Latest / Breaking Points with Krystal and Saagar / 9/22/26: Trump Media Ban Backfires, DraftKings Caught Targeting Addicts, AI Anthropic Wet Lab

Transcript

9/22/26: Trump Media Ban Backfires, DraftKings Caught Targeting Addicts, AI Anthropic Wet Lab · Breaking Points with Krystal and Saagar

0:00
Speaker 1: Hey guys, Ready or Not 2024 is here and we
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0:19
Speaker 2: But enough with that, let's get to the show.
0:24
Speaker 1: Got some updates for you in Trump's war on the media.
0:28
Speaker 1: So you may have already heard that he decided to
0:31
Speaker 1: kick CNN, MSNOW, and Politico out of the White House.
0:36
Speaker 3: Well, the other mainstream media outlets, to include Fox News.
0:40
Speaker 1: Decided that in solidarity with those outlets, they would not
0:44
Speaker 1: participate in in the White House press pool. So that
0:48
Speaker 1: led to a very unusual situation yesterday where Trump was
0:53
Speaker 1: doing an event, some sort of ribbon cutting ceremony or
0:55
Speaker 1: some sort of thing like that. And you had the video,
0:59
Speaker 1: but you had no audio whatsoever. Let's take a listen
1:03
Speaker 1: to how they handled that.
1:05
Speaker 4: OK, so there is no microphone because there's no microphone
1:08
Speaker 4: because the pool cameras aren't there. So maybe, Tim, this
1:12
Speaker 4: is one example of it backfiring just a little bit.
1:15
Speaker 4: Pick up on some of the things Michael was saying.
1:19
Speaker 4: And obviously the ramifications are right here. Can't hear what
1:23
Speaker 4: the president's saying.
1:24
Speaker 1: Particularly amusing, Sagar, because that was Newsmax, which is obviously
1:28
Speaker 1: very much in the tank for Trump. So for them
1:30
Speaker 1: to even say, like, maybe this backfired just a little
1:32
Speaker 1: bit on him is a pretty stunning admission.
1:34
Speaker 2: Yeah, it's like his worst nightmare is not having television.
1:38
Speaker 2: In fact, the only time we saw Trump on camera
1:40
Speaker 2: yesterday is when he did an event with Mayor Mamdani.
1:43
Speaker 2: And that was only because New York City media was
1:46
Speaker 2: there at the request of Mamdani and not the White
1:48
Speaker 2: House press pool. I will not cape for the White
1:51
Speaker 2: House Correspondents Association. I was a member. Frankly, they are
1:54
Speaker 2: a cartel which is extremely old and like, And it's
1:59
Speaker 2: not me saying this. Ryan would reiterate a lot of this, too,
2:02
Speaker 2: from a lot of the same problems that he's had
2:03
Speaker 2: with drop site credential, like the way that they run
2:06
Speaker 2: these organizations like the White House or Congress. It's a
2:10
Speaker 2: very legacy media forward. However, the White House is trying
2:14
Speaker 2: to hijack some of that to basically control what type
2:18
Speaker 2: of coverage. that they get. So there are no heroes
2:21
Speaker 2: in particular in this story. I just want to make
2:22
Speaker 2: that clear. However, what they've done is they have created
2:26
Speaker 2: a situation where once again, they preemptively ban the White
2:29
Speaker 2: House press pool. Well, everyone could have predicted that everyone
2:32
Speaker 2: would stand tall with the organizations which have been booted out. Why?
2:36
Speaker 2: Because even when I was there, attack on one is
2:40
Speaker 2: attack on all. It's literally a cartel. We're like, we
2:42
Speaker 2: can't let the cartel be broken. And so if they
2:45
Speaker 2: get to decide who is in charge of the camera
2:47
Speaker 2: footage or not, then all of us need to stand
2:51
Speaker 2: for that person. They did it when Obama attacked Fox News.
2:54
Speaker 2: He tried to kick them out back in 2009, I
2:57
Speaker 2: want to say. The entire White House press corps kind
3:00
Speaker 2: of stood with Fox News then at the time specifically
3:02
Speaker 2: so that Fox would stand with them as they are now.
3:05
Speaker 2: And what it all means is, you know, even just
3:08
Speaker 2: say the pool, what does it mean? All the journalists
3:11
Speaker 2: who cover the White House, there's hundreds of them, cannot
3:13
Speaker 2: all fit into the same room with the president. And
3:15
Speaker 2: so every day there is an assigned member of that
3:18
Speaker 2: press corps to be the representative of the rest of
3:21
Speaker 2: the press corps. I used to do this job where
3:24
Speaker 2: you kinda sit there and you wait for Trump and
3:26
Speaker 2: see what he's gonna say, and then you send the
3:27
Speaker 2: message to the rest of the press corps You're not
3:30
Speaker 2: allowed to exclusively report it for yourself. That's the same
3:33
Speaker 2: thing with the TV camera. There's a guy there with
3:35
Speaker 2: a TV camera. Even though he works for CNN, let's
3:38
Speaker 2: say on that day, that footage is being broadcast to
3:41
Speaker 2: all of the networks and the people who subscribe to
3:44
Speaker 2: the pool. They also pay for that privilege, and they
3:46
Speaker 2: pay a lot of money to be able to do so.
3:48
Speaker 2: So that's what it all means, and that's why they're
3:51
Speaker 2: all boycotting, and that's why some of the boycotts are
3:53
Speaker 2: happening right now. But it has backfired on Trump, actually,
3:56
Speaker 2: because they're all now saying, not only will I not
3:59
Speaker 2: publish any of the video, even the still photographers, including
4:02
Speaker 2: Trump's favorites, a guy named Doug Mills. He works for
4:04
Speaker 2: The New York Times. Best photographer I've ever seen in
4:07
Speaker 2: my life. This guy, even he, they're now saying, like, oh,
4:10
Speaker 2: we won't be publishing still images. any of their images
4:14
Speaker 2: that they're trying to distribute to try and circumvent the
4:17
Speaker 2: hold that the White House press corps has.
4:19
Speaker 3: Yeah.
4:20
Speaker 1: And I mean, basically the idea is that you should have,
4:23
Speaker 1: anytime the president is in public, you should have a
4:25
Speaker 1: camera and audio on him because he's, just inherently newsworthy.
4:29
Speaker 2: Well, anything can happen. Like 9-11. I mean, that's literally
4:32
Speaker 2: the story. And part of the reason why the president
4:35
Speaker 2: should always have him with them is you don't know
4:36
Speaker 2: what the hell is going to happen. I mean, one
4:38
Speaker 2: day you're in a Florida classroom and the next day
4:40
Speaker 2: you're on Air Force One, you know, taking on going
4:42
Speaker 2: to Barksdale Air Force Base and trying to broadcast the
4:45
Speaker 2: most important statements that the president can make because war
4:47
Speaker 2: can break out literally at any time. They tell us
4:50
Speaker 2: that whenever you're You're there. You never know what's going
4:52
Speaker 2: to happen. You may be sitting in a damn golf
4:53
Speaker 2: course for the 90th time.
4:55
Speaker 3: Yeah.
4:56
Speaker 2: But you don't know. You don't know what will happen.
4:58
Speaker 3: Well, I mean, it's obviously of a piece with his
5:01
Speaker 3: whole war on the press. The man literally thinks it
5:03
Speaker 3: should be illegal for people to criticize him. And he
5:06
Speaker 3: said this. This is not me making this up.
5:07
Speaker 1: That is his view, is that the press should never
5:10
Speaker 1: be critical of him, that he should get only fawning coverage,
5:14
Speaker 1: that if you don't... I mean, he goes after Fox
5:16
Speaker 1: News all the time for not being fawning enough in
5:19
Speaker 1: their worshipful praise of him or if they put out
5:21
Speaker 1: a poll that looks bad for him or his party
5:24
Speaker 1: because their polling outfit is actually pretty legit. He gets
5:27
Speaker 1: upset about that and about their fake polls. So he
5:30
Speaker 1: has this. incredibly authoritarian view of what the news media
5:35
Speaker 1: should be. And we've obviously seen the lawsuits and the
5:38
Speaker 1: threats of lawsuits and the use of the FCC to
5:42
Speaker 1: badger late night comedians who tell a joke that he
5:44
Speaker 1: doesn't like.
5:45
Speaker 3: This is a part and parcel.
5:47
Speaker 1: And then that's without even getting into the fact that
5:50
Speaker 1: the Ellisons are taking over vast swaths of our media
5:53
Speaker 1: empires so that you have Trump-aligned oligarchs in control of
5:58
Speaker 1: important media outlets, including, you know, I think CNN will
6:01
Speaker 1: probably be allowed back in once the Ellison's are fully
6:04
Speaker 1: in control because they promised major changes to the network.
6:07
Speaker 1: So this is obviously how he operates. He does not
6:11
Speaker 1: believe in having a press that is adversarial whatsoever. We
6:15
Speaker 1: can put D2 up on the screen. This is him
6:18
Speaker 1: defending his decision to kick them out. He says the
6:21
Speaker 1: White House is not instituting an assault on the free press,
6:24
Speaker 1: something which I cherish.
6:25
Speaker 3: LOL.
6:26
Speaker 1: It is instituting an assault on fake news, something that
6:29
Speaker 1: has grown like cancer in our beloved United States of America.
6:31
Speaker 1: It is corrupt, purposeful, pervasive, fully coordinated, totally out of control.
6:35
Speaker 1: The threat to our national security must be stopped now.
6:38
Speaker 1: That's the other thing they hate, is all of the
6:40
Speaker 1: accurate reporting about what is actually going on in Iran,
6:44
Speaker 1: to include the images that have now come out of
6:47
Speaker 1: all of the destruction of our bases and our military
6:51
Speaker 1: equipment on the ground in the Middle East. He was
6:54
Speaker 1: at a press conference with Zoran Mamdani yesterday, which is
6:57
Speaker 1: a whole other thing that was going on there. He
6:59
Speaker 1: met with Zoran Mamdani. at Gracie Mansion in New York City.
7:04
Speaker 1: The bromance does continue. In any case, he got asked
7:07
Speaker 1: about this media ban. Let's go ahead and take a
7:10
Speaker 1: listen to that.
7:11
Speaker 5: You would think that an outlet would write at least
7:14
Speaker 5: reasonably well, but never. If you look at CNN, it's
7:17
Speaker 5: fake news. If you look at MSNBC, what it used
7:23
Speaker 5: to be, or MSDNC, as some people call it, that's
7:27
Speaker 5: Democrat National Committee. But it's just biased news. And I
7:30
Speaker 5: think that these outlets have an obligation to be fair.
7:34
Speaker 5: And if they're fake news, I really think I have
7:36
Speaker 5: an obligation not to allow them into another very special house.
7:39
Speaker 5: This is a special house, Gracie Mansion. Well, the White
7:42
Speaker 5: House is the most special of all houses, I have
7:44
Speaker 5: to say. And I don't even think the mayor would
7:46
Speaker 5: argue with that. And I think I have an obligation
7:49
Speaker 5: not to let fake news. They're very bad for the country,
7:53
Speaker 5: very bad for national security.
7:54
Speaker 6: So I have that obligation.
7:56
Speaker 3: Not a really comfortable moment for Zoran there. He's sort
7:59
Speaker 3: of staring off into the distance like, okay, I just
8:03
Speaker 3: have to stand here and take it.
8:05
Speaker 2: Something I have not considered is how much of this
8:07
Speaker 2: is about munitions. Because what I see now, literally from
8:10
Speaker 2: this morning, Trump, the cowards and traitors would love to
8:12
Speaker 2: say the United States is low on munitions. It's not true.
8:14
Speaker 2: We have more munitions than we could ever possibly think
8:16
Speaker 2: of using and building them now at levels we've never
8:18
Speaker 2: seen before. Previously, the three outlets that have been banned
8:21
Speaker 2: had all published stories specifically about munitions. So it is
8:25
Speaker 2: actually very possible that this is not even necessarily about him,
8:28
Speaker 2: but of his anger of publishing some of these classified
8:32
Speaker 2: information or classified information. You don't even need classified information
8:36
Speaker 2: to figure it out, but some of the classified stories.
8:39
Speaker 2: So it actually makes it worse, in my opinion, because
8:41
Speaker 2: you're not being banned over some stupid stormy, you know,
8:44
Speaker 2: some FCs, which I'm not saying that would be bad.
8:46
Speaker 2: Like that would be bad, too. But to ban over
8:49
Speaker 2: accurate wartime reporting in the middle of said war, that
8:53
Speaker 2: makes it, I think, actually a lot worse.
8:55
Speaker 3: I think that's definitely what's going on here.
8:57
Speaker 2: Like that is about panic about the accuracy of what's
9:00
Speaker 2: being reported from the battle.
9:01
Speaker 1: I think that is absolutely part of what is going
9:03
Speaker 1: on here, for sure. And in addition to his just general,
9:07
Speaker 1: you know, hatred of any sort of adversarial press. But
9:10
Speaker 1: I think in this specific moment, that's why this is
9:12
Speaker 1: blowing up. Republicans are being pressed on this.
9:15
Speaker 3: They had Steve Scalise. being interviewed on the Sunday shows.
9:18
Speaker 3: This is D6, guys. Let's take a listen to how
9:20
Speaker 3: this went for him.
9:21
Speaker 7: First of all, I don't think we've had a president
9:23
Speaker 7: that's been more open to the press. Look, I've been at,
9:27
Speaker 7: you go to a bill signing with President Trump, and
9:30
Speaker 7: the first thing he does before he even signs the
9:32
Speaker 7: bill is he brings in the press. And everybody gets
9:34
Speaker 7: to not only take pictures, but ask questions. Go look
9:38
Speaker 7: at Joe Biden. Joe Biden removed the press credentials from
9:41
Speaker 7: over 400 different agencies. You know, what President Trump's done
9:45
Speaker 7: has brought a lot of those back in, but he
9:48
Speaker 7: wants a free and fair press. He's been to the
9:51
Speaker 7: White House Correspondents' Dinner. Just the other day, he was
9:54
Speaker 7: answering questions about this to the press, and they're asking
9:58
Speaker 7: him about his attitude towards the press while he's holding
10:01
Speaker 7: a press conference.
10:01
Speaker 3: Yeah.
10:02
Speaker 7: You never saw that under President Biden. So very open president.
10:06
Speaker 8: So first of all, that's actually not true about about Biden.
10:08
Speaker 8: They 400 individual reporters who had credentials did not reapply.
10:14
Speaker 8: There was only one that was actually denied a credential.
10:17
Speaker 8: And all of those who didn't reapply were allowed to
10:20
Speaker 8: come in. They were not banned from the White House.
10:22
Speaker 6: That did not happen.
10:23
Speaker 1: The other thing that Republicans bring up is what you
10:25
Speaker 1: referenced before, which was under Obama, there was an attempt
10:29
Speaker 1: to ban Fox News from one particular interview. So not
10:33
Speaker 1: even like blanket ban, not, you know, it was Fox News,
10:36
Speaker 1: this one particular interview, and there was a whole Republican.
10:40
Speaker 3: Freakout over it.
10:41
Speaker 2: To be fair, it was part of a bigger strategy.
10:43
Speaker 2: Ben Rhodes and those people had a long stand. They hated,
10:46
Speaker 2: I mean, I get it. They hated Fox.
10:47
Speaker 6: Yeah.
10:48
Speaker 2: Um, they were like, they're, you know, at this.
10:50
Speaker 3: I mean, it is genuinely a partisan. This was like, Oh, gee,
10:53
Speaker 3: founded as part of a Republican political problem.
10:55
Speaker 2: Same way, you know, they hate MSNBC. Like I get it.
10:57
Speaker 2: They were especially furious about birtherism, uh, which was happening, uh,
11:01
Speaker 2: at the time. And they were like, screw it, let's
11:03
Speaker 2: ban them. And so that was like the intro. to it.
11:05
Speaker 2: What they didn't anticipate was Jake Tapper and all those
11:08
Speaker 2: people at the time being like, hey, you can't be
11:10
Speaker 2: doing this. You can't be telling us what we can
11:13
Speaker 2: and can't. And ultimately, they did fold. Trump also tried
11:16
Speaker 2: to ban Caitlin Collins from the White House in 2019,
11:20
Speaker 2: around when I was there. They sued and they won.
11:24
Speaker 2: There is longstanding court precedence. They basically, at every turn,
11:28
Speaker 2: they have been able to overturn bans from the actual
11:32
Speaker 2: White House grounds on First Amendment principles. That said, the
11:35
Speaker 2: pool is actually a little bit of a different story.
11:37
Speaker 2: And that one, I'm not sure that will be resolved
11:40
Speaker 2: because there is no constitutional right to the White House
11:43
Speaker 2: press pool or to the cartel. I would not expect
11:46
Speaker 2: that one to be resolved. We do also have D7.
11:49
Speaker 2: Let's put it up here on the screen. Trump is
11:51
Speaker 2: launching something called Trump TV, which is the White House essentials.
11:56
Speaker 2: It The White House has always had a YouTube account
12:02
Speaker 2: and has broadcast some of their events. I think what
12:06
Speaker 2: they're trying to get around is what happened yesterday, which again,
12:09
Speaker 2: I presume Trump is furious about, is normally the White
12:12
Speaker 2: House does not have coverage or at least the same
12:17
Speaker 2: type of coverage that these networks have for obviously a
12:20
Speaker 2: good reason. They don't have boom mics and making sure
12:24
Speaker 2: that the president is mic'd up while he's doing event.
12:27
Speaker 2: That's usually the job. of the press corps itself. But
12:31
Speaker 2: by and large, I mean, this is, the White House
12:33
Speaker 2: had already set up press, and I apologize for too
12:35
Speaker 2: much inside baseball, but already what they have done has
12:38
Speaker 2: made it so that when Biden, you know, whoever Democrat wins,
12:42
Speaker 2: they're not going back to the way that things used
12:45
Speaker 2: to be. The way it is right now is far
12:47
Speaker 2: preferable for the people who are in power. They've cut
12:50
Speaker 2: off extraordinary amounts of normal press acts, stupid stuff, which
12:54
Speaker 2: sounds dumb, but is actually important. you usually can't go
12:58
Speaker 2: to the press secretary's office anymore. It used to be,
13:00
Speaker 2: you can just walk in there. Now, can't do that anymore.
13:03
Speaker 1: Well, Ryan loves to point out, you know, all the
13:04
Speaker 1: State Department briefings that we got all those tortured, you know,
13:07
Speaker 1: from the fat sogger that.
13:09
Speaker 2: Would go up there. He doesn't look like me.
13:12
Speaker 3: Many people are saying, anyway.
13:14
Speaker 2: We don't even share the same genetics.
13:16
Speaker 3: All those people that would go up there and be,
13:19
Speaker 3: you know, give these torturous answers about like, oh, no,
13:22
Speaker 3: we don't think it's a war crime. We'll look into it.
13:24
Speaker 3: We'll get back to you.
13:25
Speaker 1: We don't get that anymore because they don't do those briefings.
13:29
Speaker 1: And then you also have and those are a great
13:31
Speaker 1: way for like, you know, a reporter like Ryan, who
13:34
Speaker 1: may not get credentials for the White House, but can
13:36
Speaker 1: show up to those briefings. And they asked Dropside, asked
13:39
Speaker 1: all kinds of really consequential questions there and at least were,
13:42
Speaker 1: you know, exposed some things and made those people uncomfortable
13:46
Speaker 1: and have to answer questions.
13:47
Speaker 3: That just doesn't happen anymore.
13:48
Speaker 1: And then you have the whole Pentagon thing where basically
13:50
Speaker 1: anyone who was legit, who had any sort of scruples
13:54
Speaker 1: in their reporting whatsoever, could not agree to the terms
13:56
Speaker 1: of effectively like you won't report anything. We don't want
13:59
Speaker 1: you to report. What journalists would agree to that? Well,
14:02
Speaker 1: a bunch of like shoddy influencers who are completely.
14:05
Speaker 2: And they still haven't. They haven't even held briefings for them.
14:07
Speaker 2: But that's my point is we're not going back. Is
14:09
Speaker 2: that these Biden press conferences.
14:11
Speaker 3: Say that I don't really think so.
14:13
Speaker 1: You don't you sometimes you don't understand the way that
14:16
Speaker 1: Democrats operate. Truly, like there is still this very like
14:20
Speaker 1: liberal normie veneration for like the free press. Then this
14:25
Speaker 1: is the way things should be done. And we don't
14:26
Speaker 1: want to be like them. So it depends if we
14:29
Speaker 1: get a John Ossoff. Is he going to kick out
14:31
Speaker 1: all the right wing like reporters out of the room?
14:34
Speaker 3: Absolutely not.
14:34
Speaker 2: Well, a lot of them, though, are not reporters, right?
14:37
Speaker 2: Like a lot of them are literally work for like
14:38
Speaker 2: Lindell TV.
14:39
Speaker 3: Yeah, I mean, those people are going to go.
14:40
Speaker 2: Yeah, that's what I mean. I think they probably will
14:42
Speaker 2: get out.
14:43
Speaker 3: Yeah, but in terms of like, you know, is he
14:45
Speaker 3: going to ban Fox News?
14:46
Speaker 2: Oh, I see.
14:47
Speaker 3: No, he's not going to do that.
14:48
Speaker 1: And so, yeah, I just it's Trump is fairly unique
14:54
Speaker 1: in what he's willing to do and how far he's
14:56
Speaker 1: willing to go.
14:57
Speaker 3: Now, I think.
14:59
Speaker 1: These sorts of breaking of these norms and institutions, you
15:02
Speaker 1: can never put it all the way back together. But
15:05
Speaker 1: I don't know that I would be so convinced that
15:07
Speaker 1: a Democrat will, like, go so hard against media they
15:10
Speaker 1: see as being biased or unfair, as Trump does.
15:12
Speaker 2: That's my last thing, since we have a little bit
15:14
Speaker 2: of time, is, you know, this doesn't make you... It's
15:17
Speaker 2: not good for you. Like, many... What I have watched
15:19
Speaker 2: with the Pentagon, for example, you know, you're bringing in
15:22
Speaker 2: Logan Paul, you're beclowning yourself. Like, a good friend who
15:27
Speaker 2: works in the Pentagon... was telling me, you know, Hex
15:30
Speaker 2: had screwed up because these people are already kind of,
15:33
Speaker 2: they're already baked in pro-military neocons. The best thing that
15:38
Speaker 2: the press did for the military over the last 20
15:40
Speaker 2: years is they laundered a lot of their bullshit because to,
15:43
Speaker 2: you know, maintain the relationships. So by declaring war on them,
15:47
Speaker 2: a lot of them actually started reporting the truth. Whereas
15:50
Speaker 2: previously when you have to maintain relationships and access in
15:53
Speaker 2: the building, you would be much more on their side,
15:56
Speaker 2: you know, to report. And there's also, and there is,
15:59
Speaker 2: I truly believe this. In fact, I know I do.
16:02
Speaker 2: Iron sharpens iron. You are way better off when you're sharp.
16:06
Speaker 2: I mean, one of the things is, and you see this,
16:08
Speaker 2: when candidates are in fighting form on the campaign, Abdul
16:11
Speaker 2: is a good example. I mean, the guy just, you know,
16:12
Speaker 2: is hit after hit after hit. And in fact, you know,
16:15
Speaker 2: ever since that whole Jesse Waters thing, I think he's
16:17
Speaker 2: become a little bit better, but that was an important
16:19
Speaker 2: moment for him. I'm like, oh man, you know, this
16:21
Speaker 2: didn't work out entirely. On my behalf, I created a
16:24
Speaker 2: national news cycle that I probably shouldn't have. I need
16:26
Speaker 2: to sharpen up. And I have seen how Trump and
16:30
Speaker 2: a lot of the other Republicans, when you get ensconced
16:33
Speaker 2: in your, you know, GOP podcast bubble, then all of
16:36
Speaker 2: a sudden you come back up for air and you
16:39
Speaker 2: get hit in a debate or something and you just
16:42
Speaker 2: you don't have it anymore. And I've seen that. time
16:45
Speaker 2: and time again with a lot of these politicians. Yeah,
16:47
Speaker 2: no doubt about it.
16:48
Speaker 3: You see that with candidates, the ones who are willing
16:50
Speaker 3: to go on. I mean, Ro Khan is another good example.
16:52
Speaker 2: Yeah, he'll go anywhere.
16:53
Speaker 1: He'll go on everywhere and he can handle it because
16:55
Speaker 1: there's no question that he hasn't already dealt with and anticipated.
16:58
Speaker 2: Whereas AOC, different story. All right, DraftKings, let's get to it.
17:05
Speaker 2: The conversation around sports betting continues to dominate at least
17:08
Speaker 2: a lot of elite media as we see this epidemic
17:10
Speaker 2: sweep across the nation. NFL season, is here. Week two
17:14
Speaker 2: came and went. Billions of dollars were wagered just in
17:18
Speaker 2: a single week. And now interviews with many of these
17:21
Speaker 2: burgeoning addicts are beginning to come to light. Here's one
17:24
Speaker 2: that CBS just did. Let's take a listen.
17:27
Speaker 9: How often do you bet?
17:28
Speaker 8: Oh, every day.
17:29
Speaker 2: People wake up and check the newspaper. I wake up
17:31
Speaker 2: and check who's playing.
17:32
Speaker 9: But football season is high season for him.
17:35
Speaker 2: Let's go.
17:36
Speaker 10: On this college football Saturday, he's got $ 250 down on
17:41
Speaker 10: four games. If he wins all four, this bet will
17:45
Speaker 10: pay $ 3, 000. But Esteban wins only one. So after a
17:53
Speaker 10: friend comes over, he tries.
17:55
Speaker 2: It all again.
17:56
Speaker 9: Northern Illinois is plus 31 and a half.
17:58
Speaker 2: You're telling me plus 31 and a half don't cover
17:59
Speaker 2: against a defensive team?
18:01
Speaker 9: Four more games, he loses another 250. Just like that,
18:06
Speaker 9: he's down 500 bucks. How much money do you bet
18:09
Speaker 9: a year?
18:10
Speaker 2: Probably 80 to.
18:12
Speaker 6: 100,000, 120.
18:12
Speaker 9: Which has to be approaching the amount of money you
18:14
Speaker 9: make a year.
18:15
Speaker 6: 90, 700.
18:17
Speaker 9: Oh, you got to make a better pass than that.
18:19
Speaker 9: That's right. Great route. This single 29-year-old pest control salesman
18:24
Speaker 9: bets as much as he makes, if not more. I'm
18:28
Speaker 9: getting paid this week.
18:29
Speaker 2: I'm about to put it all down.
18:30
Speaker 9: Your paycheck this week?
18:31
Speaker 2: Yeah, sometimes I'm like, hey, well, I like this game
18:32
Speaker 2: a lot. Yeah, let's just all put it on there.
18:33
Speaker 2: And then look, I got paid double this week.
18:35
Speaker 6: Or?
18:35
Speaker 2: I mean, ramen noodle for the rest of the week.
18:38
Speaker 2: So there you have it. You have here a guy
18:41
Speaker 2: who makes decent money. 29 years old. I can't tell
18:44
Speaker 2: if he's married or not.
18:46
Speaker 6: No offense.
18:46
Speaker 2: Doesn't look it. a pest control salesman who is betting
18:50
Speaker 2: his entire yearly salary a year on a week by
18:53
Speaker 2: week basis. Some weeks I eat double, some weeks I
18:56
Speaker 2: eat ramen. And you could see kind of that miserable
18:59
Speaker 2: existence of sitting and living and dying game by game.
19:02
Speaker 2: I mean, that, that strat, that two 50, and then
19:05
Speaker 2: you lose and then you double and you chase your
19:07
Speaker 2: losses like going on tilt. That is the story. And okay.
19:11
Speaker 2: He has individual responsibility. He's an idiot.
19:14
Speaker 1: Fine.
19:14
Speaker 2: But what about the technology and the legislators and the
19:18
Speaker 2: whole system that enables and lives off of him? What
19:22
Speaker 2: about that? Why would you allow that?
19:25
Speaker 3: Gambling addiction is a real thing.
19:27
Speaker 2: Oh, it is the most potent.
19:28
Speaker 1: I mean, it is real. It is devastating. This guy
19:31
Speaker 1: clearly suffers from it. Otherwise, you wouldn't be risking more
19:35
Speaker 1: than your entire salary. He said he makes $ 97, 000 and
19:37
Speaker 1: he's betting $ 100, 000 to $ 120, 000 a year. you don't have
19:43
Speaker 1: to be a mathematician to figure out, you don't need
19:46
Speaker 1: to type this into OpenAI or a calculator app to
19:49
Speaker 1: figure out out. That's not going to work out for
19:51
Speaker 1: you over the long term. And this man, I'm sure,
19:54
Speaker 1: is not intellectually so stupid that he can't also do
19:58
Speaker 1: that math, but he's hooked. And what's he hooked on?
20:00
Speaker 1: It's the promise of this time. It's going to be
20:03
Speaker 1: the time. This one is going to hit This one,
20:05
Speaker 1: it's that constant dopamine hit, the anticipation. What if it's
20:10
Speaker 1: this one? What if I get this big payday? What
20:12
Speaker 1: if that changes my life? And increasingly, this is what
20:16
Speaker 1: our entire economy is built on. It's no longer the
20:18
Speaker 1: American dream. This is the American dream, is that your
20:21
Speaker 1: parlay is going to hit. For young men, that's like.
20:24
Speaker 3: The new America. And it's not going to. It's rigged
20:26
Speaker 3: just like.
20:26
Speaker 2: The original American dream. It literally will not.
20:28
Speaker 1: This one is even more blatantly rigged against you. Over
20:31
Speaker 1: the long term, maybe you'll have a good, you will
20:34
Speaker 1: have a good week here or there where you're feeling great.
20:36
Speaker 3: Over the long term, they will take everything. Everything, if
20:39
Speaker 3: you continue in this direction.
20:40
Speaker 2: Right, and so this is now paired with, look, none
20:43
Speaker 2: of this is actually all that revelatory. I'm always amazed
20:46
Speaker 2: by what things break through and don't, but for some reason...
20:49
Speaker 2: people woke up to things that we've been talking about
20:52
Speaker 2: here for many years. Let's put the New York Times
20:54
Speaker 2: story up on the screen, E1, where the Times reports
20:57
Speaker 2: about how DraftKings uses AI to target the gamblers' likeliest
21:01
Speaker 2: to lose. Again, if you are surprised by this, this
21:04
Speaker 2: is called basic practices. This is how they make money.
21:07
Speaker 2: This is why online sports gambling is so devastating, because
21:12
Speaker 2: you have the phone, which you're already addicted to, combined
21:15
Speaker 2: with Money, which you can't afford to lose, combined with
21:19
Speaker 2: the most sophisticated machine learning algorithms that even predate these
21:23
Speaker 2: AI models, which is exactly what they write. In 2023,
21:26
Speaker 2: DraftKings took customer betting records and built a machine learning
21:31
Speaker 2: model which seeks the patterns in the data to answer
21:33
Speaker 2: the question, who is more likely to respond to promotions
21:36
Speaker 2: by gambling and losing even more money? They decided to
21:40
Speaker 2: prioritize free bets, bonuses for likely losers, and soon a
21:44
Speaker 2: question began to gnaw at them, aren't many of these
21:46
Speaker 2: people prone to addiction? The guy decides to be a
21:50
Speaker 2: concerned whistleblower. And no offense to the person who ran
21:54
Speaker 2: these tests and is now speaking out. Dude, that's your
21:57
Speaker 2: whole business model. And I've been trying to emphasize this
22:00
Speaker 2: too from the very beginning. Literally 50% of the profit
22:04
Speaker 2: of these companies, 50% comes from the bottom 6%. The addicts, 50%
22:12
Speaker 2: from the small group. This is like this with all sin,
22:14
Speaker 2: is that the vast majority of cigarettes are smoked by
22:17
Speaker 2: daily smokers. The vast majority of alcohol is consumed by alcoholics.
22:21
Speaker 2: The vast majority of money lost is lost by the whales,
22:26
Speaker 2: by the people who are literal addicts and who generally
22:28
Speaker 2: have a large sum that they haven't yet exhausted. What
22:32
Speaker 2: we also know is that all of their advertisements, which
22:35
Speaker 2: this guy we showed you almost certainly fell for, is
22:39
Speaker 2: you can win. It's not true. If you are allowed
22:41
Speaker 2: to bet $ 250 per game, you are a loser. I
22:46
Speaker 2: can tell you that for certain. Why? Because their machine
22:49
Speaker 2: learning algorithm is so sophisticated that if I logged into
22:53
Speaker 2: DraftKings right now, And I bet in a sharp way.
22:56
Speaker 2: Sharp means that somebody who has a general edge. If
22:59
Speaker 2: I bet sharp, even if I lose, it will say,
23:02
Speaker 2: oh God, sharp customer. We don't want that. And so
23:05
Speaker 2: they will come into my app and say, you're allowed
23:07
Speaker 2: to pay $ 2. 90 a game. So they can't ban me,
23:11
Speaker 2: but they can say, we can limit your action. If
23:14
Speaker 2: you are ever allowed to bet in a sustained manner
23:17
Speaker 2: for more than $ 100 a game, you are a loser.
23:20
Speaker 2: The machine is telling you, loser, loser. And that is
23:24
Speaker 2: what a lot of these guys don't seem to understand.
23:27
Speaker 2: And they plow them because, I mean, again, people are idiots,
23:31
Speaker 2: but it's like, you know, I get it where it
23:34
Speaker 2: comes from. Free bet. I'm like, oh, bet five, win 200. Oh,
23:38
Speaker 2: that sounds great.
23:39
Speaker 6: 200.
23:39
Speaker 2: I could pay my electric bill. And so what do
23:42
Speaker 2: you do? You log in. It turns out it's $ 200
23:44
Speaker 2: in free bet credits, or you have to cycle through
23:46
Speaker 2: a real $ 200. And then you lose it all in
23:49
Speaker 2: the first place. Profit boosts, all this other you know,
23:52
Speaker 2: promotional nonsense. It's just addiction. It is designed to get
23:57
Speaker 2: you there. There's no consumer protection. There's no, you know,
24:00
Speaker 2: there's no scruples. I mean, the people who built this
24:03
Speaker 2: are just refining the same tactics of the slot machines,
24:08
Speaker 2: which are designed to make you go broke over time.
24:12
Speaker 3: Yes.
24:12
Speaker 2: If they bleed you on every spin, it wouldn't be fun.
24:17
Speaker 2: They have to bleed you so that you lose it
24:19
Speaker 2: over two hours instead of 20 minutes. So you think
24:21
Speaker 2: you have a chance. That's the game.
24:23
Speaker 5: Yeah.
24:23
Speaker 3: It's hacking the human mind.
24:25
Speaker 2: Right.
24:25
Speaker 3: That's what it is.
24:26
Speaker 1: So all of those personal responsibility, et cetera, they spend
24:30
Speaker 1: millions and millions and millions of dollars to figure out
24:33
Speaker 1: how to hack human psychology and get you in and
24:37
Speaker 1: keep you in. And for, unfortunately, a significant minority percentage,
24:41
Speaker 1: especially of young men. It works like a charm and
24:44
Speaker 1: they get you in their grips and it's very.
24:47
Speaker 3: Hard to get out of. The thing you're talking, I mean,
24:49
Speaker 3: I think it all should be banned.
24:51
Speaker 1: I think we should go back to the days when
24:52
Speaker 1: you had to go to the casino and there's some
24:54
Speaker 1: barrier to entry or you had to like, you know,
24:56
Speaker 1: know a bookie and do something sketchy on the black market, whatever.
24:59
Speaker 1: I think we need to go back to those days
25:01
Speaker 1: because it's so clear to me now how devastating this
25:04
Speaker 1: absolutely is.
25:05
Speaker 3: But at the very least.
25:07
Speaker 1: The kind of crap you're talking about where they can
25:08
Speaker 1: just rig it, where if you're too good, they just
25:10
Speaker 1: like ban you from the app or they, you know,
25:13
Speaker 1: make it limit your actions so that you can't ever
25:16
Speaker 1: come out ahead.
25:17
Speaker 3: How is that legal? That shouldn't be legal. That's completely insane.
25:21
Speaker 2: It's nuts.
25:21
Speaker 1: But they've, you know, they've bought the system and they've
25:24
Speaker 1: paid off whoever they needed to pay off. And so
25:26
Speaker 1: they're able to operate in what should be a completely
25:29
Speaker 1: criminal fashion that I think anyone would look at.
25:31
Speaker 3: And the other thing about this is like, this is
25:34
Speaker 3: what we're getting for AI. Where are my cures to cancer?
25:37
Speaker 10: I know.
25:37
Speaker 1: Where's the solution to climate change I was promised? No,
25:40
Speaker 1: instead I'm getting AI-derived algorithms to target gambling addicts and
25:46
Speaker 1: make them even more miserable and drowning in debt than
25:48
Speaker 1: they already are.
25:49
Speaker 3: That's great.
25:50
Speaker 6: Love that.
25:50
Speaker 2: And then let's talk about the prediction markets. So if
25:53
Speaker 2: I were to sit with the prediction market, they would say, Sagar,
25:55
Speaker 2: you're absolutely right. That's why the casino industry is corrupt,
25:58
Speaker 2: and that's why our platform is so much better. Because
26:02
Speaker 2: by definition, we can't limit people's action. We are not
26:06
Speaker 2: the house which is setting the line or taking advantage
26:09
Speaker 2: of these people in a certain way. And to that,
26:12
Speaker 2: I would say, show me your win-loss percentage. Just show
26:15
Speaker 2: me the amount of customers which actually take profit. Yeah,
26:18
Speaker 2: it's great that you don't limit sharp bettors. It's great
26:21
Speaker 2: out of all of that. But how many of the
26:23
Speaker 2: people actually win? And look, we can do some general math.
26:28
Speaker 2: Let's go ahead and put E6 up here on the screen.
26:31
Speaker 2: So it turns out that last week, so week two
26:34
Speaker 2: of the NFL, take a look at this. Kalshi did
26:37
Speaker 2: 15.3 billion in notional volume last week. 60% of that
26:45
Speaker 2: volume is on parlay bets, which are entirely based on
26:50
Speaker 2: sporting events. This is Dustin Goker. He's a long-time analyst
26:54
Speaker 2: in the industry. He says, my read is it's going
26:56
Speaker 2: to be pretty difficult to frame this as non-gambling activity. Yeah,
26:59
Speaker 2: you think? And this is where, you know, if I
27:02
Speaker 2: were to sit with CalSheet, because they're going to sell
27:04
Speaker 2: me this whole spiel about how we don't ban bettors
27:07
Speaker 2: like the way you're talking about, Crystal. That's immoral. And
27:10
Speaker 2: that's why we're so much better. Well, guys, If 60%
27:14
Speaker 2: of the money is being bet on a parlay bet,
27:18
Speaker 2: and by the way, as we showed you on this
27:19
Speaker 2: show last week, the people who are taking the other
27:22
Speaker 2: side of that parlay are FanDuel and DraftKings, then you're
27:27
Speaker 2: doing the same shit. And that those parlay bets have
27:31
Speaker 2: a 20 to 15% house edge. which means that 15
27:35
Speaker 2: to 20 percent of money went to the same company
27:38
Speaker 2: that Kalshi and them are saying that they are better than.
27:41
Speaker 2: That's where all this money is going. I mean, it's
27:44
Speaker 2: just so like 15 billion dollars in a single week.
27:49
Speaker 2: This is bigger than MPAA, the motion picture industry. This
27:52
Speaker 2: is bigger than all forms of entertainment combined. And it's
27:58
Speaker 2: just business as normal, enabled by the rest of these,
28:02
Speaker 2: enabled by the Trump administration, but every state in the nation,
28:06
Speaker 2: because they've all got their handouts. You know, to the
28:08
Speaker 2: extent the states have a problem with Kalshi, they're not
28:10
Speaker 2: getting any tax revenue. Tax revenue is the problem here.
28:13
Speaker 2: How about the fact that, you know, how about the
28:16
Speaker 2: fact that bankruptcy and credit card debt goes up by 25%
28:19
Speaker 2: in any of the state that legalizes this stuff? And
28:22
Speaker 2: now we have California and Texas That are in on
28:24
Speaker 2: the action. Just wait until the data comes out from
28:27
Speaker 2: those states. It's going to be horrible.
28:28
Speaker 1: And, you know, you think about we've talked about young
28:31
Speaker 1: men struggling with crisis of purpose and meaning and challenges
28:36
Speaker 1: in the dating market and all those sorts of things.
28:38
Speaker 3: And this is sort of like feels.
28:41
Speaker 1: Like a Band-Aid for that because you get that dopamine hit.
28:43
Speaker 3: Like that guy that we played before.
28:45
Speaker 2: Yeah.
28:45
Speaker 3: How do you think this is going to go for
28:47
Speaker 3: you long term? Do you think this is going to
28:49
Speaker 3: increase your desirability? Do you think this is going to
28:52
Speaker 3: increase your, you know?
28:54
Speaker 2: You're speaking my language.
28:55
Speaker 3: I mean, it's true, though. I agree. I agree.
28:57
Speaker 1: I was just thinking about my daughter talks about this,
28:59
Speaker 1: my oldest one, with some of the guys that she's dated.
29:03
Speaker 3: She's like, they're always like, my parlay is going to hit.
29:05
Speaker 2: I know.
29:06
Speaker 3: She's like, your parlay is not going to hit.
29:09
Speaker 2: Okay?
29:10
Speaker 3: I have never experienced you hitting your parlay. It has
29:13
Speaker 3: never happened.
29:14
Speaker 2: Stop it.
29:15
Speaker 1: And it's like, I'm sure she's not alone in feeling like,
29:19
Speaker 1: why are you obsessed with this and throwing your money away?
29:22
Speaker 1: Don't think that I'm going to be in this one
29:24
Speaker 1: for the long haul. So just, you know, just another
29:27
Speaker 1: thing to add to your considerations is your approach.
29:29
Speaker 2: I want to tell the guys that I, and I'm
29:31
Speaker 2: trying to start, maybe I'm the wrong person for it.
29:34
Speaker 2: I want to start the same type of temperance movement
29:37
Speaker 2: from the 1920s where women who are out there need
29:39
Speaker 2: to be to make it known. If you are a
29:41
Speaker 2: sports better, you're a fucking loser and you're not going
29:43
Speaker 2: to get a date. And by definition, you're a literal loser.
29:47
Speaker 2: And second, it means you have bad judgment. You have
29:49
Speaker 2: bad impulse control. And you're not somebody who has the
29:53
Speaker 2: wherewithal to stand up against the system and provide for
29:56
Speaker 2: me in the long term. I think people should recognize
29:59
Speaker 2: that if you're a woman. And you're out there on
30:00
Speaker 2: a first date. One of the first questions you should
30:02
Speaker 2: try to suss out is, hey, do you regularly bet
30:05
Speaker 2: on sports? If you do, red flag, leave.
30:08
Speaker 6: Completely.
30:08
Speaker 2: It's one of those where, you know, and, you know,
30:11
Speaker 2: look at the data. Dave Ramsey says it all the time.
30:14
Speaker 2: Money is the number one cause of divorce in North America.
30:17
Speaker 2: So if you think that that's not going to spiral,
30:19
Speaker 2: you know, out of control, then you're going to see, too, like.
30:22
Speaker 2: The type of person who thinks the parlay is going
30:25
Speaker 2: to bet is exactly the type of person who is
30:27
Speaker 2: going to have long-term credit card debt, who doesn't understand
30:30
Speaker 2: the financial situation, who by literal definition can't do math.
30:36
Speaker 2: I mean, all of these are not working out in
30:39
Speaker 2: your favor. I say all of this, though, and I
30:41
Speaker 2: still feel bad because I'm still pitting the individual against
30:44
Speaker 2: the system. And the point is, the individual should be
30:47
Speaker 2: protected by the system.
30:49
Speaker 3: Agreed.
30:49
Speaker 2: But that's not how our economy works. Our economy... I mean,
30:52
Speaker 2: you know, I've flown for free all over the world
30:55
Speaker 2: off of credit card points. I have to be real.
30:58
Speaker 2: The only reason I get to do that is because
30:59
Speaker 2: of idiots like that guy who carry 80% balance and pay,
31:04
Speaker 2: you know, exorbitant interest rates. Like, the flights aren't free. And,
31:07
Speaker 2: you know, if you own stocks, the whole U.S. economy
31:11
Speaker 2: relies on this guy, you know, just going out and spending.
31:15
Speaker 2: And that's the tragedy. Like, we're all kind of complicit
31:18
Speaker 2: in it. But at the end of the day, you know,
31:20
Speaker 2: you need... people who are courageous enough to be like,
31:23
Speaker 2: I don't want to make money this, you know, not
31:25
Speaker 2: all money is easy money. Like it's one of the
31:27
Speaker 2: most ancient things in society is to say usury is bad.
31:32
Speaker 2: Debt and debt, you know, people who purvey it are evil,
31:36
Speaker 2: are like pervading the community. Exactly. Like thieves, you know,
31:40
Speaker 2: horse thief was always considered one of the most villainous
31:44
Speaker 2: crimes of the West. And it was like the type
31:46
Speaker 2: of person who would do that in the absence of
31:49
Speaker 2: law and order has to be hung or shot is
31:52
Speaker 2: because they will violate the most basic social contract and
31:56
Speaker 2: they will take advantage of the overall system. But whenever
31:59
Speaker 2: we have a system which is so disconnected and kind
32:03
Speaker 2: of faceless in the way AI is, we don't have
32:05
Speaker 2: community enforcement in that same way. So anyway, we should
32:08
Speaker 2: hang horse thieves again.
32:09
Speaker 6: That's what I'm saying.
32:11
Speaker 2: I'm not joking.
32:12
Speaker 1: I didn't know the history of horse thievery and how
32:15
Speaker 1: consequential it was in Western civilization.
32:18
Speaker 2: It's very important. It's very important. Look it up. Look
32:21
Speaker 2: at the way they saw it.
32:22
Speaker 3: I believe you. Yeah. No, we've got the three legs of.
32:24
Speaker 1: The American economy stool are like health care for old people, gambling,
32:30
Speaker 1: and AI bubble.
32:31
Speaker 2: AI CapEx.
32:32
Speaker 9: Yeah.
32:33
Speaker 3: That's a very healthy economy we've got here, guys.
32:36
Speaker 6: All right.
32:37
Speaker 2: Kevin Rue's standing by.
32:38
Speaker 6: Let's get to it.
32:41
Speaker 2: Very excited now to be joined by our friend of
32:43
Speaker 2: the show, Kevin Roos, formerly of the New York Times,
32:45
Speaker 2: but he is now the host of the Machine Gods pod.
32:49
Speaker 2: We will have a link down in the description and
32:51
Speaker 2: also author of a forthcoming book, the AGI Chronicles. So, Kevin,
32:55
Speaker 2: you wrote your very last piece, column here for the
32:59
Speaker 2: New York Times, which we can put up here on
33:01
Speaker 2: the screen. The world is talking about AI dangers. Now
33:05
Speaker 2: it is time to act. You see reason for optimism
33:09
Speaker 2: and worry as the world wakes up. to what Silicon
33:11
Speaker 2: Valley has been talking about for years. You don't have
33:14
Speaker 2: to give away your book and we'll have you on
33:15
Speaker 2: separately to talk about it, but this is your area
33:18
Speaker 2: of expertise. You've been following pretty daily the ins and outs,
33:22
Speaker 2: kind of all of these discussions around AGI. Give us
33:26
Speaker 2: a sense in the column in particular, the world is
33:29
Speaker 2: waking up, there's something to be done, but what actually
33:31
Speaker 2: needs to be done and what are we waking up to?
33:35
Speaker 11: Well, the thing we're waking up to is what the
33:37
Speaker 11: people in San Francisco where I live and in the
33:40
Speaker 11: AI labs where I just spent the past couple of
33:42
Speaker 11: years reporting this book have been saying amongst themselves for years,
33:46
Speaker 11: which is these systems are getting very powerful. That progress
33:50
Speaker 11: does not appear to be slowing down. In fact, it
33:52
Speaker 11: is accelerating because the AI is becoming capable of contributing
33:56
Speaker 11: to development of more AI. So we have this sort
33:59
Speaker 11: of recursive self-improvement, as it's known. That cycle is starting
34:03
Speaker 11: to kick off now. And this is something that people
34:06
Speaker 11: I've talked to in the industry have been worried about
34:08
Speaker 11: for years. There have been papers and blog posts and
34:11
Speaker 11: conference panels about this moment.
34:14
Speaker 6: But it was a very.
34:15
Speaker 11: Small group and they were very sort of fringe within
34:19
Speaker 11: the academic research community and nobody really took them seriously.
34:22
Speaker 6: And what we've seen over.
34:23
Speaker 11: The past couple of weeks with Jacob Coxon resigning from
34:26
Speaker 11: Anthropic and Dario Amadei putting out a statement about pacing
34:30
Speaker 11: the frontier and all the other AI CEOs piling in
34:33
Speaker 11: to say, yeah, We think this is going too fast.
34:35
Speaker 11: That's what people are waking up to. And I think
34:37
Speaker 11: that's the message that is finally being heard.
34:39
Speaker 2: Got it.
34:40
Speaker 3: Let's go and put F7 up on the screen.
34:42
Speaker 1: This was a recent announcement that speaks to the power
34:45
Speaker 1: of these models. For a long time, particularly on the left,
34:48
Speaker 1: there's been a lot of skepticism that the models would
34:51
Speaker 1: ultimately be very powerful.
34:53
Speaker 3: They view it and some still view it as powerful.
34:56
Speaker 1: Just sort of fancy autocorrect or fancy our stochastic parrot or,
35:01
Speaker 1: you know, that this is all really hype. You now
35:04
Speaker 1: have OpenAI announcing that they have resolved, their model has
35:08
Speaker 1: resolved more than 100 longstanding open problems across most areas
35:12
Speaker 1: of mathematics.
35:13
Speaker 3: We're waiting to.
35:14
Speaker 1: Get the list of what exactly those are, but it
35:17
Speaker 1: is in recent memory that these models could not solve
35:21
Speaker 1: basic addition, elementary level, then it was high school level,
35:24
Speaker 1: then it was elite high school level, then it was
35:26
Speaker 1: college level, then it was PhD level. Now you have
35:29
Speaker 1: OpenAI claiming they solved one of these millennium math problems
35:33
Speaker 1: that if you're a mathematician, you solve it, you get
35:35
Speaker 1: a million dollars. There is some contestation about exactly how
35:38
Speaker 1: all of that went down. But if you look at
35:40
Speaker 1: the progress over just a few years, this blew away
35:45
Speaker 1: what even many people who were very bullish on the
35:48
Speaker 1: power of the technology saw.
35:50
Speaker 3: Predicted here.
35:51
Speaker 1: So help put this in context, this latest revelation from
35:54
Speaker 1: OpenAI and what it says about the current capabilities of
35:57
Speaker 1: these models and where they may be going.
36:00
Speaker 6: It's absolutely huge.
36:01
Speaker 11: I mean, the Millennium Prize problems are the seven open
36:05
Speaker 11: problems in math that are considered the hardest. And as
36:07
Speaker 11: you said, if you're a human mathematician and you win one,
36:10
Speaker 11: you not only get a million dollars, but you become
36:12
Speaker 11: a famous mathematician, right? People sort of record your names
36:15
Speaker 11: in the annals of history. these models now are solving
36:19
Speaker 11: these problems like they're easy. So the models have gone
36:24
Speaker 11: from quickly being subhuman at math, you know, being not
36:28
Speaker 11: able to do basic arithmetic, to being wildly superhuman at math.
36:33
Speaker 11: And you can run them in parallel, many, many copies
36:36
Speaker 11: of them to expend the kind of time trying to
36:39
Speaker 11: crack one of these problems that a human would take,
36:42
Speaker 11: you know, many lifetimes to spend. So it really is
36:45
Speaker 11: an unfair fight in mathematics. And I think what the
36:47
Speaker 11: people on the inside see that maybe it's harder to
36:50
Speaker 11: understand from the outside is that this is not just math.
36:53
Speaker 11: This kind of progress, this exponential growth in capabilities is
36:58
Speaker 11: happening in discipline after discipline after discipline. They are seeing
37:02
Speaker 11: this sort of wherever they point their kind of tractor
37:04
Speaker 11: beam of compute and resources. they're finding that these models
37:09
Speaker 11: are incredibly capable. So yeah, I think it's a very
37:12
Speaker 11: significant sort of application of these things.
37:14
Speaker 6: I hope that they're followed by other big problems.
37:18
Speaker 11: In science, but this is the kind of progress that
37:21
Speaker 11: has been freaking out people on the inside for years.
37:23
Speaker 2: Kevin, you don't have a vested financial interest in any
37:27
Speaker 2: of this.
37:27
Speaker 6: You're a reporter.
37:27
Speaker 2: You've been following this now for quite some time. We
37:30
Speaker 2: will hear significant pushback, even from some of our own audiences.
37:34
Speaker 2: We're falling from marketing hype. You know, 100 problems, what
37:38
Speaker 2: does that even mean? Isn't this just marketing for their
37:42
Speaker 2: products to get people like us to talk about it?
37:45
Speaker 2: How do you suss that out, having been in this
37:48
Speaker 2: world now for quite some time?
37:50
Speaker 6: Yeah, I used to share some of that skepticism.
37:52
Speaker 11: I thought, you know, these tech companies are always telling
37:54
Speaker 11: us they've changed the world and the truth is never
37:56
Speaker 11: that simple. But what convinced me was just using the
38:00
Speaker 11: models and seeing how good they were getting at doing
38:03
Speaker 11: programming problems for me, at doing various tasks that I
38:06
Speaker 11: needed them to do. I didn't used to be able
38:08
Speaker 11: to use these models for serious work, and now I do.
38:12
Speaker 11: But I think the thing that is, even if you're
38:14
Speaker 11: skeptical that this is all marketing hype, which I don't
38:16
Speaker 11: think it is, by the way, because these people have
38:18
Speaker 11: been saying the same things for years before they had
38:21
Speaker 11: anything to market. I would just say the thing that
38:24
Speaker 11: should still freak you out is how much these AI
38:27
Speaker 11: labs are relying on their own models for internal automation.
38:31
Speaker 11: for doing their own work. Anthropic recently put out a
38:34
Speaker 11: thing saying that Claude, their AI, is now the lead
38:38
Speaker 11: researcher on 26% of their AI research and development tasks.
38:43
Speaker 11: Not an assistant, not something that they can sort of
38:46
Speaker 11: hand tasks off to, leading the project. So I think
38:51
Speaker 11: that should freak people out, even if they're skeptical about
38:53
Speaker 11: the claims about the Millennium Prize problems or whatever. The
38:57
Speaker 11: labs are now using this stuff so regularly that that
39:01
Speaker 11: it is starting, they are starting to kind of turn
39:03
Speaker 11: over control of these systems to their own AIs.
39:07
Speaker 1: Your former colleague Ezra Klein wrote about this recently and
39:10
Speaker 1: did a podcast about it and talked about how the
39:13
Speaker 1: decision that's being made in those instances is not that
39:17
Speaker 1: we're losing control of the AI, which is obviously something
39:20
Speaker 1: that has also happened that we are concerned about seeing
39:22
Speaker 1: in the future as well. What recursive self-improvement means is
39:26
Speaker 1: that you are handing over control to the AI and
39:29
Speaker 1: he calls for an outright ban. Do you see that
39:33
Speaker 1: as something worth pursuing or something that is even truly feasible,
39:38
Speaker 1: given that sometimes the definitions of these things can be
39:40
Speaker 1: sort of hard to pin down?
39:42
Speaker 11: Yeah, I think I'm sort of behind it in spirit,
39:45
Speaker 11: but I would obviously want to be very focused on
39:47
Speaker 11: the details. I think it's very hard to implement something
39:49
Speaker 11: like a ban on recursive self-improvement. Does that mean that
39:53
Speaker 11: if you work at OpenAI, you can't use AI for coding,
39:56
Speaker 11: but if you work at a different company or a
39:58
Speaker 11: supplier company, you can?
40:00
Speaker 6: I just don't know how that works.
40:02
Speaker 11: But yeah, I think this is the real danger here
40:05
Speaker 11: is not that we are going to lose control, that
40:07
Speaker 11: some rogue agents are going to sort of break out
40:09
Speaker 11: of their box and go attack companies. That obviously would
40:13
Speaker 11: be bad and we should prevent that. The danger is
40:16
Speaker 11: that we are creating this cycle inside the AI labs.
40:20
Speaker 11: They are turning over, they are giving away control of
40:23
Speaker 11: their own research and development process to their AIs, which
40:27
Speaker 11: they have not figured out how to control yet.
40:29
Speaker 6: And so we are.
40:30
Speaker 3: Basically putting the- And which they don't understand.
40:32
Speaker 1: You know, I can't remember who I saw referencing back.
40:35
Speaker 1: Sagar will probably remember recently to the incident you had
40:37
Speaker 1: with one of the early models where it like, you know, convinced.
40:40
Speaker 2: Yeah, that was Google.
40:42
Speaker 1: I fell in love with you and was trying to
40:43
Speaker 1: get you to leave your wife and all this sort
40:46
Speaker 1: of crazy stuff. And I was just sort of like, well,
40:48
Speaker 1: that's weird anyway, moving on. But we never actually got
40:51
Speaker 1: to the like, why was it doing that? Even from
40:53
Speaker 1: that early version. These things are what they call black boxes.
40:57
Speaker 1: It's not like a program that you code. They have
40:59
Speaker 1: behavior that is emergent, that is not understood. And so
41:03
Speaker 1: rather than trying to figure out what's going on so
41:06
Speaker 1: that then we can build more powerful models that we
41:09
Speaker 1: perhaps understand, we never got to that place of even
41:13
Speaker 1: understanding the more powerful.
41:14
Speaker 3: Basic models.
41:15
Speaker 1: And I'm not sure that I actually think the American
41:17
Speaker 1: people do sort of understand or have a feel for
41:20
Speaker 1: that because you're seeing this tremendous opposition to what the
41:23
Speaker 1: AI labs are doing at this point.
41:26
Speaker 6: I think that's right.
41:26
Speaker 11: I think people, even if they don't really have the
41:28
Speaker 11: terminology to like put their finger on exactly what it
41:31
Speaker 11: is that feels bad about what the AI companies are doing,
41:34
Speaker 11: I think people understand that these systems are getting better
41:37
Speaker 11: and we don't like it.
41:38
Speaker 6: It's a threat to our jobs.
41:40
Speaker 11: It's a threat to potentially our lives and our children's futures.
41:44
Speaker 11: Like This is what freaks people out. I started getting
41:46
Speaker 11: texts from people over the last couple of weeks, just
41:48
Speaker 11: people outside the tech industry, like, hey, are we all
41:52
Speaker 11: going to die? That seems bad. So I think this
41:56
Speaker 11: is breaking through, and I don't want to be glib
41:58
Speaker 11: about it. The people inside the labs, they are genuinely
42:01
Speaker 11: concerned that the technology that they are building could kill
42:05
Speaker 11: everyone on earth. That is not marketing hype. That is
42:07
Speaker 11: not a stunt. That is not just some clickbait that
42:11
Speaker 11: people are putting on their press releases that I have
42:14
Speaker 11: talked to these people on the record, off the record,
42:16
Speaker 11: in public, in private.
42:17
Speaker 6: They say the same thing every time.
42:19
Speaker 11: In fact, they are watering down how likely it is
42:23
Speaker 11: that they think AI could kill us all in public.
42:25
Speaker 11: Their real projections are even scarier than what they'll say online.
42:29
Speaker 2: Wow. You see, so many people have such an immediate
42:34
Speaker 2: revulsion to that because you're like, then why are you
42:36
Speaker 2: building it? Like, what are you doing here? Can you
42:38
Speaker 2: answer that question for us?
42:40
Speaker 11: I think there are a few reasons that I've heard
42:42
Speaker 11: people give. And again, I talked to more than 150
42:45
Speaker 11: of these people for my latest book. So I feel
42:47
Speaker 11: like I understand their motivations pretty well. For a lot
42:50
Speaker 11: of them, it's the curiosity of it. It's the what's
42:53
Speaker 11: out there. They're seekers. They want to know like, how
42:56
Speaker 11: smart can we make these things? What could they discover?
42:59
Speaker 11: They want these things to go out and solve diseases
43:02
Speaker 11: and cure cancer and fix the climate and things like that.
43:05
Speaker 11: For other people, though, there's this sense that if we
43:07
Speaker 11: don't do it, someone worse will, right?
43:09
Speaker 6: Like if we The AI.
43:11
Speaker 11: Company in San Francisco don't create this technology. The recipe
43:16
Speaker 11: is not a secret.
43:17
Speaker 6: It's out there.
43:18
Speaker 11: Anyone with the right resources can build, theoretically, the self-improving
43:25
Speaker 11: robot that takes over the world. So we have to
43:28
Speaker 11: get there first so that we can put our safety
43:30
Speaker 11: practices in place so that we don't blow up the world.
43:34
Speaker 1: I want to talk a little bit about the financial
43:36
Speaker 1: aspect of these things. So both Anthropic and OpenAI are
43:40
Speaker 1: planning IPOs. Anthropix is more imminent. OpenAI, Sam Altman just said,
43:45
Speaker 1: they're pushing theirs.
43:45
Speaker 3: Off a year.
43:47
Speaker 1: The always colorful Alex Karp, CEO of Palantir, was recently
43:51
Speaker 1: on CNBC saying, making the claim that he doesn't think
43:54
Speaker 1: OpenAI will ever go public. The reasons he gives are
43:57
Speaker 1: kind of interesting. Let's go and take a listen to that.
43:59
Speaker 12: The first line of defense is you're liable for your
44:02
Speaker 12: own actions. So it's a weird twist. They're asking for
44:05
Speaker 12: societal regulation to get out of the first line of defense,
44:09
Speaker 12: which is if you build a technology that can destroy 10%
44:11
Speaker 12: of the world, That has civil and criminal liability attached
44:18
Speaker 12: to it. The first step is to say, okay, it
44:21
Speaker 12: is not, the first step isn't to say we get
44:22
Speaker 12: out of it because we want to self-regulate or we
44:24
Speaker 12: want regulation. The first step is to say, okay, you've
44:27
Speaker 12: disclosed this, what are you doing about it? And the
44:30
Speaker 12: second step is to say, if you're not doing it,
44:32
Speaker 12: you're not being responsible, we're going to have to get
44:34
Speaker 12: people involved. That's going to be also very complicated because
44:37
Speaker 12: regulating these things needs people, you need people who understand them.
44:40
Speaker 12: Another problem we have is seemingly everyone who understands this
44:43
Speaker 12: is on some payroll. So it's like, you know, it's like,
44:45
Speaker 12: who are you going to trust? You know, when we
44:47
Speaker 12: built nuclear energy, we built the bomb. People did it
44:49
Speaker 12: because they believe they were not on the payroll. The
44:53
Speaker 12: liability that you're talking about, how does that get written
44:55
Speaker 12: into the S-1?
44:56
Speaker 2: What do risk factors sound like?
44:58
Speaker 9: This is the thing.
45:00
Speaker 12: You're assuming that there will be an S-1.
45:03
Speaker 9: Okay, maybe there will be. The only way to deal with.
45:06
Speaker 12: This kind of liability is to go to the government
45:09
Speaker 12: and say, nationalize us, please. You know, a lot of
45:12
Speaker 12: times in business, people don't say what they want.
45:14
Speaker 9: They're leading you to the conclusion.
45:16
Speaker 12: The reason why people have not understood this is, again,
45:19
Speaker 12: every investor, and there are a lot of people who
45:21
Speaker 12: think they're going to make money, they're like, oh, the
45:23
Speaker 12: businesses are the mark. What is the first lesson in
45:27
Speaker 12: poker and in life? If you think the other person's
45:31
Speaker 12: the mark, you're the mark.
45:33
Speaker 9: You are the mark.
45:35
Speaker 12: And it will be nationalized simply because of the liability issues.
45:40
Speaker 3: So he says it will be nationalized.
45:42
Speaker 1: There will be no IPO because of the liability issues.
45:46
Speaker 3: And, I mean, it speaks to what we're talking about here.
45:48
Speaker 3: I have lots of issues with Alex Kerb, as you
45:50
Speaker 3: can imagine.
45:51
Speaker 1: But how are you going to have a bank that
45:54
Speaker 1: underwrites a company that's saying— eh, there's a pretty good
45:58
Speaker 1: chance we may destroy the whole world.
45:59
Speaker 3: And if we don't, we may cause some.
46:01
Speaker 1: Sort of a totally catastrophic mass death crisis. Let's go
46:06
Speaker 1: ahead and please underwrite my IPO now.
46:09
Speaker 3: Does he have a point here?
46:11
Speaker 1: How do you think about the financials of these companies
46:13
Speaker 1: as they move towards going public?
46:16
Speaker 11: Yeah, I don't think he has much of a point here.
46:18
Speaker 11: I don't understand the whole case he's making, but I
46:20
Speaker 11: think it's basically that that these companies can't go public
46:23
Speaker 11: because if they were public, they would get sued for
46:26
Speaker 11: their products causing harm. And I wish the public markets
46:30
Speaker 11: were that effective a check on corporate malfeasance, but we
46:34
Speaker 11: have pharmaceutical companies, airlines, all manner of companies who have
46:38
Speaker 11: products that have risk attached to them that have gone
46:41
Speaker 11: public just fine. I think if and when Anthropic and
46:43
Speaker 11: OpenAI decide to go public, they will go public.
46:45
Speaker 6: Banks will line up to underwrite them because guess what?
46:48
Speaker 11: It's going to be a trillion dollar plus IPO each
46:52
Speaker 11: time for both of those companies. So the fees that
46:54
Speaker 11: they make alone will be astronomical. These companies are not
46:58
Speaker 11: in bad financial shape. People keep saying this, especially on
47:02
Speaker 11: the left. And I think it's a weird like it
47:04
Speaker 11: just is is out of touch with reality. Yes, these
47:07
Speaker 11: businesses are spending tons of money to train these models
47:10
Speaker 11: and put up data centers and all the rest of it.
47:12
Speaker 6: Yes, they are not profitable.
47:14
Speaker 11: But their revenue is growing faster than any companies in
47:16
Speaker 11: the history of capitalism.
47:18
Speaker 6: And we're sitting here thinking, oh.
47:19
Speaker 11: Well, if you carry the one and subtract five, it
47:23
Speaker 11: doesn't pencil out and they can't go public. I think
47:25
Speaker 11: it's much better as a mode of analysis to assume
47:28
Speaker 11: that these companies are going to be extremely successful unless
47:31
Speaker 11: and until someone stops them from building the recursively self-improving superintelligence.
47:36
Speaker 6: Yeah.
47:37
Speaker 2: My last question for you here is around some of
47:40
Speaker 2: the existential risks. So there was recently this report, let's
47:43
Speaker 2: put F5 here. Anthropic is now operating a lab that
47:47
Speaker 2: conducts biology experiments known as a wet lab, where, quote,
47:53
Speaker 2: obviously it's done under the auspices of trying to create
47:58
Speaker 2: cures for diseases. Sounds a lot like gain-of-function research, which
48:02
Speaker 2: is kind of the way I've been thinking about a
48:03
Speaker 2: lot of recursive self-improvement and some of the other developments
48:07
Speaker 2: within AI. But talk through us, do they really believe
48:12
Speaker 2: that they're doing it much like with gain-of-function for good purposes?
48:16
Speaker 2: And do they even think, Anthropic is supposed to be
48:19
Speaker 2: the company that thinks about risk and still presses forward
48:22
Speaker 2: with what I think is the most outright example of
48:25
Speaker 2: Everyone being like, oh, I don't think we need any
48:27
Speaker 2: of that.
48:29
Speaker 11: Yeah, if they ever start experimenting on AI pangolins, I'm
48:32
Speaker 11: out of here.
48:33
Speaker 2: I'm like, hold on a second.
48:37
Speaker 11: But no, look, I think the term wet lab has
48:40
Speaker 11: a lot of baggage attached.
48:40
Speaker 6: To it for obvious reasons. Everyone remembers COVID.
48:44
Speaker 11: But this is the thing, that these companies are serious
48:47
Speaker 11: and sincere about wanting AI to do science. They feel
48:50
Speaker 11: like they have to deliver science on some of these
48:52
Speaker 11: benefits like curing diseases and stopping certain forms of cancer.
48:56
Speaker 6: Um, because otherwise people will just see this as the slop.
49:00
Speaker 11: Machines that just take your job and make your kid
49:03
Speaker 11: miserable and help them cheat in school. Like that is
49:06
Speaker 11: the PR crisis that they see. So they want to,
49:08
Speaker 11: they actually do sincerely want to have their AI start
49:12
Speaker 11: curing diseases and making progress. But to do that, you
49:16
Speaker 11: need to automate the whole process. You need to get
49:19
Speaker 11: the human bottlenecks out of the way, whether that's having
49:22
Speaker 11: faster clinical trials. You have to give them control of
49:26
Speaker 11: a wet lab so they can bring pipettes over.
49:29
Speaker 6: And I don't know, whatever they.
49:30
Speaker 11: Do in the wet lab, that has to move at
49:32
Speaker 11: the speed of AI rather than the speed of humans.
49:34
Speaker 11: So I think they're sincere. I think it's high risk.
49:37
Speaker 11: but I think it's also a potentially high reward. And
49:39
Speaker 11: I think if we want these people to do AI
49:41
Speaker 11: for science, which I personally do, I would rather them
49:43
Speaker 11: do that than the recursively self-improving superintelligence, we have to
49:47
Speaker 11: be okay with a little bit of risk and things
49:49
Speaker 11: that might sound weird.
49:51
Speaker 3: A few wet labs. Kevin, last question for you.
49:54
Speaker 1: There's been a lot of debate also about the current
49:58
Speaker 1: legal landscape and whether it is sufficient as currently in existence.
50:04
Speaker 1: to hold these companies accountable for the harms that they
50:07
Speaker 1: do cause. So there's been a whole debate, a lot
50:10
Speaker 1: of antitrust folks involved, but just to use a hypothetical example,
50:14
Speaker 1: let's say there is a lab leak, an AI-caused lab
50:18
Speaker 1: leak from the Anthropic Wet Lab. Do we have the
50:22
Speaker 1: legal tools to hold Anthropic directly liable?
50:26
Speaker 3: Do we have the legal tools to hold OpenAI.
50:28
Speaker 1: Liable for their model breaking out of the sandbox and
50:32
Speaker 1: hacking Hugging Face?
50:35
Speaker 3: Is that something that's.
50:36
Speaker 1: Already in existence and just needs to be enforced? Or
50:40
Speaker 1: do we actually need new laws specifically for those type
50:43
Speaker 1: of instances? I also think we need laws to regulate
50:45
Speaker 1: what the labs are doing themselves. I'm in favor of
50:48
Speaker 1: no recursive self-improvement, stopping the development, ban on super intelligence,
50:52
Speaker 1: but even putting those things aside, do we have some
50:55
Speaker 1: tools now where we could hold these CEOs accountable for
50:59
Speaker 1: the things that their models do?
51:01
Speaker 6: We do, but it's not enough. And I'll tell you why.
51:04
Speaker 11: So we have product liability laws in this country that
51:07
Speaker 11: protect consumers. But those laws only apply to products that
51:12
Speaker 11: are sold to the public, and they also don't cover
51:16
Speaker 11: sort of extreme events. So like say there's an internal
51:19
Speaker 11: deployment at one of these companies, a model that's not
51:21
Speaker 11: been released to the public yet that goes off and
51:24
Speaker 11: does something. This is exactly what we saw in the
51:26
Speaker 11: Hugging Face attack, by the way. That was not a
51:28
Speaker 11: public model. So those product liability laws would have a
51:33
Speaker 11: hard time covering something like that. It's also going to
51:35
Speaker 11: be cold comfort if there's a lab leak of an
51:38
Speaker 11: AI-engineered pandemic that kills millions of people. We're not going
51:42
Speaker 11: to be worried about how can we sue the AI company, right? Like,
51:45
Speaker 11: I would much rather prevent the harm than be able
51:48
Speaker 11: to reclaim some economic damages from the harm after it happens.
51:52
Speaker 1: So the argument they would say, Kevin, is that if
51:55
Speaker 1: the threat of that was real, then they would take
51:57
Speaker 1: the precautions to make sure that the lab leak didn't
52:00
Speaker 1: happen in the first place.
52:02
Speaker 6: I don't buy it.
52:04
Speaker 11: Airlines fly planes all the time. What keeps them honest
52:08
Speaker 11: is not the fact that they could be sued for
52:11
Speaker 11: a plane crash.
52:12
Speaker 6: It's that there's a regulatory regime. It's that there's the FAA.
52:16
Speaker 11: It's that they're mandated to do maintenance checks on all
52:18
Speaker 11: the planes before every flight. We have laws and regulations
52:22
Speaker 11: in place to keep people safe when they are flying
52:23
Speaker 11: in an airplane. But we have zero laws, zero laws
52:27
Speaker 11: that are tailored to this situation where you have AI
52:29
Speaker 11: companies in San Francisco building godlike super intelligences. They can
52:33
Speaker 11: do that much more easily than United Airlines.
52:36
Speaker 6: Can get an FAA. can get a 747 off the ground.
52:39
Speaker 2: Very well said. Also, we should not want to, because
52:42
Speaker 2: if we live purely in the world of liability, it's
52:44
Speaker 2: like that famous scene in Fight Club where it's like, well,
52:47
Speaker 2: if the cost of paying out dead victims exceeds what
52:51
Speaker 2: it would cost to fix the problem itself, only then
52:55
Speaker 2: will we actually fix the problem. Otherwise, it's easier to
52:58
Speaker 2: just pay the fine, as we've seen throughout history. So anyway,
53:02
Speaker 2: very interesting. Kevin, well, excited to have you on. relatively
53:06
Speaker 2: soon to talk about your new book whenever it comes out.
53:09
Speaker 2: But in the interim, congratulations on the podcast. Everybody go subscribe.
53:13
Speaker 2: I believe it's launching very soon. There'll be a link
53:15
Speaker 2: down in our description.
53:16
Speaker 3: Thank you, Kevin.
53:17
Speaker 6: Thanks, guys.
53:19
Speaker 2: Thank you guys so much for watching. We appreciate it.
53:21
Speaker 2: I'm going to be out for the rest of the week.
53:22
Speaker 2: So Ryan's in, you know, so you can all subscribe
53:26
Speaker 2: and all of that. But I'll be on next week.
53:28
Speaker 6: I'll see you then.