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