double what it spent in 2025. FORBES VALUATION We estimate Wang's net worth at $3.2 billion. The entrepreneur was the world's youngest self-made billionaire until October 2025, when Polymarket founder Shayne Coplan took over the title. TANGENT Meta is in the thick of litigation accusing it of designing addictive apps harmful to children and was recently ordered to pay $375 million in damages after a New Mexico jury ruled that the company enabled child exploitation on its platforms. A California jury also found Meta liable in a landmark social media addiction case, forcing the company to pay $3 million in damages to a woman who accused it of intentionally designing its apps to be addictive to children. Read the full story on By Antonio Pequeño IV Forbes:https://www.forbes.com/sites/antoniopequenoiv/2026/04/08/meta-shares-spike-after-tech-giant-launches-muse-spark-its-ai-bid-against-openai-google/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Meta komt na ruim een jaar eindelijk weer met een nieuw AI-model: Muse Spark. Het model markeert een opvallende koerswijziging voor het bedrijf, want het is niet open source en daarmee het tegenovergestelde van alles wat Meta eerder uitbracht. Stijn Goossens bespreekt het in deze Tech Update. Muse Spark is het eerste resultaat van Meta Superintelligence Labs, de nieuwe AI-eenheid die Mark Zuckerberg vorig jaar oprichtte nadat zijn Llama 4-modellen breed werden afgeschreven als tegenvallend. Als onderdeel van die ommezwaai investeerde Meta 14,3 miljard dollar in een belang van 49 procent in Scale AI en haalde het Alexandr Wang binnen als eerste chief AI officer in de geschiedenis van het bedrijf. Meta positioneert Muse Spark niet als het krachtigste model op de markt, maar benadrukt de efficiëntie en competitieve prestaties op specifieke taken. Het model accepteert spraak, tekst en beeldinvoer en heeft een Contemplating-modus waarbij meerdere AI-agents parallel aan een vraagstuk werken. Meta zegt ook te werken aan een open source versie. Gebruikers moeten inloggen met een bestaand Meta-account om het model te kunnen gebruiken, wat privacyvragen oproept: Meta zegt niet expliciet dat persoonsgegevens van Facebook of Instagram worden gebruikt, maar dat ligt voor de hand gezien het trainingsbeleid van het bedrijf. Het aandeel steeg meer dan zes procent na de aankondiging. Verder in deze Tech Update Federaal hof in Washington laat Pentagon toe Anthropic als veiligheidsrisico te labelen. Een panel van drie rechters wees het verzoek van Anthropic voor een tijdelijke blokkade af, terwijl een rechter in California het label eerder juist al had geblokkeerd; op 19 mei volgt de inhoudelijke behandeling in Washington. Hacker steelt meer dan 10 petabyte aan data uit Chinees supercomputercentrum. Een account onder de naam FlamingChina claimt via een gecompromitteerd VPN-domein maandenlang onopgemerkt te zijn binnengedrongen in het Nationaal Supercomputer Center in Tianjin en biedt de gestolen data, waaronder vermoedelijk militaire documenten en raketschema's, te koop aan via Telegram. See omnystudio.com/listener for privacy information.

While walking to the office, our COO Brandon Gell had his AI agent call him and go over his emails in his inbox one by one. When he arrived, he opened Gmail and confirmed she'd done everything he'd asked. "My jaw is on the floor," he messaged me.That was the moment Every got serious about setting up each employee with their own agent. Today, it's a reality—and it has completely changed how we work.Dan Shipper talked to Every COO Brandon Gell and head of platform Willie Williams for Every's AI & I about what happens when everyone at a company gets their own AI sidekick. If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Visit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI.Timestamps: 00:00 Introduction00:02:21 How Brandon built Zosia, an AI agent to run his household00:07:09 Brandon's aha moment re: using agents for work00:09:39 What happened when everyone on the team got their own agent00:12:42 How agents take on their owners' personalities, and why that matters inside an org00:23:51 Why it's important for agents to do work in public00:30:51 What we're still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem00:40:45 How we built Plus One, our hosted OpenClaw product00:47:27 The cultural shift required to make agents work at scale

Scaling AI Starts with Data & ValueIn this clip, Rakesh Doddamane, Leader – Generative AI / Responsible AI & User Experience at Philips, explains how organizations can effectively scale AI.The starting point is simple: wherever there's data, there's potential for AI

Founded in 2019, Linear is the rare company started pre-ChatGPT to have successfully reinvented itself as an agent-native business.On this episode of AI & I, Dan Shipper sat down with Karri Saarinen, cofounder and CEO of the product management tool, to discuss building a platform where humans and agents develop software together—and why the "SaaSpocalypse" isn't coming for all SaaS companies. If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Visit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI.Timestamps:0:00 Introduction 2:00 Why Linear waited to ship AI features instead of rushing to chatbots 5:06 Linear's agent platform and becoming the system that guides AI agents 7:42 Why "SaaS is dead" is a simplistic narrative 12:18 How Linear adopted AI coding tools17:45 AI's impact on product building workflows—speed versus thoughtfulness 22:18 The value of conceptual work and thinking before shipping 29:30 How AI is reshaping Linear's product strategy 37:18 Demo: Linear's agent skills, shared context, and code review workflow 47:48 The future of product development and the enduring role of human judgment

In this episode, we sit down with Alexander Selegenev, Executive Director of TMT Investments, the AIM-listed venture capital firm focused on high-growth technology companies across AI, software, and fintech.Alexander opens with an introduction to TMT's business and investment philosophy, then walks us through the firm's strategy and thesis in detail.We explore how TMT balances genuine excitement about artificial intelligence with the valuation discipline required to generate returns for shareholders.We dig into the numbers, asking why deployed capital fell sharply in 2025 compared to the prior year, and what that tells us about how the team is reading the current opportunity set. Alexander then takes us through the portfolio's core holdings, including the standout story of Scale AI, which delivered a 138% uplift in just eight months following Meta's investment, and what originally attracted TMT to the business.We also look at Bolt, now EBIT positive and active in more than 800 cities globally, and discuss how close the ride-hailing giant might be to an IPO or significant exit event. On the other side of the ledger, Alexander addresses the write-downs seen over the past year and the factors behind them.Alexander provides insight into their thinking around balancing special dividends with share buybacks and what success looks like for TMT Investments. Hosted on Acast. See acast.com/privacy for more information.

In today's episode of the Analyst Chat, Matthias Reinwarth welcomes John Tolbert to take a deep dive into the rapidly evolving world of Consumer Identity and Access Management (CIAM). As organizations manage millions, or even billions, of identities, CIAM is shifting from a standalone capability to a core component of broader digital ecosystems. Key topics: ✅ Consumer vs. B2B IAM segmentation✅ Passkeys adoption and UX gaps✅ Identity lifecycle and account recovery✅ CIAM integrations and platform ecosystems✅ AI agents and identity governance Increasing scale, regulatory pressure, and user expectations are reshaping CIAM requirements. AI agents begin to act on behalf of users, introducing new risks, but also new opportunities for automation and innovation.

OPTIMISING YOUR RECRUITER WORKFLOW WITH AI - EXAMPLES!   AI is transforming recruitment at lightning speed. Recruiters who embrace it are filling roles faster, delivering better candidate experiences, and outperforming competitors stuck in manual processes. Yet many professionals hesitate - not because they doubt AI's potential, but because they lack concrete, real-world examples of how to implement it effectively. This livestream bridges that gap with actionable demonstrations you can apply immediately!   What You'll Learn:   - Getting started with Workflow automations - Finding a single manual process is currently repeats - Asking AI to help you identify those instances! - Real live demo from experts!   Mastering techniques isn't going to happen in one episode of Brainfood Live, but getting people started isn't beyond us. This isn't just about working smarter - it's about future-proofing your career. Recruiters who integrate AI into their workflow consistently exceed KPIs, become indispensable strategic partners to hiring managers, and command higher salaries in an increasingly competitive market. Meanwhile, those who delay risk being outpaced by tech-savvy colleagues and automated alternatives. Join us to transform your daily workflow, elevate your professional value, and secure your position at the forefront of modern recruitment.   We're on Friday 27th March, 2pm GMT. Register by clicking on the green button (save my spot) and follow the channel here (recommended)     Episode 369 is sponsored by Juicebox   PeopleGPT is the leading outbound recruiting platform built on Generative AI. Find talent across 30+ data sources, get interactive Talent Insights, and reach out with personalized AI email campaigns.   Key features include: PeopleGPT (Search): the world's first people search engine that uses natural language. Describe who you're searching for and find the perfect match from over 30 data sources. Talent Insights: Visualize your talent pool with 15+ interactive charts. Refine your search and dive deep into stats based on location, employers, job titles, skills, and more. Email Outreach: Maximize candidate engagement with AI-powered email campaigns. Personalize messaging at scale using AI commands, and boost response rates by 40%. Get 15% off your Juicebox subscription with code: BRAINFOOD15   Trusted by 5000+ companies including Scale AI, Mindbloom, and Patreon. Free trial available. Try Juicebox today

Realities Remixed, formerly known as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.In a world defined by constant change, leaders must evolve from rigid hierarchies to emotionally intelligent, empowering leadership. By fostering adaptability, continuous learning, distributed leadership, and a culture of curiosity, organisations become better equipped to navigate technological disruptions, such as AI, with resilience and innovation.This week, Dave, Esmee, and Rob are joined by Jana Werner and Phil Le-Brun, Executives in Residence at AWS to explore what it really takes for organisations to thrive in a world of continuous transformation, and why rigid hierarchies, control, and over-designed change programmes so often get in the way.  TLDR00:42 – Guest introduction and overview of this week's theme01:26 – Team dig-in: A new cycle of change is on it's way19:27 – In‑depth conversation with Jana and Phil57:37 – Octopus playlist and case study highlights GuestJana Werner: https://www.linkedin.com/in/janawerner1/Phil Le-Brun: https://www.linkedin.com/in/phillebrun/https://www.theoctopusorganization.com/ A Guide to Thriving in a World of Continuous Transformation HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Yogi spent 20 years living the nightmare of enterprise accounting. As a senior finance leader at Rubrik, he watched highly paid professionals spend three weeks every month manually wrangling data into spreadsheets—a problem that caused mass burnout and multi-million dollar stock corrections.When ChatGPT launched, Yogi knew the technology was finally ready to solve the problem. In this episode, he breaks down how he left his executive track to found Maxima, how he landed massive enterprises like Scale AI and Rippling as early design partners, and why he managed to raise $41M from top-tier VCs like Kleiner Perkins and Redpoint before he even had a pitch deck.Why You Should ListenHow a 1st-time founder raised an $11M Seed and a $30M Series A in a year.Why replacing accountants with AI is a bigger opportunity than replacing SaaS tools.How to use the "Design Partner Playbook" to secure Fortune 500 customers.Why charging for an MVP creates the friction you actually need to find true PMF.The difference between selling "digital shelves" and selling "folded laundry" in the age of AI.Keywordsstartup podcast, startup podcast for founders, AI in accounting, enterprise SaaS, product market fit, finding pmf, raising seed round, raising series a, B2B sales, design partners00:00:00 Intro00:07:37 Leaving a CFO Track to Become a Founder00:11:52 Raising an $11M Seed Round from Kleiner Perkins00:20:07 The Design Partner Playbook00:22:34 Why You Must Charge Your Early Design Partners00:28:36 The Aha Moment for Product Market Fit00:33:20 Selling "Folded Laundry" Instead of "Digital Shelves"00:36:47 Raising a $30M Series A Pre-EmptivelySend me a message to let me know what you think!

Lucy Guo didn't follow a path — she built one nobody had walked before. She was trading Pokemon cards for cash in kindergarten, running bots on Neopets in second grade, and teaching herself to code before most kids knew what a startup was. By 21, she had co-founded Scale AI — one of the most consequential AI infrastructure companies ever built. By her late twenties, she had become the youngest self-made female billionaire in history.But the real story isn't the title. It's what happened before it, during it, and after it.In this conversation, Lucy breaks down what it actually took — the fundraising dynamics nobody talks about openly, the co-founder tension that led her to walk away from Scale at Series B, the detour through venture that sharpened her instincts, and how she built Passes to nine figures in under three years with almost no playbook to follow.She's also refreshingly direct about the parts of building that don't make it into press releases — firing a senior manager she'd trusted, realizing playbook executives can quietly kill a startup's culture, and why she now requires every senior hire to still do the work themselves.This one is for founders, operators, and anyone who's ever been the only one in the room.Topics Covered:Trading Pokemon cards and running Neopets bots as a kidThe Thiel Fellowship and dropping out of Carnegie MellonCo-founding Scale AI at 21 and building its early cultureFundraising as a woman — the unspoken double standardBeing the only woman on Snap's product teamWhy she walked away from Scale at the Series B stageHer venture fund and the HF0 founder residency programBuilding Passes to nine figures in under three yearsThe pay-per-minute product and creator monetization toolsHiring for competitive winners over credentialsWhy senior managers must still do IC workThe "repeated idea" dynamic in male-dominated roomsWhat the "youngest female billionaire" title actually meant to herAdvice for female founders navigating a system not built for them

In this episode, Stewart Alsop III sits down with Tom Faye — experimenter, author of The 90 Day Client Acquisition Code, and founder of Carbon Credits Marketplace — to talk about solar energy, off-grid living, and the solarpunk vision of a technology-powered utopia. They cover everything from perovskite solar cells and portable container-based solar systems, to carbon credits, ESG investing, and blockchain verification of clean energy output. The conversation also winds through AI training data, business automation, and the data labeling industry before circling back to some bigger questions about human nature, geopolitics, and what genuine self-reliance looks like in 2025. You can find Tom and his work at Carbon Credits Marketplace on LinkedIn and his energy consumption data visualization is also shared there. His book The 90 Day Client Acquisition Code is available for those looking to explore business automation further.Timestamps00:00 Introduction to Tom Fay and his work01:03 Understanding Solar Punk: Utopian Tech and Culture02:15 Current State of Solar Technology and Storage03:45 Living Off-Grid: Solar, Batteries, and Remote Work06:11 Solar Energy in Africa: Challenges and Opportunities12:21 Powering Communities with Mobile Solar Solutions16:50 The Vision of Solar Punk: Self-Sufficient Communities22:54 Existing Examples: Great Barrier Island and Others26:06 Overfishing, Environmental Challenges, and Technological Solutions28:34 Using Technology to Address Second-Order Environmental Problems36:35 Data, AI, and the Future of Energy Management43:13 Carbon Credits, Blockchain, and ESG Reporting45:27 The Geopolitics of Green Energy and Resource Control46:53 How to Connect with Tom Fay and Future ProjectsKey InsightsSolarpunk represents a genuine near-future possibility, not just an aesthetic. As solar panels and lithium batteries become cheaper and more efficient, the vision of abundant, decentralized clean energy is becoming a practical reality rather than a utopian fantasy.Perovskite solar cells are pushing efficiency roughly 22% beyond conventional panels, and the bigger revolution happening right now is on the storage side — cheaper, higher-capacity batteries are what will truly unlock solar's potential at scale.Africa may leapfrog the West on solar adoption, just as it leapfrogged landlines with mobile phones. People in energy-scarce countries viscerally understand the value of clean power in a way that people in the West, accustomed to reliable grids, simply don't.Portable solar container units — self-contained, deployable systems — already exist and are making off-grid energy viable for farms, mines, remote lodges, and even data centers, with a roughly five-to-one solar-to-load footprint required.Carbon credits generated from verified solar output, tracked via IoT smart meters and stamped on blockchain, represent a long-term business opportunity that survives political shifts because institutional investors and banks operate on independent ESG mandates.AI training data is a present and real economic opportunity, but a shrinking one. The window for humans — especially lawyers, scientists, and specialists — to get paid for their expertise is closing fast as labs pivot toward synthetic data generation.True self-reliance comes down to four things: food, water, power, and transportation. With solar and Starlink, the gap between remote wilderness and connected civilization has essentially collapsed — something unimaginable even a generation ago.

In this episode, we spotlight Gumloop, a startup that recently raised $50 million to empower employees to become AI agent builders. We also explore Gumloop's unique model-agnostic approach and how it helps companies automate tasks and scale AI adoption across their organizations.Chapters00:00 Gumloop's Mission & Funding00:52 Listener Reviews & Host's Bias03:21 Gumloop's Growth and Impact05:33 Benchmark's Investment & Gumloop's Vision08:50 Competition & Model Agnostic Approach10:33 AIbox.ai: Host's Startup LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

In this episode, we spotlight Gumloop, a startup that recently raised $50 million to empower employees to become AI agent builders. We also explore Gumloop's unique model-agnostic approach and how it helps companies automate tasks and scale AI adoption across their organizations.Chapters00:00 Gumloop's Mission & Funding00:52 Listener Reviews & Host's Bias03:21 Gumloop's Growth and Impact05:33 Benchmark's Investment & Gumloop's Vision08:50 Competition & Model Agnostic Approach10:33 AIbox.ai: Host's Startup LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we spotlight Gumloop, a startup that recently raised $50 million to empower employees to become AI agent builders. We also explore Gumloop's unique model-agnostic approach and how it helps companies automate tasks and scale AI adoption across their organizations.Chapters00:00 Gumloop's Mission & Funding00:52 Listener Reviews & Host's Bias03:21 Gumloop's Growth and Impact05:33 Benchmark's Investment & Gumloop's Vision08:50 Competition & Model Agnostic Approach10:33 AIbox.ai: Host's Startup LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we spotlight Gumloop, a startup that recently raised $50 million to empower employees to become AI agent builders. We also explore Gumloop's unique model-agnostic approach and how it helps companies automate tasks and scale AI adoption across their organizations.Chapters00:00 Gumloop's Mission & Funding00:52 Listener Reviews & Host's Bias03:21 Gumloop's Growth and Impact05:33 Benchmark's Investment & Gumloop's Vision08:50 Competition & Model Agnostic Approach10:33 AIbox.ai: Host's Startup LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

In this episode, we spotlight Gumloop, a startup that recently raised $50 million to empower employees to become AI agent builders. We also explore Gumloop's unique model-agnostic approach and how it helps companies automate tasks and scale AI adoption across their organizations.Chapters00:00 Gumloop's Mission & Funding00:52 Listener Reviews & Host's Bias03:21 Gumloop's Growth and Impact05:33 Benchmark's Investment & Gumloop's Vision08:50 Competition & Model Agnostic Approach10:33 AIbox.ai: Host's Startup LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we spotlight Gumloop, a startup that recently raised $50 million to empower employees to become AI agent builders. We also explore Gumloop's unique model-agnostic approach and how it helps companies automate tasks and scale AI adoption across their organizations.Chapters00:00 Gumloop's Mission & Funding00:52 Listener Reviews & Host's Bias03:21 Gumloop's Growth and Impact05:33 Benchmark's Investment & Gumloop's Vision08:50 Competition & Model Agnostic Approach10:33 AIbox.ai: Host's Startup LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Russell Kaplan, co-founder of Cognition — the company behind Devin — and previously at Scale AI and Tesla, joins the podcast to discuss what “software abundance” could mean for government. Our conversation covers… Why government software is so broken — Despite spending over $100B annually on IT, critical systems at agencies like the Social Security Administration and U.S. Department of the Treasury still run on decades-old code that few engineers know how to modify. How two-year software projects become three-week ones — why AI agents are particularly good at the painful migration and modernization work engineers tend to avoid. What “software abundance” actually means — AI agents can handle the tedious work of switching systems 24/7, collapsing the switching costs, and forcing software vendors to compete on value rather than locking customers into outdated systems. AI for cybersecurity — From triaging massive vulnerability backlogs to automatically fixing CVEs, AI will be essential for defending critical infrastructure as attackers gain the same tools. The coming “post-coding” world — As models converge in capability, the key bottleneck shifts from writing code to understanding problems, reviewing systems, and deciding what should be built in the first place. Plus, the future of procurement in an AI world, fraud detection in government datasets, the DMV as a software problem, and why Kaplan thinks the real skill of the future is knowing which problems matter. Thanks so much to Cognition for sponsoring this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices

Russell Kaplan, co-founder of Cognition — the company behind Devin — and previously at Scale AI and Tesla, joins the podcast to discuss what “software abundance” could mean for government. Our conversation covers… Why government software is so broken — Despite spending over $100B annually on IT, critical systems at agencies like the Social Security Administration and U.S. Department of the Treasury still run on decades-old code that few engineers know how to modify. How two-year software projects become three-week ones — why AI agents are particularly good at the painful migration and modernization work engineers tend to avoid. What “software abundance” actually means — AI agents can handle the tedious work of switching systems 24/7, collapsing the switching costs, and forcing software vendors to compete on value rather than locking customers into outdated systems. AI for cybersecurity — From triaging massive vulnerability backlogs to automatically fixing CVEs, AI will be essential for defending critical infrastructure as attackers gain the same tools. The coming “post-coding” world — As models converge in capability, the key bottleneck shifts from writing code to understanding problems, reviewing systems, and deciding what should be built in the first place. Plus, the future of procurement in an AI world, fraud detection in government datasets, the DMV as a software problem, and why Kaplan thinks the real skill of the future is knowing which problems matter. Thanks so much to Cognition for sponsoring this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices

Too many “AI for good” pilots fail before reaching the people who need them most. To solve this scaling crisis, Kanika Bahl, the CEO of Evidence Action, is stepping down from the helm of the organization to lead a new effort called the AI Access Initiative. Operating as an “AI-native NGO,” the initiative will bridge the gap between frontier tech labs and global development actors.  Rather than funding endless pilots, it is focusing on scaling proven “big bets,” including clinical decision support for overstretched health systems and AI weather forecasting for smallholder farmers. Bahl warns that the development sector must abandon “business as usual” and aim for audacious goals — including reaching half of the world's 3.5 billion people living in poverty — to prevent a rapidly widening digital divide.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Jake Hamilton, founder of Groundwire and Nockbox, to explore zero-knowledge proofs, Bitcoin identity systems, and the intersection of privacy-preserving cryptography with AI and blockchain technology. They discuss how ZK proofs could offer an alternative to invasive identity verification systems being rolled out by governments worldwide, the potential for continual learning AI models to shift the balance between centralized and open-source development, and why building secure, auditable computing infrastructure on platforms like Urbit matters more than ever as we face an explosion of AI agents and automated systems. Jake also explains Nockchain's approach to creating a global repository of cryptographically verified facts that can power trustless programmable systems, and how these technologies might converge to solve problems around supply chain security, personal data sovereignty, and resistance to censorship.Timestamps00:00 Introduction to Groundwire and Knockbox02:48 Understanding Zero-Knowledge Proofs06:04 Government Adoption of ZK Proofs08:55 The Future of Identity Verification11:52 AI and ZK Proofs: A New Era14:54 The Role of Urbit in Technology18:03 The Impact of COVID on Trust20:51 The Evolution of AI and Data Privacy23:47 The Future of AI Models26:54 The Need for Local AI Solutions29:51 Interoperability of Knockchain and BitcoinKey Insights1. Zero-Knowledge Proofs Enable Privacy-Preserving Verification: Jake explains that ZK proofs allow you to prove computational outcomes without revealing the underlying data. For example, you could prove you're over 18 without exposing your full identity or driver's license information. The proof demonstrates that a specific program ran through certain steps and reached a particular conclusion, and validating this proof is fast and compact. This technology has profound implications for age verification, identity systems, and protecting privacy while maintaining necessary compliance, potentially offering a middle path between surveillance states and complete anonymity.2. Government Adoption of Privacy Technology Remains Uncertain: There are three competing motivations driving government identity verification systems: genuine surveillance desires, bureaucratic efficiency seeking, and legitimate child protection concerns. Jake believes these groups can be separated, with some officials potentially supporting ZK-based solutions if positioned correctly. He notes the EU is exploring ZK identity verification, and UK officials have shown interest. The key is framing privacy-preserving technology as protection against "the swamp" rather than just abstract privacy benefits, which could resonate with certain political constituencies.3. The COVID Era Destroyed Institutional Trust at Unprecedented Scale: The conversation identifies COVID as potentially the largest institutional trust-burning event in human history, with numerous institutions simultaneously losing credibility with large portions of the population. This represents a dramatic shift from the boomer generation's default trust in authority figures and mainstream media. This collapse is compounded by the incoming AI revolution, creating a perfect storm where established bureaucracies cannot adapt quickly enough to manage rapidly evolving technology, leaving society in fundamentally unmanageable territory.4. Centralized AI Models Create Dangerous Dependencies: Both speakers acknowledge growing dependence on centralized AI services like Claude, with some users spending thousands monthly on tokens. This dependency creates vulnerability to price increases and service disruptions. Jake advocates for local AI deployment using models like DeepSeek R1, running on personal hardware to maintain control and privacy. The shift toward continuous learning models will fundamentally change the AI landscape, making personal data harvesting even more valuable and raising urgent questions about compensation and consent for training data contribution.5. High-Quality Training Data Is Becoming the Primary AI Bottleneck: Stewart argues that AI development is now limited more by high-quality training data than by compute power. The industry has exhausted easily accessible internet data and body-shop-style data labeling. Companies are now using specialized boutique services with techniques like head-mounted cameras for live-streaming world model training. This scarcity is subtly driving price increases across AI services and will fundamentally reshape the economics of AI development, with implications for who controls these increasingly powerful systems.6. Urbit Offers a Foundation for Trustworthy Computing: Jake positions Urbit as essential infrastructure for the AI age because its 30,000-line codebase (versus Unix's three million lines) can be understood by individual humans. Its deterministic, purely functional, and strictly typed design aims for eventual ossification—software that doesn't require constant security patches. This "tiny and diamond perfect" approach addresses the fundamental insecurity of systems requiring monthly vulnerability patches. In an era of AI agents and potential prompt injection attacks, having verifiable, comprehensible computing infrastructure becomes existentially important rather than merely desirable.7. Nockchain Creates a Global Repository of Provable Truth: Jake's vision for Nockchain combines ZK proofs with blockchain technology to create a globally available "truth repository" where verified facts can be programmatically accessed together. This enables smart contracts or programs gated on combinations of proven facts—such as temperature readings from secure devices, supply chain events, and payment confirmations. By using Nock's abstract, simple design optimized for ZK proof generation, the system can validate complex real-world conditions without exposing underlying data, creating infrastructure for coordinating action based on verifiable private information at global scale.

According to the World Bank, the real AI revolution in developing countries isn't coming from flashy mega‑models, but from small, low‑cost tools that solve local problems.Mahesh Uttamchandani, the organisation's Regional Practice Director for Digital and AI inEast Asia and Pacific and South Asia, sat down with Anshu Sharma from UN News during the India AI Impact Summit, and explained that these systems are cheaper to run, easier to adapt, and already delivering outsized impact.

Get our 5 AI workflows + 15 prompts to automate social trends research: https://clickhubspot.com/egs Ep. 400 Can you automate the most painful, time-consuming parts of marketing with AI—without sacrificing creativity or results? Kipp and Mike Futia (Founder of SCALE AI) dive into Mike's real-world blueprint for AI-powered social trendspotting and creative production for brands. Learn more on building custom AI tools (no coding experience required), the line between automation and human creative input, and which AI workflows actually move marketing metrics instead of making more work. Mentions Mike Futia https://www.linkedin.com/in/mike-futia-108709126/ Apify https://apify.com/ Claude https://claude.ai/ n8n https://n8n.io/ Zapier https://zapier.com/ Make.com https://www.make.com/ Weavy https://www.weavy.ai/ Nano Banana https://nanobananaimg.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

Innovation isn't slowing down, but in many enterprises it's becoming invisible. In this episode of Technovation, Peter High speaks with Sean Murphy, Founder and CEO of DemoHop, about why distributed work is trapping great ideas inside organizations and what CIOs can do to fix it. Sean explains how weak ties across enterprises have eroded, why peer-to-peer discovery beats status meetings, and how visibility is becoming the missing ingredient for scaling AI. Key topics include: Why innovation gets trapped in distributed organizations How science fair–style demo days reduce duplication The hidden role of weak ties in creativity and breakthrough Making enterprise AI work visible and scalable Building credibility for technology teams with business leaders

She skateboards to work, has a skydiving license, and was the world's youngest self-made female billionaire. Journalist Zing Tsjeng and BBC Business Editor Simon Jack tell the story of Lucy Guo and trace her trajectory to becoming one of the tech titans. From dropping out of college to join Peter Thiel's Fellowship, to couch-surfing as a millionaire, they follow Lucy Guo's journey to found Scale AI, a company that trains artificial intelligence for giants like OpenAI's ChatGPT, Google, and Microsoft.Good Bad Billionaire is the podcast that explores the lives of the super-rich and famous, tracking their wealth, philanthropy, business ethics, and success. There are leaders who made their money in Silicon Valley, on Wall Street and in high street fashion. From iconic celebrities and CEOs to titans of technology, the podcast unravels tales of fortune, power, economics, ambition and moral responsibility. Simon and Zing put their subjects to the test with a playful, totally unscientific scorecard — then hand the verdict over to you: are they good, bad, or simply billionaires?Here's how to contact the team: email goodbadbillionaire@bbc.com or send a text or WhatsApp to +1 (917) 686-1176. Find out more about the show and read our privacy notice at www.bbcworldservice.com/goodbadbillionaire

Carl Quintanilla, Sara Eisen, and David Faber kicked off the hour with the latest on the geopolitical front out of Davos after a headline filled 24-hours. What investors should know - plus market takeaways with Allianz' Mohamed El-Erian. In Davos: Sara was able to sit down with the CEO of Dell, in a wide-ranging interview spanning his pledge to invest in America's children to A.I. impacts on the workforce... before later on checking in with the CEO of Scale A.I. - a start-up last valued at more than $29B when Meta took a stake in the summer... and then poached then-CEO Alexandr Wang. Plus: Elon Musk being interviewed at Davos during the hour by Blackrock's Larry Fink, and the team listened in live. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.