Jaden Schaefer

really what they have is this kind of statistical understanding of patterns in data.

And of course, just this absolutely massive scale.

I think one thing that's important to remember is that intelligence itself is, you know, maybe the most, you know, like pattern recognition kind of prediction thing there is.

And once you scale that far enough, you start getting behavior that looks a lot like reasoning, but it's still just pattern recognition and prediction.

I think that's why the last few years feel a lot different.

This isn't just, you know, it doesn't feel like we have this kind of like hype cycle based on a bunch of like, oh my gosh, we're so close to X, Y, and Z and AI being able to do X, Y, and Z. Like we're seeing these systems actually work.

We're seeing them generate actual real economic value.

They're actually transforming how, you know, I work.

They're transforming how people code, how people write, research, design, build businesses.

Like

these AIs are actually helping us.

And so I think we've got past a lot of the earlier hype.

Now, of course, there's still plenty of hype today and people are overhyping many of their capabilities.

But I mean, you just have to look at how fast we've already progressed.

I think from my perspective, this is just the beginning of what these are going to be able to do, obviously, because we're seeing as you scale compute, as you scale data, they get smarter.

So I don't think we've hit a wall on where we go with those.

I think we're still super, super early.

Models are getting cheaper, faster, more capable.

You know, you can think of this in like a way you have like open AI who spends billions of dollars to train models today.

Some point in the near future, those same models are going to be trained at a fraction of the cost and anyone will be able to, you know, theoretically train those types of models.