Published on November 10, 2025 9:02 PM GMT

Excerpt from Dwarkesh Patel’s interview with Andrej Karpathy that I think is valuable for LessWrong-ers to read. I think he’s basically correct. Emphasis in bold is mine.

Andrej Karpathy 00:29:53

I guess I built the repository over a period of a bit more than a month. I would say there are three major classes of how people interact with code right now. Some people completely reject all of LLMs and they are just writing by scratch. This is probably not the right thing to do anymore.

The intermediate part, which is where I am, is you still write a lot of things from scratch, but you use the autocomplete that’s available now from these models. So when you start writing out a little piece of it, it will autocomplete for you and you can just tap through. Most of the time it’s correct, sometimes it’s not, and you edit it. But you’re still very much the architect of what you’re writing. Then there’s the vibe coding: “Hi, please implement this or that,” enter, and then let the model do it. That’s the agents.

I do feel like the agents work in very specific settings, and I would use them in specific settings. But these are all tools available to you and you have to learn what they’re good at, what they’re not good at, and when to use them. So the agents are pretty good, for example, if you’re doing boilerplate stuff. Boilerplate code that’s just copy-paste stuff, they’re very good at that. They’re very good at stuff that occurs very often on the Internet because there are lots of examples of it in the training sets of these models. There are features of things where the models will do very well.

I would say nanochat is not an example of those because it’s a fairly unique repository. There’s not that much code in the way that I’ve structured it. It’s not boilerplate code. It’s intellectually intense code almost, and everything has to be very precisely arranged. The models have so many cognitive deficits. One example, they kept misunderstanding the code because they have too much memory from all the typical ways of doing things on the Internet that I just wasn’t adopting. The models, for example—I don’t know if I want to get into the full details—but they kept thinking I’m writing normal code, and I’m not.

Dwarkesh Patel 00:31:49

Maybe one example?

Andrej Karpathy 00:31:51

You have eight GPUs that are all doing forward, backwards. The way to synchronize gradients between them is to use a Distributed Data Parallel container of PyTorch, which automatically as you’re doing the backward, it will start communicating and synchronizing gradients. I didn’t use DDP because I didn’t want to use it, because it’s not necessary. I threw it out and wrote my own synchronization routine that’s inside the step of the optimizer. The models were trying to get me to use the DDP container. They were very concerned. This gets way too technical, but I wasn’t using that container because I don’t need it and I have a custom implementation of something like it.

Dwarkesh Patel 00:32:26

They just couldn’t internalize that you had your own.

Andrej Karpathy 00:32:28

They couldn’t get past that. They kept trying to mess up the style. They’re way too over-defensive. They make all these try-catch statements. They keep trying to make a production code base, and I have a bunch of assumptions in my code, and it’s okay. I don’t need all this extra stuff in there. So I feel like they’re bloating the code base, bloating the complexity, they keep misunderstanding, they’re using deprecated APIs a bunch of times. It’s a total mess. It’s just not net useful. I can go in, I can clean it up, but it’s not net useful.

I also feel like it’s annoying to have to type out what I want in English because it’s too much typing. If I just navigate to the part of the code that I want, and I go where I know the code has to appear and I start typing out the first few letters, autocomplete gets it and just gives you the code. This is a very high information bandwidth to specify what you want. You point to the code where you want it, you type out the first few pieces, and the model will complete it.

So what I mean is, these models are good in certain parts of the stack. There are two examples where I use the models that I think are illustrative. One was when I generated the report. That’s more boilerplate-y, so I partially vibe-coded some of that stuff. That was fine because it’s not mission-critical stuff, and it works fine.

The other part is when I was rewriting the tokenizer in Rust. I’m not as good at Rust because I’m fairly new to Rust. So there’s a bit of vibe coding going on when I was writing some of the Rust code. But I had a Python implementation that I fully understand, and I’m just making sure I’m making a more efficient version of it, and I have tests so I feel safer doing that stuff. They increase accessibility to languages or paradigms that you might not be as familiar with. I think they’re very helpful there as well. There’s a ton of Rust code out there, the models are pretty good at it. I happen to not know that much about it, so the models are very useful there.

Dwarkesh Patel 00:34:23

The reason this question is so interesting is because the main story people have about AI exploding and getting to superintelligence pretty rapidly is AI automating AI engineering and AI research. They’ll look at the fact that you can have Claude Code and make entire applications, CRUD applications, from scratch and think, “If you had this same capability inside of OpenAI and DeepMind and everything, just imagine a thousand of you or a million of you in parallel, finding little architectural tweaks.”

It’s quite interesting to hear you say that this is the thing they’re asymmetrically worse at. It’s quite relevant to forecasting whether the AI 2027-type explosion is likely to happen anytime soon.

Andrej Karpathy 00:35:05

That’s a good way of putting it, and you’re getting at why my timelines are a bit longer. You’re right. They’re not very good at code that has never been written before, maybe it’s one way to put it, which is what we’re trying to achieve when we’re building these models.

Dwarkesh Patel 00:35:19

Very naive question, but the architectural tweaks that you’re adding to nanochat, they’re in a paper somewhere, right? They might even be in a repo somewhere. Is it surprising that they aren’t able to integrate that into whenever you’re like, “Add RoPE embeddings” or something, they do that in the wrong way?

Andrej Karpathy 00:35:42

It’s tough. They know, but they don’t fully know. They don’t know how to fully integrate it into the repo and your style and your code and your place, and some of the custom things that you’re doing and how it fits with all the assumptions of the repository. They do have some knowledge, but they haven’t gotten to the place where they can integrate it and make sense of it.

A lot of the stuff continues to improve. Currently, the state-of-the-art model that I go to is the GPT-5 Pro, and that’s a very powerful model. If I have 20 minutes, I will copy-paste my entire repo and I go to GPT-5 Pro, the oracle, for some questions. Often it’s not too bad and surprisingly good compared to what existed a year ago.

Overall, the models are not there. I feel like the industry is making too big of a jump and is trying to pretend like this is amazing, and it’s not. It’s slop. They’re not coming to terms with it, and maybe they’re trying to fundraise or something like that. I’m not sure what’s going on, but we’re at this intermediate stage. The models are amazing. They still need a lot of work. For now, autocomplete is my sweet spot. But sometimes, for some types of code, I will go to an LLM agent.