Dwarkesh Patel

But I meant to use that example to illustrate that it's possible to go superhuman to superhuman plus plus to superhuman plus plus plus plus.

And I'm curious if you think those gradations will continue to happen by just making the method simpler.

Or because we'll have the capability of these millions of minds who can then add complexity as needed, that will continue to be a false path even when you have billions of AI researchers or trillions of AI researchers.

Yeah, for sure.

It's interesting that both quant firms and AI labs have a culture of secrecy because both of them are operating in incredibly competitive markets and their success rests on protecting their IP.

If you're an AI researcher or engineer and you're deciding where to work, most of the quant firms or AI labs that you'll be considering will be strongly siloing their teams to minimize the risk of leaks.

Hudson River Trading takes the opposite approach.

Their teams openly share their trading strategies, and their strategy code lives in a shared monorepo.

At HRT, if you're a researcher and you have a good idea, your contribution will be broadly deployed across all relevant strategies.

This gives your work a ton of leverage.

You'll also learn incredibly fast.

You can learn about other people's research and ask questions, and you can see how everything fits together end-to-end, from the low-level execution of trades to the high-level predictive models.

HRT is hiring.

If you want to learn more, go to HudsonRiverTrading.com slash Dwarkash.

All right, back to Richard.

I guess this brings us to the topic of AI succession.

You have a perspective that's quite different from a lot of people that I've interviewed and maybe a lot of people generally.

So I also think it's a very interesting perspective.

I want to hear about it.

Yeah, I agree with all four of those arguments and the implication.