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Speaker 1: Hey, guys, Saga and Crystal here.

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Speaker 2: Every poll has shown that the Iran war is profoundly unpopular.

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Speaker 2: That is, in fact, perhaps the most unpopular war in

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Speaker 2: American history.

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Speaker 4: Trump was asked about this. Let's see what he has

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Speaker 4: to say.

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Speaker 5: Well, Americans aren't against the wark the a poll just

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Speaker 5: came out. Americans don't want had gasoline precious, but they're

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Speaker 5: not against the war. That just came out loud and

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Speaker 5: clear in a poll. Nobody wants a ran to avenuclear weapon.

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Speaker 5: Do you want a ran to avenuclear weapon? Do you

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Speaker 5: think it's good?

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Speaker 1: In fact?

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Speaker 2: Air against high gas prices, also very much against the war.

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Speaker 2: I don't have any idea of what pole he is

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Speaker 2: talking about. Harry enton Over on CNN did a breakdown

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Speaker 2: of what the polling looks like on this war.

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Speaker 4: Let's do a little fact check here.

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Speaker 6: Washington Post pol Trump's Iran war not approvedrating in early March.

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Speaker 7: Look at this.

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Speaker 6: It was way down there at minus thirteen points.

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Speaker 4: Very rare for a war to start.

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Speaker 6: Off underwater on the net approval that has approved minus disapproval.

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Speaker 6: But look at how far down we are now, right

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Speaker 6: into the Red Sea, forty points underwater according to the

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Speaker 6: Washington Post pole.

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Speaker 1: Look.

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Speaker 6: I tried to find every single poll I possibly could.

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Speaker 6: I believe this minus forty points represents the least popular

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Speaker 6: poll in terms of Trump's net approvating on the Iran

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Speaker 6: War the entire war so far?

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Speaker 1: Think the war was worth it? Iraq?

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Speaker 6: In two thousand and six, fifty eight percent said it

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Speaker 6: was not worth and again that was three years into

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Speaker 6: that war. Look at this, now you think that fifty

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Speaker 6: eight percent was high in terms of not worth it?

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Speaker 6: Sixty eight percent of Americans, more than two in three,

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Speaker 6: say the Iran war was not worth it. Just twenty

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Speaker 6: eight eight percent, Less than a third of Americans now

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Speaker 6: say the Iram War was worth it, even less than

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Speaker 6: the forty percent who said the Iraq War was worth it.

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Speaker 6: Again at this point, heading into the two thousand and

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Speaker 6: six mith term cycle, of course, we remember what happened

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Speaker 6: in two thousand and six, Republicans lost both the House

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Speaker 6: and the Senate. Among all independents, it is seventy seven percent,

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Speaker 6: more than three in four of all independence say it's

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Speaker 6: not worth it. How about GOP leaning independence. These are

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Speaker 6: independents who say that actually lean Republican. Sixty three percent

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Speaker 6: of them, more than three in five, nearly two in three,

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Speaker 6: say that, in fact, the Iram war is not worth it.

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Speaker 2: That sixty three percent number is maybe the most devastating

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Speaker 2: that of GOP leaning independence, which are people oftentimes who

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Speaker 2: claim their independence but vote for Republicans every single time.

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Speaker 2: That's the way a lot of this works in our

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Speaker 2: very partisan era. Sixty three percent say that it's not

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Speaker 2: worth it. That is extraordinary. And Politico has a write

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Speaker 2: up of a new poll looking just at MAGA voters,

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Speaker 2: and you know, overwall all they still support the president. Overall,

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Speaker 2: they still support this war. But you can see that

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Speaker 2: even among the Magabase, they are getting more and more uncomfortable.

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Speaker 2: Let's put this image up on the screen. This is

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Speaker 2: talking about gas prices, and you can see here that

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Speaker 2: a majority of Americans are blaming Iran for gas prices.

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Speaker 4: That includes MAGA.

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Speaker 2: So fifty seven percent of MAGA voters blame the war

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Speaker 2: in Iran for gas price increases than twenty four percent

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Speaker 2: blame the tariffs twenty four percent, supply chain disruptions, twenty

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Speaker 2: three percent lack of domestic oil production in the US.

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Speaker 2: I really don't think that's a problem at this point, guys,

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Speaker 2: and fourteen percent Trump's poor handling of the economy.

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Speaker 4: You know, it's a little.

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Speaker 2: Different from the way that other groups look, especially on

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Speaker 2: that number about blaming Trump directly, but in terms of

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Speaker 2: directly attributing the gas price rise to the war in Iran,

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Speaker 2: MAGA is basically where the rest of the population is.

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Speaker 2: A majority of them see the connection between what the

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Speaker 2: president has decided to do in Iran and what they

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Speaker 2: paying at the pump. Furthermore, there was an increase in

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Speaker 2: the number of MAGA voters who say that the US should.

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Speaker 4: Continue the war in Iran only.

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Speaker 2: If it doesn't increase costs, so you have more price

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Speaker 2: sensitivity at this point.

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Speaker 4: So there's a major shift there.

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Speaker 2: As you know, people just become increasingly impatient with what

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Speaker 2: is going on, and this is turning up obviously in

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Speaker 2: all of the polls of what's going.

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Speaker 4: To happen in the menterns.

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Speaker 2: I just saw an Emerson poll this morning that has

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Speaker 2: Democrats with an eleven point generic ballot lead, which would

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Speaker 2: be a vastly larger wave than what we saw even

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Speaker 2: in other waveyears for Democrats like twenty eighteen.

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Speaker 1: It's totally nuts. Can we put C three on the screen?

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Speaker 3: I actually do recommend people read this entire article level

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Speaker 3: linked to it in our newsletter because when they say

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Speaker 3: a maturity of Americans blamed the Iran war for gas prices,

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Speaker 3: they break it down by every category, not just overall

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Speaker 3: saying that the war in Iran, but they also have

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Speaker 3: it broken down by who you voted for, from Harris

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Speaker 3: to MAGA and even non MAGA. But I was looking

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Speaker 3: at some of the dissident MAGA crowd because one of

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Speaker 3: the things that a lot of the current supporters of

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Speaker 3: Trump's policy will do is they go MAGA vote self

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Speaker 3: identified MAGA support the war by ninety percent. What this

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Speaker 3: poll does is they show people who voted for Trump

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Speaker 3: at twenty twenty four. A little different, isn't it whenever

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Speaker 3: you consider what that looks like, and the so called

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Speaker 3: non Maga crowd is an increasingly a growing part of

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Speaker 3: the electorate. May not be liberals, may not even be Democrats,

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Speaker 3: maybe people who might vote Democrat, people who voted for

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Speaker 3: Trump back in twenty twenty four dramatically souring on the

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Speaker 3: war in the same percentage as the overall number.

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Speaker 1: Increasingly, if you look in this you can.

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Speaker 3: Actually see there is no political constituency outside of the

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Speaker 3: diehard boomers who watch Fox that support this war or

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Speaker 3: any even of the aims of the war.

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Speaker 1: It's media wall to wall.

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Speaker 3: You would be hard pressed outside of like Glenn Beck,

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Speaker 3: who's out there yesterday calling Trump a strategic genius.

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Speaker 1: My good luck. I mean, that's all you got left.

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Speaker 3: You got Nshpiro, and you got Glenn Beck, and you

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Speaker 3: know Jesse Waters. They's a true you know who's who

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Speaker 3: of what you would want on yourself, national.

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Speaker 2: Intellect, national intellect. The brain trust there, the true.

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Speaker 3: The real brain trust, the wise men of our time.

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Speaker 3: So yeah, that's that's really where things are right now.

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Speaker 3: WEFC five, we could put that. Nate Silver also did

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Speaker 3: an aggregation where you could see the overall and this

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Speaker 3: is you know, from all of the polls that have

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Speaker 3: come out from it, you have a pose at fifty

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Speaker 3: eight point three percent and support just around thirty three

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Speaker 3: point eight, interestingly enough, seems to be where his approval

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Speaker 3: rating is.

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Speaker 1: To just show you how how linked these two things are.

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Speaker 3: And then you add in the generic ballot which you

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Speaker 3: were talking about, and the political consequences of this. I mean,

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Speaker 3: I think it's baked in. You could end the war

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Speaker 3: today and it would still be as bad, but it's

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Speaker 3: it's only going to go in one direction, as in,

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Speaker 3: I don't think there's a lot upside necessarily an ending today,

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Speaker 3: but there's a lot of downside.

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Speaker 2: And continuing think about your think about Susan Collins in

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Speaker 2: Maine right handed a bit of a gift from the

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Speaker 2: whole Grand Platin or fallout now Democrats pretty quickly Coleester

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Speaker 2: on Troy Jackson, who has a track record in Maine

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Speaker 2: of being actually a strong, very strong electoral contender. But

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Speaker 2: she was probably feeling pretty good, you know, in a difficult,

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Speaker 2: very difficult year for her. Well, now as the war

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Speaker 2: is back on and gas prices are going back up,

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Speaker 2: we're like very likely to hit one hundred dollars a

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Speaker 2: barrel today. That's going to be immediately reflected prices. You know,

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Speaker 2: the prices go up.

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Speaker 4: Very quickly, they come down very very slowly.

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Speaker 2: She's going to be have to, you know, have to

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Speaker 2: continue defending this and deflecting and doing her little very

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Speaker 2: concerned dance this whole time. How do you think that's

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Speaker 2: going to go for her? Like it is going to

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Speaker 2: be a disaster, It is going to completely tank her

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Speaker 2: chances of holding on to that seat.

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Speaker 4: You can play that out. Iowa.

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Speaker 2: Fox News had a pull that has the Democrat up

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Speaker 2: in the Senate race in Iowa. Now, Iowa is one

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Speaker 2: of the states in the country that has been hardest

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Speaker 2: hit by all of the Trump policies, but specifically tariffs

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Speaker 2: and specifically the Iran war because of the way it

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Speaker 2: increases the fertilizer prices and creates.

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Speaker 4: All kinds of problems for them there too.

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Speaker 2: So not only in the Senate race, in the governor's race,

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Speaker 2: you have Democrats who've been consistently leading by narrow margin,

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Speaker 2: but leading in the polls in Iowa. This should be

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Speaker 2: completely off the table at this point because of the

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Speaker 2: way that rural America has shifted to the right. Those

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Speaker 2: are the sorts of things that are really being put

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Speaker 2: on the table because of Trump's disastrous policy here and

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Speaker 2: the revulsion that Americans feel against it. This let's put

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Speaker 2: this last one up on the screen here C. Seven,

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Speaker 2: because I think this really drills down on the point

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Speaker 2: of what a disaster this has been overall politically for Trump.

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Speaker 2: This is again a Fox News poll, and we've got

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Speaker 2: a comparison here from interactive polls of which party do

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Speaker 2: you trust more to handle on these variety of issues?

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Speaker 2: Twenty twenty two versus twenty twenty six. Okay, In twenty

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Speaker 2: twenty two, Republicans by thirteen points, were more trusted on inflation.

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Speaker 2: Now it's D plus ten on the economy. Back in

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Speaker 2: twenty twenty two, it was R plus fifteen. Now Democrats

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Speaker 2: are by nine points. That's the highest since two thousand

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Speaker 2: and six. On the inflation piece. That's the highest ever

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Speaker 2: recorded for trust in Democrats. Ready for this one. On immigration, Okay,

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Speaker 2: twenty twenty two, Republicans had the edge by fifteen points.

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Speaker 4: This was the signature campaign issue for Trump.

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Speaker 2: This is an area where Republicans are almost always stronger

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Speaker 2: than the Democrats. Now Democrats have pulled to a plus

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Speaker 2: one advantage basically even But the fact that you've got

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Speaker 2: Democrats even competing on this issue is such a turnabout

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Speaker 2: from the way things have been in recent years.

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Speaker 4: So it just shows you how much he has destroyed his.

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Speaker 2: Own mandate, his own agenda, and how across the board,

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Speaker 2: even on his strongest issues, he has you know, really

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Speaker 2: really frittered away any sort of mandate that he may

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Speaker 2: have had.

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Speaker 3: Yep, destroyed his entire presidency all for literally nothing. Okay,

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Speaker 3: we got to Garrison, lovely sitting by.

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Speaker 1: Let's get to it.

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Speaker 2: So we have quite a situation in developing in the

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Speaker 2: AI world. Let's go and put this Wall Street Journal

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Speaker 2: report up on the screen. Open AI models escaped and

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Speaker 2: hacked a company in cybersecurity test gone wrong. The subhead

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Speaker 2: here says before they could penetrate hugging faces, the name

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Speaker 2: of the cybersecurity company that they hacked defenses, the models

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Speaker 2: needed a way onto the internet. They found one. Joining

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Speaker 2: us now to explain what all what happened and what

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Speaker 2: all of this means is Garrison Lovely. He is a

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Speaker 2: journalist and also author of the forthcoming book Obsolete, The

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Speaker 2: AI Industry's trillion dollar race to replace Us and How

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Speaker 2: to stop It.

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Speaker 4: Great to see Garrison, see a man, Great to see you.

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Speaker 2: Yeah, of course, So just help us understand, to start with,

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Speaker 2: in non technical language, what exactly happened here?

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Speaker 8: Yeah, so a week ago, this company, hugging Base, publishes

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Speaker 8: this post and they're like, we were hacked by some AIS.

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Speaker 7: It was definitely AI, as it happened really fast.

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Speaker 8: We don't know which models they used and who was

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Speaker 8: directing them, but we caught it with our own AI

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Speaker 8: and addressed it, reported it to law enforcement. And then

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Speaker 8: on Tuesday, open Ai published as a post and we're like, hey,

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Speaker 8: that was actually us, well not exactly, it was our

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Speaker 8: AI models acting autonomously because they had broken out of

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Speaker 8: their containment and then tried to hack into hugging Face

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Speaker 8: to get answers basically to a test that they were running.

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Speaker 8: And this is the first incident like this that we

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Speaker 8: know of. It might not be the first one, because

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Speaker 8: it's possible that this happened internally at open ai, but

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Speaker 8: they just never reported it to the public, and it's

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Speaker 8: kind of hard to overstate the significance of this. People

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Speaker 8: in the AI safety community who have been worried about

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Speaker 8: AI capabilities and dangers for decades have warned for a

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Speaker 8: long time that this type of thing would happen where

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Speaker 8: you give the AI some goal and then it does

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Speaker 8: unexpected things that cause harm in the real world in.

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Speaker 7: Pursuit of that goal.

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Speaker 8: And the risks of this scale as it models get better,

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Speaker 8: And this is like a relatively low stakes version of it,

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Speaker 8: but you could imagine the ex signment being a hospital,

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Speaker 8: a bank, like the models will blackmail people in some circumstances,

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Speaker 8: and it was a test, but if they had access

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Speaker 8: to real world infrastructure that they can control and shut off,

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Speaker 8: the blackmail might actually have some some punch.

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Speaker 3: KMIT put D two up here on the screen. So

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Speaker 3: this is from a blog post that was published by

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Speaker 3: OpenAI and they spaceay specifically to gain access. The models

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Speaker 3: identified and exploited a zero day vulnerability and this was

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Speaker 3: happening quote in a sandbox testing environment. Can you translate

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Speaker 3: some of that for our audience.

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Speaker 8: Yeah, yeah, So basically, the opening IY models were operating

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Speaker 8: in this environment where they're not supposed to have access

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Speaker 8: to the outside world, the Internet, anything like that. The

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Speaker 8: models were trying to answer this evaluation. The questions were

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Speaker 8: very hard, and they're like, well, these questions are hard,

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Speaker 8: maybe it'd be easier to just get the answers to

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Speaker 8: the questions.

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Speaker 7: So maybe to do that, I'll get access to the Internet.

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Speaker 8: So it like pokes around in its environment, finds like

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Speaker 8: a little chink in the armor, and then it gets

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Speaker 8: out and like starts jumping around to different servers within

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Speaker 8: Open a Eye until it finds one that has access

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Speaker 8: to the Internet, and then it uses that to start

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Speaker 8: looking for what might have the answers to this evaluation,

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Speaker 8: finds hugging Face as a candidate, and then finds exploits

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Speaker 8: in hugging paces codebase that nobody knew existed, and then

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Speaker 8: uses those exploits and like stolen security credentials and like

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Speaker 8: all these classic hacker moves to get access to hugging Face.

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Speaker 8: And according to Opening, I successfully found the answer set

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Speaker 8: and did this all on its own and without Opening

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Speaker 8: Eye and noticing for maybe a week or more. This

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Speaker 8: is like reading between the lines from the Hugging Face

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Speaker 8: report Opening as report, we don't have a super clear

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Speaker 8: timeline and opening. I was about this because it looks

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Speaker 8: pretty bad if your models both autonomously hack an other company,

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Speaker 8: which is a crime if a human did it, but

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Speaker 8: can't be a crime because the models can't have intent legally,

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Speaker 8: but if a human did this, it would be criminal.

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Speaker 8: And then you have no idea it's happening until the

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Speaker 8: other company figures it out, reports it, and then maybe

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Speaker 8: you see the report and you're.

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Speaker 7: Like, oh shit, that sounds like us.

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Speaker 8: Then you publish it, and so this is like, yeah,

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Speaker 8: just kind of an unbelievable story across the board.

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Speaker 7: Yeah, happy to go into more detail on any of it.

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Speaker 2: Yeah, So basically, what it looks like happened is Hugging

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Speaker 2: Face detected the intrusion and came public with it. But

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Speaker 2: from open Ay's perspective, they were just like, oh, the

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Speaker 2: model got the answers.

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Speaker 4: I guess it just figured it out. They had no

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Speaker 4: idea that.

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Speaker 2: It had escaped, hacked, committed crimes in service of accomplishing

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Speaker 2: its goals. One of the one of the things I

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Speaker 2: saw sort of like, you know, cope rationale here from

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Speaker 2: people who want to downplay this is yeah, but you know,

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Speaker 2: it was still just following this goal. It's not like

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Speaker 2: it was going out there freelancing and coming up with

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Speaker 2: its own objectives to go out and cause mischief in

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Speaker 2: the world. It was just trying way too hard to

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Speaker 2: accomplish the goal that humans had ultimately set for it.

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Speaker 2: Why does that not assuage your concerns here?

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Speaker 8: Yeah, I mean I think that if it were given

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Speaker 8: like a biological you know, understanding evaluation and then it's like, well,

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Speaker 8: I'm going to hack and find the bio answer key,

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Speaker 8: then like that's more evidence, right that it's just generalizing

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Speaker 8: to like, I am going to do something you did

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Speaker 8: not intend as unrelated to the activity that you're testing

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Speaker 8: me for.

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Speaker 7: So that's worse.

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Speaker 8: But I think it's also bad that Openey wanted the

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Speaker 8: model to do this evaluation the right way, which would

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Speaker 8: not involve cheating stealing the answer key, and they definitely

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Speaker 8: didn't want their model to do crimes and to like

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Speaker 8: hack another multi billion dollar company. And so the fundamental

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Speaker 8: point is that these companies, the most sophisticated in the

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Speaker 8: world in many respects, are not able to reliably steer

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Speaker 8: or control their own models, and those models are now

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Speaker 8: autonomous and capable enough to take actions in the real

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Speaker 8: world that could have really big consequences, and those consequences

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Speaker 8: are just going to grow with time as the models

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Speaker 8: get more capable and autonomous. Uh, and the problem just

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Speaker 8: gets harder, right because like they'll just they're superhuman at

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Speaker 8: hacking in many respects now, but they could be like

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Speaker 8: super superhuman in the next generation and you'll need the

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Speaker 8: very best models to keep up. And like Hugging Face

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Speaker 8: was struggling to fend off this attack because they couldn't

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Speaker 8: use the models that open eye and anthropic, like the

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Speaker 8: very best models on the market, because those models are

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Speaker 8: like trained to not help with this type of thing

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Speaker 8: because they can't distinguish reliably whether like you're actually defending

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Speaker 8: against an attack or you're using that as a cover

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Speaker 8: to do your own attack. And so Hugging Face had

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Speaker 8: to use open weight models, which are less capable, and

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Speaker 8: then Open Eyes like we let them into our Trusted

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Speaker 8: Access Partner program as a result of this, and it's

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Speaker 8: like it's like.

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Speaker 7: A mob boss.

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Speaker 8: Yeah, it's like, oh, like nice code baits you got there,

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Speaker 8: Like a shame is something where that happened to it?

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Speaker 3: Yeah, so let's go into that. There's been a lot

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Speaker 3: of debate around this what you just talked about, these

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Speaker 3: open weight models, largely a lot of them are Chinese,

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Speaker 3: which have been released. There's a huge debate now in Washington.

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Speaker 3: I don't know if you saw this actually happened late

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Speaker 3: last night. The Secretary of the Treasury and then the

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Speaker 3: White House ais Are Michael Kratzios, both basically put out

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Speaker 3: very veiled threats saying that the Kimney K three model,

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Speaker 3: which recently beat many of the American models in terms

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Speaker 3: of testing for its power, they're claiming that it was

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Speaker 3: distilled from the Fable five.

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Speaker 1: Model released by Anthropic.

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Speaker 3: Now, obviously, as you just said, these open weight models

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Speaker 3: don't have the same.

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Speaker 1: Quote guardrails or control.

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Speaker 3: Open source open weight which they're able to run and

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Speaker 3: have been used to circumvent some of these things. I'm

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Speaker 3: curious for your general reaction, both to the geopolitics of it,

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Speaker 3: because it does seem like an actual ban on Kimmy

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Speaker 3: K three is coming sometime soon if these companies have

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Speaker 3: their way, and then to the technological piece, just again,

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Speaker 3: try to explain it and as layman term as possible,

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Speaker 3: because I know it gets complicated.

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Speaker 1: Yeah.

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Speaker 8: Yeah, So these open weight models, I mean when they're

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Speaker 8: published online, they have guardrails built into them. The thing is,

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Speaker 8: it's trivial to remove them if you have like any

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Speaker 8: technical capacity, and it costs like not very much time

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Speaker 8: or money to do so. So functionally, whatever the model

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Speaker 8: could do if it had no guardrails, like, that's the

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Speaker 8: version that will be accessible to everybody in perpetuity once

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Speaker 8: it's published. And so people worry about this in a

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Speaker 8: world where like say you make the model and it's

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Speaker 8: like capable of teaching a novice to make a novel bioweapon.

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Speaker 8: If you publish that model's weights to the world, it's

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Speaker 8: just going to exist kind of forever. And that's a

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Speaker 8: pretty scary world. And it's like bio is one of

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Speaker 8: these things which is attack dominant, so it's really hard

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Speaker 8: to defend against bioweapons cyber It's like, well, if the

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Speaker 8: good guys have access to the best models before the

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Speaker 8: bad guys by like six months, maybe you can just

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Speaker 8: patch everything and it's like defense biased if.

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Speaker 7: You have like super capable AI agents.

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Speaker 8: But yeah, we don't know yet like when or if

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Speaker 8: we'll be in the world of like there's now this

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Speaker 8: bioweapon generating machine that's just out there forever, and that

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Speaker 8: worries a lot of people, understandably. And then the case

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Speaker 8: for open weight models is that they kind of decentralize

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Speaker 8: control and power. And so if Hugging Face had access

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Speaker 8: to models that were like two years behind the state

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Speaker 8: of the art, you know, maybe they wouldn't have been

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Speaker 8: able to detect and fent off this attack. And you

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Speaker 8: just be like living at the mercy of these companies

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Speaker 8: which may or may not let you into their programs.

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Speaker 8: And if you're in a you know, middle power, if

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Speaker 8: you're in China, you also don't want to get hacked

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Speaker 8: by anybody, and you might not have access to the

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Speaker 8: best stuff. I have not looked into the report you're

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Speaker 8: talking about as much. I do think it's like you

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Speaker 8: can't really address AI and it's like jurisdiction based way, right,

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Speaker 8: Like if the US makes it hard for US developers

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Speaker 8: and companies to use open weight models from China, then

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Speaker 8: they'll still be used in other parts of the world,

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Speaker 8: and you're not going to be like safe from their effects.

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Speaker 8: Given just like the way technology and AI and software

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Speaker 8: in particular diffuses the distillation attacks that Kratzios is talking about, Like, yeah,

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Speaker 8: that seems quite likely to be true, like Kimmy will

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Speaker 8: identify as Claude if you ask it. I mean, it's

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Speaker 8: you've got to acknowledge the irony of these companies being like.

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Speaker 7: Oh no, they took great data.

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Speaker 4: It's like, well, yeah, so did you literally all of

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Speaker 4: our data? Yeah?

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Speaker 7: Yeah, Yeah. It's like a bit you know, distillation.

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Speaker 8: It's like they're just harvesting very you know, directly, like

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Speaker 8: the same type of thing. And yeah, but then the

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Speaker 8: US approach of like, all right, we got to deploy

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Speaker 8: faster and more the best models, Like that's going to

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Speaker 8: let them keep distilling the model. And so this idea

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Speaker 8: that like the US is going to win the race

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Speaker 8: by publishing more capable models faster just makes no sense

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Speaker 8: because you're just letting other countries fast follow, right, and

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Speaker 8: so it's just kind of all backwards, like if you

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Speaker 8: regulated and slowed down in the US, that would slow

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Speaker 8: down China as well.

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Speaker 4: Got it interesting? I did see.

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Speaker 2: I don't know enough about this, but Arno Bertrand, who's

441
00:21:22,920 --> 00:21:25,320
Speaker 2: and analysts I've follow on Twitter, are a very smart guy.

442
00:21:25,680 --> 00:21:29,560
Speaker 2: He's really skeptical of this claim that Kimmy K three

443
00:21:29,640 --> 00:21:32,160
Speaker 2: was distilled from Fable five simply because of the timeline.

444
00:21:32,160 --> 00:21:34,439
Speaker 2: Fable five was not out that long before Kimmy K

445
00:21:34,840 --> 00:21:37,840
Speaker 2: three was released. He's saying it would take a lot

446
00:21:37,880 --> 00:21:39,920
Speaker 2: longer if that was actually the approach. I don't know

447
00:21:39,960 --> 00:21:41,880
Speaker 2: whether that's true or not. That just wanted to throw

448
00:21:41,880 --> 00:21:44,919
Speaker 2: that into the conversation. But you know, Bernie Sanders, I

449
00:21:44,920 --> 00:21:48,520
Speaker 2: think understandably saw this hack and said, listen, this is

450
00:21:48,520 --> 00:21:50,679
Speaker 2: why we've got to regulate AI more. We have to

451
00:21:50,680 --> 00:21:52,399
Speaker 2: have more control over these things. We've putting these things

452
00:21:52,440 --> 00:21:55,080
Speaker 2: down to the public. We're trusting Sam Altman and whoever

453
00:21:55,160 --> 00:21:57,080
Speaker 2: else to you know, have our best interest at heart

454
00:21:57,119 --> 00:21:59,320
Speaker 2: and do a thorough job of testing these and make

455
00:21:59,359 --> 00:22:01,520
Speaker 2: sure that they're not going to cause problems out there

456
00:22:01,520 --> 00:22:03,560
Speaker 2: in the world. That seems like a preposterous thing to do.

457
00:22:03,960 --> 00:22:05,520
Speaker 2: At the same time, I mean, if you do have

458
00:22:05,640 --> 00:22:08,840
Speaker 2: these open weight models that have basically the same capabilities,

459
00:22:09,040 --> 00:22:13,560
Speaker 2: really undercuts the ability to regulate and constrain AI here

460
00:22:13,680 --> 00:22:17,040
Speaker 2: at home. So, you know, how do you think about

461
00:22:17,040 --> 00:22:20,840
Speaker 2: what we should do from a domestic perspective when we

462
00:22:20,920 --> 00:22:22,280
Speaker 2: live in a global world.

463
00:22:22,280 --> 00:22:23,640
Speaker 4: Technology is global, et cetera.

464
00:22:24,600 --> 00:22:26,440
Speaker 8: Yeah, yeah, I mean, I would say the open weight

465
00:22:26,480 --> 00:22:29,560
Speaker 8: models are like six months behind. Still I think there's

466
00:22:29,600 --> 00:22:31,879
Speaker 8: like this Axios headline. I think that was just inaccurate

467
00:22:33,359 --> 00:22:37,200
Speaker 8: and if Kimmy wasn't justilling for Fable, it was distilling

468
00:22:37,200 --> 00:22:39,600
Speaker 8: from Opus and other models, like I don't know exactly

469
00:22:39,600 --> 00:22:42,120
Speaker 8: the details, but it's quite likely it was distilling from

470
00:22:42,200 --> 00:22:46,520
Speaker 8: American models, at least in part. I think we obviously

471
00:22:46,520 --> 00:22:49,480
Speaker 8: need to have international rules around AI that are binding,

472
00:22:49,840 --> 00:22:53,080
Speaker 8: that don't require trusts between the parties, And there are

473
00:22:53,080 --> 00:22:57,080
Speaker 8: technical approaches you can verify what's happening and data center

474
00:22:57,200 --> 00:23:01,679
Speaker 8: is using like devices on the chips themselves, and you

475
00:23:01,720 --> 00:23:05,560
Speaker 8: could create institutions like the International Atomic Energy Agency. But

476
00:23:05,760 --> 00:23:09,960
Speaker 8: for AI, and I think this hack is just a

477
00:23:09,960 --> 00:23:13,040
Speaker 8: great example of like, you know, again, this will scale,

478
00:23:13,080 --> 00:23:15,359
Speaker 8: like this will happen again. It will be worse in

479
00:23:15,359 --> 00:23:18,880
Speaker 8: the future, like something will be worse than this. It's

480
00:23:18,920 --> 00:23:24,200
Speaker 8: almost guaranteed. And if the US decides to regulate, and China,

481
00:23:24,320 --> 00:23:26,560
Speaker 8: I mean China is regulating actually far more than the

482
00:23:26,680 --> 00:23:28,760
Speaker 8: US is and still managing to catch up quite quickly.

483
00:23:29,160 --> 00:23:31,600
Speaker 8: So I think there's like some assumptions that need to

484
00:23:31,600 --> 00:23:32,600
Speaker 8: be tested here.

485
00:23:34,280 --> 00:23:36,240
Speaker 7: But you can change the risk landscape.

486
00:23:36,280 --> 00:23:39,560
Speaker 8: You can create a kind of like a floor within

487
00:23:39,640 --> 00:23:42,560
Speaker 8: your own country, and you can also say, like, hey,

488
00:23:42,560 --> 00:23:44,280
Speaker 8: if you're a Chinese company and you want to serve

489
00:23:44,280 --> 00:23:46,119
Speaker 8: models in the United States, you have to comply with

490
00:23:46,160 --> 00:23:48,600
Speaker 8: these regulations as well. That's what states have been doing

491
00:23:48,600 --> 00:23:50,879
Speaker 8: in California and New York Illinois. If you want to

492
00:23:50,920 --> 00:23:53,720
Speaker 8: serve models in those states, you have to do certain things.

493
00:23:53,760 --> 00:23:56,439
Speaker 8: And because those are big markets, they're just going to

494
00:23:56,480 --> 00:24:00,600
Speaker 8: do those things. Yeah, but I think US should be

495
00:24:00,640 --> 00:24:02,560
Speaker 8: I mean, there are dialogues between the US and China.

496
00:24:02,600 --> 00:24:05,920
Speaker 8: I think that should continue and accelerate, and I think

497
00:24:06,320 --> 00:24:09,240
Speaker 8: ultimately in the good future there's going to be some

498
00:24:09,359 --> 00:24:12,280
Speaker 8: kind of deal between the US and China to regulate AI,

499
00:24:12,440 --> 00:24:17,480
Speaker 8: to ban certain things, to mandate other things, and that

500
00:24:17,560 --> 00:24:21,520
Speaker 8: will happen because again the bioweapon problem, like the hackers,

501
00:24:21,560 --> 00:24:24,240
Speaker 8: the terrorists, like there's going to be that at least,

502
00:24:24,920 --> 00:24:27,520
Speaker 8: And there's also just this thing of like we could

503
00:24:27,640 --> 00:24:30,840
Speaker 8: lose control of AIS. We just did we being opening

504
00:24:30,840 --> 00:24:33,320
Speaker 8: eye here, we could also lose control two AIS if

505
00:24:33,359 --> 00:24:37,000
Speaker 8: they get capable autonomous numerous enough, which is like where

506
00:24:37,040 --> 00:24:39,879
Speaker 8: things are trending. We don't know when, but that's just

507
00:24:39,960 --> 00:24:42,879
Speaker 8: like the thing that happens by default, and so we

508
00:24:43,040 --> 00:24:44,640
Speaker 8: just need to do something about it.

509
00:24:45,000 --> 00:24:47,560
Speaker 2: Garrison, what do you make of the economic landscape here,

510
00:24:47,560 --> 00:24:49,119
Speaker 2: because that was a lot of the discussion around the

511
00:24:49,200 --> 00:24:51,560
Speaker 2: Kimney K three release as well. And even if you

512
00:24:51,560 --> 00:24:54,200
Speaker 2: don't think it's as advanced as let's say, Fable five,

513
00:24:54,359 --> 00:24:57,440
Speaker 2: it's you know, they're getting pretty close here and it's

514
00:24:57,560 --> 00:25:01,479
Speaker 2: much much cheaper. Anthropic and open Ay seem to have

515
00:25:01,600 --> 00:25:04,240
Speaker 2: freaked out pretty hard and are basically calling on the

516
00:25:04,480 --> 00:25:06,840
Speaker 2: Trump administration to come in and bail them ount because

517
00:25:06,880 --> 00:25:10,800
Speaker 2: they have these incredibly mass amounts of investor money flowing

518
00:25:10,840 --> 00:25:12,879
Speaker 2: into them. It only makes sense if they're going to

519
00:25:13,160 --> 00:25:17,080
Speaker 2: get astronomical returns from replacing human labor.

520
00:25:17,640 --> 00:25:19,280
Speaker 4: How do you think this all works out?

521
00:25:19,320 --> 00:25:19,480
Speaker 8: To me?

522
00:25:19,560 --> 00:25:21,920
Speaker 4: Do you see this as a bubble?

523
00:25:22,320 --> 00:25:25,240
Speaker 2: Do you think that the future is more like AI

524
00:25:25,680 --> 00:25:27,879
Speaker 2: is a sort of a utility the way the Internet is,

525
00:25:27,880 --> 00:25:29,879
Speaker 2: and more of the money is made in the applications.

526
00:25:30,000 --> 00:25:31,679
Speaker 4: How do you view the economic pieces of this?

527
00:25:32,720 --> 00:25:34,680
Speaker 7: Yeah, it's hard to say for sure.

528
00:25:34,720 --> 00:25:37,320
Speaker 8: I think people speak very confidently on this, and it's

529
00:25:37,440 --> 00:25:39,240
Speaker 8: a really big complex topic.

530
00:25:40,480 --> 00:25:42,280
Speaker 7: I would say that prior to.

531
00:25:42,160 --> 00:25:44,760
Speaker 8: Like recent months, the case for it being a bubble

532
00:25:44,800 --> 00:25:49,480
Speaker 8: got a lot weaker because Anthropics revenue grew the fastest

533
00:25:49,480 --> 00:25:52,160
Speaker 8: the company's revenue has ever grown at that level, going

534
00:25:52,160 --> 00:25:54,480
Speaker 8: from like a few billion annualized at the start of

535
00:25:54,480 --> 00:25:57,440
Speaker 8: the year to like forty seven billion annualized by like May,

536
00:25:57,520 --> 00:25:59,639
Speaker 8: I want to say, and we don't know what it

537
00:25:59,680 --> 00:26:00,000
Speaker 8: is now.

538
00:26:01,160 --> 00:26:02,120
Speaker 7: It may have slowed down.

539
00:26:03,280 --> 00:26:05,800
Speaker 8: I think that the cost pressure or the pressure on

540
00:26:05,920 --> 00:26:09,360
Speaker 8: prices from capable open weight models, it's just always going

541
00:26:09,400 --> 00:26:12,000
Speaker 8: to be like chasing the frontier, right, and so the

542
00:26:12,840 --> 00:26:17,240
Speaker 8: American business model for these releases is like have the

543
00:26:17,359 --> 00:26:20,600
Speaker 8: very best models by capabilities, charge a premium for them,

544
00:26:21,040 --> 00:26:23,960
Speaker 8: and then keep releasing better models to stay ahead of

545
00:26:24,240 --> 00:26:28,480
Speaker 8: the open weight frontier. And that starts to break if

546
00:26:28,520 --> 00:26:30,800
Speaker 8: it takes a lot longer to publish your own models.

547
00:26:31,760 --> 00:26:35,040
Speaker 8: And so like Mythos was finished training in February, it

548
00:26:35,080 --> 00:26:40,000
Speaker 8: doesn't come out as fable until June May, and then

549
00:26:40,080 --> 00:26:42,960
Speaker 8: it's taken down and then it comes back and so

550
00:26:43,280 --> 00:26:46,320
Speaker 8: in that time, you know, Kimmy can catch up, get closer.

551
00:26:47,640 --> 00:26:52,119
Speaker 8: And so yeah, like the whole fast release like release

552
00:26:52,200 --> 00:26:54,960
Speaker 8: things is almost as soon as you have them and

553
00:26:55,080 --> 00:26:58,240
Speaker 8: capture that premium while you can starts to break if

554
00:26:58,320 --> 00:27:02,320
Speaker 8: there's like a kind of slowed down testing process. If

555
00:27:02,320 --> 00:27:06,359
Speaker 8: the government is just deciding arbitrarily or without like transparent

556
00:27:07,119 --> 00:27:10,080
Speaker 8: criteria of like what models are capable enough to release

557
00:27:10,160 --> 00:27:13,639
Speaker 8: or have guardrails that are good enough to release, then

558
00:27:13,880 --> 00:27:16,440
Speaker 8: it gives open weight models more time to eat your lunch.

559
00:27:16,880 --> 00:27:20,199
Speaker 8: And this is all predicated on the models continuing to

560
00:27:20,200 --> 00:27:23,520
Speaker 8: get better, or at least they're continuing to be demand

561
00:27:23,640 --> 00:27:26,320
Speaker 8: for the premium version of these things, which are priced

562
00:27:26,359 --> 00:27:29,080
Speaker 8: at substantially above what it costs to serve them, which

563
00:27:29,119 --> 00:27:31,160
Speaker 8: is why the open weight models are able to undercut

564
00:27:31,160 --> 00:27:31,720
Speaker 8: them so much.

565
00:27:31,880 --> 00:27:32,840
Speaker 7: Right, Like, there's this.

566
00:27:32,800 --> 00:27:35,200
Speaker 8: Big idea of like, oh, is it profitable to serve

567
00:27:35,240 --> 00:27:36,040
Speaker 8: a given customer?

568
00:27:36,280 --> 00:27:37,360
Speaker 7: The answer is clearly yes.

569
00:27:37,600 --> 00:27:40,200
Speaker 8: The financial data shows us the fact that open weight

570
00:27:40,200 --> 00:27:42,080
Speaker 8: models that are like in the same ballpark are so

571
00:27:42,160 --> 00:27:44,359
Speaker 8: much cheaper. Again, it's just showing like the raw costs,

572
00:27:44,760 --> 00:27:46,960
Speaker 8: which we don't know from the outside, but we can

573
00:27:47,320 --> 00:27:49,760
Speaker 8: infer from a bunch of reporting a bunch of other things.

574
00:27:49,840 --> 00:27:51,920
Speaker 8: And just like, think of how verbose the models are.

575
00:27:52,280 --> 00:27:54,320
Speaker 8: Would they do that if they weren't making money by

576
00:27:54,359 --> 00:27:54,800
Speaker 8: the token?

577
00:27:55,960 --> 00:27:56,200
Speaker 7: Right?

578
00:27:56,560 --> 00:27:57,800
Speaker 1: Right? That makes a lot of sense.

579
00:27:58,760 --> 00:28:01,639
Speaker 3: Really appreciate your analysis, man, I'm looking forward to the

580
00:28:01,680 --> 00:28:03,320
Speaker 3: release of your book, so thank you very much for

581
00:28:03,359 --> 00:28:03,719
Speaker 3: joining us.

582
00:28:03,760 --> 00:28:05,800
Speaker 2: Yeah, then I think it's available pre order now right,

583
00:28:05,840 --> 00:28:07,720
Speaker 2: comes out on September fifteenth, Do I have that right?

584
00:28:08,080 --> 00:28:08,520
Speaker 7: That's right?

585
00:28:08,760 --> 00:28:11,520
Speaker 2: All right, everybody go get that pre order, give us

586
00:28:11,560 --> 00:28:13,280
Speaker 2: a copy. I already got a copy. I already read it.

587
00:28:13,240 --> 00:28:17,400
Speaker 4: It's great, so I'll send you on SAA. Thanks Garson.

588
00:28:20,400 --> 00:28:22,679
Speaker 3: Very excited now to be joined by a new author,

589
00:28:22,800 --> 00:28:25,399
Speaker 3: Samuel Moyn. Let's put this book up here on the screen.

590
00:28:25,600 --> 00:28:30,120
Speaker 3: Jarontocracy in America, How the old are hoarding power.

591
00:28:29,840 --> 00:28:31,840
Speaker 1: And wealth and what to do about it? A man

592
00:28:31,960 --> 00:28:33,000
Speaker 1: after my own heart.

593
00:28:33,560 --> 00:28:37,800
Speaker 3: Professor Moyne is a professor at Yale University Law School,

594
00:28:37,840 --> 00:28:40,000
Speaker 3: and he joins us now to discuss his new book.

595
00:28:40,160 --> 00:28:42,120
Speaker 3: Thank you very much for joining us, sir, appreciate it.

596
00:28:42,400 --> 00:28:43,880
Speaker 7: Thanks so much for having me so.

597
00:28:43,920 --> 00:28:48,160
Speaker 3: In this book, you describe jerontocracy in America as both

598
00:28:48,200 --> 00:28:51,480
Speaker 3: a class element and an age element, a takeover of

599
00:28:51,880 --> 00:28:54,840
Speaker 3: American institutions, the hoarding of wealth, the hoarding of power.

600
00:28:55,080 --> 00:28:59,200
Speaker 3: You prescribe a several solutions, some more radical than the others,

601
00:28:59,280 --> 00:29:03,280
Speaker 3: including the diolution of older people's votes, awaiting younger people

602
00:29:03,360 --> 00:29:07,560
Speaker 3: mandatory retirement ages and business and in Congress, the abolition

603
00:29:07,880 --> 00:29:10,920
Speaker 3: of the United States Senate in fact, So why don't

604
00:29:10,960 --> 00:29:13,800
Speaker 3: you start with your diagnosis kind of where you're coming

605
00:29:13,800 --> 00:29:16,560
Speaker 3: from and how you came to some of these conclusions.

606
00:29:17,920 --> 00:29:21,040
Speaker 9: Well, gerontocracy is a rule of buy and for the

607
00:29:21,120 --> 00:29:25,320
Speaker 9: old people, and we have it in this country. And thankfully,

608
00:29:25,480 --> 00:29:29,240
Speaker 9: finally we've started to talk about the age of our politicians,

609
00:29:30,480 --> 00:29:35,080
Speaker 9: who at the end of their lives suffer cognitive decline

610
00:29:36,040 --> 00:29:39,280
Speaker 9: like Joe Biden, or death like so many Democrats who

611
00:29:39,360 --> 00:29:43,120
Speaker 9: died to facilitate the passage of the current president's one

612
00:29:43,160 --> 00:29:48,000
Speaker 9: big beautiful Bill, or Ruth Gater Bader Ginsburg who lost

613
00:29:48,000 --> 00:29:51,520
Speaker 9: her bet on longevity. And that debate is all to

614
00:29:51,600 --> 00:29:55,320
Speaker 9: the good, but what it misses is that there's a

615
00:29:55,440 --> 00:30:00,200
Speaker 9: much deeper phenomenon, which is elder control of the politic

616
00:30:00,360 --> 00:30:04,160
Speaker 9: system generally, no matter how old the politicians are. If

617
00:30:04,160 --> 00:30:07,480
Speaker 9: Trump doesn't make it his whole term, we might have

618
00:30:07,560 --> 00:30:10,160
Speaker 9: the youngest president ever, but we would still have the

619
00:30:10,200 --> 00:30:15,120
Speaker 9: oldest voters. And our wealth in this country is to

620
00:30:15,200 --> 00:30:19,720
Speaker 9: an astonishing extent controlled by older people. So I realized

621
00:30:19,760 --> 00:30:22,960
Speaker 9: that I should just present the receipts to try to

622
00:30:23,000 --> 00:30:26,840
Speaker 9: convince people just how deep the syndrome of gerontocracy goes.

623
00:30:28,160 --> 00:30:31,640
Speaker 2: Talk about some of your solutions, like what you would

624
00:30:31,720 --> 00:30:35,040
Speaker 2: do about this problem you perceive, Well.

625
00:30:34,920 --> 00:30:37,400
Speaker 9: It depends on the problem. I mean, I've got a

626
00:30:37,440 --> 00:30:40,320
Speaker 9: long list, and you know, it's up to the politicians

627
00:30:40,360 --> 00:30:43,480
Speaker 9: to decide, you know, if any are viable. But if

628
00:30:43,520 --> 00:30:47,480
Speaker 9: we start with the politicians, I do think, like most Americans,

629
00:30:47,480 --> 00:30:49,840
Speaker 9: that we should have age limits for office, and of

630
00:30:49,880 --> 00:30:52,160
Speaker 9: course we do at the state level in many cases

631
00:30:52,240 --> 00:30:56,720
Speaker 9: around the world. They're very familiar, including for judges. If

632
00:30:56,760 --> 00:30:59,400
Speaker 9: we only go for age limits or term limits, we're

633
00:30:59,440 --> 00:31:02,640
Speaker 9: still like to end up with a very old political class.

634
00:31:02,680 --> 00:31:06,040
Speaker 9: We might control the risk for cognitive decline and death.

635
00:31:06,080 --> 00:31:10,080
Speaker 9: But what about unfair representation in our Senate? We have

636
00:31:10,200 --> 00:31:15,320
Speaker 9: one guy under forty, and we have very few thirty

637
00:31:15,360 --> 00:31:19,160
Speaker 9: and forty year olds in the House. And so what

638
00:31:19,200 --> 00:31:23,440
Speaker 9: we need to potentially consider for politicians is youth quotas

639
00:31:23,520 --> 00:31:29,480
Speaker 9: to kind of expand the range of generational representation on elections.

640
00:31:29,600 --> 00:31:33,760
Speaker 9: I mean, you mentioned my most radical solution, but there

641
00:31:33,760 --> 00:31:37,480
Speaker 9: are a lot of modest ones, like a federal holiday

642
00:31:37,560 --> 00:31:40,600
Speaker 9: for voting so that young people aren't punished for not

643
00:31:40,720 --> 00:31:43,520
Speaker 9: yet being retired or not being powerful enough in their

644
00:31:43,600 --> 00:31:48,040
Speaker 9: jobs to leave work. We need to relax registration requirements

645
00:31:48,040 --> 00:31:54,040
Speaker 9: so they're not punished for their likely greater mobility when

646
00:31:54,560 --> 00:31:58,760
Speaker 9: they're younger. I would also argue we should have fewer

647
00:31:58,840 --> 00:32:02,400
Speaker 9: elections because we know that it's in the boring elections

648
00:32:02,800 --> 00:32:06,240
Speaker 9: that older people are likeliest to control. Young people do

649
00:32:06,280 --> 00:32:09,200
Speaker 9: show up in greater numbers for presidential elections, but just

650
00:32:09,240 --> 00:32:13,200
Speaker 9: as an example in New Mexico in twenty twenty four,

651
00:32:13,320 --> 00:32:17,400
Speaker 9: the median age of primary voters was seventy two years old.

652
00:32:17,760 --> 00:32:22,560
Speaker 9: We also need different campaign finance rules because the donors

653
00:32:22,640 --> 00:32:27,680
Speaker 9: of two politicians in a privately financed system are sixty,

654
00:32:27,840 --> 00:32:29,440
Speaker 9: seventy even eighty.

655
00:32:30,680 --> 00:32:31,360
Speaker 7: On average.

656
00:32:31,400 --> 00:32:34,200
Speaker 9: And so those are the fixes there. And then when

657
00:32:34,200 --> 00:32:38,360
Speaker 9: it comes to wealth, I advocate like higher taxes, which

658
00:32:38,400 --> 00:32:41,280
Speaker 9: means that older people who have so much more wealth

659
00:32:41,320 --> 00:32:46,080
Speaker 9: will pay more. To younger generations, I advocate incentivizing them

660
00:32:46,120 --> 00:32:50,040
Speaker 9: to leave their housing and build for seniors so that

661
00:32:50,120 --> 00:32:53,520
Speaker 9: they can, you know, downsize without fear in order to

662
00:32:53,640 --> 00:32:56,959
Speaker 9: make way for younger people. And as you mentioned on jobs,

663
00:32:57,000 --> 00:32:59,800
Speaker 9: I think many jobs should have mandatory retirement ages.

664
00:33:00,160 --> 00:33:01,000
Speaker 1: So one of the things.

665
00:33:01,040 --> 00:33:02,960
Speaker 3: What I found fascinating is that a lot of your

666
00:33:03,000 --> 00:33:05,920
Speaker 3: analysis is actually rooted in an old Marxist tradition. I'd

667
00:33:05,960 --> 00:33:09,040
Speaker 3: never heard this line before. Quote age is the modality

668
00:33:09,400 --> 00:33:12,440
Speaker 3: in which class is lived. You know, we have plenty

669
00:33:12,440 --> 00:33:13,680
Speaker 3: of time here, so why don't you give us some

670
00:33:13,760 --> 00:33:16,440
Speaker 3: of the history. I was not particularly familiar with that

671
00:33:16,480 --> 00:33:17,360
Speaker 3: strain of Marxism.

672
00:33:18,280 --> 00:33:21,040
Speaker 9: Well, so, you know, the standard Marxist view, and it's

673
00:33:21,120 --> 00:33:23,160
Speaker 9: right a lot of the time is that the problem

674
00:33:23,240 --> 00:33:28,320
Speaker 9: is class, duh. And that's often right, and it's always

675
00:33:28,400 --> 00:33:33,200
Speaker 9: right to some extent. And yet there have been folks

676
00:33:33,240 --> 00:33:38,360
Speaker 9: who said, we can't be too much into just getting

677
00:33:38,440 --> 00:33:41,720
Speaker 9: rid of every other factor. And the classic example which

678
00:33:41,720 --> 00:33:46,080
Speaker 9: Stuart Hall, the Marxist you mentioned, invoked was race, and

679
00:33:46,160 --> 00:33:49,520
Speaker 9: he said that race is the way we live out class,

680
00:33:49,640 --> 00:33:54,920
Speaker 9: both in the sense that classes racialized, the lower class

681
00:33:54,680 --> 00:33:58,040
Speaker 9: it is much more likely to be non white in

682
00:33:58,120 --> 00:34:03,920
Speaker 9: the United States, but also white people are more likely

683
00:34:04,120 --> 00:34:09,600
Speaker 9: to understand their class predicament in racially antagonistic terms. That's

684
00:34:09,600 --> 00:34:14,239
Speaker 9: how you get a candidate and presidency twice over now,

685
00:34:14,320 --> 00:34:18,400
Speaker 9: like Donald Trump. And so I'm just proposing to adapt

686
00:34:18,520 --> 00:34:21,759
Speaker 9: that view for age. It's actually a little worse in

687
00:34:21,880 --> 00:34:27,480
Speaker 9: terms of age, because we have formal laws like apartheid

688
00:34:27,600 --> 00:34:32,040
Speaker 9: for race that actually privilege older people. I think that's

689
00:34:32,360 --> 00:34:35,960
Speaker 9: just and right in many cases. For example, older people

690
00:34:36,080 --> 00:34:40,160
Speaker 9: do get more federal benefits, maybe not to the extent

691
00:34:40,239 --> 00:34:43,960
Speaker 9: they do, which is six to seven dollars they get

692
00:34:44,200 --> 00:34:48,240
Speaker 9: for everyone that's spent on a child at the federal level.

693
00:34:48,640 --> 00:34:53,080
Speaker 9: But we also have a lot of especially tax protections

694
00:34:53,120 --> 00:34:56,879
Speaker 9: designed of buy and for older people, even when they

695
00:34:56,920 --> 00:35:00,279
Speaker 9: are some of the wealthiest Americans. If you look across

696
00:35:00,320 --> 00:35:03,400
Speaker 9: the world at the billionaire class, I'm going to concede

697
00:35:03,440 --> 00:35:07,000
Speaker 9: we have a younger trillionaire, although he's not that young. Yeah,

698
00:35:07,040 --> 00:35:12,280
Speaker 9: it's not that billionaires you have a forty five percent are

699
00:35:12,600 --> 00:35:18,000
Speaker 9: between fifty and seventy, and forty five percent are above seventy.

700
00:35:18,480 --> 00:35:23,279
Speaker 9: Only ten percent of billionaires are under fifty. And so

701
00:35:24,080 --> 00:35:26,839
Speaker 9: partly we explain that by saying the older you are,

702
00:35:26,880 --> 00:35:29,880
Speaker 9: the more time you've had to accumulate and hoard. But

703
00:35:29,960 --> 00:35:33,279
Speaker 9: we also have to explain it by the privileges that

704
00:35:34,680 --> 00:35:37,399
Speaker 9: a generation in the right place at the right time

705
00:35:37,560 --> 00:35:41,759
Speaker 9: has kind of reaped by gaming the system.

706
00:35:42,160 --> 00:35:45,960
Speaker 2: So you bring up the racial wealth disparity and just

707
00:35:46,040 --> 00:35:48,120
Speaker 2: cards on the table. This is why you know, I

708
00:35:48,160 --> 00:35:50,880
Speaker 2: have some issue with your analysis because to me, it

709
00:35:50,960 --> 00:35:54,240
Speaker 2: seems like if we're pitting groups against each other, for example,

710
00:35:54,239 --> 00:35:58,360
Speaker 2: whites versus blacks, old versus young, we are dividing in

711
00:35:58,400 --> 00:36:01,640
Speaker 2: a way that ultimately is beneficial to the oligarchic class,

712
00:36:01,640 --> 00:36:04,520
Speaker 2: the Elon musk, the Sam Altman's, the Peter Thiel's, the

713
00:36:04,680 --> 00:36:07,600
Speaker 2: Mark Zuckerberg's, in a way that is beneficial from them

714
00:36:07,600 --> 00:36:11,400
Speaker 2: and distracts from what is the consistent core problem across

715
00:36:11,440 --> 00:36:14,560
Speaker 2: all of these, which is class. So let me ask

716
00:36:14,600 --> 00:36:16,879
Speaker 2: you this, with regard to the racial wealth disparity, you

717
00:36:16,920 --> 00:36:21,239
Speaker 2: propose waiting younger voters, giving them more weight with their vote,

718
00:36:21,239 --> 00:36:24,400
Speaker 2: a sort of three fifth compromise for older voters, I guess.

719
00:36:24,719 --> 00:36:26,760
Speaker 2: I mean, would you do the same thing for white people?

720
00:36:27,160 --> 00:36:29,239
Speaker 2: You say, well, as a group, you know, yeah, there's

721
00:36:29,280 --> 00:36:31,080
Speaker 2: poor whites and there's rich whites. But as a group

722
00:36:31,120 --> 00:36:34,680
Speaker 2: they have much more wealth than Black Americans, so their

723
00:36:34,760 --> 00:36:37,919
Speaker 2: vote should should count less so that we can help

724
00:36:37,960 --> 00:36:39,040
Speaker 2: even the playing field here.

725
00:36:41,080 --> 00:36:44,680
Speaker 9: So briefly, yes, but a couple of background points before

726
00:36:44,719 --> 00:36:49,600
Speaker 9: I explain my answer. First, you know, it's tough to

727
00:36:49,640 --> 00:36:52,480
Speaker 9: figure out how we're going to build a coalition to

728
00:36:52,600 --> 00:36:56,719
Speaker 9: face down oligarchy. And my ultimate view is that we

729
00:36:56,800 --> 00:37:01,160
Speaker 9: need intergenerational solidarity for the sake of inner generational equity,

730
00:37:01,320 --> 00:37:05,200
Speaker 9: just as we need a cross racial, transracial majority in

731
00:37:05,280 --> 00:37:10,600
Speaker 9: this country to face down the real evildoers. Uh, the

732
00:37:10,719 --> 00:37:15,160
Speaker 9: question is how do we analyze the situation properly, because

733
00:37:15,880 --> 00:37:19,640
Speaker 9: you know, oligarchy has a lot of tools at its disposal,

734
00:37:20,680 --> 00:37:26,040
Speaker 9: and actually, most people who've made criticisms of generational unfairness

735
00:37:26,160 --> 00:37:29,160
Speaker 9: have been in favor of oligarchy because they've wanted to

736
00:37:29,239 --> 00:37:33,680
Speaker 9: target federal benefits for old people and destroy the welfare state.

737
00:37:33,760 --> 00:37:36,720
Speaker 9: And we can't just turn our back on the idea

738
00:37:36,840 --> 00:37:42,279
Speaker 9: that there might be intergenerational unfairness that intergenerational solidarity is

739
00:37:42,320 --> 00:37:45,759
Speaker 9: going to have to solve now. Second, I never, you know,

740
00:37:45,920 --> 00:37:50,160
Speaker 9: call for diluting older people's vote, let alone stripping them

741
00:37:50,560 --> 00:37:53,000
Speaker 9: a vote. What I do, you know, suggest is that

742
00:37:53,040 --> 00:37:56,680
Speaker 9: we should think of the equality between human beings differently,

743
00:37:57,280 --> 00:38:00,839
Speaker 9: And the stake that someone who's tone has in an

744
00:38:00,840 --> 00:38:04,800
Speaker 9: election is just much greater than the stake someone who's

745
00:38:04,840 --> 00:38:08,880
Speaker 9: eighty has in an election, just because what matters is

746
00:38:08,960 --> 00:38:12,920
Speaker 9: how long we live under the policies, how much of

747
00:38:13,080 --> 00:38:17,200
Speaker 9: a say do you get in addressing long term problems?

748
00:38:17,480 --> 00:38:22,480
Speaker 9: And so my solution is inflating the weight of younger

749
00:38:22,480 --> 00:38:25,520
Speaker 9: people's votes to take account of this fact. Now, you

750
00:38:25,600 --> 00:38:28,560
Speaker 9: asked a hard question, and I'll answer it directly. We

751
00:38:28,760 --> 00:38:35,040
Speaker 9: already do try to inflate the significance of the votes

752
00:38:35,080 --> 00:38:41,040
Speaker 9: of African Americans votes, or at least we did until recently. Classically,

753
00:38:41,080 --> 00:38:44,680
Speaker 9: we did things like have majority minority districts so that

754
00:38:45,040 --> 00:38:48,879
Speaker 9: African Americans could have their districts organized so they could

755
00:38:48,920 --> 00:38:52,319
Speaker 9: have a black representative in Congress, which mattered a lot

756
00:38:52,400 --> 00:38:56,200
Speaker 9: to them, and understandably so. And you know, you can

757
00:38:56,280 --> 00:38:59,440
Speaker 9: also argue that we already have weighted voting. It's just

758
00:38:59,480 --> 00:39:04,080
Speaker 9: in favor of residents of small states whose votes because

759
00:39:04,080 --> 00:39:06,960
Speaker 9: of the Senate and the Electoral College, count way more.

760
00:39:07,480 --> 00:39:11,120
Speaker 9: And so my suggestion is we think about who's being

761
00:39:11,160 --> 00:39:15,839
Speaker 9: treated unfairly and set up rules that take account of injustice.

762
00:39:16,280 --> 00:39:19,840
Speaker 2: So I'm sticking with this point. You know, in terms

763
00:39:19,880 --> 00:39:23,480
Speaker 2: of whose voice counts more in democracy, the biggest glaring

764
00:39:23,520 --> 00:39:27,120
Speaker 2: issue is, and I mentioned it earlier, campaign finance. I mean,

765
00:39:27,280 --> 00:39:30,520
Speaker 2: isn't a big part of the reason why older voters

766
00:39:30,560 --> 00:39:34,399
Speaker 2: have more power in our political system simply that concentration

767
00:39:34,680 --> 00:39:38,120
Speaker 2: of wealth that there is, you know, a larger preponderance

768
00:39:38,120 --> 00:39:42,680
Speaker 2: of wealth among older Americans versus younger Americans. That gives

769
00:39:42,719 --> 00:39:46,399
Speaker 2: them more power in our political system. But it's not

770
00:39:46,560 --> 00:39:49,200
Speaker 2: just a problem of old versus young. It's also a

771
00:39:49,200 --> 00:39:51,839
Speaker 2: problem of the wealth among you know, with white people

772
00:39:51,920 --> 00:39:54,960
Speaker 2: versus black people. Again, the way to deal with this

773
00:39:55,200 --> 00:39:57,400
Speaker 2: is not to pit one group against the other, but

774
00:39:57,520 --> 00:40:01,520
Speaker 2: to deal with the underlying issue here of money and

775
00:40:01,600 --> 00:40:04,960
Speaker 2: politics and the way that that has completely skewed our system.

776
00:40:05,800 --> 00:40:08,480
Speaker 9: So you're making a couple of very good points. One

777
00:40:08,600 --> 00:40:11,080
Speaker 9: is the importance of reforming campaign finance.

778
00:40:11,640 --> 00:40:11,879
Speaker 8: UH.

779
00:40:11,920 --> 00:40:16,080
Speaker 9: And I provide the data about just how much older

780
00:40:16,160 --> 00:40:19,440
Speaker 9: the control there is of you know, picking candidates, of

781
00:40:19,520 --> 00:40:25,160
Speaker 9: funding them, and then having the successful politicians do their bidding,

782
00:40:25,280 --> 00:40:29,040
Speaker 9: and of course that ought to change. You're also right

783
00:40:29,160 --> 00:40:33,480
Speaker 9: that you know, this is a problem of wealth. But

784
00:40:33,880 --> 00:40:36,440
Speaker 9: you know, to go back to Stuart Hall, it's like

785
00:40:36,560 --> 00:40:41,759
Speaker 9: saying white supremacy isn't real. UH, doesn't account for how

786
00:40:41,840 --> 00:40:44,600
Speaker 9: why the class structure is the way it is. Of

787
00:40:44,600 --> 00:40:46,759
Speaker 9: course it does, and the same is true of age.

788
00:40:46,800 --> 00:40:50,799
Speaker 9: It's not accidental that so much wealth is controlled by

789
00:40:50,840 --> 00:40:55,040
Speaker 9: older Americans. Now you're right that if we want to,

790
00:40:55,160 --> 00:40:59,880
Speaker 9: you know, create solidarity, we can't create an age antagonism anymore,

791
00:41:00,040 --> 00:41:03,520
Speaker 9: and we can create a race antagonism. I'm not arguing

792
00:41:03,560 --> 00:41:08,160
Speaker 9: for either. I'm arguing for saying, to take class seriously,

793
00:41:08,680 --> 00:41:11,520
Speaker 9: we have to take age seriously, just like we have

794
00:41:11,600 --> 00:41:12,880
Speaker 9: to take race seriously.

795
00:41:12,960 --> 00:41:13,520
Speaker 4: Don't you think that.

796
00:41:13,760 --> 00:41:15,839
Speaker 2: Don't you think just on that point, don't you think

797
00:41:15,880 --> 00:41:18,560
Speaker 2: the history of racism in America is quite different from

798
00:41:18,600 --> 00:41:23,240
Speaker 2: the history of absolutely reverse agism in America, given obviously slavery,

799
00:41:23,400 --> 00:41:27,520
Speaker 2: given Jim Crow, given you know, continued legacy of that.

800
00:41:27,680 --> 00:41:30,799
Speaker 4: I mean, there's a very very clear.

801
00:41:30,560 --> 00:41:35,040
Speaker 2: Cut pattern of mass discrimination against Black Americans that doesn't

802
00:41:35,040 --> 00:41:37,520
Speaker 2: exist in terms of you know, discrimination against you.

803
00:41:37,719 --> 00:41:41,000
Speaker 9: I agree with you, but that that it's not clear

804
00:41:41,520 --> 00:41:45,239
Speaker 9: what force that has, because every form of discrimination will

805
00:41:45,280 --> 00:41:49,600
Speaker 9: have its own history. That's like saying, you know, patriarchy

806
00:41:49,680 --> 00:41:53,000
Speaker 9: isn't real because it's different from white supremacy. It also

807
00:41:53,120 --> 00:41:56,120
Speaker 9: can't be argued. I think that the only kinds of

808
00:41:56,239 --> 00:41:59,919
Speaker 9: unfairness that are real are old kinds. That's clearly fall

809
00:42:00,280 --> 00:42:03,040
Speaker 9: and it's true that this is a new kind of

810
00:42:03,120 --> 00:42:08,799
Speaker 9: discrimination because the situation of older people, mainly through the

811
00:42:08,840 --> 00:42:11,480
Speaker 9: extension of life and the fact that they're still around

812
00:42:11,520 --> 00:42:16,200
Speaker 9: in much greater numbers, and thankfully so has changed the

813
00:42:16,280 --> 00:42:20,680
Speaker 9: score in American class. And so you know, with all

814
00:42:20,760 --> 00:42:23,120
Speaker 9: due respect, I think we have to take each kind

815
00:42:23,120 --> 00:42:27,319
Speaker 9: of discrimination on its own terms, while also recognizing that

816
00:42:27,520 --> 00:42:30,040
Speaker 9: class is made up of a lot of different kinds

817
00:42:30,080 --> 00:42:30,399
Speaker 9: of fact.

818
00:42:30,480 --> 00:42:32,719
Speaker 1: Yes, I also wanted to get your response to some

819
00:42:32,760 --> 00:42:33,240
Speaker 1: of the critics.

820
00:42:33,360 --> 00:42:37,399
Speaker 3: So a friend of mine, Russ Green, he coined a term,

821
00:42:37,440 --> 00:42:41,200
Speaker 3: I love Boomer luxury communism. He wrote a critique of

822
00:42:41,239 --> 00:42:44,719
Speaker 3: your book from the right, and what he said is

823
00:42:44,760 --> 00:42:50,920
Speaker 3: that your proposal, here's aarontocracy America would actually entrench daryn

824
00:42:50,960 --> 00:42:54,480
Speaker 3: toocracy and not necessarily reverse it. So I'm just curious

825
00:42:54,520 --> 00:42:57,799
Speaker 3: for your response to his general argument. For example, I

826
00:42:57,840 --> 00:43:01,080
Speaker 3: believe your endorsement of the Bernie Sanders Social Security expansion

827
00:43:01,160 --> 00:43:05,319
Speaker 3: tax for saying that you should reject means testing whenever. Sorry,

828
00:43:05,520 --> 00:43:08,040
Speaker 3: your reason for rejecting means testing whenever it comes to

829
00:43:08,360 --> 00:43:12,040
Speaker 3: social security the general diagnosis that seniors are hard up

830
00:43:12,080 --> 00:43:15,600
Speaker 3: for retirement. So yeah, I am curious to engage with

831
00:43:15,680 --> 00:43:18,279
Speaker 3: some other critics. I think for more the right wing

832
00:43:18,320 --> 00:43:21,319
Speaker 3: of gerontocracy from more ere of your left wing kind

833
00:43:21,320 --> 00:43:22,640
Speaker 3: of Marxist analysis.

834
00:43:23,120 --> 00:43:26,440
Speaker 7: Yeah, so I liked Russ's peace.

835
00:43:26,640 --> 00:43:31,880
Speaker 9: I think he's representative of a neo liberal take on

836
00:43:32,080 --> 00:43:37,400
Speaker 9: life in general, and for many, many years, their view

837
00:43:37,600 --> 00:43:42,480
Speaker 9: has been that if you care about intergenerational justice, you

838
00:43:42,600 --> 00:43:46,880
Speaker 9: have to target entitlements for the sake of older people,

839
00:43:46,920 --> 00:43:51,600
Speaker 9: whereas I call for expanding them. And I think he's,

840
00:43:51,840 --> 00:43:54,400
Speaker 9: with all due respect again or wrong for a couple

841
00:43:54,440 --> 00:43:55,080
Speaker 9: of reasons.

842
00:43:55,200 --> 00:43:57,640
Speaker 7: One is that the main way.

843
00:43:57,440 --> 00:44:00,879
Speaker 9: That older people have control over our political system is

844
00:44:00,920 --> 00:44:04,960
Speaker 9: not that they get entitlement, especially when they're poor. It's

845
00:44:05,000 --> 00:44:11,040
Speaker 9: all the other ways, being the politicians in extraordinary numbers,

846
00:44:11,640 --> 00:44:16,680
Speaker 9: being the voters, and controlling the political system through campaign finance,

847
00:44:16,760 --> 00:44:20,080
Speaker 9: and then just wealth housing jobs. None of that has

848
00:44:20,120 --> 00:44:24,040
Speaker 9: anything to do with social security. And so I want

849
00:44:24,040 --> 00:44:29,719
Speaker 9: to argue against us that we shouldn't treat intergenerational fairness

850
00:44:29,719 --> 00:44:34,680
Speaker 9: as a pretext for attacking the elder benefits that older

851
00:44:34,680 --> 00:44:38,120
Speaker 9: people need. And as I mentioned, I actually think, contrary

852
00:44:38,120 --> 00:44:43,400
Speaker 9: to that neoliberal POV, that we should expand elder entitlements

853
00:44:43,400 --> 00:44:46,879
Speaker 9: because my firm belief is that one reason a lot

854
00:44:46,920 --> 00:44:52,839
Speaker 9: of older people do retain their wealth is that they're

855
00:44:52,880 --> 00:44:56,560
Speaker 9: going to live indefinitely long they know it, and deep

856
00:44:56,640 --> 00:44:58,960
Speaker 9: down they don't know who's going to take care of them.

857
00:44:59,040 --> 00:45:03,600
Speaker 9: Our welfare state does not, because Medicare excludes long term

858
00:45:03,640 --> 00:45:07,000
Speaker 9: maintenance of older people, which is a scandal. And so

859
00:45:07,160 --> 00:45:10,160
Speaker 9: my sens is that if we were more generous to

860
00:45:10,239 --> 00:45:15,479
Speaker 9: older people, they either would dilute their own influence over

861
00:45:15,560 --> 00:45:20,040
Speaker 9: our affairs so that it's a more just society, or

862
00:45:20,640 --> 00:45:24,719
Speaker 9: taking their power from them in exchange for care would

863
00:45:24,800 --> 00:45:26,920
Speaker 9: be a kind of just compensation.

864
00:45:26,960 --> 00:45:28,000
Speaker 1: So what's the evidence for that?

865
00:45:28,080 --> 00:45:31,319
Speaker 3: I mean, every entitlement for them has been expanded, has

866
00:45:31,360 --> 00:45:34,960
Speaker 3: been protected, whenever it comes to the way that they've

867
00:45:35,000 --> 00:45:38,320
Speaker 3: manipulated property tax rules. I don't know if you're familiar

868
00:45:38,400 --> 00:45:41,360
Speaker 3: with a more recent case where a house was passed

869
00:45:41,360 --> 00:45:44,560
Speaker 3: down in a fifty five over community to a younger woman,

870
00:45:44,680 --> 00:45:47,279
Speaker 3: and the entire community is banded together to kick her

871
00:45:47,320 --> 00:45:49,960
Speaker 3: out from the property that she was deeded because they

872
00:45:49,960 --> 00:45:52,799
Speaker 3: said it's for seniors only. I mean, there's only really

873
00:45:52,840 --> 00:45:56,480
Speaker 3: evidence of genuine hoarding and a zero sum mentality from

874
00:45:56,520 --> 00:45:58,640
Speaker 3: their perspective, So I'm not really sure why we should

875
00:45:58,640 --> 00:45:59,520
Speaker 3: give them so much grace.

876
00:46:00,040 --> 00:46:02,480
Speaker 9: So it depends what we're talking about. I mean, I

877
00:46:03,040 --> 00:46:06,600
Speaker 9: try to convince you in with all the receipts in

878
00:46:06,640 --> 00:46:11,080
Speaker 9: the book, that housing is in very large part a

879
00:46:11,160 --> 00:46:14,440
Speaker 9: crisis that's of old people's making for a few reasons.

880
00:46:14,520 --> 00:46:18,120
Speaker 9: One is that because of the insane way that we

881
00:46:18,160 --> 00:46:20,720
Speaker 9: make land use decisions in our country at these local

882
00:46:20,760 --> 00:46:26,480
Speaker 9: town meetings, older people control supply and constrain supply. Second,

883
00:46:27,120 --> 00:46:30,640
Speaker 9: as you noted, and that story exemplifies this, older people

884
00:46:30,640 --> 00:46:34,560
Speaker 9: are very averse to downsizing in part because they've made

885
00:46:34,600 --> 00:46:37,600
Speaker 9: sure there's nothing to downsize into, you know, at the

886
00:46:37,640 --> 00:46:41,520
Speaker 9: local level where these decisions are made. And that's a

887
00:46:41,520 --> 00:46:44,120
Speaker 9: crime and we should address it. And that's part of

888
00:46:44,160 --> 00:46:50,520
Speaker 9: my case. What the neoliberals say, because they're with me

889
00:46:50,640 --> 00:46:57,319
Speaker 9: on housing, is really that entitlements are too expensive and

890
00:46:57,360 --> 00:47:00,960
Speaker 9: to have intergenerational justice, we need to cut entitlements. I

891
00:47:01,000 --> 00:47:03,880
Speaker 9: think it's actually the reverse that what we need to

892
00:47:03,880 --> 00:47:07,080
Speaker 9: do is move to single payer socialized care so that

893
00:47:07,520 --> 00:47:13,280
Speaker 9: the expense medicine of medical care is cheaper, raise taxes,

894
00:47:13,640 --> 00:47:17,640
Speaker 9: which means we will get older people who are typically

895
00:47:17,880 --> 00:47:21,720
Speaker 9: the rich ones to pay if they have the means

896
00:47:22,400 --> 00:47:25,880
Speaker 9: on means testing for Social Security. You know, I steer

897
00:47:25,920 --> 00:47:29,680
Speaker 9: away from that because the traditional view about Social Security

898
00:47:29,760 --> 00:47:32,840
Speaker 9: is that it's popular only because it's a universal program

899
00:47:32,840 --> 00:47:35,799
Speaker 9: that everyone gets, and there's no reason to interfere with

900
00:47:35,840 --> 00:47:40,279
Speaker 9: that universality. If we take those rich people who are

901
00:47:40,280 --> 00:47:44,960
Speaker 9: collecting Social Security anyway and just raise their taxes, not

902
00:47:45,480 --> 00:47:49,520
Speaker 9: reduce their Social Security payment, but increase their taxes, including

903
00:47:50,280 --> 00:47:53,320
Speaker 9: on the housing they control. And so the main question

904
00:47:53,480 --> 00:47:56,960
Speaker 9: is who pays, and the rich older people should a

905
00:47:56,960 --> 00:47:58,920
Speaker 9: lot more than they do. That doesn't mean we have

906
00:47:59,000 --> 00:48:00,560
Speaker 9: to destroy the entils state.

907
00:48:00,920 --> 00:48:02,319
Speaker 2: I mean, it just seems to me that the rich

908
00:48:02,440 --> 00:48:05,000
Speaker 2: in general, not just the rich older people, but the

909
00:48:05,080 --> 00:48:07,759
Speaker 2: rich in general should pay more than what they're paying

910
00:48:07,800 --> 00:48:11,000
Speaker 2: now because just like with the Yeah, it's just like

911
00:48:11,080 --> 00:48:13,680
Speaker 2: within racial groups, like you would much rather be the

912
00:48:13,719 --> 00:48:16,880
Speaker 2: top one percent Black American or you know, for sure

913
00:48:17,120 --> 00:48:20,000
Speaker 2: millennial than you would to be in the bottom quarterer

914
00:48:20,200 --> 00:48:23,879
Speaker 2: of white people or boomers. But I wanted to ask

915
00:48:23,880 --> 00:48:26,040
Speaker 2: you too about like the root of how we ended

916
00:48:26,120 --> 00:48:29,279
Speaker 2: up here, because it didn't happen that older people were

917
00:48:29,320 --> 00:48:32,919
Speaker 2: able to accumulate wealth because they're nefarious and they hate

918
00:48:32,960 --> 00:48:34,880
Speaker 2: young people and they just want to like, you know,

919
00:48:35,040 --> 00:48:37,959
Speaker 2: save everything for themselves. They benefited from the New Deal,

920
00:48:38,120 --> 00:48:40,120
Speaker 2: you know, they benefit the end of the world of

921
00:48:40,160 --> 00:48:43,279
Speaker 2: World War two, the New Deal programs, which you know,

922
00:48:43,560 --> 00:48:46,279
Speaker 2: allowed them to be able to buy a house and

923
00:48:46,480 --> 00:48:49,080
Speaker 2: go to college for minimal cause health care was much

924
00:48:49,280 --> 00:48:50,480
Speaker 2: less expensive, et cetera.

925
00:48:50,840 --> 00:48:51,600
Speaker 4: And so isn't the.

926
00:48:51,640 --> 00:48:55,480
Speaker 2: Real solution here to go back to more of a

927
00:48:55,520 --> 00:48:59,520
Speaker 2: social democratic system rather than trying to target old people,

928
00:48:59,680 --> 00:49:03,080
Speaker 2: but to try to create a system that younger people

929
00:49:03,160 --> 00:49:06,319
Speaker 2: would benefit more from that includes more of these sort

930
00:49:06,360 --> 00:49:10,520
Speaker 2: of universal programs and public spending that the New Deal included.

931
00:49:12,520 --> 00:49:17,480
Speaker 9: I agree mostly with you, where the only place I

932
00:49:17,600 --> 00:49:22,839
Speaker 9: might resist is when you say it's either or So.

933
00:49:24,080 --> 00:49:27,280
Speaker 9: It's true that we should tax the rich no matter

934
00:49:27,400 --> 00:49:32,120
Speaker 9: their age. However, we also have a lot of formal

935
00:49:32,280 --> 00:49:36,480
Speaker 9: privileges for older people that younger people don't have, and

936
00:49:36,520 --> 00:49:42,400
Speaker 9: they're also financial privileges, and so taxation is one thing.

937
00:49:42,880 --> 00:49:45,880
Speaker 9: But if we just tax without regard to age and

938
00:49:45,960 --> 00:49:49,799
Speaker 9: focus on wealth, we won't even have seen, let alone targeted,

939
00:49:50,360 --> 00:49:54,920
Speaker 9: all of those other privileges, especially involving housing, in land

940
00:49:55,040 --> 00:49:57,960
Speaker 9: and property that older people have built into the.

941
00:49:57,880 --> 00:50:00,839
Speaker 2: System you're talking about, like the mortgage interested things like.

942
00:50:00,760 --> 00:50:04,560
Speaker 9: That or homestead exemptions. They're like, there's a very long

943
00:50:04,640 --> 00:50:06,520
Speaker 9: list and it's all in the book of all the

944
00:50:06,560 --> 00:50:12,640
Speaker 9: ways that through their voting power, which is distinctive, and

945
00:50:12,719 --> 00:50:15,359
Speaker 9: even poor older people vote a lot more than poor

946
00:50:15,440 --> 00:50:20,080
Speaker 9: younger people. Older people have privileged themselves as old, not

947
00:50:20,200 --> 00:50:23,719
Speaker 9: just as wealthy. And so my case is, you know,

948
00:50:23,800 --> 00:50:26,799
Speaker 9: really that we can't just blind ourselves to the way

949
00:50:26,920 --> 00:50:31,600
Speaker 9: class is being made and lived. Now you're you're right, absolutely,

950
00:50:31,640 --> 00:50:34,719
Speaker 9: and I'm totally with you, Crystal, that we should have

951
00:50:35,080 --> 00:50:38,840
Speaker 9: a return to, you know, a social democratic America to

952
00:50:38,880 --> 00:50:41,280
Speaker 9: the extent there ever was one, because the New Deal

953
00:50:41,680 --> 00:50:45,719
Speaker 9: left a lot to be desired, especially for if you

954
00:50:45,800 --> 00:50:49,880
Speaker 9: take intersectional oppression seriously, it didn't do much for blacks,

955
00:50:50,280 --> 00:50:53,200
Speaker 9: didn't do much for women, especially when they were workers.

956
00:50:54,200 --> 00:50:56,799
Speaker 9: And so, you know, my view as you're absolutely right,

957
00:50:56,840 --> 00:51:00,680
Speaker 9: we need to build an opportunity state for everyone of

958
00:51:00,719 --> 00:51:04,439
Speaker 9: all generations. The question is how do we get there?

959
00:51:05,000 --> 00:51:08,759
Speaker 9: And where I might differ slightly is worrying that we

960
00:51:08,800 --> 00:51:13,200
Speaker 9: can't just blind ourselves to who has power in order

961
00:51:13,239 --> 00:51:17,319
Speaker 9: to distribute power fairly. How can we do that if

962
00:51:17,320 --> 00:51:20,799
Speaker 9: we don't know how power is exercise, including in all

963
00:51:20,840 --> 00:51:25,360
Speaker 9: these diverse domains where older people do have outsized authority.

964
00:51:25,640 --> 00:51:27,839
Speaker 9: We can't get to where we both want to go.

965
00:51:28,840 --> 00:51:30,040
Speaker 1: Really enjoy this discussion.

966
00:51:30,360 --> 00:51:33,080
Speaker 2: I got one more questions to worry Mitch McConnell, alive

967
00:51:33,160 --> 00:51:35,879
Speaker 2: or dead.

968
00:51:34,800 --> 00:51:39,760
Speaker 9: I'm a you know, conspiracy stand so I would guess

969
00:51:39,760 --> 00:51:41,840
Speaker 9: he's been you know, dead for a while.

970
00:51:41,920 --> 00:51:42,840
Speaker 1: And I love it.

971
00:51:43,000 --> 00:51:46,040
Speaker 9: Uh, you know, he's he's the tip of the iceberg.

972
00:51:46,960 --> 00:51:50,640
Speaker 9: But it's it's been fascinating to see that saga unfold.

973
00:51:50,800 --> 00:51:53,640
Speaker 9: I'm going to put my money on you know, dead Mitch, not.

974
00:51:53,800 --> 00:51:57,480
Speaker 2: Live or at least like in some sort of vegetative states.

975
00:51:57,560 --> 00:52:01,120
Speaker 2: Absolutely not buying the AI photos here. Totally nuts, really

976
00:52:01,160 --> 00:52:05,960
Speaker 2: fascinating conversation. Really appreciate you, you know, laying out your thoughts

977
00:52:06,000 --> 00:52:07,520
Speaker 2: and sparring with us a little bit here.

978
00:52:07,520 --> 00:52:09,160
Speaker 3: I never thought i'd have so much in common with

979
00:52:09,200 --> 00:52:10,759
Speaker 3: a lefty Yale professor.

980
00:52:11,160 --> 00:52:12,200
Speaker 1: But you know, here we.

981
00:52:12,200 --> 00:52:13,319
Speaker 7: Go, there's only one of us.

982
00:52:13,800 --> 00:52:16,239
Speaker 1: Yeah, there are dozens. There are certainly dozens of you.

983
00:52:16,960 --> 00:52:19,440
Speaker 3: We I appreciate the conversation. Everyone should just go by

984
00:52:19,440 --> 00:52:21,080
Speaker 3: the books. Is interesting, and you actually have a lot

985
00:52:21,080 --> 00:52:23,640
Speaker 3: of good statistics. In particular, it's a big takedown of

986
00:52:23,680 --> 00:52:26,160
Speaker 3: the AARP. Maybe you'll come back.

987
00:52:26,000 --> 00:52:28,719
Speaker 1: And we'll talk about that one next any time. Appreciate you, sir, really,

988
00:52:28,760 --> 00:52:31,520
Speaker 1: thank you, really, appreciate you guys, really.

989
00:52:31,400 --> 00:52:34,480
Speaker 3: Enjoyed that conversation. Appreciate him stopping by. Thank you guys

990
00:52:34,520 --> 00:52:36,319
Speaker 3: so much for watching. There will be a Friday show

991
00:52:36,360 --> 00:52:39,240
Speaker 3: formorrow tomorrow. A lot of it will be pre taped,

992
00:52:39,239 --> 00:52:39,640
Speaker 3: et cetera.

993
00:52:39,719 --> 00:52:40,120
Speaker 1: You'll see.

994
00:52:40,280 --> 00:52:42,160
Speaker 3: We have some reasons why, but there will be. It

995
00:52:42,200 --> 00:52:44,040
Speaker 3: will be out, so don't worry. And we're about to

996
00:52:44,040 --> 00:52:45,239
Speaker 3: do the AMA so we'll see later