Logan Kilpatrick

They didn't have this general purpose intelligence.

It wasn't actually that useful.

Like making something by default that is good at playing games doesn't end up being like beneficial for most people.

But making something that's really smart and good at things that can also play games, hopefully will be the way.

And I think of like LLMs as this like delivery mechanism for intelligence that like hopefully continue to generalize across all these different domains.

I think for, um, so we're not like one of the, I was just in a meeting and we were talking about this.

One of the explicit things with AI studios, like we're not trying to solve every problem.

Um, so like really there are great tools that are out there in the ecosystem.

Some, some of which are made by Google, like Gemini CLI, some of which are made by the rest of the ecosystem.

And really the sweet spot of what we can do in AI Studio is sort of get you a feel for what the models are capable of.

Hopefully with all the vibe coding stuff we're doing and with build mode, get you a working prototype and then go and get you out into like a full-fledged sort of professional developer product.

And that could be your IDE of choice, your CLI of choice, et cetera.

And I think that's where

I think that's where the, like one of those user journeys is that people care a lot about.

I think the one you're describing of like large code bases that already exist, this is actually one of the even more complex problems that AI has to solve.

Because that like text to prompt use case actually works pretty well.

The problem with the large code bases, if folks haven't,

had to experience this before is like, there's just lots of like context buried in a bunch of different places.

And it's really difficult to sort of orchestrate this together.

And this is like a very human problem.