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On this episode of Unsupervised Learning, Razib talks to Davide Piffer, whose Substack examines genetic differences between populations. Piffer has been publishing on human genetic variation for a decade, and recently started a Substack, Piffer Pilfer, exploring similar issues in detail over a series of posts. Razib asks Piffer about the difficulties in analyzing polygenic scores from quantitative traits in ancient DNA samples. How does he do in technical terms, from genome quality to imputation to ancient populations from modern ones? Then, they discuss some of Piffer's findings, in particular, his work on pigmentation. Piffer talks about how he discovered that modern European pigmentation, and in particular, light complexion, is the product of both admixture from different populations with different characteristics and natural selection over the millennia. Piffer talks about how he discovered that selection for lighter pigmentation continued into the Iron Age.
On this episode of Unsupervised Learning, Razib again talks to George Washington University archaeologist Eric Cline. The author of 1177 B.C. - The Year Civilization Collapsed and After 1177 B.C. - The Survival of Civilizations, Cline has a new book out, Love, War, and Diplomacy: The Discovery of the Amarna Letters and the Bronze Age World They Revealed. While 1177 B.C. closed with the end of the first global civilization, that of the Eastern Mediterranean at the end of the Bronze Age, and After 1177 B.C. tells the story of those who picked up the pieces, Love, War, and Diplomacy puts the spotlight on the Late Bronze Age at its peak. Razib and Cline discuss the two major threads in Love, War, and Diplomacy: the decipherment of cuneiform and the emergence of the field of Assyriology, and the diplomatic world of Bronze Age Great Powers. Cline addresses the reality that 19th-century archaeology was not an idealized enterprise, and scholars had to compete with treasure hunters, and negotiate difficult nationalist sensitivities. He also explains how they deciphered cuneiform decades after hieroglyphs, providing an alternative view of the earliest antiquity. The discussion then focuses on the intricate and tense relationship between Egypt, Assyria, the Hittites, and the Mitanni. Cline also highlights the reality that the Amarna Letters also shed light on the bickering between the petty states of the Levant and their relationship to their hegemon, Egypt.
On this very special episode, Razib talks to paleoanthroplogists John Hawks and Chris Stringer. Hawks is a paleoanthropologist who has been a researcher and commentator in human evolutionary biology and paleoanthropology for over two decades. With a widely read weblog (now on Substack), a book on Homo naledi, and highly cited scientific papers, Hawks is an essential voice in understanding the origins of our species. He graduated from Kansas State University in 1994 with degrees in French, English, and Anthropology, and received both his M.A. and Ph.D. in Anthropology from the University of Michigan, where he studied under Milford Wolpoff. He is currently working on a textbook on the origins of modern humans in their evolutionary context. Hawks has already been a guest on Unsupervised Learning three times. Chris Stringer is affiliated with the Natural History Museum in London. Stringer is the author of African Exodus. The Origins of Modern Humanity, Lone Survivors: How We Came to Be the Only Humans on Earth and Homo Britannicus - The Incredible Story of Human Life in Britain. A proponent since the 1970's of the recent African origin of modern humans, he has also for decades been at the center of debates around our species' relationship to Neanderthals. In the 1980's, with the rise to prominence of the molecular model of "mtDNA Eve," Stringer came to the fore as a paleoanthropological voice lending support to the genetic insights that pointed to our African origins. Trained as an anatomist, Stringer asserted that the fossil evidence was in alignment with the mtDNA phylogenies, a contention that has been broadly confirmed over the last five decades. Razib, Hawks and Stringer discuss the latest work that has come out of Yuxian, China, and how it updates our understanding of human morphological diversity, and integrate it with the newest findings about Denisovans from whole genome sequencing. They talk about how we exist at a junction, with more and more data, but theories that are becoming more and more rickety in terms of explaining the patterns we see. Hawks talks about the skewing effect of selection on phylogenetic trees, while Stringer addresses the complexity of the fossil record in East Asia.
This week on Unsupervised Learning, Jacob Effron is joined by Jordan Schneider, host of China Talk, who challenges widespread assumptions about US-China AI competition. China's AI development is driven by private capital and market competition—not central government planning—with companies like DeepSeek, Alibaba, and ByteDance operating more like Silicon Valley startups than state projects. The critical bottleneck is compute: the West maintains a 10-15x advantage in advanced chips, and US export controls implemented one month before ChatGPT created a structural edge favoring America for years. Chinese companies aggressively open-source models from strategic necessity—they couldn't establish a quality gap justifying paid access like OpenAI. Jordan explains why the "Goldilocks strategy" of controlled chip dependency fails, why expert consensus opposes selling advanced semiconductors to China despite Nvidia's lobbying, and how Taiwan's invasion risk is driven more by domestic politics than AGI scenarios. China's real advantage may emerge in robotics manufacturing at scale, where they're already deploying while the US debates strategy. Inside the Politburo's AI Study Session: https://www.chinatalk.media/p/xi-takes-an-ai-masterclassSubmit your questions to Jacob here: https://docs.google.com/forms/d/1vHBYv0bTT_EgFWTjbKnLr_sn3pZnFmcFGWYVTltKEco/edit (0:00) Intro(1:45) The Chinese AI Ecosystem: Pre and Post ChatGPT(3:45) Government Influence and Private Sector Dynamics(6:40) Venture Funding and Major Players(8:36) Talent and International Collaboration(11:25) Open Source Models and Market Dynamics(15:24) What Role Does The Chinese Government Play?(31:17) US-China AI Policy and Strategic Competition(36:18) The Argument for Selling AI Accelerators(37:02) Risks of Not Selling to China(43:34) Technological Constraints and Huawei's Challenges(51:18) US-China Relations and Taiwan(1:02:46) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint
The real disruption isn't AI replacing humans, it's the shocking possibility that human labor was the economic bubble all along. In this episode, Ron Eddings sits down with Daniel Miessler, founder of Unsupervised Learning and longtime security leader, to break open why companies are hitting record profits with shrinking workforces, and what that means for your future. Daniel shares how AI agents, context management, and his Telos problem-first framework are reshaping what it means to create value in the modern economy. From Apple to Human 3.0, Daniel explains why building in public, learning fast, and solving real problems are the ultimate career edge in an AI-powered world. Impactful Moments: 00:00 - Introduction 02:00 - Jobless profit boom accelerates 05:00 - Daniel's AI journey at Apple 08:00 - Building careers around problems 12:00 - AI bubble or timing problem 15:00 - Nine-year-old codes app in two hours 18:00 - Human labor is the bubble 22:00 - Context management changes everything 26:00 - Adaptation equals survival Links: Daniel's Website: danielmiessler.com/ Daniel's Github: https://github.com/danielmiessler/ Daniel's LinkedIn: https://www.linkedin.com/in/danielmiessler/ Upcoming events: https://www.hackervalley.com/livestreams Love Hacker Valley Studio? Pick up some swag: https://store.hackervalley.com Continue the conversation by joining our Discord: https://hackervalley.com/discord Become a sponsor of the show to amplify your brand: https://hackervalley.com/work-with-us/ Join our creative mastermind and stand out as a cybersecurity professional: https://www.patreon.com/hackervalleystudio
On this episode of Unsupervised Learning, Razib talks to Alexander Cortes. Cortes is a trainer, fitness influencer and entrepreneur. He is the co-founder, along with his wife, of Ferta, a company that aims to "optimize your reproductive health and conceive naturally." Born and raised in California, Cortes began his career in the fitness industry as a personal trainer in 2010. Over the next few years he expanded his efforts online, writing about fitness and nutrition from a science-informed perspective. Cortes developed a following by offering practical advice on strength training, muscle building, and the psychological aspects of fitness to the interested general public, translating the wisdom-of-the-gym for the person on the street. In the first part of the podcast, Razib and Cortes talk about "broscience," and how it differs from "quantified self" and other movements geared toward self-optimization. They discuss how "bros" arrived on the importance and utility of peptides long before the ozempic revolution, and how the iterative and experimental methods of gym-addicted amateurs predated and anticipated what would later become conventional wisdom. Razib also explores how Cortes' particular style of broscience differs from that of others, with its stronger empirical basis and analytical orientation (and aversion to fads like "raw food"). They discuss the "peptide revolution" and how online fitness and health influencers discovered it earlier, the utility of the macromolecules in health and wellness, and what the online community discovered already that is likely to come down the clinical pipeline. In the second part of the discussion, Cortes introduces his new company, Ferta, and its situates its position in the fertility space. He explains the origin of his firm as he and his wife began to attempt to conceive in their 30s, and how difficult or easy the process was conditional on the optimizations they engaged in. Cortes explains many people struggle because they do things wrong, and don't maximize their chances by being healthy and fertile.
Recently, the new embryo-selection start-up Herasight has been in the news, finally coming out of stealth. Part of the buzz is because of the public involvement of well-known geneticists and academics like Alex Young and Joe Pickrell in Herasight's algorithm development. Additionally, Noor Siddiqi, the CEO of Orchid, a competitor to Herasight (and onetime advertiser on this podcast), was a guest on Ross Douthat's show Interesting Times, triggering another round of conversations around embryo-selection, including in The Wall Street Journal and Breaking Points. To hash out some opposing viewpoints, Unsupervised Learning decided to bring on two guests that stake out very different positions, Dr. James Lee, a psychometrician and behavior geneticist at the University of Minnesota, and Dr. Jonathan Anomaly, a philosopher and Herasight's sales lead. Lee has been on the record with his skepticism of reproductive technology, writing an op-ed in The Wall Street Journal four years ago warning against the consequences of polygenic embryo selection. Meanwhile, Anomaly's last book was Creating Future People: The Science and Ethics of Genetic Enhancement, where he advances the idea that such technologies will unlock human potential.
Fill out this short listener survey to help us improve the show: https://forms.gle/bbcRiPTRwKoG2tJx8This week on Unsupervised Learning, Jacob sits down with Nicole Brichtova and Oliver Wang, the Google researchers behind "Nano Banana" - the breakthrough AI image model that achieved unprecedented character consistency and took over social media.The conversation covers how their model fits into creative workflows, why we're still in the early innings of image AI development despite impressive current capabilities, and how image and video generation are converging toward unified models. They also share honest perspectives on current limitations, safety approaches, and why the expectation of going from prompt to production-ready content is fundamentally overhyped.(0:00) Intro(1:42) Early Nano Banana Use Cases and Character Consistency(3:05) Popular Features and User Requests(3:54) Future Frontiers in Image Models(5:26) Personalization and Aesthetic Models(7:39) Model Success and User Engagement(10:59) Product Design for Different Users(19:30) Advanced Use Cases and Future Workflows(23:14) Editing Workflows and Chatbots(25:14) Google's Image Model Applications(27:12) Milestones in Image Generation(29:30) MidJourney's Success(30:54) Future of Image Models(33:55) Image Models vs. Video Models(36:35) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint
Bret Taylor is the CEO of Sierra and Chairman of the Board of OpenAI. He previously served as co-CEO of Salesforce. I sat down with Bret to explore how the AI revolution compares to previous platform shifts and what it means for both startups and incumbents navigating this transition. (00:00) Introduction and Recent Milestone (00:38) AI Market and Historical Comparisons (02:30) Competitive Landscape and Business Models (06:02) Outcome-Based Pricing and Value Creation (13:52) Technological Shifts and Business Transitions (26:32) Adoption Challenges and Forward Deployed Engineering (37:21) Early Investment in Snowflake and Cloud Strategy (38:02) Enterprise Software Market Dynamics (38:38) AI Agents and Implementation Costs (41:06) Democratization of Software Development (43:35) The Future of Software Companies and AI Agents (49:36) Consumer Behavior and AI Agents (58:56) The Role of AI in Customer Experience (01:01:25) Career Advice in the Age of AI Executive Producer: Rashad Assir Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
On last week's episode of Unsupervised Learning, Razib spoke with Alex Nowrestah, a vice president at the Cato Institute and a strong advocate for expanding legal immigration. This week, he turned to the other side of the debate with Jason Richwhine, a resident scholar at the Center for Immigration Studies and a vocal supporter of sharply reducing immigration. Richwine earned undergraduate degrees in mathematics and political science from American University, and later a Ph.D. in public policy from Harvard. Before joining CIS, he served as deputy director of the National Institute of Standards and Technology and worked as a senior policy analyst at the Heritage Foundation. The conversation begins with an overview of the dramatic swings in U.S. immigration policy under Biden and Trump. Both note the surge of the foreign-born population in the early 2020s, with the unauthorized share now estimated at 15-16 million. Richwine faults Biden for lax border enforcement and the abuse of parole programs, and points to the comparative effectiveness of Trump's Remain in Mexico policy. He also presses the case for a moratorium, arguing that even legal immigration must be scaled back to sustainable levels. Razib and Richwine weigh the economic and cultural consequences of high-skilled immigration and close by considering whether meaningful reform is politically possible in the years ahead.
Logan is joined by Marc Benioff, the legendary co-founder and CEO of Salesforce, for a wide-ranging conversation on the rise of AI in enterprises. Marc explains how Salesforce has become the testing ground for its own “agentic” technology, using AI agents to handle customer support, boost sales, and transform marketing. He also shares his perspective on what's hype vs. reality in the AI race, the opportunities for startups, and why the future is about humans and agents working together. (00:00) Introduction and Salesforce's Lead Management (00:35) Reflecting on the Last Eight Months (01:14) The Impact of AI on Salesforce Operations (02:15) AI's Role in Customer Support and Sales (03:45) Salesforce's Vision for an Agentic Enterprise (05:00) Public Market Sentiment and AI Adoption (06:15) Salesforce's Data and Application Foundations (08:13) The Future of CRM and ITSM Markets (12:57) Managing Agents and Human Workers (17:45) Salesforce's Growth and AI Product Line (19:38) Pricing Models and Customer Success Stories (23:26) The Role of AI in Different Market Segments (28:51) Salesforce Ventures and Startup Investments (36:05) Advice for Young Professionals and Future Trends (41:04) Dreamforce Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Today on Unsupervised Learning, Razib talks to John Hawks, a paleoanthropologist who has been a researcher and commentator in human evolutionary biology and paleoanthropology for over two decades. With a widely read weblog (now on Substack), a book on Homo naledi, and highly cited scientific papers, Hawks is an essential voice in understanding the origins of our species. He graduated from Kansas State University in 1994 with degrees in French, English, and Anthropology, and received both his M.A. and Ph.D. in Anthropology from the University of Michigan, where he studied under Milford Wolpoff. He is currently working on a textbook on the origins of modern humans in their evolutionary context. Hawks has already been a guest on Unsupervised Learning three times. In this episode, Razib and Hawks focus on a very specific question: What were the different contributions to the heritage of modern humans in a world more than 200,000 years ago that was inhabited by at least half a dozen hominin species? First, Hawks takes us back to the year 2000 and his early work extending a more multiregional framework of human evolution, exploring what could be gleaned from the archaeological and paleontological record. Then Razib and Hawks discuss the ancient DNA revolution and the discovery that modern humans had ancestry from Neanderthals, as well as from an entirely new species, the Denisovans. They also examine the fact that, unlike Neanderthals, Denisovans appear to have been separated into very different regional populations that made distinct contributions to various modern populations. Razib also asks Hawks about the discovery of new pygmy human species in Luzon, as well as the current state of research on Homo naledi in South Africa and the Hobbits of Flores. Hawks contends that DNA will likely be extracted from all these lineages at some point and, if not, protein sequence data may be obtained. This would finally give researchers the statistical power to evaluate the possibility of extremely archaic admixture events. Hawks and Razib also address the potential role of natural selection driven by introgressed genes from sister lineages of humans and how this shaped modern variation.
In this episode of Unsupervised Learning, I sit down with Michael Brown, Principal Security Engineer at Trail of Bits, to dive deep into the design and lessons learned from the AI Cyber Challenge (AIxCC). Michael led the team behind Buttercup, an AI-driven system that secured 2nd place overall. We discuss: -The design philosophy behind Buttercup and how it blended deterministic systems with AI/ML -Why modular architectures and “best of both worlds” approaches outperform pure LLM-heavy -designs -How large language models performed in patch generation and fuzzing support -The risks of compounding errors in AI pipelines — and how to avoid them -Broader lessons for applying AI in cybersecurity and beyond If you’re interested in AI, security engineering, or system design at scale, this conversation breaks down what worked, what didn’t, and where the field is heading. Subscribe to the newsletter at:https://danielmiessler.com/subscribe Join the UL community at:https://danielmiessler.com/upgrade Follow on X:https://x.com/danielmiessler Follow on LinkedIn:https://www.linkedin.com/in/danielmiesslerBecome a Member: https://danielmiessler.com/upgradeSee omnystudio.com/listener for privacy information.
Send us a textWhat if I told you the biggest AI breakthroughs in pathology aren't coming from ChatGPT or generative tools—but from the quiet power of predictive analytics and machine learning?In this episode, I explore the non-generative side of artificial intelligence in pathology. These are the tools that detect tumors, segment tissue, classify images, and make predictions—without generating a single word.It's the third chapter in our guided AI series, and this time we focus on the models you're more likely to use in real-world diagnostics. You'll hear about object detection, segmentation, anomaly detection, and how these models are built using supervised and unsupervised learning—plus the pros and cons of different annotation strategies.We'll also cover why no one model fits all, and how combining simple tools like decision trees with more complex neural networks is often the key to building reliable, usable AI in pathology.Whether you're training your first model, selecting an algorithm for rare disease detection, or just want to understand what “unsupervised clustering” means—you'll find something useful here.
On this episode of Unsupervised Learning, in the wake of Elon Musk's xAI Grok chatbot turning anti-Semitic following a recent update, Razib catches up with Nikolai Yakovenko about the state of AI in the summer of 2025. Nearly three years after their first conversations on the topic, the catch up, covering ChatGPT's release and the anticipation of massive macroeconomic transformations driven by automation of knowledge-work. Yakovenko is a former professional poker player and research scientist at Google, Twitter (now X) and Nvidia (now the first $4 trillion company). With more than a decade on the leading edge computer science, Yakovenko has been at the forefront of the large-language-model revolution that was a necessary precursor to the rise of companies like OpenAI, Anthropic and Perplexity, as well as hundreds of smaller startups. Currently, he is the CEO of DeepNewz, an AI-driven news startup that leverages the latest models to retrieve the ground-truth on news-stories. Disclosure: Razib actively uses and recommends the service and is an advisor to the company. Razib and Yakovenko first tackle why Mark Zuckerberg's Meta is offering individual pay packages north of $200 million, poaching some of OpenAI's top individual contributors. Yakovenko observes that it seems Meta is giving up on its open-source Llama project, their competitor to the models that underpin OpenAI and ChatGPT (he also comments that it seems that engineers at xAI are disappointed in the latest version of Grok). Overall, though the pay-packages of AI engineers and researchers are high; there is now a big shakeout as massive companies with the money and engineering researchers pull away from their competitors. Additionally, in terms of cutting-edge models, the US and China are the only two international players (Yakovenko notes parenthetically that Chinese engineers are also the primary labor base of American AI firms). They also discuss how it is notable that almost three years after the beginning of the current booming repeated hype-cycles of artificial intelligence began to crest, we are still no closer to “artificial general intelligence” and the “intelligence super-explosion” that Ray Kurzweil has been predicting for generations. AI is partially behind the rise of companies like Waymo that are on the verge of transforming the economy, but overall, even though AI is still casting around for its killer app, big-tech has fully bought in and believes that the next decade will determine who wins the future.
On this episode of the Unsupervised Learning podcast, Razib welcomes back Ethan Strauss, a writer who has covered sports and culture for the past decade, including in the book The Victory Machine: The Making and Unmaking of the Warriors Dynasty. More recently his writing is to be found at his Substack, House of Strauss, which is notable for offering a candid take on the cross-pollination between broader culture and athletics, notably in the piece Nike's End of Men: Why Nike no longer wants us to Be Like Mike. Strauss and Razib first discuss professional sports and the different representation of various nationalities. Strauss recounts the generational attempt by the NBA to get Chinese representation to gin up a lucrative rivalry, and how it sputtered due to the reality that 1.4 billion Han Chinese seem to have less basketball talent than small nations like Croatia. Razib also asks about how and why baseball is popular in parts of Latin America and East Asia, and why there are so many more Dominicans in MLB than Mexicans. Strauss says differences between populations are so obvious in sports there's no need for complex social explanations. Then they explore the role of DEI in professional sports, and especially the NBA, and how it might be impacting decisions in the league. They recall the years around 2020, when a drive for minority representation, and in particular of blacks, was prevalent across the corporate world, and how thatimpacted professional sports. Strauss then offers his theory for why the Dallas Mavericks inexplicably traded away a potentially generational talent, Luka Dončić, and Mark Cuban's role in the choice. Finally, he highlights the racism that Jeremy Lin, one of the few Asian American stars in the 2010's, faced from fellow players.
Send us a textToday's episode explores the letter "U" in our ABCs of AI series, representing both yoU (our curious young listeners) and Unsupervised Learning. We break down how artificial intelligence systems can organize photos, music, and data by identifying similarities without being explicitly told what to look for. Through our "Sort It Like a Robot" activity, kids can experience firsthand how machines approach pattern recognition by sorting household objects and discussing the different ways things can be categorized.But beyond the technical concepts, we emphasize something crucial: despite all the amazing capabilities of AI, human qualities remain irreplaceable. Your feelings, imagination, creativity, and kindness are superpowers that no algorithm can duplicate. We discuss why it's essential to have "humans in the loop" checking AI's work, especially when machines might miss context or make incorrect assumptions based on limited information.Whether you're a tech-savvy kid or a parent looking to help your child navigate our increasingly AI-driven world, this episode offers accessible explanations and a fun hands-on activity that brings abstract concepts to life. Subscribe to AI for Kids, have your parents sign up for our newsletter at www.aidigitales.com/newsletter, and join us as we continue our journey through the ABCs of artificial intelligence!Sign up for the weekly newsletter here to get up to date news on AI for Kids: https://aidigitales.com/newsletterSupport the showHelp us become the #1 podcast for AI for Kids.Buy our new book "Let Kids Be Kids, Not Robots!: Embracing Childhood in an Age of AI"Social Media & Contact: Website: www.aidigitales.com Email: contact@aidigitales.com Follow Us: Instagram, YouTube Gift or get our books on Amazon or Free AI Worksheets Listen, rate, and subscribe! Stay updated with our latest episodes by subscribing to AI for Kids on your favorite podcast platform. Apple Podcasts Amazon Music Spotify YouTube Other Like our content, subscribe or feel free to donate to our Patreon here: patreon.com/AiDigiTales...
Chris Degnan is one of the most legendary CROs of this generation. He joined Snowflake as employee #13 and the 1st sales hire. He scaled the sales org from 0 to over $3B in ARR, spanned four CEOs, and retired as CRO after 11 years. In his first podcast post-retirement, Chris opened his CRO playbook, from early enablement to hiring rigor and fending off threats from competitors. He also reflects on lessons from working with leaders like Frank Slootman, John McMahon, and Sridhar Ramaswamy. If you're a founder or running sales at a startup, this one is for you. (00:00) Introduction to Chris's Journey at Snowflake (01:47) Navigating Leadership Changes (04:39) The Importance of Sales Methodology and Enablement (10:22) Near-Death Experiences and Company Resilience (13:39) Building a Strong Sales Organization (27:25) Hiring and Scaling the Sales Team (34:52) Board Dynamics and Mentorship (44:29) The Influence of John McMahon (46:22) Leadership Styles and Intuition (46:56) Launching Snowflake Japan (49:39) Learning from Leaders (55:10) The Importance of Competitive Moats (59:12) Snowflake vs. Databricks (01:07:45) Public vs. Private Markets (01:14:03) Sales and Marketing Synergy (01:26:17) Final Thoughts and Future Plans Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Logan sits down with Bipul Sinha, CEO and co-founder of Rubrik and former VC at Lightspeed and Blumberg Capital. Bipul shares what he learned transitioning from investor to founder, why intuition beats expertise, and how he built Rubrik into a category-defining business by betting on uncool ideas. They talk product-market fit in the AI era, what most VCs get wrong today, and why the enterprise IT market is still just getting started. It's a conversation packed with hard-earned wisdom and bold takes on building lasting companies. (00:00) Intro (01:42) Transitioning from VC to Founder (02:27) The Genesis of Rubrik (03:30) Navigating Uncertainty in Business (06:57) Product Market Fit and Early Success (08:56) Evolving with the Market (13:14) AI and Data Security (18:53) Leadership and Intuition (28:34) Building a Transparent Culture (31:52) Handling Tough Questions in Board Meetings (33:28) Changing Perspectives Over Time (34:57) Traits of Successful Entrepreneurs (36:46) The Future of Venture Capital and Startups (40:38) Balancing Forward and Lateral Motion in Business (42:35) The Impact of AI on Various Industries (01:00:28) The Evolution of Work and Technology (01:02:52) Fostering a Collaborative Company Culture (01:04:56) Looking Ahead: The Future of Rubrik Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Rick Smith (CEO, Axon) joined Logan to share the 30-year journey of building a nearly $50B public company behind the TASER, police body cameras, and now AI-powered tools like Draft One. He talks about taking Axon public in the early 2000s, navigating intense public scrutiny, and evolving from a controversial hardware startup into a software and AI pioneer. Rick also reflects on leadership lessons, regulatory battles, and his long-term mission to make the bullet obsolete. It's a candid and compelling conversation with one of the most unconventional founders in tech. (00:00) Intro (01:31) Axon: Reducing Violence Through Technology (02:12) The Evolution of Axon: From Taser to Body Cameras (04:56) Challenges and Triumphs: Going Public and Beyond (07:17) The Impact of Ferguson and the Rise of Body Cameras (11:16) Navigating Cultural and Business Shifts (17:04) The Role of AI and Future Innovations (25:26) The Taser: Technology and Purpose (34:17) Making the Bullet Obsolete: Future of Law Enforcement (37:10) Consumer Market Evolution (37:59) Proving Taser's Viability (40:17) Targeting Gun Owners (41:45) Taser-Related Deaths and Media Perception (48:07) Employee Taser Experience (50:59) Impact of Body Cameras (52:43) AI Innovations in Law Enforcement (56:15) Challenges in Product Development (01:04:27) Regulatory Hurdles (01:11:31) Leadership and Company Culture (01:14:58) Future Vision for Axon Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
On this episode of Unsupervised Learning, Razib talks to Bo Winegard and Noah Carl, the editors behind the online publication Aporia Magazine, founded in 2022. Winegard and Carl are both former academics. Winegard has a social psychology Ph.D. from Florida State University, and was an assistant professor at Marietta College. He was an editor at Quillette before moving to Aporia. Carl earned his Ph.D. in sociology from Oxford University. He was a research fellow at St. Edmund's College, Cambridge, before becoming a contributor to The Daily Skeptic and UnHerd, and a managing editor at Aporia. First, Razib asks Winegard and Carl about their respective cancellations, and the recent attacks on Aporia from the British media in particular. Winegard observes that many of the criticisms were muddled, as journalists struggled to get basic facts straight about who did what, as well as mixing up present associations among various editors with past ones. The two also address the change in the culture over the last few years, as cancellations seem to have lost some of their bite. Then Razib asks Winegard about the perception that Aporia is fixated on the third-rail of American culture: race and IQ, and its relevance to social policy and politics. Winegard talks about how he has long since said everything he has to say on the topic, but he still finds that the public conversation fails to address the possibility of cognitive differences between populations, and so keeps finding himself wading back in, to fill a gap in the discourse. Razib also asks the editors about their view of “cold winters theory,” which attempts to explain the higher IQs of temperate zone populations versus tropical ones. Then they discuss the disappointments of the MAGA movement, and its appeal to populist emotion. Winegard had hoped that despite its inchoate nature, it might have been able to pare back the radical excesses of the progressive cultural changes of the 2010's, but now he worries that overreach may up the chances that woke policies make a comeback with the inevitable political backlash in the next few years. Winegard also addresses his personal souring on reflexive anti-wokism, and Carl shares his own views from across the Atlantic, where Britain appears to follow in the US' footsteps, even if from an entirely different social-historical context. Winegard discusses the difficulties of maintaining a consistent heterodoxy in the face of tribalistic demands for conformity. Finally, they discuss the path forward for publications like Aporia that do not toe any particular party line.
Today Razib talks to Tim Lee, a previous guest on Unsupervised Learning. Lee hosts Understanding AI. Lee covered tech more generally for a decade for Washington Post, Ars Technica, and Vox.com. He has a master's degree in computer science from Princeton. Lee writes extensively about general AI issues, from Deep Research's capabilities to the state of large language models. But one of the major areas he has focused on is self-driving cars. With expansion of Waymo to Austin, and this June's debut of Tesla's robotaxis, Razib wanted to talk to Lee about the state of the industry. They discuss the controversies relating to safety and self-driving cars. Is it true, as some research suggests, that Waymo and self-driving cars are safer than human-driven cars? What about the accidents Waymos have been implicated in? Is it true that they were actually due to human error and recklessness, rather than the self-driving cars themselves? Lee also contrasts the different companies' strategies in the sector, from Waymo to Zoox to Tesla. Razib also asks him about the fact that self-driving cars' imminent arrival seems to have been overhyped five years ago, with Andrew Yang predicting trucker mass unemployment, to the reality that Waymo has now surpassed Lyft in ride volume in San Francisco. They also discuss the limitations of self-driving cars in terms of their ability to navigate cities and regions where snow might be a major impediment, and why there has been a delay in their expansion to freeway routes.
Logan sits down with Jeffrey Katzenberg, Hollywood legend and co-founder of DreamWorks, and Sujay Jaswa, former CFO of Dropbox - together, the duo behind WndrCo. They talk about building enduring companies, bridging tech and media, and what makes a great CEO partnership. The conversation also touches on storytelling as a business superpower and lessons from scaling at different stages. Whether you're a founder or a media nerd, there's something here for you. (00:00) Intro (04:26) The Genesis of the Partnership (13:06) Building and Investing in Companies (20:27) The Team and Their Roles (26:52) Decision-Making Process (33:25) Balancing Dreams and Skepticism (35:06) The Dynamics of Partnerships (37:25) Transitioning to Tech (38:45) Cultural Differences in Industries (41:26) The Value of Failure and Success (44:37) Excitement in Emerging Technologies (48:23) The Venture Capital Game (56:42) The Dropbox Talent Network (01:01:20) AI's Impact on Media and Creativity (01:06:18) Transitioning to CG Animation at DreamWorks (01:08:39) Embracing Change in the Intelligence Revolution (01:11:52) The Role of AI in Enhancing Productivity (01:14:11) Building a Consumer Cybersecurity Business (01:23:49) The Mission to Protect Children Online (01:35:17) Reflections on Partnership and Innovation Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Sholto Douglas, a Member of Technical Staff at Anthropic, joined Unsupervised Learning to break down why coding is the clearest early signal of model progress, how AI agents are already accelerating research, and what it'll take to unlock real-world breakthroughs in fields like biology and robotics. (0:00) Intro(0:48) Claude 4(1:30) Capabilities and Improvements(2:29) Practical Applications and Advice(3:04) Future of AI in Coding(4:38) Managing Multiple AI Models(11:20) The Barrier to Agents is Reliability(16:35) Agents Conducting Research(19:54) Impact of Models on World GDP(25:14) Most Important Metrics in Model Improvement(29:53) Stories of Model Creativity(32:45) How Often Will New Models Be Shipped in the Future?(39:51) Day-to-Day Work of AI Researchers(46:46) The Future of AI and Society(51:26) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint
In this episode, Logan is joined by Zach Weinberg (Co-Founder/CEO @ Curie.Bio) and Derek Thompson (writer at The Atlantic) for a candid discussion on the state of U.S. healthcare and scientific progress. They unpack what went right, and wrong, with COVID vaccine policy, the public backlash against mRNA technology, and the ripple effects on trust in science. The conversation also dives into the real reasons behind NIH budget cuts, the economics of drug discovery, and the business incentives in medical R&D. It's a sharp, thought-provoking look at the intersection of policy, innovation, and public perception. (00:00) Introduction to Drug Pricing in the US (00:23) Broad Healthcare Topics and Open-Ended Discussion (02:37) COVID-19 Vaccines: Successes and Public Perception (06:21) The Evolution of COVID-19 and Vaccine Efficacy (07:59) Public Policy and Vaccine Mandates (13:10) Impact of School Closures and Public Sentiment (19:23) NIH Funding and the Importance of Basic Research (25:04) Challenges in Science Funding and Public Perception (35:19) Government vs. Private Investment in Science (36:40) Operation Warp Speed: A Case Study (39:07) Antibiotic Resistance Crisis (43:22) The Drug Pricing Debate (44:05) Challenges in Drug Discovery (54:06) Regulatory Hurdles in Medical R&D (58:06) The Future of Drug Development (01:04:19) Concluding Thoughts Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Yotam Segev is the co-founder and CEO of Cyera, one of the fastest-growing cybersecurity startups in the world. In this episode, he joins Logan to talk about scaling Cyera from 100 to 550 employees in under two years, what it takes to operate at that speed, and why going slow can actually be riskier. They cover lessons from a tough go-to-market year, the emotional conviction behind choosing data security, and how Yotam thinks about platform expansion, hiring, and staying close to customers. It's a candid look at the mindset and mechanics behind building an elite security company at breakneck pace.(00:00) Intro(01:23) Yotam's Journey in Cybersecurity(02:30) Scaling a Company with Core Values(05:02) Founding Cyera: From Military to Startup(07:59) Entering the Venture Ecosystem(18:19) Early Challenges and Lessons Learned(22:36) Achieving Product-Market Fit(33:01) Ambitious Goals and Rapid Growth(37:39) The Future of Cybersecurity(39:07) The Cybersecurity Paradigm Shift(39:47) Entrepreneurship and Innovation in Cybersecurity(40:25) The Cat and Mouse Game of Cybersecurity(42:47) Traits of Effective CISOs(43:55) Expanding the Cybersecurity Platform(52:36) The Role of AI in Cybersecurity(01:03:25) The Impact of the October 2023 Attack on Israel(01:08:27) Leadership and Company Culture at Cyera(01:12:33) Reflections on Success and Future Goals(01:21:37) Fundraising and Partnerships(01:26:07) Hiring and Company GrowthExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
When Khan Academy launched Khanmigo, Salman Khan thought they might reach 100k users by 2025. Today, they're at 1.4 million.
Blake Scholl, founder and CEO of Boom Supersonic, is leading the boldest effort in decades to bring back commercial supersonic flight—this time with product-market fit.We talk about what went wrong with the world's first try at supersonic commercial aircraft (launched in the 70s), why Boeing hasn't introduced a new plane in over a decade, and how Blake's startup is building a jet that flies 2x faster than today's aircraft—without the sonic boom. This episode is a crash course in engineering ambition, regulatory dysfunction, and what it takes to defy gravity and incumbents.(00:00) Intro(00:40) The History and Evolution of Aviation(01:12) The Rise and Fall of Concorde(05:25) The Impact of Government and Founders on Innovation(08:57) Regulatory Challenges and Business Models(26:53) Boom's Vision for Supersonic Travel(47:10) Building Trust with Regulators(48:16) Challenges in the Aerospace Startup(49:36) Recruiting Talent from Unlikely Places(55:47) The Importance of Mission Success Events(01:01:52) Developing a Custom Jet Engine(01:22:54) Reindustrialization and Economic Strategy(01:34:42) Conclusion and Final ThoughtsExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
College sports are going through massive changes—from athlete pay drama to superconference realignment and transfer portal chaos, not to mention the giant class action lawsuit playing out now.Matt Brown, the publisher behind Extra Points and one of the top experts on the business of college athletics, joined the show to break it all down. We walked through the full history of college sports, the current money dynamics, and where things could be headed. (00:00) Meet Matt Brown: Expert in College Sports Business(03:09) The Origins of College Sports(06:31) The Evolution of College Sports Broadcasting(14:53) Title IX and Its Impact on College Athletics(17:53) The 1984 Supreme Court Decision and Its Aftermath(20:03) The SMU Death Penalty Scandal(22:19) Conference Realignment and the BCS Era(28:22) The Rise of Conference Television Networks(30:23) The Arms Race in College Sports Facilities(34:41) The Role of Boosters in College Sports(36:03) Financial Breakdown of Major College Sports Programs(37:04) Understanding Nonprofit Accounting in College Athletics(38:20) Revenue Generation in College Sports(40:34) Athletics as Enrollment Management(42:04) The Flutie Effect and University Applications(44:37) Conference Realignment and Financial Instability(48:58) The O'Bannon Case and Video Game Licensing(53:59) The Northwestern Unionization Attempt(58:19) The Alston Case and Educational Awards(01:02:11) Name, Image, and Likeness (NIL) Marketplaces(01:05:51) The Role of Collectives in College Sports(01:12:08) Dependability of Young Campaign Partners(01:13:03) Transfer Portal and Its Impact(01:15:56) Rise of NIL Agents and Handlers(01:17:40) Economic Incentives and Transfer Market(01:20:37) Challenges in NIL Enforcement(01:22:48) House Settlement and Future Implications(01:25:38) Allocation of NIL Funds by Universities(01:44:26) Potential Super Leagues and Investment Challenges(01:48:07) Concluding Thoughts on College SportsExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Aidan joined this week's Unsupervised Learning for a wide-ranging conversation on model architectures, enterprise adoption, and what's breaking in the foundation model stack. If you're building or investing in AI infrastructure, Aidan is worth listening to. He co-authored the original Transformer paper, leads one of the most advanced model labs outside of the hyperscalers, and is now building for real-world enterprise deployment with Cohere's agent platform, North. Cohere serves thousands of customers across sectors like finance, telco, and healthcare — and they've made a name for themselves by staying model-agnostic, privacy-forward, and deeply international (with major bets in Japan and Korea) (0:00) Intro(0:32) Enterprise AI(3:23) Custom Integrations and Future of AI Agents(4:33) Enterprise Use Cases for Gen AI(7:02) The Importance of Reasoning in AI Models(10:38) Custom Models and Synthetic Data(17:48) Cohere's Approach to AI Applications(23:24) Future Use Cases and Market Fit(27:11) Building a Unified Automation Platform(27:34) Strategic Decisions in the AI Journey(29:19) International Partnerships and Language Models(31:05) Future of Foundation Models(32:27) AI in Specialized Domains(34:40) Challenges in Data Integration(35:06) Emerging Foundation Model Companies(35:31) Technological Frontiers and Architectures(37:29) Scaling Hypothesis and Model Capabilities(42:26) AI Research Culture and Team Building(44:39) Future of AI and Societal Impact(48:31) Addressing AI Risks With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint
In this episode, Derek Thompson (Writer, The Atlantic) delves into the tumultuous nature of Trump's trade policies, especially regarding tariffs, and how they impact American manufacturing and global markets. They discuss the constant changes in policy, the resulting uncertainty for industries like automotive and aerospace, and the mismatch between Trump's ‘madman strategy' and effective industrial policy. The conversation also explores the broader economic consequences, including stock market volatility, housing affordability issues, and the role of government in promoting economic growth and innovation.(00:00) Intro(00:20) Trump's Trade Policy and Its Implications(01:30) The Uncertainty of Tariff Policies(02:12) Impact on American Manufacturing(05:15) Stock Market Reactions(07:00) Debating the Effectiveness of Tariffs(10:02) Wall Street vs. Main Street(18:44) Housing and Healthcare Challenges(34:53) Historical Context of Housing Regulations(41:48) The Reality of Construction Jobs(42:35) The American Dream and Housing Costs(42:57) The 30-Year Mortgage and Its Impact(43:48) Comparing Home Ownership to Stock Market Investments(45:14) Political Reception of the Book 'Abundance'(46:17) Pro-Business Democrats and Government's Role(48:38) The Need for Aggressive Democratic Leaders(51:18) The Importance of Economic Growth(01:01:26) Debating Government's Role in Industrial Policy(01:03:34) Challenges in the Semiconductor Industry(01:13:19) The Housing Problem in New York City(01:15:26) Conclusion and Final ThoughtsExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Debate between Keith Rabois and Zach Weinberg on what tariffs are actually trying to accomplish. One core theme: Tariffs aren't fully about “bringing back factories,” but rather a negotiation tool to eliminate foreign trade barriers - ultimately aiming to increase free trade, not restrict it.We also got into:- What each of them would do if they were in charge- Whether the trade deficit is a meaningful metric or just a misunderstood talking point- If tariffs could be part of an initiative to replace income tax — shifting toward a more consumption-based tax system- If tariffs could successfully be used as a non-military tool to reduce drug supply to the US- If there's a major disconnect between the new administration's rhetoric and the actual economic goals behind the policyOne of the deepest economic conversations from the show's recent history — and a rare debate where both sides had real logic behind their views.(00:00) Introduction and Host's Biases(00:46) Keith's Perspective on Tariffs(03:05) Zach's Perspective and Clarifying Questions(05:14) Debating Tariff Strategies(07:45) Economic Implications and Free Trade(13:31) Trump's Tariff Policies and Goals(16:57) Global Trade and Protectionism(25:52) Final Thoughts on Tariffs and Trade(29:16) Discussion on Trade Tariffs and Partners(30:17) Impact of Tariffs on GDP and Debt(31:20) Political Coalitions and Trade Policies(32:00) Tariffs as Consumer Taxes(33:30) Debate on Trade Deficit and Tariff Rates(36:53) Regulatory Reforms and Economic Policies(47:25) Fentanyl Crisis and Trade Negotiations(51:06) Closing Remarks and Future TopicsExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
In this freeform episode, Logan sits down with Zach Weinberg (Co-Founder and CEO of Curie.Bio) to break down two of the biggest storylines in tech: tariffs and AI.They banter through the core arguments for and against tariffs, including national security, domestic employment, and negotiation power. Plus, they revisit what's happened in past trade wars and share predictions on the real economic consequences this time around.Logan and Zach also discuss OpenAI's $40B raise and the broader race for AI dominance—can OpenAI maintain its lead against tech giants like Google and Apple? They debate the limits of product defensibility, the power of platform defaults, and the strategic moves OpenAI might need to make to stay ahead.Topics include:The arguments for and against tariffsWhat happened during past U.S. tariff cycles—and how this one comparesWhether OpenAI can maintain its edge in a world of native AI platformsA possible playbook for OpenAI to build user lock-in beyond utilityWhat this era of AI competition means for the U.S.—and what could derail ithttps://fdra.org/wp-content/uploads/2025/03/Trade-War-Lessons-from-the-Past-2025.pdf?utm_source=newsletter&utm_medium=email&utm_campaign=newsletter_axiosmacro&stream=business00:00 Intro01:35 Liberation Day and Global Trade02:13 Freeform Discussion on Various Topics02:44 Podcasting and VC Life03:32 Debating Tariffs and National Security11:26 Arguments Against Tariffs22:19 Historical Context of Tariffs26:58 Economic Predictions and Stagflation33:39 The Forgotten Lessons of Recessions36:02 The Fixed vs. Growth Mindset in Economics37:17 The Democratic Party's Shift on Economic Policies42:33 The Rise of Populism and Its Impact50:28 OpenAI's Explosive Growth and Challenges54:28 The Competitive Landscape of AI58:33 The Future of AI and Consumer Behavior01:07:20 The Role of Social Networking in AI's Future01:10:43 Wildcard: The Role of XAI and GrokExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: / @redpointai
If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs. Top guests: Noam Shazeer, Bob McGrew, Noam Brown, Dylan Patel, Percy Liang, David LuanFull Episode on Their YouTubeTimestamps* 00:00 Introduction and Excitement for Collaboration* 00:27 Reflecting on Surprises in AI Over the Past Year* 01:44 Open Source Models and Their Adoption* 06:01 The Rise of GPT Wrappers* 06:55 AI Builders and Low-Code Platforms* 09:35 Overhyped and Underhyped AI Trends* 22:17 Product Market Fit in AI* 28:23 Google's Current Momentum* 28:33 Customer Support and AI* 29:54 AI's Impact on Cost and Growth* 31:05 Voice AI and Scheduling* 32:59 Emerging AI Applications* 34:12 Education and AI* 36:34 Defensibility in AI Applications* 40:10 Infrastructure and AI* 47:08 Challenges and Future of AI* 52:15 Quick Fire Round and Closing RemarksTranscript[00:00:00] Introduction and Podcast Overview[00:00:00] Jacob: well, thanks so much for doing this, guys. I feel like we've we've been excited to do a collab for a while. I[00:00:13] swyx: love crossovers. Yeah. Yeah. This, this is great. Like the ultimate meta about just podcasters talking to other podcasters. Yeah. It's a lot. Podcasts all the way up.[00:00:21] Jacob: I figured we'd have a pretty free ranging conversation today but brought a few conversation starters to, to, to kick us off.[00:00:27] Reflecting on AI Surprises and Trends[00:00:27] Jacob: And so I figured one interesting place to start is you know, obviously it feels that this world is changing like every few months. Wondering as you guys reflect path on the past year, like what surprised you the most?[00:00:36] Alessio: I think definitely recently models we kinda on the, on the right here. Like, oh, that, well, I, I I think there's, there's like the, what surprised us in a good way.[00:00:44] May maybe in a, in a bad way. I would say in a good way. Recently models and I think the release of them right after the new reps scaling instead talked by Ilia. I think there was maybe like a, a little. It's so over and then we're so back. I'm like such a short, short period. It was really [00:01:00] fortuitous[00:01:00] Jacob: timing though, like right.[00:01:01] As pre-training died, I mean, obviously I'm sure within the labs they knew pre-training was dying and had to find something. But you know, from the outside it was it, it felt like one right into the other.[00:01:09] Alessio: Yeah. Yeah, exactly. So that, that was a good surprise,[00:01:12] swyx: I would say, if you wanna make that comment about timing, I think it's suspiciously neat that like, because we know that Strawberry was being worked on for like two years-ish.[00:01:20] Like, and we know exactly when Nome joined OpenAI, and that was obviously a big strategic bet by OpenAI. So like, for it to transition, so transition so nicely when like, pre-training is kind of tapped out to, into like, oh, now inference time is, is the new scaling law is like conv very convenient. I, I, I like if there were an Illuminati, this would be what they planned.[00:01:41] Or if we're living in a simulation or something. Yeah.[00:01:44] Open Source Models and Their Impact[00:01:44] swyx: Then you said open source[00:01:45] Alessio: as well? Yeah. Well, no, I, I think like open source. Yeah. We're discussing this on the negative. I would say the relevance of open source. I would specifically open models. Yeah, I was surprised the lack, like the llamas of the world by the lack of adoption.[00:01:56] And I mean, people use it obviously, but I would say nobody's [00:02:00] really like a huge fanboy, you know, I think the local llama community and some of the more obvious use cases really like it. But when we talk to like enterprise folks, it's like, it's cool, you know? And I think people love to argue about licenses and all of that, but the reality is that it doesn't really change the adoption path of, of ai.[00:02:18] So[00:02:19] swyx: yeah, the specific stat that I got from on anchor from Braintrust mm-hmm. In one of the episodes that we did was I think he estimated that open source model usage in work in enterprises is that like 5% and going down.[00:02:31] Jacob: And it feels like you're basically all these enterprises are in like use case discovery mode, where it's like, let's just take what we think is the most powerful model and figure out if we can find anything that works.[00:02:39] And, you know, so much of, of, of it feels like discovery of that. And then, right, as you've discovered something, a new generation of models are out and so you have to go do discovery with those. And you know, I think obviously we're probably optimistic that the that the open source models increase in uptake.[00:02:50] It's funny, I was gonna say my biggest surprise in the last year was open source related, but it was just how Fast Open Source caught up on the reasoning models. It was kind of unclear to me, like over time whether there would be, you know, [00:03:00] a compounding advantage for some of the closed source models where in the, okay, in the early days of, of scaling you know, there was a, a tight time loop, but over time, you know, would would the gap increase?[00:03:08] And if anything it feels like a trunk. You know, and I think deep seek specifically was just really surprising in how, you know, in many ways if the value of these model companies is like you have a model for a period of time and you're the only one that can build products on top of that model while you have it.[00:03:21] Like, God, that time period is a lot shorter than a, than I thought it was gonna be a year ago.[00:03:25] swyx: Yeah. I mean, again, I I, I don't like this label of how Fast Open Source caught up because it's really how Fast Deepsea caught up. Right. And now we have, like, I think some of it is that Deepsea is basically gonna stop open sourcing models.[00:03:36] Yeah. So like there, there's no team open source, there's just different companies and they choose to open source or not. And we got lucky with deep seek releasing something and then everyone else is basically distilling from deep seek and those are distillations. Catching up is such an easier lower bar than like actually catching up, which is like you, you are like from scratch.[00:03:56] You're training something that like is competitive on that front. I don't know if [00:04:00] that's happening. Like basically the only player right now is we're waiting for LA four.[00:04:03] Jordan: I mean, it's always an order of magnitude cheaper to replicate what's already been done than to create something fundamentally new.[00:04:09] And so that's why I think deep seek overall was overhyped. Right? I mean obviously it's a good open source, new entrant, but at the same time there's nothing new fundamentally there other than sort of doing it executing what's already been done really well.[00:04:21] Alessio: Yeah,[00:04:21] Jordan: right.[00:04:21] Alessio: So Well, but I think the traces is like maybe the biggest thing, I think most previous open models is like the same model, just a little worse and cheaper.[00:04:30] Yeah. Like R one is like the first model that had the full traces. So I think that's like a net unique thing in fair, open source. But yeah, I, I think like we talked about deep seek in the our n of year 2023 recap, and we're mostly focused on cheaper inference. Like we didn't really have deep, see, deep CV three[00:04:47] swyx: was out then, and we were like, that was already like talking about fine green mixture of experts and all that.[00:04:51] Like that's a great receipt to[00:04:52] Jacob: have[00:04:52] swyx: to be like, yeah.[00:04:52] Jacob: End[00:04:53] swyx: of year 20. Yeah. That's a,[00:04:54] Jacob: that's a, that's, that's an[00:04:55] swyx: impressive one. You follow the right whale believers in Twitter. It's, it's like [00:05:00] pretty obvious. I actually had like so, you know, I used to be in finance and, and a lot, a lot of my hedge fund and PE friends called me up.[00:05:06] They were like, why didn't you tip us off on deep seek? And I'm like, well, I mean, it's been there. It's, it's actually like kind of surprising that like, Nvidia like fell like what, 15% in one day? Yeah. Because deep seek and I, I think it's just like whatever the market, public market narrative decides is a story, becomes the story, but really like the technical movements are usually.[00:05:26] One to two years in the making. Before that,[00:05:27] Jacob: basically these people were telling on themselves that they didn't listen to your podcast. They've been on the end of year 22, 3. No, no,[00:05:32] swyx: no. Like yeah, we weren't, we weren't like banging the drum. So like it's also on us to be like, no, like this. This is an actual tipping point.[00:05:38] And I think I like as people who are like, our function as podcasters and industry analysts is to raise the bar or focus attention on things that you think matter. And sometimes we're too passive about it. And I think I was too passive there. I'd be, I'd be happy to own up on that.[00:05:52] Jacob: No, I feel like over time you guys have moved into this margin general role of like taking stances of things that are or aren't important and, you know I feel like you've done that with MCP of [00:06:00] late and a bunch of[00:06:00] swyx: things.[00:06:00] Yeah.[00:06:01] Challenges and Opportunities in AI Engineering[00:06:01] swyx: So like the, the general pushes is AI engineering, you know, like it's gotta, gotta wrap the shirt. And MCP is part of that, but like the, the general movement is what can engineers do above the model layer to augment model capabilities. And it turns out it's a lot. And turns out we went from like, making fun of GPT rappers to now I think the overwhelming consensus GPT wrappers is the only thing that's interesting.[00:06:20] Yeah.[00:06:21] Jacob: I remember like, Arvin from Perplexity came on our podcast and he was like, I'm proudly a rapper. Like, you know, it's like anyone that's like talking about like, you know, differentiation, like pre-product market fit is like a ridiculous thing to, to say, like, build something people want and then yeah.[00:06:33] Over time you can kind of worry about that.[00:06:35] swyx: Yeah. I, I interviewed him in 2023 and I think he may have been the first person on our podcast to like, probably be a GBT rapper. Yeah. And yeah, and obviously he's built a huge business on that. Totally. Now, now we now we all can't get enough of it. I have another one for, Oh, nice.[00:06:47] That was Alessia's one and we, we perhaps individual answers just to be interesting in the same Uber on the way up. Yeah. You just like in the, in different Oh, I was driving too. Oh, you were driving. So I actually, I mean, it was a Tesla mostly drove mine was [00:07:00] actually, it is interesting that low-code builders did not capture the AI builder market.[00:07:04] Right. AI builders being bought lovable, low-code builders being Zapier, Airtable, retool notion. Any of those, like you're not technical. You can build software.[00:07:14] misc: Yeah.[00:07:14] swyx: Somehow not all them missed it. Why? It's bizarre. Like they should have the DNA, I don't know. They should have. They already have the reach, they already have the, the distribution.[00:07:25] Like why? I I have no idea. The ability to[00:07:27] Jacob: fast follow too. Like I'm surprised there's Yeah. There's just[00:07:29] swyx: nothing. Yeah. What do you make of that? I, it seems and you know, not to come back to the AI engineering future, like it takes a, a certain kind of. Founder mindset or AI engineer mindset to be like, we will build this from whole cloth and not be tied to existing paradigms.[00:07:45] I think, 'cause I like, if I was, if I'm to, you know, you know, Wade or who's, who's, who's the Zapier person than, you know, Mike. Mike who has left the Zapier. Yeah. What's the, yeah. Like you know, Zapier, when they decided to do Zapier ai, they [00:08:00] were like, oh, you can use natural language to make Zap actions, right?[00:08:03] When Notion decided to do Notion ai, they were like, oh, you can like, you know write documents or, you know, fill in tables with, with ai. Like, they didn't do the, the, the, the next step because they already had their base and they were like, let's improve our baseline. And the other people who actually tried for to, to create a phone cloth were like, we, we got no prior preconceptions.[00:08:24] Like, let's see what we can, what kinda software people can build with like from scratch, basically. I don't know that, that's my explanation. I dunno if you guys have any retros on the AI builders?[00:08:33] Jacob: Yeah. Or, or, or did they kind of get lucky getting, you know starting that product journey? Like right as the models were reaching the inflection point?[00:08:39] There's the timing[00:08:40] swyx: issue. Yeah. Yeah, yeah. Yeah. Yeah, I don't know. Like I, I, to some extent, I think the only reason you and I are talking about it is that they, both of them have reported like ridiculous numbers. Like zero to 20 million in three months, basically, both of them. Jordan, did you have a, a big surprise?[00:08:55] Jordan: Yeah, I mean, some of what's already been discussed. I guess the only other thing would be on the Apple side in particular, I [00:09:00] think, I think you know, for the last text message summary, like, but they're[00:09:04] Jacob: funny. They're funny at how bad they had, how off they're, they're viral. Yeah.[00:09:08] Jordan: I mean, so like for the last couple years we've seen so many companies that are trying to do personal assistance, like all these various consumer things, and one of the things we've always asked is, well, apple is in prime position to do all this.[00:09:18] And then with Apple Intelligence, they just. Totally messed up in so many different ways. And then the whole BBC thing saying that the guy shot himself when he didn't. And just like, there's just so many things at this point that I would've thought that they would've ironed up their, their AI products better, but just didn't really catch on,[00:09:35] Jacob: you know, second on this list of, of generally overly broad opening questions would be anything that you guys think is kind of like overhyped or under hyped in the AI world right now?[00:09:43] Alessio: Overhyped agents framework. Sorry. Not naming any particular ones. I'm sorry. Not, not not, yeah, exactly. It's not, I, I would say they're just overall a chase to try and be the framework when the workloads are like in such flux. Yeah. That I just think is like so [00:10:00] hard to reconcile the two. I think what Harrison and Link Chain has done so amazingly, it's like product velocity.[00:10:05] Like, you know, the initial obstructions were maybe not the ending obstruction, but like they were just releasing stuff every day trying to be on top of it. But I think now we're like past that, like what people are looking for now. It's like something that they can actually build on mm-hmm. And stay on for the next couple of years.[00:10:23] And we talked about this with Brett Taylor on our episode, and it feels like, it's like the jQuery era Yeah. Of like agents and lms. It's like, it's kinda like, you know, single file, big frameworks, kinda like a lot of players, but maybe we need React. And I think people are just trying to build still Jake Barry.[00:10:39] Like, I don't really see a lot of people doing react like,[00:10:43] swyx: yeah. Maybe the, the only modification I made about that is maybe it's too early even for frameworks at all. And the thing that, and do you think[00:10:50] Jacob: there's enough stability in the underlying model layer and, and patterns to, to have this,[00:10:54] swyx: the thing is the protocol and not the framework?[00:10:56] Jacob: Yeah.[00:10:56] swyx: Because frameworks inherently embed protocols, but if you just focus on a protocol, maybe that [00:11:00] works. And obviously MCP is. The current leading mm-hmm. Area. And you know, I think the comparison there would be, instead of just jQuery, it is XML HTB requests, which is like the, the thing that enabled Ajax.[00:11:10] And that was the, the, the, the, the sort of inciting incident for JavaScripts being popular as a language.[00:11:16] Jordan: I would largely agree with that. I mean, I think on the, the react side of things, I think we're starting to see more frameworks sort of go after more of that, I guess like master is sort of like on the TypeScript side and more of like a sort of master.[00:11:28] Yeah, yeah, yeah, yeah. The traction is really impressive there. And so I think we're starting to see more surface there, but I think there's still a big opportunity. What do you have for for an over or under hyped on the under hype side? You know, I actually, I, I know I mentioned Apple already, but I think the private cloud compute side with PCC, I actually think that could be really big.[00:11:45] It's under the radar right now. Mm-hmm. But in terms of basically bringing. The on device sort of security to the cloud. They've done a lot of architecturally interesting things there. Who's they? Apple. Oh, okay. On the PCC side. And so I actually think of that.[00:11:58] swyx: So you're negative on Apple [00:12:00] Intelligence, but also on Apple Cloud,[00:12:01] Jordan: on the more of the local device.[00:12:04] Sort of, I think there'll be a lot of workloads still on device, but when you need to speak to the cloud for larger LLMs, I think that Apple has done really interesting thing on the privacy side.[00:12:13] Alessio: Yeah. We did the seed of a company that does that, so Yeah. Especially as things become more co that you set 'em up on purpose.[00:12:18] So that felt like a perfect Yeah, no, I was like, let's go Jordan, you guys concluding before this episode? Tell me about that company after. We'll chat after, but, but yes, I, I think that's like the unique the thing about LLM workflows is like you just cannot have everything be single tenant, right?[00:12:35] Because you just cannot get enough GPUs. Like even like large enterprises are used to having VPCs and like everything runs privately. But now you just cannot get enough GPUs to run in a VPC. So I think you're gonna need to be in a multi-tenant architecture, and you need, like you said, like single tenant guarantees in multi-tenant environment.[00:12:52] So yeah, it's a interesting space.[00:12:55] swyx: Yeah. What about you, Swiss? Under hypes, I want to say [00:13:00] memory. Just like stateful ai. As part of my keynote on, on for just like every, every conference I do, I do a keynote and I try to do the task of like defining an agent, just, you know, always evergreen content, every content for a keynote.[00:13:14] But I did it in a, in a way that it was like I think like a, what a researcher would do. Like you, you survey what people say and then you sort of categorize and, and go like, okay, this is the, the. What everyone calls agents and here are the groups of DEF definitions. Pick and choose. Right. And then it was very interesting that the week after that OpenAI launched their agents SDK and kind of formalized what they think agents are.[00:13:34] CloudFlare also did the same with us and none of them had memory. Yeah, it's very strange. The, pretty much like the only big lab o obviously there, there's conversation memory, but there's not memory memory like in like a, like a let's store a large across fact about you and like, you know, exceed the, the context length.[00:13:54] And here's the, if you, if you're look, if you look closely enough, there's a really good implementation of memory inside of [00:14:00] MCP when they launched with the initial set of servers. They had a memory server in there, which I, I would recommend as like, that's where you start with memory. But I think like if there was a better, I.[00:14:10] Memory abstraction, then a lot of our agents would be smarter and could learn on, on the job, which is something that we all want. And for some reason we all just like ignored that because it's just convenient to, and, but do you feel like[00:14:24] Jacob: it's being ignored or it's just a really hard problem and like lots of, I feel like lots of people are working on it.[00:14:27] Just feels like it's, it's proven more challenging.[00:14:29] swyx: Yeah. Yeah. Yeah. So, so Harrison has lang me, which I think now he's like, you know, relaunched again. And then we had letter come speak at our mm-hmm. Our conference I don't know, Zep, I think there's a bunch of other memory guys, but like, something like this I think should be normal in the stack.[00:14:44] And basically I think anything stateful should be interesting to VCs 'cause it's databases and, you know, we know how those things make money.[00:14:51] Jacob: I think on the over hype side, the only thing I'd add is like, I'm, I'm still surprised how many net new companies there are training models. I thought we were kind of like past that.[00:14:58] And[00:14:58] swyx: I would say they died end of last year. And now, [00:15:00] now they've resurfaced. Yeah. I mean they, that's one of the questions that you had down there of like, yeah. Sorry. Is there an opportunity for net new model players? I wouldn't say no. I don't know what you guys think.[00:15:08] Alessio: I, I don't have a reason to say no, but I also don't have a reason to say, this is what is missing and you should have a new model company do it.[00:15:15] But again, I'm an add here. Like, all these guys wanna[00:15:17] swyx: pursue a GI, you know, all, they all want to be like, oh, we'll, we'll like hit, you know, soda on all the benchmarks and like, they can't all do it. Yeah.[00:15:25] Jacob: I mean, look, I don't know if Ilia has the secret secret approach up his sleeve of of something beyond test time compute.[00:15:29] Mm-hmm. But it was funny, I, we had Noam Shaer on the podcast last week. I was asking him like, you know, is, is there like some sort of other algorithmic breakthrough? Would he make a Ilia? And he's like, look, I think what he is implicitly said was test time compute gets to the point where these models are doing AI engineering for us.[00:15:43] And so, you know, at that point they'll figure out the next algorithm breakthrough. Yeah. Which I thought was was pretty interesting.[00:15:47] Jordan: I agree with you folks. I think that we're most interested, at least from our side and like, you know, foundation models for specific use cases and more specialized use cases.[00:15:55] Mm-hmm. I guess the broader point is if there is something like that, that these companies can latch onto [00:16:00] and being there sort of. Known for being the best at. Maybe there's a case for that. Largely though I do agree with you that I don't think there should be, at this point, more model companies. I think it's like[00:16:09] Jacob: these[00:16:09] Jordan: unique data[00:16:09] Jacob: sets, right?[00:16:10] I mean, obviously robotics has been an area we've been really interested in. It's entirely different set of data that's required, you know, on top of like a, a good BLM and then, you know, biology, material sciences, more the specific use cases basically. Yeah. But also specific, like specific markets. A lot of these models are super generalizable, but like, you know finding opportunities to, you know, where, you know, for a lot of these bio companies, they have wet labs, like they're like running a ton of experiments or you know, same on the material sciences side.[00:16:31] And so I still feel like there's some, some opportunities there, but the core kind of like LLM agent space is it's tough, tough to compete with the big ones.[00:16:38] Alessio: Yeah. Agree. Yeah. But they're moving more into product. Yeah. So I think that's the question is like, if they could do better vertical models, why not do that instead of trying to do deep research and operator?[00:16:50] And these different things. Mm-hmm. I think that's what I'm, in my mind, it's like the agents coming[00:16:53] swyx: out too.[00:16:54] Alessio: Well. Yeah. In my, in my mind it's like financial pressure. Like they need to monetize in a much shorter timeframe [00:17:00] because the costs are so high. But maybe it's like, it's not that easy to, do[00:17:04] Jacob: you think they would be, that it would be a better business model to like, do a bunch of vertical?[00:17:07] Well, it's more like[00:17:07] Alessio: why wouldn't they, you know, like you make less enemies if you're like a model builder, right? Yeah. Like, like now with deep research and like search, now perplexity like an enemy and like a, you know, Gemini deep research is like more of an enemy. Versus if they were doing a finance model, you know?[00:17:25] Mm-hmm. Or whatever, like they would just enable so many more companies and they always have, like they had as one of the customer case studies for GBT search, but they're not building a finance based model for them. So is it because it's super hard and somebody should do it? Or is it because the new models.[00:17:41] Are gonna be so much better that like the vertical models are useless anyways. Like this is better lesson. Exactly.[00:17:46] Jacob: It still seems to be a somewhat outstanding question. I, I'd say like, all the signs of the last few years seem to be like a general purpose model is like the way to go. And, you know, you know, like training a hyper-specific model in this, in, in a domain is like, you know, maybe it's cheaper and faster, but it's not gonna be like higher quality.[00:17:59] But [00:18:00] also like, I think it's still an, I mean, we were talking to, to no and Jack Ray from Google last week, and they were like, yeah, this is still an outstanding, like, we, we check this every time we have a new model. Like whether there's you know, there that still seems to be holding. I remember like a few years ago, it felt like all the rage was like the, it was like the Bloomberg GPT model came out.[00:18:14] Everyone was like, oh, you gotta like, you know, massive data. Yeah. I had[00:18:17] swyx: a GPA, I had DP of AI of Bloomberg present on that. Yeah. That must be a really[00:18:20] Jacob: interesting episode to go back on because I feel like, like very shortly thereafter, the next opening AI model came out and just like beat it on all sorts of[00:18:25] swyx: No, it, it was a talk.[00:18:26] We haven't released it yet, but yeah, I mean it's basically they concluded that the, the closed models were better so they just Yeah. Stopped. Interesting. Exactly. So I feel like that's been the but he's I, I would be. He's very insistent that the work that they did, the team he assembled, the data that he collected is actually useful for more than just the model.[00:18:42] So like, basically everything but the model survived. What are the other things? The data pipeline. Okay. The team that they, they, they assembled for like fine tuning and implementing whatever models they, they ended up picking. Yeah, it seems like they are happy with that. And they're running with that.[00:18:57] He runs like 12, 13 [00:19:00] teams at Bloomberg just working. Jenny, I across the company.[00:19:03] Jacob: I mean, I guess we've, we've all kind of been alluding it to it right now, but I guess because it's a natural transition. You know, the other broad opening I have is just what we're paying most attention to right now. And I think back on this, like, you know, the model company's coming into the product area.[00:19:13] I mean, I think that's gonna be like, I'm fascinated to see how that plays out over the next year and kind of these like frenemy dynamics and it feels like it's gonna first boil up on like cursor anthropic and like the way that plays out over the next six months I think will be. What, what is Cursor?[00:19:26] swyx: Anthropic is, you mean Cursor versus anthropic or, yeah. And I[00:19:29] Jacob: assume, you know, over time Anthropic wants to get more into the application side of coding Uhhuh. And you know, I assume over time Cursor will wanna diversify off of, you know, just using the Anthropic model.[00:19:39] swyx: It's interesting that now Cursor is now worth like 10 billion, nine, nine, 10 billion.[00:19:43] Yeah. And like they've made themselves hard to acquire, like I would've said, like, you should just get yourself to five, 6 billion and join OpenAI. And like all the training data goes through OpenAI and that's how they train their coding model. Now it's not as complicated. Now they need to be an independent company.[00:19:57] Jacob: Increasingly, it's seems to the model companies want to get into the [00:20:00] product layer. And so seeing over the next six, 12 months does having the best model, you know let you kind of start from a cold start on the product side and, and get something in market. Or are the, you know, companies with the best products, even if they eventually have to switch to a somewhat worse, tiny bit worse model, does it not, you know, where do the developers ultimately choose to go?[00:20:16] I think that'll be super interesting. Yeah.[00:20:18] Alessio: Don't you think that Devon is more in trouble than cursor? I, I feel like on Tropic, if anything wants to move more towards, I don't think they wanna build the ID like if I think about coding, it's like kind of like, you know, you look at it like a cube, it's like the ID is like one way to get the code and then the agent is like the other side.[00:20:33] Yeah. I feel like on Tropic wants more be on the agent side and then hand you off the cursor when you want to go in depth versus like trying to build the claw. IDEI think that's not, I would say, I don't know how you think the[00:20:46] swyx: existence, a cloud code doesn't show, doesn't support what you say. Like maybe they would, but[00:20:52] Jacob: assume, like I assume both just converge eventually where you want have where will you be able to do both?[00:20:57] So,[00:20:57] swyx: so in order to be so we're, we're talking [00:21:00] about coding agents, whether it's sort of what is it? Inner loop versus auto loop, right? Like inner loop is inside cursor, inside your ID between inside of a GI commit and auto loop is between GI commits on, on the cloud. And I think like to be an outer loop coding agent, you have to be more of a, like, we will integrate with your code base, we'll sign your whatever.[00:21:17] You know, security thing that you need to sign. Yeah. That kinda schlep. I don't think the model ads wanna do that schlep, they just want to provide models. So that, that, that's, that would be my argument against like why cognition should still have, have, have some moat against anthropic just simply because they cognition would do the schlep and the biz dev and the infra that philanthropic doesn't really care about.[00:21:39] Jacob: I know the schlep is pretty sticky though. Once you do it,[00:21:41] swyx: it's very sticky. Yeah. Yeah. I mean it's, it's, it's interesting. Like, I, I think the natural winner of that should be sourcegraph. But there's another[00:21:47] Jacob: unprompted point portfolio. Nice. We, I mean they, they're[00:21:51] swyx: big supporters like very friendly with both Quinn and B and they've they've done a lot of work with Cody, but like, no, not much work on the outer [00:22:00] loop stuff yet.[00:22:01] But like any company where like they have already had, like, we've been around for 10 years, we, we like have all the enterprise contracts that you already trust us with your code base. Why would you go trust like factory or cognition as like, you know, 2-year-old startups who like just came outta MIT Like, I don't know.[00:22:17] Product Market Fit in AI[00:22:17] Jacob: I guess switching gears to the to the application side I'm curious for both of you, like how do you kind of characterize what has genuine product market fit in AI today? And I guess less, you more and your side of the investing side, like more interesting to invest in that category of the stuff that works today or kind of where the capabilities are going long term.[00:22:35] Alessio: That's hard. I was asking you to do my job for you, like, man, that's a easy, that's a layout. Tell us all your investing[00:22:40] pieces. Yeah, yeah, yeah. I, I, I would say we, well we only really do mostly seed investing, so it's hard to invest in things that already work. Yeah. That fair. Are really late. So we try to, but, but we try to be at the cusp of like, you know, usually the investments we like to make, there's like really not that much market risk.[00:22:57] It's like if this works. Obviously people are gonna [00:23:00] use it, but like it's unclear whether or not it's gonna work. So that's kind of more what we skew towards. We try not to chase as many trends and I don't know, I, you know, I was a founder myself and sometimes I feel like it's easy to just jump in and do the thing that is hot, but like becoming a founder to do something that is like underappreciated or like doesn't yet work shows some level of like dread and self, like you, you actually really believe in the thing.[00:23:25] So that alone for me is like, kind of makes me skew more towards that. And you do a lot of angel investing too, so I'm curious how,[00:23:31] swyx: Yeah, but I don't regard, I don't have, I don't use, put, put that in my mental framework of things like I come at this much more as a content creator or market analyst of like, yeah, it, it really does matter to me what has part of market fit because.[00:23:45] People, I have to answer the question of what is working now When, when people ask me,[00:23:50] Jacob: do you feel like relative to the, the obviously the hype and discourse out there, like, you know, do you feel like there's a lot of things that have product market fit or like a few things, like where a few things? Yeah.[00:23:58] swyx: I was gonna say this, so I have a list [00:24:00] of like two years ago we, I wrote the Anatomy of autonomy posts where it was like the, the first, like what's going on in agents and, and and, and, and what is actually making money. Because I think there's a lot of gen I skeptics out there. They're all like, these, these things are toys.[00:24:13] They're, they're not unreliable. And you know, why, why, why you dedicating your life to these things. And I think for me, the party market fit bar at the time was a hundred million dollars, right? Like what use cases can reasonably fit a hundred million dollars. And at the time it was like co-pilot it was Jasper.[00:24:30] No longer, but mm-hmm. You know, in that category of like help you write. Yeah. Which I think, I think was, was helpful. And then and the cursor I think was on there as, as a, as, as, as like a coding agent. Plus plus. I think that list will just grow over time of like the form factors that we know to work, and then we can just adapt the form factors to a bunch of other things.[00:24:47] So like the, the one that's the most recently added to this is deep research.[00:24:52] misc: Yeah.[00:24:52] swyx: Right. Where anything that looks like a deep research whether it's a grok version, Gemini version, perplexity version, whatever. He has an investment [00:25:00] that that he likes called Brightwave that is basically deep research for finance.[00:25:02] Yeah. And anything where like all it is like long-term agent, agent reporting and it's starting to take more and more of the job away from you and, and just give you much more reason to report. I think it's going to work. And that has some PMFI think obviously has PMF like I, I would say. It's I, I went to this exercise of trying to handicap how much money open AI made from launching open ai deep research.[00:25:25] I think it's billions. Like the, the, the mo the the she upgrade from like $20 to 200. It has to be billions in the R off. Maybe not all them will stick around, but like that is some amount of PMF that is didn't they have to immediately drop it down[00:25:38] Jacob: to the $20 tier?[00:25:39] swyx: They expanded access. I don't, I wouldn't say, which I thought was[00:25:42] Jacob: really telling of the market.[00:25:43] Right. It's like where you have a you know, I think it's gonna be so interesting to see what they're actually able to get in that 200 or $2,000 tier, which we all think is, is, you know, has a ton of potential. But I thought it was fascinating. I don't know whether it was just to get more people exposure to it or the fact that like Google had a similar product obviously, and, and other folks did too.[00:25:59] But [00:26:00] it was really interesting how quickly they dropped it down.[00:26:02] swyx: I don't, I think that's just a more general policy of no matter what they have at the top tier, they always want to have smaller versions of that in the, in the lower tiers. Yeah. And just get people exposure to it. Just, yeah, just get exposure.[00:26:12] The brand of being first to market and, and like the default choice Yeah. Is paramount to open ai[00:26:18] Jacob: though. I thought that whole thing was fascinating 'cause Google had the first product, right? Yeah. And no, like, you know, I, we[00:26:24] swyx: interviewed them. I, I, I, straight up to their faces, I was like, opening, I mocked you.[00:26:28] And they were like, yeah, well, actually curious, what's[00:26:30] Jacob: it, this is totally off topic, but whatever. Like, what is it going to take for go? Google just released some great models like a, a few weeks ago. Like I feel like it's happening. The stuff they're shipping is really cool. It's happening. Yeah, but I, I, I also, I feel like at least in the, you know, broader discourse, it's still like a drop in the bucket relative to[00:26:45] swyx: Yeah.[00:26:45] I mean, I, I can riff on, on this. I, I, but I, I think it's happening. I think it takes some time, but I am, like my Gemini usage is up. Like, I, I use, I use it a lot more for anything from like summarizing YouTube videos to the [00:27:00] native image generation Yeah. That they just launched to like flash thinking.[00:27:02] So yeah, multi-mobile stuff's great. Yeah. I run you know, and I run like a daily sort of news recap called AI news that is, 99% generated by models, and I do a bake off between all the frontier models every day. And it's every day. Like does it switch? I manual? Yes, it does switch. And I, man, I manually do it.[00:27:18] And flash is, flash wins most days. So, so like, I think it's happening. I think I was thinking, I was thinking about tracking myself like number of opens of tragedy, g Bt versus Gemini. And at some point it will cross. I think that Gemini will be my main and, and it, it, I I like that will slowly happen for a bunch of people.[00:27:37] And, and, and then that will, that'll shift. I, I think that's, that's a really interesting for developers, this is a different question. Yeah. It's Google getting over itself of having Google Cloud versus Vertex versus AI studio, all these like five different brands, slowly consolidating it. It'll happen just slowly, I guess.[00:27:53] Alessio: Yeah.[00:27:54] Yeah. I, I mean, another good example is like you cannot use the thinking models in cursor. Yeah. And I know [00:28:00] Logan killed Patrick's that they're working on it, but I, I think there's all these small things where like if I cannot easily use it, I'm really not gonna go out of my way to do it. But I do agree that when you do use them, their models are, are great.[00:28:12] So yeah. They just need better, better bridges.[00:28:15] swyx: You had one of the questions in the prep.[00:28:16] Debating Public Companies: Google vs. Apple[00:28:16] swyx: What public company are you long and short and minus Google versus, versus Apple, like, long, short. That was also my[00:28:23] Jacob: combo. I, I feel like, yeah, I mean, it does feel like Google's really cooking right now.[00:28:26] swyx: Yeah. So okay, coming back to what has product market fit[00:28:29] Jacob: now,[00:28:29] swyx: now that we come[00:28:30] Jacob: back to my complete total sidetrack,[00:28:33] Customer Support and AI's Role[00:28:33] swyx: there's also customer support.[00:28:35] We were talking on, on the car about Decagon and Sierra, obviously Brett, Brett Taylor is founder of Sierra. And yeah, it seems like there's just this, these layers of agents that'll like, I think you just look at like the income statement or like the, the org chart of any large scaled company and you start picking them off one by one.[00:28:51] What like is interesting knowledge work? And they would just kind of eat. Things slowly from the outside in. Yeah, that makes sense.[00:28:57] Alessio: I, I mean, the episode with the, [00:29:00] with Brett, he's so passionate about developer tools and Yeah. He did not do a developer tools. We spent like two hours talking about developer tools and like, all, all of that stuff.[00:29:10] And it's like, I, they a customer support company, I'm like, man, that says something. You know what I mean? Yeah. It's like when you have somebody like him who can like, raise any amount of money from anybody to do anything. Yeah. To pick customer support as the market to go after while also being the chairman of OpenAI, like that shows you that like, these things have moats and have longstanding, like they're gonna stick around, you know?[00:29:32] Otherwise he's smarter than that. So yeah, that's a, that's a space where maybe initially, you know, I would've said, I don't know, it's like the most exciting thing to, to jump into, but then if you really look at the shape of like, how the workforce are structured and like how the cost centers of like the business really end up, especially for more consumer facing businesses, like a lot of it goes into customer support.[00:29:54] AI's Impact on Business Growth[00:29:54] Alessio: All the AI story of the last two years has been cost cutting. Yeah. I think now we're gonna switch more towards growth revenue. [00:30:00] Totally. You know, like you've seen Jensen, like last year, GTC was saying the more you buy, the more you save this year is that the more you buy, the more you make. So we're hot off the[00:30:08] Jacob: press.[00:30:10] We were there. We were there. Yeah. I do think that's one of the most interesting things about the, this first wave of apps where it's like almost the easiest thing that you could you could get real traction with was stuff that, you know, for lack of a better way to frame it, like so that people had already been comfortable outsourcing the BPOs or something and kind of implicitly said like, Hey, this is a cost center.[00:30:24] Like we are willing to take some performance cut for cost in the past. You know, the, the irony of that, or what I'm really curious to see how it plays out is, you know, you, you could imagine that is the area where price competition is going to be most fierce because it's already stuff that you know, that people have said, Hey, we don't need the like a hundred percent best version of that.[00:30:42] And I wonder, you know, this next wave of apps. May prove actually even more defensible as you get these capabilities that actually are, you know, increased top line or whatnot where you're like, you take ai, go to market, for example. Like you're, you'd pay like twice as much for something that brought, like, 'cause there's just a kind of very clean ROI story to it.[00:30:59] And so [00:31:00] I wonder ultimately whether the, like this next set of apps actually ends up being more interesting than the, than the first wave.[00:31:05] Alessio: Yeah,[00:31:05] Voice AI and Scheduling Solutions[00:31:05] Jordan: I think a lot of the voice AI ones are interesting too, because you don't need a hundred percent precision recall to actually, you know, have a great product.[00:31:12] And so for example, we looked into a bunch of you know, scheduling intake companies, for example, like home services, right? For electricians and stuff like that. Today they miss 50% of their calls. So even if the AI is only effective, say 75% of the time, yeah, it's crazy, right? So if it's effective 75% of the time, that's totally fine because that's still a ton of increased revenue for the customer, right?[00:31:32] And so you don't need that a hundred percent accuracy. Yeah. And so as the models. And the reliability of these agents are getting better is totally fine, because you're still getting a ton of value in the meantime.[00:31:41] swyx: Yeah. One, this is, I don't know how related this is, but I, one of my favorite meetings at it is related one of my favorite meetings at AI Engineer Summit, it is like, like I do these, this is our first one in New York, and I it is like met the different crew than, than you meet here.[00:31:55] Like everyone here is loves developer tools, loves infra over there. They're actually more interested in [00:32:00] applications. It's kind of cool. I met this like bootstrap team that, like, they're only doing appointment scheduling for vets. They, they, yeah. And like, they're like, this is a, this is an anomaly. We don't usually come to engineering summits 'cause we usually go to vet summits and like talk to the, they're, they're like, you know, they, they're, they're literally, I'm sure it's a[00:32:16] Jordan: massive pain point.[00:32:17] They're willing to pay a lot of money.[00:32:20] Alessio: Yeah. But, but, but this is like my point about saving versus making more, it's like if an electrician takes two x more calls, do they have the bandwidth? To actually do two X more in-house and they get higher. Well, yeah, exactly. That's the thing is like, I don't think today most businesses are like structured to just like overnight two, three x the band, you know?[00:32:38] I think that's like a startup thing. Like mo most businesses then you make an[00:32:42] swyx: electrician agent. Well, no, totally. That's how do you, how do you recruiting agent for electrician, for like[00:32:49] Alessio: electrician. Great. That's a good point. How do you do lambda school for electrician? I, it's hilarious.[00:32:53] Jacob: Whack-a-mole for the bottlenecks in these businesses.[00:32:55] Like as, oh, now we have a ton of demand. Like, cool. Like where do we go?[00:32:58] swyx: Yeah.[00:32:59] Exploring AI Applications in Various Fields[00:32:59] swyx: So just to [00:33:00] round out the, the this PMF thing I think this is relevant in a certain sense of, like, it's pretty obvious that the killer agents are coding agents, support agents, deep research, right? Roughly, right. We've covered all those three already.[00:33:10] Then, then, then you have to sort of be, turn to offense and go like, okay, what's next? And like, what, what about, I[00:33:16] Jacob: mean, I also just like summarization of, of voice and conversation, right? Yep. Absolutely. We actually had that on there. I[00:33:21] swyx: just, I didn't put it as agent. Because seems less agentic, you know? But yes, still, still a good AI use case.[00:33:26] That one I, I've seen I would mention granola and what's the other one? Monterey, I think a bridge was one wanted to mention. I was say bridge. Yeah, bridge. Okay. So I'll just, I'll call out what I had on my slides. Yeah. For, for the agent engineering thing. So it was screen sharing, which I think is actually kind of, kind of underrated.[00:33:42] Like people, like an AI watching you as you do your work and just like offering assistance outbound sales. So instead of support, just being more outbound hiring, you say[00:33:51] Jacob: outbound sales has brought a market fit?[00:33:53] swyx: No, it, it, it will, it's come out. Oh, on the comp. Yeah. I was totally agree with that. Yeah. Hiring like the recruiting side education, like the, [00:34:00] the sort of like personalized teaching, I think.[00:34:02] I'm kind of shocked we haven't seen more there. Yeah. Yeah. I don't know if that's like, like it's like Duolingo is the thing. Amigo.[00:34:08] Jacob: Yeah. I mean, speak in some of these like, you know,[00:34:10] swyx: speak, practice, yeah. Interesting. And then finance, I, there's, there's a ton of finance cases that we can talk about that and then personal ai, which we also had a little bit of that, but I think personal AI is a harder to monetize, but I, I think those would be like, what I would say is up and coming in terms of like, that's what I'm currently focusing on.[00:34:27] Jacob: I feel like this question's been asked a few different ways but I'm, I'm curious what you guys think it's like, is it like, if we just froze model capabilities today, like is there, you know, trillions of dollars of application value to be unlocked? Like, like AI education? Like if we just stopped today all model development, like with this current generation of models, we could probably build some pretty amazing education apps.[00:34:44] Or like, how much of this, how much of, of all this is like contingent upon just like, okay, people have had two years with GBT four and like, you know, I don't know, six months with the reasoning models, like how much is contingent upon it just being more time with these things versus like the models actually have to get better?[00:34:58] I dunno, it's a hard question, so I'm gonna just throw it [00:35:00] to you.[00:35:00] Alessio: Yeah. Well I think the societal thing, it's maybe harder, especially in education. You know, like, can you basically like Doge. The education system. Probably you should, but like, can you, I I think it's more of a human,[00:35:14] Jacob: but people pay for all sorts of like, get ahead things outside of class and you know, certainly in other countries there's a ton of consumer spend and education.[00:35:21] It feels like the market opportunity is there.[00:35:23] swyx: Yeah. And, and private education, I think yeah, public Public is a very different, yeah. One of my most interesting quests from last year was kind of reforming Singapore's education system to be more sort of AI native, just what you were doing on the side while you were Yes.[00:35:38] That's a great, that's a great side quest. My stated goal is for Singapore to be the first country that has Python as a first language, as a, as a national language. Anyway, so, but the, the, the, the defense, the pushback I got from Ministry of Education was that the teachers would be unprepared to do it.[00:35:53] So it's like, it was like the def the, like, the it was really interesting, like immediate pushback. Was that the defacto teachers union being like, [00:36:00] resistant to change and like, okay. It's that that's par for the course. Anyway, so not, not to, not to dwell too much on that, but like yeah, I mean, like, I, I think like education is one of those things that pe everyone, like has strong opinions on.[00:36:11] 'cause they all have kids, all be the education system. But like, I think it's gonna be like the, the domain specific, like, like speak like such a amazing example of like top down. Like, we will go through the idea maze and we'll go to Korea and teach them English. Like, it's like, what the hell? And I would love to see more examples of that.[00:36:29] Like, just like really focus, like no one tried to solve everything. Just, just do your thing really, really well[00:36:34] Defensibility in AI Applications[00:36:34] Jacob: on this trend of of, of difficult questions that come up. I'm gonna just ask you the one that my partners like to ask me every single Monday, which is how do you think about defensibility at the at the app layer?[00:36:41] Alessio: Oh[00:36:41] Jacob: yeah, that's great. Just gimme an answer. I can copy paste and just like, you know, have network effects. Auto, auto response.[00:36:47] swyx: Honestly like network effects. I think people don't prioritize those enough because they're trying to make the single player experience good. But then, then they neglect the [00:37:00] multiplayer experience.[00:37:00] I think one of the I always think about like load-bearing episodes, like, you know, as, as park that you do one a week and like, you know, some of those you don't really talk about ever again. And others you keep mentioning every single podcast. And one of the, this is obviously gonna be the last one. I think the recap episodes for us are pretty load-bearing.[00:37:15] Like we, we refer to them every three months or so. And like one of them I think for us is Chai for me is chai research, even though that wasn't like a super popular one among the broader community outside of Chai, the chai community, for those who don't know, chai Research is basically a character AI competitor.[00:37:32] Right. They were bootstraps, they were founded at the same time and they have out outlasted character of de facto. Right. It's funny, like I, I would love to ask Mil a bit more about like the whole character thing, but good luck getting past the Google copy. But like, so he, like, he, like he doesn't have his own models, basically he has his own network of people submitting models to be run.[00:37:54] And I think like. That is like short term going to be hurting him because he doesn't have [00:38:00] proprietary ip. But long term he has the network network effect to make him robust to any changes in the future. And I think, like I wanna see more of that where like he's basically looking himself as kind of a marketplace and he's identified the choke point, which is will be app or the, the sort of protocol layer that interfaces between the users and the model providers.[00:38:18] And then make sure that the money kind of flows through and that works. I, I wish that more AI builders or AI founders emphasize network effects. 'cause that that's the only thing that you're gonna have with the end of the day. Yeah. And like brand deeds into network effects you.[00:38:34] Jacob: Yeah, I guess you know, harder in, in the enterprise context.[00:38:36] Right. But I mean, I feel, it's funny, we do this exercise and I feel like we talk a lot about like, you know, obviously there's, you know kind of the velocity and the breadth you're able to kind of build of product surface area. There's just like the ability to become a brand in a space. Like, I'm shocked that even in like six, nine months, how an individual company can become synonymous with like an entire category.[00:38:52] And like, then they're in every room for customers and like all the other startups are like clawing their way to try and get in like one, you know, 20th of those rooms.[00:38:59] Jordan: There's a [00:39:00] bunch of categories where we talk about an IC and it's like, oh, pricing compression's gonna happen, not as defensible. And so ACVs are gonna go down over time.[00:39:08] In actuality, some of these, the ACVs have doubled, we've seen, and the reason for that is just, you know, people go to them and pay for that premium of being that brand.[00:39:16] Jacob: Yeah. I mean, one thing I'm struck by is there's been, there was such a head fake in the early days of, of AI apps where people were like, we want this amazing defensibility story, and then what's the easiest defensibility story?[00:39:24] It's like, oh, like. Totally unique data set or like train your own model or something. And I feel like that was just like a total head fake where I don't think that's actually useful at all. It's the much less, you sound much less articulate when you're like, well the defensibility here is like the thousand small things that this company does to make like the user experience design everything just like delightful and just like the speed at which they move to kind of both create a really broad product, but then also every three, six months when a new model comes out, it's kind of an existential event for like any company.[00:39:49] 'cause if you're not the first to like figure out how to use it, someone else will. Yeah. And so velocity really matters there. And it's funny in in, in kinda our internal discussions, we've been like, man, that sounds pretty similar to like how we thought about like application SaaS [00:40:00] companies. That there isn't some like revolutionary reason you don't sound like a genius when you're like, here's applications why application SaaS company A is so much better than B.[00:40:07] But it's like a lot of little things that compound over time.[00:40:10] Infrastructure and AI: Current Trends[00:40:10] Jacob: What about the infrastructure space, guys? Like I'm curious you know. What, how do you guys think about where the interesting categories are here today and you know, like where, where, where do you wanna see more startups or, or where do you think there are too many?[00:40:21] Alessio: Yeah. Yeah, we call it kind of the L-L-M-O-S. But I would say[00:40:24] swyx: not we, I mean Andre, Andre calls it LMOS[00:40:27] Alessio: Well, but yeah, we, well everyone else just copies whatever two. And Andre, the three of you call it the LMO. Well, we have just like four words of ai framework Yeah. Yeah. That we use. And LM Os is one of them, but yeah, I mean, code execution is one.[00:40:39] We've been banging the drum, everybody now knows where investors in E two B. Mm-hmm. Memory, you know, is one that we kind of touched on before. Super interesting search we talked about. I, I think those are more not traditional infra, not like the bare metal infra. It's more like the infra around the tools for agents model, you know?[00:40:57] Which I think is where a lot of the value is gonna [00:41:00] be. The security[00:41:00] swyx: ones. Yeah.[00:41:01] Alessio: Yeah. And cyber security. I mean there's so much to be done there. And it's more like basically any area where. AI is being used by the offense. AI needs to be applied on the defense side, like email security, you know, identity, like all these different things.[00:41:16] So we've been doing a lot there as well as, you know, how do you rethink things that used to be costly, like red teaming and maybe used to be a checkbox in the past Today they can be actually helpful. Yeah. To make you secure your app. And there's this whole idea of like, semantics, right? That not the models can be good at.[00:41:32] You know, in the past everything is about syntax. It's kind of like very basic, you know, constraint rules. I think now you can start to infer semantics from things that are beyond just like simple recognition to like understanding why certain things are happening a certain way. So in the security space, we're seeing that with binary inspection, for example.[00:41:51] Like there's kinda like the syntax, but then there are like semantics of like understanding what is the scope overall really trying to do. Even though this [00:42:00] individual syntax, it's like seeing something specific. Not to get too technical, but yeah, I, I think infra overall, it's like a super interesting place if you're making use of the model, if you're just, I'm less bullish.[00:42:13] Not, not that it's not a great business, but I think it's a very capital intensive business, which is like serving the models. Mm-hmm. Yeah. I think that infra is like, great people will make money, but yeah. I, I, I don't think there's as much of a interest from, from us at[00:42:25] Jordan: least. Yeah. How, how do you guys think about what OpenAI and the big research labs will encompass as part of the developer and infra category?[00:42:31] Yeah.[00:42:31] Alessio: That, that's why I, I would say I search is the first example of one of the things we used to mention on, you know, we had X on the podcast and perplexity obviously as a, as an API. The basic idea[00:42:44] swyx: is if you go into like the chat GBT custom GPT builder, like what are the check boxes? Each of them is a startup.[00:42:50] Alessio: Yeah. And, and now they're also APIs. So now search is also an a p, we will see what the adoption is. There's the, you know, in traditional infra, like everybody wants to be [00:43:00] multi-cloud, so maybe we'll see the same Where change GPD search or open AI search. API is like, great with the open AI models because you get it all bundled in, but their price is very high.[00:43:11] If you compare it to like, you know, XI think is like five times the, the price for the same amount of research, which makes sense if you have a big open AI contract. But maybe if you're just like pick and best in breed, you wanna compare different ones. Yeah. Yeah, they don't have a code execution one.[00:43:26] I'm sure they'll release one soon. So they wanna own that too, but yeah. Same question we were talking about before, right? Did they wanna be an API company or a product company? Do you make more money building Tri g BT search or selling search? API?[00:43:38] swyx: Yeah. The, the broader lesson, instead of like going, we did applications just now.[00:43:42] And then what do you think is interesting infrastructure? Like it's not 50 50, it's not like equal weighted, like it, it's just very clearly the application layer has like. Been way more interesting. Like yes, there, there's interesting in infrastructure plays and I even want to like push back on like the, the, the whole GPU serving thing because like together [00:44:00] AI is doing well, fireworks, I mean I was, that worked.[00:44:02] Alessio: It's like data[00:44:02] Jacob: centers[00:44:03] Alessio: and inference[00:44:03] Jacob: providers,[00:44:04] Alessio: the,[00:44:04] swyx: you know,[00:44:04] Alessio: I think it's not like the capital[00:44:06] swyx: Oh, I see.[00:44:07] Alessio: I for, for again, capital efficiency. Yeah. Much larger funds. So you, I'm sure you have GPU clouds. Yeah.[00:44:13] swyx: Yeah. So that's, that's, that is one thing I have been learning in, in that you know, I think I have historically had dev tools and infra bias and so has he, and we've had to learn that applications actually are very interesting and also maybe kind of the killer application of models in a sense that you can charge for utility and not for cost.[00:44:33] Right? Which, where like most infrastructure reduces to cost plus. Yeah. Right. So, and like, that's not where you wanna be for ai. So that's, that's interesting for, for me I thought it would be interesting for me to be the only non VC in the room to be saying what is not investible. 'cause like then I then, you know, you can I, I won't be canceled for saying like, your, your whole category is, we have a great thing where like, this thing's[00:44:54] Jacob: not investible and then like three months later we're desperately chasing.[00:44:56] Exactly. Exactly. So you don't wanna be on a record space changes so [00:45:00] fast. It's like you gotta, every opinion you hold, you have to like, hold it quite loosely. Yeah.[00:45:02] swyx: I'm happy to be wrong in public, you know, I think that's how you learn the most, right? Yeah. So like, fine tuning companys is something I struggled with and still, like, I don't see how this becomes a big thing.[00:45:12] Like you kind of have to wrap it up in a broader, ser broader enterprise AI company, like services company, like a writer, AI where like they will find you and it's part of the overall offering. Mm-hmm. But like, that's not where you spike. Yeah, it's kind of interesting. And then I, I'll, I'll just kind of AI DevOps and like, there's a lot of AI SRE out there seems like.[00:45:32] There's a lot of data out there that that should be able to be plugged into your code base or, or, or your app to it's self-heal or whatever. It's just, I don't know if that's like, been a thing yet. And you guys can correct me if you're, if I'm wrong. And then the, the last thing I'll mention is voice realtime infra again, like very interesting, very, very hot.[00:45:49] But again, how big is it? Those are the, the main three that I'm thinking about for things I'm struggling with.[00:45:54] Jordan: Yeah. I guess a couple comments on the A-I-S-R-E side. I actually disagree with that one. Yeah. I think that the [00:46:00] reason they haven't sort of taken off yet is because the tech is just not there quite yet.[00:46:04] And so it goes back to the earlier question, do we think about investing towards where the companies will be when the models improve versus now? I think that's going to be, in short term we'll get there, but it's just not there just yet. But I think it's an interesting opportunity overall.[00:46:18] swyx: Yeah. It's my pushback to you is, well it's monitoring a lot of logs, right?[00:46:22] Yeah. And it's basically anomaly detection rather than. Like there's, there's a whole bunch of like stuff that can happen after you detect the anomaly, but it's really just an anomaly detection. And we've always had that, you know, like it's, this is like not a Transformers LLM use case. This is just regular anomaly detection.[00:46:38] Jordan: It's more in terms of like, it's not going to be an autonomous SRE for a while. Yeah. And so the question is how, how much can the latest sort of AI advancements increase the efficacy of going, bringing your MTTR
Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs. Top guests: Noam Shazeer, Bob McGrew, Noam Brown, Dylan Patel, Percy Liang, David Luan https://www.latent.space/p/unsupervised-learning Timestamps 00:00 Introduction and Excitement for Collaboration 00:27 Reflecting on Surprises in AI Over the Past Year 01:44 Open Source Models and Their Adoption 06:01 The Rise of GPT Wrappers 06:55 AI Builders and Low-Code Platforms 09:35 Overhyped and Underhyped AI Trends 22:17 Product Market Fit in AI 28:23 Google's Current Momentum 28:33 Customer Support and AI 29:54 AI's Impact on Cost and Growth 31:05 Voice AI and Scheduling 32:59 Emerging AI Applications 34:12 Education and AI 36:34 Defensibility in AI Applications 40:10 Infrastructure and AI 47:08 Challenges and Future of AI 52:15 Quick Fire Round and Closing Remarks Chapters 00:00:00 Introduction and Collab Excitement 00:00:58 Open Source and Model Adoption 00:01:58 Enterprise Use of Open Source Models 00:02:57 The Competitive Edge of Closed Source Models 00:03:56 DeepSea and Open Source Model Releases 00:04:54 Market Narrative and DeepSea Impact 00:05:53 AI Engineering and GPT Wrappers 00:06:53 AI Builders and Low-Code Platforms 00:07:50 Innovating Beyond Existing Paradigms 00:08:50 Apple and AI Product Development 00:09:48 Overhyped and Underhyped AI Trends 00:10:46 Frameworks and Protocols in AI Development 00:11:45 Emerging Opportunities in AI 00:12:44 Stateful AI and Memory Innovation 00:13:44 Challenges with Memory in AI Agents 00:14:44 The Future of Model Training Companies 00:15:44 Specialized Use Cases for AI Models 00:16:44 Vertical Models vs General Purpose Models 00:17:42 General Purpose vs Domain-Specific Models 00:18:42 Reflections on Model Companies 00:19:39 Model Companies Entering Product Space 00:20:38 Competition in AI Model and Product Sectors 00:21:35 Coding Agents and Market Dynamics 00:22:35 Defensibility in AI Applications 00:23:35 Investing in Underappreciated AI Ventures 00:24:32 Analyzing Market Fit in AI 00:25:31 AI Applications with Product Market Fit 00:26:31 OpenAI's Impact on the Market 00:27:31 Google and OpenAI Competition 00:28:31 Exploring Google's Advancements 00:29:29 Customer Support and AI Applications 00:30:27 The Future of AI in Customer Support 00:31:26 Cost-Cutting vs Growth in AI 00:32:23 Voice AI and Real-World Applications 00:33:23 Scaling AI Applications for Demand 00:34:22 Summarization and Conversational AI 00:35:20 Future AI Use Cases and Market Fit 00:36:20 AI Education and Model Capabilities 00:37:17 Reforming Education with AI 00:38:15 Defensibility in AI Apps 00:39:13 Network Effects and AI 00:40:12 AI Brand and Market Positioning 00:41:11 AI Application Defensibility 00:42:09 LLM OS and AI Infrastructure 00:43:06 Security and AI Application 00:44:06 OpenAI's Role in AI Infrastructure 00:45:02 The Balance of AI Applications and Infrastructure 00:46:02 Capital Efficiency in AI Infrastructure 00:47:01 Challenges in AI DevOps and Infrastructure 00:47:59 AI SRE and Monitoring 00:48:59 Scaling AI and Hardware Challenges 00:49:58 Reliability and Compute in AI 00:50:57 Nvidia's Dominance and AI Hardware 00:51:57 Emerging Competition in AI Silicon 00:52:54 Agent Authentication Challenges 00:53:53 Dream Podcast Guests 00:54:51 Favorite News Sources and Startups 00:55:50 The Value of In-Person Conversations 00:56:50 Private vs Public AI Discourse 00:57:48 Latent Space and Podcasting 00:58:46 Conclusion and Final Thoughts
On this episode of Unsupervised Learning, Razib talks to Graeme Wood. Wood is a staff writer at The Atlantic, where he usually covers geopolitics and international affairs. His work ranges from a profile of Richard Spencer, the American white nationalist public figure with whom he went to high school with, to the Islamic State. He is the author of The Way of the Strangers: Encounters with the Islamic State. Wood grew up in Dallas, Texas, and graduated from Harvard College. He also studied at the American University in Cairo, Indiana University and Deep Springs College. Today Razib talks to Wood about his piece in The Atlantic, Germany's Anti-Extremist Firewall Is Collapsing. Wood addresses the economic malaise of contemporary Germany, in particular, the former East Germany, and how that is impacting the national cultural climate. More concretely, they consider why the right-wing Alternative For Deutschland (AFD) party is so popular, and its transformation from an anti-EU party to an anti-migrant party. Wood emphasizes that Germany has become a highly polarized society when it comes to ethnicities, with very cosmopolitan cities, but small towns in rural eastern provinces where he recalls feeling like possibly the only non-white face at the local beer hall (his father is a white American while his mother is ethnically Chinese). Razib muses whether German multiculturalism as an ideology has allowed for more, not less racism, while Wood reflects on his multi-decade experience visiting the nation as an outsider.
Two weeks ago, OpenAI released its set of tools to help developers build agentic systems. Today on Unsupervised Learning, Nikunj Handa (Product Lead) and Steve Coffey (Eng Lead) answer some of the biggest questions around how developers should be thinking about building in the agentic paradigm in 2025. [0:00] Intro[0:53] OpenAI's Vision for Consumer Interaction[4:51] Building Multi-Agent Systems for Business Solutions[6:53] Challenges and Innovations in AI Fine-Tuning[13:20] Exploring Computer Use Cases and Applications[17:20] Advanced Use Cases and Developer Insights[25:29] Challenges with Context Storage and Chat Completions[26:09] Introducing the Responses API and MCP[27:16] AI Infrastructure Companies and Their Role[29:35] Building the Tools Ecosystem[30:17] Exploring Computer Use Models[31:47] The Future of AI and Developer Tools[38:36] Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint
On the latest episode of Unsupervised Learning, Jacob is joined by two of the most influential minds in AI today.
As tariff drama continues to heat up, Ryan Petersen, CEO of Flexport (one of the hottest freight forwarders in the world) came on the show to unpack the impact. Ryan also dives deep into the hidden world of US shipping, opportunities for AI automation in logistics, reflections on building Flexport, and some supply chain conspiracy theories.(00:00) Intro(01:16) Flexport's Mission and Operations(02:28) Impact of Tariffs on Businesses(05:15) Navigating New Duties and Regulations(09:19) Flexport's Strategic Response(14:39) Challenges in U.S. Shipping Policies(28:21) Union Influence on Port Automation(40:35) National Security and Trade Negotiations(41:06) Tariffs and Business Planning Challenges(42:16) Investment Opportunities in Ports(44:02) Port Automation and AI Integration(45:09) Flexport's Big Tech Launch(47:02) AI's Role in Supply Chain Management(53:14) Digitizing Freight Contracts(58:18) Lessons from Flexport's Growth(01:09:13) Conspiracy Theories in Shipping Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
On this episode of Unsupervised Learning, friend of the podcast, Charles Murray returns to chat with Razib again. Murray has been a public intellectual and scholar since the 1970's. He is the author of Losing Ground, The Bell Curve, Human Accomplishment, Real Education, Coming Apart and What it means to be a libertarian and Human Diversity, among others. Born in 1943 in Newton, Iowa, Murray has a BA from Harvard, an MA and PhD from MIT, and did a 1960's stint in the Peace Corps in Thailand. He has held positions at the American Institutions for Research, the Manhattan Institute and the American Enterprise Institute. More than four years after their last conversation, and seven years after his official retirement, Murray reflects with Razib on where he sees America going in the next decade, and what has surprised him about the last 25 years. Razib asks what it is like to be a long-standing “Never Trump conservative” and a libertarian in Trump's populist America. They also discuss the end of the “awokening” that began in the mid-2010s, and whether Murray's long exile from notice and acknowledgement from mainstream opinion-leaders and tastemakers is at an end. Murray also addresses the ideological fractures he sees on the right, and how America will deal with the last generation of mass immigration that has altered the US' demographic balance. They also discuss how taboo it still is to talk about group differences in cognitive performance, and whether America will be able to face the reality of demographics and the social consequences thereof in the 21st century.
On today's Unsupervised Learning, Mike Schroepfer (ex-CTO of Meta and founder of Gigascale Capital) reveals why energy is a key bottleneck holding AI progress back. Mike discusses how we can scale energy production to democratize AI globally and explores AI's role in climate change. He also reflects on a decade as Meta's CTO and how AI coding is transforming the CTO role. Finally, he offers predictions on the future of AI developer tools, VR, and open-source models. [0:00] Intro[0:43] AI's Role in Energy and Climate Change[4:32] Innovative Energy Solutions[14:50] Open Source and AI Development[22:35] Challenges in Chip Design[24:04] Balancing Data Center Capacity[25:55] The Future of VR and AI Integration[29:41] AI's Role in Climate Solutions[31:41] AI in Material Science and Beyond[34:31] Personal AI Assistants and Their Impact[38:47] Reflections on AI and Future Predictions[41:23] Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint
On this episode of Unsupervised Learning, Razib talks to Titus Techera, a Romanian living in Budapest, but commenting extensively on American and European culture. He is the Executive Director of the American Cinema Foundation, International Coordinator of the National Conservatism Conference and is a primary contributor to the Substack PostModernConservative. Techera also hosts a podcast for the American Cinema Foundation. Razib first talks to Techera about the 2024 Romanian presidential election that was overturned by the courts over accusations of Russian interference. Techera explains the social and cultural context of the candidate initially declared victorious against a backdrop of Romanian society's typical stock characters. Techera also discusses the tension between having a nation-state with a distinctive character and becoming part of the broader EU project that is attempting to forge unity across 27 countries. He then addresses what a “Postmodern Conservative” is in the context of the arts. Perhaps most importantly, PostModern Conservatives take the 20th century and the modernist period seriously; they are not simply reactionaries who want to return to the 19th century. Conservatives who value the arts and culture cannot simply roll the tape back; they have to engage with what has come before. Razib and Techera also consider how inferences from the sciences, like the rejection of the “blank slate,” might influence the arts. They also discuss their disagreements about the latest Dune films, Techera prefers David Lynch's attempt to adapt the book in 1984 to Denis Villeneuve's 2021 version.
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. The company operates a 24/7 public ride-hail service and provides over 150,000 trips each week across San Francisco, Los Angeles, Phoenix, and Austin, making mobility more accessible, sustainable, and safer for everyone.In this week's episode of Unsupervised Learning, we dive deep into the frontier where AI meets hardware — and there's no better guide than Vincent Vanhoucke, Distinguished Engineer at Waymo and former Head of Robotics at DeepMind. [0:00] Intro[0:50] Waymo's Technological Evolution[2:40] The Role of LLMs in Autonomous Driving[6:02] Vincent's Journey to Waymo[9:17] Challenges in Autonomous Driving[11:58] Simulation and World Models[27:44] Future Milestones and Expansion[30:10] Broader Robotics and AI[36:12] Future of General Robotics Models[38:14] Hardware vs. Software Approaches in Robotics[40:19] Challenges in Robotic Data Acquisition[40:38] Simulation vs. Real-World Data in Robotics[43:02] Human-Robot Interaction for Data Collection[45:03] Advancements in Multimodal Models[47:08] Unanswered Questions in Robotics[52:02] Reasoning Capabilities in AI[54:57] Future of Robotics and AI[1:00:51] Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint
Yamini Rangan, CEO of HubSpot (a $40 billion leader in the CRM space) shares how AI is transforming go-to-market strategies, the key lessons Yamini has learned as a first-time CEO, and the sales tactics she's mastered.She also discusses the challenges of navigating major business pivots, including how companies can successfully transition into AI-first businesses and what it takes to stay competitive in an evolving landscape.(00:00) Intro(00:56) Yamini Rangan's Background and Career Journey(02:33) Joining HubSpot and Early Challenges(03:49) Transition to CEO and Leadership Insights(07:33) Strategic Planning and Long-Term Vision(15:15) AI Transformation and Product Innovation(18:57) AI's Impact on CRM and Future Prospects(28:51) Content Strategy and Customer Engagement(37:34) Contextual AI Features for Better Usage(38:13) Human Expectations and AI(39:36) AI in Daily Productivity(42:54) The Art and Science of Sales(51:05) The Role of Curiosity and Resilience in Sales(53:23) Evolving Company Culture(55:27) Leadership Style and Management Lessons(58:27) Scaling Startups: Lessons from Workday(01:02:54) The Future of AI and Incumbents(01:14:10) Concluding Thoughts Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Andy is the founder of Artisanal Ventures and Artisanal Talent, one of Silicon Valley's top search firms. He's helped build leadership teams at companies like Databricks, Snowflake, Confluent, Abnormal Security, AcuityMD, and many more.In this episode, he shares…- How founders can differentiate in the talent war today- Maximizing the success rate of executive hires- Why interviews are a waste of time- The best ways to do references- How to choose the right search firm& more (00:00) Intro(02:02) Andy Price's Background and Career Journey(03:20) The Role of Founders in Hiring(04:32) Challenges in Early Stage Hiring(10:08) Importance of Venture Capital Brand(12:14) Effective Search Processes and Candidate Evaluation(23:27) Backchannel References and Networking(29:10) Identifying Key Players in Sales Growth(29:44) The Importance of Minimal Disruption(30:40) Effective Founder-Executive Relationships(30:57) The Role of Soak Time in Differentiation(31:52) Hiring Strategies for Rapid Growth(33:42) Common Failure Modes in Hiring(34:32) Aligning Founder and Executive Expectations(38:26) Building a Strong Talent Acquisition Team(40:51) The Talent Wars and Hiring Choke Points(44:24) Balancing Skill Sets and Company Culture(47:29) Evaluating and Upleveling Team Members(49:59) The Importance of Forecasting and Planning(51:34) Handling Executive Transitions Smoothly(59:09) The Art of Firing: Best Practices(59:32) Handling Employee Terminations with Dignity(01:02:19) Negotiating with Candidates: Tips and Tricks(01:06:31) Understanding Compensation Trends(01:08:18) Avoiding Common Founder Mistakes(01:11:28) Scaling Operations in Hypergrowth(01:15:00) Navigating the Current VC and Talent Ecosystem(01:23:34) The Importance of Specialized Search Firms(01:28:03) Adapting to the New Market Realities(01:30:46) Final Thoughts and Reflections Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
The recent controversy between WordPress and WP Engine put Matt Mullenweg (Co-Founder of WordPress, CEO of Automattic) under intense online scrutiny. In our conversation, he shared lessons from the controversy and managing through crisis, as well as this thoughts on the future of open source AI and more.(00:00) Intro(01:17) Controversy with WP Engine(03:36) Understanding Open Source and Trademarks(04:36) Automattic's Role and Contributions(08:26) Navigating Legal Battles and Community Relations(18:27) Leadership and Personal Resilience(21:49) The Impact of Social Media on CEOs(31:22) Future Outlook and Reflections(32:42) Exploring the Quinn Model and Open Source Innovations(33:17) The Evolution of AI Interfaces and User Interactions(35:36) AI as a Writing and Coding Partner(38:07) The Power of Open Source in AI Development(40:00) Commoditizing Complements: A Business Strategy(41:39) The Battle with Shopify and Open Source Models(42:33) The Impact of Open Source on Market Dynamics(43:55) USB-C Transition and Gadget Recommendations(47:53) The Benefits of Sabbaticals(53:34) The Future of WordPress and Automattic(59:12) Employee Ownership and Liquidity Programs(01:04:33) Conclusion and Final Thoughts Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
On this episode of Unsupervised Learning, third-time guest John Hawks returns after two years to discuss what we've learned in paleoanthropology since he and Razib last talked. Hawks obtained his PhD under Milford H. Wolpoff, and is currently a professor in anthropology at the University of Wisconsin. Hawks has also co-authored Almost Human: The Astonishing Tale of Homo naledi and the Discovery That Changed Our Human Story and Cave of Bones: A True Story of Discovery, Adventure, and Human Origins with Lee Berger. Razib first presses Hawks on what we know about archaic human admixture into modern populations, and particularly what we've learned about Denisovans. They discuss how many Denisovan populations there were, how many Denisovan fossil remains we have, and why it has taken so long for researchers to assign a species name to this lineage of humans. Hawks also address the puzzle of the phenomenon of why there are at least two pygmy hominin populations in Southeast Asia. Perhaps humans too are subject to island dwarfism like many other mammals? Also, Razib wonders why Southeast Asia was home to such a startling variety of humans at once prior to the arrival of modern populations. They discuss all of this in light of the framework of Out-of-Africa, the recent spread of anatomically modern humans outside of Africa. Razib questions how robust this model is today given our understanding of modern humans' extensive and repeated interactions with both Neanderthals and Denisovans. Finally, Hawks covers some controversies over fossils being sent into space that roiled the archaeological world last year.
Two weeks after Oura CEO Tom Hale started tracking and improving his sleep, he recalls, “It was like walking out of a black-and-white movie into a 4K technicolor movie… I've been missing this for 30 years.” The experience of feeling 20 again every day inspired him to apply for the Oura CEO role. Now, nearly 3 years into the job, he sat down to discuss what all founders and CEOs should consider when it comes to avoiding burnout and maximizing health and productivity. We covered his experiences with tools like continuous glucose monitoring, his thoughts on the future of wearables, and how AI insights will help us take better control of our health. (00:00) Intro(00:53) The Journey with Oura Ring(01:47) Sleep Optimization and Health Trends(05:06) Behavioral Changes for Better Sleep(09:33) Tom Hale's Professional Background(12:47) Challenges and Opportunities at Oura(22:50) The Importance of Sleep(26:05) Health Benefits of Quality Sleep(28:38) Oura's Unique Position in the Market(36:47) Consumer Choice and Healthcare Disruption(40:59) Unexpected Insights from HSA and FSA Spending(41:36) The Future of Insurance and Wearable Data(44:40) Preventative Care and Employer Incentives(47:21) The Impact of Small Choices on Health(48:52) Artificial Intelligence in Healthcare(54:08) The Role of Continuous Glucose Monitors(59:50) Expanding Oura's Market and Product Strategy(01:12:04) Navigating Leadership and Company Culture(01:19:22) Future Opportunities and Global Expansion(01:23:43) Closing Remarks and Reflections Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
Jay Chaudhry is the definition of a self-starter. Born in the Himalayas with no running water or electricity until high school, Jay has now scaled Zscaler to a $30B public company. He and his wife went all in many times over—betting their life savings and quitting their jobs to launch their first venture, Secure IT, which became a huge success. Jay then parlayed that into building multiple more self-funded, successful cybersecurity companies before founding Zscaler.In this episode, Jay shares his playbook for building disruptive companies, how he picks a market, and insights on using AI to combat modern breaches—plus his perspectives on life, family, and money. Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
In this episode, Marc Benioff (CEO, Salesforce) responds to Satya Nadella's recent predictions and shares his thoughts on the current reality of Agi. He dives into the rise of digital labor, the multi-trillion-dollar potential of agentic technology, and what the future split between software and agentic revenue might look like. Marc also discusses why CEOs need to stay grounded in delivering actionable solutions, and he emphasizes the moral obligation businesses have to retrain employees and invest in communities as AI continues to evolve.(00:00) Intro(01:45) Salesforce's AI Impact on Business(03:03) The Future of Digital Labor(05:28) Agentic AI and Customer Success(07:42) Salesforce's Competitive Edge(11:48) Marc Benioff's Response to Satya Nadella(14:16) The Role of AI in Enterprise Software(20:14) The Balance of AI and Human Labor(28:34) Salesforce's Philanthropic Efforts(36:24) The Future of AI and Regulation(40:24) Conclusion and Farewell Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
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