Andrej Karpathy: From Vibe Coding to Agentic Engineering

**Stephanie Zhan** (0:02)
We're so excited for our very first special guest. He has helped build modern AI, then explain modern AI, and then occasionally rename modern AI.
He actually helped co-found OpenAI right inside of this office, was the one who actually got autopilot working at Tesla back in the day, and he has a rare gift of making the most complex technical shifts feel both accessible and inevitable. You all know him for having coined the term vibe coding last year, but just in the last few months, he said something even more startling, that he's never felt more behind as a programmer. That's where we're starting today. Thank you, Andrej, for joining us.

**Andrej Karpathy** (0:44)
Yeah. Hello. Excited to be here and to kick us off.

**Stephanie Zhan** (0:47)
Okay. So just a couple months ago, you said that you've never felt more behind as a programmer. That's startling to hear from you of all people.
Can you help us unpack that? Was that feeling exhilarating or unsettling?

**Andrej Karpathy** (1:00)
Yeah. A mixture of both, for sure.
Well, first of all, I guess like as many of you, I've been using agentic tools like Cloud Code Adjacent Things for a while, maybe over the last year as it came out. It was very good at chunks of code, and sometimes it would mess up and you have to edit them, and it was kind of helpful. Then I would say December was this clear point where for me, I was on a break, so I had a bit more time. I think many other people were similar. And I just started to notice that with the latest models, the chunks just came out fine. And then I kept asking for more and just came out fine. And then I can't remember the last time I corrected it. And then I just trusted the system more and more, and then I was web coding. And so it was kind of a, I do think that it was a very stark transition. I think that a lot of people actually, I tried to stress this on Twitter or X, because I think a lot of people experienced AI last year as a chat GPT-adjacent thing. But you really had to look again, and you had to look as of December, because things have changed fundamentally, and especially on this agentic, coherent workflow that really started to actually work.
And so I would say that, yeah, it was just that realization that really had me go down the whole rabbit hole of just infinity side projects. My side projects folder is extremely full with lots of random things, and just I've had coding all the time. So yeah, that kind of happened in December, I would say. And I was looking at the repercussions of that sense.

**Stephanie Zhan** (2:28)
You've talked a lot about this idea of LLMs as a new computer, that it isn't just better software, it's a whole new computing paradigm. And Software 1 was explicit rules, Software 2 was learned weights, Software 3 is this.
If that's actually true, what does a team build differently the day they actually believe this?

**Andrej Karpathy** (2:50)
Right, so, yeah, exactly. So Software 1.0, I'm writing code. Software 2.0, I'm actually programming by creating datasets and training neural networks. So the programming is kind of like arranging datasets and maybe some objectives and neural network architectures. And then what happened is that basically, if you train one of these GPT models or LLMs on a sufficiently large set of tasks implicit, basically implicitly, because by training on the internet, you have to multitask all the things that are in the dataset.
These actually become kind of like a programmable computer in a certain sense. So Software 3 is kind of about, your programming now turns to prompting. And what's in the context window is your lever over the interpreter, that is the LLM, that is kind of like interpreting your context and performing computation in the digital information space. So I guess, yeah, that's kind of the transition. And I think there's a few examples of that really drove it home for me, and maybe that might be instructive. So, for example, when OpenClaw came out, when you want to install OpenClaw, you would expect that normally this is a bash script, like a shell script. So run the shell script to install OpenClaw.
But the thing is that in order to target lots of different platforms and lots of different types of computers you might run in OpenClaw, these shell scripts usually balloon up and become extremely complex. But the thing is you're still stuck in a Software 1 universe of wanting to write the code, and actually the OpenClaw installation is a copy paste of a bunch of text that you're supposed to give to your agent.