Clem Delangue

You've probably heard of DeepSeq, of Quen, of Kimi.

There are a bunch of companies and organizations in China contributing massively to the field of open source.

Well, I was asked if we were in an AI bubble, and I said we're probably not in an AI as a general field bubble, but I feel like if there's one specific domain of AI where there's

so much investment that there's maybe a risk of over-investing.

It's large language models distributed behind APIs, right?

Like you see the building of crazy data centers for it.

And obviously you see a lot of revenue growth, but with kind of like uncertain margins and certain kind of like long-term sustainability and mode for it.

So if there is a bubble, it's probably an LLM, but we'll see what happens in the next few months.

So the interesting thing is that we've had these conversations and this kind of like talking point for a while in AI when we were earlier taking face, I think six, seven years ago.

At the time it was GPT-2 and there was already like a lot of people saying that it was too dangerous to release in open source at the time, right?

It was six, seven years ago when basically it was nothing more than just an auto-complete.

I think we've seen progressively that these were quite overblown.

And I think they're also overblown today, right?

And the whole point is that, you know, Mitos, I think when it was announced, was it like three weeks ago, a month ago?

it was crazy dangerous and now it's starting to be deployed kind of like everywhere, right?

I think they just gave access to the first international organization in South Korea, I think yesterday or something like that.

And probably in a few weeks or in a few months, everyone is going to be using Mitos and not kind of like destroy the world as a result.

So I think with the current models, it's safe to release behind APIs.

It's safe to release in open source.

And it's actually the safest way because it gives everyone the capabilities to not only