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Speaker 1: Pushkin. Welcome back to Risky Business, a show about making

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Speaker 1: better decisions. I'm Maria Kanikova.

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Speaker 2: And I'm Nate Silver.

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Speaker 1: Nate. We are both in Vegas. So we're going to

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Speaker 1: start the show off with a little update of how

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Speaker 1: our first week of the world series has been going,

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Speaker 1: whether we are still as bright eyed and bushy tailed

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Speaker 1: as if we were this time last week. And then

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Speaker 1: we'll talk a little bit of politics, right, Yeah.

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Speaker 2: We'll talk about rank choice voting its implications for the

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Speaker 2: Democratic mayoral primary which is coming up in New York

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Speaker 2: as well. It's a little bit more abstractifying that in

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Speaker 2: terms of is this a good system, what does it

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Speaker 2: reward or punish? And then we'll talk about AI a

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Speaker 2: little bit, I know, a frequent topic on this show.

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Speaker 2: I was recently at the Manifest conference in Berkeley, California

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Speaker 2: before it came out here to Vegas. A little bit

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Speaker 2: of an update on the AI twenty twenty seven report,

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Speaker 2: which we talked about on a previous episode, and just

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Speaker 2: kind of how we're feeling about pe doom and kind

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Speaker 2: of how the Silicon Valley community is adjusting to the

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Speaker 2: constant developments in the sector.

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Speaker 1: Yeah, I'm looking forward to hearing what you learned at

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Speaker 1: that conference. But let's start a little closer to home

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Speaker 1: and talk about pe doom of our bank rolls. So, Wsophie,

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Speaker 1: how has week one treated unit?

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Speaker 2: So? I have fired fifteen bullets. I know it's a

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Speaker 2: violent metaphor. We're not super PC here, but a bullet

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Speaker 2: in a poker term is like an entry into a tournament. Right,

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Speaker 2: So I probably entered ten tournaments, because you can enter

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Speaker 2: some multiple times. I've had fifteen entrees into those ten tournaments.

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Speaker 2: Of those fifteen entries, I've had three caches or twenty percent,

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Speaker 2: which is pretty typical. Right. You're typically cash, depending on

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Speaker 2: the tournament, twelve to fifty percent of the time. So

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Speaker 2: I guess a little bit above average.

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Speaker 1: Right.

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Speaker 2: However, here's why it's hard to make money in the

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Speaker 2: short run, the medium run in poker tournaments.

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Speaker 1: Right, in the short run, the medium run in the

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Speaker 1: long run.

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Speaker 2: Period number one, even if you outlast ninety five percent

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Speaker 2: of people, you often only make broughly like two times

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Speaker 2: or maybe more than that. Right, So if you invest

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Speaker 2: fifteen hundred dollars in a tournament and you make it

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Speaker 2: to like the ninety fourth percent diile, the prize might

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Speaker 2: be for to have one hundred so net of the

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Speaker 2: fifteen hundred you paid to enter, right if you entered

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Speaker 2: twice and like so like. Basically, it's a very top

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Speaker 2: heavy structure where you have to get into like the

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Speaker 2: top two percent, or you have to run good and

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Speaker 2: cash in the more expensive events. Right, So the cashes

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Speaker 2: I have are in an eight hundred dollars event, fifteen

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Speaker 2: hundred dollars event, a eleven hundred dollars event. I also

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Speaker 2: entered a ten thousand dollars event that like quick Exit

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Speaker 2: three thousand dollars event quick Exit, right, you have those

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Speaker 2: Basically either have to run really really deep or have

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Speaker 2: your cash as being your higher buying events. I've done either.

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Speaker 2: Therefore I can say, oh, I have cash plenty. I've

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Speaker 2: been in the running, been in the mix, but like

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Speaker 2: still down money and that's often very typical.

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Speaker 1: How about you, Maria, So I have not played nearly

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Speaker 1: as many live events as you. I only have one

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Speaker 1: live cash so that was the first event that I played,

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Speaker 1: the eight hundred, and then I played the three thousand

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Speaker 1: dollars freeze out and did not cash that and the

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Speaker 1: monster stacks. As you know, Nate, that did not go

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Speaker 1: well for me. That was one of the ones that

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Speaker 1: you cashed. And then yesterday I played the three thousand dollars.

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Speaker 1: Basically it was a turbo, and you played that as well,

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Speaker 1: and neither one of us cashed. Luckily, I only had

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Speaker 1: to fire one bullet. I was unable to revise, so

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Speaker 1: that's good. That saved me three thousand. However, what has

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Speaker 1: kind of saved my last week is online. So I

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Speaker 1: fired a few of the online events, came in fifty

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Speaker 1: seventh out of like twelve hundred or so in one

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Speaker 1: of them for about three thousand dollars. And then last

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Speaker 1: night came in seventh firing a three hundred and fifteen

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Speaker 1: dollars event or three hundred and twenty. And that was

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Speaker 1: very or sweet because seventh was about seven thousand dollars,

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Speaker 1: which is a great ROI it was only in for

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Speaker 1: one bullet, right, you pay three hundred and twenty dollars

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Speaker 1: and you get seven thousand, which is amazing. First, as

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Speaker 1: you know, would have been a lot more. And I

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Speaker 1: was very proud of myself for getting that far because

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Speaker 1: I had an incredibly short stack. Basically the entire time,

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Speaker 1: I just kind of never had chips to work with,

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Speaker 1: and I ended up making it quite far. But that

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Speaker 1: kind of saved my my week a little bit because

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Speaker 1: live has not been going well, well, it's been going

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Speaker 1: you know, it's a tiny sample size, right, This is

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Speaker 1: kind of what you have to understand that you need

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Speaker 1: to kind of think of things in a bigger picture

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Speaker 1: as opposed to touch just saying oh, I've only because

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Speaker 1: I've only played four live events, right, So the fact

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Speaker 1: that I, yeah, so the fact that I catched.

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Speaker 2: Yeah, I don't. I have liked the online I've kind

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Speaker 2: of sworn off online poker.

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Speaker 1: Yeah, but yeah, there are definitely issue with online, but

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Speaker 1: it's it's helped. It's helped me stay more even than

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Speaker 1: negative for the first week.

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Speaker 2: The default experience at the World Series pokers, you're always

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Speaker 2: like one or two contingencies away from having like a

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Speaker 2: really great world series and one or two continues away

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Speaker 2: from having a really bad world series. Right. You know,

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Speaker 2: I made it to the very end of day two

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Speaker 2: of the Monster Stack. You are in the money, right,

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Speaker 2: And then I made a huge semi bluff if you

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Speaker 2: don't follow at home, some my bluff is when you

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Speaker 2: are hoping to get the opponent to full, but if not,

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Speaker 2: then you have a good draw that will win sometimes.

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Speaker 2: So I had basically a straight flush draw and it

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Speaker 2: was a turn, so you now have only one card

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Speaker 2: to make your hand. And the opponent I thought was

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Speaker 2: pretty strong, but I thought the odds compelled this play

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Speaker 2: and I whatever, I'm there, I'm there to run deep,

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Speaker 2: I'm the money. I went for it. He had a

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Speaker 2: set of queens. I didn't make my death.

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Speaker 1: That was not going to shold. Yes, there's it's funny.

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Speaker 1: And you say it felt like the opponent was strong,

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Speaker 1: and I think we've all had that feeling. You know,

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Speaker 1: we're like just based on the way someone's playing, based

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Speaker 1: on kind of the dynamics at the hand. You know

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Speaker 1: the person is strong, but they're strong, and they're strong, right.

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Speaker 1: A set of queens is a hand that's probably not

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Speaker 1: going to fold. If you had like naked top hair

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Speaker 1: something like that, that that might fold. And it's hard

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Speaker 1: sometimes it's hard to know the difference. But you had

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Speaker 1: a straight flushtraw. I think that you have to go

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Speaker 1: for it in that situation. And Nate, you and I

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Speaker 1: are playing for the win, not for the for them.

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Speaker 1: In cash, right, we're playing to get chips and to

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Speaker 1: win this thing. So let's do it, and let's have

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Speaker 1: a better update for our listeners next week. So, Nate,

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Speaker 1: let's turn to a totally different topic. Let's go politics

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Speaker 1: and talk a little bit about ranked choice voting. This

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Speaker 1: is correct me if I'm wrong, the second election in

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Speaker 1: New York City where the voting has been changed, so

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Speaker 1: that now we are in a situation where we have

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Speaker 1: ranked choice voting instead of the usual first past the

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Speaker 1: post system. So let's let's talk just a little bit

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Speaker 1: about what the differences are. I am correct, right, this

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Speaker 1: is the second auction where this is in play.

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Speaker 2: Correct, Yeah, only for the primary. Yes, actually it's not

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Speaker 2: used in the general election, which could also be chaotic.

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Speaker 2: We'll probably get to that later on.

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Speaker 1: Yep, all right, So normal you know, first past the post,

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Speaker 1: person who gets the most votes wins, right, and there

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Speaker 1: can be runoffs if no one gets the requisite majority.

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Speaker 1: In ranked choice voting, it doesn't quite work the same way.

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Speaker 1: You get to rank your candidates in order, you know, one, two, three, four, five.

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Speaker 1: However many ranked choices you have in a given election,

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Speaker 1: there's every Every election is a little bit different. So

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Speaker 1: let's let's pretend that we're voting for our favorite podcasters,

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Speaker 1: and just a random, random example. And let's imagine that,

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Speaker 1: you know, a bunch of people get lots of votes,

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Speaker 1: and I when you tally everyone else everyone up, I'm

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Speaker 1: Maria come in last. So I'm going to be cut

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Speaker 1: because the last person who comes in with you know,

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Speaker 1: the fewest votes is going to be cut. But what's

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Speaker 1: going to happen to my ballots? Well, I'm going to

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Speaker 1: be cut, but the people who ranked me, who wanted me,

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Speaker 1: who ranked me first, whoever they ranked after me, is

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Speaker 1: going to get all of those votes. So imagine that

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Speaker 1: everyone who voted for me actually put Nate after me. Right,

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Speaker 1: And when you cut me between the people who voted

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Speaker 1: for Nate first and the people who voted for Nate

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Speaker 1: after me, all of a sudden, Nate has lots and

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Speaker 1: lots of votes. And that's how ranked choice voting works. Right,

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Speaker 1: Your votes get redistributed.

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Speaker 2: Nate.

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Speaker 1: Congratulations, I've given you all of my votes and you're

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Speaker 1: going to be You're going to be zooming at those podcasts.

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Speaker 2: My votes got redistributed to Ezra Klein.

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Speaker 1: Apparently we tried, we tried, right, but that's actually so

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Speaker 1: you know, we joke, but there is a lot of

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Speaker 1: st know, there are pros, there cons and there's also

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Speaker 1: strategy that comes up with ranked choice voting where people

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Speaker 1: and this has actually played out in elections in the past,

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Speaker 1: where people do have coalitions and try to say, okay,

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Speaker 1: like let's try to band together so that if we're

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Speaker 1: you know, if one of us is eliminated art bo,

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Speaker 1: let's get passed to the other person. So that's kind

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Speaker 1: of ranked choice in a nutshell Nate. The last time

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Speaker 1: this was used in New York City, it was a

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Speaker 1: little bit of a disaster.

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Speaker 2: Well, it depends on who you would ask, right, So

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Speaker 2: you had Eric Adams, who's a former cop in the

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Speaker 2: conservative lane of the Democratic primary, not conserned relative to

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Speaker 2: the US overall, but they are actually a fair number

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Speaker 2: of conservatives who are Democrats in New York because there's

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Speaker 2: no point in being a Republican. So therefore, you know,

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Speaker 2: like fifteen percent of New York City Democrats voted for Trump,

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Speaker 2: which is much higher than nationwide. You had Maya Wiley,

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Speaker 2: who's the progressive candidate, and you had Catherine Garcia, who

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Speaker 2: was at the time, I believe, the Sanitation commissioner, who

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Speaker 2: was like the competent technocrat right. So what ranked choice

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Speaker 2: voting is sort of theoretically supposed to do is produce

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Speaker 2: a consensus choice where, yeah, the left likes Wiley, the

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Speaker 2: right likes Adams, and the center again relative to the

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Speaker 2: New York electorate, likes Garcia. However, you know, in principle,

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Speaker 2: you could have what's called the condors winner. It's a

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Speaker 2: French political scientist. I believe somebody condor sit where like,

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Speaker 2: let's say Garcia, because she's in the middle, that all

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Speaker 2: her voters. So if she were head to head against Wiley,

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Speaker 2: she wins because both the moderates and the conservatives prefer her.

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Speaker 2: If she's head to head against Eric Adams, she wins

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Speaker 2: because like all the moderates and the progressives prefer her. Right. However,

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Speaker 2: if the previous round is like if she's stuck in

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Speaker 2: the middle in third place, if it's you know, forty

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Speaker 2: percent Atoms, forty percent Wiley again, Conservative progressive, twenty percent Garcia,

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Speaker 2: then she's eliminated before she has a chance to beat

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Speaker 2: the other candidates one on one. What actually happened is

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Speaker 2: she just barely beat Wiley in the penultimate round and

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Speaker 2: then just barely lost to Adams in the final round. Right,

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Speaker 2: And so what you almost had happen is that the

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Speaker 2: candid who what have been the winner had to head

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Speaker 2: almost got elimina because she didn't have enough like first

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Speaker 2: choice votes after other votes were redistributed. I know that

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Speaker 2: sounds kind of crazy, but number one, I want to

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Speaker 2: emphasize that, Like, you know, one thing that helped her

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Speaker 2: was that she had agreed with Andrew Yang, who at

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Speaker 2: one point had been their frontrunner and then faded out.

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Speaker 2: You know, we're going to mutually endorse one another. Right.

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Speaker 2: It's a big burden on voters to have to list

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Speaker 2: five candides.

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Speaker 1: Yeah. No, So we've talked a lot on the show

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Speaker 1: about cognitive load, right, like how many things you can

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Speaker 1: keep in mind? Like we're busy people. People's attention is

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Speaker 1: an incredibly finite resource, Like just how much of your

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Speaker 1: time are you investing in following politics and following all

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Speaker 1: the candidates you know and trying to get into the

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Speaker 1: weeds and kind of figure out, Okay, who stands where

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Speaker 1: who do I believe in? It's much easier to say, oh, okay,

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Speaker 1: like I like this guy or this woman right then

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Speaker 1: to say okay, this in order these are my five

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Speaker 1: preferences and so, and people can get confused as well, right,

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Speaker 1: if they don't quite understand how ranked choice voting works.

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Speaker 1: They might think that it's like, oh, I'm giving points, right,

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Speaker 1: because you know, sometimes you have these voting systems where

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Speaker 1: you allocate different points to different people, and that's just

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Speaker 1: basically how it works, right, Like, oh, I gave most

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Speaker 1: of my points to Nate, I gave second most points

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Speaker 1: to Maria, like, and everyone's going to get those points

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Speaker 1: talied up. That's not how ranked choice voting works. But

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Speaker 1: people sometimes they you know, especially if you've never done

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Speaker 1: it before, it is it can be confusing. It seems

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Speaker 1: straightforward ranked five, but it's not because ranked voting systems

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Speaker 1: do differ and we're not used to it, right, We're

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Speaker 1: not used to the system where like, I don't know

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Speaker 1: any other voting in your life where you vote and

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Speaker 1: like your votes get redistributed to your second choice, right, Like,

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Speaker 1: that's that's not something that you that's not a situation

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Speaker 1: you commonly encounter. And so unless like, you know, and

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Speaker 1: you can think through the strategy of okay, what does

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Speaker 1: that mean for hand, do I rank candidates? Then you

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Speaker 1: can get an outcome that you didn't actually mean to happen.

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Speaker 2: Yeah, yeah, I know. I mean there are systems. They're alternatives,

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Speaker 2: like what is called approval voting, where you just list

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Speaker 2: as many you vote for, as many candidates you want, right,

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Speaker 2: as many candidates you like you think would be a

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Speaker 2: good mayor good president, whatever, just check the box. Like

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Speaker 2: that's a little bit more straightforward and not subject to manipulation.

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Speaker 2: You know. Also, like, guess which people take the time

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Speaker 2: to carefully think through their five preferences in New York

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Speaker 2: you get five listings, right, it's the college educated professional

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Speaker 2: class who read the New York Times in New York,

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Speaker 2: right and like and research all the candidates and do

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Speaker 2: their duty and like. So it kind of like quasi

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Speaker 2: disenfranchises voters that might be struggling a bit more or

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Speaker 2: might not be kind of as news consuming. I'm not sure,

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Speaker 2: like at quite so much, right, you know here, I mean,

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Speaker 2: so to discuss the problem, we have a good freelance

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Speaker 2: article silver Bolting about this last week, right, but like, so,

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Speaker 2: what problem is you have? Like zo Ran Mamdani a

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Speaker 2: former or current member of the Democratic Socialist of America right,

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Speaker 2: so quite left ring but has left wing but has

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Speaker 2: run like a attractive campaign. I mean he has good commercials.

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Speaker 2: He's good looking, good looking guy. Right, he has some energy.

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Speaker 2: He's young in a political environment where there's too many

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Speaker 2: fucking old people. Right, you know, is quite far left. Yeah,

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Speaker 2: is quite far left. But has proposals like free bus service,

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Speaker 2: like city run grocery stores in food deserts, a thirty

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Speaker 2: dollars an hour minimum wager. Thirty five are really high

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Speaker 2: or something like that. Right, but like it's actually stuff

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Speaker 2: that like kind of like poll tests pretty well, like

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Speaker 2: a rent freeze, Like economists hate this idea, but like,

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Speaker 2: but he has made it a very competitive race since Cuomo.

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Speaker 2: But there are plenty of voters who think, I don't

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Speaker 2: like either one. Right, Cuomo hasn't lived in New York

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Speaker 2: in thirty years. He's a meat too stuff. He's a

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Speaker 2: carp bagger. He's domineering. We don't want our own Trump.

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Speaker 1: He eats Bagel's the wrong way.

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Speaker 2: Zoran is too far to the left. People have issues

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Speaker 2: with him on Israel if they're pro Israel, they is

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Speaker 2: with him on budgeting and taxes and a lack of experience. Right,

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Speaker 2: and you have like all these like technocrats like running

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Speaker 2: in the center lane, Brad Lander, Adrian Adams, Scott Stringer,

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Speaker 2: Zilar myrie. Right, you have a hedge funder named Whitney Tilson,

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Speaker 2: I think, and so like. So you have like all

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Speaker 2: these technocrats running, and what's going to happen is that,

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Speaker 2: like you know, one of the technocrats are probably finished

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Speaker 2: in third place and be eliminated, even though in principle

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Speaker 2: you could have like an Adrian Adams or a Brad

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Speaker 2: Lander who would win head to head against these two

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Speaker 2: polarizing candidates.

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Speaker 1: And we'll be back right after this.

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Speaker 2: Now, the New York Times did something interesting, right, which

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Speaker 2: is they they said, yeah, the Times declared a year

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Speaker 2: or so ago that we're near going to endorse in

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Speaker 2: local races, right.

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Speaker 1: Which is bizarre. Right, Well, I don't.

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Speaker 2: Think newspapers should make endorsements. I think it's not that Yeah.

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Speaker 1: Fine, so if you don't think they should make an

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Speaker 1: endorsements period, that makes sense. But if they're making endorsements

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Speaker 1: in national elections, they.

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Speaker 2: Do in local rates. I mean even mostly The New

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Speaker 2: York Times endorse both Amyklobishar and Elizabeth Warren in the

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Speaker 2: twenty twenty Democratic primary, right, which did neither any favors

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Speaker 2: I don't think, right, and they do not use ranked

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Speaker 2: choice voting in most states in the primary this time,

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Speaker 2: they're like, don't vote for Zoran and rank Cuomo somewhere.

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Speaker 2: But then like rank like Lander or Tilson first, and

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Speaker 2: like I put the editorial through like different large language

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Speaker 2: models AI models, Right, so we're like, yeah, it's tell

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Speaker 2: you to vote for Brad Lander and then put Cuomo fifth.

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Speaker 2: So like so if it comes down to Cuomo versus

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Speaker 2: O run that he will he will prevail, right, but

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Speaker 2: others were confused, right, these events almost like they kind

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Speaker 2: of say all these mean things about Cuomo and the

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Speaker 2: state of vote for him anyway, right, so very very

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Speaker 2: weird and half assed. And then Lander has crossed endorsed

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Speaker 2: mam donnie, and so it just kind of like it

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Speaker 2: adds an element of randomness that to be fair, Cuomo

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Speaker 2: is almost certain to win the first choice vote. Right,

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Speaker 2: So in a normal Marri election, because there is kind

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Speaker 2: of an anti Cuomo anybody but Cuomo coalition which is

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Speaker 2: pretty broad right and have different flavors of it, right,

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Speaker 2: and a normal election, Cuomo just wins, goes to the

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Speaker 2: general election and so forth Right here, Ma'm Donnie has

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Speaker 2: a decent chant. He's at twenty five percent on polymarket.

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Speaker 2: Where as you probably know, I'm an advisor. And by

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Speaker 2: the way, I was into Mam Donnie early. I thought, like,

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Speaker 2: you know, here's a guy running a pretty energetic campaign

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Speaker 2: at a moment when like, I don't think New York

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Speaker 2: is like suddenly having some like progressive spawn. I think,

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Speaker 2: like people are sick of the establishment, and no one's

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Speaker 2: a bigger figure of the establishment than Cuomo, right, and

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Speaker 2: he and he, you know, and he's running a campaign

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Speaker 2: that feels a little bit different, and I think in

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Speaker 2: in city elections it doesn't have to be so ideological anyway.

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Speaker 2: Like I'm to his right, I would I wouldn't be

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Speaker 2: terrified by his mayorship by any means at all. Right,

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Speaker 2: but he's he's overperformed, almost underperformed. As a result, he

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Speaker 2: has a real shot. The general election is a mess, however,

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Speaker 2: because like all these candidates basically have vehicles that can

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Speaker 2: run with various minor parties in the general election. Right,

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Speaker 2: so you'll have already the current incumbent mayor, Eric Adams

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Speaker 2: is running as an independent. You'll have Republican County. I

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Speaker 2: think Curtis Silba, who's like the former Guardian Angel die right,

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Speaker 2: and you could have both like Mam Donnie if he loses,

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Speaker 2: could run on the working Famili's ticket Quoma if you lose.

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Speaker 2: I think there's some special vehicle third party that could run.

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Speaker 2: So rarely usually just a matter of the Democratic primary

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Speaker 2: mattering we're going to have a competitive general election in

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Speaker 2: New York too, it looks like and then it's very weird.

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Speaker 2: Then you get into things where like then it's no

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Speaker 2: longer ranked choice voting, and you have Cuomo and Adams

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Speaker 2: have these kind of overlap right, and so you could

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Speaker 2: see like mom Donnie wim with a plurality, even though

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Speaker 2: he probably would lose with ranked choice voting. When you

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Speaker 2: bring the more conservative voters in play, then there's probably

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Speaker 2: in anybody but Buzo run coalition. So it's it's all

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Speaker 2: kind of a mess. We like messes in.

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Speaker 1: New York, Maria, but this particular mess, it is a

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Speaker 1: particular mess. And you know something that I've seen that's

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Speaker 1: kind of interesting because normally, you know, you have people

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Speaker 1: like endorse candidates and say, you know, this is why

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Speaker 1: you vote for this one in New York because of

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Speaker 1: what happened last time, And because people don't quite get

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Speaker 1: ranked choice all the time, I've been seeing a lot

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Speaker 1: of people instead of saying, you know, vote for Zoron

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Speaker 1: or whatever it is, they say, don't rank Cuomo, right, Like,

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Speaker 1: that's the messaging has shifted to, like, just do not

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Speaker 1: put him as any of your top five choices, even

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Speaker 1: if you like would marginally like prefer him to someone else.

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Speaker 1: And so that's like game theory, game theory, game theorifying.

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Speaker 1: How do you do that game theorifying it to the

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Speaker 1: next level right now? Telling people like, don't only rank

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Speaker 1: this person, do not rank this person anywhere because we

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Speaker 1: don't want votes to trickle down there. And yeah, so

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Speaker 1: that's it's kind of an interesting way of figuring out, Okay,

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Speaker 1: how do we game this. I'm very interested to see

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Speaker 1: how it all plays out. Obviously I'm not voting in

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Speaker 1: it because I'm in about a resident, but because you know,

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Speaker 1: I have been a New York resident for many years

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Speaker 1: and still still spend a lot of time there. It

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Speaker 1: is something that is near and dear to my heart.

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Speaker 2: And I'm not voting because I am a Rechester. I

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Speaker 2: think I've told this story on the show I'm much

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Speaker 2: in Republican in New York because I want to write

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Speaker 2: you here to build against Donald Trump in twenty sixteen,

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Speaker 2: were my vote matter more? It's very hard to change

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Speaker 2: your vote. By the way, we do some breaking news here.

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Speaker 2: This podcast is not produced in real time. One of

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Speaker 2: the Maryle candidates, Brad Lander, the city comptroller, was just

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Speaker 2: arrested outside of an immigration Here. City Comptroller Brad Landers

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Speaker 2: and New York was arrested and accosted by massed federal

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Speaker 2: agents at an immigration court in Lower Manhattan. Is the

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Speaker 2: photo caption I'm reading. There is a photo of him

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Speaker 2: looking I don't know, dramatically kind of in like a

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Speaker 2: I guess it's not quite a chokehold or is it.

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Speaker 2: But he's arrested in a somewhat violent struggle. And this

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Speaker 2: is important because now Brad Lander is a going to

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Speaker 2: generate headlines and now he's a focal point. I mean,

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Speaker 2: now he kind of got the quasi New York Times

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Speaker 2: endorsement and like now Brad Landers and the news, right,

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Speaker 2: that's interesting. That could affect things. Yeah, I don't know

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Speaker 2: if it was deliberate.

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Speaker 1: If it's deliberate, it's incredibly smart because you just mentioned

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Speaker 1: a term that I actually think we should just talk

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Speaker 1: about a little bit when it comes to rank choice voting,

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Speaker 1: which is focal points. Right, when you have to rank

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Speaker 1: five candidates, when you have all this cognitive load, and

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Speaker 1: it's really difficult to figure out, Okay, where do I

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00:22:24,564 --> 00:22:29,044
Speaker 1: actually stand? Focal points matter, right, So it's actually incredibly important,

426
00:22:29,324 --> 00:22:32,444
Speaker 1: like who can command the attention, who can be in

427
00:22:32,524 --> 00:22:33,164
Speaker 1: your mind?

428
00:22:33,524 --> 00:22:33,684
Speaker 2: Right?

429
00:22:33,724 --> 00:22:38,284
Speaker 1: We have recency effects, We have those types of biases

430
00:22:38,324 --> 00:22:40,884
Speaker 1: that like if someone is on your mind for whatever

431
00:22:40,924 --> 00:22:44,644
Speaker 1: reason and has like a slightly positive bent for you

432
00:22:44,724 --> 00:22:46,884
Speaker 1: might not even remember that it was because of this arrest.

433
00:22:46,964 --> 00:22:48,804
Speaker 1: I mean, obviously now you will because it's so close

434
00:22:48,804 --> 00:22:51,124
Speaker 1: to the election, but it will make you much more

435
00:22:51,244 --> 00:22:53,044
Speaker 1: likely to be like, oh, you know, I have a

436
00:22:53,044 --> 00:22:54,884
Speaker 1: good feeling about this person. I'm going to rank them,

437
00:22:55,084 --> 00:22:57,964
Speaker 1: even if it actually has nothing to do with the election. Right, Like,

438
00:22:58,244 --> 00:23:00,404
Speaker 1: if you think about it, he didn't suddenly become a

439
00:23:00,404 --> 00:23:03,444
Speaker 1: better or a worse candidate just because of this one thing,

440
00:23:03,844 --> 00:23:06,444
Speaker 1: But in your mind, something shifts, and all of a

441
00:23:06,444 --> 00:23:11,244
Speaker 1: sudden those votes shift as well. Really interesting psychological phenomenon,

442
00:23:11,524 --> 00:23:13,964
Speaker 1: which is why if this was actually on purpose, it's

443
00:23:14,244 --> 00:23:17,524
Speaker 1: kind of a brilliant strategy. If it wasn't on purpose,

444
00:23:17,564 --> 00:23:20,924
Speaker 1: it still might actually work out, what's polymarket saying night,

445
00:23:21,924 --> 00:23:22,404
Speaker 1: So he's.

446
00:23:22,324 --> 00:23:24,524
Speaker 2: Up to two percent from like zero percent.

447
00:23:25,604 --> 00:23:27,284
Speaker 1: That's a big that's a big move in just a

448
00:23:27,284 --> 00:23:27,804
Speaker 1: few minutes.

449
00:23:28,124 --> 00:23:31,124
Speaker 2: And he's already had again this kind of New York Times.

450
00:23:31,724 --> 00:23:36,204
Speaker 2: They kind of whistled out of making an actual endorsement, right,

451
00:23:36,684 --> 00:23:40,844
Speaker 2: but they kind of implicitly endorsed him. And now if

452
00:23:40,844 --> 00:23:44,764
Speaker 2: it's Machiavelian politics and good job brand Lander, you just

453
00:23:44,804 --> 00:23:47,044
Speaker 2: gave yourself a shot.

454
00:23:49,084 --> 00:23:52,124
Speaker 1: Yeah you did, indeed. Yeah, And this is the kind

455
00:23:52,124 --> 00:23:55,204
Speaker 1: of stuff that actually ends up mattering because we're human

456
00:23:55,404 --> 00:23:57,844
Speaker 1: and you know this is the way the brain works.

457
00:23:58,004 --> 00:24:00,284
Speaker 2: No, Like just you know, name recognition is useful, right,

458
00:24:00,324 --> 00:24:03,564
Speaker 2: because you want to be listened somewhere, right, Like like

459
00:24:03,604 --> 00:24:05,764
Speaker 2: there's tons ofs around poster stuff in my neighborhood all

460
00:24:05,764 --> 00:24:07,244
Speaker 2: over the city. It's like, oh, I know this guy

461
00:24:07,404 --> 00:24:12,204
Speaker 2: is and I like, I like Lander or whatever, right,

462
00:24:12,244 --> 00:24:13,804
Speaker 2: but like I've heard of this guy, and I feel

463
00:24:13,804 --> 00:24:15,164
Speaker 2: I'll be able to listen five people and I've heard

464
00:24:15,164 --> 00:24:16,444
Speaker 2: of this guy. I'm not gonna go for somebody I

465
00:24:16,444 --> 00:24:19,964
Speaker 2: didn't know, right, So, like so this is this is interesting.

466
00:24:19,964 --> 00:24:21,724
Speaker 2: Maybe I don't. Yeah, I'm not quite sure if it's

467
00:24:21,724 --> 00:24:24,164
Speaker 2: a three way race now, but like it's it's well, well.

468
00:24:24,404 --> 00:24:28,564
Speaker 1: Yeah, we'll see what happens.

469
00:24:31,284 --> 00:24:36,484
Speaker 2: And we'll be right back after this break.

470
00:24:41,884 --> 00:24:44,724
Speaker 1: So, Nate, a few weeks ago you were at the

471
00:24:44,724 --> 00:24:48,924
Speaker 1: Manifest Conference and you had some interesting takeaways about AI

472
00:24:49,124 --> 00:24:52,524
Speaker 1: twenty twenty seven, which we've talked about on the show before,

473
00:24:53,084 --> 00:24:55,404
Speaker 1: and you know, before we get into all of that,

474
00:24:55,804 --> 00:24:58,244
Speaker 1: do you just want to let our listeners know what

475
00:24:58,444 --> 00:25:01,604
Speaker 1: exactly the Manifest Conference is because it's kind of a

476
00:25:01,644 --> 00:25:03,564
Speaker 1: big deal in the AI community, right.

477
00:25:03,924 --> 00:25:06,444
Speaker 2: Absolutely, just a week ago. I mean it feels like

478
00:25:06,484 --> 00:25:09,964
Speaker 2: I've been in Vegas for like three weeks. It's just

479
00:25:10,004 --> 00:25:12,164
Speaker 2: been eight days. I think it's by eight.

480
00:25:12,524 --> 00:25:15,684
Speaker 1: Time warps during the World Series of Fucker.

481
00:25:16,724 --> 00:25:19,724
Speaker 2: Yeah. The Manifest Conference is held by a prediction market

482
00:25:19,884 --> 00:25:23,524
Speaker 2: called Manifold, which is a market where you trade for

483
00:25:23,564 --> 00:25:25,524
Speaker 2: free trade for a world called mana are kind of

484
00:25:25,604 --> 00:25:30,484
Speaker 2: like credibility points basically, but people take it very seriously. However,

485
00:25:30,564 --> 00:25:31,844
Speaker 2: it's like kind of the de facto and there are

486
00:25:31,884 --> 00:25:34,524
Speaker 2: other conferences that I don't go to in the Bay Area.

487
00:25:34,564 --> 00:25:37,964
Speaker 2: It's like the de facto grouping of like this overlap

488
00:25:38,124 --> 00:25:45,604
Speaker 2: of prediction market nerds plus other adjacent people rationalists, effective

489
00:25:45,644 --> 00:25:48,284
Speaker 2: vutru withs. Some listeners will know what all these terms

490
00:25:48,324 --> 00:25:50,804
Speaker 2: mean if you read my book. There's a big detailed

491
00:25:50,844 --> 00:25:54,044
Speaker 2: schema of like what these different groups are, right, you know,

492
00:25:54,364 --> 00:25:57,324
Speaker 2: various nerledgses. Like a poker tournament there that I preicipaid

493
00:25:57,364 --> 00:26:00,884
Speaker 2: in some years. You know, it's kind of like effective

494
00:26:00,884 --> 00:26:07,364
Speaker 2: altruists meet degenerate gamblers, right, you know it's sponsored by.

495
00:26:07,164 --> 00:26:10,524
Speaker 1: Sorry, I'm just gonna interject, it's not defective altruists and

496
00:26:10,564 --> 00:26:14,004
Speaker 1: degenerate gamblers. But I think it was last year's Manifest

497
00:26:14,044 --> 00:26:16,324
Speaker 1: conference maybe the year before, where there was a market

498
00:26:16,364 --> 00:26:19,204
Speaker 1: on the chances of an orgy happening, and then the

499
00:26:19,244 --> 00:26:22,404
Speaker 1: market went to one hundred after the orgy happened. So

500
00:26:22,404 --> 00:26:24,724
Speaker 1: so you know, it's people have fun there.

501
00:26:24,844 --> 00:26:29,004
Speaker 2: There's lots of stuff. It's a weird enviigronment. Right. You're

502
00:26:29,044 --> 00:26:32,604
Speaker 2: on this actually quite pretty little campus in Berkeley called

503
00:26:32,644 --> 00:26:34,764
Speaker 2: light Haven, Right, but like everything kind of takes place

504
00:26:35,644 --> 00:26:39,244
Speaker 2: inside light Haven, and like there's like a like a

505
00:26:39,324 --> 00:26:42,404
Speaker 2: cuddle party at some point, and there's like a there you.

506
00:26:42,364 --> 00:26:48,044
Speaker 1: Go cuddle party listeners, cuddle party, I have big air

507
00:26:48,164 --> 00:26:48,844
Speaker 1: quotes in my.

508
00:26:49,724 --> 00:26:53,484
Speaker 2: There's all this discussion about like, uh, will the world

509
00:26:53,524 --> 00:26:56,204
Speaker 2: then because of AI? And is that? Is that good

510
00:26:56,244 --> 00:26:59,204
Speaker 2: or bad? Right? It's uh. In other way, it's a

511
00:26:59,204 --> 00:27:01,564
Speaker 2: pretty street list cloud by my friends and I the

512
00:27:01,604 --> 00:27:03,844
Speaker 2: Substack crew and I Substack is a sponsor, as is

513
00:27:03,884 --> 00:27:06,284
Speaker 2: probably Market, both of which I am an investor in.

514
00:27:06,364 --> 00:27:08,644
Speaker 2: So I'm over determined to be at this conference. So

515
00:27:08,884 --> 00:27:11,964
Speaker 2: you know we have to like sneak sneak some wine in.

516
00:27:12,044 --> 00:27:13,604
Speaker 2: We're like, you got to bring on more wine, right,

517
00:27:13,644 --> 00:27:16,044
Speaker 2: people are socializing here, you gotta bring out some more wine.

518
00:27:16,084 --> 00:27:18,524
Speaker 2: And it's like missed crowd and like, yeah, it's a

519
00:27:18,644 --> 00:27:23,484
Speaker 2: very interesting. It's weird kind of going from that environment

520
00:27:24,364 --> 00:27:26,364
Speaker 2: to the poker world where like I think I kind

521
00:27:26,364 --> 00:27:28,644
Speaker 2: of have so I castually talked about it last week,

522
00:27:28,644 --> 00:27:34,204
Speaker 2: but like the Bay Area feels distant to me, right,

523
00:27:34,244 --> 00:27:36,364
Speaker 2: I don't mean that in a pejorative way. I mean

524
00:27:36,524 --> 00:27:39,764
Speaker 2: like the fact that AI is so top of mind

525
00:27:39,884 --> 00:27:42,204
Speaker 2: for people, and that you have people who think that

526
00:27:42,364 --> 00:27:46,204
Speaker 2: like we are on the verge of an intelligence explosion

527
00:27:46,844 --> 00:27:51,324
Speaker 2: or singularity or artificial superintelligence. Right, these they're all like

528
00:27:51,364 --> 00:27:55,364
Speaker 2: slightly different terms, but that's a commonly held belief in

529
00:27:55,404 --> 00:27:59,604
Speaker 2: that community, Whereas you adventure outside of that community and

530
00:27:59,604 --> 00:28:01,524
Speaker 2: people would think you were fucking crazy to think that

531
00:28:01,564 --> 00:28:04,964
Speaker 2: we're gonna be building Dyson spheres or whatever in a

532
00:28:05,004 --> 00:28:06,924
Speaker 2: few years. Right now, I feel like I'm a little

533
00:28:06,964 --> 00:28:11,164
Speaker 2: bit of a hinge point between the kind of normy

534
00:28:11,164 --> 00:28:15,204
Speaker 2: crowd and the AI tech rationalist crowd. Like relative to

535
00:28:15,244 --> 00:28:18,924
Speaker 2: people at Manifest, I'm like, Okay, I think we're geting

536
00:28:18,924 --> 00:28:21,364
Speaker 2: a little bit ahead of ourselves, which is assuming that,

537
00:28:21,444 --> 00:28:26,084
Speaker 2: like we are on the verge of artificial super intelligence, right,

538
00:28:26,124 --> 00:28:28,844
Speaker 2: we can discuss that or not, right. And I'm also like,

539
00:28:28,884 --> 00:28:30,604
Speaker 2: I'm not so sure, Like what the fucker I mean,

540
00:28:31,484 --> 00:28:34,244
Speaker 2: you know, you're transforming all of society. Shouldn't society get

541
00:28:34,244 --> 00:28:36,444
Speaker 2: a say in that, even if you think it's good? Right?

542
00:28:37,084 --> 00:28:39,204
Speaker 2: Whereas relative to norm people think, okay, you're sleeping on

543
00:28:39,244 --> 00:28:40,884
Speaker 2: the fact that we are maybe on the verge of

544
00:28:41,604 --> 00:28:46,084
Speaker 2: profound changes even from artificial general intelligence disruptions to how

545
00:28:46,084 --> 00:28:50,164
Speaker 2: people live, how people work, certainly how people perceive the world.

546
00:28:50,204 --> 00:28:52,564
Speaker 2: And so like, I'm kind of a bridge observation there,

547
00:28:52,604 --> 00:28:54,764
Speaker 2: but like, yeah, so we talked about the AI twenty

548
00:28:54,884 --> 00:28:59,324
Speaker 2: twenty seven report on this podcast before when it came out,

549
00:28:59,404 --> 00:29:03,284
Speaker 2: which is a kind of forecast, a scenario, kind of

550
00:29:03,324 --> 00:29:05,804
Speaker 2: somewhere between. They call it like a forecaster or a prediction.

551
00:29:05,844 --> 00:29:11,324
Speaker 2: It's like a deterministic prediction conditional on fairly fast AI development,

552
00:29:11,404 --> 00:29:16,484
Speaker 2: and kind of presents one scenario where AI decides even

553
00:29:16,524 --> 00:29:19,244
Speaker 2: being Seria a threat to its growth and sends out

554
00:29:19,324 --> 00:29:22,204
Speaker 2: drones and kills every human sometime I think in the

555
00:29:22,244 --> 00:29:26,284
Speaker 2: mid to late twenty thirties, and then one scenario where

556
00:29:26,324 --> 00:29:29,444
Speaker 2: we agree to de escalate with China and the world

557
00:29:29,564 --> 00:29:34,644
Speaker 2: is profoundly transformed into an AI utopiatopia that some people

558
00:29:34,724 --> 00:29:37,484
Speaker 2: might also call a dystopia. But at least we survive

559
00:29:37,884 --> 00:29:42,364
Speaker 2: as the AI's concubines or whatever. We are exactly right,

560
00:29:43,644 --> 00:29:46,524
Speaker 2: which apparently they think is the happy ending. Right. It

561
00:29:46,604 --> 00:29:49,604
Speaker 2: was interesting that, like at that conference though, I went

562
00:29:49,604 --> 00:29:51,604
Speaker 2: to an update and they were kind of pushing back

563
00:29:51,644 --> 00:29:53,484
Speaker 2: the timelines. Right, They're like things a little bit slower

564
00:29:54,044 --> 00:29:56,444
Speaker 2: than we thought, right, you know, one big benchmark, so

565
00:29:58,124 --> 00:30:01,644
Speaker 2: ais that are agentic that for can example, search the

566
00:30:01,684 --> 00:30:04,444
Speaker 2: web to do tasks like that's still not implemented. I'm

567
00:30:04,444 --> 00:30:07,124
Speaker 2: sure it will be at some point, But maybe I

568
00:30:07,124 --> 00:30:10,724
Speaker 2: should back up, right, Like I am skeptical of claims.

569
00:30:11,204 --> 00:30:13,484
Speaker 2: So the different terms here, right, Right now, we have

570
00:30:13,604 --> 00:30:18,884
Speaker 2: what I would call spikey or patchy general intelligence for

571
00:30:19,044 --> 00:30:22,564
Speaker 2: like desk jobs, right jobs, And you know, we're not

572
00:30:22,644 --> 00:30:25,804
Speaker 2: near having like an AI plumber. We do have AI

573
00:30:26,044 --> 00:30:29,524
Speaker 2: automated driving, but that required and it's pretty good in

574
00:30:29,564 --> 00:30:31,804
Speaker 2: my opinion, but still only in a handful of cities,

575
00:30:33,044 --> 00:30:36,844
Speaker 2: require a lot of very dedicated devotion to that particular task. Right,

576
00:30:36,924 --> 00:30:40,244
Speaker 2: That's some important technology, right, But like, so first of all,

577
00:30:40,284 --> 00:30:43,364
Speaker 2: we're mostly restricting us to things in the realm of

578
00:30:43,364 --> 00:30:47,404
Speaker 2: single manipulation computer, remote desk job type of stuff, right,

579
00:30:47,684 --> 00:30:49,844
Speaker 2: and that's already kind of patchy. There are many things

580
00:30:49,844 --> 00:30:52,004
Speaker 2: that AI is already much better than humans at. But

581
00:30:52,044 --> 00:30:54,364
Speaker 2: like it can't really can't really search the web, It

582
00:30:54,364 --> 00:30:57,244
Speaker 2: can't really book a flight or make a restaurant reservation

583
00:30:57,444 --> 00:31:01,044
Speaker 2: for you, can't play poker, can't play chess. Right, The

584
00:31:01,084 --> 00:31:04,004
Speaker 2: image generation capabilities are pretty good, but you know, show

585
00:31:04,084 --> 00:31:07,684
Speaker 2: some AI artis facts, right, But yeah, you know I

586
00:31:08,084 --> 00:31:10,604
Speaker 2: think too, you know, with they but is that eventually

587
00:31:10,644 --> 00:31:15,564
Speaker 2: AIS will learn to program themselves, right, and you'll have

588
00:31:15,684 --> 00:31:20,844
Speaker 2: recursive improvement toward toward not just artificial general intelligence, but

589
00:31:20,884 --> 00:31:24,084
Speaker 2: then artificial superintelligence, right where it's discovering things that like

590
00:31:24,684 --> 00:31:28,444
Speaker 2: human beings have not discovered and and you know, become

591
00:31:28,524 --> 00:31:33,004
Speaker 2: hyper persuasive. And I am skeptical of this for various reasons, right,

592
00:31:33,004 --> 00:31:34,444
Speaker 2: One which I just think it's like kind of like

593
00:31:35,004 --> 00:31:38,444
Speaker 2: it's kind of underdetermined, right, like their uganizations like, hey, look,

594
00:31:38,844 --> 00:31:41,964
Speaker 2: so far we've been on a on a exactly a

595
00:31:42,004 --> 00:31:44,484
Speaker 2: logarithmic right, but as you add more compute, you have

596
00:31:44,804 --> 00:31:48,244
Speaker 2: these scaling laws laws, quote unquote, right, Whereas you have

597
00:31:48,324 --> 00:31:53,244
Speaker 2: more compute, things get more intelligent. And it's been a

598
00:31:53,244 --> 00:31:56,004
Speaker 2: fairly predictable trajectory so far, and so far the progress

599
00:31:56,044 --> 00:31:58,564
Speaker 2: is remarkable. Right if you had told somebody what chet

600
00:31:58,604 --> 00:32:00,964
Speaker 2: rept or Claude or Google Gemini can do, if you

601
00:32:01,004 --> 00:32:02,924
Speaker 2: told them that ten years ago, to be fucking amazed

602
00:32:03,404 --> 00:32:05,044
Speaker 2: that it can do many of these things quite well,

603
00:32:05,084 --> 00:32:07,684
Speaker 2: and some things excellently with natural language, and like they're

604
00:32:07,804 --> 00:32:10,404
Speaker 2: very very impressive, right, But like, but kind of when

605
00:32:10,444 --> 00:32:12,804
Speaker 2: it comes down to it, the argument is basically just

606
00:32:13,164 --> 00:32:14,684
Speaker 2: and I know I don't sound rigorous, so I start

607
00:32:14,724 --> 00:32:16,724
Speaker 2: a lot of time thinking about this, they're just like, well,

608
00:32:17,204 --> 00:32:19,844
Speaker 2: air linees going up, line will keep going up, and

609
00:32:19,884 --> 00:32:22,644
Speaker 2: if line keeps going up, then that inevitably means that

610
00:32:22,724 --> 00:32:25,044
Speaker 2: we pass super intelligence, right, And I don't think it's

611
00:32:25,044 --> 00:32:26,644
Speaker 2: in nonemble the line keeps going up, because I've seen

612
00:32:26,644 --> 00:32:29,844
Speaker 2: a lot of lines that keep going up until they don't.

613
00:32:30,164 --> 00:32:33,244
Speaker 2: That's one thing, right. Also, because it's trained on human data,

614
00:32:33,684 --> 00:32:35,364
Speaker 2: and so it seems like if it's trained on human

615
00:32:35,404 --> 00:32:39,204
Speaker 2: data and has human reinforcement, very importantly, without human reinforcement,

616
00:32:39,204 --> 00:32:41,484
Speaker 2: these things get pretty wacky, right, best Ti, I'm very

617
00:32:41,604 --> 00:32:45,564
Speaker 2: underwhelmed by the case for like how quickly this wollth

618
00:32:45,604 --> 00:32:49,244
Speaker 2: transformed in the physical world and how quickly it achieves

619
00:32:50,364 --> 00:32:53,644
Speaker 2: beyond human capabilities, And I feel like a heathen in

620
00:32:53,684 --> 00:32:56,644
Speaker 2: the AI community to be skeptical about this. But this

621
00:32:56,844 --> 00:33:00,284
Speaker 2: very long winded anecdote and the other reason too, I'm

622
00:33:00,324 --> 00:33:03,604
Speaker 2: sorry I'm babbling on for ye I do that sometimes, right, honestly,

623
00:33:03,604 --> 00:33:08,084
Speaker 2: Like they're not like really describing political constraints, like you know,

624
00:33:08,124 --> 00:33:10,164
Speaker 2: Sam Altman wrote, and Essa, I'm going probably write about

625
00:33:10,364 --> 00:33:13,084
Speaker 2: at the newsletter soon where it's like we're gonna have

626
00:33:13,124 --> 00:33:16,484
Speaker 2: a singularity, meaning we have this explosion of new technology

627
00:33:16,524 --> 00:33:19,444
Speaker 2: and intelligence, right, but it'll all feel pretty normal and

628
00:33:19,524 --> 00:33:22,164
Speaker 2: well cove right. I'm like, no, Sam, no, it's not

629
00:33:22,204 --> 00:33:25,284
Speaker 2: gonna happen. Right, We're not gonna have a fucking singularity

630
00:33:25,444 --> 00:33:27,604
Speaker 2: where we have as much technological progress in three years

631
00:33:27,684 --> 00:33:30,044
Speaker 2: as we had in the entire hundreds of thousands of

632
00:33:30,124 --> 00:33:33,644
Speaker 2: years of human and proto human species, right where she's like, oh, yeah,

633
00:33:34,124 --> 00:33:37,004
Speaker 2: sits look different. No one has a job anymore, right,

634
00:33:37,404 --> 00:33:40,004
Speaker 2: And you know, yeah, no, it's not gonna be just

635
00:33:40,084 --> 00:33:43,884
Speaker 2: a gentle singularity that doesn't make any fucking sense. And

636
00:33:44,004 --> 00:33:46,084
Speaker 2: the reason why it's used to go to conferences like

637
00:33:46,204 --> 00:33:49,444
Speaker 2: Manifest is that you realize that, like people are out

638
00:33:49,524 --> 00:33:52,284
Speaker 2: on a bit of an island, the Bay Area is

639
00:33:52,324 --> 00:33:54,884
Speaker 2: far away from the rest of the country, right, it's

640
00:33:54,924 --> 00:33:57,604
Speaker 2: a little insular and so like. And that affects my

641
00:33:58,444 --> 00:34:00,404
Speaker 2: views a bit because like, you're not I think sufficiently

642
00:34:00,484 --> 00:34:04,804
Speaker 2: counting for like societal political constraints, backlash and so forth.

643
00:34:04,804 --> 00:34:05,924
Speaker 2: So I at a great time at the conference, go

644
00:34:06,044 --> 00:34:07,844
Speaker 2: to connect with people who have because of course I'm

645
00:34:08,004 --> 00:34:10,484
Speaker 2: online before they've never met in person. And but yeah,

646
00:34:10,524 --> 00:34:13,124
Speaker 2: that was my experience. I went on for like ten minutes.

647
00:34:13,324 --> 00:34:15,764
Speaker 2: Maria so it's your turn to tell me what I'm

648
00:34:15,764 --> 00:34:16,484
Speaker 2: full of shit about.

649
00:34:16,804 --> 00:34:19,324
Speaker 1: No, well, well I'm not sure since I was obviously

650
00:34:19,644 --> 00:34:23,244
Speaker 1: not at the conference, but I'm actually quite curious. So

651
00:34:23,404 --> 00:34:26,044
Speaker 1: the you know, you said that the authors of the

652
00:34:26,204 --> 00:34:29,644
Speaker 1: AI twenty twenty seven report were there and that they

653
00:34:30,884 --> 00:34:35,524
Speaker 1: have kind of modified their timeline a little bit. Did

654
00:34:35,564 --> 00:34:39,004
Speaker 1: you get a chance to kind of get at whether

655
00:34:39,204 --> 00:34:43,084
Speaker 1: they're thinking has fundamentally changed as well, or whether they

656
00:34:43,244 --> 00:34:45,724
Speaker 1: now just think that, okay, it might be a little

657
00:34:45,764 --> 00:34:46,244
Speaker 1: bit slower.

658
00:34:46,404 --> 00:34:48,364
Speaker 2: Well, so for one thing, it's a little bit hard

659
00:34:48,604 --> 00:34:51,564
Speaker 2: to know, like are they making a prediction that they

660
00:34:51,604 --> 00:34:55,044
Speaker 2: would like bet on or are they trying to like

661
00:34:56,204 --> 00:35:00,844
Speaker 2: draw attention to what they view as a plausible scenario.

662
00:35:02,204 --> 00:35:06,164
Speaker 2: And there probably is some incentive you're trying to draw

663
00:35:06,164 --> 00:35:10,564
Speaker 2: attention to, like to produce more in dramatic I mean,

664
00:35:10,564 --> 00:35:12,404
Speaker 2: it's a story they had Scott Alexander, who is one

665
00:35:12,404 --> 00:35:15,084
Speaker 2: of my favorite writers. Yeah, writes format for Slate, Star

666
00:35:15,124 --> 00:35:17,524
Speaker 2: Codecs now at astrostar Codecs to like just punch up

667
00:35:17,564 --> 00:35:20,684
Speaker 2: the writing and it's a beautifully written, interesting document, right,

668
00:35:20,764 --> 00:35:22,404
Speaker 2: But like, but there's a little bit of like we

669
00:35:22,524 --> 00:35:27,364
Speaker 2: want to get attention. They let me have a chat

670
00:35:27,444 --> 00:35:29,924
Speaker 2: with them before and gave me a preview of the report, right,

671
00:35:30,004 --> 00:35:32,084
Speaker 2: and like so, like, I think you can probably like

672
00:35:33,764 --> 00:35:38,124
Speaker 2: discount a little bit based on the incentive to like

673
00:35:40,724 --> 00:35:43,564
Speaker 2: painted more robust picture and not just hedge a bit.

674
00:35:43,644 --> 00:35:46,724
Speaker 2: And like, I just think that, like I have become

675
00:35:46,764 --> 00:35:50,404
Speaker 2: increasingly unpersuaded why people think we can make this leap

676
00:35:50,764 --> 00:35:53,764
Speaker 2: from agi, which I think people will have things people

677
00:35:53,804 --> 00:35:56,564
Speaker 2: call artificial general that basically you can do most desk

678
00:35:56,684 --> 00:36:00,084
Speaker 2: jobs people, some people say all dusk jobs. Right, Let's

679
00:36:00,084 --> 00:36:02,524
Speaker 2: say you can do the large majority of dusk jobs

680
00:36:03,244 --> 00:36:07,924
Speaker 2: as well or better than the large majority of human beings. Right,

681
00:36:08,044 --> 00:36:12,044
Speaker 2: I think that's a pretty safe assumption, hedging now with

682
00:36:12,124 --> 00:36:15,804
Speaker 2: saying the large majority as opposed to all, and then

683
00:36:16,004 --> 00:36:19,044
Speaker 2: we're not that far from that. Well, we can debate that, right,

684
00:36:20,404 --> 00:36:24,084
Speaker 2: the leap to the physical world and to super intelligence.

685
00:36:24,204 --> 00:36:26,484
Speaker 2: I just I've read all these reports, I've talked to

686
00:36:26,484 --> 00:36:27,924
Speaker 2: lots of people. I just think they take that too

687
00:36:28,004 --> 00:36:29,644
Speaker 2: much for granted, and I think they need to like

688
00:36:30,284 --> 00:36:33,404
Speaker 2: treat it as a possibility. But like, I think it's

689
00:36:33,564 --> 00:36:37,204
Speaker 2: really underdetermined as far as like a persuasive explanation of that.

690
00:36:37,404 --> 00:36:40,484
Speaker 2: For me conditional upon that. Then I think these reports

691
00:36:40,644 --> 00:36:47,084
Speaker 2: like underestimate like the societal piece, society resists and pushes back,

692
00:36:47,244 --> 00:36:48,484
Speaker 2: I mean like yeah.

693
00:36:48,844 --> 00:36:51,164
Speaker 1: Well not just resisting and pushing back. I mean I

694
00:36:51,244 --> 00:36:55,764
Speaker 1: think this kind of like almost dismissive attitude, like what

695
00:36:55,884 --> 00:36:58,524
Speaker 1: you were saying with Sam Altman, like yeah, it's going

696
00:36:58,604 --> 00:37:01,404
Speaker 1: to happen and we'll cope. Like no, this is going

697
00:37:01,484 --> 00:37:06,204
Speaker 1: to cause if it does happen, even to some extent,

698
00:37:06,284 --> 00:37:09,644
Speaker 1: there's going to be massive disruption, right, and it's not

699
00:37:09,764 --> 00:37:12,884
Speaker 1: going to be peaceful. People do not like their jobs

700
00:37:13,004 --> 00:37:17,484
Speaker 1: being taken away. And you know, it's interesting because we

701
00:37:17,644 --> 00:37:21,164
Speaker 1: talked on the show before where there were you know,

702
00:37:21,484 --> 00:37:25,804
Speaker 1: union workers striking at ports where some of their jobs

703
00:37:25,964 --> 00:37:31,164
Speaker 1: we were getting automated and that was basically less work

704
00:37:31,244 --> 00:37:34,484
Speaker 1: for them, and so technically it was actually kind of better,

705
00:37:34,724 --> 00:37:38,604
Speaker 1: but practically speaking, they were like no way, right, Like

706
00:37:38,684 --> 00:37:41,844
Speaker 1: you're taking away jobs, you're taking away hours, Like we

707
00:37:41,964 --> 00:37:46,244
Speaker 1: don't want this kind of marginal efficiency from these robotic things.

708
00:37:46,324 --> 00:37:48,804
Speaker 1: We want, you know, we want our workers getting money.

709
00:37:49,084 --> 00:37:52,284
Speaker 1: And they were effective, right, like they stopped working. The

710
00:37:52,324 --> 00:37:55,844
Speaker 1: strikes actually worked, and that's kind of one one very

711
00:37:56,004 --> 00:37:59,044
Speaker 1: small piece of it and in a place where, like

712
00:37:59,084 --> 00:38:00,964
Speaker 1: the disruption, it's not like they were all going to

713
00:38:00,964 --> 00:38:03,324
Speaker 1: be out of jobs. It was just kind of it

714
00:38:03,484 --> 00:38:07,724
Speaker 1: was relatively small, probably didn't feel relatively small to them,

715
00:38:07,964 --> 00:38:11,164
Speaker 1: but it was a relatively small piece of this larger mosaic.

716
00:38:11,564 --> 00:38:13,644
Speaker 1: But if you get those kinds of protests, even with

717
00:38:13,764 --> 00:38:16,404
Speaker 1: something like that, imagine the pushback that you're going to

718
00:38:16,524 --> 00:38:21,004
Speaker 1: get in different in different areas if that starts becoming.

719
00:38:21,124 --> 00:38:22,804
Speaker 2: So, we just came off a weekend in which we

720
00:38:22,924 --> 00:38:29,044
Speaker 2: had mass political protests and no King's protest. Right, you

721
00:38:29,244 --> 00:38:34,844
Speaker 2: have maybe most importantly of all, Israel bombing Iran because

722
00:38:34,844 --> 00:38:37,044
Speaker 2: of technology. Right. I don't want to take a very

723
00:38:37,084 --> 00:38:39,404
Speaker 2: serious situation and make too cute an analogy, but like

724
00:38:39,684 --> 00:38:43,364
Speaker 2: here you have technology that like iran suclear program, that

725
00:38:43,364 --> 00:38:45,564
Speaker 2: Israel feels could be threatening to its interest or to

726
00:38:45,604 --> 00:38:47,604
Speaker 2: global interest, right, and they're bombing the hell out of

727
00:38:47,644 --> 00:38:49,844
Speaker 2: Iran right now. Right, So to think that like you're

728
00:38:49,884 --> 00:38:52,084
Speaker 2: going to have these profound transformations and the world kind

729
00:38:52,084 --> 00:38:54,884
Speaker 2: of stands idly by, right, And the fact that people

730
00:38:55,764 --> 00:38:57,564
Speaker 2: kind of in what I call the river, right, the

731
00:38:57,644 --> 00:39:01,044
Speaker 2: DC types are often clueless and dismissive about AI. It's

732
00:39:01,084 --> 00:39:03,964
Speaker 2: actually a reason to think that the political reaction is

733
00:39:04,084 --> 00:39:06,964
Speaker 2: not priced in, right, It's kind of intuitive, but because

734
00:39:07,004 --> 00:39:09,964
Speaker 2: eventually it will be people when when they're jobs, if

735
00:39:10,004 --> 00:39:12,004
Speaker 2: and when, if and when, if and when their jobs

736
00:39:12,004 --> 00:39:16,164
Speaker 2: are threatened, if and when it profoundly rebalances power, if

737
00:39:16,204 --> 00:39:22,044
Speaker 2: and when it potentially makes the US and China be

738
00:39:22,324 --> 00:39:25,164
Speaker 2: huge hedgemonds relative to the rest of the world. It's

739
00:39:25,164 --> 00:39:29,164
Speaker 2: all too superpowers. But if now Europe and the Middle

740
00:39:29,164 --> 00:39:32,844
Speaker 2: East and everyone else is behind, right, Like, what's that

741
00:39:32,964 --> 00:39:35,124
Speaker 2: going to mean? And so like, yeah, not wrestling, And

742
00:39:35,164 --> 00:39:36,564
Speaker 2: they're two ways to read that. One way to read

743
00:39:36,564 --> 00:39:39,964
Speaker 2: it is that like, okay, that means they're not pricing

744
00:39:40,044 --> 00:39:46,404
Speaker 2: in the constraints society, the legal system, resource constraints. It

745
00:39:46,484 --> 00:39:49,484
Speaker 2: just kind of seems incongruous to have this world where

746
00:39:49,564 --> 00:39:51,884
Speaker 2: the Middle East is at war. Who knows when China

747
00:39:51,884 --> 00:39:55,804
Speaker 2: and Taiwama go to war. Ukraine's still at war, right democracy.

748
00:39:55,804 --> 00:39:58,324
Speaker 2: I don't want to be overly dramatic, right, but like,

749
00:39:59,484 --> 00:40:02,924
Speaker 2: but you know, Trump presents certain threats for the US. Yeah,

750
00:40:03,044 --> 00:40:05,324
Speaker 2: and it's like, you know, global world, it's like kind

751
00:40:05,324 --> 00:40:07,644
Speaker 2: of what are we it is that going to be a.

752
00:40:07,684 --> 00:40:11,804
Speaker 1: Smooth so you'll, I mean, no, none of this is smooth.

753
00:40:11,964 --> 00:40:17,604
Speaker 1: And I think that you also cannot underestimate humans survival instinct, right,

754
00:40:17,684 --> 00:40:21,044
Speaker 1: and they're kind of their their desire not just to survive,

755
00:40:21,204 --> 00:40:23,924
Speaker 1: but to like they don't like having things taken away

756
00:40:24,004 --> 00:40:27,404
Speaker 1: from them. And you know, at some point like this

757
00:40:27,604 --> 00:40:29,004
Speaker 1: is you know, if we if we look at the

758
00:40:29,084 --> 00:40:33,004
Speaker 1: kind of big theories histories of the world, like none

759
00:40:34,404 --> 00:40:37,044
Speaker 1: none of those transitions were ever smooth, right, and and

760
00:40:37,204 --> 00:40:40,564
Speaker 1: people do start rising up and rebelling, and we're seeing

761
00:40:40,644 --> 00:40:42,804
Speaker 1: unrest all over the world and as you say, Nate,

762
00:40:42,924 --> 00:40:45,204
Speaker 1: you know, climate change, all of these things. We are

763
00:40:45,284 --> 00:40:49,844
Speaker 1: not living in a nice, peaceful, like sunny moment in

764
00:40:50,004 --> 00:40:52,284
Speaker 1: time where like everything is just going well and people

765
00:40:52,324 --> 00:40:56,564
Speaker 1: have become complacent. We were, but now I think that

766
00:40:56,884 --> 00:41:00,724
Speaker 1: that complacency is fading, and a lot of that angst

767
00:41:00,764 --> 00:41:05,124
Speaker 1: and frustration is boiling up in different ways to the surface.

768
00:41:05,204 --> 00:41:06,804
Speaker 1: So I think that this is Yeah, I think that

769
00:41:06,964 --> 00:41:10,804
Speaker 1: this is going to be profoundly disruptive. And so we'll

770
00:41:10,924 --> 00:41:15,124
Speaker 1: just see how that plays out, right and at what point,

771
00:41:15,164 --> 00:41:17,964
Speaker 1: Because I think you made a good point at the

772
00:41:18,044 --> 00:41:21,364
Speaker 1: beginning of our conversation about this that San Francisco is

773
00:41:21,404 --> 00:41:23,084
Speaker 1: a little bit of an island, and I'm putting San

774
00:41:23,124 --> 00:41:26,964
Speaker 1: Francisco here in quotes because letting it stand in for

775
00:41:27,084 --> 00:41:29,884
Speaker 1: kind of that community. And it's an echo chamber, right,

776
00:41:29,924 --> 00:41:32,564
Speaker 1: because you have these people who are all together, all

777
00:41:32,604 --> 00:41:35,004
Speaker 1: in the same place, going to the same events, talking

778
00:41:35,084 --> 00:41:39,924
Speaker 1: to each other, reinforcing each other's ideas. And when that happens,

779
00:41:40,004 --> 00:41:44,564
Speaker 1: like that's that's never good, right, Like the best things,

780
00:41:44,684 --> 00:41:48,484
Speaker 1: best ideas, best businesses, best transitions happen where you have

781
00:41:49,324 --> 00:41:52,404
Speaker 1: more outsiders and people who are open to kind of

782
00:41:52,524 --> 00:41:58,004
Speaker 1: different views, different backgrounds, you know, just different types of

783
00:41:58,084 --> 00:42:00,404
Speaker 1: people coming together. And that's not what's happening in this

784
00:42:00,484 --> 00:42:02,364
Speaker 1: little It is a little bit of a bubble. And

785
00:42:02,444 --> 00:42:04,404
Speaker 1: so I think that that's just really important to keep

786
00:42:04,444 --> 00:42:06,924
Speaker 1: in mind in terms of what we're seeing now and

787
00:42:07,004 --> 00:42:10,724
Speaker 1: also what we're likely to see unless something changes, because

788
00:42:11,084 --> 00:42:14,284
Speaker 1: you know, unless that bubble pops, like they're thinking, will

789
00:42:14,364 --> 00:42:17,844
Speaker 1: still be reinforcing each other for for a while.

790
00:42:18,484 --> 00:42:20,244
Speaker 2: Yeah. Look, it's frustrat because I don't know as much

791
00:42:20,244 --> 00:42:22,964
Speaker 2: about AI as sure as people do, right, and they're

792
00:42:23,044 --> 00:42:25,084
Speaker 2: kind of COMMUNI accusation. Well, we sent more time thinking

793
00:42:25,124 --> 00:42:26,684
Speaker 2: about this. Well, I know more about politics than they

794
00:42:26,724 --> 00:42:28,764
Speaker 2: do than any of them do, right, And so like

795
00:42:28,844 --> 00:42:30,324
Speaker 2: I'm trying to like, I'm trying to serve as a

796
00:42:30,364 --> 00:42:32,804
Speaker 2: bridge between these two kinds of communities, so to speak.

797
00:42:33,604 --> 00:42:36,484
Speaker 1: Yeah, well, let's let's see how it all plays out, Nate.

798
00:42:36,764 --> 00:42:40,004
Speaker 1: I'm very curious, you know, once you get invest invested,

799
00:42:40,244 --> 00:42:43,644
Speaker 1: once you get invited into the Manifest conference next year,

800
00:42:44,804 --> 00:42:48,724
Speaker 1: what happens and how that how that thinking might shift.

801
00:42:49,284 --> 00:42:51,604
Speaker 1: Let's definitely, you know, I think this is a topic

802
00:42:51,644 --> 00:42:53,484
Speaker 1: that we come back to often for a good reason,

803
00:42:54,084 --> 00:42:55,564
Speaker 1: and I think it's something that we have to keep

804
00:42:55,644 --> 00:43:00,324
Speaker 1: revisiting to see what's changed or hasn't changed, and how

805
00:43:00,404 --> 00:43:04,324
Speaker 1: we feel about that. On that note, let's leave the

806
00:43:04,404 --> 00:43:08,524
Speaker 1: world of AI behind and enter our other bubble, Nate,

807
00:43:08,844 --> 00:43:12,284
Speaker 1: of poker, and best of luck to the two of

808
00:43:12,404 --> 00:43:15,764
Speaker 1: us as we hit the tables this week. Let's let's

809
00:43:15,804 --> 00:43:20,444
Speaker 1: try to reclaim some of our poker glory.

810
00:43:20,844 --> 00:43:22,844
Speaker 2: Yeah, there's no poker on the show next week. That

811
00:43:22,924 --> 00:43:23,764
Speaker 2: means we sucked.

812
00:43:26,484 --> 00:43:28,644
Speaker 1: Yes, if we don't talk about it, you know how

813
00:43:28,684 --> 00:43:33,004
Speaker 1: it's going. Good luck to the two of us night,

814
00:43:33,044 --> 00:43:41,284
Speaker 1: see you at the tables. See let us know what

815
00:43:41,364 --> 00:43:43,364
Speaker 1: you think of the show. Reach out to us at

816
00:43:43,524 --> 00:43:47,124
Speaker 1: Risky Business at pushkin dot fm. And by the way,

817
00:43:47,204 --> 00:43:49,524
Speaker 1: if you're a Pushkin Plus subscriber, we have some bonus

818
00:43:49,564 --> 00:43:52,404
Speaker 1: content for you that's coming up right after the credits.

819
00:43:52,804 --> 00:43:56,004
Speaker 2: And if you're not subscribing yet, consider signing up for

820
00:43:56,244 --> 00:43:58,364
Speaker 2: just six ninety nine a month. What a nice price

821
00:43:58,764 --> 00:44:01,644
Speaker 2: you get access to all that premium content and for

822
00:44:01,724 --> 00:44:04,164
Speaker 2: listening across Pushkin's entire network of shows.

823
00:44:04,964 --> 00:44:08,004
Speaker 1: Risky Business is hosted by me Maria Kanikova and by

824
00:44:08,084 --> 00:44:08,964
Speaker 1: me Nate Silver.

825
00:44:09,484 --> 00:44:12,484
Speaker 2: The show is a co production of Pushkin Industries and iHeartMedia.

826
00:44:13,164 --> 00:44:16,564
Speaker 2: This episode was produced by Isabelle Carter. Our associate producer

827
00:44:16,644 --> 00:44:19,244
Speaker 2: is Sonia Gerwitz, Sally helm As our editor, and our

828
00:44:19,284 --> 00:44:22,764
Speaker 2: executive producer is Jacob Goldstein. Mixing by Sarah Bruger.

829
00:44:23,444 --> 00:44:24,604
Speaker 1: Thanks so much for tuning in.