Open weights do not make AI models work like open-source software

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 36:40

   now i'd love to hear your perspective on this because one of the things people have talked to me about when it comes to the anthropic business is there is that risk eventually that open source gets good enough that you can take anthropic out and put open source in yeah so you know people have i you know i think one of the things that's been true of this industry is that and you know i saw it early in the history of AI. Every community that AI has gone through, it has this set of heuristics about how things work. Like back when I was in, you know, AI back in 2014, there was an existing kind of AI and machine learning research community that like thought about things in a certain way. And we're like, this is just a fad. This is a new thing. This can't work. This can't scale. And then because of the exponential, all those things turned out to be false.

2. 37:31

   Then a similar thing happened with kind of like people deploying AI within companies to various applications. Then there was the same thought in the startup ecosystem. And I think now we're at the phase where kind of the world's business leaders, like the investors and the business, they have this whole lexicon of commoditization, you know, modes, which layer is the value going to, you know, which layer is the value going to accrue to.

3. 37:57

   And open source is this idea that you can kind of see everything that's going on, you know, that it has a significance, that it kind of undermines the fact that, you know, the idea that it undermines business. And I actually find as someone who didn't come from that world at all, who never thought in terms of that lexicon, this is one of these situations where not knowing anything often leads you to make better predictions than kind of the people who have their way of thinking about things from the last generation of tech. And, you know, this is all, I think, a long-winded way of saying, I don't think open source works the same way in AI that it has worked in other areas.

4. 38:39

   Because with open source, you can see the source code of the model. Here, we can't see inside the model. It's often called open weights instead of open source to kind of distinguish that. But a lot of the benefits, which is that many people can work on it, that it's kind of additive, it doesn't quite work in the same way. So I've actually always seen it as a red herring. When I see a new model come out, I don't care whether it's open source or not.

5. 39:06

   Like, if we talk about DeepSeek, I don't think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at, you know, the things that we, that's the only thing that I care about. It actually doesn't matter either way. Because ultimately, you have to host it on the cloud. The people who host it on the cloud do inference. These are big models. They're hard to do inference on.

6. 39:29

   And conversely, many of the things that you can do when you see the weights, you know, we're increasingly offering on clouds where you can fine tune the model. You can, you know, you know, we're even looking at ways to, you know, to to kind of, you know, investigate the activations of the model as part of like an interpretability interface. We did some little things around steering last time. So I think it's the wrong axis to think in terms of when I think about competition, I think about like which models are good.

7. 39:59

   at the tasks that we do. I think open source is actually a red herring. But if it's free and cheap to run. It's not free. You have to run it on inference and someone has to make it fast on inference.