Andrej Karpathy on open source AI
From Andrej Karpathy on Future Of AI, LLM OS, and Building Ecosystems · · Perfology Clips
“I think we have to be careful with the naming because a lot of the ones that you listed like Llama, Mron, and so on, I wouldn't actually say they're open source, right? And so like it's kind of like tossing over a binary for like an operating system, you know, like you can you can kind of work with it and it's like it's like useful, but but it's not fully useful, right? And there there are a number of what I would say is like fully open-source LLMs. So there's you know Pythia models, LLM 360, Elmo etc. So and they're fully releasing the entire infrastructure that's required to compile the the operating system right to train the model from the data to gather the data etc.”
On , Andrej Karpathy, Independent AI Researcher and Former Director of AI at Tesla/Independent, spoke about open source AI during Andrej Karpathy on Future Of AI, LLM OS, and Building Ecosystems on Perfology Clips.
About Andrej Karpathy
Andrej Karpathy has continued to speak publicly about the evolution of software development in the age of large language models. In a June 2025 keynote at the AI Startup School, he described a shift he calls "Software 3.0," where natural language prompts function as programs that direct LLMs, and he argued that LLMs have "flipped the direction of technology diffusion" by making advanced tools available to consumers before corporations or governments. He cautioned that "2025 is the year of agents" was an overstatement, saying "this is the decade of agents" and stressing that "we need humans in the loop." In 2026 appearances, Karpathy revisited the term "vibe coding," which he coined in 2025, and contrasted it with what he now calls "agentic engineering." He said vibe coding is about "raising the floor for everyone" to create software, while agentic engineering is about "preserving the quality bar" of professional software and avoiding vulnerabilities. He stated that he has "never felt more behind as a programmer" despite using agentic tools, and described LLMs as "statistical simulation circuits" rather than animal intelligence, adding that "if you'll yell at them, they're not going to work better." He also suggested that hiring for AI-era roles should involve evaluating candidates on large, real-world projects rather than traditional interviews.