Jesse Zhang

And now our talent brand has gotten a lot larger than when we were first starting.

And so it definitely has gotten easier to hire.

But at the same time, our like, the amount of people we need to hire has also gone up constantly just finding ideas of how to get new people.

I mean, we just opened up our New York office, as you know, and part of the reason for that is that, hey, there's another talent pool over here, we should leverage that.

When we first started, this was about two years ago, people were still figuring out applications.

Almost no one was doing fine tuning.

In fact, anyone that was doing fine tuning, there was a lot of writing at that time was like fine tuning, quote unquote, doesn't work because it doesn't really get you that many gains.

Another big reason to not do fine tuning at that time was that the models are changing so fast.

You're still kind of figuring out your use case.

Why invest so much time in the fine tuning?

It's not reversible.

You're going to have to throw it out next time there's a new model release.

I think now the open source models, for example, have gotten to the point where it's definitely not smart enough to do everything, but there's a lot of specific use cases where you just don't need that much intelligence.

Example would be even the agent.

Let's say the first thing the agent does is it just needs to think about like, OK, based on what the user said and everything before, what path do I go down?

What data do I need?

You can make that fine tuned model.

That model doesn't have to be good at math or coding or anything.

It's just like you just need a smaller model that's just fine tuned on that.

Nowadays, we're seeing much more of that because a lot of the applications have gotten more mature.