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Speaker 1: Hey guys, Saga and Crystal here.

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Speaker 3: We need your help to build the future of independent

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Speaker 3: news media and we hope to see you at Breakingpoints

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Speaker 3: dot Com. Turning down to the economy, a lot of

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Speaker 3: relevant news is going to put this up here on

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Speaker 3: the screen. The CEO of Craft Hinds has now said

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Speaker 3: they have the worst consumer sentiment in decades, pointing out

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Speaker 3: the drop in the number of consumers who are buying staples,

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Speaker 3: they lowered their sales outlook, and that the feeling of

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Speaker 3: US shoppers has fallen quote to a historic low. The

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Speaker 3: company now expects full organic net sales to be down

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Speaker 3: three to three point five percent, with slower growth all

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Speaker 3: also in emerging markets and pressure in the US retail.

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Speaker 3: They said that higher prices from inflation and expected headwinds

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Speaker 3: from contailed food stamp funding, we're creating a tough environment

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Speaker 3: for US shoppers. That the drop in their overall sales

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Speaker 3: has led to some seventeen percent drop in their overall

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Speaker 3: stock price compared with the seventeen percent rise in the

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Speaker 3: s and P five hundred. Higher prices from inflation, food

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Speaker 3: stamps funding are all creating a quote tough environment, they

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Speaker 3: say for shoppers. And I think that that, I mean,

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Speaker 3: that's really grim. We talked previously about the McDonald's CEO.

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Speaker 3: Now I'm not saying heinz products and McDonald's and any

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Speaker 3: of that is healthy, but it's indicative of consumer sentiment

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Speaker 3: of behavior. And what he was showing is like people

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Speaker 3: were skipping breakfast. And I think a lot of comments were, oh, well,

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Speaker 3: if they're skipping McDonald's breakfast and eating breakfast at home,

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Speaker 3: and maybe that's a good thing, but there's not actually

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Speaker 3: a ton of data that even necessarily shows that. Because

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Speaker 3: of the grocery price increase and so the fact that

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Speaker 3: staples themselves have been i'm not only more expensive, but

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Speaker 3: people are deciding not to buy them I think is

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Speaker 3: one of those like edge indicators in the grocery store

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Speaker 3: to say, oh, man, like things are really long. Yeah,

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Speaker 3: people are cutting on ketchup and mustard. I mean, by

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Speaker 3: the way, how much ketchup for people eating?

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Speaker 1: How many? How many times people buy ketchup? How about

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Speaker 1: six months a toddler?

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Speaker 4: Oh, really want to see how much ketchup your household

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Speaker 4: can go through?

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Speaker 1: Catch if you never give him ketchup?

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Speaker 2: Then disgusting because of what my son. My son would

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Speaker 2: like dip his apples lices in it and everything. Oh

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Speaker 2: that killed killed any sort of appetite I have or

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Speaker 2: had for ketchup. So our family has long cut back

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Speaker 2: on Ketchup because I refuse because gross, it's disgusting. Anyway,

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Speaker 2: let's go ahead and put C two up on the screen.

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Speaker 2: Because it's not just Kraft Higns. You also have Chipotle.

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Speaker 2: I actually didn't even realize this. They have this very

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Speaker 2: young customer base. They've really catered to them. They've been like,

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Speaker 2: you know, like, uh, there's like a whole jimbro tie in.

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Speaker 2: Here's a good way to get lots of protein for

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Speaker 2: relatively inexpensive price easy, you know, relatively affordable tastes good.

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Speaker 2: I personally I like Chipotle. I know you're big Chipotle fan, Sager,

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Speaker 2: but I used to be.

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Speaker 1: Not anymore. At the current price, I'm no longer a consumer.

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Speaker 1: It's too much.

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Speaker 2: So anyway, they say, millennials and gen zers have helped

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Speaker 2: power Chipotle sales despite rising menu prices and slow downs

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Speaker 2: at rival chains in recent months, though a harsher economic

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Speaker 2: reality is set in rising student loan paying, stagnant wege growth,

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Speaker 2: and rising health insurance costs have bumped burritos down younger

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Speaker 2: consumers priority list. They're just eating with us less frequently,

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Speaker 2: and they're eating at home more often, the Chipotle CEO,

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Speaker 2: Scott Boatwright said in an interview. And their stock is

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Speaker 2: getting to rash right now. And guess what, They're not

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Speaker 2: the only ones. I mean, you have the top line

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Speaker 2: of the stock market, you know, just keeps going.

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Speaker 4: Up, up, up. That's all because of these tech stocks.

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Speaker 2: In Vidia leading the charge, but other tech stocks that

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Speaker 2: are AI enabled, these sort of like consumer staple type

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Speaker 2: of companies, they are actually really suffering and underperforming because

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Speaker 2: you see this consumer back and we could put see

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Speaker 2: three up on the screen, which is going to have

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Speaker 2: a major impact as well. So we covered yesterday. You know,

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Speaker 2: the Trump administration was saying we're not going to pay

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Speaker 2: any SNAP benefits. A judge came in and said, you

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Speaker 2: actually have to. You have to tap these emergency funds.

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Speaker 2: So now they're saying they're going to pay half of

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Speaker 2: the SNAP benefits for November. However, there continues to be

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Speaker 2: I mean, those funds haven't hit for people yet. Here

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Speaker 2: we are November fourth, and there's no real telling when

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Speaker 2: they will hit. So in our Agriculture Department official who

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Speaker 2: oversees the SNAP program said in a court filing that quote,

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Speaker 2: there are procedural difficulties that states will likely experience which

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Speaker 2: would affect November SNAP benefits reaching households in a timely

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Speaker 2: manner and in the correctly reduced amounts. For some states,

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Speaker 2: required system changes that have to be implemented could take

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Speaker 2: weeks or several months and could lead to payment errors

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Speaker 2: and significant delays. So for the forty three million people

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Speaker 2: who are on SNAP benefits, I mean, this is just

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Speaker 2: an absolute you know, it's an absolute catastrophe. And of

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Speaker 2: course it's going to have a ripple effect throughout the economy.

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Speaker 2: And you know, some of the places that are going

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Speaker 2: to be hit the hardest are actually stores in rural

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Speaker 2: America where you have more impoverished populations, and a lot

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Speaker 2: of the revenue from the store comes from EBT, comes

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Speaker 2: from the SNAP program. So you know, even though you've

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Speaker 2: got this ruling and the Trump administration now saying okay,

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Speaker 2: we'll give you half, which is already you know, a

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Speaker 2: fifty percent reduction is a massive, massive hit. No telling

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Speaker 2: when they'll even get access to those funds either.

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Speaker 3: Yeah, I mean one of the things, well, this is

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Speaker 3: where it comes down to cost. Let's say with Chipotle

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Speaker 3: and generally, like I remember when it was six dollars

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Speaker 3: a bowl.

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Speaker 1: This actually was not that long ago, and I used

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Speaker 1: to Chipotle five.

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Speaker 3: Days a week. Literally back in my early twenties. There

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Speaker 3: was one right next to where I used to live.

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Speaker 3: I was on a name basis.

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Speaker 1: Now though, like they had.

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Speaker 3: This recent advertisement. I'm not sure if you've seen. It's

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Speaker 3: carne asada and red chimmy cheery. I was like, all right,

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Speaker 3: I gotta try it. Okay, it's like catnip. So the

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Speaker 3: overall price when I was done, keep in mind we

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Speaker 3: live in a high cost of living area.

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Speaker 1: It was twenty two dollars. I mean, look again, maybe

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Speaker 1: Rune though good. Listen, it is good. I loved it.

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Speaker 3: I'm not gonna say I didn't like it. Was it

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Speaker 3: twenty two dollars good?

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Speaker 1: I don't know.

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Speaker 3: Again, maybe Boom or Brain, but that's one of those

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Speaker 3: where you know, I don't drink alcohol.

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Speaker 1: That's kind of what I expect to pay if I go.

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Speaker 3: To like a mid tier restaurant, right Like, I want

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Speaker 3: to be sitting and getting some service, not some literally

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Speaker 3: something in a paper bag that's wrapped in foil. They're

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Speaker 3: eating on a couch when you're watching TV. That's just

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Speaker 3: in my head that should cost around eight to ten bucks.

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Speaker 3: So twenty two dollars not happening anymore. And you just

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Speaker 3: have to think about how again for young people, especially

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Speaker 3: if you eat out a lot, of course, it's about convenience.

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Speaker 3: And one of it is it used to be so

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Speaker 3: that it was a little bit more expensive to eat out,

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Speaker 3: but not all that much more, And maybe you could

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Speaker 3: justify the value in your time, Like I said, at

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Speaker 3: thirteen to eighteen bucks for a bowl, depending on like

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Speaker 3: what you're getting in there, how is that possibly even

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Speaker 3: worth it anymore?

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Speaker 1: I mean, you have to cook at home, but like,

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Speaker 1: there's no.

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Speaker 4: Joint on me there because look at how expensive beef is, right.

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Speaker 3: But exactly, so then you're like, okay, so I'm going

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Speaker 3: to eat at home. It's like, oh oh no, yeah, exactly,

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Speaker 3: you're paying for carnes out of there, or you could

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Speaker 3: pay for it at costco.

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Speaker 1: You're paying a shit ton for it, basically, no matter what.

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Speaker 3: That is the part where when you see how it pervades,

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Speaker 3: you know, even influencer culture. I think I've talked about

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Speaker 3: this about those YouTube channels where it's a like cook

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Speaker 3: like your grandma from the Great Depression. Those channels saw

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Speaker 3: a huge explosion over the last couple of years.

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Speaker 1: That's part why is home.

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Speaker 3: You know, it's so exorbitantly expensive stuff that was once

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Speaker 3: not even I mean, would you call Chipotle luxury like

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Speaker 3: I wouldn't.

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Speaker 1: It was six bucks.

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Speaker 3: I even remember in New York you would pay seven

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Speaker 3: dollars or something like. It just was not that expensive.

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Speaker 3: It was a you know, even in Midtown it was

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Speaker 3: like seven fifty. I'm pretty sure if I recall now,

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Speaker 3: I mean what fifteen dollars probably for what that's just

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Speaker 3: that's a lot of money, and you have that for everything,

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Speaker 3: because consider it's all fashioned.

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Speaker 2: And consider what the minimum wages. Right, it's still seven

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Speaker 2: to twenty five hours. Yeah, and I mean even in

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Speaker 2: states that have increased it, maybe they've upped it maybe

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Speaker 2: to fifteen dollars. So you're working an hour to get

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Speaker 2: one Chipotle bowl.

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Speaker 1: I don't even know abo buy it.

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Speaker 3: Wouldn't buy a drink, it definitely wouldn't with tactic where

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Speaker 3: I live, it would be about fifteen bucks, I think.

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Speaker 2: And this is really the story of our economutcy four

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Speaker 2: up on the screen. This is the story in the

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Speaker 2: reality of our economy today. So now the top ten

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Speaker 2: percent of income earners account for fifty percent, so half

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Speaker 2: of all spending is being done by the top ten

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Speaker 2: percent of owner earners. And so you have this incredibly

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Speaker 2: lopsided economy. And then of course what happens is all

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Speaker 2: the incentive, like if you are a business wanting to

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Speaker 2: make money, is to cater to the people who have

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Speaker 2: the money, who have money to spent, and so increasingly,

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Speaker 2: you know, the economy becomes more and more tilted and

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Speaker 2: lopsided towards catering to the affluent. There's a bunch, let's

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Speaker 2: go and put Cobac letter did a long thread with

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Speaker 2: a bunch of other indicators about the reality of the

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Speaker 2: economy today. Let's go ahead and start these. They write,

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Speaker 2: the elephant in the room. AI stocks are outperforming consumer

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Speaker 2: stocks by twenty percent plus over the last sixty days.

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Speaker 2: And as AI investment exceeds a trillion dollars per year,

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Speaker 2: car repossessions are at two thousand nine levels. There are

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Speaker 2: two US economies, rich versus poor, and AI is the

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Speaker 2: lifeline of it all. And really think about that, because

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Speaker 2: overwhelmingly the people who hold stocks are the wealthy, and

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Speaker 2: so they're benefiting from this massive, you know, continued growth

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Speaker 2: in the stock market. But if you're just a regular

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Speaker 2: wage earner trying to make it, you are getting screwed.

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Speaker 2: Let's put the next element up on the screen. So

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Speaker 2: here are the sort of consumer focused brands whose stock

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Speaker 2: prices are suffering.

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Speaker 4: They say.

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Speaker 2: On the other side of the table is a more

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Speaker 2: bleak picture as opposed to the AI stocks. Consumer facing

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Speaker 2: stocks are getting crushed late earning season. Only underscore that

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Speaker 2: take a look at the year to date performance of

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Speaker 2: these consumer stocks. General Mills and craft Times, which we

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Speaker 2: mentioned before, are in a bear market. Put the next

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Speaker 2: one up on the screen. The economy is particularly bad

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Speaker 2: for Americans who are seeking entry level jobs. The US

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Speaker 2: unemployment rate for youth graduates aged twenty to twenty four,

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Speaker 2: so people are just coming into the job market, has

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Speaker 2: averaged eight point one percent over the last three months.

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Speaker 2: That's the highest in four years. This is up sharply

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Speaker 2: from under four percent just two years ago.

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Speaker 4: When the AI boom began.

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Speaker 2: So it certainly looks like AI adoption is starting to

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Speaker 2: take a hit on the job market, especially with new

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Speaker 2: college grads in particular, or new associate degree holders. And

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Speaker 2: let's put this last one up on the screen as well.

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Speaker 2: This is a look at auto repossessions. We are now

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Speaker 2: at have seen a huge increase in just the past year,

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Speaker 2: and vehicle seizures are at the highest level that we

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Speaker 2: have seen since two thousand and nine. So you have

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Speaker 2: a huge spike in two thousand and nine great recession,

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Speaker 2: then it declines, then it climbs back up and reaches

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Speaker 2: a peak during the COVID era, which makes sense again

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Speaker 2: when people are losing their jobs and out of work

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Speaker 2: and struggling through COVID. Then it declines and now we

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Speaker 2: are back on the March up. So on the one hand,

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Speaker 2: you have these you know, record earnings for Nvidia and

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Speaker 2: all of this you know, excitement about AI and these

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Speaker 2: tech stocks that are just going up and up and up,

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Speaker 2: and then the rest of the economy tells a very

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Speaker 2: different story about what the reality is for people. And

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Speaker 2: that's what I mentioned in our a bloc our election coverage.

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Speaker 2: You know, in Trump's first term, at this point, people

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Speaker 2: were happy with where the economy was. You know, some

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Speaker 2: sixty plus percent said the economy is good versus those

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Speaker 2: saying it's poor. Now it's flipped and it's seventy plus

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Speaker 2: percent who say that the economy is actually poor and

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Speaker 2: only maybe twenty five percent that say it is doing well. So,

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Speaker 2: you you know, for people's own experience in their life,

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Speaker 2: they are really feeling the pit and feeling the pain

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Speaker 2: in spite of what the you know, top line on

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Speaker 2: the stock market is doing.

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Speaker 3: Why don't we transition AI then, So just to show

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Speaker 3: everybody what's happening with.

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Speaker 2: That, Yeah, of course, so Elon Musk went back on

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Speaker 2: with Joe Rogan and made some comments about how nobody's

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Speaker 2: gonna work anymore in the future.

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Speaker 4: Let's go ahead and take a listen to that.

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Speaker 5: Cars are cars are going to be autonomous, but there's

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Speaker 5: just so many desk jobs where really people what people

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Speaker 5: are doing is they're processing email or they're answering the phone,

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Speaker 5: and and just anything that is that that isn't moving atoms,

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Speaker 5: like anything that is not physically like doing physical work.

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Speaker 5: That will obviously be the first thing that those jobs

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Speaker 5: will be and are being eliminated by AI at a

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Speaker 5: very rapid pace. And ultimately I working will be optional

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Speaker 5: because you'll have robots plus AI and will have in

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Speaker 5: a benign scenario, universal high income, not just universal basic income,

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Speaker 5: universal high income meaning anyone can have any products of

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Speaker 5: services that they want, so you but there will be

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Speaker 5: a lot of trauma and disruption along the way.

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Speaker 6: So you anticipate a basic income from that that the

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Speaker 6: economy will boost to such an extent that a high

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Speaker 6: income would be available to almost everybody. So we'd essentially

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Speaker 6: eliminate poverty.

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Speaker 5: In the benign scenario. Yes, So like the way it

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Speaker 5: is multiple scenarios, there are multiple scenarios. There's a lot

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Speaker 5: of ways this movie can end. Like the reason I'm

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Speaker 5: so concerned about AI safety is that, like one of

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Speaker 5: the possibilities is the terminator scenario. It's not. It's not

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Speaker 5: zero percent. So that's why it's like, I'm like really

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Speaker 5: banging the drum on AI needs to be maximally truth seeking,

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Speaker 5: like don't make I don't force AI to believe a

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Speaker 5: lie like that, for example, that founding Pause were actually

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Speaker 5: a group of diverse women, or that misgendering is worse

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Speaker 5: the nuclear war because if that's the case, and then

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Speaker 5: you get the robots and the AI becomes omnipotent, it

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Speaker 5: can enforce that outcome and then then like it, unless

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Speaker 5: you're a diverse woman, you're you're out of the picture.

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Speaker 5: So we're toast.

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Speaker 2: So the benign scenario is that all the jobs are gone? Yes,

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Speaker 2: then what did he call it? The what scenario do

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Speaker 2: you call it? Basically where everyone's dead? Is the other

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Speaker 2: scenario which he says there's not a zero percent chance. Okay,

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Speaker 2: well what percent are we talking here? Not that he knows, right,

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Speaker 2: But if you talk to anyone who has been deeply

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Speaker 2: involved in this technology, none of them will tell you

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Speaker 2: that the like absolutely apocalyptic scenario is zero percent. So

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Speaker 2: we're just rolling the dice. I mean, even if it's

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Speaker 2: a one percent, one hundred chance of complete human extinction.

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Speaker 2: I don't for me, that's too high of a risk.

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Speaker 2: I don't want to take that chance, right, that seems insane.

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Speaker 2: And yet you know, right, and there's a bunch of

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Speaker 2: I mean, there's a bunch of assumptions here too. He's like, oh, well,

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Speaker 2: you know, there won't be jobs anymore, but we are

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Speaker 2: going to have a really high basic income. What indication

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Speaker 2: do you have that there's any like likelihood of that

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Speaker 2: politically occurring? Because right now you have just like a

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Speaker 2: bunch of oligarchs who are consolidating all of the money

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Speaker 2: in power, you know, and they are going to find

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Speaker 2: us to be if if they've made it so that

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Speaker 2: labor is use like, they're gonna find us useless. They're

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Speaker 2: going to want to keep us around for what for

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Speaker 2: what reason? So even that is like very optimistic and

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Speaker 2: very hopeful. And that's but that's the benign scenario that

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Speaker 2: they're contemplating here. And then sager Elon is interesting in

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Speaker 2: all of this, as I would say a bit of

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Speaker 2: a cautionary tale, because he's right that he came into

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Speaker 2: AI development very concerned about safety. That's why with Sam

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Speaker 2: Altman he sets up open AI the ideas we're going

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Speaker 2: to keep this as a nonprofit.

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Speaker 4: We're not going to make it profit driven.

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Speaker 2: It's going to be open, right, that's the open and

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Speaker 2: open AI, so people can see the development and open

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Speaker 2: source models and whatever. And then when he realize is that,

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Speaker 2: you know, other companies and Sam is taking this in

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Speaker 2: this other direction, and other companies are joined the AI

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Speaker 2: race as well, and it's basically off to it's just

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Speaker 2: an arms race, both between the companies and between the

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Speaker 2: US and China. He's like, Okay, well, I guess I'm

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Speaker 2: just joining the arms race here. And I think it's

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Speaker 2: utterly delusional to think that you're going to keep AI

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Speaker 2: quote unquote safe because it doesn't think the founding farmers

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Speaker 2: are diverse women. I mean, that's his view that he's

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Speaker 2: articulated multiple times, as he believes that if it's like

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Speaker 2: an anti woke AI, then somehow that's gonna like save

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Speaker 2: us all and mitigate us from the most consequential catastrophes.

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Speaker 2: And I think that is so incredibly delusional as to

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Speaker 2: defy like any kind.

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Speaker 4: Of live way.

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Speaker 3: I think that both are actually very dangerous, both WOKEI

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Speaker 3: and so called anti WOKI because the whole point around

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Speaker 3: it is actually about society and having feedback into both,

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Speaker 3: and that either should be controlled at a centralized life.

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Speaker 2: I mean, I think from a sort of irrelevant whether

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Speaker 2: it's what like that just completely misses the question.

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Speaker 4: I've got the problem sol.

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Speaker 3: I don't know if it's irrelevant, because I mean, part

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Speaker 3: of the thing is with oligarchical capture, as you can

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Speaker 3: all see with the Trump people is that they're incredibly

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Speaker 3: fickle and they believe nothing. So yeah, they could also

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Speaker 3: you know, it wasn't that long ago that pronouns were.

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Speaker 2: In Bia, right, But think about like when when Elon

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Speaker 2: intervened and tried to make Grock less woke, it ended

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Speaker 2: up within a matter of days calling itself. That's a

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Speaker 2: Hitler's right, And that's that's the thing, is that we

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Speaker 2: have this I think, illusion that they understand what this

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Speaker 2: technology is.

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Speaker 4: They don't. They don't.

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Speaker 2: This is not something they programmed, it's something they grew.

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Speaker 2: It's an alien intelligence that they grew, that's the best

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Speaker 2: way to think of it. And so they try to

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Speaker 2: do all these tests to figure out like is it

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Speaker 2: deceiving us? Is it doing nefarious things? Is it going

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Speaker 2: to like you know, is it going to break down

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Speaker 2: of the lab and copy itself? And they is it

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Speaker 2: going to conform when we tell it to change how

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Speaker 2: it operates? Is it going to actually conform? And they

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Speaker 2: don't actually know. They do these tests and these experiments,

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Speaker 2: and in many instances they find that it will lie

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Speaker 2: to them, it will deceive them, it will try to

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Speaker 2: blackmail them. It will copy itself onto another server to

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Speaker 2: try to prove serve its original objectives. And they'll do

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Speaker 2: some clues to try to fix that one to plug

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Speaker 2: that one hole. They have no idea whether they've fixed

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Speaker 2: the problem. They have no idea what's going on under

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Speaker 2: the service, no clue. And that's where we are today

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Speaker 2: with the limited level of AI development that we have today.

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Speaker 2: That's to say nothing of where they want these things

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Speaker 2: ultimately to go.

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Speaker 1: Well, let's pick up on that.

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Speaker 3: Rostautha just did a great interview with an AI safety

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Speaker 3: advocate D four guys.

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Speaker 1: It's going to play it.

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Speaker 7: Artificial intelligence is able to start navigating the real world.

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Speaker 7: Right so because advances in robotics, like right now, I

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Speaker 7: just watch a video showing cutting edge robots struggling to

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Speaker 7: open a refrigerator door and stock a refrigerator, right, So

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Speaker 7: would you excit expect that those advances would be supercharged

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Speaker 7: as well. So it isn't just yes, you know podcasters

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Speaker 7: and AGI researchers who are replaced, but plumbers and electricians

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Speaker 7: are replaced by robots.

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Speaker 8: Yes, exactly, and that's going to be a huge shock.

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Speaker 8: I think that most people are not really expecting something

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Speaker 8: like that. They're expecting that we sort of have AI

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Speaker 8: progress that looks kind of like it does today, where

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Speaker 8: companies run by humans are gradually like tinkering with new

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Speaker 8: robot designs and gradually like figuring out how to make

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Speaker 8: the AI good at X or Y, whereas in fact

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Speaker 8: it will be more like you already have this army

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Speaker 8: of superintelligences that are better than humans at every intellectual task,

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Speaker 8: and also that are better at learning new tasks fast

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Speaker 8: and better at figuring out how to design stuff. And

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Speaker 8: then that army of superintelligences is the thing that's figuring

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Speaker 8: out how to automate the plumbing job, which means that

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Speaker 8: they're going to be able to figure out how to

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Speaker 8: automate it much faster than an ordinary tech company full

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Speaker 8: of humans who'd be able to figure out.

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Speaker 2: So this guy is an author co author of a

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Speaker 2: sort of forecast of where I is heading AI is

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Speaker 2: heading called AI twenty twenty seven, which I read through,

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Speaker 2: and you know, the near term predictions are much more reasonable,

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Speaker 2: and the longer they go out, and obviously it becomes

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Speaker 2: more difficult to make predictions of what it's going to

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Speaker 2: look like. But what he's pointing to here is, you

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Speaker 2: know myself, including you, and I think there's a lot

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Speaker 2: of reasons to be skeptical about the idea and the

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Speaker 2: promise of AGI and what this is if it's actually

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Speaker 2: going to be as transformative people are saying, and what

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Speaker 2: he's explaining there is the reason you're going to hit

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Speaker 2: this point of exponential growth in terms of the capability

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Speaker 2: is because it's increasingly going to be taken out of

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Speaker 2: the hands of humans and you're going to have AI

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Speaker 2: training AI where it's you know, constant improvement, where you

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Speaker 2: have effectively like an army of AI that is researching

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Speaker 2: and improving itself. And the humans are just like the

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Speaker 2: high level supervisors who aren't even really capable of completely

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Speaker 2: supervised because they don't actually know. They do have to sleep,

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Speaker 2: they do get sick, they do have limited ability to

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Speaker 2: like really understand what's happening under the hood here, and

435
00:21:08,720 --> 00:21:11,400
Speaker 2: then it's in that scenario once you hit that point

436
00:21:11,720 --> 00:21:16,320
Speaker 2: where the AI is effectively training the new AI, that

437
00:21:16,400 --> 00:21:19,800
Speaker 2: you hit this kind of like exponential growth curve and

438
00:21:19,840 --> 00:21:23,199
Speaker 2: where things really start to get potentially very weird. And

439
00:21:23,240 --> 00:21:25,040
Speaker 2: obviously he's not the only one that's saying there are

440
00:21:25,119 --> 00:21:26,800
Speaker 2: a lot of people. I mean, I think most people

441
00:21:26,840 --> 00:21:29,960
Speaker 2: who actually are deeply in touch with this industry, and

442
00:21:30,200 --> 00:21:33,159
Speaker 2: whether or not they support it or not, that's the

443
00:21:33,240 --> 00:21:36,000
Speaker 2: direction they all see this ultimately going in, which is

444
00:21:36,040 --> 00:21:38,920
Speaker 2: why you see this race now for whoever can develop

445
00:21:39,040 --> 00:21:42,840
Speaker 2: that ability, whatever level of an AGI or whatever you

446
00:21:42,840 --> 00:21:45,840
Speaker 2: want to call that, whoever can develop that ability, they're

447
00:21:45,880 --> 00:21:48,000
Speaker 2: going to be the ones who are able to hit

448
00:21:48,040 --> 00:21:51,880
Speaker 2: that exponential growth curve and win the AI arms race

449
00:21:51,920 --> 00:21:55,160
Speaker 2: that is unfolding right now without any of our desire

450
00:21:55,280 --> 00:21:55,760
Speaker 2: or consent.

451
00:21:56,119 --> 00:21:59,120
Speaker 3: That's there's two scenarios and they're both so bad. One

452
00:21:59,200 --> 00:22:01,360
Speaker 3: is that they're right and that this will all progress.

453
00:22:01,400 --> 00:22:03,240
Speaker 3: And you know, I actually I still don't really believe

454
00:22:03,280 --> 00:22:07,680
Speaker 3: that they're right, but to the way that they seriously

455
00:22:07,880 --> 00:22:10,479
Speaker 3: take it and keep saying it out loud over and

456
00:22:10,520 --> 00:22:12,879
Speaker 3: over again, at the very least, if the richest and

457
00:22:12,920 --> 00:22:15,680
Speaker 3: most powerful people in the world believe something to be true,

458
00:22:15,920 --> 00:22:17,600
Speaker 3: I have to take it seriously, right because they are

459
00:22:17,640 --> 00:22:20,119
Speaker 3: the people who could They have the best ability to

460
00:22:20,240 --> 00:22:22,000
Speaker 3: enact that now. I think a lot of the times

461
00:22:22,160 --> 00:22:24,840
Speaker 3: they are talking their own book to inflate their massive

462
00:22:24,840 --> 00:22:27,480
Speaker 3: stock prices. I mean, even x You know, the reason

463
00:22:27,520 --> 00:22:30,280
Speaker 3: Elon is obsessed with AI right now is because Xai

464
00:22:30,520 --> 00:22:33,879
Speaker 3: acquired x and the entire valuation of his private forty

465
00:22:33,880 --> 00:22:37,200
Speaker 3: billion dollars boondoggle is built upon the promise of AI

466
00:22:37,400 --> 00:22:40,040
Speaker 3: and using Twitter data to use it as some sort

467
00:22:40,080 --> 00:22:41,200
Speaker 3: of training bot for Rock.

468
00:22:41,440 --> 00:22:44,240
Speaker 1: There's no profitability in Twitter. Okay, it's it's a dead business.

469
00:22:44,240 --> 00:22:44,840
Speaker 1: It's horrible.

470
00:22:45,160 --> 00:22:47,919
Speaker 3: But the point also, though, is if it fails, is

471
00:22:47,920 --> 00:22:50,440
Speaker 3: that we're basically left with some sort of dot com style,

472
00:22:50,760 --> 00:22:53,720
Speaker 3: you know, scenario which blows out the economy. I was

473
00:22:53,760 --> 00:22:55,800
Speaker 3: looking at the stat if you bought the S and

474
00:22:55,840 --> 00:22:58,840
Speaker 3: P in March of two thousand, you did not return

475
00:22:58,880 --> 00:23:00,959
Speaker 3: to baseline for like a eleven years.

476
00:23:01,040 --> 00:23:01,880
Speaker 1: That's how long it.

477
00:23:01,840 --> 00:23:04,280
Speaker 3: Took for you to actually return for it was now

478
00:23:04,280 --> 00:23:05,600
Speaker 3: obviously you should have bought in between.

479
00:23:05,680 --> 00:23:07,639
Speaker 1: You know, if your dollar cost averaging and all that.

480
00:23:07,680 --> 00:23:09,200
Speaker 3: But that's I mean, that's a lot of value and

481
00:23:09,320 --> 00:23:11,560
Speaker 3: it was wiped out eleven years of a dead zone

482
00:23:11,600 --> 00:23:12,479
Speaker 3: in the US economy.

483
00:23:12,560 --> 00:23:16,800
Speaker 2: And the Dot something is instructive as well though, because like, yes,

484
00:23:16,880 --> 00:23:19,960
Speaker 2: a bunch of these Internet companies failed, but the Internet.

485
00:23:19,720 --> 00:23:21,080
Speaker 4: Was transformation, That's correct.

486
00:23:21,240 --> 00:23:23,520
Speaker 2: So you can have a bubble pop and still have

487
00:23:24,359 --> 00:23:28,159
Speaker 2: the you know, technology technological revolution.

488
00:23:27,920 --> 00:23:30,560
Speaker 4: For good or for ill that we are you know,

489
00:23:30,600 --> 00:23:31,320
Speaker 4: facing down.

490
00:23:31,160 --> 00:23:33,399
Speaker 1: Right and bubble watch. Let's put C six up up

491
00:23:33,480 --> 00:23:34,399
Speaker 1: on the screen please.

492
00:23:34,840 --> 00:23:38,600
Speaker 3: Michael Burry of the Big Short Big Short fame apparently

493
00:23:38,680 --> 00:23:42,560
Speaker 3: now has some massive put options against the value of

494
00:23:42,680 --> 00:23:46,000
Speaker 3: Palenteer and of Nvidia, two of the high flying AI

495
00:23:46,080 --> 00:23:49,200
Speaker 3: stocks now currently. I mean, you can do the math

496
00:23:49,240 --> 00:23:51,480
Speaker 3: for yourself in terms of the value of the amount

497
00:23:51,640 --> 00:23:53,879
Speaker 3: that he's betting on those companies, and he's poised to

498
00:23:53,920 --> 00:23:57,159
Speaker 3: profit massively if those stocks do go down. Look he

499
00:23:57,200 --> 00:23:59,960
Speaker 3: and himself in terms of the release for everything. Part

500
00:24:00,119 --> 00:24:01,800
Speaker 3: of it could be publicity, but I mean those are

501
00:24:01,800 --> 00:24:05,200
Speaker 3: real positions held by obviously a legendary investor. I will

502
00:24:05,240 --> 00:24:07,639
Speaker 3: say he has been wrong a lot recently, but you

503
00:24:07,720 --> 00:24:10,080
Speaker 3: only have to be right once, As he famously was

504
00:24:10,320 --> 00:24:12,240
Speaker 3: in the movie, and you should always take what the

505
00:24:12,280 --> 00:24:15,160
Speaker 3: guy says seriously. I think a little bit more seriously

506
00:24:15,160 --> 00:24:17,280
Speaker 3: that I take is put D five on the screen.

507
00:24:17,840 --> 00:24:20,320
Speaker 1: This is John Cassidy. He's from the New Yorker.

508
00:24:20,640 --> 00:24:23,560
Speaker 3: This was he wrote a little while back, but it

509
00:24:23,600 --> 00:24:26,199
Speaker 3: was about that efficiency story that I talked about, The

510
00:24:26,560 --> 00:24:29,199
Speaker 3: Profits Draft and the Lessons of History. He is the

511
00:24:29,280 --> 00:24:31,919
Speaker 3: author of that book I can't recommend enough dot con

512
00:24:32,119 --> 00:24:35,080
Speaker 3: specifically on the dot com crash back in two thousand

513
00:24:35,119 --> 00:24:37,760
Speaker 3: and he links the same stuff that we were talking

514
00:24:37,800 --> 00:24:40,639
Speaker 3: about about how the inability to find efficiency for some

515
00:24:40,720 --> 00:24:43,600
Speaker 3: ninety five percent of users is that even though we

516
00:24:43,640 --> 00:24:47,159
Speaker 3: can cover AI job loss, that the big promises of

517
00:24:47,240 --> 00:24:50,200
Speaker 3: productivity and all that are not manifesting themselves yet.

518
00:24:50,320 --> 00:24:51,000
Speaker 1: Which is the.

519
00:24:50,880 --> 00:24:54,840
Speaker 3: Only thing propping up the nvidious stock price, open AI valuation,

520
00:24:55,080 --> 00:24:58,880
Speaker 3: thirteen billion dollar revenue, trillions of dollars in deals, AMD,

521
00:24:59,080 --> 00:25:01,760
Speaker 3: all of these other things, data center construction, Like that's

522
00:25:01,800 --> 00:25:04,919
Speaker 3: the only thing that's propping up everything right now. And

523
00:25:04,920 --> 00:25:08,000
Speaker 3: so if there's a crash, it will not only impact

524
00:25:08,160 --> 00:25:12,440
Speaker 3: overall stock valuations, it will also suck huge amounts of

525
00:25:12,440 --> 00:25:16,800
Speaker 3: money out of the economy, investment capital expenditure construction. It's

526
00:25:16,880 --> 00:25:18,639
Speaker 3: just like housing, like the housing crash did can just

527
00:25:18,680 --> 00:25:21,400
Speaker 3: crash housing, It crashed everything. Yeah, for a long time.

528
00:25:21,440 --> 00:25:23,240
Speaker 3: That's my big fear right now. And by the way,

529
00:25:23,280 --> 00:25:24,840
Speaker 3: I think this one is more likely than the AI

530
00:25:24,920 --> 00:25:27,919
Speaker 3: bots take over and kill us all. I think the

531
00:25:27,960 --> 00:25:29,320
Speaker 3: crash is the most likely scenario.

532
00:25:29,800 --> 00:25:30,159
Speaker 1: I really do.

533
00:25:30,280 --> 00:25:30,800
Speaker 4: Could be both.

534
00:25:30,840 --> 00:25:32,360
Speaker 1: Well, yeah, and you could have I don't know.

535
00:25:32,600 --> 00:25:36,280
Speaker 2: Let's take the crash scenarios. Let's take for granted that

536
00:25:36,280 --> 00:25:38,359
Speaker 2: that's the direction we're heading. I mean, think about where

537
00:25:38,400 --> 00:25:40,920
Speaker 2: we are right now. Like we just listened to Elon Musk. Obviously,

538
00:25:40,920 --> 00:25:45,560
Speaker 2: Elon has a very large personal financial incentive to push

539
00:25:45,800 --> 00:25:48,920
Speaker 2: the narrative that AI is going to be this incredibly

540
00:25:48,920 --> 00:25:53,760
Speaker 2: transformational force. Now, imagine you are a large institutional investor

541
00:25:54,160 --> 00:25:56,399
Speaker 2: and all these guys who are smart and rich and

542
00:25:56,400 --> 00:25:59,520
Speaker 2: powerful are telling you basically like listen, I'm going to

543
00:25:59,560 --> 00:26:01,480
Speaker 2: be the one make it a AGI and I'm going

544
00:26:01,560 --> 00:26:04,800
Speaker 2: to be effectively like God, king of the universe. When

545
00:26:04,840 --> 00:26:07,720
Speaker 2: that happens. That is a very powerful incentive for you

546
00:26:07,760 --> 00:26:10,280
Speaker 2: to invest with him, isn't it. So they do have

547
00:26:10,680 --> 00:26:14,840
Speaker 2: huge personal incentives to portray AI as it's going to

548
00:26:14,920 --> 00:26:18,720
Speaker 2: be this completely revolutionary thing the likes of which we've

549
00:26:18,760 --> 00:26:23,760
Speaker 2: literally never experienced before in human history on this accelerated timeline.

550
00:26:23,840 --> 00:26:26,040
Speaker 2: And so you better be on the team of the

551
00:26:26,440 --> 00:26:30,359
Speaker 2: you know, oligar god king that achieves AGI first, or

552
00:26:30,359 --> 00:26:32,280
Speaker 2: you're going to be left in the cold like all

553
00:26:32,320 --> 00:26:34,800
Speaker 2: the rest of us suckers asking for our UBI from

554
00:26:34,800 --> 00:26:37,040
Speaker 2: the federal governor or whatever he announced the scraps they

555
00:26:37,040 --> 00:26:39,680
Speaker 2: feel like giving us. So it is a pretty compelling,

556
00:26:39,840 --> 00:26:43,480
Speaker 2: you know, pretty powerful story and pretty powerful self incentive,

557
00:26:43,560 --> 00:26:46,320
Speaker 2: like huge incentive that people have in their own selfish

558
00:26:46,400 --> 00:26:48,600
Speaker 2: interest to push that narrative. So I think it is

559
00:26:48,640 --> 00:26:51,240
Speaker 2: worth keeping that in mind when you're considering the promise.

560
00:26:51,240 --> 00:26:53,080
Speaker 2: And now, like the guy that I just that we

561
00:26:53,160 --> 00:26:55,440
Speaker 2: just showed you from ross Dell, that he does not

562
00:26:55,680 --> 00:26:57,879
Speaker 2: have it, and he left Open AI because he was

563
00:26:58,000 --> 00:27:00,920
Speaker 2: concerned that they were no longer you know, worried about

564
00:27:00,920 --> 00:27:03,239
Speaker 2: the morals and ethics, and he did not see a

565
00:27:03,280 --> 00:27:07,119
Speaker 2: way that they could have achieve what's called AI alignment,

566
00:27:07,359 --> 00:27:09,960
Speaker 2: where the AI basically stays in the box that you

567
00:27:10,080 --> 00:27:12,400
Speaker 2: want it to stay in. And so that's why he

568
00:27:12,480 --> 00:27:15,560
Speaker 2: left and is now doing this work on AI safety.

569
00:27:15,840 --> 00:27:17,000
Speaker 4: But yeah, there is.

570
00:27:17,000 --> 00:27:22,760
Speaker 2: Definitely a very possible scenario where investors come to realize,

571
00:27:22,960 --> 00:27:24,720
Speaker 2: you know, I don't think that this thing is living

572
00:27:24,800 --> 00:27:27,440
Speaker 2: up to the hype, and you've got all of these

573
00:27:27,480 --> 00:27:29,760
Speaker 2: trillions of dollars in this sort of like you know,

574
00:27:29,880 --> 00:27:33,320
Speaker 2: self licking ice cream cone economic system going on here

575
00:27:33,440 --> 00:27:35,840
Speaker 2: with Nvidia at the core of it, and you could

576
00:27:35,880 --> 00:27:39,120
Speaker 2: easily see how that could all come crashing down very

577
00:27:39,160 --> 00:27:42,320
Speaker 2: quickly in an instant, or you know, China's Deep Seek

578
00:27:42,440 --> 00:27:45,960
Speaker 2: or another competitor gains a significant edge and looks like

579
00:27:46,000 --> 00:27:48,600
Speaker 2: it's going to be look gets adopted globally. They have

580
00:27:48,640 --> 00:27:51,359
Speaker 2: some advantages in that regard. Number one, the fact that

581
00:27:51,440 --> 00:27:53,919
Speaker 2: it is open source, so any anybody any in the

582
00:27:53,960 --> 00:27:56,640
Speaker 2: world can you know, download their weights and mess within

583
00:27:56,680 --> 00:27:59,160
Speaker 2: and adopt it and whatever. That could be another thing

584
00:27:59,320 --> 00:28:03,719
Speaker 2: that could cause the bubble to completely crash. So you know,

585
00:28:04,600 --> 00:28:07,480
Speaker 2: there's a number of scenarios. One is it's just completely

586
00:28:07,520 --> 00:28:09,800
Speaker 2: fake and it's a bubble and it crashes and we

587
00:28:09,880 --> 00:28:13,320
Speaker 2: never get beyond like you know, making videos of Tupac

588
00:28:13,520 --> 00:28:17,760
Speaker 2: fighting Biggie and WWF or whatever. The productivity gains never

589
00:28:17,840 --> 00:28:22,359
Speaker 2: really materialize. It's like moderately disruptive, but nothing really super crazy.

590
00:28:22,560 --> 00:28:24,040
Speaker 4: That's probably the best case scenario.

591
00:28:24,720 --> 00:28:28,280
Speaker 2: There's a scenario where the bubble crashes, but it's still

592
00:28:28,680 --> 00:28:32,119
Speaker 2: you know, the AI development actually is still that level

593
00:28:32,119 --> 00:28:34,879
Speaker 2: of transformational. And then you have the like you know,

594
00:28:34,960 --> 00:28:38,240
Speaker 2: tail end truly apocalyptic scenarios which you can't put off

595
00:28:38,240 --> 00:28:40,600
Speaker 2: the table when you consider that the people who work

596
00:28:40,640 --> 00:28:43,360
Speaker 2: with this stuff the most say, this is a real possibility.

597
00:28:43,640 --> 00:28:46,760
Speaker 2: We have to keep in mind we are trying to develop.

598
00:28:46,960 --> 00:28:50,600
Speaker 2: They are trying to develop an intelligence greater than human beings.

599
00:28:50,760 --> 00:28:53,240
Speaker 2: We've never co existed on the planet with an intelligence

600
00:28:53,240 --> 00:28:54,160
Speaker 2: that's greater than ours.

601
00:28:54,400 --> 00:28:55,680
Speaker 4: What does that mean? You know?

602
00:28:55,760 --> 00:28:58,040
Speaker 2: I mean, I think you have to take seriously the

603
00:28:58,160 --> 00:29:01,560
Speaker 2: upending of the you know, the order where that we

604
00:29:01,640 --> 00:29:05,520
Speaker 2: currently dominate and that we're trying to we are ourselves

605
00:29:05,560 --> 00:29:09,280
Speaker 2: trying to upend that order. How are we going to

606
00:29:09,320 --> 00:29:11,800
Speaker 2: be able to predict what are going to be the

607
00:29:11,840 --> 00:29:15,440
Speaker 2: actions and the outcomes of what this super intelligence does?

608
00:29:16,120 --> 00:29:18,480
Speaker 2: You know, are they going to treat us the way

609
00:29:18,520 --> 00:29:21,040
Speaker 2: we treat ants where it's basically like yeah, whether kind

610
00:29:21,040 --> 00:29:22,600
Speaker 2: of in the way it's like, you know, I don't

611
00:29:22,600 --> 00:29:23,320
Speaker 2: want them in my house.

612
00:29:23,440 --> 00:29:24,520
Speaker 4: Let me just get him out of here.

613
00:29:24,680 --> 00:29:29,400
Speaker 2: I mean, we genuinely don't know and definitionally do not

614
00:29:29,520 --> 00:29:34,480
Speaker 2: have the mental capacity to really try to figure it out.

615
00:29:35,280 --> 00:29:40,320
Speaker 3: Moving now to CZ, the notorious crypto billionaire who previously

616
00:29:40,360 --> 00:29:43,200
Speaker 3: had been indicted by the US Department of Justice. He

617
00:29:43,280 --> 00:29:46,000
Speaker 3: pled guilty. It seemed to be done and dusted in

618
00:29:46,080 --> 00:29:48,880
Speaker 3: terms of a deal until very recently when Donald Trump

619
00:29:49,200 --> 00:29:52,920
Speaker 3: pardoned him. This obviously caused a lot of consternation online

620
00:29:52,960 --> 00:29:55,920
Speaker 3: potential corruption. There were a lot of questions, and then

621
00:29:56,080 --> 00:29:59,720
Speaker 3: it exploded even more after a CBS News interview on

622
00:29:59,760 --> 00:30:03,440
Speaker 3: Sale sixty minutes whenever they asked Trump who CZ even was,

623
00:30:03,560 --> 00:30:07,120
Speaker 3: and he did not even know. This is after criticizing

624
00:30:07,200 --> 00:30:10,120
Speaker 3: Joe Biden around the auto pen scandal of pardons and

625
00:30:10,200 --> 00:30:12,880
Speaker 3: proclaiming the presidents even know who he was pardoning. So

626
00:30:13,000 --> 00:30:14,680
Speaker 3: first take a listen to Trump's answer.

627
00:30:15,000 --> 00:30:18,040
Speaker 9: He pled guilty in twenty twenty three to violating anti

628
00:30:18,160 --> 00:30:21,200
Speaker 9: money laundering laws. The government at the time said that

629
00:30:21,240 --> 00:30:26,160
Speaker 9: CZ had caused significant harm to US national security, essentially

630
00:30:26,200 --> 00:30:29,320
Speaker 9: by allowing terrorist groups like Hamas to move millions of

631
00:30:29,360 --> 00:30:31,680
Speaker 9: dollars around. Why did you pardon him?

632
00:30:31,800 --> 00:30:33,480
Speaker 8: Okay, are you ready? I don't know who he is.

633
00:30:34,120 --> 00:30:36,440
Speaker 2: I know he got a four month sentence or something

634
00:30:36,480 --> 00:30:39,200
Speaker 2: like that, and I heard it was a Biden witch hunt.

635
00:30:39,480 --> 00:30:44,760
Speaker 9: In twenty twenty five, his crypto exchange Finance helped facilitate

636
00:30:44,880 --> 00:30:49,160
Speaker 9: a two billion dollar purchase of World Liberty Financial's stable coin,

637
00:30:50,000 --> 00:30:54,640
Speaker 9: and then you pardoned CZ. How do you address the

638
00:30:54,680 --> 00:30:56,200
Speaker 9: appearance of pay for play?

639
00:30:56,320 --> 00:30:57,160
Speaker 1: Well, here's the thing.

640
00:30:57,320 --> 00:30:59,400
Speaker 2: I know nothing about it because I'm too busy doing

641
00:30:59,400 --> 00:31:00,760
Speaker 2: the other I can only tell you that.

642
00:31:01,560 --> 00:31:02,560
Speaker 9: I can only tell you this.

643
00:31:04,240 --> 00:31:05,479
Speaker 1: My sons are into it.

644
00:31:05,560 --> 00:31:08,880
Speaker 2: I'm glad they are because it's probably a great industry crypto.

645
00:31:08,960 --> 00:31:10,600
Speaker 4: I think it's good. You know, they're running a business,

646
00:31:10,600 --> 00:31:11,720
Speaker 4: they're not in government.

647
00:31:12,560 --> 00:31:14,880
Speaker 3: So he said, I knew nothing about it, and I'm

648
00:31:14,960 --> 00:31:17,920
Speaker 3: very busy, and he didn't even know who the guy

649
00:31:18,120 --> 00:31:23,080
Speaker 3: was who he partoned, which I mean, look, by the way,

650
00:31:23,120 --> 00:31:25,280
Speaker 3: you know, I think the autoped stuff is legitimate in

651
00:31:25,360 --> 00:31:28,120
Speaker 3: terms of Biden's dementia and like who was actually running

652
00:31:28,280 --> 00:31:30,560
Speaker 3: the pardon scheme. But I mean this is also equally

653
00:31:30,560 --> 00:31:32,600
Speaker 3: bad if you're because notice what does he talk about.

654
00:31:32,640 --> 00:31:34,160
Speaker 3: He's like, well, my sons are very into It's like,

655
00:31:34,160 --> 00:31:35,680
Speaker 3: so your son's asked to pardon him? Is that what

656
00:31:35,680 --> 00:31:38,000
Speaker 3: you're saying yeah, it sounds was like right, let's put

657
00:31:38,000 --> 00:31:41,120
Speaker 3: that up on the screen. Please the actual report about

658
00:31:41,160 --> 00:31:44,760
Speaker 3: the guilty, like the actual the pardon. He was sentenced

659
00:31:44,760 --> 00:31:47,280
Speaker 3: to four months in prison. In April of twenty twenty four,

660
00:31:47,400 --> 00:31:51,160
Speaker 3: pled guilty to violating US money laundering finance. Also plied

661
00:31:51,160 --> 00:31:53,680
Speaker 3: guilty and was ordered to pay four point three billion

662
00:31:54,040 --> 00:31:57,520
Speaker 3: after a US investigation said it helped users bypass sanctions.

663
00:31:57,680 --> 00:32:01,480
Speaker 3: The pardon was a big debate about house embrace of crypto,

664
00:32:02,280 --> 00:32:05,840
Speaker 3: given Trump's own family investments in the industry and concern

665
00:32:06,000 --> 00:32:08,360
Speaker 3: over whether you know, the Trump family or somebody like

666
00:32:08,400 --> 00:32:11,120
Speaker 3: that got paid. Part of the whole scandal I think

667
00:32:11,160 --> 00:32:13,440
Speaker 3: around it is the you know, we talked about trump

668
00:32:13,440 --> 00:32:16,040
Speaker 3: coin and all that behind the scenes, and you can

669
00:32:16,080 --> 00:32:19,600
Speaker 3: give a legitimate theory like case as to why like

670
00:32:20,040 --> 00:32:23,720
Speaker 3: it was quote unfair you know around crypto for money laundering,

671
00:32:23,720 --> 00:32:26,400
Speaker 3: et cetera kind of the spirit of crypto itself, and

672
00:32:26,440 --> 00:32:27,840
Speaker 3: like what that all means, because there's a lot of

673
00:32:27,840 --> 00:32:29,480
Speaker 3: crypto people who I know who said that it was

674
00:32:29,520 --> 00:32:32,320
Speaker 3: a bullshit investigation kind of in the first place, I

675
00:32:32,440 --> 00:32:35,200
Speaker 3: personally like my thing with cz is crystal. If you

676
00:32:35,240 --> 00:32:38,080
Speaker 3: will recall he was the person who was also kind

677
00:32:38,080 --> 00:32:40,520
Speaker 3: of at the heart of the whole SBF scandal and

678
00:32:40,640 --> 00:32:43,200
Speaker 3: a crash that I'm like, yeah, I would like to

679
00:32:43,200 --> 00:32:45,320
Speaker 3: see a little bit more about that, Okay, in terms

680
00:32:45,360 --> 00:32:47,880
Speaker 3: of defrauding people of billions of dollars, I don't really

681
00:32:47,880 --> 00:32:49,200
Speaker 3: care about sanctions evasion.

682
00:32:49,320 --> 00:32:52,040
Speaker 2: Yeah, yeah, let me just elaborate on that a little bit,

683
00:32:52,120 --> 00:32:54,800
Speaker 2: because it does seem to me, like, you know, okay,

684
00:32:54,840 --> 00:32:57,080
Speaker 2: you know, people say, oh, this was a bullshit investigation

685
00:32:57,200 --> 00:33:00,880
Speaker 2: and these money laundering laws whatever. Okay, but you know,

686
00:33:01,240 --> 00:33:03,520
Speaker 2: he took a deal from the government, and there were

687
00:33:03,560 --> 00:33:06,080
Speaker 2: a lot more things that were being investigated. With regard

688
00:33:06,160 --> 00:33:09,840
Speaker 2: to to CZ and with regard to Finance, the whole

689
00:33:09,880 --> 00:33:13,800
Speaker 2: structure of the thing is incredibly shady and sketchy internally.

690
00:33:13,840 --> 00:33:15,800
Speaker 2: A bunch of reports to come out about how they

691
00:33:15,800 --> 00:33:18,320
Speaker 2: had like no accounting controls what so I mean, you know,

692
00:33:18,400 --> 00:33:21,840
Speaker 2: similar to like the very rogue way that SBF ran

693
00:33:21,920 --> 00:33:25,360
Speaker 2: his company as well, and then there have been not

694
00:33:25,440 --> 00:33:28,680
Speaker 2: just allegations, but actually Finance has taken action against people

695
00:33:28,720 --> 00:33:31,760
Speaker 2: internally who were internally trading based on knowledge of what

696
00:33:31,800 --> 00:33:34,680
Speaker 2: coins were going to be listed on the exchange, because

697
00:33:34,680 --> 00:33:36,920
Speaker 2: that would give them, you know, huge advantage.

698
00:33:37,280 --> 00:33:39,200
Speaker 4: So there were all sorts.

699
00:33:39,440 --> 00:33:43,880
Speaker 2: There was a really consequential interview that he did on

700
00:33:44,000 --> 00:33:47,720
Speaker 2: CNBC that we covered here where there was real question

701
00:33:47,840 --> 00:33:49,960
Speaker 2: whether they were lying about the funds that they were

702
00:33:49,960 --> 00:33:52,520
Speaker 2: holding in reserve and whether they had enough cash to

703
00:33:52,600 --> 00:33:55,280
Speaker 2: actually cover the things that they you know, could potentially

704
00:33:55,320 --> 00:33:57,760
Speaker 2: need to cover. They had to freeze deposits for a while,

705
00:33:57,880 --> 00:34:01,480
Speaker 2: so there was there was a lot more questions and

706
00:34:01,560 --> 00:34:05,600
Speaker 2: investigations going on into this company beyond what he ultimately

707
00:34:05,720 --> 00:34:06,120
Speaker 2: fled to.

708
00:34:06,200 --> 00:34:07,680
Speaker 4: I think is important context to know.

709
00:34:07,840 --> 00:34:10,200
Speaker 3: That's very well said a friend of the show. Coffee

710
00:34:10,280 --> 00:34:12,160
Speaker 3: Zilla broke some of it down. He's an expert on

711
00:34:12,200 --> 00:34:13,400
Speaker 3: this stuff. Stick a listen.

712
00:34:13,680 --> 00:34:17,560
Speaker 10: Whether or not you're a stand of the Trump family,

713
00:34:17,680 --> 00:34:21,040
Speaker 10: it doesn't matter. I mean, this is just straight up corruption,

714
00:34:21,560 --> 00:34:23,799
Speaker 10: which we should be all against. Right, this should not

715
00:34:23,880 --> 00:34:28,960
Speaker 10: be a political issue. Unfortunately, increasingly politicians are involved in

716
00:34:29,040 --> 00:34:32,399
Speaker 10: these huge, crypto self dealing deals. But I am not

717
00:34:32,480 --> 00:34:36,239
Speaker 10: injecting politics into this, Okay, it just so happens that

718
00:34:36,520 --> 00:34:39,600
Speaker 10: they happen to be in positions of power. But don't

719
00:34:39,640 --> 00:34:41,840
Speaker 10: worry about that, right, just look at the facts please

720
00:34:41,920 --> 00:34:44,799
Speaker 10: For a moment, it all starts November twenty first, twenty

721
00:34:44,880 --> 00:34:49,480
Speaker 10: twenty three. Okay, Binance and Chengpang's out both plead guilty

722
00:34:49,640 --> 00:34:54,040
Speaker 10: in a historic four billion dollar plea deal where basically

723
00:34:54,080 --> 00:34:58,359
Speaker 10: they admitted they didn't really have anti money laundering controls

724
00:34:58,640 --> 00:35:01,480
Speaker 10: and they kind of willfully look the other way, specifically

725
00:35:01,560 --> 00:35:04,720
Speaker 10: in regards to people they probably should have not allowed

726
00:35:04,719 --> 00:35:08,320
Speaker 10: them to use their platform, such as according to the charges, terrorists,

727
00:35:08,320 --> 00:35:11,920
Speaker 10: cyber criminals, and child abusers, which you know, yeah, you

728
00:35:11,960 --> 00:35:15,200
Speaker 10: probably should block those people from using your platform now.

729
00:35:15,200 --> 00:35:17,680
Speaker 10: On April thirty of twenty twenty four, Zow is officially

730
00:35:17,719 --> 00:35:20,160
Speaker 10: sentenced to four months of prison, and of course, a

731
00:35:20,160 --> 00:35:24,320
Speaker 10: few months later, Donald Trump takes office wins the presidency. Okay,

732
00:35:24,400 --> 00:35:27,440
Speaker 10: and while he's winning the presidency, he also announces the

733
00:35:27,480 --> 00:35:30,200
Speaker 10: launch of something called World Liberty Financial, which is kind

734
00:35:30,200 --> 00:35:33,239
Speaker 10: of their big crypto project entry.

735
00:35:33,560 --> 00:35:35,480
Speaker 1: Yep, and he broke it down even more.

736
00:35:35,560 --> 00:35:37,399
Speaker 3: Put the next one up there, which you can show

737
00:35:37,560 --> 00:35:41,480
Speaker 3: the actual timeline so as he explicitly says that Trump

738
00:35:41,680 --> 00:35:44,839
Speaker 3: launches World Liberty Financial stable coin. In March twenty twenty five,

739
00:35:44,960 --> 00:35:47,920
Speaker 3: two billion dollar investment into Binance by MGX. Two billion

740
00:35:47,960 --> 00:35:51,000
Speaker 3: dollar investment was paid for in USD one. Cez admits

741
00:35:51,000 --> 00:35:53,600
Speaker 3: he applied for a pardon May twenty twenty five. October

742
00:35:53,640 --> 00:35:55,799
Speaker 3: twenty twenty five, he gets his pardon, and then the

743
00:35:55,840 --> 00:35:58,800
Speaker 3: net result is that USD one is now backed by treasuries.

744
00:35:58,960 --> 00:36:01,040
Speaker 3: This yields some sixty eight eighty million dollars a year

745
00:36:01,080 --> 00:36:03,600
Speaker 3: for this world degree financial as long as Binance has

746
00:36:03,640 --> 00:36:05,400
Speaker 3: not redeemed the two billion in USD one.

747
00:36:05,600 --> 00:36:08,400
Speaker 2: Okay, so he literally in the same month does this

748
00:36:08,440 --> 00:36:10,759
Speaker 2: two billion dollar investment that's going to pay the Trump

749
00:36:10,840 --> 00:36:13,759
Speaker 2: sixty to eighty million dollars per year and ask for

750
00:36:13,800 --> 00:36:14,240
Speaker 2: his pardon.

751
00:36:14,360 --> 00:36:15,920
Speaker 1: Yeah, it's good business if you can get it.

752
00:36:16,120 --> 00:36:18,080
Speaker 2: Just so we know, and this is the guy try. Oh,

753
00:36:18,239 --> 00:36:20,200
Speaker 2: I don't know anything about him, but my sons like him.

754
00:36:20,239 --> 00:36:23,239
Speaker 2: Oh yeah, your sons who are running this crypto scam. Yeah,

755
00:36:23,520 --> 00:36:25,799
Speaker 2: they of course they like him. So makes a lot

756
00:36:25,800 --> 00:36:26,160
Speaker 2: of sense.

757
00:36:26,239 --> 00:36:29,880
Speaker 3: Last thing was flagged here about the transcript of the

758
00:36:29,880 --> 00:36:31,960
Speaker 3: sixty minutes interview. Let's put that up on the screen.

759
00:36:32,360 --> 00:36:36,880
Speaker 3: A missing portion had this final question on crypto. Quote,

760
00:36:36,960 --> 00:36:39,360
Speaker 3: are so you're not concerned about the appearance of corruption.

761
00:36:39,680 --> 00:36:42,840
Speaker 3: Trump's edited out reply began with hesitation. He says, quote,

762
00:36:42,840 --> 00:36:45,400
Speaker 3: I can't say, because I can't say I'm not concerned.

763
00:36:45,440 --> 00:36:48,000
Speaker 3: I don't I'd rather not have you asked the question.

764
00:36:48,320 --> 00:36:50,080
Speaker 3: He went on, but I let you ask it. You

765
00:36:50,160 --> 00:36:51,319
Speaker 3: just came to me and you said, kind I ask

766
00:36:51,360 --> 00:36:53,040
Speaker 3: you a question. I said, yeah, this is the question,

767
00:36:53,239 --> 00:36:55,640
Speaker 3: O'Donnell replied, and you answered, to which Trump responded, I

768
00:36:55,680 --> 00:36:57,239
Speaker 3: don't mind. Did I let you do it? I could

769
00:36:57,280 --> 00:36:59,480
Speaker 3: have walked away. I didn't have an answer to this question.

770
00:37:00,160 --> 00:37:02,520
Speaker 3: To answer the question, he concluded, we are number one

771
00:37:02,560 --> 00:37:04,480
Speaker 3: in crypto and that's the only thing I care about.

772
00:37:04,520 --> 00:37:06,440
Speaker 3: I don't want China or anybody else to take it away.

773
00:37:06,600 --> 00:37:08,040
Speaker 3: It's a massive industry.

774
00:37:08,280 --> 00:37:13,240
Speaker 2: And you will recall that Trump sued CBS and sixty

775
00:37:13,320 --> 00:37:17,960
Speaker 2: minutes specifically one yeah over, well he got his payout.

776
00:37:18,040 --> 00:37:20,800
Speaker 4: Uh yeah, he got his payout.

777
00:37:20,560 --> 00:37:21,440
Speaker 1: If you get paid you.

778
00:37:21,400 --> 00:37:26,480
Speaker 2: In specifically over an edit of a Kamala Harris interview.

779
00:37:26,960 --> 00:37:31,120
Speaker 2: And now you have CBS, you know, controlled by Trump allies,

780
00:37:31,160 --> 00:37:34,239
Speaker 2: including Barry Wise, who he spoke of very favorably in

781
00:37:34,280 --> 00:37:37,400
Speaker 2: this interview, and they cut out this answer, which is

782
00:37:37,520 --> 00:37:41,080
Speaker 2: unflattering to him, right, they cut that out, and you

783
00:37:41,120 --> 00:37:44,680
Speaker 2: know Democrats are actually looking now at turning the tables

784
00:37:45,239 --> 00:37:46,360
Speaker 2: ensuing sixty minutes.

785
00:37:46,840 --> 00:37:50,200
Speaker 4: Sure, the editing of this interview.

786
00:37:49,800 --> 00:37:51,720
Speaker 2: But I mean, this is the These are the sorts

787
00:37:51,760 --> 00:37:54,120
Speaker 2: of like, you know, this is the way that they're

788
00:37:54,160 --> 00:37:57,719
Speaker 2: catering to power now and now that they are controlled

789
00:37:57,760 --> 00:38:00,960
Speaker 2: by Trump allies, you know, over Trump allies.

790
00:38:01,680 --> 00:38:03,719
Speaker 4: These are the sort of things to be on the

791
00:38:03,719 --> 00:38:04,319
Speaker 4: watch for here.

792
00:38:04,440 --> 00:38:07,920
Speaker 3: Yes, indeed, in not just there in terms of foreign

793
00:38:07,920 --> 00:38:10,360
Speaker 3: policy and a lot of them, oh yeah, and not

794
00:38:10,440 --> 00:38:13,800
Speaker 3: to mention entertainment, which they are currently eyeing and trying

795
00:38:13,800 --> 00:38:16,359
Speaker 3: to buy. There's a whole lot of sketchy stuff going

796
00:38:16,400 --> 00:38:18,480
Speaker 3: on behind the scenes. Okay, thank you guys so much

797
00:38:18,520 --> 00:38:20,520
Speaker 3: for watching. I know it went a little bit late today.

798
00:38:20,640 --> 00:38:23,240
Speaker 3: The live stream starts when seven thirty.

799
00:38:23,120 --> 00:38:24,959
Speaker 4: Seven thirty, Yes, check your inboment there.

800
00:38:25,440 --> 00:38:29,000
Speaker 2: Poll's close of Virginia at seven, and probably we're going

801
00:38:29,040 --> 00:38:32,120
Speaker 2: to know those results pretty quickly. Things go the you know,

802
00:38:32,160 --> 00:38:34,319
Speaker 2: if it's a span Burger, pretty clear victory, you're going

803
00:38:34,400 --> 00:38:36,560
Speaker 2: to get that one pretty quickly. So probably when we

804
00:38:36,600 --> 00:38:37,960
Speaker 2: come on the air will be able to bring you

805
00:38:38,000 --> 00:38:39,680
Speaker 2: those results. We'll be watching New Jersey.

806
00:38:39,880 --> 00:38:40,000
Speaker 3: You know.

807
00:38:40,040 --> 00:38:41,799
Speaker 2: Another one that I just thought of when we were

808
00:38:41,880 --> 00:38:44,239
Speaker 2: on the show, Graham Platner and Maine is using his

809
00:38:44,400 --> 00:38:48,840
Speaker 2: volunteer army to organize around this ballot proposition.

810
00:38:48,920 --> 00:38:49,880
Speaker 1: Yeah, what is it about me?

811
00:38:50,000 --> 00:38:53,440
Speaker 2: May It's about voting. Yeah, I think absentee ballots. I

812
00:38:53,440 --> 00:38:55,879
Speaker 2: got to check the details. But that'll be an interesting test,

813
00:38:55,960 --> 00:38:58,120
Speaker 2: you know, of his organizing strength as well.

814
00:38:58,719 --> 00:38:59,319
Speaker 4: So there's that one.

815
00:38:59,400 --> 00:39:01,880
Speaker 2: Of course, New York City mayoral race, and Ryan and

816
00:39:01,880 --> 00:39:04,920
Speaker 2: Griffin will be on the ground covering that. Hopefully we

817
00:39:05,000 --> 00:39:08,760
Speaker 2: get another like Tipsy Jamal Bowman to stop by another

818
00:39:08,920 --> 00:39:10,399
Speaker 2: other fun guest like we did last time.

819
00:39:11,080 --> 00:39:13,719
Speaker 3: Yeah, all right, New York City results. We may not

820
00:39:13,760 --> 00:39:16,319
Speaker 3: know tonight, correct, Oh they will know, well, they may

821
00:39:16,360 --> 00:39:18,479
Speaker 3: call it. We won't know the exact like we won't

822
00:39:18,520 --> 00:39:20,200
Speaker 3: have everything. They take a lot that we.

823
00:39:20,120 --> 00:39:23,279
Speaker 2: Won't have everything in But I'm you know, even on

824
00:39:23,320 --> 00:39:25,799
Speaker 2: the primary when we thought it would be we didn't

825
00:39:25,800 --> 00:39:28,040
Speaker 2: think there was any chance with rank choice voting that

826
00:39:28,080 --> 00:39:30,200
Speaker 2: we would know it that night, and we did so.

827
00:39:30,640 --> 00:39:32,600
Speaker 2: I think you're very likely to know who the next

828
00:39:32,600 --> 00:39:33,439
Speaker 2: mayor of New York City.

829
00:39:33,520 --> 00:39:35,080
Speaker 1: Oh no, no, we'll definitely know the mayor.

830
00:39:35,719 --> 00:39:37,560
Speaker 3: For me, the big question is about the fifty percent,

831
00:39:37,640 --> 00:39:40,360
Speaker 3: like Kenny Crack fifty or not. I wonder whether I

832
00:39:40,440 --> 00:39:42,360
Speaker 3: think we'll have a sense of such issues with me.

833
00:39:42,640 --> 00:39:43,359
Speaker 4: I think we'll have a.

834
00:39:43,280 --> 00:39:45,319
Speaker 2: Sense of whether he's on track for that or we

835
00:39:45,360 --> 00:39:46,879
Speaker 2: won't have all the results in, but I think we'll

836
00:39:46,920 --> 00:39:48,480
Speaker 2: have a sense of whether he's headed for that kind

837
00:39:48,480 --> 00:39:49,080
Speaker 2: of a victory.

838
00:39:49,160 --> 00:39:49,880
Speaker 4: So there we go.

839
00:39:50,120 --> 00:39:51,239
Speaker 1: Okay, see you guys later