Richard is the founder of Recursive, You.com, & AIX Ventures. 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.

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 later GPT work, 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 • 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, 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 the danger of 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, replication, and why AI does not necessarily need to fear being turned off • High agency, choosing ambitious goals, and advice for people building with AI