At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon! From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round. In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more. You can get his book “The Eureka Machine” here! We go deep on Recursive’s early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence: We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today’s LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford’s GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself. We discuss: * The Eureka Machine and Richard’s vision for an AI that can automate invention * Why Richard is optimistic about superintelligence for science and technology * Why AI hard-takeoff scenarios may underestimate physical and economic constraints * The risks of regulating intelligence itself instead of specific AI applications * Reward hacking and why increasingly intelligent AI makes objective design harder * Richard’s critique of Anthropic’s constitution and constitutional AI * Alignment vs. personalization and whose values an AI should follow * Why open-source AI matters for resilience, competition, and geopolitical soft power * Why Richard left You.com’s frontier-model work to start Recursive * Recursive self-improvement and automating the process of AI research * Whether today’s LLM paradigm is enough — and why Richard is less bullish on world models * DecaNLP, early prompt-based generalization, and the research that influenced GPT * Why rejected research can shape entire technological timelines * Open-endedness, evolutionary approaches, and rainbow teaming * What happens if AI systems begin setting their own goals * Why simple objectives like profit maximization can produce dangerous reward hacks * Recursive’s long-term plan to apply self-improving AI to science * The compute, hardware, and economic constraints on AI takeoff * Recursive’s early NanoChat, NanoGPT, and GPU kernel optimization results * Why automating AI research could reduce years of work to weeks * Reward engineering and what makes auto-research systems actually work * The AI Economist and using simulations to test economic policy * Whether LLMs can realistically simulate people and entire economies * Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress * Recursive’s near-term focus on AI for AI research * Harness optimization, sandboxing, and web search as core agent infrastructure * You.com and the search stack for AI agents * AI in finance, backtesting, and data leakage * Richard’s three fundamental components and ten “spaces” of intelligence * The theoretical upper bounds of vision, communication, knowledge, and computation * Creative intelligence, metacognition, and AI-generated goals * Survival and replication and why AI does not necessarily need to fear being turned off * High agency and ambitious goals and Richard’s advice for people building with AI Richard Socher * X: https://x.com/RichardSocher * LinkedIn: https://www.linkedin.com/in/richardsocher/ Timestamps 00:00:00 The Eureka Machine and Superintelligence 00:02:23 AI Optimism, Slow Takeoff, and Regulation 00:07:56 AI Safety, Reward Hacking, and Anthropic’s Constitution 00:11:49 Alignment, Personalization, and Open Source AI 00:15:46 Why Richard Started Recursive 00:20:03 Recursive Self-Improvement and the Founding Team 00:22:55 Are Today’s LLMs Enough? 00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time 00:34:38 Open-Endedness and Evolutionary AI 00:36:38 What Happens When AI Chooses Its Own Goals? 00:41:16 Superintelligence for Science 00:42:40 GPUs, Compute, and the Limits of AI Takeoff 00:45:07 Recursive’s Results: AI Beating Humans and Their Agents 00:49:14 Reward Engineering and Auto Research 00:53:12 The AI Economist and Simulating Entire Economies 00:58:07 LLM Simulations, Personas, and Mode Collapse 01:03:38 Recursive’s Roadmap, Agents, Search, and Finance 01:09:13 The Upper Bounds and Spaces of Intelligence 01:30:21 Goals, High Agency, and Advice for Builders Transcript Introduction: Richard Socher and the Eureka Machine Swyx [00:00:00]: We’re here in a studio with Vibhu and myself and Richard Socher. Welcome. Richard Socher [00:00:06]: Thanks for having me. Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it’s your life’s goal. What is the Eureka Machine? Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It’s essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for. Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you’ve written. Richard Socher [00:00:50]: That’s right, yeah. I finished it last year, a little bit before we started Recursive, and now we’re gonna try to build parts of that. Swyx [00:00:57]: You finished it last year. It’s July. What takes so long? Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow. Richard Socher [00:01:04]: It’s ridiculous. That whole industry is just unfathomably slow. Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I’m really glad it’s finally coming out in September this year. Swyx [00:01:14]: We might have AGI by then. Like, we don’t know. Vibhu [00:01:18]: Any key takeaway that you’re most excited to put in here? Techno-Optimism, AI Upside, and Slow Takeoff Richard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries. Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well. Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he’s right on the techno-optimism. Swyx [00:02:23]: Where do you think optimists get in trouble? Richard Socher [00:02:26]: Like, you shouldn’t have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don’t want them to use it for. It’s a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there’s bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can’t share the illegal content as quickly, or we should make the hard drive smaller so you can’t store as much illegal content.” But I’m like, “That’s not how you regulate that.” that’s like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don’t want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don’t want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it’s let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don’t consider enough are fairly easily regulated, compared to, what the doom
