Aaron Levie

We've got this great dynamic, which is you're now seeing improvements

TPUs sort of bring down costs on the Google side.

That's going to obviously continue to drive competition for the NVIDIAs of the world to have to continue to improve their pricing and their kind of gains.

So I think I basically, asterisk that I could totally be wrong, I think costs are not going to be the underlying problem with AI.

The things today where to your exact point, you've got to run something through a model 20 times.

Either, in a year from now, you'll run it through a model five times because the model will be 4x better, or it'll be 25% the cost because some other efficiency gain has happened that has caused the amount of times you run it through the model to be just much cheaper.

You can solve it through either variable.

Either the token goes down or the quality of the token goes up.

One or the other, you're going to end up being able to outrun this.

I'm talking my own book for a second, but this is where I think having the software layer be different from the model layer ends up being useful because we don't have any, I mean, we have got great relationships and friendships and partnerships, but we don't have like a technical bias toward one of the AI vendors.

So we will go where the model is best able to solve a particular problem in that domain.

And so you can easily imagine an agent in the future that says, okay, for legal contracts, I like Claude 3-7.

For financial documents, I like Gemini 2-5.

For large projects,

Code bases, I like Grok 3, 5, or whatever.

And it will route based on whatever data type it sees or the length of the data type or some other nuance.

It's not that hard.

We can technically do that today.

It's not that elaborate because we haven't found the need.

But you would want to rely on your vendor to do that, so a la box.