Hey, this is Paris. I hope you enjoyed the Data Vampire series that we did back in October.

It's had a fantastic response. And for that series, I spoke to a bunch of experts.

And now we're releasing the full-length versions of those interviews for our supporters over on patreon.com.

And I wanted to give you a preview of what those interviews sound like.

So you can consider whether to go to

patreon.com slash tech won't save us, become a supporter yourself, so you can learn even more

about the important topics that we dug into in that special series. So enjoy this clip from my

interview with Alex Hanna. Why are these generative AI models so computationally intensive?

Yeah, so they're computationally intensive because they are so large,

and the process of training is a pretty computationally intensive process.

So it depends on how far you want to go back.

But if you go back to the original innovation of neural networks

and the kind of advent of back propagation,

back propagation is just a really intensive process

because you have this architecture.

When I say architecture, it's the kind of actual structure of the neural network.

There's a few that are more or less popular.

It depends on, you know, the latest and greatest large language model, you know,

has a certain kind of architecture, you know, and it differs.

You know, like I can't imagine there's a lot of daylight

between the cutting edge models at OpenAI versus Anthropic versus Google. Maybe there's some

kind of differentiation that they have. But at the end of the day, the actual parameter fitting,

...