Limboy
Andrej Karpathy: Software Is Changing (Again)

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 (Powered by https://ChapterMe.co) - 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/

本次演讲中,前特斯拉人工智能总监 Andrej Karpathy 探讨了软件的演进,将其划分为三个阶段:软件 1.0(传统代码)、软件 2.0(神经网络)和软件 3.0(大型语言模型)。他强调,大型语言模型(LLM)正以前所未有的方式改变着软件开发,它们可以通过自然语言进行编程,并催生了“部分自主应用”。Karpathy 将 LLM 比作操作系统和公用事业,并指出了其高昂的计算成本和独特的“心理学”特性,如幻觉和顺行性遗忘症。他还分享了利用 LLM 进行快速原型开发的经验,并提出了为 AI 代理构建软件的建议,例如创建 LLM 友好的数据格式和文档。他总结道,未来十年将是部分自主产品蓬勃发展的时代。