Lex Fridman

And nowhere is that more true than in the process of idea development.

As I talked about in a recent episode with Michael Levin, do we have ideas or do ideas have us?

And ideas have a way of kind of occupying, for a time or for a generation, the brains of multiples of people.

And those ideas are formed and shaped and modified and evolved.

across those brains, utilizing those brains, or the brains do the modification.

Whichever it is, you want to have the best tools for the job, and Miro's incredible for that.

Converts sticky notes, screenshots, and so on into diagrams or prototypes in minutes.

Super easy to use, makes teamwork fun, help your teams develop great ideas into results with Miro.

Go to Miro.com to find out how.

That's M-I-R-O dot com.

This episode is also brought to you by Chevron, an energy company that delivers affordable, reliable energy to US data centers.

I'm attending NeurIPS in San Diego, which is a machine learning conference.

And boys, one thing clear, aside all the fascinating technical details...

the turmoil of the research world, the excitement, the wonder, the mystery, beyond all that is the reality that in order for the scaling laws to hold, the compute needs to scale.

And for the compute to scale,

We need energy.

In the United States, the scaling of the energy infrastructure is essential because the demand for electricity is growing at an unprecedented scale.

Chevron is working hard to provide multi-gigawatts of delivered power with the flexibility to scale further.

Energy is not an easy problem, especially if the scaling laws hold, especially if there's benefits to the products that rely on artificial intelligence, both for the training side and the inference side.

And frankly, I think it is the inference side that will over time consume more and more energy and require more and more compute.