Podcasts about hbs

In this episode, Gies Business professor Eren Ahsen shares his unconventional journey from mathematics and electrical engineering to machine learning in healthcare and ultimately business education. He discusses how AI evolved from an academic pursuit into a transformative force in medicine and organizations, why combining multiple algorithms leads to better decisions, and how business leaders can thoughtfully integrate AI into real-world workflows. With insights on bias, human judgment, and the future of business schools, Eren makes the case for cross-disciplinary, human-centered AI that improves lives without removing the human touch.

Should applicants use AI in their admissions essays? With extreme caution, as we hear this week.

The HBS co-hosts are diligently at work prepping for Season 16 so, in the meantime, enjoy this "Minibar" episode from Jennifer Kling explaining the merits and demerits of employing Hanlon's Razor in our everyday lives! Full episode notes available at this link:https://hotelbarpodcast.com/podcast/hanlons-razor---------------------SUBSCRIBE to the podcast now to automatically download new episodes!SUPPORT Hotel Bar Sessions podcast on Patreon here! (Or by contributing one-time donations here!)BOOKMARK the Hotel Bar Sessions website here for detailed show notes and reading lists, and contact any of our co-hosts here.Hotel Bar Sessions is also on Facebook, YouTube, BlueSky, Instagram, and TikTok. Like, follow, share, duet, whatever... just make sure your friends know about us! ★ Support this podcast on Patreon ★

Tony Husband, who will change his hat to the Host Broadcast Service, and Jorge Perez Navarro-heading to Telemundo for World Cup coverage- joined Jon and Niko Moreno to look at their respective assignmentsTony will be in Philly and JPN heads to MiamiWe look at the matches at The Linc and El Tri in depth

Presenting an episode of The Founder Mindset with Reza Satchu, a new show from HBS

As we enter summer, we talk about how applicants can strategically approach those early MBA application dates. Plus, we demystify the ‘career vision'. 

A fire sale? An arms race? However you term it, business schools are slashing rates – we look at how applicants can score a scholarship.

In this compilation episode of The Parlor Room Presents: Hello AI, host and Harvard Business School Online Creative Director Chris Linnane gathers HBS faculty to share actionable advice for mid-career professionals navigating the AI landscape. Featuring professors Christina Wallace, Jake Cook, Iavor Bojinov, and Joe Fuller, the conversation explores how mid-career professionals can build AI fluency, apply their domain expertise, and create value in a rapidly evolving workplace. From identifying entrepreneurial opportunities to experimenting with AI tools and leading organizational change, guests share what it takes to stay adaptable, relevant, and competitive in an AI-driven future. GUESTS Christina Wallace, Senior Lecturer of Business Administration Jake Cook, Lecturer of Business Administration Iavor Bojinov, James Dinan and Elizabeth Miller Associate Professor of Business Administration Joseph Fuller, MBA Class of 1960 Professor of Management Practice RESOURCES Catch up on previous episodes of The Parlor Room, featuring faculty from this compilation episode: ⁠Christina Wallace on Developing an Entrepreneurial Mindset⁠⁠ ⁠⁠Iavor Bojinov on AI Adoption, Trust, and Decision-Making⁠ ⁠Joe Fuller on AI and Rethinking Work

Hey HBs! It's the final part of this book! You better get ready to find out who the murder is! Bonus Content: Sabrina putting the mystery's puzzle pieces together in real time, and so much more! Mel: Hoopla! OMG! Librarian HBs, if you know of a way for Sabrina and other rural HBs to get Hoopla, email us! Sabrina: space operas with giant, complicated, overarching plots. Inspired by The Zion Warrior series by Regine Abel, Susan Trombley, and more. This Friday on PATREON, I tell Sabrina all about KISS THE VILLAIN by Rina Kent! Y'all. There's so much SA it's bananas. Also, this Friday on PATREON is a LIVE HANG! Come play with us on Discord at 8pm ET (May 22) because we're setting up our Stardew Valley farm! Want to support the show? Rate and review us on your favorite podcast app! It super helps the algorithm connect us to new listeners.  Credits: Theme Music: Brittany Pfantz  Art: Author Kate Prior (her newest release MATED TO MY EX is out now!!)  Want to tell us a story, ask about advertising, or anything else? Email: heavingbosomspodcast (at) gmail  Follow our socials:  Instagram @heavingbosoms Tiktok @heaving_bosoms  Facebook group: the Heaving Bosoms Geriatric Friendship Cult The above contains affiliate links, which means that when purchasing through them, the podcast gets a small percentage without costing you a penny more. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

We talk over the picks on Poets&Quants's inaugural Career & Admissions Bestseller List, from ‘How to Win Friends and Influence People' to ‘What Colour is Your Parachute'. 

Hey y'all! It's part 5 of The Bride! The final week of this epic book! This last 20% of this novel is so action packed that it'll make your head spin. Bonus Content: demanding bribes for adulting, severe aversions to heights, necrophilia jokes that Mel understands right in front of you, and so much more! Lady Loves: Mel: Hoopla! OMG! Librarian HBs, if you know of a way for Sabrina and other rural HBs to get Hoopla, email us! Sabrina: space operas with giant, complicated, overarching plots. Inspired by The Zion Warrior series by Regine Abel, Susan Trombley, and more. This Friday on PATREON, I tell Sabrina all about KISS THE VILLAIN by Rina Kent! Y'all. There's so much SA it's bananas. Want to support the show? Rate and review us on your favorite podcast app! It super helps the algorithm connect us to new listeners.  Credits: Theme Music: Brittany Pfantz  Art: Author Kate Prior (her newest release MATED TO MY EX is out now!!)  Want to tell us a story, ask about advertising, or anything else? Email: heavingbosomspodcast (at) gmail  Follow our socials:  Instagram @heavingbosoms Tiktok @heaving_bosoms  Facebook group: the Heaving Bosoms Geriatric Friendship Cult The above contains affiliate links, which means that when purchasing through them, the podcast gets a small percentage without costing you a penny more. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this faculty spotlight, Professor Aimee Barbeau of Gies College of Business explains how she introduces first-year students to business through ethics, experiential learning, and real-world impact projects. She challenges common misconceptions about capitalism by framing business as a value-creating, ethical practice and shows how tools like AI and hands-on corporate partnerships help students build practical skills and rethink the role of business in society.

AI is rapidly reshaping the MBA - and some business schools are racing ahead faster than others. In this episode, who leads the charge and what questions MBA applicants should ask about AI adoption.

Matt Weinzierl is Senior Associate Dean for Faculty Research and Development at Harvard Business School, where he is the Joseph and Jacqueline Elbling Professor of Business Administration in the Business, Government, and the International Economy Unit, and a Research Associate at the National Bureau of Economic Research. From 2022 through 2025, he served as Faculty Chair of the MBA Program at HBS, where he also teaches courses on economic policy and the space sector. His research focuses on the optimal design of economic policy, in particular taxation, with an emphasis on better understanding the philosophical principles underlying policy choices, and on the commercialization of the space sector and its economic implications. Prior to completing his PhD in economics at Harvard University in 2008, Professor Weinzierl served as the Staff Economist for Macroeconomics on the President's Council of Economic Advisers and worked in the New York office of McKinsey & Company.  Professor Weinzierl has written on a range of topics in optimal taxation and optimal economic policy more generally. His work in Positive Optimal Tax Theory has focused on identifying and formalizing the goals for tax policy that hold sway among the public, political and economic leaders, and leading tax thinkers, and then characterizing the implications of using those objectives in the analysis of optimal taxation.Professor Weinzierl currently serves as Senior Associate Dean for Faculty Development and Research. He previously served as Senior Associate Dean, Chair of the MBA Program and as Chair of the MBA Required Curriculum (RC). Prior to those positions, he was the coursehead for Business, Government, and the International Economy (BGIE), an RC course, and Chair of MBA Community Standards and the Conduct Review Board at HBS. He has created and currently teaches two courses in the Elective Curriculum: The Role of Government in Market Economies (RoGME) and Space, Public and Commercial Economics (SPACE).Space to Grow: Unlocking the Final Economic Frontierhttps://shorturl.at/5W1QU

So what exactly is an asshole? Is it a settled character type, or just a way of behaving that anyone might fall into on a bad day? Why does asshole behavior provoke us as it does, and why does it seem so much harder to resist now than it once was? If assholes are produced by social conditions (and they appear to be), what conditions produce them, and which ones might produce fewer?This episode takes Aaron James's 2012 bestseller, Assholes: A Theory, as its central provocation. James defines the asshole as someone (almost always a man) who "systematically allows himself to enjoy special advantages in interpersonal relations out of an entrenched sense of entitlement that immunizes him against the complaints of other people." The HBS co-hosts work with this definition and push on it where it falls short. Bob makes the case that contemporary capitalism, supercharged by the compare-and-contrast machinery of social media, has transfigured a vice into a virtue: in our current moment, assholery is increasingly mistaken for strength. Jen draws on Rousseau's distinction between amour de soi and amour-propre to ask what social conditions cultivate the asshole disposition. And Leigh asks what we can do, practically, in our classrooms and in our daily encounters, to make environments less hospitable to assholes in the first place.Grab a drink and join us as we try to figure out what makes an asshole an asshole — and what, if anything, can be done about the apparent abundance of them in our current moment.Full episode notes available at this link:https://hotelbarpodcast.com/podcast/aholes---------------------SUBSCRIBE to the podcast now to automatically download new episodes!SUPPORT Hotel Bar Sessions podcast on Patreon here! (Or by contributing one-time donations here!)BOOKMARK the Hotel Bar Sessions website here for detailed show notes and reading lists, and contact any of our co-hosts here.Hotel Bar Sessions is also on Facebook, YouTube, BlueSky, Instagram, and TikTok. Like, follow, share, duet, whatever... just make sure your friends know about us! ★ Support this podcast on Patreon ★

In this compilation episode of The Parlor Room Presents: Hello AI, host and Harvard Business School Online Creative Director Chris Linnane gathers HBS faculty to share actionable advice for mid-career professionals. Featuring Professors Felix Oberholzer-Gee, Nien-hê Hsieh, Sunil Gupta, and Linda Hill, the conversation explores how to stay relevant in an AI-driven workplace by embracing new tools, building human skills, and adopting a “wayfinder” mindset to grow and create value. GUESTS Felix Oberholzer-Gee, Andreas Andresen Professor of Business Administration Nien-hê Hsieh, Kim B. Clark Professor of Business Administration Sunil Gupta, Edward W. Carter Professor of Business Administration Linda Hill, Wallace Brett Donham Professor of Business Administration RESOURCES Catch up on previous episodes of The Parlor Room: Nien-hê Hsieh on Ethical AI, Decision-Making, and Investing Felix Oberholzer-Gee on the Frameworks of Business Strategy Sunil Gupta on Data-Driven Digital Marketing Strategies Linda Hill on Leading Through AI-Driven Change

Thinking about a dual-degree MBA? In this episode, we break down the most popular pairings – from MBA/MPP to MBA/JD – who they make sense for, costs and career trade-offs.

KJ is the nickname she chose, the armor she built, the stage she never stopped standing on. But behind the confidence and the cultural commentary and the perfectly matched lipstick is Kristen: the middle child who felt like the ugly duckling, the founder who nearly didn't make it out, the mother whose daughter became her reason to keep going. In this episode of Sense of Self, Dr. Gowri Aragam sits down with Kristen Jones Miller, better known as KJ, co-founder of Mented Cosmetics, host of the Queen Things podcast, content creator, professor, and soon-to-be author, for the conversation behind the brand. What unfolds is a story about identity built layer by layer: from a girl in Columbus who learned to be funny because she had to, to an HBS grad who walked into entrepreneurship knowing the odds and doing it anyway, to a founder who spent a year watching her company deteriorate in real time while quietly, privately, almost silently falling apart. And about what it looks like to sell the thing you built from your own wounds, and somehow find your way back to loving it again. This one is equal parts hilarious, devastating, and deeply alive. Meet KJ! Kristen Jones Miller, known as KJ, is the co-founder of Mented Cosmetics, a beauty brand celebrating the beauty of darker skin tones, which she co-founded in 2017 out of Harvard Business School. She is the creator and host of the Queen Things podcast, a content creator, a professor at Ohio State University, and a soon-to-be author. She lives in Ohio with her husband K'idar, and their daughter Kayla. Connect with KJ: Instagram: https://www.instagram.com/kjmiller/ TikTok: https://www.tiktok.com/@iam_kjmiller Queen Things Podcast: https://tr.ee/I0h4kIx-e1 Mented Cosmetics: https://www.mentedcosmetics.com/ Connect with Sense of Self: Subscribe for more episodes like this: AppleSense of Self Follow the Sense of Self Podcast:instagram.com/senseofself.podcastwww.senseofselfpod.com Connect with Dr. Gowri Aragam: Instagram: instagram.com/drgowriaragam Website: Drgowriaragam.com 00:00 Chasing What Lights You Up 00:33 Meet KJ and the Show 03:26 Origins of the Name KJ 04:14 Middle Child Roots 07:11 Privilege and Expectations 10:55 Idyllic Columbus Bubble 12:21 Skin Tone and Humor 15:14 KJ as Armor 18:37 Meeting Her Husband 21:02 College to Business School 23:37 Entrepreneurship North Star 28:52 Building Mented Beauty 33:01 Founder Foundation Talk 33:51 Oprah Favorite Things High 35:22 Maternity Leave Cash Crunch 36:56 Highs Lows Reality Check 39:08 Postpartum Depression Signs 40:07 Zoloft Decision Relief 43:08 Losing Optimism Signal 47:09 Reunion Breakdown 2023 49:39 Con Artist Deal Spiral 50:35 Board Ultimatum Hail Mary 52:37 Vulnerability Survival Mode 56:27 Relief After Sale 57:59 Content Creator Era 01:00:10 Multihyphenate Identity 01:02:30 Parenting Passion First 01:03:54 Final Thanks Subscribe This episode includes discussion of postpartum depression and suicidal ideation, which may be distressing for some listeners. If you find yourself experiencing difficult emotions, please consider pausing the episode, returning at another time, and reaching out to a trusted person or a professional. Thanks from all of us at Sense of Self. This episode was edited by Brie Mittan This episode was produced by Dr. Gowri Aragam and Brie Mittan A note on ethics, process, and safety: The individuals in this podcast have graciously shared their stories and it's important to note that while these discussions are enriching and enlightening, they are not a substitute for therapy or mental healthcare.Please note that each guest has given their consent to participate, had full control over what aspects of their journey were shared, and either currently engages in therapy or has done so in the past.Thanks from all of us at Sense of Self

How much do rankings actually matter to employers? Plus, we talk about career pivots into consulting

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

The Khan Academy founder and HBS grad returns to discuss AI disruption—preparing for The Great Reskilling and challenging the status quo in instruction, assessment, and credentials.

In this compilation episode of The Parlor Room Presents: Hello AI, host and Harvard Business School Online Creative Director Chris Linnane gathers HBS faculty to share actionable advice for early-career professionals. Featuring Professors Nien-hê Hsieh, Christina Wallace, Joe Fuller, and Iavor Bojinov, the conversation explores how to build skills, develop expertise, and create value while working alongside AI. From using AI tools effectively to strengthening human skills like communication, curiosity, and empathy, faculty share what it takes to adapt, grow, and stand out in today's evolving workplace. GUESTS Nien-hê Hsieh, Kim B. Clark Professor of Business Administration Christina Wallace, Senior Lecturer of Business Administration Joseph Fuller, MBA Class of 1960 Professor of Management Practice Iavor Bojinov, James Dinan and Elizabeth Miller Associate Professor of Business Administration RESOURCES Catch up on previous episodes of The Parlor Room, featuring faculty from this compilation episode: Nien-hê Hsieh on Ethical AI, Decision-Making, and Investing Christina Wallace on Developing an Entrepreneurial Mindset Iavor Bojinov on AI Adoption, Trust, and Decision-Making You can also watch The Parlor Room on YouTube.

From volatility to greatest gains: we dig into the ups and downs of this year's list

Every founder who has ever handed sales off too early has paid for it. Every single one.In this episode, John sits down with Lou Shipley, a Harvard Business School lecturer, board member, investor, and author of Unlikely Entrepreneurs, to dig into why sales is still the most misunderstood function in business, and why founders who treat it as a second-class citizen almost always fail. Lou draws from decades of experience running companies, building sales cultures from scratch, and teaching MBAs how to sell before they ever launch a product.From cold-calling encyclopedia buyers to opening Asia for Avid Technology to building the sales curriculum at HBS, Lou has lived every stage of the sales journey, and he's done it at the highest level.If you're a founder, a sales professional, or anyone trying to understand what it actually takes to build a company in the age of AI, this conversation will challenge everything you think you know about selling. Visit www.jbarrows.com and learn how you can Make It Happen.What You'll LearnWhy founders who delegate sales too early almost always failHow Lou built one of Harvard's most in-demand coursesThe cultural disdain of salesWhy sales is not about convincing anyone of anythingHow to use AI as a learning tool instead of an answer machineWhat the Guy Kawasaki GPT experiment revealedWhy curiosity is the most important professional skill in the AI eraWhat SaaS companies should be doing right nowHow to build a farm system for sales talent17 stories that prove anyone can build something worth buyingLou Shipley is a multi-time tech CEO, entrepreneur, and enterprise software leader with over 25 years of experience driving growth and innovation. He has led several successful startups through rapid expansion and acquisition, including Black Duck, WebLine (acquired by Cisco), Reflectent (acquired by Citrix), and VMTurbo. In addition to his executive leadership, Lou serves on multiple boards, teaches technology sales at Harvard Business School, and is a respected mentor, speaker, and commentator in the tech industry.Connect with Lou Shipley:Website: https://www.loushipley.com/LinkedIn: https://www.linkedin.com/in/loushipley/Grab a copy of Lou Shipley's book, “Unlikely Entrepreneurs: Wins, Losses, and Crucial Lessons on Building Great Companies,“ on Amazon: https://www.amazon.com/Unlikely-Entrepreneurs-Lou-Shipley/dp/1394345895/John Barrows is a sales trainer, speaker, and founder of JB Sales with over 25 years of experience in the industry. He has made hundreds of cold calls a week, led startups to acquisition, and trained high-performing teams at companies like Salesforce, LinkedIn, Amazon, and Okta. Through JB Sales, John focuses on practical sales execution—helping reps fill pipeline, close deals, and build trust with buyers in today's AI-driven sales environment.Connect with John Barrows:LinkedIn: https://www.linkedin.com/in/johnbarrows/ Instagram: https://www.instagram.com/johnmbarrows/TikTok: https://www.tiktok.com/@johnmbarrowsCheck out John's Membership: https://go.jbarrows.com/Join John's Newsletter: https://www.jbarrows.com/newsletter

Violence is everywhere right now... or is it?When you press people to define "violence," you'll often find that their grasp on the concept is slippery at best. We think we know what it means, but that certainty tends to evaporate the moment someone asks whether a slur counts as violence, or a system that denies you healthcare until you die counts as violence, or refusing to recognize someone's existence does. A lot of our most heated disagreements about violence happen prior to the moral disagreements we may have which actions count as violent. Our core disagreements are conceptual ones, and we're usually having them without realizing it.What, if anything, ties physical force to structural oppression? Is there a definition of violence capacious enough to hold both together without becoming so broad it is evacuated of meaning altogether? When the word "violence" gets attached to something, what exactly are we expecting people to do — morally and politically?In this episode, the HBS co-hosts work through these questions with many disagreements (but no fisticuffs!) along the way. They take up Hegel's argument that recognition is a life-or-death struggle, and Hannah Arendt's claim that violence is always a symptom of political failure. They look at the way entertainment media trains us to see violence as cleaner and more effective than it ever actually is, and how actions that involve "bodily harm" might constitute the easiest, but least satisfying, definition of violence. Leigh reflects on her year directing the M.K. Gandhi Institute Institute for Nonviolence and why she's no longer the pacifist she was then. Jen, as past President of Concerned Philosophers for Peace, draws a sharp line between caring about peace and believing violence is never warranted. Meanwhile, Bob wonders why Americans are not more violently opposed to their lack of basic social securities, like healthcare.Grab a drink and join us as we slow the word "violence" down and look at what it actually means, and what it does an does not accomplish in our language and lives... all from the relatively safe place of the hotel bar!Full episode notes available at this link:https://hotelbarpodcast.com/podcast/violence---------------------SUBSCRIBE to the podcast now to automatically download new episodes!SUPPORT Hotel Bar Sessions podcast on Patreon here! (Or by contributing one-time donations here!)BOOKMARK the Hotel Bar Sessions website here for detailed show notes and reading lists, and contact any of our co-hosts here.Hotel Bar Sessions is also on Facebook, YouTube, BlueSky, Instagram, and TikTok. Like, follow, share, duet, whatever... just make sure your friends know about us! ★ Support this podcast on Patreon ★

Poets&Quants Founder John A. Byrne bids farewell and introduces Executive Editor Pola Lem

Nikhil Jain applied four times before getting in. This is what he discovered once he got there

In this compilation episode of The Parlor Room Presents: Hello AI, host and Harvard Business School Online Creative Director Chris Linnane gathers HBS faculty to share actionable advice for early-career professionals. From pairing AI skills with foundational business knowledge to building relationships and making strategic career decisions, faculty share what it takes to grow and succeed in an AI-driven world. GUESTS Linda Hill, Wallace Brett Donham Professor of Business Administration Sunil Gupta, Edward W. Carter Professor of Business Administration Felix Oberholzer-Gee, Andreas Andresen Professor of Business Administration Willy Shih, Robert and Jane Cizik Baker Foundation Professor of Management Practice in Business Administration Jake Cook, Lecturer of Business Administration RESOURCES Catch up on previous episodes of The Parlor Room: Linda Hill on Leading Change and the Paradoxes of Management (https://hbs.me/4svxhhmz) Felix Oberholzer-Gee on the Frameworks of Business Strategy (https://hbs.me/mvuumck9) Sunil Gupta on Data-Driven Digital Marketing Strategies (https://hbs.me/43s5u336) You can also watch The Parlor Room on YouTube: https://hbs.me/2fh4jtxp

Gies Business professor Sandra Corredor explores one of the biggest misconceptions students have about business: the idea that there's always a correct decision. Drawing from her research and teaching, she explains how success comes from thoughtful design, attention to detail, and embracing uncertainty - both in corporate strategy and personal career paths.

How Wharton dominates rankings of business school academic research

We discuss the 20th anniversary of the AIGAC (Association of International Graduate Admissions Consultants)

Mike's setting up the menu for the summer and that includes books and even a chat with Daniel from HBS waste removal including a texter's shining endorsement!!See omnystudio.com/listener for privacy information.

What if accounting isn't about numbers—but about uncovering the story behind them?In this episode of Poets&Quants' Faculty Spotlight, Gies College of Business professor Fei Du joins John A. Byrne to share how intellectual curiosity, and a desire for freedom, shaped her path into academia. What began as a practical career choice in China evolved into a passion for decoding financial statements as “clues” to how organizations think and act.Du explains how accounting, when done right, is less about calculation and more about interpretation. Through real-world examples, from COVID-era market reactions to corporate promotion systems, she shows how financial data reveals deeper truths about strategy, incentives, and human behavior.

What you need to know about the one-year option

What international students experience in a program abroad 

We analyze the severe downturn in international applicants due to the Trump administration

What we really think of the latest ranking from Fortune which has Wharton on top and Stanford missing

Special guest Conrad Chua, former executive director of The Cambridge MBA, helps us dissect the newest FT ranking

In this week's MBA Admissions podcast we began by discussing the current state of the MBA admissions season. We continue to see several top MBA programs rolling out their Round 2 interview invites. Next week UPenn / Wharton and INSEAD are scheduled to release their interview invites and we speculate that MIT Sloan will, too. We then briefly discussed our new interview prep tool,  Clear Admit's MBA Interview simulator  Thus far, we have seen broad adoption of this tool, and we expect word to continue to spread! The MBA interview simulator is trained on Clear Admit's extensive catalogue of interview resources including our interview archive and interview guides. Graham noted we are scheduled for our monthly AMA YouTube Livestream later today. Here is Clear Admit's YouTube channel, https://www.youtube.com/@ClearAdmitMBA Graham also highlighted MBA webinar events that are on the horizon that Clear Admit is hosting. We are hosting a series for MiM programs which is scheduled for February 24 and 25. Clear Admit is also hosting events with London Business School and Vanderbilt / Owen later this week. On March 19, we are hosting a series of online panel discussions focused on international students who are targeting the top MBA programs in the United States. Finally, we are excited to announce our in-person admissions event, the MBA Fair, to be scheduled in Atlanta, on May 11. Signups for all these events are here: https://www.clearadmit.com/events Graham then highlighted a recently published article from Clear Admit's Fridays from the Frontlines series, highlighting a veteran who is at Notre Dame / Mendoza.  Graham then noted three admissions tips which all focus on the interview experience: MBA interview etiquette, questions for the admissions interviewer, and post-interview follow-up. Graham addressed a recently published Real Humans piece that focuses on Class of 2027 HBS students. Then finally, we discussed this week's roll out of the Financial Times ranking. For this week, for the candidate profile review portion of the show, Alex selected two ApplyWire entries and one DecisionWire entry: This week's first MBA admissions candidate is a deferred admissions candidate who appears to have a very strong profile but still needs to take the GMAT. This week's second MBA applicant is from India, works in finance, and has a perfect 340 on the GRE test. This week's final MBA candidate is deciding between Wharton and Sloan with a scholarship. This episode was recorded in Paris, France and Cornwall, England. It was produced and engineered by the fabulous Dennis Crowley in Philadelphia, USA. Thanks to all of you who've been joining us and please remember to rate and review this show wherever you listen!

The bank's head of global talent, an HBS grad, explains the value of cultivating careers, keeping churn low, hiring from within, and focusing on local markets and communities. Also, AI adoption, skills-based hiring, the pivotal role of managers, and training leaders to navigate turbulence.  

The GRE and TOEFL are for sale. Here's why and what it means for test taking

This year, round 3 applications are less a Hail Mary pass than ever before due to slumping application volume at top business schools

Gies College of Business marketing professor Aric Rindfleisch reflects on why he chose marketing and how his research on materialism reveals why buying more doesn't lead to happiness. He discusses his passion for teaching in the College's fully online iMBA program, the balance between digital and analog worlds, and why business schools must put humanity at their core in an AI-driven world.

In this episode of the Healthy, Wealthy and Smart podcast, Dr. Karen Litzy interviews Pete Moore, founder of Integrity Square, discussing the evolution of the health, active lifestyle, and outdoor sector, known as Halo. They explore the shortcomings of the term 'wellness', the importance of understanding business valuations and KPIs, and the emotional readiness required for business transitions. Pete shares insights on navigating growth, preparing for exits, and the significance of knowing one's competitors and market position. Takeaways The term 'wellness' is outdated and not serving the industry. Understanding your market position is crucial for business success. Local libraries can be valuable resources for business research. Key performance indicators (KPIs) are essential for evaluating business health. Emotional readiness is as important as financial readiness for business transitions. Knowing your competitors helps in strategic planning. Valuations are driven by more than just revenue multipliers. Founders often overlook the importance of mental preparation for exits. Networking and mentorship are vital for entrepreneurial growth. Continuous learning and adaptation are key to success in the Halo sector.  Chapters 00:00 Introduction to Halo and Wellness 01:49 Navigating Business Growth and Exits 03:22 Understanding Valuations and KPIs 05:54 Emotional Readiness for Business Transitions 06:56 Quickfire Insights for Entrepreneurs More About Pete: Pete is the Founder, Managing Partner and Chief Dream Architect at Integrity Square ("ISQ"), a leading boutique financial advisory firm focused on the $4.7T Health, Active Lifestyle, Outdoor ("HALO") sector.  Since founding ISQ in 2010, the firm has played an active advisory role in 100+ mergers & acquisitions, private placements and advisory assignments across North America. Pete Moore and his team have also invested in passionate entrepreneurs at HigherDOSE, XTEND, and Promotion Vault.  ISQ's media and "live education" properties include HALO Talks, the leading B2B podcast in the sector, Time To Win Again, and the HALO Academy, an Executive Education Bootcamp Series. Prior to ISQ, Pete was Head of the Active Lifestyle & Wellness Group at Sagent Advisors (2003-2010.) Prior to 2003, Pete was co-founder of FitnessInsite, a SasS sales management platform with 1500+ clients (based in AZ.) At FitnessInsite, Pete invested his personal capital, leveraged his credit cards and learned what it takes to manage a startup. Pete built his business and financial acumen on top of the foundation laid at three critical positions early in his career: Senior Associate at Brockway Moran & Partners, the private equity owner of Gold's Gym International, Inc;  worked as an Associate at Donaldson, Lufkin & Jenrette; and an Analyst at Chase Securities. (Now JP Morgan.)  ISQ saw a need for a deeper & more useful level of education in the HALO sector. In response, we launched the HALO Talks podcast, with 500+ completed interviews and over 120,000 downloads. HALO Talks has become a "must listen" for anyone working or investing in the sector.  Pete graduated from Emory University (BBA, 1994) and received his MBA from Harvard Business School (1999.) While at HBS, he co-founded IRON PLANET, the leading B2B auction site for used heavy equipment, which was sold to Ritchie Bros for $758 million.   His hobbies include: Football, basketball, tennis, podcasting, amateur ventriloquism, pro bono DJ and fitness enthusiast.    Notable Stats: Wingspan 76", 33 yard dash at 4.3 seconds. Resources from this Episode: Pete's Website Pete on LinkedIn Jane Sponsorship Information: Book a one-on-one demo here Mention the code LITZY1MO for a free month Follow Dr. Karen Litzy on Social Media: Karen's Instagram Karen's LinkedIn  Subscribe to Healthy, Wealthy & Smart: YouTube Website Apple Podcast Spotify SoundCloud Stitcher iHeart Radio

This episode with my friend and HBS classmate Todd Wilcox was recorded before Todd was nominated for his current role as Assistant Secretary of State, Bureau of Diplomatic Security. We speak about his background and views on business and the world. Todd Wilcox was sworn in as Assistant Secretary of State for Diplomatic Security (DS) on October 14, 2025. In this role, he leads the security and law enforcement arm of the U.S. Department of State, ensuring a safe environment for U.S. foreign policy operations. He oversees a global team of Special Agents, Diplomatic Couriers, Security Engineering Officers, Security Technical Specialists, contractors, and administrative personnel.Mr. Wilcox brings decades of leadership experience as a decorated combat veteran, former CIA case officer, and successful entrepreneur. Before joining the State Department, he founded Patriot Defense in 2005, a company dedicated to supporting those who defend America. He served as its Chief Executive Officer for 10 years before transitioning to Executive Chairman, where he guided the company's vision and acquisition strategy.Prior to his business career, Mr. Wilcox served as an Arabic-speaking CIA Field Operations Officer focused on Middle East and counterterrorism issues. His final assignment was as the CIA Liaison Officer to the FBI's Joint Terrorism Task Force in Orlando. He joined the CIA in 1997 after serving in the U.S. Army, where he earned the Green Beret as a Special Forces A-Team commander and completed a combat tour during Operation Desert Storm.Mr. Wilcox's achievements have been recognized by the U.S. Army ROTC Hall of Fame, Ernst & Young's Entrepreneur of the Year award, and the Orlando Business Journal's Veterans of Influence Award. He has served on the boards of the National Defense University Foundation, RAND Corporation's Center for Middle East Public Policy, and the Orlando Economic Partnership.An active member of the Young Professionals Organization (YPO), Mr. Wilcox also serves as Treasurer of Business Force, a nonprofit political action committee.

In this episode, Adam Torres and Ibrahim Sagna, Executive Chairman at Silverbacks Holdings, about investing in high-growth African companies across tech, sports, entertainment, and media. Ibrahim shares Silverbacks' founder-focused strategy, why global revenue and cross-border scale matter, and how diaspora demand is reshaping opportunity for African-led businesses worldwide. About Ibrahim Sagna Ibrahim Sagna is the Executive Chairman of Silverbacks Holdings, a private investment firm focused on tech, entertainment, and sports, with nine profitable exits since 2019.  Silverbacks' landmark investments include Uber backed Moove, Stripe backed Wave Mobile Money, Netflix movies producer Forever7 Entertainment, DAZN and Warner Bros Music Africa sponsored African Warriors Fighting Championship (AWFC), as well as the NBA Africa tournament participating basketball team, Cape Town Tigers.  He serves on several boards and hosts the "IN THE VALLEY" business podcast. His 30 year career includes high finance roles at IMF, Africa Finance Corporation, Afreximbank, Rwanda Capital Markets Authority, Millennium and ECP.  He holds degrees from Boston College, INSEAD, LBS, and HBS. About Silverbacks Holdings Silverbacks Holdings backs dominant platform builders in underserved markets, primarily across Africa and its vicinity. The firm supports founders after product-market fit to sustain industry leadership, strengthen governance, and expand internationally. As a data-driven capital allocator, Silverbacks Holdings seeks alpha by investing in tech-enabled, export-oriented businesses across high-growth sectors including technology, entertainment, sports, and the creative economy—industries seen as key drivers of job creation and regional advancement. Follow Adam on Instagram at https://www.instagram.com/askadamtorres/ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: https://missionmatters.lpages.co/podcastguest/ Visit our website: https://missionmatters.com/ More FREE content from Mission Matters here: https://linktr.ee/missionmattersmedia Learn more about your ad choices. Visit podcastchoices.com/adchoices

When it comes to working in military intelligence, strong leadership skills and the ability to make quick decisions under pressure are key. Just as important to a mission's success is being a good team player.Those were the lessons and skills Chris Stillwell '24 carried into his two career pivots after his time working as a military intelligence officer for the U.S. Army. His first pivot landed him a role at Kearney in Dubai focusing on M&A integration and strategy consulting. Chris then decided to pursue an MBA at Rice Business to sharpen his financial skills and pivot once again into the world of investment banking. Now an investment banking associate at Bank of America, Chris joins co-host Brian Jackson '21 to discuss his military experience, why he chose Rice, how the program helped him make a major career transition, and his advice to those considering an MBA to pursue new career opportunities. Episode Guide:00:00 Introduction to Chris Stillwell01:03 Military Intelligence: Separating Fact From Fiction02:15 Roles and Responsibilities in the Army03:08 Leadership and Decision Making in High-Pressure Situations08:07 From Military to Consulting09:49 Living Abroad: Challenges and Cultural Insights15:02 Transitioning to an MBA at Rice University18:13 Involvement and Networking at Rice20:56 Entering Investment Banking: Preparation and Challenges25:37 Day-to-Day in Investment Banking28:46 Advice for Career Pivoters and VeteransThe Owl Have You Know Podcast is a production of Rice Business and is produced by University FM.Episode Quotes:The moment Chris realized that Rice gave him an edge over his peers[20:48] Brian: Going into investment banking, was there, like, now an elevated sense of confidence of, Okay, I've done this before; I'll do it again?[20:56] Chris: Maybe some blind confidence sometimes. Yeah, you could even ask my parents. I went home for like four days for the Christmas break the year I was recruiting. And I was studying flashcards with my mom of all the IB 400 questions. And I was like, “I'm not going to get a job. You know, like all these people around me are much smarter than me. There's a really—we've got a really talented pool of candidates that are recruiting this year.” But you know, I felt like at the end of the day, the Finance Association and Rice, just the classes I took, really prepared me to understand the basics of finance, the basics that are expected of the interview process. And then, going forward, I saw when I started as an intern at the bank, I went to New York for a week…We were training with all these people from all these different schools, going to all these different groups in the bank, and some people didn't even know what a DCF was or didn't know how to do it that well, I should say. We were doing some practice problems, and I was like, “Wow, we're actually far ahead of a lot of these other schools and people.” So that was kind of good to see that Rice really put an effort into training us up. What Chris learned about leadership through three career pivots[30:15] There are certain people who can be leaders and are very good at being leaders. But being a good leader in the military might not translate to being a good leader at banking. And a lot of times you actually see that, or you see military officers leave the military and go into the corporate world and not be as successful. Because I really think you do need to tailor your leadership style to the one the industry you're working in, and two, the people you're working with, you know, different ways of operating motivate people differently. Like in the military, you could yell at somebody and hold them to a higher standard and maybe they'll do it. But if you yelled at somebody like, you know, a marketing job, they probably would shut down and that'd be the end of it. It really doesn't work the same. The leadership style is something that you have to adjust to the area you're working in.On how his military experience strengthened his teamwork skills[04:03] In the military, you are a leader, but you learn how to be a good follower as well. And I think what you do with that is that you are able to have great teamwork. You're able, like in my current job now, I have an analyst underneath me, but I have people like VPs and MDs above me and I can understand what their intent is and what we need to get accomplished in our day-to-day job, but also articulate to the people below me, Hey, this is the intent and this is how we do it. So it's kind of been very helpful in those soft skills.On how Rice gave him the academic foundation he needed[16:49] My reasons for going to Rice were great, but once I got there, I appreciated it a lot more. I really got exposed to, I mean, I was looking for some things like smaller classrooms for example. Like a lot of people we hire from Kearney were from Yale or HBS, and their class size was like a thousand people. And maybe you didn't have a lot of rigor in terms of academics. I think Rice, especially in the first term, really forces you to go to classes to do your homework, to learn the materials. And that was attractive to me as well, because I didn't come from a finance background at all. So I didn't even know what a DCF was before I came to Rice. So I was very grateful at that, you know, getting to Rice and realizing that it was such a good platform to be integrated into.Show Links: TranscriptGuest Profile:Chris Stillwell | LinkedIn

Most leaders have mandated AI pilots, but few can claim it's fundamentally changed their operations. Why is the gap between experiment and transformation so persistent? Courtney Baker, David DeWolf, and Mohan Rao discuss how to escape the "forever pilot" trap in part three of our change management series. They explore why tools start the change but rituals sustain it, and how to shift AI from a special project to the way business gets done. Pete Buer also joins to break down new research from HBS on why AI-enabled teams outperform lone power users—and the new management skills required to lead them. Then, Pete interviews Scott D. Anthony, Clinical Professor at Dartmouth's Tuck School of Business. Scott explains why you should treat AI as a teammate rather than an oracle, using it to challenge groupthink while navigating the organizational politics of data access. Insights you won't want to miss: Why internal "product-market fit" for AI tools expires every 90 days. The "gym analogy" for building decision-making wisdom. The critical difference between one-way and two-way door decisions. Watch the full episode on YouTube: https://www.youtu.be/4_m9fzfEao4 Try Knownwell free for 30 days: https://www.knownwell.com/30days Get Scott Anthony's new book, Epic Disruptions: 11 Innovations That Shaped Our Modern World. 

Shotgun Spratling and Chris Trevino return to podcast form to break down USC's disappointing 42-27 loss to Oregon on the road that leaves the Trojans still searching for their first win in Autzen Stadium since 2011.  Shotgun takes on the 'Two-Minute Drill' first, discussing USC's growth as a program, but how it still measures up short to that national-title-contending tier. Chris spends his time talking about some Oregon experiences, including a disastrous La Quinta Inn stay.   The HBs move into the 'Stock Up, Stock Down' segment, which features the Trojan special teams, freshman wide receiver Tanook Hines, safety Kennedy Urlacher, tackling on the road and much more.  The Helium Boys then dive into the second half of the show to see if the Trojans can close out the year with nine wins in the regular season against crosstown rival UCLA, one of the worst teams in the Big Ten.  Chris puts Shotgun through another edition of Take It or Leave It with takes on Lincoln Riley's hot seat for 2026, Makai Lemon's Biletnikoff race and tackles for a loss. The Overtime period features Thanksgiving themes from Turkey Bowls to cousins.  To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

This episode will completely change the way you think about communication. What you learn will boost your influence and make you more confident.If you… Overthink what you said hours after a meeting, Freeze up when it's your turn to talk, Or want to sound more confident without faking it… This is your blueprint for better conversations at work, in relationships, and in life. Today, Mel sits down with Dr. Alison Wood Brooks: Harvard professor, researcher, author, and one of the world's leading experts on the science of communication. Her course at Harvard Business School, all about communication, is one of the most popular classes there – and in this episode she shares the exact methods she teaches to students at HBS. You're going to learn the exact tools and strategies that will help you communicate with more confidence, ease, and clarity. You'll learn: -What makes conversation feel so hard and how to make it easier -The #1 mistake people make when they talk -How to feel more confident even when you're anxious, blanking out, or unsure of what to say -Why we misread people constantly (and how to stop doing it) -How to recover from awkward moments or miscommunication -The skill every great leader, parent, and partner has in common Whether you want to build better relationships, stop second-guessing yourself, or speak up with more purpose and ease, this episode will give you the science, tools, and confidence to do it. For more resources related to today's episode, click here for the podcast episode page. If you liked the episode, check out this one next: How To Handle Difficult People & Take Back Your Peace and PowerConnect with Mel:  Get Mel's newsletter, packed with tools, coaching, and inspiration.Get Mel's #1 bestselling book, The Let Them TheoryWatch the episodes on YouTubeFollow Mel on Instagram The Mel Robbins Podcast InstagramMel's TikTok Subscribe to SiriusXM Podcasts+ to listen to new episodes ad-freeDisclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.