Andrej Karpathy talks about the importance of GUIs for working with AI systems—and why we still need to keep a tight leash on agents. Even with fast output, the human remains the bottleneck.
Andrej Karpathy's keynote on June 17, 2025 at AI Startup School in San Francisco. Slides provided by Andrej: https://drive.google.com/file/d/1a0h1mkwfmV2PlekxDN8isMrDA5evc4wW/view?usp=sharing Chapters: 00:00 - Intro 01:25 - Software evolution: From 1.0 to 3.0 04:40 - Programming in English: Rise of Software 3.0 06:10 - LLMs as utilities, fabs, and operating systems 11:04 - The new LLM OS and historical computing analogies 14:39 - Psychology of LLMs: People spirits and cognitive quirks 18:22 - Designing LLM apps with partial autonomy 23:40 - The importance of human-AI collaboration loops 26:00 - Lessons from Tesla Autopilot & autonomy sliders 27:52 - The Iron Man analogy: Augmentation vs. agents 29:06 - Vibe Coding: Everyone is now a programmer 33:39 - Building for agents: Future-ready digital infrastructure 38:14 - Summary: We’re in the 1960s of LLMs — time to build Drawing on his work at Stanford, OpenAI, and Tesla, Andrej sees a shift underway. Software is changing, again. We’ve entered the era of “Software 3.0,” where natural language becomes the new programming interface and models do the rest. He explores what this shift means for developers, users, and the design of software itself— that we're not just using new tools, but building a new kind of computer. More content from Andrej: https://www.youtube.com/@AndrejKarpathy Thoughts (From Andrej Karpathy!) 0:49 - Imo fair to say that software is changing quite fundamentally again. LLMs are a new kind of computer, and you program them *in English*. Hence I think they are well deserving of a major version upgrade in terms of software. 6:06 - LLMs have properties of utilities, of fabs, and of operating systems → New LLM OS, fabbed by labs, and distributed like utilities (for now). Many historical analogies apply - imo we are computing circa ~1960s. 14:39 - LLM psychology: LLMs = "people spirits", stochastic simulations of people, where the simulator is an autoregressive Transformer. Since they are trained on human data, they have a kind of emergent psychology, and are simultaneously superhuman in some ways, but also fallible in many others. Given this, how do we productively work with them hand in hand? Switching gears to opportunities... 18:16 - LLMs are "people spirits" → can build partially autonomous products. 29:05 - LLMs are programmed in English → make software highly accessible! (yes, vibe coding) 33:36 - LLMs are new primary consumer/manipulator of digital information (adding to GUIs/humans and APIs/programs) → Build for agents! Some of the links: - Software 2.0 blog post from 2017 https://karpathy.medium.com/software-2-0-a64152b37c35 - How LLMs flip the script on technology diffusion https://karpathy.bearblog.dev/power-to-the-people/ - Vibe coding MenuGen (retrospective) https://karpathy.bearblog.dev/vibe-coding-menugen/ Apply to Y Combinator: https://ycombinator.com/apply Work at a startup: https://workatastartup.com
Andrej Karpathy is a legendary AI researcher, engineer, and educator. He's the former director of AI at Tesla, a founding member of OpenAI, and an educator at Stanford. Please support this podcast by checking out our sponsors: - Eight Sleep: https://www.eightsleep.com/lex to get special savings - BetterHelp: https://betterhelp.com/lex to get 10% off - Fundrise: https://fundrise.com/lex - Athletic Greens: https://athleticgreens.com/lex to get 1 month of fish oil EPISODE LINKS: Andrej's Twitter: http://twitter.com/karpathy Andrej's YouTube: http://youtube.com/c/AndrejKarpathy Andrej's Website: http://karpathy.ai Andrej's Google Scholar: http://scholar.google.com/citations?user=l8WuQJgAAAAJ Books mentioned: The Vital Question: https://amzn.to/3q0vN6q Life Ascending: https://amzn.to/3wKIsOE The Selfish Gene: https://amzn.to/3TCo63s Contact: https://amzn.to/3W3y5Au The Cell: https://amzn.to/3W5f6pa PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 0:58 - Neural networks 6:01 - Biology 11:32 - Aliens 21:43 - Universe 33:34 - Transformers 41:50 - Language models 52:01 - Bots 58:21 - Google's LaMDA 1:05:44 - Software 2.0 1:16:44 - Human annotation 1:18:41 - Camera vision 1:23:46 - Tesla's Data Engine 1:27:56 - Tesla Vision 1:34:26 - Elon Musk 1:39:33 - Autonomous driving 1:44:28 - Leaving Tesla 1:49:55 - Tesla's Optimus 1:59:01 - ImageNet 2:01:40 - Data 2:11:31 - Day in the life 2:24:47 - Best IDE 2:31:53 - arXiv 2:36:23 - Advice for beginners 2:45:40 - Artificial general intelligence 2:59:00 - Movies 3:04:53 - Future of human civilization 3:09:13 - Book recommendations 3:15:21 - Advice for young people 3:17:12 - Future of machine learning 3:24:00 - Meaning of life SOCIAL: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Reddit: https://reddit.com/r/lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
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 * Labelbox helps you get data that is more detailed, more accurate, and higher signal than you could get by default, no matter your domain or training paradigm. Reach out today at https://labelbox.com/dwarkesh * Mercury helps you run your business better. It’s the banking platform we use for the podcast — we love that we can see our accounts, cash flows, AR, and AP all in one place. Apply online in minutes at https://mercury.com * Google’s Veo 3.1 update is a notable improvement to an already great model. Veo 3.1’s generations are more coherent and the audio is even higher-quality. If you have a Google AI Pro or Ultra plan, you can try it in Gemini today by visiting https://gemini.google 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
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I've yet encountered. Here are links to the individual talks and the full live streams for the two days: 1. Foundations of Deep Learning (Hugo Larochelle, Twitter) - https://youtu.be/zij_FTbJHsk 2. Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) - https://youtu.be/u6aEYuemt0M 3. Deep Learning for Natural Language Processing (Richard Socher, Salesforce) - https://youtu.be/oGk1v1jQITw 4. TensorFlow Tutorial (Sherry Moore, Google Brain) - https://youtu.be/Ejec3ID_h0w 5. Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU) - https://youtu.be/rK6bchqeaN8 6. Nuts and Bolts of Applying Deep Learning (Andrew Ng) - https://youtu.be/F1ka6a13S9I 7. Deep Reinforcement Learning (John Schulman, OpenAI) - https://youtu.be/PtAIh9KSnjo 8. Theano Tutorial (Pascal Lamblin, MILA) - https://youtu.be/OU8I1oJ9HhI 9. Deep Learning for Speech Recognition (Adam Coates, Baidu) - https://youtu.be/g-sndkf7mCs 10. Torch Tutorial (Alex Wiltschko, Twitter) - https://youtu.be/L1sHcj3qDNc 11. Sequence to Sequence Deep Learning (Quoc Le, Google) - https://youtu.be/G5RY_SUJih4 12. Foundations and Challenges of Deep Learning (Yoshua Bengio) - https://youtu.be/11rsu_WwZTc Full Day Live Streams: Day 1: https://youtu.be/eyovmAtoUx0 Day 2: https://youtu.be/9dXiAecyJrY Go to http://www.bayareadlschool.org for more information on the event, speaker bios, slides, etc. Huge thanks to the organizers (Shubho Sengupta et al) for making this event happen. CONNECT: - If you enjoyed this video, please subscribe to this channel. - AI Podcast: https://lexfridman.com/ai/ - Show your support: https://www.patreon.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Twitter: https://twitter.com/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Slack: https://deep-mit-slack.herokuapp.com
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Andrej Karpathy recently coined the term “vibe coding” to describe how LLMs are getting so good that devs can simply “give in to the vibes, embrace exponentials, and forget that the code even exists.” We dive into this new way of programming and what it means for builders in the age of AI. Apply to Y Combinator: https://ycombinator.com/apply Chapters (Powered by https://bit.ly/chapterme-yc) - 0:00 Intro 0:42 What is vibe coding? 1:00 What founders in the current YC batch are saying 4:35 Debugging and building systems 6:59 The models people are using now 10:01 What percentage of code is being written by LLM’s? 11:58 What changed and what stayed the same? 18:08 How Triplebyte did candidate assessments and how would that change in this era 21:37 Key skills that will remain relevant 23:01 How do you develop taste without classical training? 30:59 Outro