Unsupervised Learning with Jacob Effron

Hosted by by Redpoint Ventures

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101

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Aug 2026

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About the show

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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August 3, 20261 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 a necessary part of collective defense. They connect this directly to the Fable ban episode as a possible preview of a coming frontier-model licensing regime, then turn to the OpenAI and Hugging Face security incident as a landmark, publicly legible moment of autonomous AI causing real-world harm. From there the conversation shifts to business strategy: how much value are companies actually handing over to frontier labs by building on their APIs, and what does the Grok and Cursor data advantage really tell us about the future value of real-world usage data versus purchased training data. They close with a rapid-fire tour of the news cycle, covering SSI's mysterious $5B raise, the rumored Stripe and OpenRouter acquisition, Google's continued underperformance despite structural advantages, and OpenAI's leadership speculation, plus a reflection on venture's swing back toward deep tech and physical infrastructure. (0:00) Intro (2:41) China's Open Source Models Catch Up (7:42) Does Distillation Explain China's Rise? (13:54) The Geopolitical Risk of Chinese AI Models (20:28) Should the Government Restrict Open Models? (22:06) What Are the Labs Really Learning From You? (29:22) Future of Government Regulation (40:50) The OpenAI-Hugging Face Hack (47:59) Grok, Cursor, and the Value of Real Data (56:52) SSI and OpenRouter (1:02:52) Venture Capital's Return to Deep Tech (1:05:16) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

July 31, 20261 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 also discusses why he left xAI to start River AI, the three bets behind it, and why he's betting on local hardware, not just software, for personal AI. On the enterprise side, he tackles whether companies will actually train their own models or if it's just a cost play, and makes the case that proprietary labs like OpenAI and Anthropic are facing a real business squeeze. He's skeptical that stacking specialized RL domains generalizes the way pre-training scale did, and is candid about the uncomfortable reality that today's frontier open-weight models are almost entirely Chinese. He closes on what's actually needed to push model progress beyond coding into non-verifiable domains, and the broader implications of where AI is headed next. (0:00) Intro (1:17) Writing Fiction on Where AI Is Headed (4:46) Cracking Agents Beyond Coding (10:29) Why Igor Left to Start River (12:22) River's Three Big Bets (18:06) Weights vs. Memory: The Personalization Debate (22:04) Should Enterprises Train Their Own Models? (25:10) Are Proprietary Labs Losing Their Edge? (32:16) The China Open-Source Problem (44:19) The Elon Call That Started xAI (50:18) Thoughts on Cursor Acquisition (52:16) What's Actually Bottlenecking AI (56:55) Humans, Machines, and Staying Relevant (1:01:29) Igor's Odds This All Goes Well With your host: @jacobeffron - Managing Director at Redpoint

July 16, 20261 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 past shifts where the boundaries were at least knowable, and that this uncertainty is what fuels both AGI hype and doomerism without resolving anything. Benedict unpacks why capabilities remain jagged, meaning usage is jagged too, why coding became the first real enterprise use case thanks to scalable verification, and why most consumer and enterprise use cases still have to be invented by entrepreneurs rather than emerging spontaneously once models improve. He also lays out why foundation model labs may end up structurally like TSMC rather than Windows, valuable but bounded rather than owning the entire stack, walks through why automation has historically meant more work rather than less (using a hundred years of rising accountant headcount as evidence), and explains why industries like Uber and Airbnb, or Caterpillar and the internet, show just how unevenly this kind of technology actually lands. Throughout, he offers candid, historically grounded takes on OpenAI's product sprawl versus Anthropic's narrow coding bet, Apple's stumbled AI moment, and why most companies, unlike Silicon Valley, have far bigger priorities than AI on their minds. (0:00) Intro (1:31) Is AI Bigger Than the Internet? (10:10) Barriers of Getting From Demos to Daily Use (20:15) Why Job Predictions Fail (25:52) Where's the Moat? (33:55) Will Models Eat the App Layer? (39:25) When Average Isn't Enough and Models Don't Work (45:58) Reflections on OpenAI (55:04) Consumer Usage Is Still Shallow (58:51) What's Required for More Enterprise Adoption (1:03:47) Opinion on Sora (1:06:27) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

July 9, 202650 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-generated experimentation rather than internet data. He closes by reconsidering mainstream AI safety arguments and offers a sweeping vision of self-replicating robot societies eventually colonizing the solar system. (0:00) Intro (1:24) How Close Is Superhuman AI? (2:27) Why ChatGPT Didn't Surprise Him (3:21) The Path to Recursive Self-Improvement (9:01) Will AI Takeoff Feel Sudden? (11:02) Intelligence Means Efficiency (12:32) Advice for Labs: Beyond Human-Biased Data (17:10) Artificial Curiosity and the Theory of Fun (21:33) When Do We Get the AI Scientist? (24:07) AI Chemistry, MOFs, and Carbon Capture (25:04) Robotics Reality Check (28:23) The Data Center Bet: Overbuilt? (31:48) Open Source vs. Closed Labs (34:25) Does Being First to RSI Create a Moat? (38:06) AI Safety and Alignment Skepticism (43:44) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

June 12, 20261 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 model have its biggest supporters questioning whether safety framing is masking competitive positioning. The conversation runs through the full state of the lab wars, including Rob doubling down on his Sam Altman ouster prediction and the Bret Taylor succession theory, why Google's structural advantages remain intact despite falling behind on coding, what xAI's Cursor acquisition is really for, and Ari's claim that compute constraints could push labs to suspend their APIs entirely. The back half digs into the physical bottlenecks underneath it all, from atom and x-ray lithography startups challenging ASML to H100 prices reversing their decline, before closing with predictions: recursive self-improvement is closer than it was six months ago but slower than the takeoff narratives suggest, robotics is nearing its GPT-3 moment, and Anthropic's next chapter may be life sciences. (0:00) Intro (1:40) Coding Agents Cross a Threshold (3:29) Is Open-Weight AI in Retreat? (7:37) Cost Crunch & Scaffolding (12:13) The "Apps Are Cooked" Debate (16:37) Sam Altman Under Scrutiny (19:44) Anthropic's Fable Backlash (23:24) How Big a Step Change Is Fable? (26:50) What's Going On at Google? (33:20) Could the APIs Go Away? (34:11) Breaking the Semiconductor Bottleneck (35:42) Beyond EUV: Atom & X-Ray Lithography (37:23) Implications of a Compute Shortage (40:20) Do Alt Chips Actually Help? (43:43) SpaceX, xAI & the Cursor Acquisition (48:50) How Close Are We to RSI? (52:21) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

June 3, 20261 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 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. (0:00) Intro (1:12) Transformers vs. Human Learning (8:37) How Do We Get Physical World Generalization? (10:52) What Comes After Transformers (13:59) How Much Have Agents Improved Lukasz's AI Research Productivity? (17:21) How Close Is an AI Research Intern? (26:06) RL Beyond Verifiable Tasks (35:38) App Companies: Build Models or Lean on Labs? (46:21) Multimodal Is Still Missing Something (49:46) OpenAI's Bet on Reasoning (55:26) The AI Coding Wars (59:26) Focus vs. Keeping Embers Burning (1:02:09) Open Source vs. Closed Source Gap (1:05:15) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

June 1, 202656 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 tried to prevent the AI race we're now all living through, and now finds himself one of its central protagonists. He used to believe a single lab could carry the safety burden to AGI; he now sees safety as a collective action problem only governments can solve. He hedged DeepMind's research bets across every promising direction, and as a result missed the two most consumer-defining moments in modern AI — ChatGPT and Claude Code. He nearly spun DeepMind out of Google with a secret $1B Reid Hoffman pledge backing him, but never used the leverage and stayed — and won a Nobel Prize the next year. The episode also zooms out to the structural forces shaping the race — why hyperscalers can't out-recruit concentrated-bet labs, why Sebastian gives OpenAI roughly 50/50 odds of being absorbed by next summer, why he thinks Anthropic should IPO right now, and what the personal histories between Demis, Elon, and Sam reveal about who actually trusts whom. (0:00) Intro (2:04) Was the AI Race Inevitable? (4:03) The 2015 Safety Summit Backfire (7:15) Can Governments Actually Fix This? (9:26) How the World Misread DeepMind (11:27) Why Google Never Makes the Concentrated Bet (15:51) Project Mario: The Secret Spinout Plan (19:43) What Demis Actually Regrets (23:46) Venture Startups vs. Tech Behemoths (27:50) Controlling the Narrative (30:40) The Talent War and Hiring Brand (34:08) David Silver and the RL True Believers (38:21) Demis, Elon, and the Evil Genius Feud (42:39) Great Man Theory vs. Inevitability (45:00) What Demis Didn't Want Published With your host: @jacobeffron - Managing Director at Redpoint

May 22, 202659 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. (0:00) Intro (1:36) Why World Models (4:21) The GPT Moment for Video (7:51) What Makes Omni a World Model (10:04) World Models & Robotics (12:37) Evaluating Physics in AI (14:51) Consumer Agents & Spark (18:39) Scaffolding & the Bitter Lesson (22:06) Memory & Continual Learning (26:54) Research Bets Inside Big Labs (32:30) Post-Training RL is Greenfield (35:57) What Real Intelligence Looks Like (39:11) RL Generalization (43:00) Advice for Founders (46:40) Can AI Truly Innovate? (49:48) Recursive Self-Improvement (52:14) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

May 15, 20261 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 representations rather than generating pixel-level predictions, and explains why this approach is essential for robotics, industrial applications, and any system that needs to operate beyond the substrate of language. Yann also reveals the real story behind his departure from Meta (he had zero technical influence on Llama, contrary to public narrative), the genesis of his Tapestry project for sovereign open-source AI, why he believes LLMs are intrinsically unsafe, where he diverges from his fellow Turing laureates Hinton and Bengio, and why he predicts the industry will recognize the paradigm shift by early 2027. Throughout, he offers candid reflections on the tension between research and product at major labs, and why he intentionally headquartered AMI Labs in Paris with zero Silicon Valley VC money. (0:00) Introduction (01:45) Why LLMs Aren't the Path to Intelligence (07:51) AMI and World Models (12:07) The JEPA Architecture Explained (15:55) Problems with Robotics Models Today (20:37) Silicon Valley Herd Behavior (28:18) Tapestry: Sovereign AI for the Rest of the World (35:49) OpenAI Is the Next Sun Microsystems (40:51) Why Yann's Views Diverged from Hinton & Bengio (44:32) LLMs Are Intrinsically Unsafe (58:00) Why Yann Left Meta (1:00:26) Reflections on FAIR (1:12:11) Advice for PhD Students LeWorldModel Paper: https://arxiv.org/abs/2603.19312 With your host: @jacobeffron - Partner at Redpoint With your host: @jacobeffron - Managing Director at Redpoint

April 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. (0:00) Intro (1:17) What the Top AI Engineers Are Thinking About (2:13) Has AI Infra Finally Stabilized? (6:39) When Does Doing RL In-House Make Sense? (11:26) Why Selling Dev Tools to Agents is Different (17:18) AI Coding Wars (29:04) Consumer AI Plateau (30:22) Codex vs Claude Code (44:52) 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 With your host: @jacobeffron - Managing Director at Redpoint