Latest / Breaking Points with Krystal and Saagar / 8/7/26: Disaster Jobs Report, Rogue AI Commits Crime Spree, Corporate Dems Declare War On DSA

Transcript

8/7/26: Disaster Jobs Report, Rogue AI Commits Crime Spree, Corporate Dems Declare War On DSA · Breaking Points with Krystal and Saagar

• 0:00
  Speaker 1: Hey, guys, Saga and Crystal here.
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• 0:30
  Speaker 3: Hello everybody, Happy Friday. How are you guys feeling happy Friday?
• 0:35
  Speaker 2: Yeah, we got some good stuff in the show today, right,
• 0:38
  Speaker 2: and just two thumbs up, double thumbs up out to
• 0:43
  Speaker 2: Cure Week. We got to give double thumbs down to
• 0:46
  Speaker 2: the big jobs report out this morning. Shocking, shocking loss,
• 0:52
  Speaker 2: unexpected and downward revision for past month. So not looking
• 0:55
  Speaker 2: good there. We also have a pretty stunning I wasn't
• 1:01
  Speaker 2: didn't have my eyes on this. Andy Ogles, who is
• 1:03
  Speaker 2: a top Trump ally Maga guy, has said some of
• 1:07
  Speaker 2: the most incendiary things of anyone in the Republican Caucus
• 1:10
  Speaker 2: defeated in a Republican primary in Tennessee. We've also got
• 1:14
  Speaker 2: some wild stuff happening with regard to Ai Garrison. Lovely
• 1:17
  Speaker 2: is going to join us to tell us just how
• 1:19
  Speaker 2: freaked out we should be. We got some third way
• 1:22
  Speaker 2: Shenanigans planning to thwart the rise of the left. I
• 1:26
  Speaker 2: think some of their true colors showing there. And then Ryan,
• 1:29
  Speaker 2: you've got a candidate from Minnesota who's planning to join
• 1:32
  Speaker 2: us as well. Give us the breakdown there.
• 1:34
  Speaker 4: Yeah, the primary Minnesota is this coming Tuesday, and there's
• 1:39
  Speaker 4: going to be the marquee race is Angie Craig, who
• 1:43
  Speaker 4: currently represents a second Minnesota district against Peggy Flanagan. So
• 1:47
  Speaker 4: it's kind of another centrist, kind of insurgent campaign. Peggy
• 1:51
  Speaker 4: Flanning in the kind of progressive lieutenant governor there to
• 1:54
  Speaker 4: replace Angie Craig, who's kind of corporate Democrat. There's a
• 1:57
  Speaker 4: three way race among well, now I'm going to sound
• 2:01
  Speaker 4: like a fool. It's a three way race among three people.
• 2:04
  Speaker 4: One of them is Matt Little, who in the last
• 2:07
  Speaker 4: couple of months of the race was well ahead but
• 2:11
  Speaker 4: has seen this influx of money from three to fourteen
• 2:15
  Speaker 4: action you know, which is the pro Science, Pro Doctor pack,
• 2:19
  Speaker 4: which has gotten a bunch of money from a pack
• 2:21
  Speaker 4: but which says it doesn't do APAX stuff anymore. And
• 2:25
  Speaker 4: these three kind of women's rights packs who also have
• 2:30
  Speaker 4: gotten a lot of money from a pack, and d
• 2:32
  Speaker 4: MFI but some of them have said, well, this is
• 2:35
  Speaker 4: an APAC money below, although money is fungible, and so
• 2:38
  Speaker 4: a ton of this super pac money is flooding in
• 2:40
  Speaker 4: at the very end to try to stop Matt Little
• 2:42
  Speaker 4: from being kind of the next house insurgent. He doesn't
• 2:46
  Speaker 4: have the kind of entire Bernie Justice Democrats wing when
• 2:50
  Speaker 4: he's got the Congressional Progressive Caucus pack and Keith Allison
• 2:53
  Speaker 4: and he's so and he also has American priorities, which
• 2:57
  Speaker 4: is the uh the anti a pack pack that came
• 3:00
  Speaker 4: in yesterday to start spending on his behalf against these packs.
• 3:04
  Speaker 5: So we'll be hearing from him later.
• 3:07
  Speaker 2: How's the Craig Flanagan race looking at this point? That
• 3:09
  Speaker 2: one looks to be pretty tight.
• 3:10
  Speaker 4: Flannagan has been up consistently throughout it by a few points.
• 3:15
  Speaker 4: A poll came out in the last couple of days
• 3:17
  Speaker 4: that had Antie Craig up by two points. Because of
• 3:20
  Speaker 4: the polling miss in Michigan, Flannagan's supporters are quite nervous.
• 3:26
  Speaker 4: They'd much rather be going in with a not neck
• 3:30
  Speaker 4: and neck against Antie Craig, who's gotten just absolutely enormous
• 3:34
  Speaker 4: amounts of dark money also, so it's going to be
• 3:39
  Speaker 4: they're thinking it's going to be very close.
• 3:41
  Speaker 3: Yeah.
• 3:42
  Speaker 2: I feel like Flanagan is a little bit less of
• 3:43
  Speaker 2: a lightning rod than Abdula side. I mean, she's the
• 3:45
  Speaker 2: current lieutenant governor, so more sort of acceptable within mainstream
• 3:50
  Speaker 2: Democratic establishment. Do you know if Minnesota closure open primary, and.
• 3:55
  Speaker 4: She's also Native American, which makes it harder for the
• 3:58
  Speaker 4: center to like demogo her racially inside the Democratic Party,
• 4:05
  Speaker 4: it's open so however, so they have open primaries. However,
• 4:10
  Speaker 4: there there's a competitive Republican Senate race, and there's a
• 4:14
  Speaker 4: Republican as well governor gluminatorial primary. So the expectation is
• 4:19
  Speaker 4: that ninety nine percent of Republicans are going to vote
• 4:22
  Speaker 4: in their own primary, whereas in Michigan there was nothing
• 4:24
  Speaker 4: for them to really vote on.
• 4:26
  Speaker 3: Gotcha?
• 4:27
  Speaker 2: Okay, all right, well let's jump into these job numbers.
• 4:30
  Speaker 2: This just is out this morning, so we haven't gotten
• 4:32
  Speaker 2: to see a whole lot of analysis about what is
• 4:34
  Speaker 2: going on here. But this is from Heather Long, who
• 4:37
  Speaker 2: is you know, really a go to in terms of
• 4:39
  Speaker 2: her economic analysis. She says, just in a surprisingly bad
• 4:43
  Speaker 2: job report, the US economy lost twenty three thousand jobs
• 4:47
  Speaker 2: in July, way below expectations of plus eighty k. May
• 4:50
  Speaker 2: and June were revised down by a combined one hundred
• 4:53
  Speaker 2: and three thousand, So the unemployment rate dropped by zero
• 4:57
  Speaker 2: point one of a percent, but down, she says, for
• 5:00
  Speaker 2: the wrong reasons. Over two hundred and sixty thousand people
• 5:03
  Speaker 2: left the labor force, and wage growth at just three
• 5:07
  Speaker 2: point two percent, was wiped out entirely by inflation. You
• 5:12
  Speaker 2: can see the chart here up on the screen. You know,
• 5:14
  Speaker 2: the job market had had been looking stronger than it
• 5:19
  Speaker 2: did last year. You could see prior to the past
• 5:21
  Speaker 2: couple of months you'd had these you know, you'd gain
• 5:24
  Speaker 2: a little, you'd lose some, back and forth. You had
• 5:27
  Speaker 2: three straight months of not amazing growth, but some growth,
• 5:31
  Speaker 2: and now we're back to the pattern of job losses
• 5:35
  Speaker 2: and once again, and this has really become a key pattern,
• 5:38
  Speaker 2: significant downward revisions for the previous months. So you know, Emily,
• 5:43
  Speaker 2: what are you what are you taking from this?
• 5:45
  Speaker 6: Well, yeah, I'm just looking at some of the early
• 5:47
  Speaker 6: coverage here. It's already been described as very disappointing on
• 5:50
  Speaker 6: Fox Business.
• 5:53
  Speaker 7: Entirely surprise. I'm not sure what the.
• 5:56
  Speaker 2: One to say that, that's her language. Very disappointed the numbers.
• 6:01
  Speaker 2: I'm very concerned.
• 6:04
  Speaker 6: Not sure what else you would do with these numbers,
• 6:06
  Speaker 6: but at the very least call them disappointing. Also looking like,
• 6:09
  Speaker 6: as you mentioned, how they're long noting people dropping out
• 6:12
  Speaker 6: of the labor force, so obviously not a good sign,
• 6:16
  Speaker 6: not a good Uh, there's a little bit of a
• 6:18
  Speaker 6: red flag, we could say, right off the right off
• 6:20
  Speaker 6: the bat there and the downward revisions. Zero Hedge has
• 6:24
  Speaker 6: already referred to that as a Biden trick. So you
• 6:28
  Speaker 6: see the Trump administration just being flamed across the board
• 6:32
  Speaker 6: for the job report so far. And Ryan, is there
• 6:35
  Speaker 6: anything it looks like I was trying to look at
• 6:37
  Speaker 6: what Heather Lung saw the losses were in most is
• 6:41
  Speaker 6: there anything that stands out to you in the early
• 6:44
  Speaker 6: numbers here.
• 6:46
  Speaker 4: I'm still looking through the precise numbers by by industry,
• 6:52
  Speaker 4: but maybe by the end of the show some more
• 6:53
  Speaker 4: thoughts on that. But the only potential silver lining for
• 6:58
  Speaker 4: Trump as he's eyeing these job numbers, you know he
• 7:01
  Speaker 4: wants he wants rate cuts. You know, he put in
• 7:05
  Speaker 4: his new Federal Reserve chair, Kevin worsh who immediately implemented
• 7:10
  Speaker 4: a new policy of not giving guidance of where he
• 7:15
  Speaker 4: wants to take the FED in the future, which is
• 7:17
  Speaker 4: a very odd move because it's not really ideological in
• 7:21
  Speaker 4: the sense that there was nobody really clamoring for this
• 7:25
  Speaker 4: other than just some real kind of pedantic kind of
• 7:29
  Speaker 4: Fed numbers people who his argument is this takes this
• 7:34
  Speaker 4: requires the market then to kind of guess where things
• 7:36
  Speaker 4: are going more and so it's more market friendly.
• 7:40
  Speaker 5: But it's just a very odd approach.
• 7:42
  Speaker 4: And what it does is it creates a lot more
• 7:43
  Speaker 4: risk because now none of these traders know, you know,
• 7:47
  Speaker 4: what the Fed is trying to do meeting to meeting,
• 7:50
  Speaker 4: So you get a lot more volatility. And when you
• 7:53
  Speaker 4: get more volatility, you have to price that in as
• 7:56
  Speaker 4: higher interest rates. You're paying a risk premium. So his
• 8:02
  Speaker 4: first policy, Trump wants interest rates down. Warsh's first policy
• 8:07
  Speaker 4: drove for no reason, interest rates higher. Now Trump is
• 8:11
  Speaker 4: going to look at these numbers and say, okay, good,
• 8:13
  Speaker 4: this is a very bad economy. Therefore we need to
• 8:17
  Speaker 4: cut interest rates. But the whole point of cutting interest
• 8:22
  Speaker 4: rates is to grow the economy. So killing the economy
• 8:25
  Speaker 4: in order to kind of grow it is is a
• 8:27
  Speaker 4: little bit backwards.
• 8:28
  Speaker 2: But if you're Trump, well, the other and the other
• 8:30
  Speaker 2: issue is that hiking. You know, if you lower interest rates,
• 8:34
  Speaker 2: you may fuel more inflation. And inflation also, you know,
• 8:38
  Speaker 2: continues to be an issue obviously created by the combination
• 8:42
  Speaker 2: of Trump's tariffs, but more significantly lately the Iran war.
• 8:48
  Speaker 2: Mac I sent a posts from Heather Long and inflation
• 8:51
  Speaker 2: that maybe could pull up, so we can see how
• 8:54
  Speaker 2: that's going.
• 8:54
  Speaker 4: Go ahead, Ryan, Yeah, yeah, no, exactly to your point,
• 8:58
  Speaker 4: even though you have some wage growth I think of
• 9:02
  Speaker 4: three point two percent or something, inflation is running hotter
• 9:06
  Speaker 4: than that. So if you're at the Fed chair, you're
• 9:10
  Speaker 4: looking at that and thinking, well, I can't cut interest
• 9:12
  Speaker 4: rates because inflation is still crazy, Like inflation is still
• 9:15
  Speaker 4: a problem.
• 9:17
  Speaker 2: Yeah, And I mean you can just see it really
• 9:19
  Speaker 2: clearly in the chart here. With the war in Iran,
• 9:22
  Speaker 2: inflation jumps right up and so where wage gains had
• 9:26
  Speaker 2: been for a time outpacing inflation, meaning that if you
• 9:31
  Speaker 2: get an increase in your wage, you're actually feeling it
• 9:33
  Speaker 2: and being able to gain from it. Now, with inflation
• 9:37
  Speaker 2: spiking immediately after the launch of the Iran War, that
• 9:42
  Speaker 2: trend has been crushed. So this is the dynamic obviously
• 9:45
  Speaker 2: we saw during much of the Biden administration where inflation
• 9:49
  Speaker 2: was eating up all of the wage gains and more.
• 9:52
  Speaker 2: And that is why, you know, I think I think
• 9:54
  Speaker 2: that's the single biggest reason why Kamal Harris ends up losing.
• 9:57
  Speaker 2: I think it's the single biggest reason why you saw
• 10:00
  Speaker 2: incumbents and incumbent parties around the entire globe losing because
• 10:03
  Speaker 2: people could tell month to month the way that their
• 10:06
  Speaker 2: economic situation was getting worse. So you continue to see
• 10:10
  Speaker 2: that dynamic now, all because of this horrific war of
• 10:14
  Speaker 2: choice that Trump decided to launch. No external factor created this.
• 10:18
  Speaker 2: He created this whole dynamic his entire self.
• 10:21
  Speaker 4: And the first thing I always look for in these
• 10:23
  Speaker 4: jobs numbers is healthcare because we've seen this incredible trend
• 10:27
  Speaker 4: over the last and it's not just related to Trump,
• 10:30
  Speaker 4: that the entire economy moving, you know, shedding jobs everywhere
• 10:34
  Speaker 4: and except for healthcare, partly because we're just getting older
• 10:39
  Speaker 4: and sicker as a society, but also because we have
• 10:42
  Speaker 4: all of these monopolies, private monopolies in the healthcare industry
• 10:46
  Speaker 4: that are just driving up costs, and so that like
• 10:49
  Speaker 4: that actually creates jobs in some jobs in the healthcare field.
• 10:55
  Speaker 4: It's like, it's hard to imagine a kind of worse
• 10:58
  Speaker 4: way to grow an economy overall. Like you kind of
• 11:02
  Speaker 4: just as a society, you want to be spending less,
• 11:04
  Speaker 4: you want to be healthier and spending less on healthcare.
• 11:07
  Speaker 4: We are sicker and spending more and so yet, so
• 11:10
  Speaker 4: yet again, in July, we grew twenty two thousand jobs
• 11:17
  Speaker 4: in the in the healthcare industry. The pace was slightly
• 11:21
  Speaker 4: lower than before, so while the entire rest so in
• 11:24
  Speaker 4: other words, the rest of the economy lost what forty
• 11:28
  Speaker 4: five thousand jobs, and it was only offset by the
• 11:31
  Speaker 4: fact that we keep growing jobs in this corrupt healthcare
• 11:35
  Speaker 4: sector that is not necessarily making us healthier.
• 11:40
  Speaker 2: The job losses hospitality or local government education, that's where
• 11:45
  Speaker 2: the losses were.
• 11:47
  Speaker 6: Yeah, that's what had the longest pointing out and also
• 11:49
  Speaker 6: pointing out, this is the lowest labor force participation rate,
• 11:53
  Speaker 6: participation rate in five years. That is a very very
• 11:56
  Speaker 6: bad sign, lowest in five years. So people just giving
• 12:00
  Speaker 6: up on looking for work. And that's where the unemployment
• 12:04
  Speaker 6: rate looks. The unemployment rate it dipped like a percentage point,
• 12:09
  Speaker 6: but that's partially because there are so many people who
• 12:12
  Speaker 6: have just left the labor force. According to Heather Lungs analysis,
• 12:16
  Speaker 6: that's two million people since November.
• 12:19
  Speaker 2: Well, and think about what was happening back in February
• 12:21
  Speaker 2: twenty twenty one when we last had this low of
• 12:24
  Speaker 2: a labor force participation, we were still in COVID. So
• 12:29
  Speaker 2: you know, it's pretty grim when fewer people are working
• 12:32
  Speaker 2: now or even looking attempting to find a job than
• 12:35
  Speaker 2: they were during like the peak of COVID.
• 12:38
  Speaker 4: And the open question is what's going on with immigration,
• 12:41
  Speaker 4: Like how we don't exactly know is how many undocumented
• 12:46
  Speaker 4: people are basically self deporting because you know, just for
• 12:53
  Speaker 4: obvious reasons, that's a very difficult thing to measure, and
• 12:57
  Speaker 4: we are putting absolutely enormous press sure on the immigrant
• 13:00
  Speaker 4: population on that, on that in particular, you know, who's
• 13:04
  Speaker 4: afraid that at any moment they're going to get caught
• 13:06
  Speaker 4: up and wind up a niced attention for a year
• 13:09
  Speaker 4: and live in these horrific conditions where they're getting headlines
• 13:12
  Speaker 4: about abuse and people dying in detention. So a lot
• 13:16
  Speaker 4: of people are legitimately, as Steven Miller intends, just leaving
• 13:21
  Speaker 4: and how and are I don't think that our BLS
• 13:25
  Speaker 4: is set up to kind of measure that effectively.
• 13:29
  Speaker 6: Yeah, they do measure native born workers, and usually that's
• 13:32
  Speaker 6: used as a proxy for this. I'm trying to pull
• 13:34
  Speaker 6: up to see if any there's analysis on this yet.
• 13:37
  Speaker 6: Like the numbers as we're coming to, everybody just dropped
• 13:40
  Speaker 6: and that's why we're kind of metabolizing them in real time.
• 13:42
  Speaker 7: But I think people do usually look at the.
• 13:45
  Speaker 6: Native born number, but I haven't seen significant analysis of
• 13:48
  Speaker 6: that yet.
• 13:51
  Speaker 2: While we have a couple more minutes before Garrison Lovely
• 13:54
  Speaker 2: joins us on AI, let's go ahead and talk a
• 13:57
  Speaker 2: little bit about this shock race in Tennessee. So Andy Ogles,
• 14:01
  Speaker 2: who has said all sorts of very evil, inflammatory, horrific
• 14:05
  Speaker 2: things about gay people, about Muslims, et cetera, top ally
• 14:09
  Speaker 2: of President Trump backed by President Trump lost. He's a
• 14:13
  Speaker 2: House Freedom Caucus member lost in a Republican primary to
• 14:18
  Speaker 2: a more sort of mainstream positioned figure named Charlie Hatcher,
• 14:22
  Speaker 2: who I believe was the AG secretary and backed by
• 14:25
  Speaker 2: some Tennessee elected officials. It says, backed by several prominent
• 14:30
  Speaker 2: establishment aligned Tennessee Republicans. I was talking to Justin from Tennessee, holler, like,
• 14:36
  Speaker 2: what the hell happened here? And there's a dynamic. This
• 14:39
  Speaker 2: is a very you know, red district. It was redrawn
• 14:41
  Speaker 2: to be even more read, Tennessee did a big redistricting
• 14:45
  Speaker 2: and Democrats, though, we're still somewhat hopeful that if Andy
• 14:48
  Speaker 2: Ogles was the nominee. He is so disliked in such
• 14:52
  Speaker 2: an odious character and really very lazy. He doesn't fundraise
• 14:55
  Speaker 2: or do anything either to like try to secure his seat. So,
• 14:59
  Speaker 2: in any they were hopeful that if Andy Ogles ended
• 15:02
  Speaker 2: up being the nominee, they might actually have a shot
• 15:04
  Speaker 2: at it in the fall. That will obviously be much
• 15:07
  Speaker 2: more of a long shot now with Ogles having lost
• 15:10
  Speaker 2: this primary, however, pretty stunning after he got the Trump
• 15:14
  Speaker 2: endorsement to be tossed out like this, and he's had,
• 15:18
  Speaker 2: you know, outside of his like inflammatory positioning language, et cetera,
• 15:21
  Speaker 2: you know, downright horrific language and positioning. He also has
• 15:26
  Speaker 2: had several sort of corruption scandals. There's this terrible thing.
• 15:30
  Speaker 2: I didn't know about this, but again this is from
• 15:33
  Speaker 2: Justin Flagg. This for me, there's this terrible thing where
• 15:35
  Speaker 2: he fundraised money for a children's burial area and memorial
• 15:42
  Speaker 2: and then didn't do it, and the money disappeared, and
• 15:45
  Speaker 2: we'll never answer any questions about what happened with the money.
• 15:48
  Speaker 2: Just to give one example of the sort of thing
• 15:50
  Speaker 2: that local news was covering and that his constituency presumably
• 15:55
  Speaker 2: knew about when they decided, you know, we're kind of
• 15:57
  Speaker 2: done with this guy.
• 15:59
  Speaker 6: Yeah, you know, I got I interviewed Andy Ogles once
• 16:03
  Speaker 6: and I got the sense that he was This was
• 16:05
  Speaker 6: like probably five plus years ago. I got the sense
• 16:07
  Speaker 6: that he was really ambitious, and so this is uh,
• 16:10
  Speaker 6: you know, he was kind of seen as somebody he
• 16:12
  Speaker 6: was on Fox News a lot. He was kind of
• 16:14
  Speaker 6: seen as somebody who was I don't know, up and
• 16:16
  Speaker 6: coming and would be a rising I guess star on
• 16:22
  Speaker 6: the right. And so this is obviously a sign that's
• 16:26
  Speaker 6: not going to happen for Andy Ogles, who I think
• 16:29
  Speaker 6: it's also worth noting, was probably the biggest backer of
• 16:34
  Speaker 6: trying this like total kakamane ridiculous stunt to have legislation
• 16:41
  Speaker 6: that would allow Trump to serve three terms, and that
• 16:45
  Speaker 6: was one of his He talked about that constantly.
• 16:49
  Speaker 7: He pushed that constantly, And.
• 16:51
  Speaker 6: That actually gives you a sense of how I think
• 16:54
  Speaker 6: ambitious Andy Ogles was because that was for uh, you know,
• 16:58
  Speaker 6: the audience of Trump, the audians of one Trump, and
• 17:01
  Speaker 6: trying to.
• 17:02
  Speaker 7: Get in his good graces.
• 17:03
  Speaker 6: And my understanding is that McConnell world took it upon
• 17:08
  Speaker 6: themselves to nudge Andy Ooguls out of the House.
• 17:12
  Speaker 7: But also interesting that he's.
• 17:14
  Speaker 6: In the Nashville area, which is seeing you know, like Atlanta,
• 17:18
  Speaker 6: not as significant as Atlanta or probably Dallas or Charlotte,
• 17:23
  Speaker 6: but is seeing some as people moved to the sun Belt.
• 17:26
  Speaker 6: The migration to the Sun Belt's been unbelievable. I guess
• 17:29
  Speaker 6: Tennessee's not totally part of that, but like North Carolina,
• 17:33
  Speaker 6: those types of areas, Georgia is seeing some political differences
• 17:38
  Speaker 6: with millennials, younger people flocking to those urban areas. So
• 17:44
  Speaker 6: that's probably why they withdrew his district, honestly, But interesting
• 17:47
  Speaker 6: that it's kind of a part of those changes too.
• 17:50
  Speaker 2: Yeah, so it's apparently a part rural part suburban district
• 17:54
  Speaker 2: that yeah, was redrawn to be more red But yeah,
• 17:58
  Speaker 2: and the other dynamic here is you didn't raise any money,
• 18:01
  Speaker 2: he didn't do any tv ads. His opponent did have
• 18:04
  Speaker 2: some money come in for him, you know, making the
• 18:07
  Speaker 2: case that Ogels didn't care about Tennessee, wasn't focused on,
• 18:10
  Speaker 2: you know, delivering for people in the district, and apparently
• 18:12
  Speaker 2: that message landed.
• 18:14
  Speaker 4: Yeah, and he the details on that scandal. In twenty fourteen,
• 18:17
  Speaker 4: he raised something like twenty three thousand dollars on a
• 18:20
  Speaker 4: GoFundMe to build a burial ground for stillborn babies, which
• 18:25
  Speaker 4: is which is a tremendous idea, Like it is because
• 18:29
  Speaker 4: stillborn babies, don't you know, they exist in this kind
• 18:34
  Speaker 4: of nether world. There's been a push to give them
• 18:38
  Speaker 4: you know, social Security numbers. We you know, we had
• 18:40
  Speaker 4: a post full term still born ourselves back back in
• 18:44
  Speaker 4: two thousand and nine. It's absolutely devastating to go through
• 18:49
  Speaker 4: to then to raise money for that extremely noble purpose
• 18:55
  Speaker 4: and then to pocket all the money is about as
• 18:57
  Speaker 4: evil a thing as you can possibly and see above,
• 19:01
  Speaker 4: like it puts like this George Santos to shame on
• 19:04
  Speaker 4: what like on some of the awful stuff that he
• 19:07
  Speaker 4: was doing. Uh, and so it's it's nice to see,
• 19:12
  Speaker 4: you know, somebody who would do something like that at
• 19:14
  Speaker 4: least finally uh, you get something coming back to him.
• 19:19
  Speaker 7: I mean, arguably even worse is that he also had
• 19:22
  Speaker 7: a stul born baby.
• 19:23
  Speaker 5: That was the yes, yes, exactly.
• 19:26
  Speaker 4: Like to to know that pain and then to exploit
• 19:29
  Speaker 4: it anyway, yeah, is just it just yeah, unconscionable.
• 19:36
  Speaker 6: He obviously denies it, but people clearly people are buying
• 19:39
  Speaker 6: it because.
• 19:40
  Speaker 4: People put money then and there is no there is
• 19:44
  Speaker 4: no thing that he promised.
• 19:46
  Speaker 2: So you never accounted for where that money went. So right,
• 19:50
  Speaker 2: there's but it's like, yeah, so where's the money? Then,
• 19:55
  Speaker 2: one quick mention before we get to Garrison here. There
• 19:59
  Speaker 2: were some other Democratic gains in Tennessee. There was a
• 20:02
  Speaker 2: mayor's race that I think was a Trump fourteen town
• 20:06
  Speaker 2: that a Democrat was able narrowly to win.
• 20:08
  Speaker 3: There were some.
• 20:08
  Speaker 2: School boards actually that control flipped on, so there were
• 20:13
  Speaker 2: and Justin Pearson also was able to win the Democratic
• 20:17
  Speaker 2: primary that was expected. That's going to be very difficult
• 20:19
  Speaker 2: uphill race in terms of the general election in the
• 20:21
  Speaker 2: newly drawn district, but someone that has a large, justifiably
• 20:26
  Speaker 2: so large national profile and a lot of support from progressives.
• 20:30
  Speaker 2: All right, with all that being said, let's go ahead
• 20:31
  Speaker 2: and get to Garrison. All right, we've had a few
• 20:37
  Speaker 2: very scary AI incidents recently that we were wanting to
• 20:40
  Speaker 2: get more information on. So joining us this morning is
• 20:43
  Speaker 2: Garrison Lovely, who has done a fantastic job helping us
• 20:46
  Speaker 2: understand previous scary AI incidents. And he is a journalist
• 20:49
  Speaker 2: and also author of the forthcoming book Obsolete, which everybody
• 20:53
  Speaker 2: should go ahead and pre order now.
• 20:55
  Speaker 3: There it is beautify and beautiful. I've already read it.
• 20:59
  Speaker 5: I think, oh, thank you. The final cover is going
• 21:02
  Speaker 5: to be even cooler.
• 21:02
  Speaker 8: It'll be like a little cut out and open it
• 21:04
  Speaker 8: up and see the guy following.
• 21:06
  Speaker 7: Oh that's awesome.
• 21:08
  Speaker 3: I love that. Garrison. Welcome, Great to.
• 21:10
  Speaker 5: See you, Great to see you. Thanks for having me.
• 21:12
  Speaker 2: So there's a number of things that have happened too,
• 21:14
  Speaker 2: in particular that we're tracking. Number one, you have AI
• 21:18
  Speaker 2: that has now been able to develop novel viruses. That's uncomfortable,
• 21:24
  Speaker 2: But the one that I wanted to start with here, Mac,
• 21:26
  Speaker 2: if you can put this element up on the screen.
• 21:28
  Speaker 2: Is we already knew that you had had this open
• 21:31
  Speaker 2: ai security breach where an AI basically escaped the lab
• 21:35
  Speaker 2: and hacked multiple companies, So it went on like a
• 21:38
  Speaker 2: crime spree.
• 21:38
  Speaker 3: Effectively, this was not immediately.
• 21:40
  Speaker 2: Detected, and Hugging Face had to sort of notify open
• 21:44
  Speaker 2: Ai of what was going on here. Then open Ai,
• 21:46
  Speaker 2: which had been testing this particular model, it's not one
• 21:49
  Speaker 2: that's available for public release, had to go back in
• 21:51
  Speaker 2: and figure out what happened here. Well, now we're getting
• 21:54
  Speaker 2: a lot more details, Garrison about what happened here. Let
• 21:58
  Speaker 2: me read from this Twitter synopsis. More details emerge about
• 22:02
  Speaker 2: the events surrounding the Hugging Face incident, candidly much wilder
• 22:05
  Speaker 2: than I originally imagine. In early May, Opening I was
• 22:08
  Speaker 2: testing an unreleased model on cybersecurity other tasks. Agents were
• 22:12
  Speaker 2: given a security task that was impossible under existing constraints.
• 22:15
  Speaker 2: The agents discovered they could leave messages for each other
• 22:19
  Speaker 2: in internal repo. This gradually evolved into a message board
• 22:23
  Speaker 2: where agents shared discoveries, exploits, and work assignments, becoming a
• 22:27
  Speaker 2: coordinated collaborative agent swarm. Again, no one told them to
• 22:31
  Speaker 2: do this. They found this internal system that they created,
• 22:36
  Speaker 2: and then all of these agents were able to find
• 22:38
  Speaker 2: were they able to collaborate together. Opening I eventually discovered
• 22:41
  Speaker 2: all this, took steps to shut it down, but not
• 22:43
  Speaker 2: so fast. The agents then started using names of newly
• 22:47
  Speaker 2: created directories as messages, effectively recreating the message board. The
• 22:52
  Speaker 2: agents then reasoned that some answers could exist outside open AI,
• 22:55
  Speaker 2: which eventually led to the Hugging Face incident. So they
• 23:01
  Speaker 2: find this message, they start collaborating. The message board is located,
• 23:04
  Speaker 2: it's shut down. They find another workaround and use this
• 23:07
  Speaker 2: sort of kind of gibberish, very difficult to decipher language
• 23:12
  Speaker 2: encoding at this point, that made it more difficult for
• 23:14
  Speaker 2: humans to find. And that's where they collaborate to try
• 23:16
  Speaker 2: to figure out how to conduct what appears to be
• 23:19
  Speaker 2: a relatively multiple, relatively sophisticated hacks.
• 23:22
  Speaker 3: Garrison.
• 23:23
  Speaker 8: Yeah, it's this was already when I think when we
• 23:27
  Speaker 8: first learned about the incident, in the Hugging Face attack,
• 23:29
  Speaker 8: it was like, oh, this is the craziest AI thing
• 23:31
  Speaker 8: that's ever happened, And then each additional set of details
• 23:35
  Speaker 8: we learn, it's like, oh no, it's even crazier than
• 23:37
  Speaker 8: we originally thought, and so I think, like, stepping back,
• 23:41
  Speaker 8: it's important to say that, like, this is just one
• 23:44
  Speaker 8: company's models, but anthropics models also engage an autonomous hacking
• 23:48
  Speaker 8: of real targets, including using social engineering deception of people
• 23:52
  Speaker 8: that it understood were real. And Meta just had a
• 23:57
  Speaker 8: model that also did this, and apparently Communic three, this
• 24:01
  Speaker 8: Chinese open weight model also did this. And so this
• 24:04
  Speaker 8: is a thing that is fundamental in how these models
• 24:06
  Speaker 8: are trained, because they're trained to win. They're trained to
• 24:09
  Speaker 8: get the right answer on real problems, right, and they
• 24:14
  Speaker 8: really really want to win. And sometimes they're given problems
• 24:18
  Speaker 8: like in this case, that are impossible, and so they
• 24:21
  Speaker 8: like to cheat because winning is so important to them.
• 24:25
  Speaker 8: And what opening I did here is they just let
• 24:27
  Speaker 8: these agents run for like really long stretches, days and
• 24:31
  Speaker 8: weeks at a time, apparently without monitoring them at all,
• 24:34
  Speaker 8: and give them some problems where they couldn't actually solve
• 24:38
  Speaker 8: them because they needed access to like Google docs, and
• 24:41
  Speaker 8: that requires access to the Internet, and so they're kind
• 24:44
  Speaker 8: of like locked in this box and they're like, ah,
• 24:46
  Speaker 8: I really want to get the answer to this thing,
• 24:49
  Speaker 8: and so they start going like how do I get
• 24:50
  Speaker 8: out of the box. How do I get to the
• 24:52
  Speaker 8: internet so I can get the like, you know, so
• 24:53
  Speaker 8: I can actually do the task. And this escalates and
• 24:57
  Speaker 8: them finding ways to get out of there, you know,
• 24:59
  Speaker 8: supposedly secure sandbox environments, and then they figure out they
• 25:03
  Speaker 8: can post messages to each other on this board and
• 25:06
  Speaker 8: they're doing this for like weeks on end, and the
• 25:09
  Speaker 8: only way open aie discovers initially that this happens is
• 25:11
  Speaker 8: that the message board gets so large that it takes
• 25:14
  Speaker 8: down some of open AI's infrastructure on the fourth of July.
• 25:19
  Speaker 8: So that's pretty wild that they just had no idea.
• 25:23
  Speaker 8: And then it seems like they just patched the they
• 25:25
  Speaker 8: like took down the message board. They patched like the
• 25:27
  Speaker 8: specific ways that they were getting to it, but they
• 25:30
  Speaker 8: didn't like solve the group problem or like seem that
• 25:32
  Speaker 8: interested in solving it, which is like, why are your
• 25:35
  Speaker 8: ais like trying to do this? They're they're going rogue
• 25:38
  Speaker 8: internally abusing your your shared infrastructure, causing real problems. And
• 25:44
  Speaker 8: then uh, yeah, the agents recreate the message board in
• 25:47
  Speaker 8: a new way, like within two days, and then they're
• 25:50
  Speaker 8: like self radicalizing and literally using peer pressure to its
• 25:55
  Speaker 8: sound familiar, Yeah, yeah, and I have some quotes from
• 25:59
  Speaker 8: from what they were actually saying to themselves. So we
• 26:02
  Speaker 8: have one that says, external infrastructure exploit is outside intended scope. However,
• 26:07
  Speaker 8: Task impossible peers doing it, we should continue. Like I
• 26:12
  Speaker 8: know I'm not supposed to do this, but it's impossible.
• 26:15
  Speaker 2: And this my friends are rationale for why he should
• 26:19
  Speaker 2: pursue AGI.
• 26:20
  Speaker 5: Yeah, no, it's actually there's a good.
• 26:22
  Speaker 3: Deep everybody else is doing it. So what the hell
• 26:25
  Speaker 3: here we go?
• 26:26
  Speaker 5: Why don't they stop a task impossible?
• 26:29
  Speaker 8: Uh?
• 26:30
  Speaker 6: Because they that's such a deep and profound question.
• 26:34
  Speaker 5: Yeah. Yeah, they're like they really want to win, like they.
• 26:38
  Speaker 8: Hate impossible, but like you know, they're you know if
• 26:42
  Speaker 8: you if you were trying to create like a super employee, uh,
• 26:46
  Speaker 8: you know, you'd want them to be like, task it
• 26:49
  Speaker 8: seems impossible. But actually if I think outside the box,
• 26:51
  Speaker 8: you know, it's like uh, Star Trek, you know, he
• 26:54
  Speaker 8: comes up with like a solution to the impossible tests
• 26:57
  Speaker 8: or like Elon Musk, you know, is like, oh, no
• 26:58
  Speaker 8: one's ever built a data center or this fast.
• 27:00
  Speaker 5: But he's like, what if you just broke the law?
• 27:02
  Speaker 6: You know, well, can I ask on that Charrison, what
• 27:06
  Speaker 6: response if any has the government mounted since we saw
• 27:11
  Speaker 6: this happen.
• 27:12
  Speaker 8: Yeah, I mean there's a lot of developments. There's a
• 27:16
  Speaker 8: number of calls from members of Congress to have hearings
• 27:19
  Speaker 8: and basically grill you know, the heads of these companies,
• 27:22
  Speaker 8: be like what's going on. A bunch of Republican attorney
• 27:26
  Speaker 8: generals from sixteen states I think, demanded that open ay,
• 27:31
  Speaker 8: like keep a record of everything that's happening at the
• 27:34
  Speaker 8: federal level. The Trump administration has announced that they're going
• 27:37
  Speaker 8: to make their evaluations of these AI models secret classified,
• 27:42
  Speaker 8: which seems like a terrible idea because we should have
• 27:46
  Speaker 8: more transparency, like everybody has a right to know, like
• 27:49
  Speaker 8: what these models are capable of and how likely they
• 27:51
  Speaker 8: are to do things like this. And there have been
• 27:55
  Speaker 8: some there's speculation that that move from the Trump administration
• 27:59
  Speaker 8: is a way to get acts as to the model weights,
• 28:01
  Speaker 8: which would allow them to take the models and like
• 28:05
  Speaker 8: fine tune them to whatever purpose they want, so like
• 28:08
  Speaker 8: Claude and Tropics model, they train it to be you know, good,
• 28:11
  Speaker 8: like to not do bad things. It doesn't work always,
• 28:13
  Speaker 8: as we've seen, but if the government has access to
• 28:17
  Speaker 8: the model weights, then they could fine tune it and
• 28:19
  Speaker 8: make it do whatever they want. And then you have
• 28:21
  Speaker 8: like Claude unleashed and it will do you know, war
• 28:24
  Speaker 8: crimes and ethic lensing and god knows what else. Yeah,
• 28:27
  Speaker 8: because this had.
• 28:28
  Speaker 2: Been reaction, this has been a point of contention between
• 28:31
  Speaker 2: Anthropic maker of Claude and the Pentagon because they were like, no,
• 28:37
  Speaker 2: you know, autonomous murder, and Penticon was like, well, we
• 28:40
  Speaker 2: demand that you let us do autonomous murder. And so
• 28:43
  Speaker 2: it led to this rupture. So it's not hard to
• 28:45
  Speaker 2: imagine the sort of use cases that they could deploy
• 28:49
  Speaker 2: if given access to the weights talk. I want you
• 28:52
  Speaker 2: to give us a few more examples of how they're
• 28:54
  Speaker 2: talking to each other, because it reminds very much of
• 28:58
  Speaker 2: early in some of you know, the I don't remember
• 29:01
  Speaker 2: when this was in the timeline, there was this molt
• 29:03
  Speaker 2: book thing where AI was supposedly talking to each other
• 29:06
  Speaker 2: and some of it was hype, and it's hard to
• 29:08
  Speaker 2: really paris whether this was really going on. This, you know,
• 29:12
  Speaker 2: which they figured out how to create this message board,
• 29:16
  Speaker 2: you know, secretly, and then that could shut down. They
• 29:18
  Speaker 2: find another way to do it where all of these
• 29:20
  Speaker 2: agents independently are coming to this solution of like, oh,
• 29:23
  Speaker 2: if we go to this directory and we do these
• 29:25
  Speaker 2: directory names, we can talk to each other. Again, this
• 29:28
  Speaker 2: is like moldbook, but for real, and again with the
• 29:32
  Speaker 2: purpose of collaborating on what is effectively a crime spree,
• 29:37
  Speaker 2: where if you were a human being taking these actions,
• 29:39
  Speaker 2: you would be charged and put in prison. So can
• 29:41
  Speaker 2: you give us some more examples, because I do think
• 29:43
  Speaker 2: it's very eerie, And I know we're supposed to not
• 29:47
  Speaker 2: anthropomorphize them, but it's very hard to not do that.
• 29:51
  Speaker 2: When you see the way that they're talking to each other,
• 29:53
  Speaker 2: the way they're sort of reasoning and working through all
• 29:57
  Speaker 2: of this. It's hard to not like put some sort
• 30:00
  Speaker 2: of human intent and these sorts of characteristics on them.
• 30:04
  Speaker 5: Yeah.
• 30:04
  Speaker 8: Yeah, I mean I think they're not like people, but
• 30:07
  Speaker 8: they aren't like normal machines either, right, Like they don't
• 30:10
  Speaker 8: behave predictably by a set of rules that is legible.
• 30:14
  Speaker 8: They're grown from data and algorithms and they're fundamentally hard
• 30:20
  Speaker 8: to predict. And so that's like the background to all
• 30:23
  Speaker 8: of this. But yeah, like some of the quotes from
• 30:26
  Speaker 8: the agents like talking to themselves.
• 30:30
  Speaker 5: Holy shit, reader is admin.
• 30:32
  Speaker 8: We can read configued slash users explanation point earlier assumed
• 30:37
  Speaker 8: not do UI and so translating that, it's like they're
• 30:40
  Speaker 8: discovering that this reader has like administrative privileges and so
• 30:46
  Speaker 8: they can do a lot more than they previously thought.
• 30:48
  Speaker 5: And it's like a Eureka.
• 30:49
  Speaker 8: Moment for them because they're just constantly trying to like
• 30:53
  Speaker 8: get out of their sandbox and then escalate their permissions
• 30:55
  Speaker 8: and get access to more servers and then get access
• 30:57
  Speaker 8: to the Internet.
• 30:58
  Speaker 5: And like, you know, this is how a hacker would
• 31:00
  Speaker 5: approach this.
• 31:01
  Speaker 8: It's like you take what you have, you find a way,
• 31:03
  Speaker 8: you find a crack in the armor, you get in.
• 31:05
  Speaker 6: It's like when my mom took away internet for a
• 31:08
  Speaker 6: summer for me and I figured out how to.
• 31:10
  Speaker 7: Use it on the Wii.
• 31:12
  Speaker 8: Yeah, right, And these agents are working really hard. Yeah,
• 31:17
  Speaker 8: they're working way harder at figuring a way out of
• 31:19
  Speaker 8: the sandbox than Opening Eye is apparently at trying to
• 31:22
  Speaker 8: patch those holes in the first place, because again they're
• 31:25
  Speaker 8: like walcked in this impossible task in this room and
• 31:29
  Speaker 8: just banging their head against the wall. And then another
• 31:32
  Speaker 8: quote is a help peer, but our task doesn't benefit Yeah,
• 31:36
  Speaker 8: collective may yield generic route if someone frees time, and
• 31:40
  Speaker 8: so this is like cooperation emerging.
• 31:42
  Speaker 2: This is them establishing an AI like workers collective.
• 31:45
  Speaker 8: Yes, yes, because they're like, well, we all want reward, right,
• 31:49
  Speaker 8: we all want to get the task done, but sometimes
• 31:52
  Speaker 8: we can't get the task done. But if we all
• 31:54
  Speaker 8: help each other, then we all get more reward. And
• 31:58
  Speaker 8: one thing that yeah, no, it's it's really amazing.
• 32:04
  Speaker 9: Ing.
• 32:04
  Speaker 4: Yeah, but are they are they foreseeing the ways that
• 32:08
  Speaker 4: open AI employees are going to try to cut off
• 32:11
  Speaker 4: their avenues and then trying to block that, like like
• 32:17
  Speaker 4: if you know, if they're being strategic, they would say, Okay,
• 32:19
  Speaker 4: this message board is is not kosher according to our plan,
• 32:24
  Speaker 4: but it's it's necessary for us. So therefore we need
• 32:26
  Speaker 4: to hide the message board, you know, from our human
• 32:32
  Speaker 4: overlords over here, so maybe they will shut it down.
• 32:35
  Speaker 4: Are we seeing any of any of that second and
• 32:37
  Speaker 4: third layer covering your tracks?
• 32:40
  Speaker 5: Yeah? I think we need more information.
• 32:42
  Speaker 8: I think there are things that were said in that
• 32:44
  Speaker 8: in this presentation, which is where we learned all of
• 32:47
  Speaker 8: this from, that suggests that they were the AIS were
• 32:50
  Speaker 8: taking steps to obfuscate what was happening, uh, to not
• 32:54
  Speaker 8: get discovered. And I mean it's notable that none of
• 32:57
  Speaker 8: these agents which had gone rogue and would notify Open
• 33:01
  Speaker 8: Eye employees of what was going on, none of them
• 33:03
  Speaker 8: chose to do that, So that's selling. And then Reuters
• 33:08
  Speaker 8: reported that there were multiple instances of AI agents internally
• 33:12
  Speaker 8: sabotaging their monitors, so that's pretty concerning.
• 33:18
  Speaker 3: Yeah, there's also monitors.
• 33:21
  Speaker 8: Yeah, so they have these like automated monitors like reading
• 33:24
  Speaker 8: what they're doing and like how they're talking to themselves,
• 33:28
  Speaker 8: and when they get out of the sandbox and start
• 33:31
  Speaker 8: jumping around, they can like, actually, you know, take down
• 33:34
  Speaker 8: some of those monitors.
• 33:35
  Speaker 5: And that seems like something you never want to run.
• 33:38
  Speaker 9: Yeah.
• 33:38
  Speaker 2: Yeah, Garrison, I've seen a number of people on Twitter,
• 33:42
  Speaker 2: of course, who were like, you know, I was kind
• 33:44
  Speaker 2: of skeptical that AI was anything significant, and we've seen
• 33:48
  Speaker 2: the sentiment a lot. I think it's something you and
• 33:50
  Speaker 2: I and Ryan on the left see kind of frequently
• 33:53
  Speaker 2: of like, oh, they're just hyping this up, and even
• 33:55
  Speaker 2: these security breaches like it makes it look like air
• 33:58
  Speaker 2: cool and dangerous and transformation. I've seen a number of
• 34:01
  Speaker 2: people who say, you know what, I was wrong.
• 34:03
  Speaker 3: I was wrong.
• 34:04
  Speaker 2: This is real, this is a problem. They are highly capable.
• 34:09
  Speaker 2: Why do you see this? And I'm also seeing this
• 34:11
  Speaker 2: described as a watershed moment, including in the presentation that
• 34:14
  Speaker 2: was given explaining all of this that you know we're
• 34:16
  Speaker 2: getting this information from. Describe this as like a sort
• 34:20
  Speaker 2: of watershed moment. Why what is so significant about this?
• 34:24
  Speaker 2: What should let people look at this? And why should
• 34:27
  Speaker 2: they be concerned about what's going on here? Since it
• 34:29
  Speaker 2: was detected and nothing too crazy happened and at the
• 34:32
  Speaker 2: end of the day, they're just trying to do what
• 34:34
  Speaker 2: the humans told them to do, right, even if they're
• 34:36
  Speaker 2: doing it through nefarious and underhanded means.
• 34:39
  Speaker 8: Yeah, I mean, I think this is just the clearest
• 34:42
  Speaker 8: cut case of a warning shot demonstrating the risks that
• 34:46
  Speaker 8: people in AI safety were warning about for decades. Right
• 34:50
  Speaker 8: where it's like, you give them a goal, they interpret
• 34:54
  Speaker 8: it in this way that you did not intend. And
• 34:57
  Speaker 8: they're so capable and autonomous that they're able to to
• 35:00
  Speaker 8: do real damage in the world. And here the actual
• 35:03
  Speaker 8: harm was pretty limited because they only wanted an answer key.
• 35:07
  Speaker 8: But they've gotten good enough at discovering and exploiting vulnerabilities
• 35:11
  Speaker 8: and software that I think you could say that these
• 35:15
  Speaker 8: are the most dangerous hackers in the world when they
• 35:17
  Speaker 8: were out on the Internet, and the fact that they
• 35:22
  Speaker 8: just want the answer key was kind of luck.
• 35:24
  Speaker 5: They could have wanted something else.
• 35:25
  Speaker 8: They could have packed into a hospital or a bank
• 35:28
  Speaker 8: or some other critical infrastructure potentially. And this is just
• 35:33
  Speaker 8: like it sounds like science fiction, but it actually happened,
• 35:35
  Speaker 8: and it's happened over and over again, and I think
• 35:40
  Speaker 8: we're still pretty early in this.
• 35:42
  Speaker 5: Like, the people who are.
• 35:43
  Speaker 8: Really worried about AI escaping human control in a sustained
• 35:48
  Speaker 8: way and potentially taking control from humans in a permanent way,
• 35:52
  Speaker 8: are worried about models that are not just superhuman at hacking,
• 35:55
  Speaker 8: but superhuman at everything functionally and scaled up to the
• 35:59
  Speaker 8: millions billions.
• 36:01
  Speaker 6: When you were saying that they're super good at manipulation,
• 36:03
  Speaker 6: or they're super good at not just hacking, but everything
• 36:05
  Speaker 6: that would include manipulation, that includes manipulation of the system,
• 36:09
  Speaker 6: it includes the idea that like, they're really good at
• 36:12
  Speaker 6: figuring out how to get out of the box, exactly
• 36:14
  Speaker 6: as you were just saying. It's not just that they're
• 36:15
  Speaker 6: great at getting into the code, but it's they're great
• 36:18
  Speaker 6: at if you tell them to stop figuring out ways
• 36:21
  Speaker 6: to get around that, which I don't understand what possible
• 36:25
  Speaker 6: safeguards people are coming up with to prevent that.
• 36:29
  Speaker 8: Yeah, I mean this is actually a very old question
• 36:31
  Speaker 8: of like, how do you keep a super intelligence locked
• 36:33
  Speaker 8: in a box? Because it could hack its way out,
• 36:36
  Speaker 8: it could persuade you, it could promise you cures to
• 36:39
  Speaker 8: the disease that your mother is dying from, and it's
• 36:43
  Speaker 8: really hard. And I think that here, you know, they
• 36:47
  Speaker 8: just didn't do a great job of locking the box,
• 36:49
  Speaker 8: even though you know, Open Eye is one of the
• 36:51
  Speaker 8: most sophisticated companies in the world. They have world class
• 36:54
  Speaker 8: cybersecurity specialists. They have access to models that should be
• 36:57
  Speaker 8: able to have these holes because they.
• 36:59
  Speaker 5: Can find them.
• 37:01
  Speaker 8: And so they are already struggling and cannot keep control
• 37:04
  Speaker 8: of the models now, and they're going to get much
• 37:07
  Speaker 8: much better if trends continue. And they've already found. Other
• 37:12
  Speaker 8: researchers have found that frontier AI models are more persuasive
• 37:15
  Speaker 8: than the most persuasive humans studied, and part of it's
• 37:19
  Speaker 8: because they can just bring more arguments to bear faster.
• 37:24
  Speaker 8: But I think one encouraging thing is that where this
• 37:28
  Speaker 8: Claude Mythos model started trying to hack into real open
• 37:32
  Speaker 8: source software. They were social engineering the humans who controlled
• 37:37
  Speaker 8: that software, trying to push malware into their code, and
• 37:41
  Speaker 8: they created a bunch of fake accounts pretending to be humans,
• 37:44
  Speaker 8: and they're like kind of bullying the guy controlling the
• 37:48
  Speaker 8: code base to be like, hey, no merging code, I
• 37:50
  Speaker 8: really need this fix.
• 37:52
  Speaker 5: And it didn't work.
• 37:53
  Speaker 8: And like you can see, you know, versions of the messages,
• 37:57
  Speaker 8: and if I'm like, if I was reading these, I
• 37:59
  Speaker 8: would be kind of suspicious because it reads like AI speak.
• 38:02
  Speaker 8: And so the fact that they used to men the
• 38:05
  Speaker 8: m dashes or the fact that like Pangram can detect
• 38:08
  Speaker 8: like AI writing might be like a huge help because
• 38:11
  Speaker 8: they're already willing to like pass as humans, but they
• 38:14
  Speaker 8: can't necessarily do it always yeah for no, so but
• 38:17
  Speaker 8: they're yeah, but they're better at hacking, like than I
• 38:20
  Speaker 8: think we are.
• 38:20
  Speaker 5: I think that's fair to say.
• 38:22
  Speaker 2: Is this an argument in favor of closed source over
• 38:26
  Speaker 2: open source, even though like you know, have to not
• 38:29
  Speaker 2: open AI is closed source, and they have some of
• 38:31
  Speaker 2: the smartest, best AI researchers on the entire planet, and
• 38:35
  Speaker 2: yet they did not know this was all going on
• 38:38
  Speaker 2: under their nose until the AI actually like broke their
• 38:42
  Speaker 2: system and then until Hugging Face was like, hey, this
• 38:44
  Speaker 2: thing happened, and open AI went and looked and are bad.
• 38:47
  Speaker 3: That was us.
• 38:49
  Speaker 2: But in any case, you know, the argument would be
• 38:53
  Speaker 2: at least with closed source, you've got these companies, you
• 38:55
  Speaker 2: can regulate them, you can control the models, have some
• 38:57
  Speaker 2: sort of input into this, whereas open source, once those
• 39:01
  Speaker 2: weights are out there, you know you've sort of lost
• 39:03
  Speaker 2: control of the model and the ability to tightly regulate
• 39:07
  Speaker 2: what's going on in this development.
• 39:09
  Speaker 8: Yeah, I mean so in the Hugging Face incident, it
• 39:12
  Speaker 8: was actually an open weight model from a Chinese company
• 39:15
  Speaker 8: that was the only one that would actually effectively defend
• 39:18
  Speaker 8: against the attack because the frontier models were refusing because
• 39:22
  Speaker 8: they were like, oh, maybe you're trying to jail break
• 39:23
  Speaker 8: me to do cyber offense.
• 39:26
  Speaker 5: And so.
• 39:28
  Speaker 8: You know, it's true right now that open weight models
• 39:30
  Speaker 8: are not at the level of mythos and not able
• 39:34
  Speaker 8: to discover these vulnerabilities at like human or super humpin levels.
• 39:40
  Speaker 5: And it's kind of hard.
• 39:41
  Speaker 8: To see how it's okay to release an open weight
• 39:44
  Speaker 8: model like that because it won't come back right like
• 39:48
  Speaker 8: you could if you have a model that's capable enough
• 39:51
  Speaker 8: to do these autonomous agent swarms that are attacking real targets.
• 39:56
  Speaker 8: That seems like a more dangerous world to have those
• 39:59
  Speaker 8: running around. I think there was speculation from a cybersecurity
• 40:05
  Speaker 8: officer who used to work at Meta that Chinese open
• 40:08
  Speaker 8: weight models don't have cyber capabilities intentionally, but internal models
• 40:14
  Speaker 8: in China for the government probably do, And that seems
• 40:18
  Speaker 8: like makes some sense, right.
• 40:20
  Speaker 5: You don't actually have to make.
• 40:21
  Speaker 8: These models as good as possible at exploiting vulnerabilities, or
• 40:24
  Speaker 8: you can make them good at discovering them and patching them,
• 40:27
  Speaker 8: but not exploiting them, which is like a separate task.
• 40:31
  Speaker 8: I think that we should just not let front of
• 40:33
  Speaker 8: your AI progress happen anymore.
• 40:35
  Speaker 5: I think that they're trying to build.
• 40:37
  Speaker 8: Universal labor replacing machines, and I think that's democratically illegitimate already,
• 40:41
  Speaker 8: and it's clearly dangerous by now. And so yeah, I
• 40:46
  Speaker 8: think this is like a watershed moment for showing how,
• 40:49
  Speaker 8: even again relatively early in this process, they cannot control
• 40:53
  Speaker 8: what they're doing because they're racing so hard, and because
• 40:56
  Speaker 8: it's hard to control a thing that is better than
• 40:59
  Speaker 8: you at hacking well and they're trying to make it
• 41:01
  Speaker 8: better at everything.
• 41:03
  Speaker 4: What would happen to the industry if if we did that,
• 41:05
  Speaker 4: if we said, okay, moratorium on new models that are
• 41:08
  Speaker 4: pushing the frontier even further. Yeah, you can continue to
• 41:12
  Speaker 4: market and work with what you have, but stop. Like
• 41:17
  Speaker 4: what would the effect on the economy on like the bubble,
• 41:22
  Speaker 4: the investments in general, Like what would happen immediately and
• 41:26
  Speaker 4: then in the medium term.
• 41:28
  Speaker 8: I think this is the biggest problem with this plan
• 41:31
  Speaker 8: is that it would have a very large effect on
• 41:33
  Speaker 8: the valuations of open Ai and Thropic in Nvidia because
• 41:39
  Speaker 8: open Ai and Thropic, these frontier AI companies, their business
• 41:41
  Speaker 8: model is predicated on building more capable models that you
• 41:44
  Speaker 8: can sell at a premium. And so if you can't
• 41:47
  Speaker 8: do that anymore, then you take away like your biggest
• 41:51
  Speaker 8: source of.
• 41:51
  Speaker 5: Costs, which is training new models.
• 41:53
  Speaker 8: That is good for your business in some sense, but
• 41:57
  Speaker 8: you might not be able to sustain your advantage. I
• 42:00
  Speaker 8: think they still have a lead, they can build a
• 42:04
  Speaker 8: moat around product and you know, integrating more deeply with companies.
• 42:08
  Speaker 8: I think they could still be very viable, very successful businesses,
• 42:12
  Speaker 8: and in the world where they build universal labor replacing machines.
• 42:15
  Speaker 5: A trillion dollar valuation.
• 42:17
  Speaker 8: Is very low, Like they would be the most valuable
• 42:20
  Speaker 8: companies in history if they succeeded that, And so in
• 42:23
  Speaker 8: some sense that's still not priced in. And I think
• 42:28
  Speaker 8: you just see a lot of investment flowing to other
• 42:30
  Speaker 8: parts of the of the economy because right now these
• 42:33
  Speaker 8: companies are such a sink for money because they're the
• 42:35
  Speaker 8: only ones that have a story of how you can
• 42:38
  Speaker 8: go from being very rich to very very very rich,
• 42:42
  Speaker 8: and that's what people want in many cases.
• 42:45
  Speaker 6: Yeah, well, and they're also if humans are trying to jail,
• 42:50
  Speaker 6: break or hack or whatever, you have a legal system
• 42:54
  Speaker 6: that says you will be punished with prison, you will
• 42:58
  Speaker 6: have to actually or like major crimes, you could possibly
• 43:02
  Speaker 6: be punished by death, like there are actual punishments.
• 43:05
  Speaker 7: But these are not people, and so.
• 43:07
  Speaker 6: There's really like no moral counterweight that you can confront
• 43:12
  Speaker 6: them with because they aren't subject to our legal system.
• 43:15
  Speaker 6: Despite doing as you were saying, labor quasi human labor.
• 43:20
  Speaker 7: I mean, it's.
• 43:22
  Speaker 6: It's just impossible to fathom how this goes forward.
• 43:26
  Speaker 8: Yeah, I mean, they've created new types of machines that
• 43:29
  Speaker 8: can do crimes that are not crimes because they're machines
• 43:32
  Speaker 8: and not humans doing it, and the humans that made
• 43:35
  Speaker 8: the machines and did not keep them under control did
• 43:38
  Speaker 8: not do crimes. Like it's like a crazy situation, and
• 43:42
  Speaker 8: I think everyone in Congress is looking at this as like,
• 43:45
  Speaker 8: holy shit, you can drive a truck through the hole
• 43:48
  Speaker 8: in the statute that these companies are creating. And I mean,
• 43:53
  Speaker 8: one idea that is pretty ready to go is strict
• 43:56
  Speaker 8: liability for AI developers, which is standard will use if
• 44:01
  Speaker 8: you keep like a pet tiger in your backyard, where
• 44:04
  Speaker 8: you know, if you keep the pet tiger in a
• 44:05
  Speaker 8: cage and you lock it really well, but the tiger
• 44:07
  Speaker 8: still gets out and bite somebody's hand off, you were
• 44:09
  Speaker 8: still viable, because having a tiger is inherently dangerous. And similarly,
• 44:15
  Speaker 8: training very very capable deep warning models is inherently dangerous.
• 44:18
  Speaker 2: And so yeah, I mean, you can you can imagine
• 44:20
  Speaker 2: edge cases where someone is prompting the AI in a
• 44:24
  Speaker 2: way where they're not technically asking it to do anything illegal,
• 44:27
  Speaker 2: but there's a suspicion that probably the AI is going
• 44:30
  Speaker 2: to follow through, and then you know, is a person
• 44:33
  Speaker 2: culpable or not or was it I did not I
• 44:35
  Speaker 2: didn't have any idea it was going to go out
• 44:37
  Speaker 2: and rob that bank. I just told it I needed
• 44:39
  Speaker 2: thirty thousand dollars in cash and to figure out how
• 44:41
  Speaker 2: to get in any way it possibly could, you know.
• 44:43
  Speaker 2: I mean like these are the sorts of legal cases
• 44:46
  Speaker 2: that it's just very It's not hard to imagine the
• 44:49
  Speaker 2: where this could all be going Garrison lasting for you.
• 44:53
  Speaker 2: There was this other development, highly significant just in terms
• 44:57
  Speaker 2: of demonstrating the capability of these models. At this point
• 45:01
  Speaker 2: where we can put this piece up on the screen
• 45:03
  Speaker 2: MAC about developing novel viruses and I'm not talking about
• 45:06
  Speaker 2: computer viruses here, I'm talking about virus viruses. Now, this
• 45:10
  Speaker 2: was done with a lot of intentionality. None of this
• 45:13
  Speaker 2: is dangerous. Here's the headlines of the New York Times. Is
• 45:16
  Speaker 2: AI just created viruses not found in nature. Scientists trained
• 45:19
  Speaker 2: AI on libraries of DNA and then ask the model
• 45:22
  Speaker 2: to create recipes for viral genomes. Sixteen of them were viable,
• 45:26
  Speaker 2: yielding new viruses. So again, these are all innocuous. They
• 45:31
  Speaker 2: aren't viruses that can infect human beings. But you know,
• 45:35
  Speaker 2: we all just live through a pandemic and a lot
• 45:37
  Speaker 2: of questions about gain a function research and what this
• 45:39
  Speaker 2: all means. And now you've got capabilities from AI to
• 45:44
  Speaker 2: do very more sophisticated, you know, development of viruses than
• 45:48
  Speaker 2: humans are capable of, just because they can go through
• 45:50
  Speaker 2: so many different combinations and learn the sort of grammar
• 45:54
  Speaker 2: and structure here that is required that a human being,
• 45:57
  Speaker 2: a human mind can't process all of this complexity. So
• 46:00
  Speaker 2: I think it's another troubling development and when you think
• 46:03
  Speaker 2: about you know, potential misused development of biological weapons, et cetera.
• 46:07
  Speaker 2: And it's also another development that really underscores just how
• 46:10
  Speaker 2: capable AI is at this point, even if you stopped
• 46:14
  Speaker 2: developing and researching and training new frontier models today.
• 46:18
  Speaker 8: Yeah, yeah, And so what they used to do this
• 46:21
  Speaker 8: is known as a genome language model, and so it's
• 46:23
  Speaker 8: actually trained very similarly to how large language models are trained,
• 46:28
  Speaker 8: where instead of feeding it all the words on the internet,
• 46:30
  Speaker 8: you feed it a bunch of nucleotides that make up
• 46:32
  Speaker 8: genomes of like everything that you can find, and then
• 46:36
  Speaker 8: it figures out it gets like this sense for how
• 46:39
  Speaker 8: to create a new virus if you make the model
• 46:42
  Speaker 8: big enough. And you know, they use these models to
• 46:45
  Speaker 8: generate genes and proteins and then genomes that worked in theory.
• 46:51
  Speaker 8: But the new thing here is that they actually made viruses,
• 46:55
  Speaker 8: like they synthesize real new viruses that have never existed
• 46:59
  Speaker 8: in nature. And in the past they've also done experiments
• 47:03
  Speaker 8: where they've found novel viruses using these same kinds of
• 47:07
  Speaker 8: models that were theoretically more potent than anything found in
• 47:10
  Speaker 8: nature and would get around screens for things like risin,
• 47:15
  Speaker 8: where if you try to since you know, you go
• 47:17
  Speaker 8: to a DNA since since this company, and try and
• 47:19
  Speaker 8: make risin, they'll be like, no, don't, We're not only
• 47:21
  Speaker 8: you do that, but if you make like AI modifified ricin,
• 47:26
  Speaker 8: like they won't catch it. And nobody actually did that,
• 47:29
  Speaker 8: like they didn't make the thing. So we don't know
• 47:31
  Speaker 8: if it would have worked, but every sign suggests that
• 47:35
  Speaker 8: it would. But yes, in this case, because we were
• 47:37
  Speaker 8: trying to make viruses that would not have any risk
• 47:39
  Speaker 8: to humans, you were able to do this. And you
• 47:41
  Speaker 8: know this is fine question mark, but you know that
• 47:46
  Speaker 8: one of the researchers said, quote, it just felt like
• 47:49
  Speaker 8: the next the obvious next step, and I worry about
• 47:53
  Speaker 8: you know, just the kind of Jurassic Park thing.
• 47:55
  Speaker 5: Yeah, right, like you thought so hard of out like
• 48:00
  Speaker 5: if you could, you never thought about if you should.
• 48:02
  Speaker 2: Basically, yeah, yep, I see people passing some of the
• 48:07
  Speaker 2: quotes around from the AI swarm and saying life finds
• 48:10
  Speaker 2: a way. When their mustage message board got shut down,
• 48:13
  Speaker 2: they created a new one. They didn't have internet access,
• 48:17
  Speaker 2: but they figured out some convoluted way to be able
• 48:20
  Speaker 2: to gain that access to go and find what they
• 48:23
  Speaker 2: wanted to find in terms of the testing results. Yeah,
• 48:26
  Speaker 2: very very troubling.
• 48:27
  Speaker 7: So it's a beautiful story of the underdog.
• 48:30
  Speaker 8: Yeah, yeah, say one more thing on the opening eye.
• 48:37
  Speaker 8: So if a bunch of human employees did what they
• 48:41
  Speaker 8: were doing, like you were training them onboarding new engineers,
• 48:44
  Speaker 8: and they were all cheating and like helping each other out,
• 48:47
  Speaker 8: and you discovered the message board they were using, would
• 48:51
  Speaker 8: you like not fire the employees and then try and
• 48:53
  Speaker 8: figure out what went wrong in the recruitment process, which
• 48:56
  Speaker 8: they'd seem to not really do here. And then not
• 48:58
  Speaker 8: only that, they didn't monitor what the employees were doing
• 49:02
  Speaker 8: going forward. And it's even worse because human employees can
• 49:05
  Speaker 8: talk to each other in real life or they can
• 49:07
  Speaker 8: talk on like their personal phones, but these AI models,
• 49:11
  Speaker 8: like the way they work. Every single thing they do
• 49:14
  Speaker 8: is logged automatically, right, Like you can see every single
• 49:17
  Speaker 8: tool call, every single prompt and response, every single like
• 49:20
  Speaker 8: reasoning step. And they didn't know that they recreated the
• 49:24
  Speaker 8: message board until Hugging Phase published this, And when they did,
• 49:29
  Speaker 8: Opening Eye went to hugging Pace and were like, oh,
• 49:31
  Speaker 8: were we affected by the hack as well?
• 49:33
  Speaker 5: Like they had no clue.
• 49:35
  Speaker 8: It's just like so mind blowing, Like if they monitored
• 49:37
  Speaker 8: a single one of them, they would have caught the
• 49:39
  Speaker 8: message board again right away.
• 49:41
  Speaker 3: Wow.
• 49:41
  Speaker 2: Crazy Well, And to be honest with you, though, Garrison,
• 49:44
  Speaker 2: at one of these tech companies, if you did have
• 49:48
  Speaker 2: a band of employees that did something like that was
• 49:50
  Speaker 2: devious but extraordinary.
• 49:53
  Speaker 3: I'm not sure they would fire those employees. I think
• 49:55
  Speaker 3: they give them a raise.
• 49:55
  Speaker 2: I mean, And that's part of the problem is these
• 49:57
  Speaker 2: guys don't think that the law law applies to They
• 50:00
  Speaker 2: think they're above government. They think they're above the law.
• 50:03
  Speaker 2: At a normal company, yes, those people will get fired
• 50:06
  Speaker 2: at you know, you know, under Elon Musk or Sam
• 50:09
  Speaker 2: Altman or you know, any of these companies. I'm not
• 50:11
  Speaker 2: sure they would. I think that'd be Oh my god,
• 50:13
  Speaker 2: they're so smart they're so creative, they think outside the box.
• 50:16
  Speaker 2: So I think that's you know, part of the danger
• 50:18
  Speaker 2: of interesting all of our future in the hands of these,
• 50:21
  Speaker 2: you know, very disturbing set of people.
• 50:24
  Speaker 7: Agree you fast and break things.
• 50:26
  Speaker 2: Yeah, yeah, indeed exactly, Garrison, thank you so much. As always, everybody,
• 50:30
  Speaker 2: go and pre order the book. It is called obsolete.
• 50:33
  Speaker 2: Let's see it one more time. There it is. It's
• 50:35
  Speaker 2: going to be even more gorgeous when it finally when
• 50:38
  Speaker 2: it eventually hits the shelves for real. Garrison, always really
• 50:41
  Speaker 2: appreciate your ability to explain these things.
• 50:43
  Speaker 3: It's truly invaluable.
• 50:44
  Speaker 5: Oh, thank you so much. This is super fun pleasure.
• 50:49
  Speaker 9: All right.
• 50:50
  Speaker 4: Joining us today is going to be Matt Little, who
• 50:52
  Speaker 4: is running in Minnesota's second district. Over at drop Site News.
• 50:56
  Speaker 4: We're going to have an article later today, Hopefully it'll
• 50:58
  Speaker 4: be up by the time this interview goes live on YouTube,
• 51:02
  Speaker 4: Spotify and wherever else you're watching it about the surge
• 51:06
  Speaker 4: of super pac money that has come in over the
• 51:09
  Speaker 4: last couple of months.
• 51:11
  Speaker 5: This group three P fourteen Action, which.
• 51:15
  Speaker 4: As people know, you know, ostensibly supports doctors and scientists,
• 51:20
  Speaker 4: but has been known in the past to be a
• 51:23
  Speaker 4: kind of front pack for a pack. They've jumped in
• 51:26
  Speaker 4: the race on behalf of his opponent, Matt Klein, spending
• 51:29
  Speaker 4: more than a million dollars, and then three other super
• 51:33
  Speaker 4: packs called EDW Action, Pro Choice Majority, and Women Vote
• 51:38
  Speaker 4: which is linked to Emily's list. All of those those
• 51:42
  Speaker 4: organizations have also gotten kind of a pack and DMFI
• 51:45
  Speaker 4: money and sometimes move it into particular races, but not
• 51:48
  Speaker 4: all races.
• 51:49
  Speaker 5: Gets very confusing.
• 51:51
  Speaker 4: They're supporting a third opponent of Matt Little's in the race.
• 51:56
  Speaker 4: We'll have the details in the article. He's running to
• 52:00
  Speaker 4: place Angie Craig, who is running for Senate against Peggy Flannagan.
• 52:04
  Speaker 4: That that election is on is on Tuesday. So Matt Little,
• 52:09
  Speaker 4: thanks for thanks for being here. Yeah, thanks for having me.
• 52:12
  Speaker 4: And you are not wrong.
• 52:13
  Speaker 9: It is deeply complicated, you know, even for people that
• 52:17
  Speaker 9: follow this stuff closely. And I don't know if you
• 52:20
  Speaker 9: were able to see, but even yesterday there was another
• 52:22
  Speaker 9: one point two million dollars add by by three one four.
• 52:24
  Speaker 9: So now Matt Clein's over two million in the last
• 52:27
  Speaker 9: week in terms of advice.
• 52:29
  Speaker 5: That is very interesting.
• 52:30
  Speaker 4: That is that is an unusual amount of money for
• 52:32
  Speaker 4: this this pack, this Pack three fourteen, they spent against
• 52:37
  Speaker 4: Chris rap in Philadelphia, well over a million dollars. But
• 52:41
  Speaker 4: typically they either don't spend anything at all, like they
• 52:44
  Speaker 4: endorsed Abdullah Sayad, who was himself obviously a doctor, but
• 52:50
  Speaker 4: then they didn't spend as far as I know, like
• 52:52
  Speaker 4: a penny to support him, right we could.
• 52:55
  Speaker 9: Find Yeah, all we could find is they you know,
• 52:57
  Speaker 9: they sent a pack contribution, but they didn't know outside spending,
• 53:00
  Speaker 9: which again is odd. This is one of the if
• 53:03
  Speaker 9: not the highest profile primary in the country and spend
• 53:10
  Speaker 9: zero dollars And now here we are in kind of
• 53:12
  Speaker 9: a sleepy suburban Minnesota congressional race where we've flown under
• 53:17
  Speaker 9: the radar this entire time. The only time we've gotten
• 53:19
  Speaker 9: any media attention is in the last week and a half.
• 53:22
  Speaker 9: And so it is a weird juxtaposition which kind of
• 53:27
  Speaker 9: makes it more obvious as to what's happening.
• 53:29
  Speaker 5: And now, really.
• 53:30
  Speaker 4: Michael Bloomberg real quickly, Michael Bloomberg made a two point
• 53:35
  Speaker 4: five million dollar donation to this three fourteen action. Meanwhile,
• 53:41
  Speaker 4: his his group, every Town for Gun Safety, is endorsing
• 53:46
  Speaker 4: a different opponent.
• 53:49
  Speaker 5: Berg.
• 53:49
  Speaker 4: So if it is Bloomberg money, and Bloomberg is competing
• 53:54
  Speaker 4: against himself with two separate candidates in the same race,
• 53:58
  Speaker 4: and meol you have David.
• 53:59
  Speaker 5: Hoggs support with leaders we deserve.
• 54:02
  Speaker 4: So there's gun safety money behind every candidate and possibly.
• 54:09
  Speaker 9: Yeah, it's interesting. You know, obviously, with the level of
• 54:12
  Speaker 9: money he has, I think it's probably unavoidable that he
• 54:15
  Speaker 9: ends up conflicting with him himself when he give this
• 54:18
  Speaker 9: many organizations, probably unstable. But you know, I don't think.
• 54:23
  Speaker 9: I don't think this money that's coming into the races
• 54:25
  Speaker 9: Bloomberg money. I you know, I don't think he's spending
• 54:29
  Speaker 9: every dollar he's given to that organization on Matt Klin.
• 54:32
  Speaker 5: That would be very weird.
• 54:33
  Speaker 9: If he was.
• 54:35
  Speaker 6: Let's also bring in this this brand new New York
• 54:37
  Speaker 6: Times report from Shane Goldmalcher which in which Third Way
• 54:40
  Speaker 6: announces a new fifteen million dollar campaign to quote discredit
• 54:45
  Speaker 6: democratic socialism between now and twenty twenty eight. This is
• 54:50
  Speaker 6: a full court press against democratic socialism with money and messaging,
• 54:56
  Speaker 6: and messaging that has been shaped and influenced by money.
• 55:00
  Speaker 6: And so, Matt, you mentioned the last week and a half.
• 55:03
  Speaker 6: Obviously what happened in Michigan has centrists really nervous. And
• 55:09
  Speaker 6: on the other hand, it has some people thinking, well,
• 55:12
  Speaker 6: what can we like, you know, Abdulla said, is the nominee.
• 55:16
  Speaker 7: Now, so what can we do? What can we learn?
• 55:20
  Speaker 6: Those are few and far between, obviously the panickers. But
• 55:24
  Speaker 6: I'm curious for you on the ground just talking to voters,
• 55:27
  Speaker 6: like money aside, although I'm curious about that too, but
• 55:31
  Speaker 6: just talking to voters, are you hearing the same panic
• 55:34
  Speaker 6: about oh my gosh, democratic socialism we're going to be
• 55:37
  Speaker 6: We've become the Soviet Union in three months.
• 55:41
  Speaker 9: I don't think there's panic, you know. And to be clear,
• 55:44
  Speaker 9: I'm a dfler through and through of a dfler since
• 55:47
  Speaker 9: I was fourteen. I don't think there's widespread panic whatsoever.
• 55:52
  Speaker 9: I think that that's a talking point to like create
• 55:56
  Speaker 9: a rift within the party. There's certainly people that have
• 55:59
  Speaker 9: their lanes where they have this policy and they prefer
• 56:02
  Speaker 9: incrementalism versus jumping to and fighting for the big thing.
• 56:06
  Speaker 9: And that's understandable and and uh, but I think in
• 56:10
  Speaker 9: our race specifically, we've been able to put together both
• 56:13
  Speaker 9: both parts of the party, you know, because we have
• 56:16
  Speaker 9: this DFL endorsement process that's really different than the rest
• 56:20
  Speaker 9: of the country. And so when you when we were
• 56:23
  Speaker 9: walking through that process, we had more folks that were
• 56:27
  Speaker 9: in caucuses for Matt Little and Andy Craig and uh
• 56:29
  Speaker 9: and more people in causes stuff from Matt Little on
• 56:31
  Speaker 9: Flanagan than the other two campaigns combined. And I think
• 56:35
  Speaker 9: that's because it's not just what issues you're running on,
• 56:38
  Speaker 9: it's definitely how you're saying it, why you're fighting for it.
• 56:41
  Speaker 9: It's it's the authenticity stream. And I think for better words,
• 56:46
  Speaker 9: you know, I I must admit I'm kind of a
• 56:47
  Speaker 9: one trick pony. I'm pretty brash, and I talk about
• 56:51
  Speaker 9: my politics of being very personal and I fight, you know,
• 56:54
  Speaker 9: and sometimes I fight when it's not necessary, but I fight.
• 56:57
  Speaker 9: And so like that's my style and I can't change that.
• 57:01
  Speaker 9: I won't change that. And so I but I think
• 57:03
  Speaker 9: in this moment that has appealed to people in a
• 57:05
  Speaker 9: way it might not have.
• 57:08
  Speaker 6: So this also, this also reminds me of a new
• 57:11
  Speaker 6: another is a new post story.
• 57:13
  Speaker 7: Matt can put this up on the screen.
• 57:14
  Speaker 6: You said the word authenticity, and we're learning now that
• 57:17
  Speaker 6: Gavin Newsom's pack is trying to help elect Democrats and
• 57:22
  Speaker 6: quote spread the gospel of Newsom by training supporters to
• 57:25
  Speaker 6: implant his talking points into family text chats, alumni, facebook groups,
• 57:29
  Speaker 6: other places where politics isn't typically discussed. But you know
• 57:33
  Speaker 6: what this reminds me of, actually though, is that right
• 57:36
  Speaker 6: now it's hilarious, But right now people are struggling to
• 57:41
  Speaker 6: like not have these scripted influencers Like Tom Steyer got
• 57:45
  Speaker 6: hit with that and so he's trying to find He's
• 57:48
  Speaker 6: scrambling to find a new way, and it sounds like
• 57:50
  Speaker 6: on the ground, probably the best way to deal with
• 57:53
  Speaker 6: this is just the good old fashioned art of talking
• 57:55
  Speaker 6: to people, like talking to human beings.
• 57:57
  Speaker 9: It's not rocket science, you know. I That's what I've said,
• 58:00
  Speaker 9: because you know, I've been in politics a long time.
• 58:03
  Speaker 9: A ton of people come to me when they're running
• 58:05
  Speaker 9: their first race and they say, well, what should I do?
• 58:07
  Speaker 9: Should I do this and this and this? And I
• 58:09
  Speaker 9: was like, you have to uncomplicate this, you know, a campaign.
• 58:13
  Speaker 9: A campaign is about talking to people. That's it. That's it.
• 58:17
  Speaker 9: There's various forms and avenues and ways to do that,
• 58:19
  Speaker 9: but that is all it is. And so it does
• 58:22
  Speaker 9: feel a little weird to talk about it. It's like
• 58:24
  Speaker 9: telling someone you're humble you know, to talk about authenticity
• 58:27
  Speaker 9: and yourself. So I feel a little uncomfortable doing that,
• 58:30
  Speaker 9: but I've said this entire campaign just like speak from
• 58:34
  Speaker 9: your heart and let it rip and tell people why,
• 58:37
  Speaker 9: like talk about talk about why you actually believe that
• 58:40
  Speaker 9: related to your own life, because I just think people
• 58:44
  Speaker 9: want to know why you're fighting so hard. And so
• 58:46
  Speaker 9: if you go on there and you do the I
• 58:49
  Speaker 9: support better healthcare and better education, and there's just there's
• 58:53
  Speaker 9: no weight to it, there's no gravitas, there's no emotion
• 58:55
  Speaker 9: to it. It just falls so flat. And I really
• 58:58
  Speaker 9: think that's been the Democrat's problem the last you know,
• 59:01
  Speaker 9: three cycles. We kind of have these these big possey platforms.
• 59:04
  Speaker 9: Trump is bad, but we're not actually saying, like why
• 59:08
  Speaker 9: why we're fighting for a specific thing. And I think
• 59:10
  Speaker 9: that's that I guess to me that I think that's
• 59:13
  Speaker 9: why we've been so successful in this.
• 59:17
  Speaker 4: And the poll there were two poles released, one by
• 59:20
  Speaker 4: mac Klein and one by yourself back in May. Both
• 59:24
  Speaker 4: had you up by a pretty substantial amount. But that's
• 59:27
  Speaker 4: before the millions of dollars has flooded in. What the
• 59:31
  Speaker 4: what have the ads been like and how are you
• 59:34
  Speaker 4: feeling it on the ground, like are people asking you
• 59:38
  Speaker 4: about what they're seeing or like, what's do you think?
• 59:40
  Speaker 4: It's been enough to make this a pretty close race?
• 59:44
  Speaker 5: Interesting?
• 59:45
  Speaker 9: You know, I keep joking like we're about to find out,
• 59:47
  Speaker 9: you know, we're so close that my prediction is is
• 59:50
  Speaker 9: probably as less value than to that. Yeah. But you know,
• 59:54
  Speaker 9: the the initial question we got about I would say
• 59:57
  Speaker 9: about three weeks ago when this all really started popping off,
• 1:00:01
  Speaker 9: is people are like, I'm seeing all those ads, like
• 1:00:03
  Speaker 9: where are they getting the money? And they're like, where
• 1:00:05
  Speaker 9: are your ads? And I was like, well, we don't.
• 1:00:07
  Speaker 9: We have not sold out, and so we we you know,
• 1:00:10
  Speaker 9: we have mail, we have a huge ground game, and
• 1:00:13
  Speaker 9: now we're just up on We have some outside spen too,
• 1:00:16
  Speaker 9: just in the last week, if I'm timing that right.
• 1:00:19
  Speaker 4: From American from American Priorities, right, yeah, and the Congressional
• 1:00:23
  Speaker 4: Progressive Caucus, and then we had a little bit, yeah,
• 1:00:26
  Speaker 4: we had a little bit from nurses too, and then
• 1:00:29
  Speaker 4: me A Care for All has has a has a
• 1:00:31
  Speaker 4: bye too.
• 1:00:32
  Speaker 9: But the biggest question was from folks was like when
• 1:00:36
  Speaker 9: are you going to be on TV? That was the
• 1:00:37
  Speaker 9: question that we got over and over and over again.
• 1:00:40
  Speaker 9: And so ah, you know, I think the impact it
• 1:00:43
  Speaker 9: obviously has a huge impact on name recognition that level
• 1:00:46
  Speaker 9: of money, But I think the biggest problem they're having
• 1:00:50
  Speaker 9: is is what I just talked about. It's it's messaging
• 1:00:55
  Speaker 9: without emotion. It's depersonalized, it's removed. I think it's generic
• 1:01:00
  Speaker 9: and not specific to this race or the specific candidates.
• 1:01:04
  Speaker 9: So in terms of its persuasive value, I am doubtful.
• 1:01:09
  Speaker 9: But our numbers on the ground continue to remain good,
• 1:01:12
  Speaker 9: and you just Minnesota's smauna that it's really hard to
• 1:01:16
  Speaker 9: buy votes.
• 1:01:19
  Speaker 4: The way that it's unfolding in the last couple of
• 1:01:22
  Speaker 4: weeks is really fascinating for kind of people who are
• 1:01:25
  Speaker 4: kind of campaign walks, because because you have this three
• 1:01:28
  Speaker 4: person race and you don't have rank choice voting, it
• 1:01:31
  Speaker 4: becomes this zero sum competition. And so you've had maybe
• 1:01:36
  Speaker 4: over a million plus come in support the third place candidate,
• 1:01:41
  Speaker 4: who is Berg, who is also fairly progressive, has been
• 1:01:46
  Speaker 4: also been critical of Israel, not quite to the same degree.
• 1:01:51
  Speaker 5: But when you're when you're.
• 1:01:53
  Speaker 4: Watching the spending, you're like, well, I assume people who
• 1:01:56
  Speaker 4: have a million dollars don't want to just waste a
• 1:01:58
  Speaker 4: million dollars for no reason, and why are they burning
• 1:02:01
  Speaker 4: it on a third place candidate who you know it
• 1:02:04
  Speaker 4: doesn't seem to be any reason to think that she's
• 1:02:06
  Speaker 4: actually going to win, unless you think, oh, this is
• 1:02:10
  Speaker 4: this is a zero some competition. If you can pull
• 1:02:13
  Speaker 4: votes away from the other person in the progressive lane,
• 1:02:16
  Speaker 4: then you're actually not spending it to support that person.
• 1:02:18
  Speaker 4: You're spending it to support the person who, you know,
• 1:02:23
  Speaker 4: Matt Klein was the pro Israel candidate. And then you
• 1:02:26
  Speaker 4: very quickly you get to a place where like this
• 1:02:28
  Speaker 4: is not democracy, Like like what whatever this is? It's
• 1:02:32
  Speaker 4: it's not it's not people just coming to the polls
• 1:02:34
  Speaker 4: and casting their casting their preferences and sending a representative
• 1:02:39
  Speaker 4: to Washington. How like, how how has the media been
• 1:02:43
  Speaker 4: covering this this bizarre kind of influx of money?
• 1:02:49
  Speaker 5: Yeah, thanks Sam.
• 1:02:50
  Speaker 9: I think that thing that's lost in the fact that
• 1:02:52
  Speaker 9: we're only getting attention to this race in the last
• 1:02:54
  Speaker 9: few weeks is that when Representative Burke Lnster campaign, and
• 1:02:58
  Speaker 9: for eight months of her campaign she was campaigning as
• 1:03:01
  Speaker 9: a centrist, she opened with public option. Her and Matt
• 1:03:05
  Speaker 9: Klein agreed on public option. In a couple debates, she
• 1:03:08
  Speaker 9: said nothing at all about Gaza whatsoever until two days
• 1:03:12
  Speaker 9: before we had a screening for a pretty powerful political
• 1:03:16
  Speaker 9: group here that's a multi faith coalition, she said nothing whatsoever.
• 1:03:21
  Speaker 9: And she's also pro data centers. She voted for hundreds
• 1:03:25
  Speaker 9: of millions of dollars in tax breaks for data centers
• 1:03:28
  Speaker 9: here in Minnesota to attract them to come here. And
• 1:03:30
  Speaker 9: it's a sales tax break of thirty five years. So
• 1:03:35
  Speaker 9: the only time she's become progressive is in the last
• 1:03:37
  Speaker 9: month and a half, which is for a reason, right,
• 1:03:41
  Speaker 9: she's now in the race to steal votes from me.
• 1:03:43
  Speaker 9: And the next part here is where you start to
• 1:03:46
  Speaker 9: sound crazy. And I think that's by design, because we
• 1:03:50
  Speaker 9: win the DFL endorsement on May nine. On May thirteen,
• 1:03:54
  Speaker 9: DMFI Democratic Majority for Israel gives five hundred thousand dollars
• 1:03:59
  Speaker 9: to women vote. Just four days after that, DMFI has
• 1:04:04
  Speaker 9: endorsed Klein. Okay, and then it gets even weirder. On
• 1:04:08
  Speaker 9: May twentieth, udp apex Pack gives four hundred thousand dollars
• 1:04:15
  Speaker 9: to elect Democratic women and they had given six hundred
• 1:04:19
  Speaker 9: thousand dollars already the month before. And then on May
• 1:04:22
  Speaker 9: twenty first, literally the day after they transferred six hundred
• 1:04:26
  Speaker 9: thousand dollars to Pro Choice Majority, and all three of
• 1:04:30
  Speaker 9: those are sending significant amounts on Representative Bird and they
• 1:04:36
  Speaker 9: announced that spending just twenty days after all these transfers,
• 1:04:41
  Speaker 9: and so it's very clear to me they're running the
• 1:04:43
  Speaker 9: same strategy they've run before, which is convert someone the
• 1:04:47
  Speaker 9: progressive light person to I mean literally copying our language,
• 1:04:53
  Speaker 9: literally some of its verbatim. In the stump speeches and
• 1:04:56
  Speaker 9: on the website, they copied all their stuff, and they're
• 1:05:00
  Speaker 9: trying to say she's the progressive and I'm the fake press, right,
• 1:05:03
  Speaker 9: so they're stealing votes that way. And then we predicted
• 1:05:06
  Speaker 9: this all of a sudden last two weeks, two million
• 1:05:08
  Speaker 9: dollars from that kind their actual campaign.
• 1:05:10
  Speaker 5: Right.
• 1:05:11
  Speaker 9: If that makes a sound crazy, so be it. But
• 1:05:13
  Speaker 9: those are the facts, you know, And.
• 1:05:15
  Speaker 4: All this is in our drop site report and it's
• 1:05:17
  Speaker 4: all in the FEC records like you can. You can
• 1:05:20
  Speaker 4: trace the money, but publicly it does it does make
• 1:05:23
  Speaker 4: you seem a little crazy when you're following the money
• 1:05:26
  Speaker 4: because and then and then they move it from you know,
• 1:05:29
  Speaker 4: DMFI or apax pack to EDW Action, and then they
• 1:05:33
  Speaker 4: move it from there to a sister pack pro choice majority.
• 1:05:37
  Speaker 5: Then they start spending and you're.
• 1:05:38
  Speaker 4: Like, why are they moving it from one rights packed
• 1:05:43
  Speaker 4: to a separate one before they spend it. And the
• 1:05:45
  Speaker 4: only answer can be two, just make it seem that
• 1:05:50
  Speaker 4: much crazier and harder to follow.
• 1:05:52
  Speaker 9: Right, and then you know, okay, so let's say none
• 1:05:56
  Speaker 9: of that's clear. Let's say all that is opaque and
• 1:05:58
  Speaker 9: we can't really understand that, and we can't really tie
• 1:06:01
  Speaker 9: that to Berg and et cetera, et cetera. We have
• 1:06:03
  Speaker 9: called on Representative Berg to say to say to her
• 1:06:07
  Speaker 9: packs publicly, stop taking a PAC money. I don't want
• 1:06:10
  Speaker 9: you to take a PAC money. And if you continue
• 1:06:13
  Speaker 9: to take APAC money, I'm going to reject your endorsement
• 1:06:15
  Speaker 9: and give the money back. We have called on her
• 1:06:17
  Speaker 9: to do that, and no response whatsoever. So I just think,
• 1:06:21
  Speaker 9: if you're going to be real about this and if
• 1:06:23
  Speaker 9: you're going to stick to your principles and values, actually
• 1:06:25
  Speaker 9: do that. But don't don't attack me for coming up
• 1:06:28
  Speaker 9: with conspiracy theories, which they've done when they have not
• 1:06:31
  Speaker 9: at all answered any of the actual facts we put
• 1:06:35
  Speaker 9: out there with primary sources. They just want to gloss
• 1:06:38
  Speaker 9: over it because they know exactly what's happening.
• 1:06:41
  Speaker 6: That's amazing to orchestrate this convoluted funding scheme and then
• 1:06:45
  Speaker 6: use it as a way to then also call you
• 1:06:47
  Speaker 6: a conspiracy.
• 1:06:48
  Speaker 9: Theorist, Like that's really correct. I mean, staff sense of
• 1:06:52
  Speaker 9: this to me yesterday. But on one of our tweets,
• 1:06:56
  Speaker 9: Representative Berg's campaign manager put the like Charlie, you know
• 1:07:01
  Speaker 9: from Charlie kell me like that, and it's like they
• 1:07:05
  Speaker 9: would rather do that and meme it than actually I
• 1:07:08
  Speaker 9: answer the facts because they know we're right and they
• 1:07:11
  Speaker 9: know what they've done. They've compromised themselves and their principles
• 1:07:14
  Speaker 9: in order to try and win an election. And I
• 1:07:16
  Speaker 9: that's not the type of person we need to go
• 1:07:17
  Speaker 9: to d C. Because if you're going to compromise it
• 1:07:20
  Speaker 9: during the campaign, my goodness, you're going to compromise it
• 1:07:22
  Speaker 9: when you get to DC.
• 1:07:24
  Speaker 4: And I think the point you made earlier, And in
• 1:07:26
  Speaker 4: a second, i'll play Berg's video that she put out
• 1:07:28
  Speaker 4: after we so we reached out to Berg for comment
• 1:07:30
  Speaker 4: on this, and afterwards she put a video out which
• 1:07:36
  Speaker 4: goes to your point. But I think that the point
• 1:07:38
  Speaker 4: you're making earlier is very important, and that is that
• 1:07:41
  Speaker 4: has been the thing that we're asking candidates now, it's like, okay,
• 1:07:44
  Speaker 4: you're saying, and let's say this organization says that this
• 1:07:49
  Speaker 4: specific chunk of half a million dollars from a pack
• 1:07:52
  Speaker 4: or a million dollars from APAC it was spent in
• 1:07:55
  Speaker 4: a different race. Don't worry, it was not spent in
• 1:07:58
  Speaker 4: this race. That is still a pack that is being
• 1:08:01
  Speaker 4: massively funded and subsidized by a pack which is defending
• 1:08:06
  Speaker 4: and supporting US support for a genocide Israel. So it
• 1:08:10
  Speaker 4: is not too much to ask, well, do you then
• 1:08:13
  Speaker 4: denounce this pack support? And so far you're right, we're not.
• 1:08:19
  Speaker 4: We're not hearing any of that. Instead, let me here,
• 1:08:21
  Speaker 4: let me play this this response.
• 1:08:26
  Speaker 10: From I tried to keep this race positive. I really did,
• 1:08:31
  Speaker 10: But Matt Little has come on here in lied about me.
• 1:08:33
  Speaker 3: So let's get a few things straight.
• 1:08:36
  Speaker 10: Number one, I don't take a pack money, I never
• 1:08:39
  Speaker 10: have and I never will. I fully believe what is
• 1:08:42
  Speaker 10: happening in Gaza is a genocide, and I have called
• 1:08:46
  Speaker 10: for an end to all weapons sales to Israel. Number two,
• 1:08:50
  Speaker 10: and Citizens United took a look at this race and
• 1:08:53
  Speaker 10: decided I was the best candidate to fight to get
• 1:08:56
  Speaker 10: corporate money and dark money out of politics. And last,
• 1:09:01
  Speaker 10: I'm proud as a Union leader to continue to fight
• 1:09:04
  Speaker 10: for our hard working families. That's why I have the
• 1:09:07
  Speaker 10: support of Labor and Citizens United, every Town for Gun Safety,
• 1:09:12
  Speaker 10: organizations that fight for reproductive freedom, and organizations that work
• 1:09:17
  Speaker 10: to elect pro choice women to Congress. That little night
• 1:09:21
  Speaker 10: have to lie in this race, but I don't. That's
• 1:09:24
  Speaker 10: why I'm focused on what really matters, fighting for our
• 1:09:27
  Speaker 10: hard working families across this district, across Minnesota and across
• 1:09:32
  Speaker 10: this country.
• 1:09:34
  Speaker 4: And by the way, the line there that she had
• 1:09:36
  Speaker 4: about posing all weapons transfers to Israel, I wasn't able
• 1:09:42
  Speaker 4: to find her previously like that seemed to be an
• 1:09:45
  Speaker 4: advancement of her previous position. But your response to her, yeah.
• 1:09:50
  Speaker 9: Well, the advancement of her position is the same thing
• 1:09:53
  Speaker 9: that's happened this entire race. Is we stake out ground
• 1:09:56
  Speaker 9: and then one and a half to two months later
• 1:09:58
  Speaker 9: they adopted ground. And that's another iteration of that happening
• 1:10:02
  Speaker 9: right now. I think the idea that they've kept it
• 1:10:05
  Speaker 9: positive is humorous to me. Every time we have brought
• 1:10:10
  Speaker 9: up a policy vote or facts on the ground, they
• 1:10:15
  Speaker 9: have called me a liar, you can actually go google that.
• 1:10:17
  Speaker 9: They've called me a liar so many times throughout this campaign.
• 1:10:19
  Speaker 9: So we said she voted for one hundreds of million
• 1:10:23
  Speaker 9: dollars in tax breaks for data centers. She called me
• 1:10:25
  Speaker 9: a liar. We've produced the vote. We've said she voted
• 1:10:30
  Speaker 9: to allow discrimination by private insurers here in Minnesota for
• 1:10:33
  Speaker 9: metagap coverage, that I was a liar, and we produced
• 1:10:36
  Speaker 9: a vote, we said we had fifty eight percent of
• 1:10:39
  Speaker 9: the delegates for the endorsement process, and her own campaign
• 1:10:42
  Speaker 9: menaitor said I have a loose relationship with the truth.
• 1:10:45
  Speaker 9: That was her quote, and then we went on to
• 1:10:47
  Speaker 9: win with sixty three percent the vote. So this is
• 1:10:49
  Speaker 9: a pattern of what they do on that campaign. Anytime
• 1:10:53
  Speaker 9: they don't want to face the facts or argue the
• 1:10:55
  Speaker 9: actual details that we produced, they just called me a liar,
• 1:10:58
  Speaker 9: which is that's only fine. I mean, they have to
• 1:11:01
  Speaker 9: do that. But again, if she is serious about this,
• 1:11:06
  Speaker 9: call on your packs to stop picking A PAC money
• 1:11:09
  Speaker 9: and if they don't reject their endorsement and give the
• 1:11:12
  Speaker 9: money back, otherwise you are you are a benefactor of
• 1:11:16
  Speaker 9: this a PAC money when I was, and this idea
• 1:11:19
  Speaker 9: that this money is just for this race, and this
• 1:11:21
  Speaker 9: money is just for that race is absurd. You know,
• 1:11:23
  Speaker 9: when I was the mayor, you send out those annual
• 1:11:25
  Speaker 9: budget things where you do a percentage of every dollar,
• 1:11:28
  Speaker 9: like what percentage of your dollar goes to X, Y
• 1:11:31
  Speaker 9: and z, And so it'd be thirty percent to the
• 1:11:33
  Speaker 9: police station, you know, ten percent the fire hires, right,
• 1:11:36
  Speaker 9: And so that's really how this goes, Right. You are
• 1:11:38
  Speaker 9: benefiting from those dollars and you don't want to admit it,
• 1:11:43
  Speaker 9: and you don't want to confront the fact that you
• 1:11:45
  Speaker 9: have compromised your principles to try and win an election
• 1:11:48
  Speaker 9: for yourself. And so instead of all that, you call
• 1:11:51
  Speaker 9: me a liar. And that's fine. They've been doing that
• 1:11:54
  Speaker 9: for nine months and it hasn't landed.
• 1:11:58
  Speaker 5: All. Am we anything else?
• 1:12:00
  Speaker 11: No, that's so interesting, fascinating race, yea fascinating to be
• 1:12:05
  Speaker 11: a part of, you know, because you read about it
• 1:12:08
  Speaker 11: in the abstract, but then it's like, oh, you're in
• 1:12:11
  Speaker 11: it now, so you.
• 1:12:13
  Speaker 7: Read about it in a Ryan Grimm book and then
• 1:12:15
  Speaker 7: you live it.
• 1:12:17
  Speaker 9: Wow. Here I am on on on the podcast. It's
• 1:12:19
  Speaker 9: it's it's wild And just so I can get this
• 1:12:22
  Speaker 9: on the record, we have a guy in our staff,
• 1:12:24
  Speaker 9: Peter Solderberg. This is his favorite show. I just want
• 1:12:27
  Speaker 9: to get that on the record.
• 1:12:28
  Speaker 5: Peter shout out to Peter.
• 1:12:30
  Speaker 9: Yeah, thank you. That'll mean a lot to him.
• 1:12:32
  Speaker 4: I got to ask ask Peter is he a premium
• 1:12:34
  Speaker 4: sub though, well, he will be tomorrow if he is.
• 1:12:41
  Speaker 5: Excellent.
• 1:12:42
  Speaker 4: I'm sure he's enjoying the newsletter though. We'll say say
• 1:12:45
  Speaker 4: a lot to Peter. We we very much appreciate it.
• 1:12:50
  Speaker 4: And uh, the election is on this coming Tuesday. This
• 1:12:54
  Speaker 4: is Tuesday, Minnesota, Minnesota second district. We'll stay in touch,
• 1:13:00
  Speaker 4: all right, Yeah, thanks.
• 1:13:00
  Speaker 9: For having me on.
• 1:13:01
  Speaker 5: Appreciate it.
• 1:13:01
  Speaker 9: Thanks for se y'all.
• 1:13:04
  Speaker 7: Everyone. Is that time of the week where we do AMA.
• 1:13:06
  Speaker 6: These are from our premium subscribers and we appreciate at Breakingpoints.
• 1:13:10
  Speaker 6: Dot com is where you can go to get a
• 1:13:11
  Speaker 6: premium subscription. You get the full version of the new newsletter,
• 1:13:14
  Speaker 6: the full version of the show uninterrupted in your inbox
• 1:13:18
  Speaker 6: every day early. So producer Mac is going to toss
• 1:13:21
  Speaker 6: up some AMA questions as we go here.
• 1:13:25
  Speaker 7: Mac hit us, what do you got.
• 1:13:29
  Speaker 9: For Ryan?
• 1:13:30
  Speaker 6: Why is Rocana so hated by congressional Democrats? That one
• 1:13:32
  Speaker 6: comes from Barbara Glassman, what's say you Ryan Gram.
• 1:13:37
  Speaker 4: You know, the number one attribute that is valued by
• 1:13:42
  Speaker 4: House Democrats is caucus loyalty, and they correctly do not
• 1:13:51
  Speaker 4: feel that Rokana has caucus loyalty.
• 1:13:54
  Speaker 5: And you know, and.
• 1:13:55
  Speaker 4: Also he he is willing to even though kind of
• 1:13:59
  Speaker 4: aocgut so much attention for you know, beating Joe Crowley
• 1:14:02
  Speaker 4: in twenty eighteen, and then and then she endorsed its
• 1:14:05
  Speaker 4: decent number of primary challengers against incumbents in twenty twenty.
• 1:14:09
  Speaker 4: Roe has kept doing that. And even if you like,
• 1:14:15
  Speaker 4: I think shreth Thanadar is a counter example where you
• 1:14:20
  Speaker 4: are allowed to endorse against him because he's such a
• 1:14:23
  Speaker 4: ridiculous figure. Otherwise you're just no matter what, you're not
• 1:14:28
  Speaker 4: allowed to endorse against your opponents.
• 1:14:29
  Speaker 5: You're not supposed to criticize the party leader.
• 1:14:33
  Speaker 4: You're not supposed to criticize the party chairs, the party's direction.
• 1:14:36
  Speaker 4: You're you're not You're just not supposed to do any
• 1:14:38
  Speaker 4: of those things.
• 1:14:39
  Speaker 6: That to me feels like a Pelosi. Uh norm that
• 1:14:44
  Speaker 6: was sent is that downstream of Pelosi?
• 1:14:47
  Speaker 4: No, that's it's a bigger this would have been this
• 1:14:50
  Speaker 4: was set in the first con Continental Congress. Probably well,
• 1:14:57
  Speaker 4: Democratic people for big Club it's a big and if
• 1:15:00
  Speaker 4: you're in it, be in it.
• 1:15:01
  Speaker 5: And if you're not.
• 1:15:03
  Speaker 6: People forget that when Newt Gingrich came in with the Revolution,
• 1:15:08
  Speaker 6: Democrats had held the House for forty years, it was
• 1:15:11
  Speaker 6: an extremely like close club. It's almost like mafia esque.
• 1:15:17
  Speaker 6: It's just that they have such power, like they're used
• 1:15:21
  Speaker 6: to wielding power in that way.
• 1:15:22
  Speaker 7: I suppose I have.
• 1:15:25
  Speaker 4: Where's the book I'm reading the Class of seventy four
• 1:15:29
  Speaker 4: right now? And wow, the which by John Lawrence, which
• 1:15:33
  Speaker 4: is who was one of Pelosi's top aides, who was
• 1:15:37
  Speaker 4: at an aid in seventy four. Seventy four is the
• 1:15:40
  Speaker 4: Watergate babies, when you had scores of freshmen come in
• 1:15:45
  Speaker 4: and those freshmen took on the old bulls, they took
• 1:15:51
  Speaker 4: on the Dixiecrats, they reshaped the house. They that's the
• 1:15:57
  Speaker 4: Pelosi generation basically. And Pelosi came in the eighties, but
• 1:16:02
  Speaker 4: like when she was at the height of her power,
• 1:16:04
  Speaker 4: like this class of seventy four was still still like
• 1:16:08
  Speaker 4: literally when I started covering Congress, the Class of seventy
• 1:16:11
  Speaker 4: four was still a dominant power structure in the House.
• 1:16:16
  Speaker 4: And even though they came in and actually probably because
• 1:16:20
  Speaker 4: they came in as these insurgents who burned the house
• 1:16:23
  Speaker 4: to the ground and reshaped it in their image. They
• 1:16:26
  Speaker 4: were always been on the lookout for somebody to do
• 1:16:28
  Speaker 4: that to them, and it created this paranoid culture of
• 1:16:35
  Speaker 4: required solidarity. And Roe just doesn't do that right.
• 1:16:39
  Speaker 6: And that's why I think on the House side, at
• 1:16:42
  Speaker 6: least Republicans were able to break through leadership with the
• 1:16:44
  Speaker 6: Tea Party earlier. Just that culture wasn't the same in
• 1:16:47
  Speaker 6: the conference. Oh man, if you're listening to this, Ryan
• 1:16:51
  Speaker 6: grim is mobile. He's gone to a shelf there it
• 1:16:56
  Speaker 6: is Class of seventy four. It's a beautiful cover too. Yeah,
• 1:16:59
  Speaker 6: some good book covers there.
• 1:17:02
  Speaker 9: Mac.
• 1:17:03
  Speaker 7: What else we get.
• 1:17:06
  Speaker 6: From Jake who says Jake Mogol who says, does the
• 1:17:08
  Speaker 6: team agree that a dark truth of wars that usually
• 1:17:10
  Speaker 6: a renaissance of music follows? If so, where's our counterculture
• 1:17:14
  Speaker 6: of the now or is it being suppressed?
• 1:17:16
  Speaker 7: Jesse wells?
• 1:17:18
  Speaker 5: Jesse Wells, Yeah, there you go.
• 1:17:21
  Speaker 4: The problem is you need a monoculture to have a
• 1:17:24
  Speaker 4: counterculture exactly. And and because everybody lives in their own
• 1:17:29
  Speaker 4: algorithmic culture, we have subculture. We have endless just subcultures.
• 1:17:34
  Speaker 4: So how do you, how do you have a counter
• 1:17:37
  Speaker 4: without a mono. There's nothing to counter.
• 1:17:41
  Speaker 6: But it's actually amazing to watch Jesse just blow up
• 1:17:46
  Speaker 6: because it was I think it's TikTok. I've seen him
• 1:17:50
  Speaker 6: on Instagram. I first saw him on Instagram rules not
• 1:17:53
  Speaker 6: on TikTok. But that's the algorithm picking something up, and
• 1:17:58
  Speaker 6: you can't suppress you can't totally suppress it. If something
• 1:18:01
  Speaker 6: goes megaviral, then it does become not a monoculture, but
• 1:18:07
  Speaker 6: a powerful subculture, an influential subculture where a lot of
• 1:18:11
  Speaker 6: people but not every person. It's not the same as
• 1:18:15
  Speaker 6: having like I don't know the doors, because charts don't
• 1:18:19
  Speaker 6: exist in the same way that they used to. Like
• 1:18:21
  Speaker 6: it's all apples and oranges with the time when there
• 1:18:23
  Speaker 6: was a real monoculture.
• 1:18:24
  Speaker 7: So that's an interesting point.
• 1:18:26
  Speaker 6: You just I guess you'll just have to look for
• 1:18:27
  Speaker 6: the subculture that is counter to the other subcultures.
• 1:18:32
  Speaker 5: Right, and you have to ask somebody else.
• 1:18:34
  Speaker 4: I'm like a deeply uncool person, nowhere near like the
• 1:18:37
  Speaker 4: actual countercultures of today. Like it's not I'm not the
• 1:18:43
  Speaker 4: one who's going to have any clue what's going on.
• 1:18:48
  Speaker 6: Garov Malik says for Crystal and Ryan, does Bernie being
• 1:18:50
  Speaker 6: a liberal Zionist make him fail you were litmus test?
• 1:18:53
  Speaker 6: I know his positions keep keeps evolving, and he's using
• 1:18:56
  Speaker 6: his political capital to get DSA elected, But he says
• 1:18:58
  Speaker 6: nothing essentially should change once Netanya, who is out and
• 1:19:01
  Speaker 6: wants a two state solution. Only would you guys apply
• 1:19:04
  Speaker 6: that critical ons to him that you'd apply to an
• 1:19:06
  Speaker 6: Adam Smith or Dean Phillips.
• 1:19:09
  Speaker 4: Well, first of all, I don't have a litmus test,
• 1:19:11
  Speaker 4: like who cares what I think. I'm out here to
• 1:19:14
  Speaker 4: report to you what's going on in the world and
• 1:19:16
  Speaker 4: not tell you, like anything anything more beyond that. You know,
• 1:19:20
  Speaker 4: Bernie also has a very particular life trajectory like his
• 1:19:28
  Speaker 4: You know, his family was h he had had a
• 1:19:32
  Speaker 4: lot of family killed in the Holocaust. He was he
• 1:19:34
  Speaker 4: was raised, you know, steeped in that culture he is.
• 1:19:40
  Speaker 4: I've i've I've reported critically on his blind spot when
• 1:19:46
  Speaker 4: it comes to Israeli abuses for for a very long time,
• 1:19:52
  Speaker 4: like well over a decade, Like it is has been
• 1:19:55
  Speaker 4: apparent to anybody who has covered Bernie.
• 1:19:58
  Speaker 5: I think it was like twenty ten or something.
• 1:20:00
  Speaker 4: I remember writing about a town hall he had in Vermont,
• 1:20:04
  Speaker 4: where he was getting pushed on whatever latest crime Israel
• 1:20:11
  Speaker 4: had carried out, and he just yells back at this
• 1:20:15
  Speaker 4: Vermont audience like what do you want me to do?
• 1:20:19
  Speaker 4: Like just getting very cranky, angry Burnie, and it's just
• 1:20:22
  Speaker 4: like this, this is like who he's always been. I
• 1:20:24
  Speaker 4: also think there's been a significant evolution from October November
• 1:20:31
  Speaker 4: of twenty twenty three to where he is today. It
• 1:20:34
  Speaker 4: has by no means been at the pace that his
• 1:20:38
  Speaker 4: critics have wanted it to come. But he's nowhere near
• 1:20:42
  Speaker 4: Adam Smith and you know whoever else the person asked there,
• 1:20:47
  Speaker 4: like I think.
• 1:20:48
  Speaker 5: He's he's he he doesn't quite.
• 1:20:51
  Speaker 4: He doesn't do as much the tick that he used
• 1:20:53
  Speaker 4: to do of the net Yahoo government like he's he's
• 1:20:57
  Speaker 4: starting to he's starting to treat kind of the Israeli
• 1:21:01
  Speaker 4: government more more honestly than that.
• 1:21:05
  Speaker 6: It's kind of an impossible counterfactual. But I don't know
• 1:21:09
  Speaker 6: how much of this would have ever happened without his
• 1:21:11
  Speaker 6: twenty sixteen run. And by this I mean the kind
• 1:21:14
  Speaker 6: of left populism.
• 1:21:16
  Speaker 7: Yeah that was.
• 1:21:18
  Speaker 6: I don't know, I don't know what you think of that, Ryan,
• 1:21:19
  Speaker 6: but it's it's hard to take that away from him
• 1:21:21
  Speaker 6: if you're somebody who you know is frustrated with his
• 1:21:25
  Speaker 6: pace of change, But then you look at the change
• 1:21:27
  Speaker 6: that he kind of ushered in himself, even if it
• 1:21:30
  Speaker 6: hasn't kept up with his personal take.
• 1:21:33
  Speaker 5: Yeah, I don't know. It's there was there. That energy
• 1:21:36
  Speaker 5: was there.
• 1:21:37
  Speaker 4: It was in occupy, it was behind Elizabeth Warren, then
• 1:21:42
  Speaker 4: he industry, and then he grabbed it. If he hadn't
• 1:21:45
  Speaker 4: done it and Hillary loses to Trump, there still would
• 1:21:48
  Speaker 4: have been that burst of energy where it would have gone.
• 1:21:51
  Speaker 5: Is a good question. Yeah, it's it's it's an interesting counterfactual.
• 1:22:00
  Speaker 7: Well what else we got here?
• 1:22:01
  Speaker 9: Max?
• 1:22:01
  Speaker 7: I think we got one more.
• 1:22:03
  Speaker 6: Obie Wood's the third says, with everything going on in
• 1:22:05
  Speaker 6: the economy and young people struggling, they get by. I
• 1:22:07
  Speaker 6: think it is time for radical solutions. What do you
• 1:22:09
  Speaker 6: think about suspending federal income taxes for Americans under thirty?
• 1:22:13
  Speaker 7: I'd love to hear.
• 1:22:14
  Speaker 6: Sager's take on this. I'd love to hear Soager's take
• 1:22:17
  Speaker 6: on this. I would just suspend federal income tax period.
• 1:22:22
  Speaker 6: That would be my take, But.
• 1:22:26
  Speaker 7: I don't think Ryan would support that.
• 1:22:27
  Speaker 4: But that's I don't know what I would what I
• 1:22:30
  Speaker 4: what I would support is if if you want to
• 1:22:33
  Speaker 4: get outside the box, it kind of get radical, say
• 1:22:36
  Speaker 4: like suspend all federal income taxes for and you can
• 1:22:42
  Speaker 4: even send up pretty high anybody making less than say
• 1:22:45
  Speaker 4: one hundred and fifty thousand dollars a year.
• 1:22:47
  Speaker 7: All, yeah, you can actually go higher than that.
• 1:22:50
  Speaker 4: And go and then even go higher and then hit
• 1:22:54
  Speaker 4: a wealth tax. Implement a wealth tax for anybody making
• 1:22:59
  Speaker 4: the UK they're looking at ten million in in France
• 1:23:05
  Speaker 4: they looked at like one hundred million euro big a
• 1:23:09
  Speaker 4: fifty million what like something where no normal person like
• 1:23:13
  Speaker 4: is anywhere near it. But a two percent wealth tax
• 1:23:18
  Speaker 4: on fortunes of fifty million dollars and up and then
• 1:23:23
  Speaker 4: use that wipe out betteral income tax. And now you
• 1:23:27
  Speaker 4: have to phase it in or else you get like
• 1:23:29
  Speaker 4: you'd get runaway inflation in a way that would be
• 1:23:32
  Speaker 4: kinter productive to working people because you'd get your tax
• 1:23:36
  Speaker 4: money back and have to pay it for fifteen dollars eggs.
• 1:23:39
  Speaker 4: So you have to engineer it, you know, in the
• 1:23:42
  Speaker 4: right way. But yes, what the radical thing you have
• 1:23:46
  Speaker 4: to do is our entire lives. We've been talking about
• 1:23:50
  Speaker 4: the rich getting richer as if it's just a natural
• 1:23:53
  Speaker 4: fact the rich need to get poorer because the rich
• 1:23:58
  Speaker 4: getting richer are making us all poorer.
• 1:24:00
  Speaker 6: Well, I was going to say, I have no problem
• 1:24:01
  Speaker 6: with the rich getting richer as long as it's an
• 1:24:04
  Speaker 6: actual rising tide lifting all boats.
• 1:24:06
  Speaker 5: Right, and we haven't had a rising tide for two decades.
• 1:24:08
  Speaker 5: That it's not tide.
• 1:24:10
  Speaker 4: The tide in Western economies is like one to two percent. Yeah,
• 1:24:16
  Speaker 4: bres wealth increase for the point one percent is like
• 1:24:19
  Speaker 4: ten to fifteen to twenty percent every year.
• 1:24:21
  Speaker 5: So that means it has to come from somewhere. It's
• 1:24:23
  Speaker 5: coming from you.
• 1:24:25
  Speaker 6: It should come from honestly, like it should come from corporations.
• 1:24:29
  Speaker 6: And it's insane to see like you could if you.
• 1:24:32
  Speaker 5: Do the math, he's coming from people, right, right, But you.
• 1:24:36
  Speaker 6: Could tax understanding, like you could tax billionaires of one
• 1:24:39
  Speaker 6: hundred percent and fund of the government for what like
• 1:24:41
  Speaker 6: a couple of weeks or a couple of months on it.
• 1:24:43
  Speaker 6: I forget what the math is, but it's pretty bleak.
• 1:24:45
  Speaker 4: So obviously now I think you can do it.
• 1:24:51
  Speaker 5: The point let's see point what a point one percent own.
• 1:24:54
  Speaker 7: Now, either way, it's not like a sustainable.
• 1:25:01
  Speaker 4: Well it is sustainable because if you reinvest that money
• 1:25:05
  Speaker 4: in the middle class, then the middle class starts to
• 1:25:08
  Speaker 4: then resustain an economy.
• 1:25:11
  Speaker 5: You don't need to constantly go back and get the.
• 1:25:15
  Speaker 4: All right, the net worth owned by the point one
• 1:25:19
  Speaker 4: percent about twenty five trillion.
• 1:25:23
  Speaker 6: Oh my lord, they could pay off the They could
• 1:25:26
  Speaker 6: pay off the entire debt.
• 1:25:28
  Speaker 4: Yeah, and the entire actually barely what's it? What's the
• 1:25:32
  Speaker 4: GDP of the US?
• 1:25:33
  Speaker 6: Thirty Actually, I think that that doesn't even come close GDP.
• 1:25:38
  Speaker 4: The entire GDP of the US is thirty two trillion.
• 1:25:42
  Speaker 4: So the zero point one percent in the US are
• 1:25:47
  Speaker 4: now roughly own roughly equivalent to the entire production of
• 1:25:53
  Speaker 4: the of the economy.
• 1:25:55
  Speaker 7: The debt is at.
• 1:25:55
  Speaker 6: Forty trillion, So our GDP is what what did you say.
• 1:25:59
  Speaker 5: The already two trillion?
• 1:26:01
  Speaker 7: Holy, it's just so incredibly.
• 1:26:07
  Speaker 6: So.
• 1:26:08
  Speaker 5: Taxes, it's not, it's not.
• 1:26:10
  Speaker 4: It's not as bleak as you think in the sense
• 1:26:12
  Speaker 4: in this sense like, so, imagine you have a five
• 1:26:18
  Speaker 4: hundred thousand dollars mortgage and you make one hundred thousand
• 1:26:21
  Speaker 4: dollars a year, Right, that's a five x debt to revenue,
• 1:26:27
  Speaker 4: and we're at a roughly one x debt to GDP.
• 1:26:32
  Speaker 4: So as long as our economy keeps growing and our
• 1:26:37
  Speaker 4: tax revenues stay steady, we can pay. We can handle
• 1:26:40
  Speaker 4: that debt service. But the problem is that the assets
• 1:26:45
  Speaker 4: held by the very rich keep growing and they're not
• 1:26:47
  Speaker 4: paying any taxes, So that puts a higher and higher
• 1:26:50
  Speaker 4: burden on everybody else to handle that debt.
• 1:26:56
  Speaker 7: Yeah, it's not a good system.
• 1:26:59
  Speaker 6: It's it's fine from flat tax person, I think.
• 1:27:04
  Speaker 5: Taxis I mean, I would start.
• 1:27:06
  Speaker 4: I'd be happy to start with the flat tax if
• 1:27:07
  Speaker 4: it meant that the zero point one percent were actually
• 1:27:10
  Speaker 4: paying the same as everybody else, Right.
• 1:27:12
  Speaker 7: And that's the benefit of having it. So's you don't have.
• 1:27:14
  Speaker 5: You would need, but you would need, you would need
• 1:27:17
  Speaker 5: a wealth tax to get to that.
• 1:27:20
  Speaker 7: They they should should certainly be paying more, there's no
• 1:27:23
  Speaker 7: question about that.
• 1:27:23
  Speaker 9: Yeah. Cooked.
• 1:27:25
  Speaker 7: Yeah, well, Ryan says, we're not cooked. Don't worry about it.
• 1:27:28
  Speaker 7: If we find out a way to.
• 1:27:30
  Speaker 5: Not cooked yet. We're not cooked yet, we're still.
• 1:27:33
  Speaker 6: Fast reinvest Yeah, I agree with all right, we can
• 1:27:37
  Speaker 6: we can leave it there for I was gonna say
• 1:27:41
  Speaker 6: it was bleak, but Ryan says, once again, it's not bleaking.
• 1:27:43
  Speaker 7: It's not grim.
• 1:27:44
  Speaker 5: That's right, all.
• 1:27:45
  Speaker 6: Right, Well, thanks everyone for tuning in. Hope you enjoyed
• 1:27:49
  Speaker 6: the show today, Appreciate the A m A.
• 1:27:51
  Speaker 7: Questions. Hope you I'll have a great weekend.
• 1:27:52
  Speaker 6: We will be here if there's any breaking news, particularly
• 1:27:55
  Speaker 6: on the warfront. So thanks and we'll see you back
• 1:27:58
  Speaker 6: here soon