Unsupervised Learning with Jacob Effron

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

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

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

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

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