Jaden Schaefer

We realized we needed to have much bigger models.

The data sets we realized had to get much larger.

And then training runs went from, you know, like it used to be like hours to weeks.

And then it started getting pushed into months.

And eventually we've arrived at a lot of these large language models.

And we have the kind of AI that can read, write, reason, and talk.

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.