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Unsupervised Learning with Jacob Effron

by Redpoint Ventures

We probe the sharpest minds in AI in search for the truth about what’s real today, what will be real in the future and what it all means for businesses and the world. If you’re a builder, researcher or investor navigating the AI world, this podcast will help you deconstruct and understand the most important breakthroughs and see a clearer picture of reality. Follow this show and consider enabling notifications to stay up to date on our latest episodes.

Unsupervised Learning is a podcast by Redpoint Ventures, an early-stage venture capital fund that has invested in companies like Snowflake, Stripe, and Mistral.

Hosted by Redpoint investor Jacob Effron alongside Patrick Chase, Jordan Segall and Erica Brescia.

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  • 21 episodes
  • Avg 1 hr 2 min
  • English

Episodes 21

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  • August 3 · 1 hr 12 min

    AI Vibe Check: Chinese Open Models, Distillation & The Hugging Face Breach

    In this installment of their recurring roundtable, Jacob, Ari, and Rob dig into the accelerating Chinese open-source model race, debating whether Kimi K3 has actually closed the gap with the US frontier or just looks like it has, and whether distillation fully explains China's progress. That opens into the messier fight over open-weight models generally, with the group dissecting the backlash against Anthropic's stance and asking whether powerful open models are inherently dangerous or actually

  • #92
    July 31 · 1 hr 4 min

    Ep 92: xAI Co-Founder Unpacks the Future of Model Development

    Igor Babuschkin, co-founder of River AI and formerly a co-founder of xAI, joins to unpack a career that spans nearly every major AI lab: he led the StarCraft and AlphaCode work at DeepMind, joined OpenAI's reasoning team years before o1 shipped, and co-founded xAI, where he helped stand up the Colossus data center in roughly 120 days and reflects candidly on what it's actually like working with Elon Musk day to day, plus what the Cursor acquisition actually unlocked for Grok's coding models. He

  • #91
    July 16 · 1 hr 13 min

    Ep 91: Top AI Analyst Unpacks Today's AI Hype Cycle

    Benedict Evans, one of tech's most widely-read analysts, joins Jacob Effron. The conversation centers on Benedict's core thesis that comparing AI's scale to past platform shifts (the internet, mobile, PCs) is analytically useless, and that the more productive move is studying how those previous technologies actually evolved economically to reason about where AI's value will accrue. He argues the one genuine difference this time is that we don't know AI's physical or scientific limits, unlike pas

  • #90
    July 9 · 50 min

    Ep 90: AI Pioneer Jürgen Schmidhuber on the State of AI Today

    Dr. Jürgen Schmidhuber, a renowned scientist and AI researcher widely regarded as one of the pioneers in the field, originated key ideas behind today's transformers, LSTMs, and recursive self-improvement through his lab's work. He argues that true AGI remains bottlenecked by physical hardware, that today's AI data center investments are headed for a correction as open-source keeps pace with closed labs, and that the path to general intelligence runs through artificial curiosity and self-generate

  • June 12 · 1 hr 6 min

    AI Vibe Check: Lab Wars, Why APIs Might Vanish & Future Predictions

    Six months after their last roundup, Jacob sits down with Ari Morcos (Datology AI CEO, former Meta AI researcher) and Rob Toews (Radical Ventures partner, Forbes AI columnist) to take stock of an AI landscape that has shifted dramatically: coding agents crossing the long-time-horizon threshold has turned engineers into managers of agents, near-frontier open weight AI looks like it may be disappearing as Meta and the Chinese labs pull back, and Anthropic's restrictions on its newly released Fable

  • #89
    June 3 · 1 hr 13 min

    Ep 89: AI Research Legend’s Honest Assessment of Where We Are

    This episode with Lukasz Kaiser, co-author of the seminal "Attention Is All You Need" transformer paper and former researcher at both Google Brain and OpenAI, is a wide-ranging conversation about the fundamental limits of current AI architectures and whether transformers will continue to dominate or eventually give way to something new. Lukasz brings a rare dual perspective: deep belief in how far the current paradigm has taken us (he's an enthusiastic daily Codex user who's seen 10x productivit

  • #88
    June 1 · 56 min

    Ep 88: Unpacking DeepMind's Quest for SuperIntelligence with Demis Hassabis' Biographer

    Sebastian Mallaby spent three years and 30+ hours interviewing Demis Hassabis in the back of a British pub to write The Infinity Machine, and the conversation uses that reporting to surface the most underexplored figure in AI. Demis founded the original AI lab in 2010, won a Nobel Prize, runs models that consistently top the leaderboards, and yet remains so unrecognized that Sebastian's own publisher worried no one would buy a book with his face on the cover. The throughline is a paradox: Demis

  • #87
    May 22 · 59 min

    Ep 87: Gemini Co-Lead on World Models, RL's Next Domains & Continual Learning

    Oriol Vinyals, VP of Research at Google DeepMind and co-lead of the Gemini program, joins Jacob the day after Google I/O to unpack the research underpinning Google's latest announcements and where frontier AI is heading. The conversation moves from world models (why Google has uniquely bet on them as a path to AGI, what the "GPT moment" for video and images would look like, and how they connect to robotics and simulation) to agents (the Spark release, why the system and model need to be optimize

  • #86
    May 15 · 1 hr 21 min

    Ep 86: Yann LeCun on Leaving Meta, Breaking The LLM Paradigm, & Why Hinton is Wrong

    Yann LeCun, Turing Award winner and former Chief AI Scientist at Meta, joins Jacob Effron. The conversation centers on Yann's contrarian thesis that LLMs are a dead-end on the path to human-level intelligence, despite being useful products — because they can't predict the consequences of their actions, can't plan, and fundamentally can't model the messy, high-dimensional real world. He unpacks his alternative architecture, JEPA (Joint Embedding Predictive Architecture), which learns abstract rep

  • #85
    April 23 · 54 min

    Ep 85: Has AI Infra Stabilized, FM Vibe Shift, & What's Next for Coding Agents

    This episode is a wide-ranging conversation between Jacob and Swyx (Shawn Wang), an AI engineer, podcaster, and now operator at Cognition, who sits at a uniquely informed intersection of builder, investor, and community organizer in the AI world. The two cover the current state of the AI engineering zeitgeist: from the stabilization of agent infrastructure and the surprising stickiness of Claude Code, to the competitive dynamics of the AI coding wars, the rise of open models, the threat to tradi

  • #84
    April 9 · 58 min

    Ep 84: OpenAI’s Chief Scientist on Continual Learning Hype, RL Beyond Code, & Future Alignment Directions

    Jakub Pachocki, OpenAI's Chief Scientist, sits down with Jacob to cover the full arc of where AI research stands today and where it's headed. The conversation spans the explosive growth of coding agents and what it signals about near-term AI capability, the use of math and physics benchmarks as proxies for general intelligence, how reinforcement learning is being extended beyond easily-verified domains toward longer-horizon tasks, and what it means to run a research organization at the precise m

  • #83
    April 2 · 54 min

    Ep 83: Owning the System of Record, AI-Native Org Charts, & Why ITSM is The Most Vulnerable Legacy Category

    Serval is one of the fastest-growing AI-native enterprise software companies right now, and this episode is a rare inside look at the deliberate architectural, go-to-market, and talent decisions behind that growth. Jake Stauch breaks down why he made the contrarian bet to build a full system of record rather than layer on top of existing tools, why ITSM is more vulnerable to AI disruption than CRM, ERP, or HRIS, and how Serval is winning Fortune 500 deals against a $14B incumbent with a fraction

  • #82
    March 11 · 54 min

    Ep 82: Behind Legora's $550M Raise, Model Competition, Doubling Revenue Every Quarter, & US Expansion

    Max Jungestål, CEO of Legora, joins Jacob Effron and Logan Bartlett to discuss the company's $550M Series D and share a candid account of what building an AI-native company at speed actually looks like from the inside. Max argues that the AI application layer requires a fundamentally different operating model than traditional SaaS, one built on low ego, constant reinvention, and a willingness to watch nine months of work get washed away by a model update. He walks through how step-function impr

  • #81
    January 29 · 1 hr 2 min

    Ep 81: Ex-OpenAI Researcher On Why He Left, His Honest AGI Timeline, & The Limits of Scaling RL

    This episode features Jerry Tworek, a key architect behind OpenAI's breakthrough reasoning models (o1, o3) and Codex, discussing the current state and future of AI. Jerry explores the real limits and promise of scaling pre-training and reinforcement learning, arguing that while these paradigms deliver predictable improvements, they're fundamentally constrained by data availability and struggle with generalization beyond their training objectives. He reveals his updated belief that continual lear

  • Dec 18, 2025 · 1 hr 18 min

    AI Vibe Check: The Actual Bottleneck In Research, SSI’s Mystique, & Spicy 2026 Predictions

    Ari Morcos and Rob Toews return for their spiciest conversation yet. Fresh from NeurIPS, they debate whether models are truly plateauing or if we're just myopically focused on LLMs while breakthroughs happen in other modalities. They reveal why infinite capital at labs may actually constrain innovation, explain the narrow "Goldilocks zone" where RL actually works, and argue why U.S. chip restrictions may have backfired catastrophically—accelerating China's path to self-sufficiency by a decade.

  • #80
    Dec 15, 2025 · 48 min

    Ep 80: CEO of Surge AI Edwin Chen on Why Frontier Labs Are Diverging, RL Environments & Developing Model Taste

    Edwin Chen is the founder and CEO of Surge AI, the data infrastructure company behind nearly every major frontier model. Surge works with OpenAI, Anthropic, Meta, and Google, providing the high-quality data and evaluation infrastructure that powers their models. Edwin reveals why optimizing for popular benchmarks like LMArena is "basically optimizing for clickbait," how one frontier lab's models regressed for 6-12 months without anyone knowing, and why the industry's approach to measurement is

  • #79
    Dec 10, 2025 · 56 min

    Ep 79: OpenAI's Head of Product on How the Best Teams Build, Ship and Scale AI Products

    This episode features Olivier Godement, Head of Product for Business Products at OpenAI, discussing the current state and future of AI adoption in enterprises, with a particular focus on the recent releases of GPT 5.1 and Codex. The conversation explores how these models are achieving meaningful automation in specific domains like coding, customer support, and life sciences: where companies like Amgen are using AI to accelerate drug development timelines from months to weeks through automated re

  • #78
    Dec 5, 2025 · 1 hr 13 min

    Ep 78: Jordan Schneider, Host of China Talk, on AI Race, Key Policy Decisions & Unpacking Geopolitical Chip Tension

    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

  • #77
    Dec 2, 2025 · 42 min

    Ep 77: Anthropic’s Dianne Na Penn on Opus 4.5, Rethinking Model Scaffolding & Safety as a Competitive Advantage

    This episode features Dianne Na Penn, a senior product leader at Anthropic, discussing the launch of Claude Opus 4.5 and the evolution of frontier AI models. The conversation explores how Anthropic approaches model development—balancing ambitious capability roadmaps with user feedback, making strategic bets on areas like agentic coding and computer use while deliberately avoiding others like image generation. Dianne shares insights on the shifting nature of AI evaluation (moving beyond saturated

Showing 1–20 of 21 episodes