Hey everybody, Rob Maurer here, and today we are joined by James Dauman to talk about

Tesla's approach on autonomous driving specifically on neural networks.

James has a ton of knowledge on this space, he was actually on the podcast back in 2018

for those of you that have listened for a long time, and today we're continuing some

of those conversations, learning a little bit more about Tesla's strategy here.

So this is a multi-part conversation, make sure to subscribe for part two, and with that,

we'll jump right in.

I've been doing neural networks for a really long time, although it hasn't been a job.

And lately it's turned into an investing thesis.

So for the last decade or so, I've been a full-time investor.

I just invest my own portfolio, and my MO is, I look for companies where the technology

difference in their approach is something which is a good fit for my background and expertise.

And then I tend to do deep dives on them.

And so Tesla's one of my, Tesla's actually my deep learning company.

It's 10 years ago before I got into Tesla.

When I was looking for companies that would be able to keep using,

make good use of computation as it continues to expand.

And you probably know that Moore's Law is, it's been something that it keeps going.

And people inside the field think it's going to die every year.

I understand that there's this joke in the printing business about how the second

book Gutenberg printed was about how the publishing industry was doomed.

And Moore's Law is kind of like that.

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