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Unsupervised Learning with Jacob Effron: Daily Summaries Delivered
by 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. 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.
Latest Episodes
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- 2 days ago1h 13m
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 productivity gains in his own research), while maintaining genuine intellectual humility about whether transformers can truly generalize the way humans do. The episode weaves together questions about data efficiency, the non-verifiable RL frontier, the coding agent revolution, the open vs. closed source gap, and what the next architectural leap might look like: all filtered through the lens of someone who helped build the foundation the entire field is standing on. Intro Transformers vs. Human Learning How Do We Get Physical World Generalization? What Comes After Transformers How Much Have Agents Improved Lukasz's AI Research Productivity? How Close Is an AI Research Intern? RL Beyond Verifiable Tasks App Companies: Build Models or Lean on Labs? Multimodal Is Still Missing Something OpenAI's Bet on Reasoning The AI Coding Wars Focus vs. Keeping Embers Burning Open Source vs. Closed Source Gap Quickfire With your host: @jacobeffron - Managing Director at Redpoint
- 4 days agoconversational56 min6-min read
Ep 88: Unpacking DeepMind's Quest for SuperIntelligence with Demis Hassabis' Biographer
Sebastian Mallaby's book, The Infinity Machine, offers a deep dive into Demis Hassabis, the co-founder and CEO of DeepMind, revealing his intellectual journey, leadership style, and evolving views on AI safety and competition. The conversation explores how Hassabis’s background in neuroscience and his quasi-spiritual drive to understand intelligence shaped DeepMind’s trajectory, contrasting sharply with figures like Sam Altman and Elon Musk.
- 2 weeks ago59 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 optimized jointly, and why scaffolding will eventually be written by models themselves). Oriol gets into the mechanics of memory in models, drawing on his cognitive neuroscience background to argue that file-system-style non-parametric memory is more practical than baking memory into weights at serving scale. He shares his views on the limits of RL today (LLMs are data-limited in a way that game-playing RL never was), why training on narrow domains like math and code generalizes surprisingly well, and what a true "Move 37" moment for science or ML research would look like. Throughout, he reflects on the unique advantages of being inside Google (TPU co-design, end-to-end revenue stability, the merger of Brain and DeepMind), the trade-offs between focus and exploration in research orgs, and why he believes AGI in some meaningful sense may already be here, even if the goalposts keep moving. Intro Why World Models The GPT Moment for Video What Makes Omni a World Model World Models & Robotics Evaluating Physics in AI Consumer Agents & Spark Scaffolding & the Bitter Lesson Memory & Continual Learning Research Bets Inside Big Labs Post-Training RL is Greenfield What Real Intelligence Looks Like RL Generalization Advice for Founders Can AI Truly Innovate? Recursive Self-Improvement Quickfire With your host: @jacobeffron - Managing Director at Redpoint
- 3 weeks agointerview1h 21m7-min read
Ep 86: Yann LeCun on Leaving Meta, Breaking The LLM Paradigm, & Why Hinton is Wrong
Yann LeCun argues that while large language models (LLMs) are useful for language tasks, they are not a viable path toward human-like intelligence due to their lack of planning, world modeling, and ability to predict action consequences. His new company, AMLabs, is advancing JEPA (Joint Embedding Predictive Architecture) to build scalable world models that enable data-efficient, generalizable AI for real-world applications like robotics and industrial control.
- Apr 23, 202654 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 traditional SaaS, and the frontier questions around world models, memory, and what it actually means for AI to "understand" something. The episode is grounded in practitioner-level candor, with Swyx offering real takes from running AIE conferences, working inside Cognition, and thinking deeply about what the next wave of AI-native software development looks like. Intro What the Top AI Engineers Are Thinking About Has AI Infra Finally Stabilized? When Does Doing RL In-House Make Sense? Why Selling Dev Tools to Agents is Different AI Coding Wars Consumer AI Plateau Codex vs Claude Code Future of Open Models 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
- Apr 9, 202658 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 moment the models themselves are starting to accelerate the research. Jakub shares a candid take on the competitive landscape, why chain-of-thought monitoring is one of the most promising tools in the alignment toolkit, and — with unusual directness — why the concentration of power enabled by highly automated AI organizations is a societal problem that doesn't yet have an obvious solution. Intro Research Intern Capability Timelines Math Breakthroughs RL Beyond Verifiable Tasks RL vs In-Context Allocating Compute Internally AI for Science Pattern Matching Solving the Hardest Math Problems Chain of Thought Monitoring Generalization and Value Alignment in Models Inside OpenAI 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
- Apr 2, 202654 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 of the resources. Beyond the product, Jake gets into the organizational decisions that underpin Serval's velocity — why recruiting is the #1 job of every employee, how to prevent talent bar decay as you scale from 8 to 200 people, and how the role of the manager is shifting as ICs own more scope than ever. Threading it all together is a founder's honest account of what it means to build a horizontal software company when the models are improving, the infrastructure is shifting, and the window to displace a legacy incumbent is open but won't stay open forever. 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
- Mar 11, 202654 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 improvements in the underlying models, particularly Opus 4.5 and 4.6, have repeatedly forced Legora to rebuild core product features from scratch, and why he sees that as a feature, not a bug. On the legal industry, Max offers a ground-level view of how AI is actually diffusing through law firms, less through top-down mandates and more through competitive pressure between firms and, increasingly, from enterprise clients demanding efficiency from their outside counsel. He pushes back on the viability of AI-native law firms, dismisses outcome-based pricing as harder than it looks, and makes the case for why foundation model competition creates tailwinds rather than threats for a company with Legora's depth. The episode closes with a detailed look at the US expansion strategy, including the deliberate cultural decisions, like flying all New York hires to Stockholm for onboarding, that Max believes are the real source of Legora's compounding advantage. [0:00] Intro [1:16] Legora's Series D Story [3:24] Why You Need Low Ego to Build in AI [5:58] From 60% to 100% Accuracy in One Summer [7:04] Law Firm Economics Shift [14:09] Pricing Seats Vs Outcomes [18:31] Why Foundation Models Entering Legal Helps Legora [30:10] Convincing a 75-Year-Old Partner to Go All In [33:02] Hiring Legal Engineers [34:32] Running an AI-Native Company [35:57] The Opus 4.5 Christmas Breakthrough [40:02] Building With Customers [44:01] All In On US Expansion [51:22] Stockholm Startup DNA 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
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About Unsupervised Learning with Jacob Effron
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. 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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