Latest / Breaking Points with Krystal and Saagar / 9/29/26: OpenAI Pulls Model Over Safety Concerns, Morning Joe Admits Bibi Knew Oct 7 Was Coming, DraftKings VIP For Undercover Addicts

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9/29/26: OpenAI Pulls Model Over Safety Concerns, Morning Joe Admits Bibi Knew Oct 7 Was Coming, DraftKings VIP For Undercover Addicts · Breaking Points with Krystal and Saagar

• 0:00
  Speaker 1: Hey guys, Ready or Not 2024 is here and we
• 0:04
  Speaker 1: here at Breaking Points are already thinking of ways we
• 0:06
  Speaker 1: can up our game for this critical election.
• 0:08
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• 0:20
  Speaker 2: But enough with that, let's get to the show.
• 0:25
  Speaker 1: There's, as usual, a lot going on in the wild
• 0:28
  Speaker 1: world of AI, and we're happy to be joined by
• 0:30
  Speaker 1: Garrison Lovely, who is a journalist focused on these things
• 0:33
  Speaker 1: and author of the new book that is actually out now.
• 0:36
  Speaker 1: It is called Obsolete, the AI Industry's Trillion-Dollar Race to
• 0:40
  Speaker 1: Replace Us and How to Stop It. This book is
• 0:42
  Speaker 1: literally flying off the shelves. Garrison, we will put a
• 0:46
  Speaker 1: link in the description of the best place to find
• 0:48
  Speaker 1: it at this point because it's actually out of stock
• 0:50
  Speaker 1: in a number of places, so... sign of how much
• 0:53
  Speaker 1: interest there is in the topic in general, in this
• 0:56
  Speaker 1: book in particular. So congrats and great to see you, Garrison.
• 0:59
  Speaker 3: Thank you. And we are working on getting more copies, everybody, ASAP.
• 1:02
  Speaker 4: I'm sure that you are.
• 1:03
  Speaker 1: I'm sure that your people are certainly working on that.
• 1:07
  Speaker 1: We want to get to some of the specifics of
• 1:08
  Speaker 1: the book, which focuses on the threat to, as the
• 1:12
  Speaker 1: title implies, make human labor obsolete. But I wanted to
• 1:16
  Speaker 1: start with some of the latest news here with regard
• 1:18
  Speaker 1: to OpenAI, if we can put C1B up on the screen.
• 1:22
  Speaker 1: This is a pretty drastic move that they are making.
• 1:26
  Speaker 1: They are scrapping the release of of their new AI
• 1:30
  Speaker 1: model over safety concerns. They said they were having all
• 1:34
  Speaker 1: sorts of issues. They didn't get into too much specifics
• 1:36
  Speaker 1: around this latest model. Of course, we know a lot
• 1:39
  Speaker 1: of the safety incidents, not all, but a lot of
• 1:41
  Speaker 1: the safety incidents in recent months have originated from OpenAI.
• 1:47
  Speaker 1: And in addition, Sam Altman has announced that they have
• 1:49
  Speaker 1: not only scrapped this release, they have stopped training new
• 1:53
  Speaker 1: models internally at all. So what is going on here, Garrison?
• 1:57
  Speaker 1: Are we getting the full story about what is happening
• 2:00
  Speaker 1: internally at OpenAI?
• 2:01
  Speaker 5: Yeah, I mean, so the big picture is that OpenAI
• 2:04
  Speaker 5: has been racing even harder than usual to make models
• 2:08
  Speaker 5: more and more capable.
• 2:10
  Speaker 3: And they're using this technique called reinforcement learning.
• 2:12
  Speaker 5: To basically make the models better at winning, achieving the outcome,
• 2:16
  Speaker 5: routing around obstacles, whatever it takes. And this produces a
• 2:21
  Speaker 5: side effect of creating models that sometimes hack and cheat
• 2:24
  Speaker 5: and deceive to win. And now these models are better
• 2:27
  Speaker 5: than humans at hacking, right? And so they're able to
• 2:31
  Speaker 5: discover and exploit novel vulnerabilities in their environments.
• 2:35
  Speaker 3: And last week there was a model that was unreleased.
• 2:38
  Speaker 5: Maybe this one, actually, I think they said it was
• 2:40
  Speaker 5: not this one that hacked its way out of its
• 2:44
  Speaker 5: environment and got onto the internet again. And this was
• 2:48
  Speaker 5: caught much faster and they shut it down with it
• 2:49
  Speaker 5: a few hours, which is still a pretty long time
• 2:51
  Speaker 5: if you think about what these things can do in
• 2:53
  Speaker 5: that time.
• 2:54
  Speaker 2: True.
• 2:55
  Speaker 3: And so I think people there are pretty freaked out.
• 2:57
  Speaker 5: And it's like they spent months trying to make their
• 3:00
  Speaker 5: environments hardened for these AI models. But if they keep
• 3:03
  Speaker 5: having to kind of race them to patch all the holes,
• 3:09
  Speaker 5: which AIs, again, are better at finding than the humans,
• 3:12
  Speaker 5: then it creates like a really... difficult problem to get
• 3:15
  Speaker 5: useful work out of these without creating enormous amounts of risk.
• 3:18
  Speaker 2: Garrison, we have another report. Let's put C2 up here
• 3:22
  Speaker 2: on the screen, where these top AI researchers from multiple
• 3:26
  Speaker 2: companies have called for urgent oversight of self-improving systems. So
• 3:30
  Speaker 2: this was a paper. It was published yesterday by OpenAI's
• 3:34
  Speaker 2: chief scientist, Anthropix co-founder, Microsoft's chief scientist, and Meta's vice president,
• 3:40
  Speaker 2: of AI. They write, automating AI research would set off
• 3:43
  Speaker 2: an intelligence explosion. Such a development would compress years of
• 3:47
  Speaker 2: progress into months and outpace humans' ability to understand it.
• 3:51
  Speaker 2: Writing in personal capacity, the paper's author said policymakers should
• 3:54
  Speaker 2: urgently demand more visibility into how far companies have already
• 3:59
  Speaker 2: automated their own model development. Can you translate some of
• 4:02
  Speaker 2: this for us?
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  Speaker 5: So the industry is racing to build these systems that
• 4:07
  Speaker 5: can improve themselves and train the next generation of AI models.
• 4:11
  Speaker 5: And for a long time, people have theorized that this
• 4:14
  Speaker 5: could kick off, as I call it, an intelligence explosion,
• 4:17
  Speaker 5: where it loops and continues making better and better AI models,
• 4:21
  Speaker 5: compressing potentially years or decades of AI progress in a
• 4:25
  Speaker 5: span of months or weeks. And this has been the
• 4:28
  Speaker 5: explicit goal of the industry. And recently, we've now seen
• 4:31
  Speaker 5: more calls to regulate or even just stop this. And
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  Speaker 5: I think we should not do this at all. I
• 4:36
  Speaker 5: don't think there's a safe way to do this anytime
• 4:38
  Speaker 5: soon or probably ever. And so it's encouraging to see
• 4:42
  Speaker 5: more attention called to it. And of course, people will
• 4:45
  Speaker 5: say they're just advertising, right? Because this idea is a
• 4:48
  Speaker 5: great way to get out of the economic problems that
• 4:51
  Speaker 5: these companies have. If you can get to this magical
• 4:54
  Speaker 5: point where the AI can just improve itself and become
• 4:57
  Speaker 5: massively more efficient and performant, then the training costs can
• 5:02
  Speaker 5: come down and the prices can go up. But it's
• 5:06
  Speaker 5: also worth noting that Jeffrey Hinton, Joshua Bengio, two of
• 5:09
  Speaker 5: the godfathers of deep learning, and the two most cited
• 5:12
  Speaker 5: scientists in the world were also on this paper. as
• 5:14
  Speaker 5: well as a number of independent academics. So I think
• 5:17
  Speaker 5: the concern from many people on this, and probably most
• 5:19
  Speaker 5: of the authors, is quite sincere. And there are strong
• 5:23
  Speaker 5: theoretical reasons to believe that recursive self-improvement would actually result
• 5:27
  Speaker 5: in something pretty remarkable and quite dangerous.
• 5:30
  Speaker 1: Yeah, well, given the fact they already don't have control
• 5:32
  Speaker 1: of their models or having to stop training or having
• 5:34
  Speaker 1: to pull models that they were ready to release, I
• 5:37
  Speaker 1: think supercharging that process and taking humans more out of
• 5:41
  Speaker 1: the loop, frankly, sounds like a completely insane thing to
• 5:43
  Speaker 1: do at this point. I want to turn more specifically
• 5:46
  Speaker 1: to the topics you tackle in your book, which, guys,
• 5:49
  Speaker 1: I have already recommended this book many times, but I
• 5:51
  Speaker 1: want to recommend it again. You get into the financial models,
• 5:55
  Speaker 1: which I think is an increasingly important question as anthropic
• 5:58
  Speaker 1: charges headlong into a potential IPO and open AI supposed
• 6:02
  Speaker 1: to follow quickly thereafter. There's a lot of discussion about
• 6:05
  Speaker 1: how good the finances of these companies are. Are they
• 6:08
  Speaker 1: real businesses? They're losing billions of dollars, all these sorts
• 6:10
  Speaker 1: of things. You get into that. You get into some
• 6:13
  Speaker 1: of the existential risk questions as well. But the focus
• 6:16
  Speaker 1: of the book is on the stated goal, so the
• 6:19
  Speaker 1: positive vision of AI in their view, which is they
• 6:23
  Speaker 1: can only justify these multi-trillion dollar valuations if they are, quote,
• 6:28
  Speaker 1: adding that level of productivity to companies. Well, what does
• 6:30
  Speaker 1: that mean? That means replacing all of human labor. And
• 6:33
  Speaker 1: they have out and out, and you used their own words,
• 6:36
  Speaker 1: come out and said, that is our goal. That's what
• 6:39
  Speaker 1: we want to accomplish. So you argue, I think quite compellingly,
• 6:43
  Speaker 1: we should take them at their word and we should
• 6:45
  Speaker 1: not allow them to try to move in that direction.
• 6:50
  Speaker 1: So just lay out your argument a little bit for people, Garrison,
• 6:53
  Speaker 1: that you also lay out in the book.
• 6:57
  Speaker 2: Yeah.
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  Speaker 5: So the basic idea is that there's this tiny group
• 7:00
  Speaker 5: of people backed by the best resourced organizations in the
• 7:03
  Speaker 5: world that's trying to render us obsolete.
• 7:06
  Speaker 3: And almost none of us want that to happen.
• 7:08
  Speaker 5: But we're letting it happen because we're less aware that
• 7:11
  Speaker 5: it's happening or because we don't think it'll work or
• 7:14
  Speaker 5: because we don't think we can stop them.
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  Speaker 3: But they are trying. It might work.
• 7:18
  Speaker 5: So the only surefire way to prevent it is if
• 7:21
  Speaker 5: we stop them. And the book explains how.
• 7:23
  Speaker 2: Something I'm curious about, Garrison as well, just kind of
• 7:26
  Speaker 2: throughout this, is with these unemployment claims, because they have
• 7:32
  Speaker 2: come out. many times about unemployment, but in kind of
• 7:37
  Speaker 2: similar to safety as language, they set it in a
• 7:39
  Speaker 2: vacuum when there were no political consequences. Now that there
• 7:43
  Speaker 2: are consequences, they're changing their tune. They're like, well, actually
• 7:46
  Speaker 2: it hasn't worked out as we as expected. I think
• 7:50
  Speaker 2: there is kind of something to that. And that, you know,
• 7:53
  Speaker 2: one of the common retorts I've seen to a lot
• 7:55
  Speaker 2: of the safety as language is these people are actually
• 7:58
  Speaker 2: not very good at forecasting their own technology. What do
• 8:02
  Speaker 2: you make of that?
• 8:04
  Speaker 5: I would say that the people who are best at
• 8:05
  Speaker 5: forecasting AI capabilities have been people from the AI safety community.
• 8:11
  Speaker 5: So like Ryan Greenblatt and Ajay Akotra, two of the
• 8:13
  Speaker 5: investigators on the meter report into the hugging face hacks.
• 8:17
  Speaker 5: I think we're the top two people out of 400
• 8:19
  Speaker 5: or so predicting AI progress. And experts, I was on
• 8:24
  Speaker 5: an expert panel, we all underrated AI progress, except for
• 8:27
  Speaker 5: these two folks. It's sometimes hard to translate that progress
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  Speaker 5: into real world impact.
• 8:34
  Speaker 3: Right.
• 8:34
  Speaker 5: Like benchmark scores and real world utility are not the
• 8:37
  Speaker 5: same thing.
• 8:39
  Speaker 3: But it is true that we have not.
• 8:41
  Speaker 5: Seen macro job loss in the way that Dario Amadei
• 8:45
  Speaker 5: predicted might have started happening by now. And, you know,
• 8:48
  Speaker 5: he said one to five years when he said that
• 8:51
  Speaker 5: 50 percent of entry level white collar work could go away.
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  Speaker 3: And the thing I come to in the book is
• 8:56
  Speaker 3: like this could happen very quickly.
• 8:58
  Speaker 2: Right.
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  Speaker 3: It might not just be this gradual thing.
• 9:01
  Speaker 5: AI progress, if you look at a chart, is this
• 9:03
  Speaker 5: smooth exponential, but we experience it in fits and starts.
• 9:07
  Speaker 5: So the AI goes from not being able to code
• 9:10
  Speaker 5: well enough to use it to being faster and better
• 9:13
  Speaker 5: than the typical human to AI can't hack things very
• 9:17
  Speaker 5: well to AI is now better at hacking than humans are.
• 9:21
  Speaker 5: And so, you know, engines were worse than horses for
• 9:24
  Speaker 5: a long time, but they steadily got better. At some point,
• 9:27
  Speaker 5: there's a tipping point. And then horses dropped. And so
• 9:31
  Speaker 5: jobs could be the same thing where you can not
• 9:33
  Speaker 5: do the job end to end. And then suddenly the
• 9:36
  Speaker 5: AI becomes reliable enough to actually do those jobs. And
• 9:39
  Speaker 5: then like everybody's at risk if you can work from
• 9:42
  Speaker 5: a remote desk and there's not a premium on a
• 9:45
  Speaker 5: human being being on the other end of the line.
• 9:47
  Speaker 1: So let me get you to respond specifically to David
• 9:50
  Speaker 1: Sachs on this point, who's tying together, you know, he
• 9:53
  Speaker 1: wants to knock down the existential risk claims. And he's
• 9:56
  Speaker 1: also trying to knock down the unemployment, the obsoleting unemployment.
• 10:00
  Speaker 2: Claims.
• 10:00
  Speaker 4: Let's go ahead and put this up on the screen, C3.
• 10:02
  Speaker 1: He says, last year, the same labs, politicians, NGOs, and
• 10:06
  Speaker 1: media outlets now pushing the extinction hoax were running a
• 10:10
  Speaker 1: full-scale campaign that AI would cause a jobs apocalypse. We
• 10:13
  Speaker 1: were told it would be a white-collar bloodbath, 10% to 20% unemployment,
• 10:17
  Speaker 1: half of entry-level jobs gone. Obama amplified it. Over a
• 10:21
  Speaker 1: year later, unemployment is at 4.1%, no economy-wide displacement, a
• 10:26
  Speaker 1: million new jobs created by AI, according to The Economist.
• 10:30
  Speaker 1: Anthropic's own chief economist says the data looks like skill augmentation,
• 10:33
  Speaker 1: not job destruction. So any admission by the Doomer Labs
• 10:36
  Speaker 1: politicians and NGOs that they were wrong, any humility about
• 10:39
  Speaker 1: scaring the public with wild extrapolations dressed up as forecasts?
• 10:43
  Speaker 6: Nope.
• 10:43
  Speaker 1: They just move on to the next hoax with the
• 10:45
  Speaker 1: same false certitude and fake precision.
• 10:48
  Speaker 4: Garrison, your thoughts.
• 10:49
  Speaker 5: Well, first, I'm responding to David Sachs' AI because Pangram
• 10:52
  Speaker 5: flagged it as AI.
• 10:53
  Speaker 2: All right, but Pangram's kind of bullshit.
• 10:57
  Speaker 3: No, Pangram's real. One in 10,000 false negative rate.
• 11:01
  Speaker 2: I don't know. Or false positive, sorry. I've seen it
• 11:03
  Speaker 2: flag things just because if it mimics human rights.
• 11:07
  Speaker 1: Okay, but this sounds like AI in fairness.
• 11:09
  Speaker 3: It's definitely AI. I will bet money on this one.
• 11:12
  Speaker 6: Yeah, yeah.
• 11:13
  Speaker 2: But he's pro-AI, so not a hypocrite. Yeah, it's not hypocritical.
• 11:16
  Speaker 4: He's pro-AI, so there you go.
• 11:18
  Speaker 2: I used to edit my stuff sometimes. Sorry. Hands off.
• 11:21
  Speaker 2: It's true.
• 11:23
  Speaker 5: And so, yeah, I mean, Dario Amadei's claim that 10%
• 11:27
  Speaker 5: to 20% white-collar unemployment was in one to five years,
• 11:30
  Speaker 5: and that was a bit over a year ago. So
• 11:32
  Speaker 5: we're in that window. It has not really been falsified.
• 11:35
  Speaker 5: And like I said, it could happen much faster than
• 11:37
  Speaker 5: people expect. The data is showing less people getting fired
• 11:41
  Speaker 5: or laid off because of AI and more people not
• 11:44
  Speaker 5: getting hired in the first place. So young workers in
• 11:48
  Speaker 5: AI-exposed jobs like software engineers, they're just down 20% roughly
• 11:55
  Speaker 5: relative to other jobs, those openings. And so people are
• 11:59
  Speaker 5: finding jobs elsewhere. And the economy is, from a jobs perspective,
• 12:03
  Speaker 5: about as good as it's ever been. but, you know,
• 12:08
  Speaker 5: Opus 5.5 just came out and just seeing what it
• 12:11
  Speaker 5: can do in rendering video and motion graphics. Like somebody's like,
• 12:15
  Speaker 5: come up with a really good sizzle reel if you're
• 12:18
  Speaker 5: like a motion designer.
• 12:20
  Speaker 3: And what it produced in, I don't know, 20 minutes
• 12:22
  Speaker 3: looked very impressive.
• 12:24
  Speaker 5: I'm not somebody who works in this space, but if
• 12:27
  Speaker 5: I saw somebody's motion reel on their website and they
• 12:29
  Speaker 5: did that, I'd be like, wow, this.
• 12:30
  Speaker 3: Person's really good.
• 12:32
  Speaker 5: And so I think, just going to see more and
• 12:35
  Speaker 5: more capabilities come online with these models and like this
• 12:39
  Speaker 5: is not anthropics main focus right they're trying to make
• 12:42
  Speaker 5: coding agents and agents that can do ai r d
• 12:45
  Speaker 5: as fast as possible to kick off the recursive self-improvement
• 12:48
  Speaker 5: we talked about but it can now also just make
• 12:52
  Speaker 5: videos from scratch not using diffusion models but just literally
• 12:55
  Speaker 5: coding the video and the audio and i feel like
• 12:59
  Speaker 5: that will have implications on the job market and you
• 13:02
  Speaker 5: know people have been wrong again at projecting how capabilities
• 13:07
  Speaker 5: translate to real-world impacts.
• 13:10
  Speaker 3: But as these systems get.
• 13:11
  Speaker 5: More capable, more reliable, more autonomous, they'll get closer and
• 13:15
  Speaker 5: closer to being able to fully replace those jobs.
• 13:19
  Speaker 1: Can you address the radiologist question here? Because this was
• 13:24
  Speaker 1: a sort of infamous prediction of Jeffrey Hinton, who's considered
• 13:27
  Speaker 1: one of the godfathers of AI, who also has been
• 13:29
  Speaker 1: out sounding the alarm about what he sees as existential
• 13:32
  Speaker 1: risk here. And he had predicted that this would be
• 13:34
  Speaker 1: one of the first careers to be fully automated because
• 13:37
  Speaker 1: the AI is already very good at doing the technical
• 13:41
  Speaker 1: job of a radiologist. And in fact, what you've seen
• 13:44
  Speaker 1: is this augmentation where there was more demand for radiologists
• 13:48
  Speaker 1: than previously the market could meet. And so you've actually
• 13:50
  Speaker 1: had more radiologists hired now that they can use these
• 13:54
  Speaker 1: AI tools to help.
• 13:55
  Speaker 4: Them in their work.
• 13:56
  Speaker 1: So does this disprove the idea that AI is going
• 14:00
  Speaker 1: to displace a significant amount of the workforce?
• 14:04
  Speaker 5: This is the quintessential example of an overconfident prediction that
• 14:08
  Speaker 5: mistakes are technical capability in a specific task with being
• 14:12
  Speaker 5: able to replace an entire job. I think Hinton just
• 14:15
  Speaker 5: wasn't familiar enough with what radiologists do. I don't think
• 14:20
  Speaker 5: it disproves the general point. There are plenty of jobs.
• 14:24
  Speaker 5: About half of the US economy are jobs that can
• 14:26
  Speaker 5: be done from a remote desk. If you could keep
• 14:29
  Speaker 5: doing your job during COVID, then you're at risk of
• 14:32
  Speaker 5: being automated by AI models that can use computers. And
• 14:36
  Speaker 5: radiologists were just never that type of job. And so
• 14:39
  Speaker 5: it's a mistake. And I think it's fair to criticize
• 14:42
  Speaker 5: Hinton for it. And he has had a mea culpa
• 14:45
  Speaker 5: about it. And in general, I think we should take
• 14:49
  Speaker 5: all of these claims with a grain of salt. Dario
• 14:51
  Speaker 5: Amadei is not an economist. Jeffrey Hinton is not an economist.
• 14:56
  Speaker 5: But the economists have also just been very wrong about AI.
• 14:59
  Speaker 5: And almost everybody has dramatically underrated how quickly it would improve.
• 15:04
  Speaker 5: And it's just incredibly interdisciplinary question of like the technical piece,
• 15:10
  Speaker 5: how that fits into the political economy and.
• 15:13
  Speaker 3: How society reacts to it.
• 15:15
  Speaker 5: Because I think a lot of people did not expect
• 15:18
  Speaker 5: the backlash that we're seeing to AI and data centers
• 15:21
  Speaker 5: in particular to get this big, this fast. Like I
• 15:23
  Speaker 5: expected people to wake up politically, but on a longer timeline.
• 15:27
  Speaker 5: And so the world is just becoming harder and harder
• 15:30
  Speaker 5: to understand and predict.
• 15:34
  Speaker 2: There's a lot of discussion around Muse, you know, the
• 15:37
  Speaker 2: personal agent that's been released by Meta. It's been enormously
• 15:39
  Speaker 2: successful as a product, at least so far, one of
• 15:42
  Speaker 2: the most downloaded apps on the app store. I saw
• 15:45
  Speaker 2: a great joke, which kind of encapsulated everything. They're like,
• 15:48
  Speaker 2: Muse wants you to automate all of your annoying tasks
• 15:51
  Speaker 2: so you can spend your time doing what you value most,
• 15:53
  Speaker 2: watching short form video content, which is true. I mean, empirically,
• 15:58
  Speaker 2: if you go on your screen time for the vast
• 16:00
  Speaker 2: majority of Americans, you have the time to do basically anything.
• 16:04
  Speaker 2: you're rotting your brain on short-form video content. I do
• 16:07
  Speaker 2: it sometimes too. I get it.
• 16:08
  Speaker 4: It's relaxing. More Twitter scrolling in my case.
• 16:10
  Speaker 2: Or doom scrolling. Doom scrolling is like reading in the
• 16:14
  Speaker 2: equivalent of modern, you know, in terms of doom scrolling,
• 16:18
  Speaker 2: Twitter is like what reading used to be. And then
• 16:20
  Speaker 2: watching short-form video content is what watching a movie or
• 16:23
  Speaker 2: like a well-put-together television show used to be. Both, you know,
• 16:27
  Speaker 2: bad for the brain. That's one thing I'm curious, you know,
• 16:30
  Speaker 2: for your reaction, Garrison. I know you differ a little
• 16:32
  Speaker 2: bit politically, but what if it's neither job loss and non-extinction,
• 16:38
  Speaker 2: and yet it just amplifies everybody to become even more
• 16:42
  Speaker 2: addicted to their phone and watching as much short-form video
• 16:46
  Speaker 2: content as possible? Like having it book your flights, having
• 16:48
  Speaker 2: it order your groceries, having it create this like, you know, very,
• 16:53
  Speaker 2: this like automated thing existence of luxury phone usage where
• 16:57
  Speaker 2: you can just sit at home, do nothing. Yeah. Be,
• 17:00
  Speaker 2: be Wally. Have it order your door dash delivery. So
• 17:03
  Speaker 2: you can just sit there and scroll, scroll potentially with
• 17:06
  Speaker 2: your neural link chip. You can just sit there and
• 17:08
  Speaker 2: even scroll with your eyes. What do you think of
• 17:10
  Speaker 2: that possibility?
• 17:11
  Speaker 3: I mean, it sounds chillingly plausible.
• 17:14
  Speaker 2: Um, that's my median. That's what I think is going
• 17:16
  Speaker 2: to happen.
• 17:17
  Speaker 3: Yeah.
• 17:17
  Speaker 2: Yeah.
• 17:17
  Speaker 5: No, I think this like promise of a friction free
• 17:20
  Speaker 5: life is what Silicon Valley is selling in many ways.
• 17:23
  Speaker 5: And it's, sounds appealing in some sense, but also like
• 17:27
  Speaker 5: friction is what makes life interesting. And I'm worried that
• 17:31
  Speaker 5: people are going to just have fewer and fewer social
• 17:33
  Speaker 5: interactions as they already are, as we do more things
• 17:36
  Speaker 5: like order from apps and order our groceries delivered and
• 17:40
  Speaker 5: just do everything through the phone or computer as we
• 17:43
  Speaker 5: become more and more miserable.
• 17:47
  Speaker 3: Yeah, it's really concerning. I don't think it's at odds
• 17:49
  Speaker 3: with the labor stuff.
• 17:50
  Speaker 5: And I think the obsoleting project crystal, you talked about
• 17:53
  Speaker 5: it as like replacing our jobs. And that's definitely the
• 17:56
  Speaker 5: main thing that I, you know, I'm thinking about here
• 17:58
  Speaker 5: in addition to the risks of creating machines that can
• 18:01
  Speaker 5: fully replace our labor.
• 18:03
  Speaker 3: But I think it's more than just jobs.
• 18:05
  Speaker 5: It's like replacing the creative output of humans and replacing
• 18:10
  Speaker 5: all of these interactions that we would normally have had
• 18:12
  Speaker 5: to had to do, you know, daily life and creating
• 18:17
  Speaker 5: like a thing where you are just interacting with a
• 18:20
  Speaker 5: machine or your machines are interacting with other machines to
• 18:23
  Speaker 5: make your life as easy as possible.
• 18:25
  Speaker 3: And you just don't even think about it anymore. And
• 18:28
  Speaker 3: that seems kind of dystopian.
• 18:31
  Speaker 5: In addition to creating all these risks as these agents
• 18:33
  Speaker 5: are interacting with each other and you have your own
• 18:36
  Speaker 5: little swarm interacting with all the other swarms doing who
• 18:38
  Speaker 5: knows what and having all kinds of unpredictable and undesirable
• 18:43
  Speaker 5: effects on the world. as everything becomes optimized for your
• 18:47
  Speaker 5: attention and your money. And yeah, I'm like increasingly concerned
• 18:52
  Speaker 5: about this as well.
• 18:54
  Speaker 1: Put C4 up on the screen. So I threw some
• 18:56
  Speaker 1: things at you that challenge your thesis about the likelihood
• 19:00
  Speaker 1: of labor replacement. But this seems really to support your
• 19:03
  Speaker 1: thesis that you are going to see significant labor replacement.
• 19:06
  Speaker 1: And it's showing up in a place that I think, again,
• 19:09
  Speaker 1: was not predicted by really anyone. You see it, as
• 19:12
  Speaker 1: you said, among young people graduating from college in the
• 19:15
  Speaker 1: most AI-exposed professions.
• 19:17
  Speaker 4: You also see it in employment in the arts, right?
• 19:20
  Speaker 1: So the full slopification of the arts landscape appears to
• 19:25
  Speaker 1: be well underway. Over the last four years, America's lost
• 19:29
  Speaker 1: more than 200,000 jobs in creative fields like publishing, film, TV,
• 19:33
  Speaker 1: graphic design. We're stretched for arts jobs outside of major recessions.
• 19:39
  Speaker 1: He goes on in another tweet to say perhaps the
• 19:41
  Speaker 1: best evidence that this is AI-driven arts job loss is
• 19:44
  Speaker 1: that all digital arts industries are losing jobs while most
• 19:47
  Speaker 1: live arts still chug along. So if you're doing a
• 19:52
  Speaker 1: theatrical production, performing arts, those things are still continuing at
• 19:57
  Speaker 1: the same rate. But if you're doing graphic design, if
• 20:00
  Speaker 1: you're doing streaming and social media, digital arts are falling
• 20:04
  Speaker 1: off a cliff. So, you know, to me, again, this
• 20:08
  Speaker 1: is an indication that AI will replace people, is already
• 20:13
  Speaker 1: replacing people, and that it's starting in an area where
• 20:16
  Speaker 1: you would think that the human touch would be most valued,
• 20:21
  Speaker 1: and yet they're the first ones to be jettisoned.
• 20:25
  Speaker 4: Yeah.
• 20:25
  Speaker 5: I mean, if we think about like market testing focus
• 20:28
  Speaker 5: groups for movies and different content online and like the
• 20:32
  Speaker 5: optimization of short form video, With generative AI, you can
• 20:36
  Speaker 5: optimize every piece of it. There are these fully automated
• 20:39
  Speaker 5: TikTok accounts that are doing like A-B testing where they
• 20:43
  Speaker 5: try different versions of the video, but for not just
• 20:46
  Speaker 5: two versions, but like any number of things. And so
• 20:49
  Speaker 5: you can create this like perfect slop, right? And that
• 20:55
  Speaker 5: is hard to compete with if you're a human creator.
• 20:57
  Speaker 5: And I think for certain types of things like film
• 21:01
  Speaker 5: and TV shows and like the kind of, biggest budget
• 21:04
  Speaker 5: and the highest effort and the most like auteur type
• 21:07
  Speaker 5: media humans will have an advantage here but it's already
• 21:11
  Speaker 5: the case that people really prefer ai content in almost
• 21:15
  Speaker 5: everything like visual arts poetry writing like if you do
• 21:18
  Speaker 5: blind tests and when you tell them it's ai they
• 21:20
  Speaker 5: go like they're horrified in many cases but they're these
• 21:24
  Speaker 5: systems are trained to please people And so it makes
• 21:27
  Speaker 5: sense that they would be good at generating stuff that
• 21:29
  Speaker 5: pleases people and be better than us at it. Because
• 21:33
  Speaker 5: humans make stuff that's like a little challenging sometimes and unexpected. And,
• 21:37
  Speaker 5: you know, some people like that, but most people just want,
• 21:40
  Speaker 5: you know, the kind of easy stuff.
• 21:42
  Speaker 4: Yeah. Well, and humans make plenty of slop too.
• 21:44
  Speaker 1: I mean, if you go and like scroll on Netflix,
• 21:46
  Speaker 1: it's not like it's all highbrow.
• 21:47
  Speaker 2: Literally what I'm doing while you guys are speaking is
• 21:50
  Speaker 2: I said, because I saw a tweet today and it said,
• 21:54
  Speaker 2: AI music is going to displace all music within the
• 21:57
  Speaker 2: next two years. Super hyperbolic. Somebody made a good point.
• 22:01
  Speaker 2: They're like, guys, the number one music in the country
• 22:04
  Speaker 2: is like Taylor Swift and Pink. All right? It's not
• 22:06
  Speaker 2: that hard to reverse engineer in terms of, look, I
• 22:10
  Speaker 2: like Taylor Swift. I like Pink, too, despite whatever her
• 22:13
  Speaker 2: politics and.
• 22:14
  Speaker 4: All that would be.
• 22:14
  Speaker 2: I've never liked Pink.
• 22:15
  Speaker 4: I just want to go on record as that.
• 22:16
  Speaker 2: She says that once.
• 22:16
  Speaker 4: I do like Taylor Swift, but not.
• 22:17
  Speaker 2: A fan of Pink. But I'm scrolling here. I'm looking
• 22:20
  Speaker 2: at Netflix, and this is the stuff people are watching. Unabomber,
• 22:23
  Speaker 2: which I'm obsessed with the Unabomber story. This is a
• 22:25
  Speaker 2: dog shit movie, like 23%, number one on Netflix. Number two,
• 22:29
  Speaker 2: Breadwinner with Nate Bargatze. Number three, I think this is
• 22:32
  Speaker 2: a Demon Slayer. I don't know if this is in
• 22:34
  Speaker 2: the K-pop. universe number four top gun maverick actually a
• 22:37
  Speaker 2: decent movie number five is the ministry of ungentlemanly warfare
• 22:41
  Speaker 2: dog shit knock in the cabin haven't even watched it
• 22:44
  Speaker 2: guarantee you isn't the.
• 22:45
  Speaker 1: Number one show in the country still that like ncis
• 22:47
  Speaker 1: i thought it was that live pd like oh is
• 22:50
  Speaker 1: it i know i.
• 22:51
  Speaker 2: Thought it was ncis uh like ncis.
• 22:53
  Speaker 4: Reality show garbage all.
• 22:55
  Speaker 2: That stuff yeah and then number 10 on netflix is
• 22:57
  Speaker 2: the puss in boots the last witch which i didn't
• 22:59
  Speaker 2: even know existed So, I mean, this is what people
• 23:02
  Speaker 2: are actually watching. Like, let's be honest, like, AI slop
• 23:06
  Speaker 2: is far, 90% of what most people consume already is
• 23:10
  Speaker 2: already slop. The Wire, The Sopranos, Breaking Bad, those are like,
• 23:15
  Speaker 2: those are the outliers. That's what, and by the way,
• 23:18
  Speaker 2: not even that many people watch them. You know, they're
• 23:20
  Speaker 2: overrepresented in the elites and the highly educated, who mostly have,
• 23:24
  Speaker 2: you know, better taste, no offense. But, It's just true.
• 23:28
  Speaker 2: And you know, the stuff that most people watch is
• 23:30
  Speaker 2: law and order. Like it's just, it seems pretty obvious,
• 23:33
  Speaker 2: I think. So when I see, you know, there's a
• 23:36
  Speaker 2: lot of people in the arts who push back against
• 23:38
  Speaker 2: us and they're like, they could never do what we do.
• 23:41
  Speaker 2: I just don't think that that's fundamentally true. And I
• 23:43
  Speaker 2: think that that, you know, the Netflix point I just
• 23:45
  Speaker 2: made bears it out. People watch literal shit just to
• 23:51
  Speaker 2: fill big portions of their day. And that's obvious in
• 23:54
  Speaker 2: the number one hits on Netflix on a, you know,
• 23:57
  Speaker 2: year over year basis. And in terms of their watch time.
• 24:01
  Speaker 3: Yeah. I think intentionality is an essential part of what
• 24:04
  Speaker 3: makes something art.
• 24:05
  Speaker 2: Yeah.
• 24:06
  Speaker 3: But in many of these cases, there's not much intentionality, right?
• 24:08
  Speaker 5: It's like executives being what will people watch and they're
• 24:11
  Speaker 5: looking at the data and it's, there's not much like
• 24:13
  Speaker 5: human creative input that that's happening here. Um, and I
• 24:18
  Speaker 5: think something that's been kind of strange is watching like
• 24:21
  Speaker 5: these Opus 5.5 videos with a very minimal prompt. just
• 24:24
  Speaker 5: make a lot of choices and then not producing something bad. Like,
• 24:29
  Speaker 5: and people might roast me for this, but just search
• 24:31
  Speaker 5: like Opus 5.5 in Twitter or some other place and
• 24:34
  Speaker 5: just like watch a few of the things and like,
• 24:37
  Speaker 5: just tell me what you think, because I did not
• 24:40
  Speaker 5: expect them to be able to do this anytime soon.
• 24:43
  Speaker 5: And like this being like generating these music videos from
• 24:47
  Speaker 5: scratch that like sync up and, you know, produce like
• 24:51
  Speaker 5: all these graphics that are relevant to the thing and, and,
• 24:54
  Speaker 5: making jokes.
• 24:55
  Speaker 3: And like, there was one that's like, what's your TikTok feed?
• 24:57
  Speaker 3: And it's like for a language model.
• 24:58
  Speaker 5: And, you know, I'm laughing at it because it's like
• 25:01
  Speaker 5: showing different, like getting reward for getting the right.
• 25:04
  Speaker 3: Answers on all the unit tests passing.
• 25:06
  Speaker 5: And it's like, that's like a nerdy joke that appeals
• 25:08
  Speaker 5: to me in particular and will appeal to most people
• 25:10
  Speaker 5: because it's like literally Claude making its own experience. And
• 25:14
  Speaker 5: so there's kind of like meta fiction or meta humor
• 25:17
  Speaker 5: type things are possible now.
• 25:19
  Speaker 3: And it's concerning.
• 25:21
  Speaker 5: I don't like it. It's bad that These things are
• 25:24
  Speaker 5: so good at this because I don't want humans to
• 25:27
  Speaker 5: be replaced by machines. And I don't want us to
• 25:29
  Speaker 5: be outcompeted by these like soulless corporate AIs that are
• 25:35
  Speaker 5: pretending to be nice and friendly and making very persuasive
• 25:37
  Speaker 5: propaganda as well.
• 25:39
  Speaker 3: Right. Like they're not just going to be talking to people.
• 25:41
  Speaker 3: They'll be making music videos about how like there's nothing
• 25:44
  Speaker 3: to worry about. This is all going to be fine.
• 25:45
  Speaker 3: Look at the pretty colors.
• 25:47
  Speaker 5: You know, it's it's kind of like, again, ahead of schedule.
• 25:51
  Speaker 1: Yeah, and that's another thing that I think people don't
• 25:54
  Speaker 1: realize either is this is the worst that they'll be.
• 25:57
  Speaker 1: There's sort of an assumption that it's going to, if
• 26:00
  Speaker 1: you have any sort of a refined eye, read as
• 26:02
  Speaker 1: slop forever.
• 26:03
  Speaker 4: I don't think that's true.
• 26:06
  Speaker 1: We already have, there's this novel in France that's like
• 26:09
  Speaker 1: won awards. that is at least allegedly AI written. And
• 26:13
  Speaker 1: it was like some highfalutin literary novel that a lot
• 26:18
  Speaker 1: of very refined taste type of people really loved. And
• 26:24
  Speaker 1: you put it into, what's the name of the thing
• 26:25
  Speaker 1: that detects the AI? You put it in there and
• 26:27
  Speaker 1: it's like 100% it's AI. So we're already there with
• 26:31
  Speaker 1: it's very difficult to distinguish. And I just, I think
• 26:35
  Speaker 1: people will in theory prefer human art, but we can
• 26:40
  Speaker 1: already see the numbers in terms of the market. Like
• 26:42
  Speaker 1: we've outsourced art to the market, just like we've outsourced
• 26:45
  Speaker 1: everything else to the market. So ultimately that's what's going
• 26:47
  Speaker 1: to determine how far this goes and how many will
• 26:50
  Speaker 1: be made obsolete. And so to me, that's such a
• 26:54
  Speaker 1: It's very powerful proof. This is not going to stay
• 26:57
  Speaker 1: with digital arts. It's not going to stay with coders
• 27:00
  Speaker 1: because already it's automating out of existence some of the
• 27:03
  Speaker 1: things that you would have thought humans would have had
• 27:06
  Speaker 1: a preference for the humans. And it's just being proven wrong.
• 27:11
  Speaker 1: I mean, I have no illusions about... Right now, I
• 27:13
  Speaker 1: think the AI podcasters aren't as good as we are,
• 27:16
  Speaker 1: but at certain things.
• 27:16
  Speaker 2: They are.
• 27:17
  Speaker 4: There was an AI podcast that went through all of
• 27:19
  Speaker 4: the Epstein files.
• 27:20
  Speaker 2: Yeah, I remember that.
• 27:21
  Speaker 4: And was able.
• 27:22
  Speaker 1: I mean, I don't have time to read tens of
• 27:24
  Speaker 1: thousands of Epstein emails. We did extensive coverage here.
• 27:28
  Speaker 3: Mm-hmm.
• 27:28
  Speaker 1: And their, you know, their AI podcasters were kind of
• 27:31
  Speaker 1: like dull and not as nearly as engaging as we are, obviously.
• 27:36
  Speaker 1: But in terms of their just ability to assimilate all
• 27:38
  Speaker 1: of this information, put it together in a coherent narrative,
• 27:41
  Speaker 1: I can't compete with that. So it is not mine. Like,
• 27:44
  Speaker 1: it's not hard for me to imagine a future in
• 27:46
  Speaker 1: which I'm competing. And on some senses, I already am
• 27:49
  Speaker 1: not just with other human creators, but that there's like robots, right?
• 27:54
  Speaker 1: who people have actual emotional attachments to who are in
• 27:59
  Speaker 1: this space and doing a very.
• 28:01
  Speaker 4: Good job at it.
• 28:02
  Speaker 2: Yeah.
• 28:02
  Speaker 5: I do think people will continue to prefer podcasts and
• 28:06
  Speaker 5: shows made by people where you can see and hear
• 28:09
  Speaker 5: the person's voice.
• 28:10
  Speaker 4: Yeah.
• 28:10
  Speaker 5: No, really like these videos, you know, when you see
• 28:12
  Speaker 5: a music video, you're not like looking at the person
• 28:15
  Speaker 5: who made it.
• 28:15
  Speaker 2: Right.
• 28:16
  Speaker 5: And so this parasocial relationship that these podcasts really are
• 28:20
  Speaker 5: based around in many cases is I think will keep
• 28:23
  Speaker 5: humans involved for longer.
• 28:26
  Speaker 3: And I think there will always be a market for
• 28:28
  Speaker 3: live music.
• 28:29
  Speaker 5: And I think we're going to see actually more preference
• 28:32
  Speaker 5: for that type of thing as the internet becomes increasingly
• 28:35
  Speaker 5: like you're looking at this amazing thing and it's like, wait,
• 28:37
  Speaker 5: is this real? And that feeling is, it just has
• 28:41
  Speaker 5: sucked the joy out of like viral videos. And it's
• 28:45
  Speaker 5: a small thing compared to all of the other shit.
• 28:47
  Speaker 5: But like, man, that's a bummer. And we didn't need
• 28:50
  Speaker 5: to let it happen.
• 28:51
  Speaker 2: All right, Garrison, you're going to stick around for us.
• 28:52
  Speaker 2: You're going to help us with our AMA. So let's
• 28:54
  Speaker 2: get to that.
• 28:58
  Speaker 1: Turning back to Israel, where Benjamin Netanyahu is facing a
• 29:01
  Speaker 1: very tough reelection bid amidst revelations about just how many
• 29:06
  Speaker 1: warnings he had received in advance of October 7th. This
• 29:10
  Speaker 1: news cycle is becoming so overwhelming that even Joe and
• 29:13
  Speaker 1: Mika over on Morning Joe are paying attention. Let's take
• 29:16
  Speaker 1: a listen to what they had to say.
• 29:17
  Speaker 7: If you remember when this all happened, this horrific historic event,
• 29:22
  Speaker 7: the border was wide open.
• 29:24
  Speaker 4: The gates were empty.
• 29:27
  Speaker 7: They had practically opened the door for this attack, or
• 29:30
  Speaker 7: at least moved security away from one of the main gates.
• 29:34
  Speaker 6: And in some instances, while women were being raped, is
• 29:40
  Speaker 6: were being gunned down, while children were being killed in
• 29:42
  Speaker 6: front of their parents and parents were being killed in
• 29:45
  Speaker 6: front of their children and the elderly were being tortured,
• 29:48
  Speaker 6: abused and killed. The government did little to nothing.
• 29:53
  Speaker 3: Hours.
• 29:54
  Speaker 6: It takes Netanyahu and his government.
• 29:56
  Speaker 8: 11 hours, 10 hours, nine hours to even respond to
• 30:02
  Speaker 8: their country being invaded by terrorists and their people being tortured.
• 30:07
  Speaker 2: The worst slaughter of Jews.
• 30:10
  Speaker 6: On any day since the Holocaust.
• 30:14
  Speaker 8: And Netanyahu was warned repeatedly that it was coming. And
• 30:18
  Speaker 8: yet he continued funding Hamas through Qatar.
• 30:23
  Speaker 1: Not all that surprising that they're going in on Netanyahu
• 30:26
  Speaker 1: because a lot of liberal Zionists have made him the
• 30:28
  Speaker 1: boogeyman of the situation. What is surprising is that a
• 30:31
  Speaker 1: few times now, including you could hear Mika kind of
• 30:34
  Speaker 1: hinting towards it there, they have sort of suggested that
• 30:37
  Speaker 1: he may have known and sort of allowed this to transpire.
• 30:41
  Speaker 1: So let's take a look at the recent evidence. We
• 30:43
  Speaker 1: have a new report out from Haaretz that provides even
• 30:46
  Speaker 1: more details about what went down here. So A number
• 30:50
  Speaker 1: of reports, the first one coming from Haaretz to begin with,
• 30:54
  Speaker 1: a liberal Israeli newspaper, said that the leader of the
• 30:57
  Speaker 1: UAE had warned Netanyahu multiple times, was upset that Netanyahu
• 31:02
  Speaker 1: hadn't taken it seriously. And then after the fact... Netanyahu
• 31:05
  Speaker 1: went to him and said, hey, you've got to deny
• 31:07
  Speaker 1: that this all went down in a secret visit. So
• 31:10
  Speaker 1: this is the latest from Haaretz. This builds on their
• 31:13
  Speaker 1: own previous report. We can also put D4 up on
• 31:17
  Speaker 1: the screen. This is The New York Times. They have
• 31:19
  Speaker 1: more information here.
• 31:21
  Speaker 4: About a warning.
• 31:22
  Speaker 1: This particular article is also about the warnings coming from
• 31:26
  Speaker 1: the UAE. They say that it lacks specific details, and
• 31:29
  Speaker 1: it never reached the chiefs of Israel's security establishment, meaning
• 31:33
  Speaker 1: that Netanyahu was warned and he did not take that
• 31:38
  Speaker 1: to the Israeli security establishment. He just kept it to himself.
• 31:43
  Speaker 1: Let's put the next one up on the screen. This
• 31:45
  Speaker 1: is another warning that he received. This is from the
• 31:47
  Speaker 1: Wall Street Journal. They say that Egypt also warned Netanyahu
• 31:52
  Speaker 1: of impending attacks on Israel before October 7th, again, saying that, hey,
• 31:56
  Speaker 1: we see activity here.
• 31:58
  Speaker 4: You need to take this seriously.
• 32:00
  Speaker 1: And they were disturbed by how little credence he seemed
• 32:03
  Speaker 1: to give to this.
• 32:05
  Speaker 4: Then you add on top of this, we.
• 32:07
  Speaker 1: Already knew, Sagar, that there had been, in 2022, the
• 32:11
  Speaker 1: Israeli intelligence community actually got their hands on the October
• 32:17
  Speaker 1: 7th plans— And they dismissed it because they were like,
• 32:20
  Speaker 1: I don't think Hamas would do that. I don't think
• 32:21
  Speaker 1: that they have the capability to do that. So they
• 32:23
  Speaker 1: didn't take it serious. So they had the specific plans.
• 32:26
  Speaker 1: He had warning from the leader of the UAE, warnings
• 32:28
  Speaker 1: from the leader of Egypt. There were those women who
• 32:31
  Speaker 1: were in charge of surveillance of Gaza who said, I
• 32:34
  Speaker 1: think they're planning something that's dismissed and discarded.
• 32:37
  Speaker 4: The IDF is.
• 32:39
  Speaker 1: Moved from near Gaza to the West Bank. So you
• 32:44
  Speaker 1: have very little security near the border with Gaza. You
• 32:48
  Speaker 1: have this music festival that's happening right there that was
• 32:51
  Speaker 1: not supposed to go on until October 7th. They had
• 32:53
  Speaker 1: to ask the IDF for an extension. And it's granted,
• 32:57
  Speaker 1: even as all of this concern about what's going on
• 33:00
  Speaker 1: with Hamas and Gaza is swirling. So people can be
• 33:03
  Speaker 1: forgiven for taking a look at this and going, listen,
• 33:06
  Speaker 1: this is either an incredible failure of competence, incredible arrogance
• 33:11
  Speaker 1: to think, you know, oh, my adversary here, they wouldn't
• 33:13
  Speaker 1: do this.
• 33:13
  Speaker 4: I know better. They're incapable, etc.
• 33:16
  Speaker 1: Or he allowed this to transpire because he thought it
• 33:20
  Speaker 1: would enable exactly the genocidal actions that he has taken.
• 33:24
  Speaker 1: So You can't blame people for putting two and two
• 33:27
  Speaker 1: together and thinking that may be the case. Now, I
• 33:29
  Speaker 1: think it's just as plausible that he had that level
• 33:32
  Speaker 1: of arrogance, that he ignored the warnings, that he didn't
• 33:34
  Speaker 1: take it seriously, that he didn't even bring it to
• 33:36
  Speaker 1: the Israeli intel chiefs. security chiefs to let them know
• 33:41
  Speaker 1: what these foreign leaders were talking about. But it shows
• 33:44
  Speaker 1: you how mainstream it has become that even on Morning Joe,
• 33:47
  Speaker 1: they're saying, yeah, this looks weird. Hillary Clinton even said
• 33:50
  Speaker 1: something to the same regard.
• 33:51
  Speaker 2: Right. Yeah, it does seem to be a little bit
• 33:53
  Speaker 2: of a cop out. Also, where's Donny Deutch to tell
• 33:56
  Speaker 2: me that that's anti-Semitic on that show?
• 33:59
  Speaker 1: I mean, can you imagine in the immediate wake of
• 34:02
  Speaker 1: October 7th when we were looking at some of these
• 34:05
  Speaker 1: things and going, what's going on here? I mean, there's
• 34:07
  Speaker 1: no way they would have freaked out if we like,
• 34:11
  Speaker 1: you know, went on their show and they.
• 34:12
  Speaker 2: Did any of it. They did freak out, of course.
• 34:14
  Speaker 4: Yeah.
• 34:14
  Speaker 2: Blacklisted from a lot of places because of some of
• 34:17
  Speaker 2: the commentary from that time. Not that I care, but
• 34:19
  Speaker 2: the point around all of it is, look, let's just
• 34:22
  Speaker 2: look at the incontrovertible evidence. And I remember, you know,
• 34:25
  Speaker 2: 9-11 is an easy comparison where and I think this
• 34:28
  Speaker 2: is all fair. If you go back and you look,
• 34:31
  Speaker 2: every single piece to stop 9-11 was in possession of
• 34:34
  Speaker 2: the United States government. You could even say Bush. I mean,
• 34:37
  Speaker 2: I remember when that got declassified in 05. It was
• 34:40
  Speaker 2: an earth-shaking moment. The intel report that reached Bush's desk,
• 34:45
  Speaker 2: I think it was like August 7th. I used to
• 34:48
  Speaker 2: have the date memorized. It was like August something, 2001.
• 34:50
  Speaker 2: Bin Laden determined to attack the United States, the very
• 34:53
  Speaker 2: top of the presidential list. intelligence briefing. That was enough
• 34:56
  Speaker 2: for most Americans to be like, oh my God, they knew.
• 34:58
  Speaker 2: Then the looming tower came out and you find out
• 35:01
  Speaker 2: all of the different pieces, every single piece there to stop.
• 35:04
  Speaker 2: I mean, the evidence for the government knew about 9-11,
• 35:07
  Speaker 2: either let it happen through incompetence or let it happen,
• 35:09
  Speaker 2: you know, et cetera, is far worse than the evidence
• 35:13
  Speaker 2: that Netanyahu knew everything he needed to know about October 7th.
• 35:17
  Speaker 2: He gets this call directly from MBZ, who by the way,
• 35:20
  Speaker 2: Netanyahu is there today, I believe. I mean, kind of
• 35:23
  Speaker 2: weird situation happening in the wake of all this. Of course,
• 35:27
  Speaker 2: everybody is denying the report. It appears that the Egyptians
• 35:31
  Speaker 2: knew a lot, and they're like, hey, you have a
• 35:33
  Speaker 2: real problem on your hands. And you can understand exactly why,
• 35:36
  Speaker 2: is in every time in the past when Hamas does
• 35:39
  Speaker 2: some sort of attack on Israel, they use it as
• 35:42
  Speaker 2: a casus bella to go in and do whatever they
• 35:44
  Speaker 2: want and to shore up the domestic I don't know
• 35:49
  Speaker 2: necessarily if they thought October 7th was going to go
• 35:52
  Speaker 2: as bad as they thought. I think they probably thought
• 35:54
  Speaker 2: it was going to be more like a median incursion
• 35:56
  Speaker 2: as opposed to as well planned.
• 35:58
  Speaker 4: But to say— I mean, even Hamas did not think
• 36:00
  Speaker 4: it would go the way.
• 36:00
  Speaker 2: That it would. Good point, is that even the planners
• 36:03
  Speaker 2: of the attack had no idea it would be as successful,
• 36:05
  Speaker 2: you know, by their standards as it would be. Although,
• 36:08
  Speaker 2: you know, in the long run, I think it was
• 36:09
  Speaker 2: obviously a disaster. But what happened is they thought that
• 36:13
  Speaker 2: it was a controllable mow-the-lawn situation. That's their paradigm. That's
• 36:17
  Speaker 2: their framework. And what it ended up unleashing was not
• 36:21
  Speaker 2: only a disaster for all of the 1,000 plus people
• 36:24
  Speaker 2: who were killed on October 7th, but then for the
• 36:28
  Speaker 2: hundreds of thousands of Palestinians either killed or wounded and
• 36:33
  Speaker 2: just setting the entire region on fire. And I do
• 36:36
  Speaker 2: think that this stuff matters. It matters because it's very obvious,
• 36:41
  Speaker 2: and it wouldn't be the very first time in history
• 36:42
  Speaker 2: that you knew something was going to happen And you
• 36:44
  Speaker 2: either let it through incompetence or potentially through basically just
• 36:49
  Speaker 2: wanting it to happen. and then you have to be
• 36:51
  Speaker 2: responsible for the consequences. As I understand it, this has
• 36:54
  Speaker 2: become a big controversy in Israel, actually. It's like, in Israel,
• 36:58
  Speaker 2: this is not going down. They're not whitewashing it the
• 37:01
  Speaker 2: way that a lot of people in the American press are.
• 37:03
  Speaker 1: Well, and Netanyahu has also, through starting various other wars,
• 37:08
  Speaker 1: blocked any action of investigation and accountability within Israel. There
• 37:14
  Speaker 1: was supposed to be an after-action report, because even on
• 37:17
  Speaker 1: the day of the response— Netanyahu was sleeping for the
• 37:20
  Speaker 1: first three hours.
• 37:22
  Speaker 4: It was complete chaos.
• 37:24
  Speaker 1: You know, there was very little organized military security response
• 37:29
  Speaker 1: to what was unfolding. And so even if you just
• 37:32
  Speaker 1: look at the day of, you're like, what the hell
• 37:34
  Speaker 1: happened here?
• 37:34
  Speaker 4: None of that has happened.
• 37:35
  Speaker 1: The only things we've gotten is from this sort of
• 37:38
  Speaker 1: journalistic investigation in terms of an official government report. That
• 37:41
  Speaker 1: has not occurred because Netanyahu, in the same way that
• 37:44
  Speaker 1: he pushes off indefinitely his corruption trial, has pushed off
• 37:46
  Speaker 1: a reckoning over that and has now solidified his political
• 37:50
  Speaker 1: standing enough so that it looks like He is unlikely
• 37:56
  Speaker 1: with his party to win a majority, but it also
• 37:58
  Speaker 1: looks like the opposition is unlikely to win a majority.
• 38:01
  Speaker 1: So you're likely to end up with this situation where
• 38:03
  Speaker 1: he's sort of like a caretaker.
• 38:04
  Speaker 4: Prime minister and lives to fight another day. And that's
• 38:06
  Speaker 4: what he is incredibly good at.
• 38:09
  Speaker 1: However, you know, perhaps these latest scandals end up being
• 38:13
  Speaker 1: too much for him. It's very difficult. to say how
• 38:15
  Speaker 1: this will all unfold. I'm certainly no expert on domestic
• 38:19
  Speaker 1: Israeli politics. However, this is a significant development here, just
• 38:23
  Speaker 1: in terms of our understanding of this society. Put D6
• 38:26
  Speaker 1: up on the screen. This was highlighted by Shael Ben-Ephraim.
• 38:31
  Speaker 1: There was a poll that just came out about the
• 38:33
  Speaker 1: fact that they are banning the Arab parties from competing
• 38:38
  Speaker 1: in elections. And he says the vast majority of Jews
• 38:40
  Speaker 1: in Israel support disqualifying all of these Arab parties.
• 38:44
  Speaker 4: 55% of all voters want to disqualify them.
• 38:46
  Speaker 1: That means about 65% of Jewish Israelis support the move.
• 38:51
  Speaker 1: He goes on to opine, never let them say it's
• 38:53
  Speaker 1: a fringe. Israel is a racist state to its core.
• 38:55
  Speaker 1: This flies directly in the face of all this.
• 38:57
  Speaker 4: It's a democracy.
• 38:58
  Speaker 1: And as Arab Israelis, they get to vote and they
• 39:01
  Speaker 1: have full participation and they're treated just the same as
• 39:04
  Speaker 1: Jewish Israelis. Clear bullshit here. And then they have even
• 39:09
  Speaker 1: gone so far, put D7 up on the screen, as
• 39:12
  Speaker 1: to start to police who they allow citizenship in their
• 39:17
  Speaker 1: country based on their political beliefs. So this is a
• 39:21
  Speaker 1: seminal event within Israel. They have frozen what's called Aliyah,
• 39:25
  Speaker 1: which is the, you know, quote unquote, return of Jews
• 39:28
  Speaker 1: to Israel territory. granting of citizenship and ability to reside there.
• 39:33
  Speaker 1: They have revoked that for two Jewish activists because they've
• 39:37
  Speaker 1: been involved in what's called protective presence in the West Bank.
• 39:40
  Speaker 1: where you go, and I mean, it's exactly like what
• 39:43
  Speaker 1: it sounds like. You're there, and since you're a Jew,
• 39:46
  Speaker 1: so you have some moral standing in the eyes of
• 39:48
  Speaker 1: this racist apartheid state, you hope that your presence helps
• 39:51
  Speaker 1: to protect the Palestinians that you are there with. So
• 39:54
  Speaker 1: you're sort of using your privilege in this situation to
• 39:57
  Speaker 1: try to protect Palestinians. And Israel now is saying, okay, well,
• 40:01
  Speaker 1: then you can't be part of this state. It's no
• 40:03
  Speaker 1: longer any Jew can come here, and this is their
• 40:05
  Speaker 1: safe haven. It's now the ones that have the political
• 40:08
  Speaker 1: views that we're comfortable with who are not going to
• 40:11
  Speaker 1: stand up for human rights of Palestinians.
• 40:12
  Speaker 2: Almost like they're defining who's Jewish based on their support
• 40:15
  Speaker 2: for the government and the state.
• 40:17
  Speaker 3: Interesting.
• 40:18
  Speaker 2: Which would maybe enhance anti-Semitism globally across the world when
• 40:22
  Speaker 2: the only Jewish state is going to define Jewishness for
• 40:26
  Speaker 2: not whether what you worship or don't or I guess
• 40:29
  Speaker 2: by their definition like blood heritage compared to instead. their
• 40:34
  Speaker 2: own political positions on what's happening in the West. No,
• 40:37
  Speaker 2: it's nuts. You know, I mean, Aliyah always seemed to
• 40:41
  Speaker 2: be one of those ironclad things. It's like, yep, you're
• 40:43
  Speaker 2: a Jew. You get to come here. That's the whole point.
• 40:46
  Speaker 2: And now, I mean, that was literally the foundation of
• 40:48
  Speaker 2: their entire.
• 40:49
  Speaker 4: That's the whole idea.
• 40:50
  Speaker 2: Safe havens for all Jews. Yeah.
• 40:51
  Speaker 1: Not the ones that have the views that we are
• 40:54
  Speaker 1: comfortable with.
• 40:54
  Speaker 2: That's why they get to do what they do, right?
• 40:56
  Speaker 2: You know, in their own internal.
• 40:59
  Speaker 4: Narrative.
• 40:59
  Speaker 2: I'm just.
• 41:00
  Speaker 4: Yeah.
• 41:01
  Speaker 2: Tells you a lot.
• 41:02
  Speaker 4: Yeah.
• 41:02
  Speaker 1: But I mean, you see this rhetoric with, you know,
• 41:05
  Speaker 1: with Zionists here in the United States where it suggested
• 41:08
  Speaker 1: that if you are an anti-Zionist Jew, you're not a
• 41:10
  Speaker 1: real Jew.
• 41:11
  Speaker 2: Yeah.
• 41:11
  Speaker 4: So it's.
• 41:12
  Speaker 2: According to the Israelis, that's true.
• 41:13
  Speaker 1: It's that's that is now Israel state policy effectively. Last
• 41:18
  Speaker 1: thing I wanted to update here on is Google Maps
• 41:22
  Speaker 1: just updated Gaza with new satellite images. So for the
• 41:26
  Speaker 1: first time, you can go. You can go on Google
• 41:29
  Speaker 1: Maps and type in Gaza. You can type in Rafah,
• 41:32
  Speaker 1: which is now just a pile of rubble, and you
• 41:35
  Speaker 1: can see what has been done here. And it's absolutely horrifying.
• 41:41
  Speaker 1: You look at these places and it's particularly, I don't know,
• 41:44
  Speaker 1: there's something really just deeply dystopian, disturbing about the fact
• 41:49
  Speaker 1: that you'll see these little pinpoints on the map, you know,
• 41:52
  Speaker 1: just like you would in any city of like a
• 41:54
  Speaker 1: restaurant or something that used to be there. And you
• 41:57
  Speaker 1: zoom in and it's just rubble. These are people's homes.
• 42:01
  Speaker 1: These are schools. These are, you know, mosques. This is everything.
• 42:05
  Speaker 1: Entire cities. You're going to tell me this was a
• 42:07
  Speaker 1: target operation to take out the terrorists.
• 42:09
  Speaker 4: Like, bullshit. This is why no one believes you.
• 42:11
  Speaker 2: I mean, they don't even say it anymore.
• 42:12
  Speaker 1: No one believes anything. That's exactly right. The amount of
• 42:16
  Speaker 1: rubble that has been created there, the amount of destruction,
• 42:18
  Speaker 1: lives lost. We still have no idea what the real
• 42:20
  Speaker 1: death count is. And they still won't let in international journalists.
• 42:24
  Speaker 1: to be able to see and document for themselves. So
• 42:28
  Speaker 1: this is the best we can get, this glimpse from
• 42:31
  Speaker 1: Google Maps of the extent of the full-scale annihilation that
• 42:36
  Speaker 1: they committed here in the Gaza Strip with our tax dollars.
• 42:39
  Speaker 2: Very depressing. All right, let's get to DraftKings. Turning now
• 42:43
  Speaker 2: to DraftKings. All right, let's put this up here on
• 42:45
  Speaker 2: the screen from ProPublica. I love these types of stories
• 42:49
  Speaker 2: where a lot of the Atlantic and ProPublica, like these people,
• 42:54
  Speaker 2: magazine writers will do these long exposés where they bet
• 42:59
  Speaker 2: as problem gamblers and then see what happens. So this
• 43:02
  Speaker 2: is from Jake Pearson over at ProPublica. He says, I
• 43:04
  Speaker 2: deliberately bet like a problem gambler. DraftKings made me a VIP.
• 43:10
  Speaker 2: So that sounds really bad, but I want to dig
• 43:12
  Speaker 2: into just how bad it is and how this reveals
• 43:15
  Speaker 2: the entire scam of the sports gambling industry. So let's
• 43:20
  Speaker 2: start putting some of these elements up here on the screens.
• 43:23
  Speaker 2: Jake Pearson was given some $ 20, 000 by ProPublica. They bankrolled
• 43:28
  Speaker 2: his account. He decided, quote, I waved my first red
• 43:31
  Speaker 2: flag during a Knicks game by loss chasing, attempting to
• 43:35
  Speaker 2: recoup my continued losses by making more and more big bets. Soon,
• 43:39
  Speaker 2: I was already down $ 2, 000, most of it blown in
• 43:43
  Speaker 2: one night. Okay, let's continue to go. So the next day,
• 43:46
  Speaker 2: an email popped in his inbox. Welcome to the DraftKings
• 43:50
  Speaker 2: VIP showcase. I couldn't believe it. My night of loss
• 43:54
  Speaker 2: chasing was being rewarded. Reading the fine print I was floored,
• 43:58
  Speaker 2: the more I spent on long odds wagers, the faster
• 44:02
  Speaker 2: I would advance through the ranks. Continue. What you see
• 44:05
  Speaker 2: is a month in, he was betting so much that
• 44:09
  Speaker 2: DraftKings quick tab deposit buttons had actually changed inside the
• 44:14
  Speaker 2: app to $ 500, $ 750, and $ 950, making it that much
• 44:15
  Speaker 2: easier to mindlessly transfer money into my account I was
• 44:22
  Speaker 2: also getting pummeled with push notifications of inviting me to
• 44:26
  Speaker 2: bet more. Just linger on this, guys, because I want
• 44:27
  Speaker 2: to show. everybody the sheer insanity of what this all
• 44:31
  Speaker 2: looks like. So take a look. Boost yourself. You've unlocked
• 44:33
  Speaker 2: an MLB reward. Step up to the plate. Daily MLB
• 44:37
  Speaker 2: profit boost. Drama builds in game one. Can the Knicks
• 44:40
  Speaker 2: hit two out of three in the final three minutes?
• 44:42
  Speaker 2: Cleveland versus New York remains tight in the second half.
• 44:45
  Speaker 2: The stars are here. The stars delivered yesterday. Will Ronaldo
• 44:48
  Speaker 2: and K do the same thing? Both find the back
• 44:50
  Speaker 2: of the net today. Plus 200. Win or go home.
• 44:53
  Speaker 2: Boost your NBA single game parlay before tonight's Cavs Pistons
• 44:57
  Speaker 2: tip off Claim now. Look at this bombardment of what
• 45:01
  Speaker 2: he was getting. Okay, let's continue and we'll see. So
• 45:04
  Speaker 2: he goes, I eventually decided to set in-app limits on
• 45:08
  Speaker 2: how much I could deposit, how much time I would
• 45:10
  Speaker 2: spend on the app. I received first of four promotional
• 45:14
  Speaker 2: push alerts a half hour later. So this guy is
• 45:18
  Speaker 2: trying to internally show DraftKings to try and trip the
• 45:23
  Speaker 2: wires where they are, again, by their own company policies,
• 45:28
  Speaker 2: not supposed to be doing this. And instead, as you see,
• 45:33
  Speaker 2: what is happening is that they are making as much
• 45:37
  Speaker 2: money as possible for off of the gambling addicts. So continue. Eventually,
• 45:42
  Speaker 2: he comes around. He says, and he asked them, the
• 45:45
  Speaker 2: system worked the way it was supposed to me. I
• 45:48
  Speaker 2: didn't even qualify for a manual review despite depositing $ 21, 000
• 45:52
  Speaker 2: into my account in just eight weeks. When I asked
• 45:56
  Speaker 2: if DraftKings ever cut people off, this is what I get,
• 46:00
  Speaker 2: is found response to be revealing is that the system worked.
• 46:04
  Speaker 2: Does DraftKings ever cut somebody off based on their betting
• 46:06
  Speaker 2: history and not because they've disclosed that they have a problem? Quote, yes,
• 46:09
  Speaker 2: we do that all the time. She repeated the same
• 46:12
  Speaker 2: answer when I asked her how often. I pressed again
• 46:14
  Speaker 2: and a spokesperson interjected telling me he would take the
• 46:17
  Speaker 2: question as a follow-up. Later on, the company told me
• 46:19
  Speaker 2: we do not share. the specific number of closures. So
• 46:23
  Speaker 2: basically you need to go up to the, you know,
• 46:27
  Speaker 2: you have to like call draft Kings and be like,
• 46:29
  Speaker 2: please don't let me bet. I am an addict for
• 46:32
  Speaker 2: them to not basically to cut you off. They're not
• 46:35
  Speaker 2: going to tell you the number because the number is
• 46:36
  Speaker 2: obviously minuscule in terms of how many times.
• 46:38
  Speaker 1: And their excuse was, well, you weren't betting between 1
• 46:41
  Speaker 1: 30 AM and 5 30 AM.
• 46:43
  Speaker 4: So we thought it was cool.
• 46:44
  Speaker 1: Like that's Crazy that you think like that is the requirement.
• 46:48
  Speaker 1: And we have no idea if that's even real or
• 46:51
  Speaker 1: they just like made that up to pretend they had
• 46:53
  Speaker 1: some justification.
• 46:54
  Speaker 2: It's total bullshit. I'll tell you why. It's because there
• 46:56
  Speaker 2: are a lot of sharp bettors who what they do
• 46:58
  Speaker 2: is they actually program their phones or hire people to
• 47:01
  Speaker 2: go on their phones and check their bets at 2.30
• 47:03
  Speaker 2: in the morning. because that mimics gambling addict behavior, which
• 47:07
  Speaker 2: means DraftKings will shower you in more promotional free bet,
• 47:11
  Speaker 2: which then you can take advantage of. So it's completely false.
• 47:14
  Speaker 2: The more you check at 3.30 in the morning, some
• 47:16
  Speaker 2: tip for the gamblers out there, if you want as
• 47:18
  Speaker 2: much free bet as possible, wake your ass up at
• 47:21
  Speaker 2: 3.35 in the morning and show DraftKings you can't sleep
• 47:24
  Speaker 2: because all you're thinking about is betting and they will
• 47:27
  Speaker 2: give you free money. That is what this industry is.
• 47:29
  Speaker 4: That is so dark.
• 47:30
  Speaker 2: Why am I focusing on this? Because guys, this is
• 47:33
  Speaker 2: how they make their money. So we have talked here
• 47:35
  Speaker 2: extensively about how they limit sharp bettors. So if you're
• 47:38
  Speaker 2: actually good, you're screwed, right? You have to go through
• 47:41
  Speaker 2: all these hoops and create fake accounts and mimic addict behavior,
• 47:44
  Speaker 2: et cetera, just to be able to put some money down.
• 47:47
  Speaker 2: The vast majority of their profits, nearly 50% of their profits,
• 47:50
  Speaker 2: come from people like this, the real people like this.
• 47:53
  Speaker 2: Only 3% of the people account for almost 50% of
• 47:57
  Speaker 2: the profit at these companies. And so the addicts are
• 48:00
  Speaker 2: the ones who pay all the bills. Remember, this is
• 48:03
  Speaker 2: the same for cigarettes, for alcohol, for all vice, is
• 48:07
  Speaker 2: that the vast majority of vice is consumed by the addicts.
• 48:12
  Speaker 2: And in this case, you can actually see that if
• 48:15
  Speaker 2: you cut the addict off, which again, their policy says
• 48:19
  Speaker 2: that they should do, and even laws say that they
• 48:22
  Speaker 2: should do, they will go bust instantaneously. This entire thing
• 48:26
  Speaker 2: is only sustainable because of the addict. And that's the
• 48:30
  Speaker 2: tragic part around all of this. Now, you all remember
• 48:33
  Speaker 2: last week we covered that AI policy, where they can
• 48:36
  Speaker 2: do this even more effectively. Put this up here on
• 48:38
  Speaker 2: the screen from my friends over at gamblingharm.org, a great organization.
• 48:43
  Speaker 2: I encourage people to support them. Well, you would ask,
• 48:47
  Speaker 2: after seeing all this AI stuff, hey, wouldn't you guys
• 48:52
  Speaker 2: want to do something about this, the regulators? Listen to this.
• 48:56
  Speaker 2: The Massachusetts Gaming Commission defended its regulatory approach around DraftKings
• 49:03
  Speaker 2: and said the agency has its own AI task force
• 49:06
  Speaker 2: and has funded research into AI and online gambling through
• 49:09
  Speaker 2: the University of Nevada and that they will, quote, engage
• 49:12
  Speaker 2: with DraftKings regarding the report. In other words, they're not
• 49:16
  Speaker 2: doing anything about it. They are defending DraftKings. in the
• 49:21
  Speaker 2: way that you can actually look at this. They say
• 49:23
  Speaker 2: there are troubling things to be raised. We need to
• 49:25
  Speaker 2: find out the facts with no disrespect to the New
• 49:27
  Speaker 2: York Times, etc. This is something true across any retail experience,
• 49:33
  Speaker 2: anything That a customer facing has these same challenges and
• 49:36
  Speaker 2: determining the potential for AI to exploit customers. He called
• 49:40
  Speaker 2: it a typical retail experience. This is the guy.
• 49:44
  Speaker 4: That might be true, by the way, which is also dystopian.
• 49:46
  Speaker 2: I was going to say, it is true. And that's
• 49:48
  Speaker 2: the problem. This morning I showed Crystal this viral post
• 49:52
  Speaker 2: of a guy ordering Uber to his house, $ 76, ordering
• 49:55
  Speaker 2: to his neighbor's house, $ 26. This is our economy now.
• 49:59
  Speaker 2: is exploiting these people. Uber, I get price gouging. I
• 50:02
  Speaker 2: hate it, obviously, but I guess that's a little different.
• 50:04
  Speaker 2: At least they're providing you something. This is designed to
• 50:07
  Speaker 2: milk you. If they allow you to bet, you're going
• 50:10
  Speaker 2: to lose. And the more I hammer this home to people,
• 50:13
  Speaker 2: the more that I am hoping it makes a difference,
• 50:17
  Speaker 2: is you just have to know, just because you're one
• 50:19
  Speaker 2: of those people who can place a bet and move on,
• 50:22
  Speaker 2: the reason you aren't able to is because of people
• 50:24
  Speaker 2: like this. And they are literally killing themselves in a
• 50:27
  Speaker 2: historic fact. Like, Suicide rate amongst gambling addiction is the
• 50:32
  Speaker 2: highest of every single addiction. It is the highest. It
• 50:36
  Speaker 2: is the type of addiction. you can hit rock bottom
• 50:39
  Speaker 2: and no one will know. Would you know if I
• 50:41
  Speaker 2: went broke? No, you would have no idea. Everybody knows
• 50:44
  Speaker 2: when an alcoholic is a rock bottom. Everybody knows when
• 50:46
  Speaker 2: a drug addict is on rock bottom. They're in the
• 50:48
  Speaker 2: middle of Skid Row, you know, selling themselves for hits.
• 50:52
  Speaker 2: Nobody knows. You know, you can just wake up one
• 50:54
  Speaker 2: day and you will have lost everything. And you're going
• 50:57
  Speaker 2: to lose your marriage. You're going to lose your kids.
• 50:58
  Speaker 2: You're going to lose your entire future. And you're, you know,
• 51:01
  Speaker 2: unless you get some serious help, that's another problem. The
• 51:03
  Speaker 2: recidivism is rate for, imagine being a gambling addict in
• 51:07
  Speaker 2: the year 2026. What do you do? At least in
• 51:10
  Speaker 2: the past, you could say, all right, I'm self-banning myself
• 51:13
  Speaker 2: from the casino, right? And so then you have to
• 51:15
  Speaker 2: go illegally gamble. And even then they would never offer.
• 51:17
  Speaker 4: Harder to hide it from your family, your wife, whatever.
• 51:20
  Speaker 2: I mean, this, you know, the phone, the iPad, you
• 51:22
  Speaker 2: can go, I mean, you can go to a gas
• 51:24
  Speaker 2: station and you can gamble in some of these states.
• 51:26
  Speaker 4: I Do you see that?
• 51:28
  Speaker 2: It's so bad.
• 51:29
  Speaker 4: Do you see that? I know you did.
• 51:30
  Speaker 1: This AI Kalshi ad where the boyfriend thinks his girlfriend
• 51:35
  Speaker 1: is cheating on him because she's constantly on her phone.
• 51:38
  Speaker 1: But it turns out she's not cheating on him, at
• 51:40
  Speaker 1: least not technically.
• 51:41
  Speaker 4: She's betting on Kalshi all the time.
• 51:43
  Speaker 2: That is cheating, by the way. It's financial infidelity.
• 51:45
  Speaker 4: And that's what he discovers in this AI. And he's
• 51:48
  Speaker 4: so relieved. Oh, I'm sure she's doing great and winning
• 51:50
  Speaker 4: lots of money. So it's all fine and good.
• 51:52
  Speaker 1: The other interesting thing about the ProPublica investigation is that
• 51:57
  Speaker 1: this reporter who is trying to mimic the behaviors of
• 52:02
  Speaker 1: a problem gambler, of someone who has gambling addiction but
• 52:04
  Speaker 1: doesn't actually have any.
• 52:05
  Speaker 4: Addictive issues with it himself— Even he.
• 52:08
  Speaker 1: Is kind of like tempted by the siren song of it.
• 52:11
  Speaker 2: Of course you are.
• 52:12
  Speaker 4: He says he won.
• 52:13
  Speaker 1: Big because he bet on the Knicks to win the
• 52:14
  Speaker 1: NBA championship or bet something on the Knicks and won
• 52:17
  Speaker 1: big on that and was actually up. And he was like,
• 52:20
  Speaker 1: I was like, maybe I'm good at this.
• 52:22
  Speaker 4: Oh, yeah.
• 52:22
  Speaker 1: And then, of course, you know, two weeks later, then
• 52:25
  Speaker 1: you're down thousands of dollars.
• 52:26
  Speaker 2: Have you ever done it?
• 52:27
  Speaker 4: Thousands of dollars. Have I ever bet on?
• 52:28
  Speaker 2: Yeah.
• 52:28
  Speaker 4: Yeah, I've bet some on sports.
• 52:29
  Speaker 2: No, not it's not for I mean, gambling is anything.
• 52:33
  Speaker 2: Go to a casino and play craps. The most fun
• 52:35
  Speaker 2: game in the casino. you think you're a god. If
• 52:38
  Speaker 2: you're on a hot roll, you're like, I'm a god.
• 52:40
  Speaker 2: These hands are lucky, right? And the next thing you know,
• 52:42
  Speaker 2: you're gonna roll seven, you're gonna lose every hand that
• 52:44
  Speaker 2: comes on the table. But I get it too. I understand.
• 52:47
  Speaker 2: It is a rush, like no, roulette, when you hit
• 52:49
  Speaker 2: that number, when you hit a zero, It's a drug.
• 52:53
  Speaker 1: And something about something about your like mental processes, you
• 52:57
  Speaker 1: tend to remember more the ones that you got right.
• 53:00
  Speaker 3: Of course.
• 53:00
  Speaker 4: Then you're like the ones that you got wrong. That
• 53:02
  Speaker 4: was a fluke. The ones you got right.
• 53:04
  Speaker 1: Well, that was because of your skill and knowledge and
• 53:06
  Speaker 1: because you're good at this. So, yeah, Kyle, for a while,
• 53:08
  Speaker 1: Kyle and I were betting like, you know, five different
• 53:10
  Speaker 1: golf events because he's like, I know this sport really well. Well,
• 53:14
  Speaker 1: even with him, he does know the sport.
• 53:15
  Speaker 2: Trust me. Yeah.
• 53:17
  Speaker 4: Even with that, it didn't matter. It was losing.
• 53:19
  Speaker 2: See, that's the thing is that they weaponize knowledge where
• 53:23
  Speaker 2: knowing a sport well doesn't matter. The real bettors, they
• 53:27
  Speaker 2: don't know anything about the sports. The real ones don't
• 53:29
  Speaker 2: even watch the games. All they do is look. They
• 53:32
  Speaker 2: have a computerized model with AI and they're looking at
• 53:36
  Speaker 2: numbers for the probabilities. As long as their internal probability
• 53:41
  Speaker 2: is a mismatch to the DraftKings number, they bet it.
• 53:44
  Speaker 2: They don't care about the sport. Your knowledge of the
• 53:46
  Speaker 2: sport means nothing. And I can guarantee you the DraftKings
• 53:49
  Speaker 2: 10,000 engineers, they know a shitload more about it than
• 53:52
  Speaker 2: you do. And so that's what you're up against. So
• 53:55
  Speaker 2: your knowledge will buy you absolutely nothing.
• 53:57
  Speaker 4: And we're very bad at statistics and probability.
• 54:00
  Speaker 2: Oh, it's so bad.
• 54:01
  Speaker 1: Like the natural human instincts on that thing are complete trash.
• 54:05
  Speaker 1: So you think your parlay is like, oh, these are
• 54:07
  Speaker 1: all reasonable outcomes. Like I could totally see these all hitting.
• 54:10
  Speaker 1: And so if you're not someone who's like studiously running
• 54:14
  Speaker 1: the numbers and yeah, it's a disaster.
• 54:29
  Speaker 2: So you'd be like, hey, 1,000, that's incredible, like 1,000
• 54:33
  Speaker 2: to one, when if you were to look at the statistics,
• 54:36
  Speaker 2: the number of times this ever happened, let's say, and
• 54:38
  Speaker 2: I'm making this up off the top of my head,
• 54:39
  Speaker 2: would be once in the last 20 years. So in reality,
• 54:43
  Speaker 2: even if you did hit it, you are massively being underpaid.
• 54:46
  Speaker 2: That's how they rob you, is that your idiot brain goes,
• 54:49
  Speaker 2: oh my God, 1,000 to one? That's a great deal.
• 54:51
  Speaker 2: It should be 10,000 to one, right? The real number
• 54:54
  Speaker 2: should be like 10,000 or 100,000. Or something like that
• 54:56
  Speaker 2: to one. But your ape brain goes, oh my god,
• 55:00
  Speaker 2: 1,000 to one. So even when you win, you're actually losing.
• 55:03
  Speaker 2: The vig that they're taking from you is something that, again,
• 55:06
  Speaker 2: you're not good at math and you're probably drunk or
• 55:08
  Speaker 2: high whenever you're placing the bet. You're not thinking rationally.
• 55:11
  Speaker 2: But that's why the people who are the sharp bettors,
• 55:13
  Speaker 2: they don't even care about the sport. For them, they
• 55:15
  Speaker 2: look at that number. If there's a mismatch, they take it.
• 55:17
  Speaker 4: Yeah.
• 55:18
  Speaker 2: And so you'll never beat it.
• 55:19
  Speaker 4: Yeah.
• 55:20
  Speaker 1: And you're in there and they show you like, oh, well,
• 55:21
  Speaker 1: if you did this bet, you'd make this much more money.
• 55:23
  Speaker 1: And so, oh, let me get the real payoff here
• 55:26
  Speaker 1: and not this like measly.
• 55:27
  Speaker 2: But you don't know the real ads. That's how they
• 55:29
  Speaker 2: weaponize it. So stay away. Please take it away. Ladies,
• 55:32
  Speaker 2: take it away from your husband. Delete the app. Tell
• 55:34
  Speaker 2: me to divorce him. Do it. It's going to be
• 55:36
  Speaker 2: good for you. All right. We're about to do our
• 55:38
  Speaker 2: AMA now with Garrison. Let's get to it.