Dwarkesh PodcastAndrej Karpathy on Dwarkesh Patel: Why Agents Take a Decade
Why pre-training and gradient descent produce ghosts rather than agents: Karpathy maps the biological gaps that make the decade of agents the honest frame.
Episode Details
EPISODE INFO
- Released
- October 17, 2025
- Duration
- 2h 26m
- Channel
- Dwarkesh Podcast
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
The Andrej Karpathy episode. During this interview, Andrej explains why reinforcement learning is terrible (but everything else is much worse), why AGI will just blend into the previous ~2.5 centuries of 2% GDP growth, why self driving took so long to crack, and what he sees as the future of education. It was a pleasure chatting with him. 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒
- Transcript: https://dwarkesh.substack.com/p/andrej-karpathy
- Apple Podcasts: https://podcasts.apple.com/us/podcast/andrej-karpathy-agi-is-still-a-decade-away/id1516093381?i=1000732326311
- Spotify: https://open.spotify.com/episode/3iIYVmmhXwh3fOumypWVpC?si=33d37708b2b44e2f
𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒
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To sponsor a future episode, visit https://dwarkesh.com/advertise 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 – AGI is still a decade away 00:30:33 – LLM cognitive deficits 00:40:53 – RL is terrible 00:50:26 – How do humans learn? 01:07:13 – AGI will blend into 2% GDP growth 01:18:24 – ASI 01:33:38 – Evolution of intelligence & culture 01:43:43 - Why self driving took so long 01:57:08 - Future of education
SPEAKERS
Andrej Karpathy
guestDwarkesh Patel
hostNarrator
other
EPISODE SUMMARY
In this episode of Dwarkesh Podcast, featuring Andrej Karpathy and Dwarkesh Patel, Andrej Karpathy on Dwarkesh Patel: Why Agents Take a Decade explores andrej Karpathy explains AI agents, RL flaws, and future education revolution Andrej Karpathy argues we’re not building animal-like intelligences but "ghosts": digital systems trained via imitation and gradient descent that differ fundamentally from evolved brains. He thinks the coming era will be the "decade of agents," not the "year," because current LLM-based agents lack robustness, memory, continual learning, and real autonomy, and each extra "nine" of reliability is hard-won. He is sharply critical of today’s reinforcement learning and LLM-judge-based methods as noisy, gameable, and prone to collapse, and expects several new algorithmic breakthroughs (reflection, better credit assignment, multi-agent self-play, rich synthetic data) before we get truly capable agents. Looking forward, he is focusing on education via his new project Eureka, aiming to build a “Starfleet Academy” that combines deeply engineered learning ramps, AI tools, and eventually AI tutors so humans can become vastly more capable rather than sidelined in an AI-driven world.
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