Latest / Breaking Points with Krystal and Saagar / 7/23/26: War Historically Unpopular, AI Hack Meltdown, Old People Debate

Transcript

7/23/26: War Historically Unpopular, AI Hack Meltdown, Old People Debate · Breaking Points with Krystal and Saagar

0:00
Speaker 1: Hey, guys, Saga and Crystal here.
0:01
Speaker 2: Independent media just played a truly massive role in this election,
0:05
Speaker 2: and we are so excited about what that means for
0:07
Speaker 2: the future of this show.
0:08
Speaker 3: This is the only place where you can find honest
0:10
Speaker 3: perspectives from the left and the right that simply does
0:13
Speaker 3: not exist anywhere else.
0:14
Speaker 2: So if that is something that's important to you, please
0:17
Speaker 2: go to Breakingpoints dot com. Become a member today and
0:19
Speaker 2: you'll get access to our full shows, unedited, ad free,
0:22
Speaker 2: and all put together for you every morning in your inbox.
0:25
Speaker 3: We need your help to build the future of independent
0:27
Speaker 3: news media, and we hope to see you at Breakingpoints
0:29
Speaker 3: dot com.
0:31
Speaker 2: Every poll has shown that the Iran war is profoundly unpopular.
0:34
Speaker 2: That is, in fact, perhaps the most unpopular war in
0:37
Speaker 2: American history.
0:38
Speaker 4: Trump was asked about this. Let's see what he has
0:40
Speaker 4: to say.
0:40
Speaker 5: Well, Americans aren't against the wark the a poll just
0:44
Speaker 5: came out. Americans don't want had gasoline precious, but they're
0:48
Speaker 5: not against the war. That just came out loud and
0:50
Speaker 5: clear in a poll. Nobody wants a ran to avenuclear weapon.
0:55
Speaker 5: Do you want a ran to avenuclear weapon? Do you
0:57
Speaker 5: think it's good?
0:58
Speaker 1: In fact?
0:58
Speaker 2: Air against high gas prices, also very much against the war.
1:02
Speaker 2: I don't have any idea of what pole he is
1:04
Speaker 2: talking about. Harry enton Over on CNN did a breakdown
1:07
Speaker 2: of what the polling looks like on this war.
1:10
Speaker 4: Let's do a little fact check here.
1:11
Speaker 6: Washington Post pol Trump's Iran war not approvedrating in early March.
1:15
Speaker 7: Look at this.
1:15
Speaker 6: It was way down there at minus thirteen points.
1:17
Speaker 4: Very rare for a war to start.
1:19
Speaker 6: Off underwater on the net approval that has approved minus disapproval.
1:22
Speaker 6: But look at how far down we are now, right
1:25
Speaker 6: into the Red Sea, forty points underwater according to the
1:28
Speaker 6: Washington Post pole.
1:29
Speaker 1: Look.
1:30
Speaker 6: I tried to find every single poll I possibly could.
1:33
Speaker 6: I believe this minus forty points represents the least popular
1:37
Speaker 6: poll in terms of Trump's net approvating on the Iran
1:40
Speaker 6: War the entire war so far?
1:42
Speaker 1: Think the war was worth it? Iraq?
1:43
Speaker 6: In two thousand and six, fifty eight percent said it
1:45
Speaker 6: was not worth and again that was three years into
1:47
Speaker 6: that war. Look at this, now you think that fifty
1:49
Speaker 6: eight percent was high in terms of not worth it?
1:52
Speaker 6: Sixty eight percent of Americans, more than two in three,
1:56
Speaker 6: say the Iran war was not worth it. Just twenty
1:59
Speaker 6: eight eight percent, Less than a third of Americans now
2:03
Speaker 6: say the Iram War was worth it, even less than
2:06
Speaker 6: the forty percent who said the Iraq War was worth it.
2:08
Speaker 6: Again at this point, heading into the two thousand and
2:10
Speaker 6: six mith term cycle, of course, we remember what happened
2:13
Speaker 6: in two thousand and six, Republicans lost both the House
2:15
Speaker 6: and the Senate. Among all independents, it is seventy seven percent,
2:20
Speaker 6: more than three in four of all independence say it's
2:23
Speaker 6: not worth it. How about GOP leaning independence. These are
2:26
Speaker 6: independents who say that actually lean Republican. Sixty three percent
2:30
Speaker 6: of them, more than three in five, nearly two in three,
2:34
Speaker 6: say that, in fact, the Iram war is not worth it.
2:37
Speaker 2: That sixty three percent number is maybe the most devastating
2:39
Speaker 2: that of GOP leaning independence, which are people oftentimes who
2:43
Speaker 2: claim their independence but vote for Republicans every single time.
2:46
Speaker 2: That's the way a lot of this works in our
2:47
Speaker 2: very partisan era. Sixty three percent say that it's not
2:50
Speaker 2: worth it. That is extraordinary. And Politico has a write
2:55
Speaker 2: up of a new poll looking just at MAGA voters,
2:58
Speaker 2: and you know, overwall all they still support the president. Overall,
3:01
Speaker 2: they still support this war. But you can see that
3:04
Speaker 2: even among the Magabase, they are getting more and more uncomfortable.
3:09
Speaker 2: Let's put this image up on the screen. This is
3:12
Speaker 2: talking about gas prices, and you can see here that
3:16
Speaker 2: a majority of Americans are blaming Iran for gas prices.
3:20
Speaker 4: That includes MAGA.
3:21
Speaker 2: So fifty seven percent of MAGA voters blame the war
3:25
Speaker 2: in Iran for gas price increases than twenty four percent
3:29
Speaker 2: blame the tariffs twenty four percent, supply chain disruptions, twenty
3:32
Speaker 2: three percent lack of domestic oil production in the US.
3:34
Speaker 2: I really don't think that's a problem at this point, guys,
3:36
Speaker 2: and fourteen percent Trump's poor handling of the economy.
3:39
Speaker 4: You know, it's a little.
3:40
Speaker 2: Different from the way that other groups look, especially on
3:44
Speaker 2: that number about blaming Trump directly, but in terms of
3:47
Speaker 2: directly attributing the gas price rise to the war in Iran,
3:51
Speaker 2: MAGA is basically where the rest of the population is.
3:54
Speaker 2: A majority of them see the connection between what the
3:57
Speaker 2: president has decided to do in Iran and what they
4:00
Speaker 2: paying at the pump. Furthermore, there was an increase in
4:04
Speaker 2: the number of MAGA voters who say that the US should.
4:08
Speaker 4: Continue the war in Iran only.
4:11
Speaker 2: If it doesn't increase costs, so you have more price
4:16
Speaker 2: sensitivity at this point.
4:17
Speaker 4: So there's a major shift there.
4:18
Speaker 2: As you know, people just become increasingly impatient with what
4:21
Speaker 2: is going on, and this is turning up obviously in
4:24
Speaker 2: all of the polls of what's going.
4:25
Speaker 4: To happen in the menterns.
4:26
Speaker 2: I just saw an Emerson poll this morning that has
4:28
Speaker 2: Democrats with an eleven point generic ballot lead, which would
4:32
Speaker 2: be a vastly larger wave than what we saw even
4:36
Speaker 2: in other waveyears for Democrats like twenty eighteen.
4:39
Speaker 1: It's totally nuts. Can we put C three on the screen?
4:41
Speaker 3: I actually do recommend people read this entire article level
4:44
Speaker 3: linked to it in our newsletter because when they say
4:47
Speaker 3: a maturity of Americans blamed the Iran war for gas prices,
4:50
Speaker 3: they break it down by every category, not just overall
4:54
Speaker 3: saying that the war in Iran, but they also have
4:56
Speaker 3: it broken down by who you voted for, from Harris
4:59
Speaker 3: to MAGA and even non MAGA. But I was looking
5:02
Speaker 3: at some of the dissident MAGA crowd because one of
5:05
Speaker 3: the things that a lot of the current supporters of
5:09
Speaker 3: Trump's policy will do is they go MAGA vote self
5:12
Speaker 3: identified MAGA support the war by ninety percent. What this
5:15
Speaker 3: poll does is they show people who voted for Trump
5:18
Speaker 3: at twenty twenty four. A little different, isn't it whenever
5:20
Speaker 3: you consider what that looks like, and the so called
5:23
Speaker 3: non Maga crowd is an increasingly a growing part of
5:26
Speaker 3: the electorate. May not be liberals, may not even be Democrats,
5:29
Speaker 3: maybe people who might vote Democrat, people who voted for
5:32
Speaker 3: Trump back in twenty twenty four dramatically souring on the
5:35
Speaker 3: war in the same percentage as the overall number.
5:38
Speaker 1: Increasingly, if you look in this you can.
5:40
Speaker 3: Actually see there is no political constituency outside of the
5:44
Speaker 3: diehard boomers who watch Fox that support this war or
5:47
Speaker 3: any even of the aims of the war.
5:49
Speaker 1: It's media wall to wall.
5:51
Speaker 3: You would be hard pressed outside of like Glenn Beck,
5:54
Speaker 3: who's out there yesterday calling Trump a strategic genius.
5:57
Speaker 1: My good luck. I mean, that's all you got left.
5:59
Speaker 3: You got Nshpiro, and you got Glenn Beck, and you
6:02
Speaker 3: know Jesse Waters. They's a true you know who's who
6:07
Speaker 3: of what you would want on yourself, national.
6:08
Speaker 2: Intellect, national intellect. The brain trust there, the true.
6:12
Speaker 3: The real brain trust, the wise men of our time.
6:15
Speaker 3: So yeah, that's that's really where things are right now.
6:17
Speaker 3: WEFC five, we could put that. Nate Silver also did
6:20
Speaker 3: an aggregation where you could see the overall and this
6:24
Speaker 3: is you know, from all of the polls that have
6:26
Speaker 3: come out from it, you have a pose at fifty
6:28
Speaker 3: eight point three percent and support just around thirty three
6:31
Speaker 3: point eight, interestingly enough, seems to be where his approval
6:33
Speaker 3: rating is.
6:34
Speaker 1: To just show you how how linked these two things are.
6:38
Speaker 3: And then you add in the generic ballot which you
6:40
Speaker 3: were talking about, and the political consequences of this. I mean,
6:43
Speaker 3: I think it's baked in. You could end the war
6:45
Speaker 3: today and it would still be as bad, but it's
6:47
Speaker 3: it's only going to go in one direction, as in,
6:49
Speaker 3: I don't think there's a lot upside necessarily an ending today,
6:52
Speaker 3: but there's a lot of downside.
6:53
Speaker 2: And continuing think about your think about Susan Collins in
6:56
Speaker 2: Maine right handed a bit of a gift from the
6:59
Speaker 2: whole Grand Platin or fallout now Democrats pretty quickly Coleester
7:02
Speaker 2: on Troy Jackson, who has a track record in Maine
7:05
Speaker 2: of being actually a strong, very strong electoral contender. But
7:08
Speaker 2: she was probably feeling pretty good, you know, in a difficult,
7:11
Speaker 2: very difficult year for her. Well, now as the war
7:14
Speaker 2: is back on and gas prices are going back up,
7:17
Speaker 2: we're like very likely to hit one hundred dollars a
7:19
Speaker 2: barrel today. That's going to be immediately reflected prices. You know,
7:23
Speaker 2: the prices go up.
7:23
Speaker 4: Very quickly, they come down very very slowly.
7:26
Speaker 2: She's going to be have to, you know, have to
7:28
Speaker 2: continue defending this and deflecting and doing her little very
7:31
Speaker 2: concerned dance this whole time. How do you think that's
7:34
Speaker 2: going to go for her? Like it is going to
7:36
Speaker 2: be a disaster, It is going to completely tank her
7:38
Speaker 2: chances of holding on to that seat.
7:40
Speaker 4: You can play that out. Iowa.
7:42
Speaker 2: Fox News had a pull that has the Democrat up
7:45
Speaker 2: in the Senate race in Iowa. Now, Iowa is one
7:48
Speaker 2: of the states in the country that has been hardest
7:50
Speaker 2: hit by all of the Trump policies, but specifically tariffs
7:54
Speaker 2: and specifically the Iran war because of the way it
7:57
Speaker 2: increases the fertilizer prices and creates.
7:59
Speaker 4: All kinds of problems for them there too.
8:01
Speaker 2: So not only in the Senate race, in the governor's race,
8:04
Speaker 2: you have Democrats who've been consistently leading by narrow margin,
8:07
Speaker 2: but leading in the polls in Iowa. This should be
8:10
Speaker 2: completely off the table at this point because of the
8:13
Speaker 2: way that rural America has shifted to the right. Those
8:16
Speaker 2: are the sorts of things that are really being put
8:18
Speaker 2: on the table because of Trump's disastrous policy here and
8:21
Speaker 2: the revulsion that Americans feel against it. This let's put
8:25
Speaker 2: this last one up on the screen here C. Seven,
8:27
Speaker 2: because I think this really drills down on the point
8:29
Speaker 2: of what a disaster this has been overall politically for Trump.
8:33
Speaker 2: This is again a Fox News poll, and we've got
8:36
Speaker 2: a comparison here from interactive polls of which party do
8:39
Speaker 2: you trust more to handle on these variety of issues?
8:41
Speaker 2: Twenty twenty two versus twenty twenty six. Okay, In twenty
8:45
Speaker 2: twenty two, Republicans by thirteen points, were more trusted on inflation.
8:50
Speaker 2: Now it's D plus ten on the economy. Back in
8:55
Speaker 2: twenty twenty two, it was R plus fifteen. Now Democrats
8:59
Speaker 2: are by nine points. That's the highest since two thousand
9:03
Speaker 2: and six. On the inflation piece. That's the highest ever
9:05
Speaker 2: recorded for trust in Democrats. Ready for this one. On immigration, Okay,
9:11
Speaker 2: twenty twenty two, Republicans had the edge by fifteen points.
9:15
Speaker 4: This was the signature campaign issue for Trump.
9:20
Speaker 2: This is an area where Republicans are almost always stronger
9:23
Speaker 2: than the Democrats. Now Democrats have pulled to a plus
9:26
Speaker 2: one advantage basically even But the fact that you've got
9:29
Speaker 2: Democrats even competing on this issue is such a turnabout
9:33
Speaker 2: from the way things have been in recent years.
9:36
Speaker 4: So it just shows you how much he has destroyed his.
9:38
Speaker 2: Own mandate, his own agenda, and how across the board,
9:41
Speaker 2: even on his strongest issues, he has you know, really
9:45
Speaker 2: really frittered away any sort of mandate that he may
9:48
Speaker 2: have had.
9:49
Speaker 3: Yep, destroyed his entire presidency all for literally nothing. Okay,
9:53
Speaker 3: we got to Garrison, lovely sitting by.
9:55
Speaker 1: Let's get to it.
9:58
Speaker 2: So we have quite a situation in developing in the
10:01
Speaker 2: AI world. Let's go and put this Wall Street Journal
10:03
Speaker 2: report up on the screen. Open AI models escaped and
10:07
Speaker 2: hacked a company in cybersecurity test gone wrong. The subhead
10:11
Speaker 2: here says before they could penetrate hugging faces, the name
10:14
Speaker 2: of the cybersecurity company that they hacked defenses, the models
10:18
Speaker 2: needed a way onto the internet. They found one. Joining
10:22
Speaker 2: us now to explain what all what happened and what
10:24
Speaker 2: all of this means is Garrison Lovely. He is a
10:26
Speaker 2: journalist and also author of the forthcoming book Obsolete, The
10:31
Speaker 2: AI Industry's trillion dollar race to replace Us and How
10:34
Speaker 2: to stop It.
10:34
Speaker 4: Great to see Garrison, see a man, Great to see you.
10:36
Speaker 2: Yeah, of course, So just help us understand, to start with,
10:39
Speaker 2: in non technical language, what exactly happened here?
10:43
Speaker 8: Yeah, so a week ago, this company, hugging Base, publishes
10:46
Speaker 8: this post and they're like, we were hacked by some AIS.
10:50
Speaker 7: It was definitely AI, as it happened really fast.
10:53
Speaker 8: We don't know which models they used and who was
10:55
Speaker 8: directing them, but we caught it with our own AI
10:58
Speaker 8: and addressed it, reported it to law enforcement. And then
11:02
Speaker 8: on Tuesday, open Ai published as a post and we're like, hey,
11:05
Speaker 8: that was actually us, well not exactly, it was our
11:07
Speaker 8: AI models acting autonomously because they had broken out of
11:12
Speaker 8: their containment and then tried to hack into hugging Face
11:16
Speaker 8: to get answers basically to a test that they were running.
11:20
Speaker 8: And this is the first incident like this that we
11:23
Speaker 8: know of. It might not be the first one, because
11:26
Speaker 8: it's possible that this happened internally at open ai, but
11:28
Speaker 8: they just never reported it to the public, and it's
11:32
Speaker 8: kind of hard to overstate the significance of this. People
11:35
Speaker 8: in the AI safety community who have been worried about
11:38
Speaker 8: AI capabilities and dangers for decades have warned for a
11:43
Speaker 8: long time that this type of thing would happen where
11:46
Speaker 8: you give the AI some goal and then it does
11:49
Speaker 8: unexpected things that cause harm in the real world in.
11:52
Speaker 7: Pursuit of that goal.
11:54
Speaker 8: And the risks of this scale as it models get better,
11:59
Speaker 8: And this is like a relatively low stakes version of it,
12:02
Speaker 8: but you could imagine the ex signment being a hospital,
12:04
Speaker 8: a bank, like the models will blackmail people in some circumstances,
12:08
Speaker 8: and it was a test, but if they had access
12:11
Speaker 8: to real world infrastructure that they can control and shut off,
12:15
Speaker 8: the blackmail might actually have some some punch.
12:18
Speaker 3: KMIT put D two up here on the screen. So
12:20
Speaker 3: this is from a blog post that was published by
12:22
Speaker 3: OpenAI and they spaceay specifically to gain access. The models
12:27
Speaker 3: identified and exploited a zero day vulnerability and this was
12:33
Speaker 3: happening quote in a sandbox testing environment. Can you translate
12:37
Speaker 3: some of that for our audience.
12:39
Speaker 8: Yeah, yeah, So basically, the opening IY models were operating
12:43
Speaker 8: in this environment where they're not supposed to have access
12:45
Speaker 8: to the outside world, the Internet, anything like that. The
12:49
Speaker 8: models were trying to answer this evaluation. The questions were
12:53
Speaker 8: very hard, and they're like, well, these questions are hard,
12:57
Speaker 8: maybe it'd be easier to just get the answers to
12:59
Speaker 8: the questions.
13:01
Speaker 7: So maybe to do that, I'll get access to the Internet.
13:04
Speaker 8: So it like pokes around in its environment, finds like
13:07
Speaker 8: a little chink in the armor, and then it gets
13:11
Speaker 8: out and like starts jumping around to different servers within
13:15
Speaker 8: Open a Eye until it finds one that has access
13:18
Speaker 8: to the Internet, and then it uses that to start
13:21
Speaker 8: looking for what might have the answers to this evaluation,
13:26
Speaker 8: finds hugging Face as a candidate, and then finds exploits
13:30
Speaker 8: in hugging paces codebase that nobody knew existed, and then
13:34
Speaker 8: uses those exploits and like stolen security credentials and like
13:37
Speaker 8: all these classic hacker moves to get access to hugging Face.
13:42
Speaker 8: And according to Opening, I successfully found the answer set
13:47
Speaker 8: and did this all on its own and without Opening
13:50
Speaker 8: Eye and noticing for maybe a week or more. This
13:54
Speaker 8: is like reading between the lines from the Hugging Face
13:55
Speaker 8: report Opening as report, we don't have a super clear
13:58
Speaker 8: timeline and opening. I was about this because it looks
14:01
Speaker 8: pretty bad if your models both autonomously hack an other company,
14:05
Speaker 8: which is a crime if a human did it, but
14:07
Speaker 8: can't be a crime because the models can't have intent legally,
14:10
Speaker 8: but if a human did this, it would be criminal.
14:13
Speaker 8: And then you have no idea it's happening until the
14:15
Speaker 8: other company figures it out, reports it, and then maybe
14:18
Speaker 8: you see the report and you're.
14:18
Speaker 7: Like, oh shit, that sounds like us.
14:21
Speaker 8: Then you publish it, and so this is like, yeah,
14:26
Speaker 8: just kind of an unbelievable story across the board.
14:29
Speaker 7: Yeah, happy to go into more detail on any of it.
14:31
Speaker 2: Yeah, So basically, what it looks like happened is Hugging
14:34
Speaker 2: Face detected the intrusion and came public with it. But
14:39
Speaker 2: from open Ay's perspective, they were just like, oh, the
14:41
Speaker 2: model got the answers.
14:42
Speaker 4: I guess it just figured it out. They had no
14:44
Speaker 4: idea that.
14:44
Speaker 2: It had escaped, hacked, committed crimes in service of accomplishing
14:49
Speaker 2: its goals. One of the one of the things I
14:52
Speaker 2: saw sort of like, you know, cope rationale here from
14:55
Speaker 2: people who want to downplay this is yeah, but you know,
14:57
Speaker 2: it was still just following this goal. It's not like
14:59
Speaker 2: it was going out there freelancing and coming up with
15:02
Speaker 2: its own objectives to go out and cause mischief in
15:05
Speaker 2: the world. It was just trying way too hard to
15:07
Speaker 2: accomplish the goal that humans had ultimately set for it.
15:11
Speaker 2: Why does that not assuage your concerns here?
15:15
Speaker 8: Yeah, I mean I think that if it were given
15:18
Speaker 8: like a biological you know, understanding evaluation and then it's like, well,
15:22
Speaker 8: I'm going to hack and find the bio answer key,
15:25
Speaker 8: then like that's more evidence, right that it's just generalizing
15:28
Speaker 8: to like, I am going to do something you did
15:31
Speaker 8: not intend as unrelated to the activity that you're testing
15:34
Speaker 8: me for.
15:35
Speaker 7: So that's worse.
15:37
Speaker 8: But I think it's also bad that Openey wanted the
15:40
Speaker 8: model to do this evaluation the right way, which would
15:43
Speaker 8: not involve cheating stealing the answer key, and they definitely
15:48
Speaker 8: didn't want their model to do crimes and to like
15:50
Speaker 8: hack another multi billion dollar company. And so the fundamental
15:55
Speaker 8: point is that these companies, the most sophisticated in the
15:59
Speaker 8: world in many respects, are not able to reliably steer
16:02
Speaker 8: or control their own models, and those models are now
16:05
Speaker 8: autonomous and capable enough to take actions in the real
16:08
Speaker 8: world that could have really big consequences, and those consequences
16:12
Speaker 8: are just going to grow with time as the models
16:15
Speaker 8: get more capable and autonomous. Uh, and the problem just
16:18
Speaker 8: gets harder, right because like they'll just they're superhuman at
16:21
Speaker 8: hacking in many respects now, but they could be like
16:23
Speaker 8: super superhuman in the next generation and you'll need the
16:26
Speaker 8: very best models to keep up. And like Hugging Face
16:30
Speaker 8: was struggling to fend off this attack because they couldn't
16:33
Speaker 8: use the models that open eye and anthropic, like the
16:36
Speaker 8: very best models on the market, because those models are
16:38
Speaker 8: like trained to not help with this type of thing
16:41
Speaker 8: because they can't distinguish reliably whether like you're actually defending
16:45
Speaker 8: against an attack or you're using that as a cover
16:47
Speaker 8: to do your own attack. And so Hugging Face had
16:50
Speaker 8: to use open weight models, which are less capable, and
16:54
Speaker 8: then Open Eyes like we let them into our Trusted
16:56
Speaker 8: Access Partner program as a result of this, and it's
16:58
Speaker 8: like it's like.
17:00
Speaker 7: A mob boss.
17:01
Speaker 8: Yeah, it's like, oh, like nice code baits you got there,
17:03
Speaker 8: Like a shame is something where that happened to it?
17:05
Speaker 3: Yeah, so let's go into that. There's been a lot
17:07
Speaker 3: of debate around this what you just talked about, these
17:09
Speaker 3: open weight models, largely a lot of them are Chinese,
17:13
Speaker 3: which have been released. There's a huge debate now in Washington.
17:16
Speaker 3: I don't know if you saw this actually happened late
17:18
Speaker 3: last night. The Secretary of the Treasury and then the
17:21
Speaker 3: White House ais Are Michael Kratzios, both basically put out
17:24
Speaker 3: very veiled threats saying that the Kimney K three model,
17:28
Speaker 3: which recently beat many of the American models in terms
17:31
Speaker 3: of testing for its power, they're claiming that it was
17:34
Speaker 3: distilled from the Fable five.
17:37
Speaker 1: Model released by Anthropic.
17:39
Speaker 3: Now, obviously, as you just said, these open weight models
17:43
Speaker 3: don't have the same.
17:44
Speaker 1: Quote guardrails or control.
17:46
Speaker 3: Open source open weight which they're able to run and
17:48
Speaker 3: have been used to circumvent some of these things. I'm
17:52
Speaker 3: curious for your general reaction, both to the geopolitics of it,
17:54
Speaker 3: because it does seem like an actual ban on Kimmy
17:57
Speaker 3: K three is coming sometime soon if these companies have
18:00
Speaker 3: their way, and then to the technological piece, just again,
18:03
Speaker 3: try to explain it and as layman term as possible,
18:06
Speaker 3: because I know it gets complicated.
18:08
Speaker 1: Yeah.
18:08
Speaker 8: Yeah, So these open weight models, I mean when they're
18:11
Speaker 8: published online, they have guardrails built into them. The thing is,
18:14
Speaker 8: it's trivial to remove them if you have like any
18:17
Speaker 8: technical capacity, and it costs like not very much time
18:20
Speaker 8: or money to do so. So functionally, whatever the model
18:23
Speaker 8: could do if it had no guardrails, like, that's the
18:25
Speaker 8: version that will be accessible to everybody in perpetuity once
18:28
Speaker 8: it's published. And so people worry about this in a
18:31
Speaker 8: world where like say you make the model and it's
18:34
Speaker 8: like capable of teaching a novice to make a novel bioweapon.
18:38
Speaker 8: If you publish that model's weights to the world, it's
18:41
Speaker 8: just going to exist kind of forever. And that's a
18:45
Speaker 8: pretty scary world. And it's like bio is one of
18:49
Speaker 8: these things which is attack dominant, so it's really hard
18:52
Speaker 8: to defend against bioweapons cyber It's like, well, if the
18:57
Speaker 8: good guys have access to the best models before the
19:00
Speaker 8: bad guys by like six months, maybe you can just
19:02
Speaker 8: patch everything and it's like defense biased if.
19:04
Speaker 7: You have like super capable AI agents.
19:08
Speaker 8: But yeah, we don't know yet like when or if
19:11
Speaker 8: we'll be in the world of like there's now this
19:14
Speaker 8: bioweapon generating machine that's just out there forever, and that
19:18
Speaker 8: worries a lot of people, understandably. And then the case
19:23
Speaker 8: for open weight models is that they kind of decentralize
19:26
Speaker 8: control and power. And so if Hugging Face had access
19:30
Speaker 8: to models that were like two years behind the state
19:32
Speaker 8: of the art, you know, maybe they wouldn't have been
19:34
Speaker 8: able to detect and fent off this attack. And you
19:38
Speaker 8: just be like living at the mercy of these companies
19:40
Speaker 8: which may or may not let you into their programs.
19:43
Speaker 8: And if you're in a you know, middle power, if
19:46
Speaker 8: you're in China, you also don't want to get hacked
19:48
Speaker 8: by anybody, and you might not have access to the
19:52
Speaker 8: best stuff. I have not looked into the report you're
19:56
Speaker 8: talking about as much. I do think it's like you
20:01
Speaker 8: can't really address AI and it's like jurisdiction based way, right,
20:05
Speaker 8: Like if the US makes it hard for US developers
20:08
Speaker 8: and companies to use open weight models from China, then
20:12
Speaker 8: they'll still be used in other parts of the world,
20:14
Speaker 8: and you're not going to be like safe from their effects.
20:18
Speaker 8: Given just like the way technology and AI and software
20:22
Speaker 8: in particular diffuses the distillation attacks that Kratzios is talking about, Like, yeah,
20:29
Speaker 8: that seems quite likely to be true, like Kimmy will
20:31
Speaker 8: identify as Claude if you ask it. I mean, it's
20:35
Speaker 8: you've got to acknowledge the irony of these companies being like.
20:38
Speaker 7: Oh no, they took great data.
20:39
Speaker 4: It's like, well, yeah, so did you literally all of
20:43
Speaker 4: our data? Yeah?
20:44
Speaker 7: Yeah, Yeah. It's like a bit you know, distillation.
20:46
Speaker 8: It's like they're just harvesting very you know, directly, like
20:49
Speaker 8: the same type of thing. And yeah, but then the
20:53
Speaker 8: US approach of like, all right, we got to deploy
20:55
Speaker 8: faster and more the best models, Like that's going to
20:58
Speaker 8: let them keep distilling the model. And so this idea
21:01
Speaker 8: that like the US is going to win the race
21:03
Speaker 8: by publishing more capable models faster just makes no sense
21:07
Speaker 8: because you're just letting other countries fast follow, right, and
21:11
Speaker 8: so it's just kind of all backwards, like if you
21:14
Speaker 8: regulated and slowed down in the US, that would slow
21:16
Speaker 8: down China as well.
21:18
Speaker 4: Got it interesting? I did see.
21:20
Speaker 2: I don't know enough about this, but Arno Bertrand, who's
21:22
Speaker 2: and analysts I've follow on Twitter, are a very smart guy.
21:25
Speaker 2: He's really skeptical of this claim that Kimmy K three
21:29
Speaker 2: was distilled from Fable five simply because of the timeline.
21:32
Speaker 2: Fable five was not out that long before Kimmy K
21:34
Speaker 2: three was released. He's saying it would take a lot
21:37
Speaker 2: longer if that was actually the approach. I don't know
21:39
Speaker 2: whether that's true or not. That just wanted to throw
21:41
Speaker 2: that into the conversation. But you know, Bernie Sanders, I
21:44
Speaker 2: think understandably saw this hack and said, listen, this is
21:48
Speaker 2: why we've got to regulate AI more. We have to
21:50
Speaker 2: have more control over these things. We've putting these things
21:52
Speaker 2: down to the public. We're trusting Sam Altman and whoever
21:55
Speaker 2: else to you know, have our best interest at heart
21:57
Speaker 2: and do a thorough job of testing these and make
21:59
Speaker 2: sure that they're not going to cause problems out there
22:01
Speaker 2: in the world. That seems like a preposterous thing to do.
22:03
Speaker 2: At the same time, I mean, if you do have
22:05
Speaker 2: these open weight models that have basically the same capabilities,
22:09
Speaker 2: really undercuts the ability to regulate and constrain AI here
22:13
Speaker 2: at home. So, you know, how do you think about
22:17
Speaker 2: what we should do from a domestic perspective when we
22:20
Speaker 2: live in a global world.
22:22
Speaker 4: Technology is global, et cetera.
22:24
Speaker 8: Yeah, yeah, I mean, I would say the open weight
22:26
Speaker 8: models are like six months behind. Still I think there's
22:29
Speaker 8: like this Axios headline. I think that was just inaccurate
22:33
Speaker 8: and if Kimmy wasn't justilling for Fable, it was distilling
22:37
Speaker 8: from Opus and other models, like I don't know exactly
22:39
Speaker 8: the details, but it's quite likely it was distilling from
22:42
Speaker 8: American models, at least in part. I think we obviously
22:46
Speaker 8: need to have international rules around AI that are binding,
22:49
Speaker 8: that don't require trusts between the parties, And there are
22:53
Speaker 8: technical approaches you can verify what's happening and data center
22:57
Speaker 8: is using like devices on the chips themselves, and you
23:01
Speaker 8: could create institutions like the International Atomic Energy Agency. But
23:05
Speaker 8: for AI, and I think this hack is just a
23:09
Speaker 8: great example of like, you know, again, this will scale,
23:13
Speaker 8: like this will happen again. It will be worse in
23:15
Speaker 8: the future, like something will be worse than this. It's
23:18
Speaker 8: almost guaranteed. And if the US decides to regulate, and China,
23:24
Speaker 8: I mean China is regulating actually far more than the
23:26
Speaker 8: US is and still managing to catch up quite quickly.
23:29
Speaker 8: So I think there's like some assumptions that need to
23:31
Speaker 8: be tested here.
23:34
Speaker 7: But you can change the risk landscape.
23:36
Speaker 8: You can create a kind of like a floor within
23:39
Speaker 8: your own country, and you can also say, like, hey,
23:42
Speaker 8: if you're a Chinese company and you want to serve
23:44
Speaker 8: models in the United States, you have to comply with
23:46
Speaker 8: these regulations as well. That's what states have been doing
23:48
Speaker 8: in California and New York Illinois. If you want to
23:50
Speaker 8: serve models in those states, you have to do certain things.
23:53
Speaker 8: And because those are big markets, they're just going to
23:56
Speaker 8: do those things. Yeah, but I think US should be
24:00
Speaker 8: I mean, there are dialogues between the US and China.
24:02
Speaker 8: I think that should continue and accelerate, and I think
24:06
Speaker 8: ultimately in the good future there's going to be some
24:09
Speaker 8: kind of deal between the US and China to regulate AI,
24:12
Speaker 8: to ban certain things, to mandate other things, and that
24:17
Speaker 8: will happen because again the bioweapon problem, like the hackers,
24:21
Speaker 8: the terrorists, like there's going to be that at least,
24:24
Speaker 8: And there's also just this thing of like we could
24:27
Speaker 8: lose control of AIS. We just did we being opening
24:30
Speaker 8: eye here, we could also lose control two AIS if
24:33
Speaker 8: they get capable autonomous numerous enough, which is like where
24:37
Speaker 8: things are trending. We don't know when, but that's just
24:39
Speaker 8: like the thing that happens by default, and so we
24:43
Speaker 8: just need to do something about it.
24:45
Speaker 2: Garrison, what do you make of the economic landscape here,
24:47
Speaker 2: because that was a lot of the discussion around the
24:49
Speaker 2: Kimney K three release as well. And even if you
24:51
Speaker 2: don't think it's as advanced as let's say, Fable five,
24:54
Speaker 2: it's you know, they're getting pretty close here and it's
24:57
Speaker 2: much much cheaper. Anthropic and open Ay seem to have
25:01
Speaker 2: freaked out pretty hard and are basically calling on the
25:04
Speaker 2: Trump administration to come in and bail them ount because
25:06
Speaker 2: they have these incredibly mass amounts of investor money flowing
25:10
Speaker 2: into them. It only makes sense if they're going to
25:13
Speaker 2: get astronomical returns from replacing human labor.
25:17
Speaker 4: How do you think this all works out?
25:19
Speaker 8: To me?
25:19
Speaker 4: Do you see this as a bubble?
25:22
Speaker 2: Do you think that the future is more like AI
25:25
Speaker 2: is a sort of a utility the way the Internet is,
25:27
Speaker 2: and more of the money is made in the applications.
25:30
Speaker 4: How do you view the economic pieces of this?
25:32
Speaker 7: Yeah, it's hard to say for sure.
25:34
Speaker 8: I think people speak very confidently on this, and it's
25:37
Speaker 8: a really big complex topic.
25:40
Speaker 7: I would say that prior to.
25:42
Speaker 8: Like recent months, the case for it being a bubble
25:44
Speaker 8: got a lot weaker because Anthropics revenue grew the fastest
25:49
Speaker 8: the company's revenue has ever grown at that level, going
25:52
Speaker 8: from like a few billion annualized at the start of
25:54
Speaker 8: the year to like forty seven billion annualized by like May,
25:57
Speaker 8: I want to say, and we don't know what it
25:59
Speaker 8: is now.
26:01
Speaker 7: It may have slowed down.
26:03
Speaker 8: I think that the cost pressure or the pressure on
26:05
Speaker 8: prices from capable open weight models, it's just always going
26:09
Speaker 8: to be like chasing the frontier, right, and so the
26:12
Speaker 8: American business model for these releases is like have the
26:17
Speaker 8: very best models by capabilities, charge a premium for them,
26:21
Speaker 8: and then keep releasing better models to stay ahead of
26:24
Speaker 8: the open weight frontier. And that starts to break if
26:28
Speaker 8: it takes a lot longer to publish your own models.
26:31
Speaker 8: And so like Mythos was finished training in February, it
26:35
Speaker 8: doesn't come out as fable until June May, and then
26:40
Speaker 8: it's taken down and then it comes back and so
26:43
Speaker 8: in that time, you know, Kimmy can catch up, get closer.
26:47
Speaker 8: And so yeah, like the whole fast release like release
26:52
Speaker 8: things is almost as soon as you have them and
26:55
Speaker 8: capture that premium while you can starts to break if
26:58
Speaker 8: there's like a kind of slowed down testing process. If
27:02
Speaker 8: the government is just deciding arbitrarily or without like transparent
27:07
Speaker 8: criteria of like what models are capable enough to release
27:10
Speaker 8: or have guardrails that are good enough to release, then
27:13
Speaker 8: it gives open weight models more time to eat your lunch.
27:16
Speaker 8: And this is all predicated on the models continuing to
27:20
Speaker 8: get better, or at least they're continuing to be demand
27:23
Speaker 8: for the premium version of these things, which are priced
27:26
Speaker 8: at substantially above what it costs to serve them, which
27:29
Speaker 8: is why the open weight models are able to undercut
27:31
Speaker 8: them so much.
27:31
Speaker 7: Right, Like, there's this.
27:32
Speaker 8: Big idea of like, oh, is it profitable to serve
27:35
Speaker 8: a given customer?
27:36
Speaker 7: The answer is clearly yes.
27:37
Speaker 8: The financial data shows us the fact that open weight
27:40
Speaker 8: models that are like in the same ballpark are so
27:42
Speaker 8: much cheaper. Again, it's just showing like the raw costs,
27:44
Speaker 8: which we don't know from the outside, but we can
27:47
Speaker 8: infer from a bunch of reporting a bunch of other things.
27:49
Speaker 8: And just like, think of how verbose the models are.
27:52
Speaker 8: Would they do that if they weren't making money by
27:54
Speaker 8: the token?
27:55
Speaker 7: Right?
27:56
Speaker 1: Right? That makes a lot of sense.
27:58
Speaker 3: Really appreciate your analysis, man, I'm looking forward to the
28:01
Speaker 3: release of your book, so thank you very much for
28:03
Speaker 3: joining us.
28:03
Speaker 2: Yeah, then I think it's available pre order now right,
28:05
Speaker 2: comes out on September fifteenth, Do I have that right?
28:08
Speaker 7: That's right?
28:08
Speaker 2: All right, everybody go get that pre order, give us
28:11
Speaker 2: a copy. I already got a copy. I already read it.
28:13
Speaker 4: It's great, so I'll send you on SAA. Thanks Garson.
28:20
Speaker 3: Very excited now to be joined by a new author,
28:22
Speaker 3: Samuel Moyn. Let's put this book up here on the screen.
28:25
Speaker 3: Jarontocracy in America, How the old are hoarding power.
28:29
Speaker 1: And wealth and what to do about it? A man
28:31
Speaker 1: after my own heart.
28:33
Speaker 3: Professor Moyne is a professor at Yale University Law School,
28:37
Speaker 3: and he joins us now to discuss his new book.
28:40
Speaker 3: Thank you very much for joining us, sir, appreciate it.
28:42
Speaker 7: Thanks so much for having me so.
28:43
Speaker 3: In this book, you describe jerontocracy in America as both
28:48
Speaker 3: a class element and an age element, a takeover of
28:51
Speaker 3: American institutions, the hoarding of wealth, the hoarding of power.
28:55
Speaker 3: You prescribe a several solutions, some more radical than the others,
28:59
Speaker 3: including the diolution of older people's votes, awaiting younger people
29:03
Speaker 3: mandatory retirement ages and business and in Congress, the abolition
29:07
Speaker 3: of the United States Senate in fact, So why don't
29:10
Speaker 3: you start with your diagnosis kind of where you're coming
29:13
Speaker 3: from and how you came to some of these conclusions.
29:17
Speaker 9: Well, gerontocracy is a rule of buy and for the
29:21
Speaker 9: old people, and we have it in this country. And thankfully,
29:25
Speaker 9: finally we've started to talk about the age of our politicians,
29:30
Speaker 9: who at the end of their lives suffer cognitive decline
29:36
Speaker 9: like Joe Biden, or death like so many Democrats who
29:39
Speaker 9: died to facilitate the passage of the current president's one
29:43
Speaker 9: big beautiful Bill, or Ruth Gater Bader Ginsburg who lost
29:48
Speaker 9: her bet on longevity. And that debate is all to
29:51
Speaker 9: the good, but what it misses is that there's a
29:55
Speaker 9: much deeper phenomenon, which is elder control of the politic
30:00
Speaker 9: system generally, no matter how old the politicians are. If
30:04
Speaker 9: Trump doesn't make it his whole term, we might have
30:07
Speaker 9: the youngest president ever, but we would still have the
30:10
Speaker 9: oldest voters. And our wealth in this country is to
30:15
Speaker 9: an astonishing extent controlled by older people. So I realized
30:19
Speaker 9: that I should just present the receipts to try to
30:23
Speaker 9: convince people just how deep the syndrome of gerontocracy goes.
30:28
Speaker 2: Talk about some of your solutions, like what you would
30:31
Speaker 2: do about this problem you perceive, Well.
30:34
Speaker 9: It depends on the problem. I mean, I've got a
30:37
Speaker 9: long list, and you know, it's up to the politicians
30:40
Speaker 9: to decide, you know, if any are viable. But if
30:43
Speaker 9: we start with the politicians, I do think, like most Americans,
30:47
Speaker 9: that we should have age limits for office, and of
30:49
Speaker 9: course we do at the state level in many cases
30:52
Speaker 9: around the world. They're very familiar, including for judges. If
30:56
Speaker 9: we only go for age limits or term limits, we're
30:59
Speaker 9: still like to end up with a very old political class.
31:02
Speaker 9: We might control the risk for cognitive decline and death.
31:06
Speaker 9: But what about unfair representation in our Senate? We have
31:10
Speaker 9: one guy under forty, and we have very few thirty
31:15
Speaker 9: and forty year olds in the House. And so what
31:19
Speaker 9: we need to potentially consider for politicians is youth quotas
31:23
Speaker 9: to kind of expand the range of generational representation on elections.
31:29
Speaker 9: I mean, you mentioned my most radical solution, but there
31:33
Speaker 9: are a lot of modest ones, like a federal holiday
31:37
Speaker 9: for voting so that young people aren't punished for not
31:40
Speaker 9: yet being retired or not being powerful enough in their
31:43
Speaker 9: jobs to leave work. We need to relax registration requirements
31:48
Speaker 9: so they're not punished for their likely greater mobility when
31:54
Speaker 9: they're younger. I would also argue we should have fewer
31:58
Speaker 9: elections because we know that it's in the boring elections
32:02
Speaker 9: that older people are likeliest to control. Young people do
32:06
Speaker 9: show up in greater numbers for presidential elections, but just
32:09
Speaker 9: as an example in New Mexico in twenty twenty four,
32:13
Speaker 9: the median age of primary voters was seventy two years old.
32:17
Speaker 9: We also need different campaign finance rules because the donors
32:22
Speaker 9: of two politicians in a privately financed system are sixty,
32:27
Speaker 9: seventy even eighty.
32:30
Speaker 7: On average.
32:31
Speaker 9: And so those are the fixes there. And then when
32:34
Speaker 9: it comes to wealth, I advocate like higher taxes, which
32:38
Speaker 9: means that older people who have so much more wealth
32:41
Speaker 9: will pay more. To younger generations, I advocate incentivizing them
32:46
Speaker 9: to leave their housing and build for seniors so that
32:50
Speaker 9: they can, you know, downsize without fear in order to
32:53
Speaker 9: make way for younger people. And as you mentioned on jobs,
32:57
Speaker 9: I think many jobs should have mandatory retirement ages.
33:00
Speaker 1: So one of the things.
33:01
Speaker 3: What I found fascinating is that a lot of your
33:03
Speaker 3: analysis is actually rooted in an old Marxist tradition. I'd
33:05
Speaker 3: never heard this line before. Quote age is the modality
33:09
Speaker 3: in which class is lived. You know, we have plenty
33:12
Speaker 3: of time here, so why don't you give us some
33:13
Speaker 3: of the history. I was not particularly familiar with that
33:16
Speaker 3: strain of Marxism.
33:18
Speaker 9: Well, so, you know, the standard Marxist view, and it's
33:21
Speaker 9: right a lot of the time is that the problem
33:23
Speaker 9: is class, duh. And that's often right, and it's always
33:28
Speaker 9: right to some extent. And yet there have been folks
33:33
Speaker 9: who said, we can't be too much into just getting
33:38
Speaker 9: rid of every other factor. And the classic example which
33:41
Speaker 9: Stuart Hall, the Marxist you mentioned, invoked was race, and
33:46
Speaker 9: he said that race is the way we live out class,
33:49
Speaker 9: both in the sense that classes racialized, the lower class
33:54
Speaker 9: it is much more likely to be non white in
33:58
Speaker 9: the United States, but also white people are more likely
34:04
Speaker 9: to understand their class predicament in racially antagonistic terms. That's
34:09
Speaker 9: how you get a candidate and presidency twice over now,
34:14
Speaker 9: like Donald Trump. And so I'm just proposing to adapt
34:18
Speaker 9: that view for age. It's actually a little worse in
34:21
Speaker 9: terms of age, because we have formal laws like apartheid
34:27
Speaker 9: for race that actually privilege older people. I think that's
34:32
Speaker 9: just and right in many cases. For example, older people
34:36
Speaker 9: do get more federal benefits, maybe not to the extent
34:40
Speaker 9: they do, which is six to seven dollars they get
34:44
Speaker 9: for everyone that's spent on a child at the federal level.
34:48
Speaker 9: But we also have a lot of especially tax protections
34:53
Speaker 9: designed of buy and for older people, even when they
34:56
Speaker 9: are some of the wealthiest Americans. If you look across
35:00
Speaker 9: the world at the billionaire class, I'm going to concede
35:03
Speaker 9: we have a younger trillionaire, although he's not that young. Yeah,
35:07
Speaker 9: it's not that billionaires you have a forty five percent are
35:12
Speaker 9: between fifty and seventy, and forty five percent are above seventy.
35:18
Speaker 9: Only ten percent of billionaires are under fifty. And so
35:24
Speaker 9: partly we explain that by saying the older you are,
35:26
Speaker 9: the more time you've had to accumulate and hoard. But
35:29
Speaker 9: we also have to explain it by the privileges that
35:34
Speaker 9: a generation in the right place at the right time
35:37
Speaker 9: has kind of reaped by gaming the system.
35:42
Speaker 2: So you bring up the racial wealth disparity and just
35:46
Speaker 2: cards on the table. This is why you know, I
35:48
Speaker 2: have some issue with your analysis because to me, it
35:50
Speaker 2: seems like if we're pitting groups against each other, for example,
35:54
Speaker 2: whites versus blacks, old versus young, we are dividing in
35:58
Speaker 2: a way that ultimately is beneficial to the oligarchic class,
36:01
Speaker 2: the Elon musk, the Sam Altman's, the Peter Thiel's, the
36:04
Speaker 2: Mark Zuckerberg's, in a way that is beneficial from them
36:07
Speaker 2: and distracts from what is the consistent core problem across
36:11
Speaker 2: all of these, which is class. So let me ask
36:14
Speaker 2: you this, with regard to the racial wealth disparity, you
36:16
Speaker 2: propose waiting younger voters, giving them more weight with their vote,
36:21
Speaker 2: a sort of three fifth compromise for older voters, I guess.
36:24
Speaker 2: I mean, would you do the same thing for white people?
36:27
Speaker 2: You say, well, as a group, you know, yeah, there's
36:29
Speaker 2: poor whites and there's rich whites. But as a group
36:31
Speaker 2: they have much more wealth than Black Americans, so their
36:34
Speaker 2: vote should should count less so that we can help
36:37
Speaker 2: even the playing field here.
36:41
Speaker 9: So briefly, yes, but a couple of background points before
36:44
Speaker 9: I explain my answer. First, you know, it's tough to
36:49
Speaker 9: figure out how we're going to build a coalition to
36:52
Speaker 9: face down oligarchy. And my ultimate view is that we
36:56
Speaker 9: need intergenerational solidarity for the sake of inner generational equity,
37:01
Speaker 9: just as we need a cross racial, transracial majority in
37:05
Speaker 9: this country to face down the real evildoers. Uh, the
37:10
Speaker 9: question is how do we analyze the situation properly, because
37:15
Speaker 9: you know, oligarchy has a lot of tools at its disposal,
37:20
Speaker 9: and actually, most people who've made criticisms of generational unfairness
37:26
Speaker 9: have been in favor of oligarchy because they've wanted to
37:29
Speaker 9: target federal benefits for old people and destroy the welfare state.
37:33
Speaker 9: And we can't just turn our back on the idea
37:36
Speaker 9: that there might be intergenerational unfairness that intergenerational solidarity is
37:42
Speaker 9: going to have to solve now. Second, I never, you know,
37:45
Speaker 9: call for diluting older people's vote, let alone stripping them
37:50
Speaker 9: a vote. What I do, you know, suggest is that
37:53
Speaker 9: we should think of the equality between human beings differently,
37:57
Speaker 9: And the stake that someone who's tone has in an
38:00
Speaker 9: election is just much greater than the stake someone who's
38:04
Speaker 9: eighty has in an election, just because what matters is
38:08
Speaker 9: how long we live under the policies, how much of
38:13
Speaker 9: a say do you get in addressing long term problems?
38:17
Speaker 9: And so my solution is inflating the weight of younger
38:22
Speaker 9: people's votes to take account of this fact. Now, you
38:25
Speaker 9: asked a hard question, and I'll answer it directly. We
38:28
Speaker 9: already do try to inflate the significance of the votes
38:35
Speaker 9: of African Americans votes, or at least we did until recently. Classically,
38:41
Speaker 9: we did things like have majority minority districts so that
38:45
Speaker 9: African Americans could have their districts organized so they could
38:48
Speaker 9: have a black representative in Congress, which mattered a lot
38:52
Speaker 9: to them, and understandably so. And you know, you can
38:56
Speaker 9: also argue that we already have weighted voting. It's just
38:59
Speaker 9: in favor of residents of small states whose votes because
39:04
Speaker 9: of the Senate and the Electoral College, count way more.
39:07
Speaker 9: And so my suggestion is we think about who's being
39:11
Speaker 9: treated unfairly and set up rules that take account of injustice.
39:16
Speaker 2: So I'm sticking with this point. You know, in terms
39:19
Speaker 2: of whose voice counts more in democracy, the biggest glaring
39:23
Speaker 2: issue is, and I mentioned it earlier, campaign finance. I mean,
39:27
Speaker 2: isn't a big part of the reason why older voters
39:30
Speaker 2: have more power in our political system simply that concentration
39:34
Speaker 2: of wealth that there is, you know, a larger preponderance
39:38
Speaker 2: of wealth among older Americans versus younger Americans. That gives
39:42
Speaker 2: them more power in our political system. But it's not
39:46
Speaker 2: just a problem of old versus young. It's also a
39:49
Speaker 2: problem of the wealth among you know, with white people
39:51
Speaker 2: versus black people. Again, the way to deal with this
39:55
Speaker 2: is not to pit one group against the other, but
39:57
Speaker 2: to deal with the underlying issue here of money and
40:01
Speaker 2: politics and the way that that has completely skewed our system.
40:05
Speaker 9: So you're making a couple of very good points. One
40:08
Speaker 9: is the importance of reforming campaign finance.
40:11
Speaker 8: UH.
40:11
Speaker 9: And I provide the data about just how much older
40:16
Speaker 9: the control there is of you know, picking candidates, of
40:19
Speaker 9: funding them, and then having the successful politicians do their bidding,
40:25
Speaker 9: and of course that ought to change. You're also right
40:29
Speaker 9: that you know, this is a problem of wealth. But
40:33
Speaker 9: you know, to go back to Stuart Hall, it's like
40:36
Speaker 9: saying white supremacy isn't real. UH, doesn't account for how
40:41
Speaker 9: why the class structure is the way it is. Of
40:44
Speaker 9: course it does, and the same is true of age.
40:46
Speaker 9: It's not accidental that so much wealth is controlled by
40:50
Speaker 9: older Americans. Now you're right that if we want to,
40:55
Speaker 9: you know, create solidarity, we can't create an age antagonism anymore,
41:00
Speaker 9: and we can create a race antagonism. I'm not arguing
41:03
Speaker 9: for either. I'm arguing for saying, to take class seriously,
41:08
Speaker 9: we have to take age seriously, just like we have
41:11
Speaker 9: to take race seriously.
41:12
Speaker 4: Don't you think that.
41:13
Speaker 2: Don't you think just on that point, don't you think
41:15
Speaker 2: the history of racism in America is quite different from
41:18
Speaker 2: the history of absolutely reverse agism in America, given obviously slavery,
41:23
Speaker 2: given Jim Crow, given you know, continued legacy of that.
41:27
Speaker 4: I mean, there's a very very clear.
41:30
Speaker 2: Cut pattern of mass discrimination against Black Americans that doesn't
41:35
Speaker 2: exist in terms of you know, discrimination against you.
41:37
Speaker 9: I agree with you, but that that it's not clear
41:41
Speaker 9: what force that has, because every form of discrimination will
41:45
Speaker 9: have its own history. That's like saying, you know, patriarchy
41:49
Speaker 9: isn't real because it's different from white supremacy. It also
41:53
Speaker 9: can't be argued. I think that the only kinds of
41:56
Speaker 9: unfairness that are real are old kinds. That's clearly fall
42:00
Speaker 9: and it's true that this is a new kind of
42:03
Speaker 9: discrimination because the situation of older people, mainly through the
42:08
Speaker 9: extension of life and the fact that they're still around
42:11
Speaker 9: in much greater numbers, and thankfully so has changed the
42:16
Speaker 9: score in American class. And so you know, with all
42:20
Speaker 9: due respect, I think we have to take each kind
42:23
Speaker 9: of discrimination on its own terms, while also recognizing that
42:27
Speaker 9: class is made up of a lot of different kinds
42:30
Speaker 9: of fact.
42:30
Speaker 1: Yes, I also wanted to get your response to some
42:32
Speaker 1: of the critics.
42:33
Speaker 3: So a friend of mine, Russ Green, he coined a term,
42:37
Speaker 3: I love Boomer luxury communism. He wrote a critique of
42:41
Speaker 3: your book from the right, and what he said is
42:44
Speaker 3: that your proposal, here's aarontocracy America would actually entrench daryn
42:50
Speaker 3: toocracy and not necessarily reverse it. So I'm just curious
42:54
Speaker 3: for your response to his general argument. For example, I
42:57
Speaker 3: believe your endorsement of the Bernie Sanders Social Security expansion
43:01
Speaker 3: tax for saying that you should reject means testing whenever. Sorry,
43:05
Speaker 3: your reason for rejecting means testing whenever it comes to
43:08
Speaker 3: social security the general diagnosis that seniors are hard up
43:12
Speaker 3: for retirement. So yeah, I am curious to engage with
43:15
Speaker 3: some other critics. I think for more the right wing
43:18
Speaker 3: of gerontocracy from more ere of your left wing kind
43:21
Speaker 3: of Marxist analysis.
43:23
Speaker 7: Yeah, so I liked Russ's peace.
43:26
Speaker 9: I think he's representative of a neo liberal take on
43:32
Speaker 9: life in general, and for many, many years, their view
43:37
Speaker 9: has been that if you care about intergenerational justice, you
43:42
Speaker 9: have to target entitlements for the sake of older people,
43:46
Speaker 9: whereas I call for expanding them. And I think he's,
43:51
Speaker 9: with all due respect again or wrong for a couple
43:54
Speaker 9: of reasons.
43:55
Speaker 7: One is that the main way.
43:57
Speaker 9: That older people have control over our political system is
44:00
Speaker 9: not that they get entitlement, especially when they're poor. It's
44:05
Speaker 9: all the other ways, being the politicians in extraordinary numbers,
44:11
Speaker 9: being the voters, and controlling the political system through campaign finance,
44:16
Speaker 9: and then just wealth housing jobs. None of that has
44:20
Speaker 9: anything to do with social security. And so I want
44:24
Speaker 9: to argue against us that we shouldn't treat intergenerational fairness
44:29
Speaker 9: as a pretext for attacking the elder benefits that older
44:34
Speaker 9: people need. And as I mentioned, I actually think, contrary
44:38
Speaker 9: to that neoliberal POV, that we should expand elder entitlements
44:43
Speaker 9: because my firm belief is that one reason a lot
44:46
Speaker 9: of older people do retain their wealth is that they're
44:52
Speaker 9: going to live indefinitely long they know it, and deep
44:56
Speaker 9: down they don't know who's going to take care of them.
44:59
Speaker 9: Our welfare state does not, because Medicare excludes long term
45:03
Speaker 9: maintenance of older people, which is a scandal. And so
45:07
Speaker 9: my sens is that if we were more generous to
45:10
Speaker 9: older people, they either would dilute their own influence over
45:15
Speaker 9: our affairs so that it's a more just society, or
45:20
Speaker 9: taking their power from them in exchange for care would
45:24
Speaker 9: be a kind of just compensation.
45:26
Speaker 1: So what's the evidence for that?
45:28
Speaker 3: I mean, every entitlement for them has been expanded, has
45:31
Speaker 3: been protected, whenever it comes to the way that they've
45:35
Speaker 3: manipulated property tax rules. I don't know if you're familiar
45:38
Speaker 3: with a more recent case where a house was passed
45:41
Speaker 3: down in a fifty five over community to a younger woman,
45:44
Speaker 3: and the entire community is banded together to kick her
45:47
Speaker 3: out from the property that she was deeded because they
45:49
Speaker 3: said it's for seniors only. I mean, there's only really
45:52
Speaker 3: evidence of genuine hoarding and a zero sum mentality from
45:56
Speaker 3: their perspective, So I'm not really sure why we should
45:58
Speaker 3: give them so much grace.
46:00
Speaker 9: So it depends what we're talking about. I mean, I
46:03
Speaker 9: try to convince you in with all the receipts in
46:06
Speaker 9: the book, that housing is in very large part a
46:11
Speaker 9: crisis that's of old people's making for a few reasons.
46:14
Speaker 9: One is that because of the insane way that we
46:18
Speaker 9: make land use decisions in our country at these local
46:20
Speaker 9: town meetings, older people control supply and constrain supply. Second,
46:27
Speaker 9: as you noted, and that story exemplifies this, older people
46:30
Speaker 9: are very averse to downsizing in part because they've made
46:34
Speaker 9: sure there's nothing to downsize into, you know, at the
46:37
Speaker 9: local level where these decisions are made. And that's a
46:41
Speaker 9: crime and we should address it. And that's part of
46:44
Speaker 9: my case. What the neoliberals say, because they're with me
46:50
Speaker 9: on housing, is really that entitlements are too expensive and
46:57
Speaker 9: to have intergenerational justice, we need to cut entitlements. I
47:01
Speaker 9: think it's actually the reverse that what we need to
47:03
Speaker 9: do is move to single payer socialized care so that
47:07
Speaker 9: the expense medicine of medical care is cheaper, raise taxes,
47:13
Speaker 9: which means we will get older people who are typically
47:17
Speaker 9: the rich ones to pay if they have the means
47:22
Speaker 9: on means testing for Social Security. You know, I steer
47:25
Speaker 9: away from that because the traditional view about Social Security
47:29
Speaker 9: is that it's popular only because it's a universal program
47:32
Speaker 9: that everyone gets, and there's no reason to interfere with
47:35
Speaker 9: that universality. If we take those rich people who are
47:40
Speaker 9: collecting Social Security anyway and just raise their taxes, not
47:45
Speaker 9: reduce their Social Security payment, but increase their taxes, including
47:50
Speaker 9: on the housing they control. And so the main question
47:53
Speaker 9: is who pays, and the rich older people should a
47:56
Speaker 9: lot more than they do. That doesn't mean we have
47:59
Speaker 9: to destroy the entils state.
48:00
Speaker 2: I mean, it just seems to me that the rich
48:02
Speaker 2: in general, not just the rich older people, but the
48:05
Speaker 2: rich in general should pay more than what they're paying
48:07
Speaker 2: now because just like with the Yeah, it's just like
48:11
Speaker 2: within racial groups, like you would much rather be the
48:13
Speaker 2: top one percent Black American or you know, for sure
48:17
Speaker 2: millennial than you would to be in the bottom quarterer
48:20
Speaker 2: of white people or boomers. But I wanted to ask
48:23
Speaker 2: you too about like the root of how we ended
48:26
Speaker 2: up here, because it didn't happen that older people were
48:29
Speaker 2: able to accumulate wealth because they're nefarious and they hate
48:32
Speaker 2: young people and they just want to like, you know,
48:35
Speaker 2: save everything for themselves. They benefited from the New Deal,
48:38
Speaker 2: you know, they benefit the end of the world of
48:40
Speaker 2: World War two, the New Deal programs, which you know,
48:43
Speaker 2: allowed them to be able to buy a house and
48:46
Speaker 2: go to college for minimal cause health care was much
48:49
Speaker 2: less expensive, et cetera.
48:50
Speaker 4: And so isn't the.
48:51
Speaker 2: Real solution here to go back to more of a
48:55
Speaker 2: social democratic system rather than trying to target old people,
48:59
Speaker 2: but to try to create a system that younger people
49:03
Speaker 2: would benefit more from that includes more of these sort
49:06
Speaker 2: of universal programs and public spending that the New Deal included.
49:12
Speaker 9: I agree mostly with you, where the only place I
49:17
Speaker 9: might resist is when you say it's either or So.
49:24
Speaker 9: It's true that we should tax the rich no matter
49:27
Speaker 9: their age. However, we also have a lot of formal
49:32
Speaker 9: privileges for older people that younger people don't have, and
49:36
Speaker 9: they're also financial privileges, and so taxation is one thing.
49:42
Speaker 9: But if we just tax without regard to age and
49:45
Speaker 9: focus on wealth, we won't even have seen, let alone targeted,
49:50
Speaker 9: all of those other privileges, especially involving housing, in land
49:55
Speaker 9: and property that older people have built into the.
49:57
Speaker 2: System you're talking about, like the mortgage interested things like.
50:00
Speaker 9: That or homestead exemptions. They're like, there's a very long
50:04
Speaker 9: list and it's all in the book of all the
50:06
Speaker 9: ways that through their voting power, which is distinctive, and
50:12
Speaker 9: even poor older people vote a lot more than poor
50:15
Speaker 9: younger people. Older people have privileged themselves as old, not
50:20
Speaker 9: just as wealthy. And so my case is, you know,
50:23
Speaker 9: really that we can't just blind ourselves to the way
50:26
Speaker 9: class is being made and lived. Now you're you're right, absolutely,
50:31
Speaker 9: and I'm totally with you, Crystal, that we should have
50:35
Speaker 9: a return to, you know, a social democratic America to
50:38
Speaker 9: the extent there ever was one, because the New Deal
50:41
Speaker 9: left a lot to be desired, especially for if you
50:45
Speaker 9: take intersectional oppression seriously, it didn't do much for blacks,
50:50
Speaker 9: didn't do much for women, especially when they were workers.
50:54
Speaker 9: And so, you know, my view as you're absolutely right,
50:56
Speaker 9: we need to build an opportunity state for everyone of
51:00
Speaker 9: all generations. The question is how do we get there?
51:05
Speaker 9: And where I might differ slightly is worrying that we
51:08
Speaker 9: can't just blind ourselves to who has power in order
51:13
Speaker 9: to distribute power fairly. How can we do that if
51:17
Speaker 9: we don't know how power is exercise, including in all
51:20
Speaker 9: these diverse domains where older people do have outsized authority.
51:25
Speaker 9: We can't get to where we both want to go.
51:28
Speaker 1: Really enjoy this discussion.
51:30
Speaker 2: I got one more questions to worry Mitch McConnell, alive
51:33
Speaker 2: or dead.
51:34
Speaker 9: I'm a you know, conspiracy stand so I would guess
51:39
Speaker 9: he's been you know, dead for a while.
51:41
Speaker 1: And I love it.
51:43
Speaker 9: Uh, you know, he's he's the tip of the iceberg.
51:46
Speaker 9: But it's it's been fascinating to see that saga unfold.
51:50
Speaker 9: I'm going to put my money on you know, dead Mitch, not.
51:53
Speaker 2: Live or at least like in some sort of vegetative states.
51:57
Speaker 2: Absolutely not buying the AI photos here. Totally nuts, really
52:01
Speaker 2: fascinating conversation. Really appreciate you, you know, laying out your thoughts
52:06
Speaker 2: and sparring with us a little bit here.
52:07
Speaker 3: I never thought i'd have so much in common with
52:09
Speaker 3: a lefty Yale professor.
52:11
Speaker 1: But you know, here we.
52:12
Speaker 7: Go, there's only one of us.
52:13
Speaker 1: Yeah, there are dozens. There are certainly dozens of you.
52:16
Speaker 3: We I appreciate the conversation. Everyone should just go by
52:19
Speaker 3: the books. Is interesting, and you actually have a lot
52:21
Speaker 3: of good statistics. In particular, it's a big takedown of
52:23
Speaker 3: the AARP. Maybe you'll come back.
52:26
Speaker 1: And we'll talk about that one next any time. Appreciate you, sir, really,
52:28
Speaker 1: thank you, really, appreciate you guys, really.
52:31
Speaker 3: Enjoyed that conversation. Appreciate him stopping by. Thank you guys
52:34
Speaker 3: so much for watching. There will be a Friday show
52:36
Speaker 3: formorrow tomorrow. A lot of it will be pre taped,
52:39
Speaker 3: et cetera.
52:39
Speaker 1: You'll see.
52:40
Speaker 3: We have some reasons why, but there will be. It
52:42
Speaker 3: will be out, so don't worry. And we're about to
52:44
Speaker 3: do the AMA so we'll see later