And if your company has no value as a result, that's how the market works.

We should.

I would argue we should do three things.

One, we should actually probably pass policy to to guard against catastrophic outcomes.

This is a role of government.

This is an important role of government.

Don't kill us.

And you're close from evacuating much of southern Europe.

All

right.

So, so this, this brings me to the part of, sorry, that's kind of a loaded question.

That's a totally reasonable question.

It's a question that everyone should be asking.

It's a question that a lot of people are asking.

I have no, I don't take any issue with the question.

Barbara, you're hitting a chord, right?

So what do you think are some of the biggest misconceptions when it comes to AI, especially in a corporate setting?

I think right now people are grossly overestimating the economic detriment of AI and grossly underestimating the emotional one.

So October 1st, 2024, the Longshoremen go on strike.

I don't know if you remember this.

The dock workers of America walk off the eastern seaboard.

And on the morning of October 2nd, the head of the Longshoremen, Harold Daggett, goes on CNN and gives this very long, pretty powerful interview.

And he opens the interview by pointing at the camera.

How old?

you a

baseball fan? Yeah.

Third inning.

It's funny.

People either pick second inning or eighth inning.

I've heard some people do nine.

Third is great.

And I think one of the questions that comes up a lot, again, as consumers and as investors, is what does the AI renaissance mean for the future of work? How does it empower humans, you know, rather than replace them? What does that look like in the next, you know, 5, 10, 15 years in your view?

In your mind, has that been shattered or do we just need to reassess what that moat was? Because it seemed like a lot of that moat was based on capital and scale.

Well, I'll start by saying I think outside of the investment world, I think a lot of people have been waiting for a moment where we started to see the research commoditize.

And certainly, this is one of those moments.

I mean, seeing a Chinese firm produce a GPT-4 equivalent model of any kind was remarkable.

Then to see it actually be so economically efficient is truly astounding.

Calling a paradigm shattered because of one model is pretty complicated.

But I do think it is a reminder to everyone that just because we live in a world today where models require an exceptional amount of compute, an exceptional amount of data, an exceptional amount of energy, does not mean that we will always live in that world.

When we're looking at the development of AI and the idea of the U.S. having dominance or at least a lead when it comes to AI, how significantly has that changed? Do we need to see a government response, a private company response, a partnership response? What would you say?