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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

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Speaker 2: This is everybody's business from Bloomberg Business Week. I'm Stacy

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Speaker 2: Bannock Smith.

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Speaker 3: And I'm Max Chafkin and Max.

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Speaker 4: Twenty twenty five is coming to a close. It has

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Speaker 4: been quite a year, honestly, very hard to process all

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Speaker 4: of it.

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Speaker 3: But you know, here we are.

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Speaker 5: This is my favorite time of the year because we

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Speaker 5: get to break the format. We get to do different

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Speaker 5: things on the show as we process.

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Speaker 2: Yes, and you know, we're looking out over this year

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Speaker 2: and we have to ask ourselves, like, what are the

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Speaker 2: big themes.

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Speaker 4: That have merged in the economy for twenty twenty five.

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Speaker 5: Well, Stacy, you and I knew that we had too.

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Speaker 5: I'm too big for us, too big for two people. Yeah,

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Speaker 5: even too big for Bloomberg, as big as Bloomberg.

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Speaker 3: As we needed a team that would cross many newsrooms.

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Speaker 4: And yes and many genres.

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Speaker 3: Of the greatest podcasters.

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Speaker 2: We know a kind of bad signal across the ech

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Speaker 2: podcasting world.

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Speaker 4: We like to think of it as the legends of

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Speaker 4: pod Yes, and here they are.

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Speaker 2: We have Tracy Alloway, seer of Markets, keeper of the

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Speaker 2: vix and host of Odd Loots, Keeper of the vix.

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Speaker 6: That's a new one.

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Speaker 3: Thank you.

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Speaker 7: If Tracy goes on holiday, that is no Vix.

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Speaker 5: Bad voice you. Here is Felix Salmon, economic sense maker,

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Speaker 5: the King of Context, the host of Slave Money, and

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Speaker 5: our colleague contributor to Bloomberg Weekend.

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Speaker 7: I'm Blueberg Ideas and Culture. It's very flicy and.

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Speaker 2: Last, but definitely not least, the great Robert Smith economic

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Speaker 2: story seeker able to break down the economy in a

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Speaker 2: single minute. Contributing host for Planet Money and of Pushkin's

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Speaker 2: new business history podcast.

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Speaker 8: I'm the only little with the visitor's badge here. I'm

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Speaker 8: very excited.

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Speaker 2: Now we have assembled you all to talk about the

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Speaker 2: year that was twenty twenty five, and we thought we

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Speaker 2: would do it by looking across a bunch of different

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Speaker 2: categories and different parts of the economy. But before we start,

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Speaker 2: because we all deal with numbers a lot every day,

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Speaker 2: we thought we would have you all pick a number,

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Speaker 2: one number that you thought summed up or distilled some

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Speaker 2: part of the twenty twenty five economy. Tracy Alloway, keeper

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Speaker 2: of the vis let's start with you number the year.

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Speaker 6: Okay, Well, I was kind of torn between selecting a

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Speaker 6: number that was like a little bit more unknown versus

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Speaker 6: one of the big giant market cap numbers of one

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Speaker 6: of the big tech stocks that just keeps going up

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Speaker 6: and up and up. But in the end I went

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Speaker 6: with the first one. My number is forty percent.

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Speaker 3: Okay.

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Speaker 6: It is the amount of twenty twenty five US GDP

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Speaker 6: growth that is estimated to be generated by AI investment.

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Speaker 6: So nearly half the US economy is now driven by AI.

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Speaker 7: Capital spending, well half the delta.

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Speaker 8: Sure, yeah, no, no.

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Speaker 6: No, but it's actually wait wait, but it's a bigger

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Speaker 6: driver of the US economy now than consumer spending growth,

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Speaker 6: which is crazy.

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Speaker 7: But consumer spending is still like twenty times bigger than

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Speaker 7: the AI.

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Speaker 6: I'm not sure anymore, honestly.

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Speaker 7: Yeah no, I mean, if you're looking at what is

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Speaker 7: one of the great things about capitalism is that people

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Speaker 7: don't care about large numbers. They only care about growing numbers.

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Speaker 7: And so if you care about what is going to

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Speaker 7: make the US economy even bigger than it is already, yeah.

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Speaker 8: I think that is true.

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Speaker 1: And the US economy has grown what on average around

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Speaker 1: three percent a year, which is amazing, right, three percent

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Speaker 1: adds up to this enormous success that the United States

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Speaker 1: has had, and so the components of that growth of

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Speaker 1: the three percent is incredibly important because it's different every

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Speaker 1: year what is driving the growth of the US economy.

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Speaker 1: So in that sense, like in terms of what's driving

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Speaker 1: the growth, Yeah, it's been an AI year.

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Speaker 5: Are we going to spend the whole time talking about AI?

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Speaker 5: Because I feel like we could, like at least time,

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Speaker 5: I think.

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Speaker 4: To say, Felix Salmon, what is your number?

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Speaker 7: My number is one hundred and fifteen million dollars?

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Speaker 3: Someone just like an art? Is it like painting or something?

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Speaker 4: The banana?

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Speaker 7: And there was a painting that sold for two hundred

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Speaker 7: and thirty eight million dollars, so roughly half a Gustav Klint.

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Speaker 6: Is there your your annual wardrobe budget?

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Speaker 8: I was going to think an apartment we can see

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Speaker 8: from here.

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Speaker 7: It is none of those. It is the legal bill

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Speaker 7: that Charlie Javis managed to run up in her legal

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Speaker 7: fight against JP Morgan. Well actually technically in her legal

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Speaker 7: fight against the US attorneys who were prosecuting her criminally

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Speaker 7: for doing all manner of criminal things when she sold

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Speaker 7: her company to JP Morgan, but because JP Morgan had

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Speaker 7: like Director's Novices insurance and she was a director of

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Speaker 7: an officer or something. She got JP Morgan to pay

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Speaker 7: all of her legal bills. She went to Alex Spiro,

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Speaker 7: who is a friend of the part I'm sure who lawyer,

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Speaker 7: Elon Musk lawyer and various other you know people like that,

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Speaker 7: and he managed to build out twenty one hundred dollars

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Speaker 7: an hour and run up one hundred and fifteen twenty

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Speaker 7: one hundred dollars an hour, ran up one hundred and

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Speaker 7: fifteen million dollars of legal bills, which they then presented

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Speaker 7: to JP Morgan and said, you have to pay this

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Speaker 7: after losing you know, even more than that on buying

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Speaker 7: her company in the first place. Now they have to

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Speaker 7: pay the legal bills to center to jail. It's amazing story.

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Speaker 4: Wait, still stuck on two one hundred dollars an hour.

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Speaker 4: I just feel like I'm in the wrong.

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Speaker 8: It's one hundred dollars added to it.

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Speaker 1: It's kind of a tip tip which as we know,

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Speaker 1: is going to be tax free.

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Speaker 3: What did she do?

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Speaker 5: I honestly, I have trouble keeping straight the fraudulent fintech

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Speaker 5: companies from the not fraudulent ones.

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Speaker 4: That's affair some kindtech thing, right, Yeah.

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Speaker 7: She she had this company that claimed to have millions

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Speaker 7: of customers of people with student loans and that she

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Speaker 7: would help them optimize and refinance and do all of

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Speaker 7: the and find the good deals and the government forgiveness

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Speaker 7: and all the rest of it that you can that

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Speaker 7: you're looking for when you have student loans. She did

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Speaker 7: not have the four million customers that she promised Shaping

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Speaker 7: Morgan she had. She had like twelve, and so yeah,

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Speaker 7: that was the end of that.

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Speaker 5: It was a breakout year for Alex Spiro. He's like,

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Speaker 5: no longer even the Elon guy.

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Speaker 3: He's got a hole.

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Speaker 4: I'm gonna charge what I'm worth.

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Speaker 2: After listening to like motivational tapes in the car, he

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Speaker 2: was like, you know what, I'm gonna make it twenty

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Speaker 2: one hundred dollars.

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Speaker 4: Robert Smith number the year.

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Speaker 8: My number is two point two four.

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Speaker 1: It's gonna be impossible to guess, but I think global

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Speaker 1: and think it involves sex.

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Speaker 4: Oh my gosh, Robert.

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Speaker 7: Is this the total fertility rate in some country?

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Speaker 1: It is the total fertility rate of the plan of

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Speaker 1: the plan two point twenty four, meaning the average woman

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Speaker 1: has two point two four kids. It is barely above

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Speaker 1: the replacement level, which is two point one. It's been

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Speaker 1: going down for decades.

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Speaker 7: Or one hundredth of alex Buro's exactly right.

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Speaker 1: That is how they measure, yes, And so it does

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Speaker 1: appear that during my lifetime, may it be long, the

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Speaker 1: globe will stop growing in population and will begin to decline.

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Speaker 7: I believe that this was also the year in which

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Speaker 7: more than half of the planet lives in a country

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Speaker 7: with a TFA of less than two point one. Absolutely,

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Speaker 7: because China now has a total fertility rate of less

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Speaker 7: than two point one, and once China's flips, then you

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Speaker 7: have more than half the planet.

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Speaker 6: Do you remember the nineties, we were always told that

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Speaker 6: overpopulation was the problem, and now we have Elon Musk

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Speaker 6: obviously saying that we have to have babies to save capitalism.

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Speaker 4: It's a big about.

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Speaker 2: There was also like the Malthusian theory, member of like well,

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Speaker 2: like there's good, we can't run out of food, like

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Speaker 2: the population will self correct.

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Speaker 4: I can't really get out of that mindset and into

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Speaker 4: the mindset of like now we've got to make more people.

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Speaker 1: But you have to start thinking like we don't have

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Speaker 1: an economics profession that has ever dealt with shrinking population

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Speaker 1: globally I mean, yeah, for one country, right, but.

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Speaker 7: The really tough one. But no, I think that Elon

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Speaker 7: has the solution to this, which is pay every woman

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Speaker 7: who has a baby thirty million dollars and then that

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Speaker 7: will be enough of incentive to get people to have

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Speaker 7: more babies.

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Speaker 4: I don't know if I can quite get behind it.

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Speaker 7: And of course only does it for women who have

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Speaker 7: his baby, But in principle at.

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Speaker 3: Scales, I'm kind of depressed about this, Robert.

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Speaker 7: One of the most interesting debates in the world right

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Speaker 7: now is the one about is it good for the

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Speaker 7: planet to have babies or to not have babies? Because

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Speaker 7: for the past twenty years or so, everyone that's been

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Speaker 7: told that the carbon footprint of having a baby is

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Speaker 7: absolutely enormous, especially once you consider the carbon footprint like

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Speaker 7: the babies they will wind up having, and so on

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Speaker 7: and so forth, and the best thing you can have

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Speaker 7: to save the plant, the best thing you can do

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Speaker 7: to save the planet is to have few or even

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Speaker 7: no babies. And now, for the first time, people are

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Speaker 7: coming out with, actually, no, we need to have children,

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Speaker 7: otherwise we won't have the people who are going to

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Speaker 7: be necessary to solve all of these problems.

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Speaker 1: It will rapidly shrink too. You've heard of sort of

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Speaker 1: geometric growth on the way up. Yeah, we're not prepared

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Speaker 1: for geometric growth on the way down. Like the human

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Speaker 1: population will rapidly shrink, and then we're gonna have to

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Speaker 1: figure out, like, is it okay if a GDP goes

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Speaker 1: negative every year?

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Speaker 6: It would be nice if the population shrunk and we

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Speaker 6: saw wage growth go up with it, like we did.

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Speaker 6: Do you remember the black personally? But there was a

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Speaker 6: big population decline and the value of HU and labor

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Speaker 6: went up. I suspect that won't happen this time.

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Speaker 1: Well, housing will be very cheap, like that's another another

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Speaker 1: good thing, right, I'm glad we've been talking about Elon.

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Speaker 5: Musk is my number? And this is maybe a hint,

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Speaker 5: just generally is Musk adjacent?

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Speaker 4: Okay? Musk adjacent?

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Speaker 7: Go?

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Speaker 4: What is your number?

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Speaker 3: Okay? Two hundred and fourteen billion? Anyway?

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Speaker 6: His bonus or his salary, well, whoa Elon would never

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Speaker 6: He doesn't get out of bed for less than that

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Speaker 6: a monthly?

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Speaker 3: It is any any other guesses.

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Speaker 7: The value of his steak in SpaceX.

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Speaker 3: Higher?

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Speaker 5: This is I should have said this is a dubious number.

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Speaker 5: I don't know if that helps. This is the claimed

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Speaker 5: savings by Doge, the number from.

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Speaker 3: The Doge wall of made up to do It's there

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Speaker 3: is evidence it's on the website.

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Speaker 5: Remember Elon Musk said initially that they were going to

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Speaker 5: save two trillion dollars. That was kind of I think

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Speaker 5: that was at a rally in Madison Square Garden that

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Speaker 5: I went to, UH only like a year ago, and

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Speaker 5: then it was kind of knocked down to a billion

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Speaker 5: dollars and here.

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Speaker 3: We are two hundred and fourteen billion.

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Speaker 7: I think there is Big Bulls still maintaining that website

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Speaker 7: is still is it still being Yes?

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Speaker 8: What happened?

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Speaker 5: Okay, my understanding on Big Balls. We'll have to check

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Speaker 5: this after this if we finished recording. I believe Big

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Speaker 5: Balls is back in the Trump administration, but I don't

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Speaker 5: think he's on the UH.

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Speaker 3: I think he's on website. Dude.

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Speaker 5: The funniest thing on this to me anyway, is that

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Speaker 5: the Airbnb guy is like the chief design officer for

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Speaker 5: the US government and and now like there are a

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Speaker 5: couple of websites like you're starting to see his work

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Speaker 5: and they are like they look like night like twenty

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Speaker 5: ten startup.

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Speaker 7: Go go to Trump cards dot Gov and it really

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Speaker 7: does look like Abbah.

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Speaker 6: It looks like you need to put sunglasses on for all.

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Speaker 5: The gold, like rounded corners and like the hell Vetica

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Speaker 5: fond It's like, it's so so strange, especially if you

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Speaker 5: live through that time which were which was so closely

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Speaker 5: identified with Obama and the Airbnb guys were.

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Speaker 7: Like, you know, you know what has rounded corners and

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Speaker 7: it's woke is Calibri.

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Speaker 4: That's the new font.

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Speaker 3: The State.

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Speaker 7: The State Department under Biden swe from Times New Roban

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Speaker 7: Roman to Calibri on the grounds that it's easier to

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Speaker 7: read for people who find it hard to read words.

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Speaker 7: And then Marco Rubio has switched back to Times New Romans,

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Speaker 7: saying that means you are being woke, and through Calibri

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Speaker 7: under the bus, calling it a woke.

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Speaker 6: Font sometimes New Roman. You know how much men think about.

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Speaker 7: Time every like multiple times a day.

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Speaker 4: The best spot.

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Speaker 2: All right, Legends of pod those are our numbers of

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Speaker 2: the year. But now we have some categories, stories of

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Speaker 2: the year. You all sent me your stories and I

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Speaker 2: pulled a few from each of you.

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Speaker 4: Some of you have an interesting mind meld happening, which

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Speaker 4: was was quite interesting.

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Speaker 8: Not just our obsession with fertility.

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Speaker 4: Uh not just your obsession with fertility exactly for yourself.

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Speaker 8: Brother, You're the one that chimed in.

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Speaker 4: With all the facts, all right, all right, yeah, fonts

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Speaker 4: and fertility. So the first category was flubb of the Year.

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Speaker 2: And for this one, Tracy, I'm gonna let you go first,

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Speaker 2: and then Robert and Tracy you had kind of a

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Speaker 2: mind meld here.

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Speaker 6: I mean, this is the obvious one, right, So it's

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Speaker 6: the Liberation Day rollout of the Trump Administration's tariffs.

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Speaker 9: Because poster, my fellow Americans, this is liberation Day waiting

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Speaker 9: for a long time. April second five will forever be

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Speaker 9: remembered as today American industry was reborn, the day America's

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Speaker 9: destiny was reclaimed.

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Speaker 3: And the day that we began.

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Speaker 9: To make America wealthy again.

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Speaker 6: Yeah, it just it did not have to be that way,

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Speaker 6: and yet it was. And even though you know a

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Speaker 6: lot of the market reaction has since recovered, it was

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Speaker 6: still an enormous amount of chaos and it's still an

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Speaker 6: enormous amount of chaos for businesses that is ongoing. And again,

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Speaker 6: it just did not have to be like that.

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Speaker 7: But it was such an amazing buying opportunity for the

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Speaker 7: stock market.

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Speaker 1: Sure, sure, but it's hard to believe, you know, because

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Speaker 1: he hires the best people, but like his staff completely

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Speaker 1: failed him. This is the biggest economic moment he's going

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Speaker 1: to have, one that transformed the world and failed him.

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Speaker 4: Like formula wise.

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Speaker 8: Well the formula.

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Speaker 1: We we got the formula from AI, right, and then

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Speaker 1: they put on their mine.

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Speaker 8: Was more specific about that chart.

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Speaker 1: The tariffs imposed on the uninhabited islands, the herd of

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Speaker 1: McDonald Islands, inhabited only by penguins and.

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Speaker 6: Seals, got it, got more penguins, just.

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Speaker 1: Saying we haven't gotten a lot of money from that

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Speaker 1: island since we imposed ten percent terrritories.

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Speaker 2: That was my favorite moment of liberation d I've found terrifying.

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Speaker 2: When I was watching that poster being unveiled, I was like,

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Speaker 2: this is the end of the economy.

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Speaker 4: I was so worried.

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Speaker 2: I had done a lot of reporting on like Argentina

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Speaker 2: and like big tariffs being imposed suddenly by countries.

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Speaker 4: I was so worried. The only thing that helped me

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Speaker 4: sleep at night was the penguins.

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Speaker 7: I might so I have the exact opposite terror, right,

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Speaker 7: I have this terror that we have right, now in

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Speaker 7: this country tariffs at levels that have not been seen

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Speaker 7: since like the nineteen thirties or earlier. They have transformed

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Speaker 7: global trade. They have rewired the entire global economy. And somehow,

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Speaker 7: because you know, Stonks are doing great, like, there isn't

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Speaker 7: that panic anymore and no one is losing sleep over this,

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Speaker 7: and we have managed to normalize it. And if we

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Speaker 7: were still in this sort of post liberation day panic zone,

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Speaker 7: that would feel more sensible to me.

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Speaker 6: Well, I'm still losing sleep. And also just on a

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Speaker 6: personal note, i bought something from Holland a few months ago,

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Speaker 6: actually many months ago now, and I'm still waiting for

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Speaker 6: it to be shipped because they can't figure out the

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Speaker 6: US tariff system that was back in September.

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Speaker 8: Is it a stroop waffle? It is not.

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Speaker 7: Is it a windmill?

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Speaker 8: I'm not going to tell you it's a then you

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Speaker 8: paid too much?

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Speaker 5: So it was definitely, like unquestionably a political screw up.

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Speaker 5: I think, like basically Trump's presidency has gone downhill. I

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Speaker 5: think pretty much since that moment, are we one hundred

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Speaker 5: percent sure it was an economic screw up? Like to

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Speaker 5: your point, Felix, nothing bad has really happened.

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Speaker 7: A lot of bad things have happened, and to Tracy's point,

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Speaker 7: they have been kind of overshadowed by AI. And if

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Speaker 7: it wasn't for the AI, things would look a lot worse.

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Speaker 7: And a lot of the bad things will continue to happen.

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Speaker 7: It will continue to get worse in the future unless

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Speaker 7: the Supreme Court decides that the whole thing is illegal.

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Speaker 8: Like, there's still half the.

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Speaker 5: Thing, right, We'll still have a bunch of tariffs even

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Speaker 5: if they roll that a liberation day is illegal to.

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Speaker 6: The care, which is another failure of the rollout right,

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Speaker 6: is we don't know if these are legal or not,

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Speaker 6: and at some point maybe half of them are going

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Speaker 6: to get rolled back.

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Speaker 4: There numbers so many exceptions. Yeah, that's the thing.

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Speaker 2: The numbers keep changing. Hundreds of times they've changed. There

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Speaker 2: have been so many exceptions. I think there have been

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Speaker 2: estimates that as much as a third of what we

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Speaker 2: import is like exceptioned from the tariffs. So I'm not

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Speaker 2: sure if the impact is as devastating.

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Speaker 7: Look at Canada. Canada's my favorite one, which is like

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Speaker 7: the entire country of Canada is up in arms over

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Speaker 7: these tariffs. Basically, the entire country of Canada has just

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Speaker 7: boycotted traveling to the United States, and they have this

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Speaker 7: tiny little asterisk underneath the massive terriff saying unless it's

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Speaker 7: covered by USMCA, which is ninety plus percent of all

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Speaker 7: of the trade between the US and Canada. You like, yeah,

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Speaker 7: it's all exceptions. Everything is exceptions.

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Speaker 6: The big ironery here is if you think about the

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Speaker 6: Trump administration, they say they hate red tape and bureaucracy, right,

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Speaker 6: think of how many man hours are being spent out

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Speaker 6: pouring over customs forms. I can't imagine.

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Speaker 1: But luckily, luckily, we all have our jobs at the

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Speaker 1: Apple iPhone factory down the road, where we all work

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Speaker 1: our shifts, the American made fingers deck stras, so manufacturing's back.

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Speaker 2: This feels like a good moment to seg we to

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Speaker 2: a person of the year, Max, Your answer was by

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Speaker 2: far that was fascinating to me.

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Speaker 4: I want you to explain it.

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Speaker 5: Well. I just feel like, obviously the person of the year,

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Speaker 5: like objectively, is probably Donald Trump for all the reasons

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Speaker 5: we're talking about.

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Speaker 3: I picked Sho Hee Otani, who.

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Speaker 5: Is arguably, or I would argue, the greatest baseball player

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Speaker 5: of all time, greater than Babe Ruth. He put together

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Speaker 5: an amazing performance in the World Series.

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Speaker 8: Get there are the right.

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Speaker 7: Fields, gone sky scraping home run from Shoo Tani.

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Speaker 3: We are living through a wonderful, wonderful baseball moment.

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Speaker 5: Show Heyo Tani pitched in a game and hit home

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Speaker 5: runs and even Babe Ruth, the greatest baseball player of

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Speaker 5: all time, did not do that. It's really freaking cool.

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Speaker 5: The World Baseball Classic, by the way, coming in March.

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Speaker 3: Good times.

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Speaker 4: Mark your calendars, mar your calendars.

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Speaker 1: Were we allowed to pick happy news. That's way too

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Speaker 1: that's way too positive.

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Speaker 8: For this year.

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Speaker 4: Okay, Now our feud of the year, Felix Few to

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Speaker 4: the air. You and Robert had kind of an interesting

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Speaker 4: mind meld hair but fire away.

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Speaker 7: So my yeah, I went full versus mask, which was

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Speaker 7: a famous shouting match that almost became a fist.

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Speaker 8: Should we show them? It was chest to chess. There

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Speaker 8: was shoving.

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Speaker 5: It was a.

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Speaker 2: Maybe her chest they were doing the leg, yeah they were.

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Speaker 7: Yeah, it was like alpha males trying to back war totally.

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Speaker 7: And that in the way and that like within you

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Speaker 7: could hear them screaming at each other from the Oval office.

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Speaker 8: Like an ambassador to Italy or something like that.

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Speaker 1: Had her overheard this whole thing and allegedly Bestn't called

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Speaker 1: Musk a total fraud.

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Speaker 4: Wow, he seems so relaxed.

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Speaker 5: I say, like he contains multitudes, because his vibe is

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Speaker 5: not the like I'm gonna throw it, I'm gonna take

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Speaker 5: a swing.

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Speaker 6: Well that was when he got the eye injury as well. Right,

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Speaker 6: that was like supposedly from that fight.

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Speaker 5: I mean Musk said it was his kid, so right,

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Speaker 5: but we all know boom.

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Speaker 10: Yeah.

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Speaker 5: I was just watching around with the lex and I said,

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Speaker 5: go ahead, punch me in the face, and he did.

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Speaker 3: Turns out, even a five year old punching you in

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Speaker 3: the in the face actually.

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Speaker 7: Extra the best, you know, for people who knew him

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Speaker 7: over the past cup lived decades, did have this incredibly

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Speaker 7: physically aggressive side to him, and he he you know,

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Speaker 7: in good old hedge fund manager style. He goes very

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Speaker 7: very hard when he goes. And this was, like, to

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Speaker 7: be clear, a fight over who should get to nominate

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Speaker 7: the head of the I R S. It does not

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Speaker 7: sound like a thing to really scream at people.

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Speaker 4: That's literally what the fight was about.

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Speaker 3: Yeah.

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Speaker 5: One of the reasons I'm Dubious has actually happened is

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Speaker 5: because well I just think that one of the ways

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Speaker 5: you get Trump's attention is by performing, like performing aggression

439
00:21:38,920 --> 00:21:42,920
Speaker 5: and like being a part of this like reality television show.

440
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Speaker 5: And I think whether or not Beston really shoved Musk

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Speaker 5: or whacked him or whatever, it was a very savvy

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Speaker 5: move because it's a move that like was it yeah,

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Speaker 5: within because within Trump world, like this is a way they.

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Speaker 4: Would all get fired if we did this in the workplaces.

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Speaker 5: Yeah, but if your boss is as you know, as

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Speaker 5: different as all, Like you know, remember there was this

447
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Speaker 5: power struggle going on in the it's it's it's easy

448
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Speaker 5: to forget that, like Elon Musk was for like a

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Speaker 5: very short amount of time, like the most powerful person

450
00:22:09,320 --> 00:22:10,000
Speaker 5: in the Trump.

451
00:22:10,280 --> 00:22:15,040
Speaker 7: I know, the entire development world really really wishes that

452
00:22:15,160 --> 00:22:17,520
Speaker 7: Marco Rubio had done this to mask over the USA.

453
00:22:17,600 --> 00:22:20,440
Speaker 7: I d cuts right, Yeah, Like all you needed to do,

454
00:22:20,520 --> 00:22:23,679
Speaker 7: it turns out, was shout at him very loudly in

455
00:22:23,720 --> 00:22:25,560
Speaker 7: the White House and then that would get him to

456
00:22:25,640 --> 00:22:27,600
Speaker 7: bank off and we could have saved you know, half

457
00:22:27,640 --> 00:22:30,640
Speaker 7: a million lives. But apparently we hadn't worked that out

458
00:22:30,680 --> 00:22:33,320
Speaker 7: at that point. And now you know, all of these children.

459
00:22:33,040 --> 00:22:35,720
Speaker 6: Are dying Americans who go to France. Know that talking

460
00:22:35,800 --> 00:22:38,640
Speaker 6: louder always works, That's true.

461
00:22:39,280 --> 00:22:40,960
Speaker 4: They're just pretending that understand.

462
00:22:42,720 --> 00:22:50,440
Speaker 1: Meme of the Year, Robert So my meme of the year,

463
00:22:50,680 --> 00:22:53,040
Speaker 1: and I laugh every time I see it is a

464
00:22:53,280 --> 00:22:57,119
Speaker 1: still from when President Trump toured the FED construction.

465
00:22:57,560 --> 00:22:58,600
Speaker 6: That's a good one.

466
00:22:58,280 --> 00:23:00,400
Speaker 8: And there he is wearing a hard hat.

467
00:23:00,480 --> 00:23:03,040
Speaker 1: Jay Powell is also wearing a hard hat, you know,

468
00:23:03,200 --> 00:23:05,960
Speaker 1: chair of the FED. And they're walking along and Trump

469
00:23:06,040 --> 00:23:10,199
Speaker 1: just starts making up some numbers about cost overruns.

470
00:23:10,320 --> 00:23:12,280
Speaker 10: It looks like it said about three point one billion,

471
00:23:12,520 --> 00:23:15,840
Speaker 10: one up a little bit or a lot, so the

472
00:23:15,880 --> 00:23:17,960
Speaker 10: two point seven is now three point one.

473
00:23:19,600 --> 00:23:20,960
Speaker 9: Yeah, it just came out.

474
00:23:21,640 --> 00:23:24,160
Speaker 7: Yeah, I haven't heard that from anybody the FED.

475
00:23:25,400 --> 00:23:26,200
Speaker 10: It just came out.

476
00:23:27,000 --> 00:23:31,160
Speaker 1: And poor Jay Powell, who actually takes numbers seriously looks.

477
00:23:31,000 --> 00:23:34,679
Speaker 8: Confused as disturbed. He picks up a piece of paper

478
00:23:34,720 --> 00:23:36,040
Speaker 8: and has a like what.

479
00:23:36,880 --> 00:23:37,400
Speaker 4: We should say?

480
00:23:37,400 --> 00:23:40,600
Speaker 2: It was the sort of the restoration thesion building.

481
00:23:41,240 --> 00:23:41,560
Speaker 5: This was.

482
00:23:41,680 --> 00:23:45,960
Speaker 7: This was j Powell's version of the famous Jonathan Swan

483
00:23:46,080 --> 00:23:50,560
Speaker 7: reactions to Donald Trump during his HBO actuals an HBO interview.

484
00:23:50,680 --> 00:23:53,840
Speaker 7: The whey he's just looking at Trump thing what that

485
00:23:54,040 --> 00:23:55,280
Speaker 7: is talking about?

486
00:23:55,960 --> 00:23:58,800
Speaker 8: I have the numbers in front of me. Oh my gosh, yes,

487
00:23:59,000 --> 00:24:00,680
Speaker 8: and so no, it gets it pops.

488
00:24:00,440 --> 00:24:04,159
Speaker 1: Up all the time where it's just like with lines

489
00:24:04,240 --> 00:24:06,840
Speaker 1: like oh, you know me, me looking at the credit

490
00:24:06,880 --> 00:24:09,320
Speaker 1: card bill after my kids ticked the credit cards out

491
00:24:09,400 --> 00:24:09,840
Speaker 1: or something like that.

492
00:24:09,880 --> 00:24:10,280
Speaker 8: It's like.

493
00:24:12,880 --> 00:24:14,800
Speaker 2: He had a very he had a very pronounced frown,

494
00:24:15,400 --> 00:24:17,320
Speaker 2: right because like Trump handed him the bill, he just

495
00:24:17,560 --> 00:24:18,919
Speaker 2: very very.

496
00:24:19,000 --> 00:24:23,200
Speaker 7: You just five billion dollars on you building and he's like, no,

497
00:24:23,280 --> 00:24:24,280
Speaker 7: I didn't, and he was like.

498
00:24:24,280 --> 00:24:26,600
Speaker 2: Nope, you've added in a different building.

499
00:24:27,800 --> 00:24:28,960
Speaker 3: We're going to play a game later.

500
00:24:29,560 --> 00:24:31,320
Speaker 5: And what I wanted to do but I'm not going

501
00:24:31,400 --> 00:24:34,560
Speaker 5: to do, was have clips of Trump talking about building

502
00:24:34,600 --> 00:24:36,960
Speaker 5: renovations and we would have to guess whether it was

503
00:24:37,040 --> 00:24:39,240
Speaker 5: from whether the quote was from an h G t

504
00:24:39,440 --> 00:24:40,920
Speaker 5: V episode.

505
00:24:40,720 --> 00:24:42,399
Speaker 6: Or I would be so.

506
00:24:44,520 --> 00:24:45,480
Speaker 4: Or a un speech.

507
00:24:45,880 --> 00:24:49,840
Speaker 5: He talks about building materials and buildings so often they're

508
00:24:49,960 --> 00:24:54,679
Speaker 5: old gold ecce walls, stacy.

509
00:24:54,880 --> 00:24:57,159
Speaker 7: Everybody like the fact that he spends half of his

510
00:24:58,080 --> 00:25:02,320
Speaker 7: address to the General and to the world saying like,

511
00:25:03,080 --> 00:25:06,520
Speaker 7: I bid on renovating this building. They declined my bid.

512
00:25:06,560 --> 00:25:08,640
Speaker 7: If I'd done it, it would have been marble impact.

513
00:25:10,200 --> 00:25:11,359
Speaker 4: He actually doesn't look at the.

514
00:25:13,160 --> 00:25:15,000
Speaker 7: Which and also, by the way, I just want to say,

515
00:25:15,040 --> 00:25:17,159
Speaker 7: for the record, since I'm at Bloomberg and everything we

516
00:25:17,200 --> 00:25:19,720
Speaker 7: say here is true, the un Building is the most

517
00:25:19,720 --> 00:25:21,480
Speaker 7: beautiful building in New York. I love it so much.

518
00:25:21,640 --> 00:25:22,640
Speaker 4: Is a beautiful building.

519
00:25:23,160 --> 00:25:24,480
Speaker 3: Yes, I am.

520
00:25:24,560 --> 00:25:26,480
Speaker 7: I am happy that Donald Trump did not get his

521
00:25:26,560 --> 00:25:27,000
Speaker 7: paws on.

522
00:25:27,000 --> 00:25:31,119
Speaker 2: It, all right, CEO Tweet of the Year slash Social

523
00:25:31,200 --> 00:25:32,320
Speaker 2: media post of the Year.

524
00:25:36,359 --> 00:25:37,160
Speaker 4: Tracy Alloway.

525
00:25:37,200 --> 00:25:39,240
Speaker 3: This is you wait? Is this the ACMAN division or

526
00:25:39,240 --> 00:25:40,200
Speaker 3: the non Acmen.

527
00:25:39,920 --> 00:25:41,400
Speaker 4: Division non Acman division.

528
00:25:43,080 --> 00:25:46,680
Speaker 6: This one was tough because of Acmen, but I went

529
00:25:46,720 --> 00:25:48,479
Speaker 6: with a non Acman one. And there's a little bit

530
00:25:48,480 --> 00:25:52,080
Speaker 6: of recency bias in here as well, but it is pallanteer.

531
00:25:52,200 --> 00:25:58,800
Speaker 6: CEO Alex Karp announcing the Neurodivergent Fellowship after his appearance

532
00:25:58,920 --> 00:26:02,120
Speaker 6: at the Deal Book com from Amazing where he couldn't

533
00:26:02,280 --> 00:26:05,600
Speaker 6: quite sit still and made a lot of erratic hand movements.

534
00:26:05,640 --> 00:26:08,960
Speaker 6: And the funniest thing about the fellowship is they've they

535
00:26:09,000 --> 00:26:11,200
Speaker 6: put up the application form and I took a look

536
00:26:11,200 --> 00:26:15,240
Speaker 6: at it and there's no like requirements listed at all,

537
00:26:15,680 --> 00:26:17,800
Speaker 6: So I don't know if they're measuring people on the

538
00:26:17,800 --> 00:26:20,840
Speaker 6: basis of like how good they perform or how neuro

539
00:26:20,920 --> 00:26:22,440
Speaker 6: divergent they actually are.

540
00:26:22,560 --> 00:26:25,439
Speaker 5: I'm just so confused because I thought Alex Karp was

541
00:26:25,520 --> 00:26:31,280
Speaker 5: really against diversity, equity and inclusion and various important and

542
00:26:31,359 --> 00:26:33,520
Speaker 5: you know, emphasizing differences and so on.

543
00:26:33,760 --> 00:26:34,919
Speaker 3: Yeah, it seems strange.

544
00:26:34,960 --> 00:26:36,440
Speaker 5: I've also seen a lot of clips of him where

545
00:26:36,440 --> 00:26:39,840
Speaker 5: he isn't moving around that erratically, which is another thing

546
00:26:39,920 --> 00:26:42,520
Speaker 5: that I don't know. He's clearly doing something right though,

547
00:26:42,520 --> 00:26:45,760
Speaker 5: because the stock is well, not lately, but it's.

548
00:26:47,040 --> 00:26:47,399
Speaker 4: Robert.

549
00:26:47,400 --> 00:26:49,600
Speaker 2: You also had a very funny tweet I wanted you

550
00:26:49,680 --> 00:26:50,480
Speaker 2: to talk about.

551
00:26:50,760 --> 00:26:53,280
Speaker 1: Oh well, So I do not follow a lot of

552
00:26:53,320 --> 00:26:55,520
Speaker 1: CEOs on Twitter, because thank god, my job does not

553
00:26:55,600 --> 00:26:58,400
Speaker 1: require it, but I did see one because I see

554
00:26:58,440 --> 00:27:02,560
Speaker 1: this guy's tweets all the time. Brian Johnson, fintech CEO

555
00:27:02,960 --> 00:27:04,600
Speaker 1: and life extension Google.

556
00:27:04,720 --> 00:27:07,639
Speaker 4: He's the one who's like always injecting the blood of He.

557
00:27:09,119 --> 00:27:12,359
Speaker 7: Has trademarked the phrase never die.

558
00:27:13,400 --> 00:27:15,359
Speaker 1: And he stands like shirtless next to his son and

559
00:27:15,400 --> 00:27:18,439
Speaker 1: me like, you can't tell the difference. I'm like, so,

560
00:27:19,840 --> 00:27:23,360
Speaker 1: I'm kind of I'm like cringey and concerned about him

561
00:27:23,359 --> 00:27:27,320
Speaker 1: all the time. But but but in the last few

562
00:27:27,320 --> 00:27:30,639
Speaker 1: weeks he sent he sent a tweet and it started out, guys,

563
00:27:31,119 --> 00:27:35,360
Speaker 1: I finally have a girlfriend, and he says, like it's

564
00:27:35,560 --> 00:27:37,760
Speaker 1: basically a coworker of his, and it's like she fits

565
00:27:37,800 --> 00:27:38,960
Speaker 1: me like a puzzle piece.

566
00:27:39,720 --> 00:27:41,680
Speaker 8: And it was a very long love letter to you.

567
00:27:42,440 --> 00:27:44,679
Speaker 1: And I was actually kind of moved because I'm like,

568
00:27:45,440 --> 00:27:48,000
Speaker 1: maybe now you can live a normal life, like like

569
00:27:48,280 --> 00:27:50,960
Speaker 1: it seems like you were searching for something in all

570
00:27:51,000 --> 00:27:54,960
Speaker 1: of this weird supplements and and nighttime emissions, but like

571
00:27:55,560 --> 00:27:57,719
Speaker 1: you can finally just like go on a date and

572
00:27:57,720 --> 00:27:59,880
Speaker 1: have a picnic and like watch Netflix.

573
00:28:00,680 --> 00:28:03,000
Speaker 6: Or maybe in ten years he's going to start preserving

574
00:28:03,040 --> 00:28:04,120
Speaker 6: his girlfriend in like.

575
00:28:05,240 --> 00:28:07,120
Speaker 4: Yeah, kind of a bride of Frankenstein's.

576
00:28:07,200 --> 00:28:07,760
Speaker 6: You never know.

577
00:28:08,000 --> 00:28:12,560
Speaker 7: Brian Johnson is definitely emblematic and Alex Cup is as well.

578
00:28:12,720 --> 00:28:17,879
Speaker 7: Of the way in which people become very, very weird

579
00:28:18,000 --> 00:28:20,640
Speaker 7: after they become billionaires.

580
00:28:20,960 --> 00:28:23,520
Speaker 4: That's true. It doesn't seem to be good for people.

581
00:28:24,160 --> 00:28:25,879
Speaker 7: Yeah, I would, I would say, don't do it.

582
00:28:26,320 --> 00:28:27,800
Speaker 3: I think it's getting more pronounced too.

583
00:28:28,000 --> 00:28:30,240
Speaker 2: I don't think there's a danger of me becoming a billionaire,

584
00:28:30,280 --> 00:28:31,320
Speaker 2: although I don't know.

585
00:28:31,400 --> 00:28:35,000
Speaker 5: It's but like the there are greater incentives also to

586
00:28:35,080 --> 00:28:37,720
Speaker 5: be weird, Like I was saying, like Alex Karp has

587
00:28:37,760 --> 00:28:40,240
Speaker 5: gotten more publicly weird over the last couple of years

588
00:28:40,240 --> 00:28:42,200
Speaker 5: and with the whole like meme stock thing or whatever.

589
00:28:42,440 --> 00:28:45,040
Speaker 7: And well, I mean Bet Ackman is another good example,

590
00:28:45,160 --> 00:28:47,720
Speaker 7: and his investors are hell well, I mean, Tracy, you

591
00:28:47,720 --> 00:28:50,880
Speaker 7: odd the best to us. How do Bill Ackman's investors

592
00:28:50,920 --> 00:28:52,360
Speaker 7: think of Bill Ackman these days?

593
00:28:52,440 --> 00:28:53,920
Speaker 6: I'm gonna go with no comment.

594
00:28:54,040 --> 00:28:58,080
Speaker 5: Right, different investor community, right, I mean, like, are there

595
00:28:58,080 --> 00:29:00,920
Speaker 5: a lot of retail investors like right Robin types putting

596
00:29:00,960 --> 00:29:01,680
Speaker 5: money into actmen?

597
00:29:01,800 --> 00:29:03,880
Speaker 7: Well, they don'tely have a vehicle yet, yeah.

598
00:29:03,720 --> 00:29:06,000
Speaker 6: Yeah, I mean they definitely follow what you said. So

599
00:29:06,120 --> 00:29:09,040
Speaker 6: like the Fanny Mae Freddie Mack move, people were really

600
00:29:09,080 --> 00:29:09,760
Speaker 6: interested in that.

601
00:29:10,160 --> 00:29:12,160
Speaker 1: But billionaires used to be boring, I mean, or at

602
00:29:12,200 --> 00:29:14,160
Speaker 1: least like the whole idea was if you were Warren

603
00:29:14,200 --> 00:29:17,480
Speaker 1: Buffett or Bill Gates, you didn't have to perform for

604
00:29:17,520 --> 00:29:21,600
Speaker 1: the world like you were super rich and running successful companies.

605
00:29:21,360 --> 00:29:22,160
Speaker 7: Like that was enough.

606
00:29:22,200 --> 00:29:24,360
Speaker 1: Your plate was full and people really looked up to

607
00:29:24,360 --> 00:29:25,320
Speaker 1: you and admired you.

608
00:29:25,720 --> 00:29:27,680
Speaker 8: And now I don't know what this mix is.

609
00:29:27,720 --> 00:29:30,480
Speaker 6: This is important, Actually, this is my thesis. Okay, there

610
00:29:30,560 --> 00:29:33,880
Speaker 6: used to be natural limits on capitalism are either in

611
00:29:33,920 --> 00:29:38,360
Speaker 6: the form of higher taxes or in terms of religion. Right,

612
00:29:38,520 --> 00:29:40,840
Speaker 6: it was good to give to charity. You didn't want

613
00:29:40,880 --> 00:29:43,200
Speaker 6: to become a billionaire because you wanted to give away

614
00:29:43,200 --> 00:29:47,720
Speaker 6: at least some of your wealth. Now, with prosperity, gospel

615
00:29:47,840 --> 00:29:50,680
Speaker 6: and all of that and just the general like cultural shift,

616
00:29:50,960 --> 00:29:52,600
Speaker 6: you want to be as rich as possible, and you

617
00:29:52,680 --> 00:29:53,240
Speaker 6: just keep going.

618
00:29:53,360 --> 00:29:59,520
Speaker 7: And when Warren Buffett wound up having some very odd

619
00:29:59,560 --> 00:30:01,880
Speaker 7: relationship between his secretary and his wife and all the

620
00:30:01,920 --> 00:30:03,720
Speaker 7: rest of it, it was kept very quiet and he

621
00:30:03,760 --> 00:30:07,959
Speaker 7: never talked about it. When Alex Cup has a weird relationship,

622
00:30:08,120 --> 00:30:10,160
Speaker 7: he's like, he comes out in public and says, I'm

623
00:30:10,240 --> 00:30:11,880
Speaker 7: geographically monogamous.

624
00:30:13,760 --> 00:30:23,000
Speaker 4: All right. Product of the year, Felix Salmon, Oh la boo.

625
00:30:22,880 --> 00:30:25,480
Speaker 3: Boos, Tracy.

626
00:30:25,600 --> 00:30:28,280
Speaker 6: If you did not have that down, that would have

627
00:30:28,280 --> 00:30:31,600
Speaker 6: been great. A small labuobu collection, Yes, well, I just

628
00:30:31,640 --> 00:30:33,720
Speaker 6: have one. Someone gave it to me. I feel like

629
00:30:33,760 --> 00:30:36,360
Speaker 6: I need to specify that. But another one had appeared

630
00:30:36,360 --> 00:30:39,240
Speaker 6: at your desk. That was someone else's laboobu that they

631
00:30:39,280 --> 00:30:43,320
Speaker 6: brought in in La Boobo solidarity. But here here's the thing.

632
00:30:43,400 --> 00:30:46,400
Speaker 6: I never wanted one. I was kind of like interested

633
00:30:46,400 --> 00:30:49,160
Speaker 6: in them as a cultural and economic phenomenon because it

634
00:30:49,200 --> 00:30:53,320
Speaker 6: was the first cultural export of you know, huge significance

635
00:30:53,360 --> 00:30:55,800
Speaker 6: from China. But I did not want one personally, and

636
00:30:55,800 --> 00:30:58,160
Speaker 6: then someone gave it to me, and I actually really

637
00:30:58,200 --> 00:31:00,120
Speaker 6: love it, and I kind of want another one. I

638
00:31:00,120 --> 00:31:01,360
Speaker 6: don't know what's happening why.

639
00:31:02,040 --> 00:31:04,000
Speaker 4: I don't know, because a little evil bunnies.

640
00:31:04,240 --> 00:31:07,200
Speaker 6: I don't know, like it's it Actually it's really soft.

641
00:31:07,520 --> 00:31:09,000
Speaker 6: It kind of looks at you funny.

642
00:31:09,160 --> 00:31:12,360
Speaker 1: This is how addictions start, you know, I know free,

643
00:31:12,400 --> 00:31:13,320
Speaker 1: I was just gonna try it on.

644
00:31:13,560 --> 00:31:15,480
Speaker 8: That's right now. I think about them all the time.

645
00:31:15,520 --> 00:31:15,880
Speaker 4: That's right.

646
00:31:15,920 --> 00:31:18,880
Speaker 6: My labooboo dealer in Chinatown gave me the first.

647
00:31:18,720 --> 00:31:19,400
Speaker 8: One to three.

648
00:31:20,040 --> 00:31:21,200
Speaker 7: Have you named your livery booth?

649
00:31:21,280 --> 00:31:21,680
Speaker 3: I have not.

650
00:31:21,880 --> 00:31:23,720
Speaker 6: No, I haven't gone that far. Although I know you

651
00:31:23,760 --> 00:31:25,840
Speaker 6: can buy little outfits for them like.

652
00:31:25,840 --> 00:31:29,680
Speaker 7: An American girl dolls, and if your Naomi's asaka, you

653
00:31:29,680 --> 00:31:30,040
Speaker 7: can be.

654
00:31:30,080 --> 00:31:33,400
Speaker 4: Dazzle them, yes, and sell them for enormous amounts of money.

655
00:31:33,560 --> 00:31:36,400
Speaker 6: If I start bedazzling my labooboes, you guys will put

656
00:31:36,400 --> 00:31:40,280
Speaker 6: me in some sort of like rehab facility, right, yes, okay, yes,

657
00:31:40,480 --> 00:31:43,120
Speaker 6: Well Felix, though, why was this your product of the air?

658
00:31:43,840 --> 00:31:48,840
Speaker 7: Partly for exactly the reason that Dracy gave, which is

659
00:31:48,960 --> 00:31:54,160
Speaker 7: the people love to compare them to beanie babies into

660
00:31:54,280 --> 00:32:00,080
Speaker 7: other crazies that have been largely domestic. But the interesting

661
00:32:00,120 --> 00:32:04,000
Speaker 7: thing about Laboo Boos is that and a Chinese import.

662
00:32:04,480 --> 00:32:09,360
Speaker 7: And if you are PopMart, which makes Laboo Boos, the

663
00:32:09,520 --> 00:32:13,680
Speaker 7: entire United States is just a kind of afterthought for you.

664
00:32:13,680 --> 00:32:16,080
Speaker 7: You know, what you really care about is China. That's

665
00:32:16,080 --> 00:32:18,880
Speaker 7: where the real market is. And then there's natural spill

666
00:32:18,880 --> 00:32:20,840
Speaker 7: over into the rest of the world and people are like, oh,

667
00:32:20,840 --> 00:32:23,200
Speaker 7: the Americans like them do and that's great, and but like,

668
00:32:23,400 --> 00:32:27,960
Speaker 7: it's the first time I can remember America falling for

669
00:32:28,160 --> 00:32:33,400
Speaker 7: a huge sort of toy kraze that wasn't American and

670
00:32:33,480 --> 00:32:36,520
Speaker 7: where American main character driving We're not the main character

671
00:32:36,560 --> 00:32:36,920
Speaker 7: at all.

672
00:32:37,080 --> 00:32:39,360
Speaker 5: I picked TikTok for the same reason, for the like

673
00:32:39,480 --> 00:32:42,640
Speaker 5: Chinese export thing, and just like, oh yeah, the fact

674
00:32:42,640 --> 00:32:45,400
Speaker 5: that we started the year like TikTok was banned. There

675
00:32:45,400 --> 00:32:48,440
Speaker 5: were all these good memes I don't remember, but one

676
00:32:48,480 --> 00:32:50,840
Speaker 5: of my thoughts on meme was, were there all these

677
00:32:50,840 --> 00:32:53,840
Speaker 5: funny red note memes where like people were posting like

678
00:32:53,960 --> 00:32:55,480
Speaker 5: Chinese translations of.

679
00:32:55,560 --> 00:32:56,960
Speaker 3: Various US memes.

680
00:32:57,120 --> 00:32:59,920
Speaker 5: They were like random people in the US being like,

681
00:33:00,040 --> 00:33:03,320
Speaker 5: here's to my Chinese handler, like gagging around about the

682
00:33:03,880 --> 00:33:06,640
Speaker 5: suggestion that TikTok is secretly, you know, a Chinese spy

683
00:33:06,680 --> 00:33:09,120
Speaker 5: app or something, and and yeah, we we had this

684
00:33:09,240 --> 00:33:11,920
Speaker 5: brief flirtation with this app called Red Note, which now

685
00:33:11,960 --> 00:33:12,840
Speaker 5: everyone has forgotten.

686
00:33:12,840 --> 00:33:16,120
Speaker 7: But TikTok, to be clear, has been banned all year.

687
00:33:17,640 --> 00:33:20,320
Speaker 8: A law. It is a law of the United States

688
00:33:20,360 --> 00:33:23,080
Speaker 8: of America. Everyone just laughing, But the.

689
00:33:22,960 --> 00:33:25,200
Speaker 4: White House has a TikTok account that has like.

690
00:33:25,160 --> 00:33:28,239
Speaker 5: A lot and they all got together we decided to

691
00:33:28,240 --> 00:33:32,320
Speaker 5: ban it. Democrats and Republicans. It was like basically everyone agreed.

692
00:33:32,560 --> 00:33:36,560
Speaker 5: And then when little threat to take away TikTok, and

693
00:33:36,840 --> 00:33:40,000
Speaker 5: you know, everybody simultaneously decided that was a bad idea.

694
00:33:40,160 --> 00:33:43,880
Speaker 1: No, everyone except the law, and I know we've all

695
00:33:43,920 --> 00:33:44,760
Speaker 1: lost our minds.

696
00:33:44,760 --> 00:33:47,080
Speaker 4: We learned that they don't matter that much.

697
00:33:47,200 --> 00:33:50,120
Speaker 7: Now it goes to the point where you know, Apple,

698
00:33:50,880 --> 00:33:53,720
Speaker 7: which is a Laura Briting company, did what it was

699
00:33:53,760 --> 00:33:57,000
Speaker 7: required to do by the law and removed TikTok from

700
00:33:57,000 --> 00:34:01,360
Speaker 7: the app store, and the White House had to beg

701
00:34:01,640 --> 00:34:04,360
Speaker 7: Apple to put it back and Apple is like, but

702
00:34:04,440 --> 00:34:06,600
Speaker 7: there's a law against it. We have to follow the law.

703
00:34:06,760 --> 00:34:09,560
Speaker 7: And eventually, like that, you know, the Solicitor General had

704
00:34:09,560 --> 00:34:11,560
Speaker 7: to send all of these letters saying like, no, honestly,

705
00:34:11,640 --> 00:34:13,480
Speaker 7: you can do it, we will not prosecute you, and

706
00:34:13,560 --> 00:34:14,800
Speaker 7: eventually they relented.

707
00:34:14,800 --> 00:34:19,480
Speaker 4: It feels like a trap. All right, now we have

708
00:34:20,040 --> 00:34:21,360
Speaker 4: Bill Ackman's tweets.

709
00:34:26,239 --> 00:34:27,200
Speaker 3: This is now Max.

710
00:34:27,280 --> 00:34:31,160
Speaker 2: You requested this category specifically, talk about why you requested

711
00:34:31,160 --> 00:34:31,560
Speaker 2: the category.

712
00:34:31,600 --> 00:34:34,239
Speaker 5: First of all, the official name of this category is

713
00:34:34,320 --> 00:34:37,400
Speaker 5: CEO Social Media Posts of the Year ACMAN Division.

714
00:34:37,600 --> 00:34:40,279
Speaker 3: It's like how in cross country skiing you have skate

715
00:34:40,320 --> 00:34:43,200
Speaker 3: skiing and Classic. They're gold medals in both.

716
00:34:43,680 --> 00:34:48,400
Speaker 5: I picked this because Akman's ACMEN is such a memorable poster.

717
00:34:48,480 --> 00:34:49,640
Speaker 3: I don't know if he's a good poster.

718
00:34:49,760 --> 00:34:52,720
Speaker 5: He I would say he does too many words in general,

719
00:34:52,840 --> 00:34:53,640
Speaker 5: but but he is.

720
00:34:53,880 --> 00:34:57,600
Speaker 7: He should take notes from Joe Wis and just keep

721
00:34:57,640 --> 00:34:58,040
Speaker 7: it sure.

722
00:34:58,239 --> 00:35:00,920
Speaker 5: But like, he has made a lot of news with

723
00:35:01,120 --> 00:35:04,359
Speaker 5: his tweets, and I picked my favorite this year.

724
00:35:04,719 --> 00:35:05,279
Speaker 3: Now do you do?

725
00:35:05,320 --> 00:35:08,080
Speaker 5: You all remember when Ackman was trying to become a

726
00:35:08,080 --> 00:35:10,000
Speaker 5: professional tennis player earlier this year.

727
00:35:10,280 --> 00:35:11,640
Speaker 4: This is not true?

728
00:35:11,680 --> 00:35:12,200
Speaker 6: Is that true?

729
00:35:12,560 --> 00:35:12,799
Speaker 8: True?

730
00:35:13,680 --> 00:35:14,760
Speaker 3: He got himself.

731
00:35:14,880 --> 00:35:16,640
Speaker 4: That doesn't sound true.

732
00:35:17,120 --> 00:35:17,840
Speaker 8: Mid fifties.

733
00:35:19,640 --> 00:35:23,400
Speaker 5: He got himself into a tennis match, and he played poorly.

734
00:35:23,920 --> 00:35:29,400
Speaker 5: And then on July tenth, he did a longish post

735
00:35:29,480 --> 00:35:32,680
Speaker 5: on X the Everything app where he explained what had happened,

736
00:35:32,719 --> 00:35:34,880
Speaker 5: and he said, I can speak in front of an

737
00:35:34,920 --> 00:35:37,240
Speaker 5: audience of a thousand people or in a TV studio

738
00:35:37,280 --> 00:35:39,840
Speaker 5: on a broad range of topics that any preparation, and

739
00:35:39,840 --> 00:35:42,680
Speaker 5: without a twinge of fear. But yesterday I had my

740
00:35:42,840 --> 00:35:47,360
Speaker 5: first real experience of stage fright. It wasn't his athletic failures.

741
00:35:47,400 --> 00:35:50,120
Speaker 5: It wasn't the fact that he's again in his fifties

742
00:35:50,320 --> 00:35:51,720
Speaker 5: pretending to be a tennis player.

743
00:35:51,920 --> 00:35:55,280
Speaker 3: It was stage fright. And and he in fact sort.

744
00:35:55,040 --> 00:35:57,480
Speaker 4: Of blamed about tennis his well.

745
00:35:57,480 --> 00:35:59,560
Speaker 5: He said he had never played in front of a crowd.

746
00:36:00,040 --> 00:36:03,800
Speaker 5: But then he also right, No, was that some smaller

747
00:36:03,880 --> 00:36:09,240
Speaker 5: match in like Rhode Island. He claimed that the pros

748
00:36:09,280 --> 00:36:12,000
Speaker 5: he was playing against him had gone easy, and that

749
00:36:12,160 --> 00:36:15,000
Speaker 5: threw him off his game because because.

750
00:36:14,719 --> 00:36:16,720
Speaker 8: He's paying the money.

751
00:36:16,920 --> 00:36:18,759
Speaker 1: It's like, oh, everyone I've ever played with that I've

752
00:36:18,760 --> 00:36:20,320
Speaker 1: paid lots of money to is let.

753
00:36:20,200 --> 00:36:21,440
Speaker 4: Me win one thing.

754
00:36:21,480 --> 00:36:24,160
Speaker 6: I'll say we should all aspire to have the confidence

755
00:36:24,200 --> 00:36:26,600
Speaker 6: of Bill Ackman. That's something you can say, right.

756
00:36:26,880 --> 00:36:29,520
Speaker 7: I think that is such a thing as too much confident.

757
00:36:30,600 --> 00:36:34,960
Speaker 5: I think all rich guys should attempt to be professional athletes.

758
00:36:35,040 --> 00:36:37,080
Speaker 5: I think it's a good It would be fun for us,

759
00:36:37,200 --> 00:36:38,319
Speaker 5: it would be humbling for them.

760
00:36:38,440 --> 00:36:43,120
Speaker 7: Whatever happened to the monks about cage.

761
00:36:43,560 --> 00:36:46,759
Speaker 5: I spent eighteen months trying to make that happen, and

762
00:36:46,800 --> 00:36:49,200
Speaker 5: it still has yet to uh to happen.

763
00:36:49,440 --> 00:36:52,759
Speaker 8: You got to build the octagon here at Bloomberg. It's

764
00:36:52,800 --> 00:36:53,640
Speaker 8: waiting for you.

765
00:36:54,160 --> 00:36:57,600
Speaker 2: And finally, Tracy, you also had a good Bill Ackman

766
00:36:57,640 --> 00:36:58,280
Speaker 2: tweet moment.

767
00:36:58,400 --> 00:37:01,000
Speaker 6: Oh well, speaking of confidence. For me, it has to

768
00:37:01,040 --> 00:37:02,600
Speaker 6: be the may I meet you? Line?

769
00:37:04,360 --> 00:37:04,880
Speaker 8: Definitely.

770
00:37:05,000 --> 00:37:08,680
Speaker 6: This is the tweet that like launched a thousand million

771
00:37:08,960 --> 00:37:13,239
Speaker 6: confused women around America, right, Like these guys coming up

772
00:37:13,239 --> 00:37:14,880
Speaker 6: to you and saying, may I meet you?

773
00:37:15,320 --> 00:37:15,560
Speaker 5: Wait?

774
00:37:15,719 --> 00:37:17,120
Speaker 4: Explain? Explain this tweet?

775
00:37:17,280 --> 00:37:20,680
Speaker 6: So he basically said his technique for picking up women

776
00:37:21,000 --> 00:37:23,200
Speaker 6: is to approach them and say may I meet you?

777
00:37:23,320 --> 00:37:27,200
Speaker 6: And he claimed to never have received a no. Now,

778
00:37:27,239 --> 00:37:29,600
Speaker 6: I will say the people who tried to adopt this

779
00:37:29,719 --> 00:37:33,640
Speaker 6: technique and experiment with it, you know, they filmed videos

780
00:37:33,640 --> 00:37:36,360
Speaker 6: for TikTok and things like that, they have less success.

781
00:37:36,560 --> 00:37:38,360
Speaker 3: Let's put it that we say this was going to

782
00:37:38,400 --> 00:37:39,640
Speaker 3: fix a fertility crisis.

783
00:37:42,520 --> 00:37:44,080
Speaker 4: This also doesn't feel real.

784
00:37:44,400 --> 00:37:46,160
Speaker 3: This is definitely a bad line, right, I don't know.

785
00:37:46,200 --> 00:37:47,759
Speaker 3: I've been married for a long time. I meet you,

786
00:37:47,920 --> 00:37:48,440
Speaker 3: I meet you.

787
00:37:48,960 --> 00:37:51,200
Speaker 6: I don't think it's great. But in the spirit of

788
00:37:51,200 --> 00:37:54,520
Speaker 6: the holiday and being charitabed, that's exactly what I was

789
00:37:54,560 --> 00:37:56,319
Speaker 6: going to say. Do you remember when negging was all

790
00:37:56,320 --> 00:37:57,960
Speaker 6: the rage and guys would just come up to you

791
00:37:58,000 --> 00:38:01,440
Speaker 6: and insult you to your off your and it was

792
00:38:01,480 --> 00:38:02,040
Speaker 6: like so weird.

793
00:38:02,120 --> 00:38:03,879
Speaker 4: Oh god, yeah that was a thing.

794
00:38:03,960 --> 00:38:04,560
Speaker 8: This thing.

795
00:38:05,239 --> 00:38:07,719
Speaker 4: Yeah, so so improvement things are getting better.

796
00:38:07,800 --> 00:38:10,240
Speaker 6: Not sure perfect, it's a low bar, but sure.

797
00:38:10,440 --> 00:38:12,879
Speaker 2: It's like you were saying, Robert, it's not about how

798
00:38:12,920 --> 00:38:15,120
Speaker 2: big the market is, it's whether or not it's improving.

799
00:38:15,760 --> 00:38:21,080
Speaker 7: Yeah, when they're improving the DC market just a little

800
00:38:21,080 --> 00:38:23,319
Speaker 7: bit by introducing may I meet you?

801
00:38:31,600 --> 00:38:33,080
Speaker 3: Okay, so that was good.

802
00:38:33,120 --> 00:38:37,440
Speaker 11: We we looked, we looked bad at we looked on

803
00:38:37,600 --> 00:38:40,759
Speaker 11: the year, and and we thought it would be fun

804
00:38:40,800 --> 00:38:42,759
Speaker 11: to play a game, to sort of do a year

805
00:38:42,760 --> 00:38:44,319
Speaker 11: in review by way of a game.

806
00:38:44,520 --> 00:38:46,319
Speaker 5: I know all these answers, so I will not be playing,

807
00:38:46,320 --> 00:38:48,000
Speaker 5: but you will be playing, and I will be keeping schools.

808
00:38:48,080 --> 00:38:48,720
Speaker 7: This is a quiz.

809
00:38:48,880 --> 00:38:53,680
Speaker 5: The game is called who is Donald Trump talking about?

810
00:38:53,920 --> 00:38:57,000
Speaker 5: Oh well, no, there will be okay, so let me

811
00:38:57,080 --> 00:38:59,040
Speaker 5: let me just just some hints here. There will be

812
00:38:59,120 --> 00:39:03,040
Speaker 5: some people who some critics of the Trump administration might

813
00:39:03,120 --> 00:39:07,239
Speaker 5: describe as sick offense. There will also be some foils,

814
00:39:07,239 --> 00:39:11,200
Speaker 5: some adversaries, a whole bunch of different people that Trump

815
00:39:11,280 --> 00:39:13,560
Speaker 5: has found himself in the same room with. This was

816
00:39:13,640 --> 00:39:16,200
Speaker 5: like a year I think defined by this kind of

817
00:39:16,239 --> 00:39:20,400
Speaker 5: like reality show presidency. And I'm going to play the clip.

818
00:39:20,440 --> 00:39:22,479
Speaker 5: I think let's start with Robert on this first one.

819
00:39:22,920 --> 00:39:24,920
Speaker 5: If he if he misses it, one of.

820
00:39:24,880 --> 00:39:26,560
Speaker 4: You can sign this game.

821
00:39:26,880 --> 00:39:28,879
Speaker 7: Here's his only one. I'll be able to get. I'll

822
00:39:28,880 --> 00:39:29,799
Speaker 7: be able to get that one.

823
00:39:29,840 --> 00:39:33,800
Speaker 3: Here's our first Here is our first clue. This woman

824
00:39:34,120 --> 00:39:34,680
Speaker 3: is a winner.

825
00:39:34,719 --> 00:39:37,040
Speaker 10: So you know, we've become very close friends, all of

826
00:39:37,160 --> 00:39:41,000
Speaker 10: us said, because their stock market today, at our stock

827
00:39:41,080 --> 00:39:44,680
Speaker 10: market today hit an all time high.

828
00:39:45,160 --> 00:39:47,080
Speaker 8: Is it is it the president of Mexico?

829
00:39:47,320 --> 00:39:47,480
Speaker 9: Yeah?

830
00:39:49,320 --> 00:39:52,320
Speaker 6: Who it is? It's the japan Prime minister.

831
00:39:52,760 --> 00:40:02,000
Speaker 5: Yes, yes, yes, who was you know recently elected? Yeah, praising, praising, Tracy,

832
00:40:02,000 --> 00:40:04,360
Speaker 5: do you know, like, what is the status of the

833
00:40:04,440 --> 00:40:07,000
Speaker 5: Trump Japan relationship at the moment.

834
00:40:07,160 --> 00:40:09,560
Speaker 6: I don't know. I mean I feel like it changes

835
00:40:09,600 --> 00:40:11,480
Speaker 6: minute to minute, right, and it kind of depends on

836
00:40:11,520 --> 00:40:13,680
Speaker 6: who he spoke to you last, So don't ask.

837
00:40:13,760 --> 00:40:15,760
Speaker 3: Okay, that is a point to Tracy.

838
00:40:16,480 --> 00:40:21,280
Speaker 5: Wait, I need to yeah, thank you, all right, keeping school,

839
00:40:21,360 --> 00:40:22,160
Speaker 5: Robert's keeping score.

840
00:40:22,239 --> 00:40:25,480
Speaker 3: That's great. Okay. Second question, this one is for Felix,

841
00:40:26,760 --> 00:40:27,680
Speaker 3: and Baron got.

842
00:40:27,560 --> 00:40:29,840
Speaker 10: To meet him, and I think he respects his father

843
00:40:29,920 --> 00:40:32,440
Speaker 10: a little bit more now, just the fact that I

844
00:40:32,480 --> 00:40:34,520
Speaker 10: introduce you. So I just want to thank you both

845
00:40:34,560 --> 00:40:37,320
Speaker 10: for being here. Thank you very much, really and honor.

846
00:40:37,440 --> 00:40:39,600
Speaker 5: Okay, So this is someone, as you heard in that

847
00:40:39,640 --> 00:40:45,400
Speaker 5: clip that Trump introduced to Baron, Trump sou.

848
00:40:45,239 --> 00:40:46,600
Speaker 4: And Baron was clearly impressed.

849
00:40:46,719 --> 00:40:50,120
Speaker 5: Baron was hard to impress that kid, Felix.

850
00:40:50,120 --> 00:40:50,680
Speaker 3: What do you got?

851
00:40:51,040 --> 00:40:53,000
Speaker 7: I'm just gonna have. I have no idea. So I'm

852
00:40:53,000 --> 00:40:54,960
Speaker 7: going to take a wild guess at show.

853
00:40:54,800 --> 00:41:00,960
Speaker 5: Hao, Tony, I can start giving hints kid Rock not

854
00:41:01,120 --> 00:41:01,680
Speaker 5: kid Rock.

855
00:41:01,960 --> 00:41:03,319
Speaker 3: That's a good guess, all right.

856
00:41:03,719 --> 00:41:06,439
Speaker 5: So I'm gonna give you a hint, one of which

857
00:41:06,480 --> 00:41:09,120
Speaker 5: which is that the World Cup is coming to our

858
00:41:09,160 --> 00:41:10,640
Speaker 5: shores next year.

859
00:41:10,840 --> 00:41:13,839
Speaker 3: Oh, it's not messy, but you.

860
00:41:13,840 --> 00:41:16,399
Speaker 6: Can the other one with a billion children.

861
00:41:18,120 --> 00:41:22,240
Speaker 3: Close the Spanish guy, not Spanish or Portuguese. Thank you.

862
00:41:25,440 --> 00:41:26,480
Speaker 4: I feel like I should.

863
00:41:26,200 --> 00:41:28,319
Speaker 6: Get half a point for I knew who it was.

864
00:41:28,880 --> 00:41:29,280
Speaker 3: Tracy.

865
00:41:29,320 --> 00:41:32,920
Speaker 5: Half a point, Felix half a point, Felix one. Tracy

866
00:41:32,960 --> 00:41:34,320
Speaker 5: gets a half all right, fine.

867
00:41:34,360 --> 00:41:35,640
Speaker 6: Thank you, thank you.

868
00:41:36,080 --> 00:41:38,840
Speaker 5: All right, here's the next one. This is for Tracy.

869
00:41:39,040 --> 00:41:40,799
Speaker 5: She's on fire so far.

870
00:41:41,080 --> 00:41:43,959
Speaker 3: He is a very strong, very good leader.

871
00:41:44,000 --> 00:41:46,400
Speaker 10: He's a he's a nice man, but he could be nasty,

872
00:41:48,320 --> 00:41:49,760
Speaker 10: maybe as nasty as anybody.

873
00:41:49,880 --> 00:41:51,280
Speaker 8: It's a rare male nasty.

874
00:41:52,200 --> 00:41:55,440
Speaker 6: Yeah, that's a good point. That's a tough one because

875
00:41:55,440 --> 00:41:57,799
Speaker 6: it could be a number of people. But I'm gonna

876
00:41:57,800 --> 00:41:58,360
Speaker 6: go with President.

877
00:41:59,680 --> 00:42:02,239
Speaker 5: That is a really really good guess. But it is

878
00:42:02,280 --> 00:42:04,759
Speaker 5: not jes anybody else.

879
00:42:04,600 --> 00:42:05,120
Speaker 4: Is it Putin?

880
00:42:05,320 --> 00:42:08,160
Speaker 3: It is not Celensi.

881
00:42:08,280 --> 00:42:12,279
Speaker 5: It is an important world leader, but not of as

882
00:42:12,320 --> 00:42:16,560
Speaker 5: big an economy as China Hungarian.

883
00:42:16,840 --> 00:42:22,080
Speaker 3: No, it's Mark. Yeah.

884
00:42:22,400 --> 00:42:28,480
Speaker 6: I don't think many people would describe Mark Carney as nasty.

885
00:42:28,719 --> 00:42:31,600
Speaker 7: Yeah, he describes a lot of Canadians.

886
00:42:32,960 --> 00:42:37,840
Speaker 5: That's a compliment, though coming from Trump, it's not an insult.

887
00:42:38,719 --> 00:42:39,120
Speaker 7: I think he.

888
00:42:39,200 --> 00:42:41,160
Speaker 3: Means it as a complaint. I don't know. Okay, here's

889
00:42:41,239 --> 00:42:42,000
Speaker 3: here's the next one.

890
00:42:42,320 --> 00:42:46,280
Speaker 1: Just to recap feelix with two points, Tracy one point five,

891
00:42:46,440 --> 00:42:47,239
Speaker 1: Robert has zero.

892
00:42:47,760 --> 00:42:48,560
Speaker 4: I also have zero.

893
00:42:49,160 --> 00:42:52,480
Speaker 5: You're getting your question now here. Okay, this is a

894
00:42:52,520 --> 00:42:57,280
Speaker 5: fun one. Now this is a a two person question. Okay,

895
00:42:57,360 --> 00:42:59,440
Speaker 5: you're the you're a host, so you know yours is harder.

896
00:42:59,800 --> 00:43:00,040
Speaker 6: No.

897
00:43:00,280 --> 00:43:01,600
Speaker 3: I call them the Bobbsey Twins.

898
00:43:01,600 --> 00:43:04,000
Speaker 9: They're the most different human beings I've ever met.

899
00:43:04,360 --> 00:43:06,680
Speaker 10: They're both great, but one is a little bit different

900
00:43:06,719 --> 00:43:09,560
Speaker 10: than the other, like by about two hundred yards.

901
00:43:09,440 --> 00:43:12,520
Speaker 3: The Bobbsey Twins. Who in the.

902
00:43:14,160 --> 00:43:16,880
Speaker 5: This is hard Who in the Trump administration might you

903
00:43:16,960 --> 00:43:19,080
Speaker 5: describe as the Bobbsey Twins?

904
00:43:19,440 --> 00:43:21,560
Speaker 4: Like two different people in the Trump administry?

905
00:43:21,640 --> 00:43:21,960
Speaker 8: Different?

906
00:43:22,000 --> 00:43:22,840
Speaker 3: He says, they're different.

907
00:43:23,000 --> 00:43:25,680
Speaker 4: Okay, I feel like this might be.

908
00:43:28,560 --> 00:43:31,479
Speaker 7: Are they biologically related to No?

909
00:43:32,400 --> 00:43:33,960
Speaker 3: Okay, one of them.

910
00:43:33,920 --> 00:43:35,720
Speaker 4: Is one of the melon musk No.

911
00:43:35,719 --> 00:43:38,280
Speaker 5: No, one of them is Scott Besson.

912
00:43:39,120 --> 00:43:43,719
Speaker 4: One of them is Scott Besson and Rubio.

913
00:43:44,239 --> 00:43:44,560
Speaker 3: Nope.

914
00:43:46,080 --> 00:43:53,120
Speaker 5: Think about like Besson's affect and the affect of other people's.

915
00:43:55,719 --> 00:43:57,520
Speaker 3: Twins.

916
00:43:57,800 --> 00:44:01,200
Speaker 7: Because Bessons and Lutnik were famously in competition to see

917
00:44:01,520 --> 00:44:03,640
Speaker 7: both of them wanted the Treasury sectory job, and that

918
00:44:03,840 --> 00:44:06,000
Speaker 7: was the big Is.

919
00:44:06,040 --> 00:44:08,600
Speaker 5: That a point for feelings? That's a point for feelings. Okay,

920
00:44:09,360 --> 00:44:12,080
Speaker 5: so where do we have? Felix is up to three? Now, okay,

921
00:44:12,120 --> 00:44:15,120
Speaker 5: here we go, Robert, don't worry, there's a second round,

922
00:44:15,120 --> 00:44:16,560
Speaker 5: all right, here we go.

923
00:44:16,760 --> 00:44:17,600
Speaker 4: Is there a prize?

924
00:44:17,719 --> 00:44:18,880
Speaker 3: Yes, there is a prize I should have.

925
00:44:19,400 --> 00:44:22,080
Speaker 10: He's different than you know, your average captain. He came

926
00:44:22,120 --> 00:44:25,240
Speaker 10: out of nowhere. I said, he has a great campaign matters.

927
00:44:25,239 --> 00:44:28,600
Speaker 10: You're standing over there. He came out of He came

928
00:44:28,640 --> 00:44:30,640
Speaker 10: out of nowhere. When'd you start off at one or two?

929
00:44:30,800 --> 00:44:33,160
Speaker 10: And then I watched who is this guy?

930
00:44:33,440 --> 00:44:35,680
Speaker 8: I know it from the first sentence? Is zill Ron?

931
00:44:35,840 --> 00:44:36,879
Speaker 3: Yeah? Or mom died?

932
00:44:36,960 --> 00:44:39,920
Speaker 5: That was one of the strangest and most surprising moments

933
00:44:40,080 --> 00:44:41,960
Speaker 5: in the White It's interesting because.

934
00:44:41,760 --> 00:44:44,000
Speaker 2: I feel like I had been all for kind of

935
00:44:44,000 --> 00:44:45,560
Speaker 2: reaching across the aisle. I do not like all the

936
00:44:45,600 --> 00:44:48,239
Speaker 2: divisiveness in our country right now, but that moment made

937
00:44:48,320 --> 00:44:49,799
Speaker 2: me so deeply uncomfortable.

938
00:44:50,080 --> 00:44:52,839
Speaker 4: Why was that press conference so I couldn't watch it?

939
00:44:52,960 --> 00:44:53,000
Speaker 5: Like?

940
00:44:53,239 --> 00:44:57,440
Speaker 3: Really, Oh my gosh, it was for me.

941
00:44:57,560 --> 00:45:01,920
Speaker 7: It was very possibly the most impressive performance that I

942
00:45:02,040 --> 00:45:04,840
Speaker 7: seems on and I'm done, he gave, and that he's sitting.

943
00:45:05,120 --> 00:45:08,480
Speaker 7: He's standing in the White House next to Donald Trump,

944
00:45:08,520 --> 00:45:11,000
Speaker 7: who is grinning and doing thumbs up, and he is

945
00:45:11,760 --> 00:45:15,000
Speaker 7: very very consciously not smiling and not putting his thumbs

946
00:45:15,080 --> 00:45:18,400
Speaker 7: up and trying to convey to his face that no,

947
00:45:18,520 --> 00:45:21,640
Speaker 7: I am in no way a fan of this guy, while.

948
00:45:22,200 --> 00:45:24,040
Speaker 6: You know, he's a fine line to walk.

949
00:45:24,200 --> 00:45:25,719
Speaker 8: It is, and it was Trump's.

950
00:45:25,840 --> 00:45:28,600
Speaker 1: It was really one of Trump's best lines about being

951
00:45:28,640 --> 00:45:29,920
Speaker 1: called a fascist.

952
00:45:29,600 --> 00:45:33,239
Speaker 8: Yes, which is Trump's like, yeah, go ahead and say that, Like.

953
00:45:34,239 --> 00:45:36,759
Speaker 7: I don't think I don't think Trump minds being called

954
00:45:36,760 --> 00:45:39,640
Speaker 7: a fascist. I genuinely don't think he finds that to

955
00:45:39,680 --> 00:45:40,560
Speaker 7: be insulting.

956
00:45:40,719 --> 00:45:41,160
Speaker 8: He said.

957
00:45:41,200 --> 00:45:44,279
Speaker 3: So he said as much. He's easier that way. All Right,

958
00:45:44,640 --> 00:45:47,800
Speaker 3: here we go. This is our sixth question, this, Felix,

959
00:45:47,840 --> 00:45:48,520
Speaker 3: this one is for you.

960
00:45:48,680 --> 00:45:51,239
Speaker 8: I love to have him. I love watching him on television.

961
00:45:51,280 --> 00:45:53,840
Speaker 10: I'd love to have him come up and explain his

962
00:45:53,960 --> 00:45:57,720
Speaker 10: true feelings, but maybe not his truestlings.

963
00:45:57,719 --> 00:45:59,279
Speaker 8: That might be going a little bit too far.

964
00:46:00,000 --> 00:46:03,040
Speaker 5: Okay, So this is someone that Trump enjoys watching on

965
00:46:03,080 --> 00:46:07,160
Speaker 5: television but doesn't really want to look inside his brain,

966
00:46:07,200 --> 00:46:08,600
Speaker 5: doesn't want to does it?

967
00:46:08,680 --> 00:46:11,719
Speaker 3: Maybe doesn't want to get too close. It's a member

968
00:46:11,719 --> 00:46:12,719
Speaker 3: of the Trump administration.

969
00:46:13,280 --> 00:46:18,520
Speaker 7: Oh oh, I'm just gonna come out and say Rubio,

970
00:46:18,800 --> 00:46:21,000
Speaker 7: because he's the only member of the Trump administration I

971
00:46:21,000 --> 00:46:22,120
Speaker 7: can think of off my head.

972
00:46:22,200 --> 00:46:30,439
Speaker 3: Okay, not Rubio. Hang in there for anyone to take.

973
00:46:30,760 --> 00:46:32,840
Speaker 5: This is a person who was very involved also with

974
00:46:32,880 --> 00:46:34,600
Speaker 5: the first Trump presidency.

975
00:46:35,040 --> 00:46:39,560
Speaker 3: He's on the younger side. Oh, Milla, yes, Stephen.

976
00:46:39,239 --> 00:46:42,600
Speaker 7: Miller, Oh yeah, you do not want to look inside

977
00:46:42,600 --> 00:46:45,960
Speaker 7: that guy. I mean, I hate to agree with Donald Trump,

978
00:46:46,000 --> 00:46:47,160
Speaker 7: but he's right about.

979
00:46:48,800 --> 00:46:48,920
Speaker 5: Right.

980
00:46:49,520 --> 00:46:51,120
Speaker 3: All right, Robert, where are we now?

981
00:46:51,160 --> 00:46:51,200
Speaker 5: What?

982
00:46:51,320 --> 00:46:53,360
Speaker 3: What are the scores? We have two more questions.

983
00:46:54,320 --> 00:46:56,800
Speaker 1: Felix is running away with it all right, four points,

984
00:46:56,880 --> 00:47:00,120
Speaker 1: Tracy at one point five? I have one and Stacy

985
00:47:00,640 --> 00:47:01,200
Speaker 1: nothing yet?

986
00:47:01,320 --> 00:47:04,400
Speaker 4: Right is the power of yet?

987
00:47:04,840 --> 00:47:07,200
Speaker 10: Despite that, it's having very little lipack because we have,

988
00:47:07,320 --> 00:47:09,680
Speaker 10: you know, we have all of these things happening. But

989
00:47:09,680 --> 00:47:12,040
Speaker 10: it has an impact on housing to a certain extent.

990
00:47:12,280 --> 00:47:13,839
Speaker 10: He's a fool, He's a stupid man.

991
00:47:14,520 --> 00:47:16,799
Speaker 6: I feel like I know it, but I can't remember now.

992
00:47:17,120 --> 00:47:19,560
Speaker 4: I bet I know, I bet I know too.

993
00:47:20,640 --> 00:47:23,759
Speaker 6: Impact on housing, I don't know. I'm blinking. Give me

994
00:47:23,800 --> 00:47:24,160
Speaker 6: a hint.

995
00:47:24,239 --> 00:47:26,680
Speaker 3: Okay, we're overthinking this.

996
00:47:27,360 --> 00:47:29,760
Speaker 1: Donald Trump thinks it has a big impact on housing,

997
00:47:29,800 --> 00:47:31,080
Speaker 1: but your economic brain is.

998
00:47:31,000 --> 00:47:33,520
Speaker 8: Like, well, does it really have this affected housing?

999
00:47:34,440 --> 00:47:37,080
Speaker 3: Robert's meme federal funds?

1000
00:47:37,160 --> 00:47:37,279
Speaker 5: Right?

1001
00:47:38,080 --> 00:47:42,400
Speaker 6: Oh, Pal, that's yeah, you're right.

1002
00:47:42,640 --> 00:47:45,680
Speaker 5: It's confusing because Trump did nominate Powell, and there was

1003
00:47:45,719 --> 00:47:47,600
Speaker 5: a time when he didn't go around calling him a

1004
00:47:47,640 --> 00:47:48,239
Speaker 5: stupid man.

1005
00:47:48,320 --> 00:47:50,440
Speaker 3: But now that that time is Do you remember.

1006
00:47:50,160 --> 00:47:54,800
Speaker 7: When Trump nominated Powell because he was tall, unlike Johnny Yellen,

1007
00:47:54,840 --> 00:47:55,160
Speaker 7: who was.

1008
00:47:55,400 --> 00:47:58,360
Speaker 4: Yes, I do. That broke my heart and I never

1009
00:47:58,440 --> 00:48:01,360
Speaker 4: forgave him. I loved Anna Yellen. I loved Jennie.

1010
00:48:01,480 --> 00:48:04,319
Speaker 5: All right, last question, Yes, this is this is gonna

1011
00:48:04,320 --> 00:48:07,640
Speaker 5: be worth two points, and just because I want to

1012
00:48:07,640 --> 00:48:08,280
Speaker 5: make it interesting.

1013
00:48:09,640 --> 00:48:18,560
Speaker 3: Okay, she will win it. Here we go, Okay.

1014
00:48:19,520 --> 00:48:20,760
Speaker 8: I like them as people.

1015
00:48:21,680 --> 00:48:23,680
Speaker 9: I've listened to them music over the years.

1016
00:48:23,680 --> 00:48:25,960
Speaker 5: They are characters to really are.

1017
00:48:26,719 --> 00:48:27,920
Speaker 9: I don't know if you saw it last night.

1018
00:48:27,960 --> 00:48:30,239
Speaker 10: The speeches, they were great. They really made some great

1019
00:48:30,239 --> 00:48:32,960
Speaker 10: speeches last night, and they're very popular.

1020
00:48:33,120 --> 00:48:37,400
Speaker 2: I'm going to defer to like Tracy, no take it.

1021
00:48:38,160 --> 00:48:42,480
Speaker 2: I'm probably wrong with the village people wrong okay, oh.

1022
00:48:42,480 --> 00:48:43,480
Speaker 4: Because he loves them.

1023
00:48:43,520 --> 00:48:45,759
Speaker 3: Any other guests, but they're not people, and it's more

1024
00:48:45,800 --> 00:48:46,279
Speaker 3: than one.

1025
00:48:46,440 --> 00:48:47,600
Speaker 8: It is a they don't give speech.

1026
00:48:47,960 --> 00:48:48,920
Speaker 3: It's a group of people.

1027
00:48:49,200 --> 00:49:04,040
Speaker 12: But it's singing right No, yes, maybe, oh, oh.

1028
00:49:01,680 --> 00:49:05,560
Speaker 7: Isn't Jenny or that it's not kiss kiss.

1029
00:49:06,760 --> 00:49:07,120
Speaker 3: There we go.

1030
00:49:08,400 --> 00:49:09,360
Speaker 7: Tracy has.

1031
00:49:10,880 --> 00:49:11,439
Speaker 3: Coming forward.

1032
00:49:13,239 --> 00:49:17,319
Speaker 6: I knew my nineteen eighties rock very knowledge coming Yeah.

1033
00:49:17,640 --> 00:49:20,960
Speaker 5: At the Kennedy Center recently, Trump awarded medals.

1034
00:49:21,040 --> 00:49:22,279
Speaker 3: Yeah literally kiss.

1035
00:49:23,600 --> 00:49:23,960
Speaker 8: Yeah.

1036
00:49:24,000 --> 00:49:26,680
Speaker 5: They were wearing their makeup for part of the ceremony

1037
00:49:26,760 --> 00:49:28,879
Speaker 5: and and no makeup and the other half.

1038
00:49:28,960 --> 00:49:31,920
Speaker 3: Also, I believe Sylvester Sallone.

1039
00:49:31,520 --> 00:49:33,279
Speaker 5: Got an award to I had a clip there but

1040
00:49:33,280 --> 00:49:35,279
Speaker 5: but didn't have time to play it. Tracy, you are

1041
00:49:35,280 --> 00:49:40,319
Speaker 5: the winner. You win a bunch of Swedish like a

1042
00:49:40,360 --> 00:49:41,560
Speaker 5: bunch of Swedish liquorice.

1043
00:49:42,000 --> 00:49:46,480
Speaker 6: Wow, your producer, Yes, wow, Swedish lickorish, which, thank you.

1044
00:49:46,760 --> 00:49:53,440
Speaker 6: I actually hate this. My husband loves it. Okay, yeah, okay.

1045
00:49:53,440 --> 00:49:56,040
Speaker 3: It tastes like pea according to Stacy van Smith.

1046
00:49:56,600 --> 00:50:01,960
Speaker 4: Well, thank you, thank you for I stand by that assessment.

1047
00:50:02,280 --> 00:50:05,040
Speaker 7: I'm never have been happy to lose the contest.

1048
00:50:06,000 --> 00:50:09,520
Speaker 4: We're all winners. Yes, no, I mean it really does though.

1049
00:50:10,000 --> 00:50:12,560
Speaker 5: And with that, Stacy, I think we should We should rap,

1050
00:50:12,600 --> 00:50:15,520
Speaker 5: We Should Rap. Twenty twenty five are our Legends of

1051
00:50:15,600 --> 00:50:19,960
Speaker 5: Pod Episode Complete Legends of Pod Robert Smith, Felix Samon,

1052
00:50:20,040 --> 00:50:20,760
Speaker 5: Tracy Alloway.

1053
00:50:20,800 --> 00:50:22,840
Speaker 3: Thank you all for being here, well.

1054
00:50:22,719 --> 00:50:23,680
Speaker 6: Thank you for inviting us.

1055
00:50:23,680 --> 00:50:24,680
Speaker 7: It was so much fun.

1056
00:50:25,360 --> 00:50:28,160
Speaker 5: We will do this next year with more clips, more contests.

1057
00:50:28,160 --> 00:50:29,600
Speaker 5: Maybe I'll do the HDTV things.

1058
00:50:30,200 --> 00:50:31,560
Speaker 3: Oh yes both.

1059
00:50:32,440 --> 00:50:35,080
Speaker 8: Will it be the same legends or are there new legends?

1060
00:50:35,680 --> 00:50:37,880
Speaker 7: Do we have to fight to retain a place?

1061
00:50:38,120 --> 00:50:38,719
Speaker 8: Yeah?

1062
00:50:38,880 --> 00:50:42,600
Speaker 4: Cage match, No, No, the Legends are locked.

1063
00:50:49,600 --> 00:50:52,440
Speaker 5: This show is produced by Stacy Wong. Magnus Hendrickson is

1064
00:50:52,440 --> 00:50:56,000
Speaker 5: our supervising producer, and Amy Kean our executive producer. Sam

1065
00:50:56,080 --> 00:50:59,760
Speaker 5: Rogic handles engineering, and Dave Percell fact checks. Sage Bauman

1066
00:50:59,800 --> 00:51:03,360
Speaker 5: heads Bloomberg Podcasts Special thanks to Jeff Muscus, Julie Rubin,

1067
00:51:03,560 --> 00:51:07,520
Speaker 5: Charlie Gorvin, Joshua Devaux, Angel Recchio, and Marie Ling. If

1068
00:51:07,560 --> 00:51:09,759
Speaker 5: you have a minute, please rate and review our show.

1069
00:51:09,840 --> 00:51:10,920
Speaker 8: It'll mean a lot to us.

1070
00:51:10,960 --> 00:51:12,840
Speaker 5: And if you have a story that should be our business,

1071
00:51:13,000 --> 00:51:16,120
Speaker 5: email us at Everybody's at Bloomberg dot net. That's everybody

1072
00:51:16,160 --> 00:51:18,880
Speaker 5: with an s at Bloomberg dot net. Thanks for listening,

1073
00:51:19,040 --> 00:51:21,439
Speaker 5: Happy holidays and we will see you in twenty twenty

1074
00:51:21,440 --> 00:51:21,640
Speaker 5: six