Nick Talken

Their role is to say, OK, this is the infinite space.

I know if I'm making a paint.

it needs to have at least 50% water.

Okay, right there, you've now constrained that problem much more than any possible combination.

And I know it needs to have these type of attributes.

And it can't have 100 ingredients because I can't manufacture that reasonably.

So the scientist comes in with a real-world constraint building, and they start to put constraints on it.

And that's how you, first of all, solve the problem sooner.

It's also how you reduce the amount of compute to go brute force this problem.

And I think...

It comes into, you know, the question we get all the time is, you know, are we replacing scientists?

For sure, we're trying to replace a piece of the scientific process that they're going through.

Are we trying to replace the entire scientists?

Absolutely not, because the scientists still need to come in and give those guardrails for how the AI should go approach the problem.

If we're headed to a place of like huge amounts of prosperity, right, where we're starting to automate these tasks, you know, at some point we may get to a point where everyone's job has fully been replaced.

So then the question isn't, you know, is that the inevitable end state at some point in the future?

The question is like, what's the last job that's going to be out there, right?

I think like scientists and some of those types of fields, those are going to be some of the last jobs that are out there because they're very challenging roles to be in to just fully autonomous, fully automize the entire process.

And so I think if I was a scientist listening to this conversation, I wouldn't worry too much about it today.

These generations, I think we're going to have a lot of cool tools to play with and not something that's just going to completely replace it.