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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 BusinessWeek. I'm Stacey Bannocksmith.

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Speaker 3: I'm Max Chefkin Stacy. It's the holiday season. I feel

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Speaker 3: like we've been in the holiday season for a while,

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Speaker 3: but it's still he like it never, it's never gonna end.

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Speaker 3: But you know what, we're companies are finally in the black.

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Speaker 3: All these retail stores, right they depend on holiday sales.

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Speaker 3: We're about halfway through, and now is the time when

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Speaker 3: they're making money. And one of the ways they're making money,

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Speaker 3: as we'll learn in one of our segments, is through

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Speaker 3: these buy now, pay later loans.

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Speaker 2: Yes, one of the ways they're making money is by

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Speaker 2: demanding less money.

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Speaker 1: It's odd.

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Speaker 3: Amanda mal is going to talk about that, and speaking

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Speaker 3: of buying things, we're going to talk about the hot

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Speaker 3: item of twenty twenty five. I'm of course talking about

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Speaker 3: the Trump meme coin as well as the Marania meme

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Speaker 3: coin with Zeke.

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Speaker 1: Fox, our colleague crypto expert.

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Speaker 3: He's coming here to talk to talk about his new

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Speaker 3: story in BusinessWeek about all the weird ways that crypto

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Speaker 3: and politics are intersecting.

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Speaker 4: And Max for our underrated story.

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Speaker 2: You have brought us the story, and it is apparently

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Speaker 2: about age inclusive scores or something.

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Speaker 3: That's right, Yeah, right, it's about opening up professional.

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Speaker 1: Sports to the elder, to the wizendelder, to the wizend elder. Excellent,

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Speaker 1: All right, Stacey.

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Speaker 3: It's been a couple of weeks since we've gotten any

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Speaker 3: kind of economic data. Maybe I'm maybe I hallucinate that,

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Speaker 3: but it feels like it's been a while. But now

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Speaker 3: all of a sudden, there's a lot a lot of

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Speaker 3: information coming out about the economy.

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Speaker 1: What kind of mix, I guess, yes.

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Speaker 4: I mean, you're right.

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Speaker 2: The government shutdown meant that a lot of government data

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Speaker 2: wasn't collected and wasn't you know, given out. And it's

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Speaker 2: hard because this is a really feels like a really

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Speaker 2: crucial time in the economy. Things feel a little bit

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Speaker 2: fragile and shaky. This week, we did get some jobs

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Speaker 2: information out, some of the jobs numbers. It was mixed, Yeah,

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Speaker 2: I would say mixed to not great. Three major takeaways here.

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Speaker 2: The first one is the unemployment rate crept up a

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Speaker 2: little bit more to four point six percent. It's not

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Speaker 2: good that it's been rising rising, but under five percent

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Speaker 2: is still historically really low, so it's not terrible news,

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Speaker 2: but it's not great. Also, there's a really interesting statistic

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Speaker 2: in there that the share of workers working more than

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Speaker 2: one job has risen to five point seven percent, which

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Speaker 2: is the highest it's been in twenty five years, which

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Speaker 2: may point to maybe people not being able to afford

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Speaker 2: things as much, or having to take on multiple jobs.

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Speaker 3: Or wages being not growing as quickly as people would hope,

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Speaker 3: or maybe there's just a lot of freaking jobs in

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Speaker 3: the economy and all so many jobs.

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Speaker 4: Different styles of working.

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Speaker 2: I think that's a really good point that this isn't

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Speaker 2: necessarily bad, that like the way we work is a

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Speaker 2: little different now. But in addition to that, what you

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Speaker 2: said about the hiring, I think is the big job's

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Speaker 2: news of this shar which is the hiring is just

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Speaker 2: at the lowest level it's been in years and years

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Speaker 2: and years. The job market is a little bit dead,

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Speaker 2: and layoffs are rising slightly, and so I think people

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Speaker 2: workers do not feel great about the job market. Everyone

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Speaker 2: feels a little bit like they're kind of holding onto

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Speaker 2: their jobs. And then in certain sectors, the only sector

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Speaker 2: that's like really hiring this year was healthcare everything else

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Speaker 2: almost was shedding jobs or barely adding jobs. I talked

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Speaker 2: with one worker for a Business Week story, This guy,

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Speaker 2: Brett Vergara.

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Speaker 4: He worked at a big tech company.

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Speaker 2: He was actually a project manager on an AI team,

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Speaker 2: and so he thought his job was pretty safe. But

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Speaker 2: then he was laid off in October, and he said,

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Speaker 2: right now, trying to apply for jobs is really tough.

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Speaker 5: When you're just applying into the void and never hearing anything,

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Speaker 5: it can definitely be tough to keep up momentum.

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Speaker 1: Be like, but let me do that again.

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Speaker 5: And it's always fun too. When you get a rejection,

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Speaker 5: like immediately after applying something, it's like, okay, well it

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Speaker 5: does have like this to just completely ruin your day.

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Speaker 5: I wish I was better at being able to shrug

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Speaker 5: those things off, but I am not. I'm not the

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Speaker 5: best at that.

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Speaker 3: Yeah, that's a I mean, that's a universal thing. Oh yeah,

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Speaker 3: that's all. That's all, you know, whatever is going on

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Speaker 3: with the economy. I think that is a feeling people

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Speaker 3: have right this kind of Yes, you got to apply

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Speaker 3: to a bunch of things cold, there's a lot of rejection, and.

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Speaker 2: There's a lot of AI built into the job system now,

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Speaker 2: so so what he's talking about like this, Sometimes the

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Speaker 2: second you fill out an application, they're like, Nope, you

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Speaker 2: don't fit this bill. So it is like kind of

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Speaker 2: shocking to get immediately.

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Speaker 3: Have you ever tried to return something on Amazon and

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Speaker 3: you have to talk to the chatbot? Like, yeah, it's

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Speaker 3: like that, but you're applying for it's like circular, like

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Speaker 3: my future and the thing on the other side of

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Speaker 3: it is just like thank you for.

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Speaker 4: Dear applicant and the other thing. Brett said.

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Speaker 2: So, at the same time this is happening, he's also

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Speaker 2: hearing about more and more layoffs in his field, and

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Speaker 2: he said, on the one hand, really it's kind of

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Speaker 2: comforting because he's seeing really good people losing their job.

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Speaker 2: It's like, okay, well, really really great people with great

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Speaker 2: skills are losing their jobs. But then also he's realizing

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Speaker 2: that now they're all applying for the same jobs.

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Speaker 5: When you hear that next wave of layoffs from Amazon

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Speaker 5: or like Target or Meta or Google or whatever, it's.

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Speaker 6: Like, oh, I'm now competing against all those people immediately, yeah,

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Speaker 6: and like and like you know, and it's like, oh,

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Speaker 6: we're getting put against each other even though we're in

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Speaker 6: the same circumstances or similar circumstances. But yeah, it's like, oh,

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Speaker 6: all these people who I also know who are plenty

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Speaker 6: qualified as well.

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Speaker 2: And he said the people he knows that do have

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Speaker 2: jobs are also unhappy because they feel stuck. So it's

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Speaker 2: just a it's a bad moment in the job market,

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Speaker 2: I think exactly because of the hiring numbers.

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Speaker 3: Yeah, all right, well let's look ahead, writer, Yes, maybe twenty.

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Speaker 2: Twenty twenty six. I mean, there are some signs that

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Speaker 2: twenty twenty six might be getting better. We've got good

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Speaker 2: inflation numbers. Inflation seems to have come down a little.

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Speaker 2: The data is a little difficult because a lot it

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Speaker 2: wasn't collected in the normal way because of the shutdown.

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Speaker 2: But here's hoping that the hiring rate picks up in

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Speaker 2: the new year.

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Speaker 1: All right, well, we wanted to hear about your New

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Speaker 1: year's resolution.

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Speaker 4: Yes, it's the resolution time.

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Speaker 3: We sent Charlie Gorvin, Bloomberg reporter out onto the streets

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Speaker 3: to sort of ask people what their resolutions were, what

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Speaker 3: their what their financial resolutions were, what their personal resolutions were,

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Speaker 3: And this is what he came back with.

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Speaker 1: Do you have a resolution with that?

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Speaker 3: Again?

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Speaker 5: Yeah, I have a new year's resolution, get outside more.

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Speaker 3: Yeah, I would like to read more books, probably trying

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Speaker 3: to better myself mentally than smogan less weeds.

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Speaker 7: We're entering twenty twenty six with a big question mark,

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Speaker 7: and what are.

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Speaker 1: You hoping to shop forward forty years? The way to

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Speaker 1: that came, whatever I need.

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Speaker 3: Well, I'm planning on getting rid of my startphone this year,

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Speaker 3: so I'm like, I'll have more time to not like

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Speaker 3: look at my Facebook reels because.

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Speaker 1: Everyone says like it's good, healthy Gord to Virginia. But

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Speaker 1: let's see how long that lasts. My new year's resolution

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Speaker 1: is to watch a fewer reels. My new year's resolution.

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Speaker 8: I think that just being a city like this, growing

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Speaker 8: my career, connect with old friends and new ones, I

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Speaker 8: think it's something that I really want to be present for.

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Speaker 7: In January is like an arbitrary number. It's an arbitrary date.

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Speaker 7: It's like, if you want to change your life, do

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Speaker 7: it tomorrow. I'm also at a certain age where I'm like,

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Speaker 7: I'm really just like happy to be alive.

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Speaker 2: I do have to say I love the woman who's

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Speaker 2: like my resolution is to shop more. That is the

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Speaker 2: American economy saying yes.

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Speaker 3: Yeah, I'm just struck by how prominent Facebook reels are

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Speaker 3: in that in those ress like.

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Speaker 4: Yeah, well I think less screen time.

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Speaker 3: Right, Yeah, they want to. I was thinking, maybe my

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Speaker 3: resolution is going to be like lots of screen time.

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Speaker 3: Oh yeah, I'm just going deep in Facebook.

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Speaker 4: Shot more and spend more time online.

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Speaker 3: Yeah.

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Speaker 4: I need to help these last time with friends.

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Speaker 3: I need to help these trillion dollar online you know,

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Speaker 3: internet companies increase their per user time spent every every week.

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Speaker 3: I'm going to do my part. I like that because

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Speaker 3: the stock market needs it.

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Speaker 4: Excellent.

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Speaker 2: Well, I feel like I'm going to eat more sugar

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Speaker 2: and try to spend more time inside alone. So, Max,

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Speaker 2: we just heard a bunch of resolutions, and obviously, you know,

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Speaker 2: the difficult economy right now is weighing on everybody's minds.

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Speaker 3: Yeah, lot of uncertainty, and I know that the way

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Speaker 3: I deal with uncertainty is by putting it off into

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Speaker 3: the future.

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Speaker 1: I think.

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Speaker 2: I think that's always a good plan, and in fact,

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Speaker 2: a lot of people are doing that, not just with

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Speaker 2: their futures, but with their finances. We've got senior reporter

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Speaker 2: Amanda Mole with us. She is the author of the

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Speaker 2: Buying Power column at BusinessWeek, Amanda, you just wrote a

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Speaker 2: great article about buy now, Pay later.

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Speaker 9: Yes, the topic of our time is really yes, earlyist,

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Speaker 9: I would argue, so if you don't mind first describing

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Speaker 9: what buy now pay later is and then also just

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Speaker 9: talk about what's been happening lately. Yeah, So buy Now

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Speaker 9: Pay Later, which is often abbreviated BMPL, is like a

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Speaker 9: fintech type of business. They specialize in basically what is

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Speaker 9: like short term micro loans. So if you are buying

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Speaker 9: a new couch, if you want a new outfit for

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Speaker 9: your birthday, if you want a new handbag that you

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Speaker 9: don't want to pay for right now, then what you

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Speaker 9: can do at retailers that offer buy now, pay later

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Speaker 9: vendors for payment is that you can split that purchase

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Speaker 9: up into small payments over a short period of time.

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Speaker 9: The most common type of buy now, pay later loan

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Speaker 9: is a what they call pay in four, which splits

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Speaker 9: it up and splits the purchase up into quarters roughly,

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Speaker 9: and then you pay like every two weeks, every week,

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Speaker 9: every month, like it depends on the terms of the

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Speaker 9: lender you're dealing with. These lending decisions are made algorithmically

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Speaker 9: very quickly at the point of sale for in most cases,

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Speaker 9: there's also like membership programs that some of them offer.

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Speaker 2: Also, like when you're online buying stuff, I feel like

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Speaker 2: this will often come up. It would like, would you

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Speaker 2: with this instead of putting your credit card.

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Speaker 9: In right, And on a lot of like individual product

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Speaker 9: pages that you're if you're looking at, like a new refrigerator,

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Speaker 9: it might say this is the price today or as

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Speaker 9: low as however much with a firm or with Klarna,

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Speaker 9: these services are pretty widely advertised within the shopping experience.

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Speaker 9: They are part of the decision calculus for a lot

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Speaker 9: of purchases in the US, and they are getting more

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Speaker 9: and more popular and more and more difficult to understand

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Speaker 9: exactly how they function within the world of consumer finance

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Speaker 9: because they do not, you know, do a hard credit pull.

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Speaker 9: The debt you accrue to them generally does not show

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Speaker 9: up on your credit score or on your credit report.

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Speaker 9: So yeah, until your default, they some of them might

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Speaker 9: start messing with your credit history if you stop paying.

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Speaker 2: I mean it sounds a lot like a credit card

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Speaker 2: in some ways, right, Like you you buy the thing,

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Speaker 2: and then you know your credit card payment comes in

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Speaker 2: and we all know, you know how credit card works,

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Speaker 2: so if you if you don't pay awfully for the month,

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Speaker 2: you end up paying a little bit more by note

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Speaker 2: pay later. I think if you make the four payments,

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Speaker 2: you don't pay interest. But what happens if you don't

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Speaker 2: make those payments.

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Speaker 9: In a lot of cases, especially with these like four

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Speaker 9: part loans, there's no interest. If you opt into something longer,

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Speaker 9: there may be interest. The more you spread it out,

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Speaker 9: the more they're gonna charge you, generally, and it depends

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Speaker 9: on what kind of loan you take out with them

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Speaker 9: as to how it works. If you stop paying, some

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Speaker 9: of them will just sort of like continue trying to collect.

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Speaker 9: Some of them, depending on the size of the loan

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Speaker 9: and the policies of the particular lender, you maybe get

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Speaker 9: hit with interest that wouldn't have been there otherwise. They

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Speaker 9: may pursue you through the credit bureaus if they can.

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Speaker 1: They come kind of take your fridge back. Though, if

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Speaker 1: you don't pay. Is it like a car loan? No,

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Speaker 1: not necessarily. Yeah.

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Speaker 9: They most of these loans are like fairly small for

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Speaker 9: a couple hundred, couple thousand dollars, and a lot of

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Speaker 9: them are done sort of informally relative to like taking

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Speaker 9: out a car loan.

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Speaker 3: At first glance, it really seems like one of these

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Speaker 3: zerp businesses that zero interest rate policy.

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Speaker 1: So like a bunch of tech.

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Speaker 3: Companies got big when interest rates were really low because

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Speaker 3: basically borrowing money was free. So giving away a gazillion

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Speaker 3: interest free loans to buy refrigerators is like maybe a

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Speaker 3: good deal when interest rates are low, the economy is good,

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Speaker 3: et cetera, et cetera. But seems like not obviously like

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Speaker 3: a great business to me today, And so I'm wondering,

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Speaker 3: like what is happening there, Like how have they managed

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Speaker 3: to keep growing and how are these businesses actually making

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Speaker 3: money if they're just like giving away free money to

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Speaker 3: anyone without even bothering to like check their credit.

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Speaker 1: Yeah.

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Speaker 9: Well, the main revenue stream that they have is that

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Speaker 9: they do transaction fees with the retailers they partner with.

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Speaker 9: So if you use Klarna, which is the market leader

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Speaker 9: both in the US and worldwide in this type of service,

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Speaker 9: it's a Swedish company, to buy a new designer handbag

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Speaker 9: and it costs you three thousand dollars, Klarna will be

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Speaker 9: paid a portion of that sale, a small percentage of

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Speaker 9: that sale by the retailer, and this happens with credit

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Speaker 9: cards too, interchange fees, transaction fees, swhite fees, if you

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Speaker 9: use an electronic form of payment anything other than cash.

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Speaker 9: Really there is a little bit of money going from

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Speaker 9: the retailer to the person who processes that payment, so

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Speaker 9: that is generally how they make their money. Some of

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Speaker 9: them also offer longer term, larger loans that do charge interest,

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Speaker 9: so they do have interest payments coming in from some

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Speaker 9: of their borrowers.

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Speaker 3: So this is like an entry point to having a

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Speaker 3: deeper financial relationship with you the borrower. I see this,

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Speaker 3: I click on it, I buy the fridge. Yeah, and

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Speaker 3: then Klarna convinces me down the road to borrow some

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Speaker 3: more money from them.

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Speaker 1: Yeah.

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Speaker 9: And like as with any sort of like consumer choice,

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Speaker 9: like getting people to do something the first time that

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Speaker 9: they're not used to doing or that they're not sure

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Speaker 9: if it will work, is like the big thing. Once

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Speaker 9: you have made a by now pay later purchase, you're

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Speaker 9: really likely to make a second one. In a recent

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Speaker 9: survey that lending Tree did of like couple thousand by

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Speaker 9: now pay Later users, the majority of them had at

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Speaker 9: some point in the past had two or more loans

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Speaker 9: going at once. And it's definitely true that these types

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Speaker 9: of lenders over index in their customer base with types

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Speaker 9: of people who are not necessarily credit worthy by traditional

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Speaker 9: credit card issuer or lender standards. Their customer base, which

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Speaker 9: is about ninety one million people in the US right now,

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Speaker 9: so it's a huge Yeah, it's grown a lot in

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Speaker 9: the past several years. So their customer base tends to

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Speaker 9: skew young, it tends to skew single. There's a lot

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Speaker 9: of young parents that use it. There's a lot of

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Speaker 9: overlap between people who are young and single and have

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Speaker 9: a child. So because they don't have a credit history,

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Speaker 9: they have other debt where.

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Speaker 2: They might be able to get a credit card with

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Speaker 2: a really high interest rate, but wouldn't be able to

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Speaker 2: get like great credit right.

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Speaker 9: And what's interesting is that in the numbers that I've seen,

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Speaker 9: like the percentage of people who use by Now Pay

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Speaker 9: Later who have missed a payment or been laid on

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Speaker 9: a payment in the past year is really big and

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Speaker 9: it's growing. That same Lending Tree survey found that it

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Speaker 9: was forty one percent of by Now Pay Later borrowers

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Speaker 9: in the last year had missed a payment.

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Speaker 4: Wow, And that's why to be way higher than a

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Speaker 4: credit card.

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Speaker 1: Yeah, that's a lot of people.

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Speaker 9: And it's up from thirty four percent the year before

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Speaker 9: and thirty one percent the year before that, so people

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Speaker 9: are missing more payments. However, the default rate on these

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Speaker 9: is only like two to three percent, which is much

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Speaker 9: lower than a traditional credit card, which is closer to

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Speaker 9: ten percent.

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Speaker 3: We talked about this when we were recapping the Black

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Speaker 3: Friend sales, which were good, but there was a lot

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Speaker 3: of binow pay later in there, and Saty and I

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Speaker 3: were kind of like trying to tease out good thing

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Speaker 3: or bad thing?

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Speaker 1: Is this bad or good? Like to me, that sounds like.

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Speaker 3: People don't have enough money they're spending me on their means.

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Speaker 3: You tack that on with default rates going up on

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Speaker 3: credit cards and you're like, uh oh, this seems bad.

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Speaker 3: This seems like the kind of like fintech industry has

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Speaker 3: like gotten together with the retail industry to convince people

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Speaker 3: to spend money that they don't have.

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Speaker 1: Yeah.

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Speaker 9: I think it's something that like should be looked at

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Speaker 9: as a red flag, although we don't necessarily know exactly

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Speaker 9: what it means at this point. The Black Friday to

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Speaker 9: Cyber Monday or like Thanksgiving to Cyber Monday period was

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Speaker 9: enormous for by Now Pay Later. It was usage of

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Speaker 9: those services was up I think six percentage points over

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Speaker 9: last year according to one survey. And on Cyber Monday,

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Speaker 9: over a billion dollars in e commerce spending was done

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Speaker 9: via Bye Now Pay Later services and the U which

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Speaker 9: is the first time it's cracked a billion dollars in

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Speaker 9: a day. And you know, about seven percent of all

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Speaker 9: online purchases that day were by Now Pay Later, which

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Speaker 9: is for a type of payment plan that only really

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Speaker 9: became widely available in the US like ten years ago.

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Speaker 2: Ish really kind of really got traction during the pandemic,

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Speaker 2: I think.

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Speaker 4: I mean, it's not that old.

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Speaker 9: Yeah, it's really not that old. Some of these companies

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Speaker 9: started in the twenty tens. Klarna was started in Sweden

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Speaker 9: and then came here in the twenty tens. This is

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Speaker 9: all like fairly new.

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Speaker 3: I mean.

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Speaker 2: The other thing I think I've heard a lot about

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Speaker 2: buy Now Pay Later is that some things are big purchases,

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Speaker 2: like couches and refrigerators. But also by Now pay Later

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Speaker 2: has started to show up in things like grocery services.

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Speaker 3: And we got to talk about the burrito back security,

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Speaker 3: reno back security.

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Speaker 2: I mean, people are splitting up very small purchases, which

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Speaker 2: seems like another potential red flag and maybe a signal

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Speaker 2: from consumers that like of distress.

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Speaker 4: But when do you think, yeah.

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Speaker 9: You know, if you look at like the purchase of

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Speaker 9: a refrigerator, the purchase of a new laptop, a new

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Speaker 9: iPhone because your one just broke and you didn't plan

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Speaker 9: for that expense something like that, if you look at

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Speaker 9: an option to pay it over the course of a

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Speaker 9: couple of months at no interest, like that is a

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Speaker 9: rational decision to make, Like, I think that there are

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Speaker 9: good use cases for these, especially when consumer credit card

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Speaker 9: interest rates have have really risen in the.

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Speaker 1: Past several years.

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Speaker 4: Then there's the burrito.

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Speaker 9: Then there's the burritos.

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Speaker 3: Yeah, so are there so in case people haven't followed

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Speaker 3: this like you can, there are some door dash offers

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Speaker 3: the car and so that has led to jokes like

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Speaker 3: people are buying burritos in four parts and then that

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Speaker 3: is l which is yeah, gross, And that has led

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Speaker 3: to another level of of sort of joke, which is

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Speaker 3: the prospect that these finance companies that are lending you

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Speaker 3: money to buy burritos are then securitizing that debt and

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Speaker 3: selling them to like Goldman Sacks or something as burrito

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Speaker 3: backed securities are there, Burrito securities, abandon them all.

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Speaker 1: Essentially, Yeah, Like.

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Speaker 9: I think in a in a manner of speaking, yes,

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Speaker 9: because the pot of money that these lenders are drawing

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Speaker 9: from is you know, private investors, it's private equity, it's

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Speaker 9: all of these things that are how private credit, private credit,

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Speaker 9: it's how you know, any company that needs a big

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00:19:25,359 --> 00:19:29,199
Speaker 9: pot of money is getting money nowadays. Klarna is a

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Speaker 9: public company now they went public in September, but a

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Speaker 9: lot of these services either aren't public or they're part

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Speaker 9: of like a much larger company that like isn't necessarily

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Speaker 9: a bank, so it doesn't have to hear to banking

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Speaker 9: regulations and transparency. So like where how much debt exists

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Speaker 9: in this form, and like where it is, and like

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Speaker 9: what it was used on, and the likelihood of getting

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Speaker 9: paid back is just sort of difficult to discern, I would.

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Speaker 2: Say, Amanda Mall, thank you so much for talking with us.

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Speaker 4: Come back later. Yeah, thanks for having me, Thanks for

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Speaker 4: being here.

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Speaker 3: Now, all right, Stacey, we have talked about crypto a

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Speaker 3: bunch this year, partly because big story in twenty twenty five,

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Speaker 3: partly because, uh, you know, crypto currencies, the acid class

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Speaker 3: have boomed.

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Speaker 1: Uh.

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Speaker 3: Some of that I think because of hype caused by

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Speaker 3: the Trump administration, and some of it because the Trump

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Speaker 3: administration itself and in particular Trump and his family members

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Speaker 3: are like making tons and tons of money.

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Speaker 2: Now they're releasing the crypto adventures.

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Speaker 1: This is all kind of crazy.

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Speaker 3: There are a bunch of crazy little angles of this,

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Speaker 3: and the craziest one, I think are these meme coins,

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Speaker 3: the Trump coin, and we have in our midst Zeke

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Speaker 3: Fox of Bloomberg BusinessWeek. He just wrote a story with

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Speaker 3: Max Abelson, Donald and Malania Trump's terrible, tacky, seemingly legal

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Speaker 3: meme coin adventure. Zeke is also the author of Number

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Speaker 3: Go Up, which is like the best book that has

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Speaker 3: been written about the weird world of crypto and ZEGI

435
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Speaker 3: is here now.

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Speaker 1: Heyzeke, Hey, thanks for having me.

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Speaker 3: All Right, So before we talk about this Trump coin,

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Speaker 3: which is what your story is about, I just have

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Speaker 3: to ask you, like, before Donald Trump was elected, it

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Speaker 3: kind of seemed like this entire industry was gonna go away,

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Speaker 3: and like the I may have been, I think I've

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Speaker 3: made this point the last time you were on this podcast,

443
00:21:29,400 --> 00:21:32,879
Speaker 3: But like there were all these guys to former titans

444
00:21:32,880 --> 00:21:37,240
Speaker 3: of industry who are like in prison or disgraced, and

445
00:21:37,760 --> 00:21:40,520
Speaker 3: the whole industry seemed like in the duldrums. And if

446
00:21:40,520 --> 00:21:43,040
Speaker 3: you wake up now and look and like, not only

447
00:21:43,040 --> 00:21:45,040
Speaker 3: are they all back, but they are all in like

448
00:21:45,359 --> 00:21:49,160
Speaker 3: positions of kind of power, And I'm wondering, like how

449
00:21:49,800 --> 00:21:50,560
Speaker 3: that happened.

450
00:21:50,720 --> 00:21:52,840
Speaker 1: Yeah, I mean, it's it's pretty amazing.

451
00:21:53,320 --> 00:21:57,800
Speaker 8: The regulators in the US were kind of a sleep

452
00:21:57,800 --> 00:22:01,840
Speaker 8: at the wheel during the lastpto boom under Biden, and

453
00:22:01,840 --> 00:22:05,120
Speaker 8: then towards the end they woke up and they said, hey,

454
00:22:05,400 --> 00:22:09,080
Speaker 8: all of this like crypto trading are doing, we don't

455
00:22:09,119 --> 00:22:11,240
Speaker 8: actually think almost any of this is legal at all,

456
00:22:11,680 --> 00:22:15,720
Speaker 8: and they filed big lawsuits against all of the big

457
00:22:15,720 --> 00:22:22,520
Speaker 8: crypto exchanges like coinbase, Binance. These lawsuits basically would have

458
00:22:22,600 --> 00:22:27,520
Speaker 8: put the crypto industry out of business, and the industry

459
00:22:28,760 --> 00:22:32,080
Speaker 8: realized they had a political problem and that there could

460
00:22:32,080 --> 00:22:36,840
Speaker 8: be a political solution, and they tried two different approaches.

461
00:22:37,760 --> 00:22:41,160
Speaker 8: One group raised a lot of money and they donated

462
00:22:41,200 --> 00:22:44,879
Speaker 8: a lot to congressional candidates. The crypto industry actually spent

463
00:22:44,960 --> 00:22:47,920
Speaker 8: more on the last election cycle than any other industry,

464
00:22:47,960 --> 00:22:48,880
Speaker 8: even like oil.

465
00:22:48,960 --> 00:22:52,040
Speaker 4: Right, yes, more than like tech or anything.

466
00:22:52,840 --> 00:22:56,040
Speaker 8: They spent one hundred and fifty million. They spent against

467
00:22:56,080 --> 00:22:59,480
Speaker 8: one senator who had said some kind of critical things

468
00:22:59,520 --> 00:23:02,600
Speaker 8: about crypt but didn't even really seem to have strong

469
00:23:02,600 --> 00:23:05,959
Speaker 8: opinions about it, Shared Brown. They spent forty million dollars

470
00:23:06,000 --> 00:23:10,200
Speaker 8: on his election and defeated him. He was replaced by

471
00:23:10,240 --> 00:23:14,560
Speaker 8: a used car dealer with some crypto experience.

472
00:23:15,600 --> 00:23:21,040
Speaker 3: Bernie Marino, Yes, and a used car entrepreneur an owner

473
00:23:21,119 --> 00:23:21,920
Speaker 3: of dealerships.

474
00:23:22,359 --> 00:23:26,640
Speaker 8: So but there there was a second group of crypto people.

475
00:23:27,720 --> 00:23:32,479
Speaker 8: They said, you know what, this lobbying is all fine,

476
00:23:32,840 --> 00:23:35,920
Speaker 8: but the Democrats are the ones who've gone after us.

477
00:23:36,680 --> 00:23:39,040
Speaker 1: Why don't we get behind Trump?

478
00:23:40,200 --> 00:23:47,120
Speaker 8: And this wing was led by some really enthusiastic bitcoin fans,

479
00:23:47,520 --> 00:23:52,720
Speaker 8: in particular the guy who runs Bitcoin magazine, and he

480
00:23:52,800 --> 00:23:57,400
Speaker 8: runs this annual conference for every year, like ten thousand

481
00:23:57,440 --> 00:24:01,000
Speaker 8: bitcoin fans get together and talk about how cool bitcoin

482
00:24:01,080 --> 00:24:03,159
Speaker 8: is and how it's going to solve all the world's problems.

483
00:24:03,640 --> 00:24:09,000
Speaker 8: And last year he arranged a fundraiser before the conference.

484
00:24:09,600 --> 00:24:12,280
Speaker 8: They got people to pledge something like twenty five million

485
00:24:12,320 --> 00:24:16,400
Speaker 8: dollars to Trump. An RFK who was then a candidate.

486
00:24:17,040 --> 00:24:19,960
Speaker 8: He was supposed to be the main attraction, but he

487
00:24:20,000 --> 00:24:25,360
Speaker 8: got bumped for Trump. He held this fundraiser. He came

488
00:24:25,400 --> 00:24:29,000
Speaker 8: out on stage and he gave this speech in which

489
00:24:29,040 --> 00:24:33,360
Speaker 8: he promised the crypto industry everything they could have hoped for.

490
00:24:33,840 --> 00:24:36,560
Speaker 8: He said he would fire the head of the SEC

491
00:24:36,640 --> 00:24:39,960
Speaker 8: the one who had filed all these lawsuits against the

492
00:24:40,000 --> 00:24:43,200
Speaker 8: biggest players. He said that if the if crypto's going

493
00:24:43,240 --> 00:24:45,520
Speaker 8: to the moon, the US should lead the way.

494
00:24:47,800 --> 00:24:50,720
Speaker 2: And because this is like maybe a critical time for

495
00:24:51,080 --> 00:24:52,600
Speaker 2: crypto at this moment.

496
00:24:52,960 --> 00:24:54,639
Speaker 1: Well, and Trump wanted money.

497
00:24:54,880 --> 00:24:56,879
Speaker 3: I mean, he wanted their support, he wanted their he

498
00:24:56,920 --> 00:24:59,320
Speaker 3: wanted their dollars and their votes. I think so the

499
00:24:59,440 --> 00:25:02,879
Speaker 3: industry they get behind Trump, and I think the plan

500
00:25:03,280 --> 00:25:06,440
Speaker 3: is he's gonna do a bunch of things to help

501
00:25:06,480 --> 00:25:09,240
Speaker 3: them from a regulatory and he's gonna he's gonna fire

502
00:25:09,240 --> 00:25:12,600
Speaker 3: Gary Gensler, he's gonna make a strategic picking reserve, he's gonna,

503
00:25:12,760 --> 00:25:14,440
Speaker 3: you know, make a do a bunch of stuff that's

504
00:25:14,440 --> 00:25:15,840
Speaker 3: gonna be great for the industry.

505
00:25:16,240 --> 00:25:17,240
Speaker 1: No, that's the plan.

506
00:25:17,520 --> 00:25:19,639
Speaker 3: And then there's like this one, this thing that I

507
00:25:19,680 --> 00:25:23,000
Speaker 3: think they did not entirely count on, which is that

508
00:25:23,080 --> 00:25:27,280
Speaker 3: he would also enter the industry himself. So, like just

509
00:25:27,320 --> 00:25:30,040
Speaker 3: before the inauguration, we get this trump coin.

510
00:25:30,520 --> 00:25:37,760
Speaker 8: Yes, so it's inauguration weekend and some of these crypto

511
00:25:37,840 --> 00:25:42,800
Speaker 8: guys who had backed Trump put on a crypto ball

512
00:25:42,960 --> 00:25:46,919
Speaker 8: in DC at a big auditorium just down the street

513
00:25:46,920 --> 00:25:50,200
Speaker 8: from the White House, and who's who of the crypto

514
00:25:50,240 --> 00:25:56,840
Speaker 8: industry had gathered there, and Trump on truth Social announces

515
00:25:57,000 --> 00:26:02,480
Speaker 8: that he's created trump Coin, and people aren't sure what

516
00:26:02,520 --> 00:26:04,800
Speaker 8: to make of it. This is a meme coin, and

517
00:26:05,640 --> 00:26:10,920
Speaker 8: I want to explain what they are. So a lot

518
00:26:10,960 --> 00:26:16,160
Speaker 8: of cryptocurrencies make promises that they're going to make finance

519
00:26:16,200 --> 00:26:19,359
Speaker 8: more efficient, that they're going to make it cheaper to

520
00:26:19,400 --> 00:26:21,240
Speaker 8: transfer money across borders, or.

521
00:26:21,280 --> 00:26:24,200
Speaker 4: Stable coin like they're going to be pegged to a currency.

522
00:26:24,119 --> 00:26:25,280
Speaker 1: Be pegged to a currency.

523
00:26:25,520 --> 00:26:29,400
Speaker 8: It's like a new venture that's new trading venture, like defive,

524
00:26:29,640 --> 00:26:33,280
Speaker 8: and a meme coin is none of that. It's a

525
00:26:33,280 --> 00:26:38,640
Speaker 8: coin that transparently does not do anything, and it's just saying, hey,

526
00:26:38,640 --> 00:26:40,800
Speaker 8: here I am this is kind of fun.

527
00:26:41,320 --> 00:26:43,760
Speaker 1: If people buy it, it will go up.

528
00:26:43,760 --> 00:26:45,119
Speaker 4: It's like a joke sometimes.

529
00:26:45,200 --> 00:26:48,159
Speaker 1: Okay, So so my question is, and this is kind.

530
00:26:48,000 --> 00:26:50,879
Speaker 2: The day of the cyberball at the center of your story,

531
00:26:51,080 --> 00:26:52,280
Speaker 2: just before the inauguration.

532
00:26:54,040 --> 00:26:55,920
Speaker 1: How involved was Trump in this?

533
00:26:56,040 --> 00:26:59,000
Speaker 3: I mean, like he We've read a lot of stories

534
00:26:59,240 --> 00:27:02,199
Speaker 3: suggesting he ate a lot of money or on paper,

535
00:27:02,560 --> 00:27:04,680
Speaker 3: a huge amount of money initially and then less money

536
00:27:04,760 --> 00:27:09,239
Speaker 3: is it as it fell? How involved was Trump? And

537
00:27:09,320 --> 00:27:12,639
Speaker 3: like who are the players in this Trump coin?

538
00:27:13,320 --> 00:27:17,760
Speaker 8: What we were told that in the weeks before inauguration

539
00:27:17,880 --> 00:27:19,760
Speaker 8: down at mar A Lago, there was kind of a

540
00:27:19,760 --> 00:27:22,320
Speaker 8: parade of crypto guys coming through. A lot of them

541
00:27:22,359 --> 00:27:25,560
Speaker 8: had donated to the inauguration, that donated to the campaign,

542
00:27:25,920 --> 00:27:28,840
Speaker 8: and that there was this push to get this meme

543
00:27:28,880 --> 00:27:33,280
Speaker 8: coin done. There's a belief that a president Alex meme

544
00:27:33,359 --> 00:27:37,680
Speaker 8: coin was somehow more legal than a president.

545
00:27:37,960 --> 00:27:40,760
Speaker 2: It had to be locked into place. That's why it

546
00:27:40,800 --> 00:27:42,000
Speaker 2: happened at the cyberball.

547
00:27:42,320 --> 00:27:43,240
Speaker 1: Yes, now.

548
00:27:44,600 --> 00:27:47,160
Speaker 8: I want to say, though there are no rules about

549
00:27:47,200 --> 00:27:50,040
Speaker 8: mean coins, no one. I couldn't even tell you what

550
00:27:50,520 --> 00:27:52,720
Speaker 8: why a president Alex meme coin is better than a

551
00:27:52,720 --> 00:27:57,119
Speaker 8: president's meme coin legally speaking. So something else weird happened

552
00:27:57,119 --> 00:28:00,000
Speaker 8: that weekend, though, which was that when Trump announced his coin.

553
00:28:00,680 --> 00:28:02,880
Speaker 8: I mean, this is by far the biggest celebrity who's

554
00:28:02,920 --> 00:28:04,919
Speaker 8: ever done a meme coin. It's like, I don't know

555
00:28:04,920 --> 00:28:07,920
Speaker 8: what niche hobby to compare it to, but like he's

556
00:28:07,960 --> 00:28:08,240
Speaker 8: like the.

557
00:28:08,200 --> 00:28:10,200
Speaker 1: Hawk Tua of America is what you're saying.

558
00:28:12,560 --> 00:28:15,600
Speaker 8: So the coin really got going, it's got it gets

559
00:28:15,680 --> 00:28:19,800
Speaker 8: up to like seventy bucks in that day with it

560
00:28:20,200 --> 00:28:21,000
Speaker 8: on that weekend.

561
00:28:21,119 --> 00:28:23,200
Speaker 1: But then I'm.

562
00:28:24,400 --> 00:28:28,040
Speaker 8: Something something else happened that within the crypto world was

563
00:28:28,119 --> 00:28:29,000
Speaker 8: viewed as a rugging.

564
00:28:29,640 --> 00:28:32,080
Speaker 2: Oh okay, what happened was, yeah, because how did the

565
00:28:32,080 --> 00:28:34,320
Speaker 2: crypt so how did the people the crypto ball feel

566
00:28:34,320 --> 00:28:37,120
Speaker 2: as this coin was launching? Like are they excited? Does

567
00:28:37,119 --> 00:28:39,080
Speaker 2: this feel not quite right?

568
00:28:39,240 --> 00:28:42,320
Speaker 8: The crypto ball during at that time, people there were

569
00:28:42,440 --> 00:28:45,000
Speaker 8: worried that Trump had been hacked because there was this

570
00:28:45,080 --> 00:28:48,720
Speaker 8: trend of hackers getting into people's Twitter and announcing meme coins.

571
00:28:49,040 --> 00:28:52,000
Speaker 3: Oh say, there's nothing stopping you from launching your own

572
00:28:52,120 --> 00:28:54,400
Speaker 3: trump coin that has nothing to do with Trump, Like

573
00:28:54,520 --> 00:28:57,360
Speaker 3: I just call it the dj T token or whatever.

574
00:28:57,440 --> 00:28:59,800
Speaker 3: And it wasn't totally clear that he was involved with

575
00:28:59,840 --> 00:29:03,440
Speaker 3: it until he truthed. Until her even though there was

576
00:29:03,480 --> 00:29:04,600
Speaker 3: some concern that was.

577
00:29:04,560 --> 00:29:06,680
Speaker 4: Not people were just like this isn't real.

578
00:29:06,840 --> 00:29:09,920
Speaker 8: Yeah, okay, So it becomes clear it's real, it's really

579
00:29:10,040 --> 00:29:14,320
Speaker 8: going and then Milania announces her own meme coin.

580
00:29:14,760 --> 00:29:19,720
Speaker 2: Okay, and this one takes off two but less so

581
00:29:19,840 --> 00:29:20,200
Speaker 2: I remember.

582
00:29:20,280 --> 00:29:24,600
Speaker 8: Less So then crashes and it brings trump Coin down

583
00:29:24,640 --> 00:29:29,520
Speaker 8: with it, and people that day, on the day the

584
00:29:29,600 --> 00:29:33,040
Speaker 8: Millennia launch, when when the Millennia crashes, trump Coin goes

585
00:29:33,120 --> 00:29:36,080
Speaker 8: down to trump Coin has not crashed as hard as

586
00:29:36,080 --> 00:29:39,280
Speaker 8: Millennia coin. It's down about like ninety percent. It's kind

587
00:29:39,280 --> 00:29:39,680
Speaker 8: of been a.

588
00:29:40,880 --> 00:29:42,000
Speaker 4: Is a lot down.

589
00:29:42,320 --> 00:29:45,240
Speaker 8: Milania coin is down ninety nine percent, which is a

590
00:29:45,280 --> 00:29:45,960
Speaker 8: lot worse.

591
00:29:46,200 --> 00:29:48,600
Speaker 2: What was the time frame here between when the coin

592
00:29:48,680 --> 00:29:51,920
Speaker 2: launched and when it lost ninety slash ninety nine percent

593
00:29:51,960 --> 00:29:52,720
Speaker 2: of its value?

594
00:29:53,000 --> 00:29:56,480
Speaker 8: Trump Coin it got all the way we're talking about

595
00:29:56,680 --> 00:29:59,360
Speaker 8: the decline from the peak once it hit seventy I

596
00:29:59,360 --> 00:30:01,400
Speaker 8: think within like a day of that it lost at

597
00:30:01,480 --> 00:30:04,040
Speaker 8: least half its value. All the action is that weekend.

598
00:30:04,400 --> 00:30:08,920
Speaker 8: It's been much less exciting since then. So again, a

599
00:30:08,960 --> 00:30:11,640
Speaker 8: meme coin is nothing. There's never any expectation.

600
00:30:11,960 --> 00:30:13,480
Speaker 4: It's not backed bad thing.

601
00:30:13,560 --> 00:30:15,760
Speaker 8: That the President's going to do anything to make this

602
00:30:15,840 --> 00:30:18,880
Speaker 8: meme coin valuable. The only thing he can do is

603
00:30:18,960 --> 00:30:24,480
Speaker 8: talk about it, and when he started talking about his

604
00:30:24,600 --> 00:30:29,080
Speaker 8: wife's coin. This is kind of like a breaking of

605
00:30:29,160 --> 00:30:32,640
Speaker 8: the implicit contract with your meme coin buyers. How come

606
00:30:33,640 --> 00:30:37,840
Speaker 8: when you announce a meme coin, you are basically promising

607
00:30:37,880 --> 00:30:40,440
Speaker 8: that you will promote that coin for at least a

608
00:30:40,440 --> 00:30:43,160
Speaker 8: couple of days. You can't then start talking about your

609
00:30:43,160 --> 00:30:44,360
Speaker 8: family member's coins.

610
00:30:44,520 --> 00:30:47,440
Speaker 1: Wait, so it's like he's taking too much from the problem.

611
00:30:47,480 --> 00:30:49,840
Speaker 2: Is he like promoted his coin and then immediately promoted

612
00:30:50,120 --> 00:30:53,200
Speaker 2: its like too many coin launches and at the same time, yes,

613
00:30:53,320 --> 00:30:54,440
Speaker 2: why is that breaking the code?

614
00:30:54,480 --> 00:30:55,400
Speaker 4: That doesn't make any sense to me.

615
00:30:56,000 --> 00:30:59,320
Speaker 8: The meme coin thrives on attention, and if you start

616
00:30:59,400 --> 00:31:01,720
Speaker 8: driving a into some other coin.

617
00:31:01,640 --> 00:31:02,720
Speaker 4: You're splitting attention.

618
00:31:02,960 --> 00:31:06,880
Speaker 8: And also, there's only so many people that want to

619
00:31:06,920 --> 00:31:10,440
Speaker 8: gamble on meme coins, and they only have so much money.

620
00:31:10,760 --> 00:31:12,240
Speaker 8: And I'm going to say like a lot of money,

621
00:31:12,280 --> 00:31:15,280
Speaker 8: like billions of dollars, but not like trillions. So if

622
00:31:15,320 --> 00:31:17,760
Speaker 8: you start telling people about a new coin.

623
00:31:17,600 --> 00:31:18,760
Speaker 4: It's not a deep market.

624
00:31:19,040 --> 00:31:22,320
Speaker 8: They're going to sell their existing coins to get in

625
00:31:22,360 --> 00:31:23,040
Speaker 8: on the new one.

626
00:31:23,560 --> 00:31:26,240
Speaker 3: The view is that he got greedy, right, It's like

627
00:31:26,320 --> 00:31:27,440
Speaker 3: it's like you're getting greedy.

628
00:31:27,520 --> 00:31:31,640
Speaker 8: Yeah, so people weren't sure I mean, it seems hard

629
00:31:31,680 --> 00:31:35,680
Speaker 8: to believe, but like, did Donald and Millenia.

630
00:31:35,880 --> 00:31:38,600
Speaker 1: Speak to each other about these coins? Were these two

631
00:31:38,720 --> 00:31:40,840
Speaker 1: opposing camps. It's a little bit.

632
00:31:40,720 --> 00:31:41,400
Speaker 4: Blowing my mind.

633
00:31:41,440 --> 00:31:44,640
Speaker 2: So the problem that the crypto community felt was not

634
00:31:44,840 --> 00:31:46,840
Speaker 2: that someone who was about to go into the White

635
00:31:46,840 --> 00:31:48,800
Speaker 2: House had launched a crypto coin and all.

636
00:31:48,680 --> 00:31:49,960
Speaker 4: The potential implications of that.

637
00:31:50,040 --> 00:31:53,760
Speaker 2: The problem was that he launched another coin which took

638
00:31:53,760 --> 00:31:57,640
Speaker 2: attention away from that and potentially was like rugging the

639
00:31:57,720 --> 00:32:01,600
Speaker 2: people who had believed in him enough to buy his coin.

640
00:32:02,880 --> 00:32:06,200
Speaker 3: Yes, to be clear, No, there were people in the

641
00:32:06,200 --> 00:32:08,640
Speaker 3: crypto world who hated this, in particular the camp that

642
00:32:08,760 --> 00:32:11,000
Speaker 3: Ze brought up early on, the people who were like

643
00:32:11,040 --> 00:32:14,000
Speaker 3: investing in campaigns and like they found this.

644
00:32:14,200 --> 00:32:15,680
Speaker 1: There's some crypto people who didn't like this.

645
00:32:15,800 --> 00:32:19,440
Speaker 8: Yeah, I mean, I will say so, it's a lot

646
00:32:19,480 --> 00:32:22,160
Speaker 8: of people would say they found this distasteful. This isn't

647
00:32:22,200 --> 00:32:24,880
Speaker 8: what crypto stands for. And now what I will note

648
00:32:25,160 --> 00:32:27,600
Speaker 8: is that they were all very quick to list the

649
00:32:27,640 --> 00:32:31,000
Speaker 8: meme coin on their trading platforms, and that when I

650
00:32:31,040 --> 00:32:34,600
Speaker 8: think what some of them told me is that yet

651
00:32:34,960 --> 00:32:37,840
Speaker 8: this is kind of a helpful conflict of interest, like

652
00:32:37,920 --> 00:32:40,000
Speaker 8: Trump has made a lot of promises to us.

653
00:32:40,320 --> 00:32:42,560
Speaker 1: We don't know if he's going to follow through. But

654
00:32:42,760 --> 00:32:47,160
Speaker 1: now he's in on it himself. He's got regulate crypto, right.

655
00:32:47,000 --> 00:32:47,920
Speaker 4: He's got skin in the game.

656
00:32:48,080 --> 00:32:48,680
Speaker 1: Is that a reason?

657
00:32:48,800 --> 00:32:52,400
Speaker 3: Yeah, that's a great point, great great transition, because I

658
00:32:52,440 --> 00:32:54,640
Speaker 3: wanted to ask you, like how much money he made

659
00:32:54,760 --> 00:32:59,640
Speaker 3: and and like on paper there were some crazy estimates.

660
00:32:59,840 --> 00:33:02,560
Speaker 3: It's gone down a lot. I mean, we're how rich

661
00:33:02,680 --> 00:33:05,960
Speaker 3: did the president get and the president's wife on these

662
00:33:06,480 --> 00:33:09,120
Speaker 3: on these two meme coins.

663
00:33:09,320 --> 00:33:12,480
Speaker 8: Yeah, so it can be a bit tricky to calculate

664
00:33:12,520 --> 00:33:15,720
Speaker 8: because it's so easy to create wealth on paper. If

665
00:33:15,720 --> 00:33:18,720
Speaker 8: I make a billion zeke coins, I saw you one

666
00:33:18,760 --> 00:33:23,760
Speaker 8: for a dollar, Now I'm a billionaire technically. But because uh,

667
00:33:23,800 --> 00:33:27,680
Speaker 8: and on paper, the Trumps at one point were sitting

668
00:33:27,680 --> 00:33:31,280
Speaker 8: on fifty billion dollars of meme coin.

669
00:33:31,400 --> 00:33:32,280
Speaker 4: Do they own it?

670
00:33:33,040 --> 00:33:35,920
Speaker 8: We don't know exactly what they're split is with their

671
00:33:36,080 --> 00:33:42,520
Speaker 8: business partners, but yes. Now, because a lot of this

672
00:33:42,720 --> 00:33:47,520
Speaker 8: trading happens on the blockchain, where it can be if

673
00:33:47,520 --> 00:33:50,680
Speaker 8: you know what to look for, you can gather a

674
00:33:50,680 --> 00:33:52,720
Speaker 8: lot of data about how the about the trading.

675
00:33:54,480 --> 00:33:55,080
Speaker 1: I talked to.

676
00:33:55,120 --> 00:34:00,640
Speaker 8: Two crypto research firms Chain Analysis and bubble maps. Together,

677
00:34:00,720 --> 00:34:05,000
Speaker 8: they had calculated that the Trump family made more than

678
00:34:05,040 --> 00:34:08,120
Speaker 8: three hundred and fifty million dollars off these two meme

679
00:34:08,160 --> 00:34:08,960
Speaker 8: coins combined.

680
00:34:09,080 --> 00:34:12,400
Speaker 3: And that's a lot that money that they've coins that

681
00:34:12,440 --> 00:34:15,960
Speaker 3: they've sold and turned out of paper into dollars real money.

682
00:34:16,120 --> 00:34:21,400
Speaker 8: Yes, I mean this really beats Trump sneakers, Trump guitars, Trump.

683
00:34:21,200 --> 00:34:23,879
Speaker 2: Fragrance fight Fight Fight did not make nearly three hundred

684
00:34:23,880 --> 00:34:26,680
Speaker 2: and fifty dollars thousand dollars million, oh.

685
00:34:26,719 --> 00:34:28,960
Speaker 1: Million, three hundred and fifty million. Yeah.

686
00:34:29,080 --> 00:34:29,279
Speaker 3: Yeah.

687
00:34:29,400 --> 00:34:34,080
Speaker 1: And now Trump he has acted.

688
00:34:33,920 --> 00:34:36,680
Speaker 8: And this seems very believable that he really does not

689
00:34:36,800 --> 00:34:39,279
Speaker 8: know very much about any of this at a.

690
00:34:39,560 --> 00:34:42,080
Speaker 2: Well, he's old school, I mean, in fairness, it is

691
00:34:42,120 --> 00:34:44,440
Speaker 2: a whole world and it's not his world.

692
00:34:44,680 --> 00:34:48,200
Speaker 8: Yes, and so at a press conference, on his first

693
00:34:48,239 --> 00:34:51,279
Speaker 8: press conference as president, uh, he was asked about this

694
00:34:51,680 --> 00:34:53,759
Speaker 8: and he flipped the question on the reporter and was like,

695
00:34:54,080 --> 00:34:56,640
Speaker 8: how much did I make on that? And the reporter

696
00:34:56,840 --> 00:34:59,520
Speaker 8: was like, as a president, I think like billions of dollars.

697
00:34:59,560 --> 00:35:02,759
Speaker 8: The data wasn't quite so good then, and he was like, ooh,

698
00:35:03,480 --> 00:35:05,640
Speaker 8: but it's nothing compared to he was standing with like

699
00:35:05,640 --> 00:35:08,799
Speaker 8: Elon Musk, He's like, yeah, billions, who cares? At a

700
00:35:08,840 --> 00:35:11,040
Speaker 8: different press conference, and this was one of my favorite

701
00:35:12,440 --> 00:35:18,320
Speaker 8: parts of the story. The Press secretary was asked about

702
00:35:18,719 --> 00:35:23,120
Speaker 8: on an event that dinner that Trump was hosting for

703
00:35:23,200 --> 00:35:26,480
Speaker 8: the top buyers of his meme coin, and she said

704
00:35:28,239 --> 00:35:30,400
Speaker 8: it wasn't relevant that he was doing that in his

705
00:35:30,520 --> 00:35:34,560
Speaker 8: personal time, Like he just clocked out for the day.

706
00:35:35,000 --> 00:35:38,320
Speaker 1: He's not president anymore, just whatever he does. Who cares?

707
00:35:38,680 --> 00:35:41,920
Speaker 3: Zeke Fox, author of Number Go Up, Inside Crypto's Wild

708
00:35:42,000 --> 00:35:44,600
Speaker 3: Rise and Staggery Fall, Thanks for being here.

709
00:35:45,080 --> 00:35:52,840
Speaker 8: Thanks Facts, Thanks Stacy, Stacy.

710
00:35:53,480 --> 00:35:56,200
Speaker 3: I had to share a little listener feedback that came

711
00:35:56,239 --> 00:36:00,800
Speaker 3: into the Everybody's Business mailbox last week. Really fun email

712
00:36:01,160 --> 00:36:05,920
Speaker 3: from Matt in regards to our conversation about Donald Trump

713
00:36:06,000 --> 00:36:08,840
Speaker 3: and the difference between football and guess.

714
00:36:08,800 --> 00:36:11,200
Speaker 2: Yes, this is when Donald Trump was like, we should

715
00:36:11,200 --> 00:36:13,719
Speaker 2: call soccer football, which is very right.

716
00:36:13,800 --> 00:36:18,239
Speaker 3: Shock. I was offended on behalf of all American sports chauvinists.

717
00:36:18,400 --> 00:36:21,280
Speaker 3: But here's Matt, as an avid fan of the beautiful

718
00:36:21,280 --> 00:36:24,000
Speaker 3: game in North America, I would welcome being able to

719
00:36:24,000 --> 00:36:26,680
Speaker 3: call the sport football or football like the rest of

720
00:36:26,719 --> 00:36:29,160
Speaker 3: the world. Soccer is just one more mistake we got

721
00:36:29,200 --> 00:36:32,319
Speaker 3: from the British, much like imperial units, which we should

722
00:36:32,360 --> 00:36:35,120
Speaker 3: have dumped Inton Boston Harbor, along with the tea worth

723
00:36:35,160 --> 00:36:38,320
Speaker 3: noting as well that Britain itself has since fixed both mistakes.

724
00:36:38,760 --> 00:36:42,319
Speaker 3: He goes on to talk about how popular soccer is,

725
00:36:42,800 --> 00:36:45,960
Speaker 3: and then says the NFL, in contrast, is an American

726
00:36:46,080 --> 00:36:49,440
Speaker 3: entertainment franchise and could be renamed something more catchy by

727
00:36:49,520 --> 00:36:53,960
Speaker 3: their immense marketing team, perhaps touchdown exclamation point or I

728
00:36:54,000 --> 00:36:57,200
Speaker 3: don't know, desperate quarterbacks say thanks for the fun podcast,

729
00:36:57,239 --> 00:36:57,759
Speaker 3: I'll sign up.

730
00:36:57,800 --> 00:37:00,440
Speaker 2: He wants to change the name of football to desperate quarterback.

731
00:37:00,440 --> 00:37:01,279
Speaker 1: You want to change it?

732
00:37:01,320 --> 00:37:04,520
Speaker 3: Okay, I don't think that one, Matt Matt in Seattle, Sorry,

733
00:37:04,560 --> 00:37:05,919
Speaker 3: desperate Quarterbacks is not good.

734
00:37:06,000 --> 00:37:09,200
Speaker 1: But touchdown exclamation point excellent.

735
00:37:09,280 --> 00:37:12,040
Speaker 2: I think you think the game of football should now

736
00:37:12,040 --> 00:37:14,239
Speaker 2: be called the game of American football should now be

737
00:37:14,239 --> 00:37:15,080
Speaker 2: called touchdown.

738
00:37:15,280 --> 00:37:18,960
Speaker 3: I am on the record saying that it's soccer and that,

739
00:37:19,080 --> 00:37:22,080
Speaker 3: and I think Matt is very confused, Like I understand

740
00:37:22,160 --> 00:37:24,680
Speaker 3: like this, there's kind of like a patriotism thing here,

741
00:37:24,760 --> 00:37:27,240
Speaker 3: like we dumped soccer, but we have football.

742
00:37:27,320 --> 00:37:29,839
Speaker 1: But but like football is also a British word.

743
00:37:29,880 --> 00:37:32,680
Speaker 3: So I just think I am on the record saying

744
00:37:32,719 --> 00:37:35,440
Speaker 3: it should be called the NFL should be called football.

745
00:37:35,840 --> 00:37:38,279
Speaker 3: The beautiful game should be soccer. But I do like

746
00:37:38,360 --> 00:37:40,360
Speaker 3: Matt's idea that touchdown.

747
00:37:40,600 --> 00:37:42,319
Speaker 1: Okay, it has to be with the exclamation point.

748
00:37:42,440 --> 00:37:45,120
Speaker 2: I mean, I just feel like football, you know, Listen,

749
00:37:45,239 --> 00:37:46,360
Speaker 2: I love free trade.

750
00:37:46,480 --> 00:37:48,680
Speaker 4: I mean, I think globalization is a very positive force.

751
00:37:48,760 --> 00:37:51,239
Speaker 2: I am a person I like to consider myself a

752
00:37:51,280 --> 00:37:52,280
Speaker 2: cosmopolitan person.

753
00:37:52,600 --> 00:37:57,120
Speaker 4: And it's just football. I don't know why break it.

754
00:37:57,480 --> 00:37:58,719
Speaker 1: Yeah, No, that's so much.

755
00:37:59,040 --> 00:38:02,120
Speaker 2: I feel like there's so much changing in our world

756
00:38:02,200 --> 00:38:05,279
Speaker 2: right now. I just want this one thing to say

757
00:38:05,280 --> 00:38:05,640
Speaker 2: the same.

758
00:38:06,160 --> 00:38:07,760
Speaker 1: Okay, thank you MAV for the email.

759
00:38:07,880 --> 00:38:09,200
Speaker 4: Yes, thank you for the email.

760
00:38:09,320 --> 00:38:09,600
Speaker 1: Everyone.

761
00:38:09,640 --> 00:38:12,160
Speaker 3: Please keep writing in everybody's at Bloomberg dot net.

762
00:38:12,160 --> 00:38:13,719
Speaker 1: We really like the emails we keep reading.

763
00:38:13,760 --> 00:38:20,280
Speaker 2: Okay, Well, while we were on the topic of sports, Max,

764
00:38:20,400 --> 00:38:23,400
Speaker 2: you have an underrated story also about sports.

765
00:38:23,440 --> 00:38:24,920
Speaker 4: What's what's your underrated story?

766
00:38:25,280 --> 00:38:28,480
Speaker 3: You're probably familiar with this problem we have in American

767
00:38:28,560 --> 00:38:32,120
Speaker 3: society and in American corporations where you have these kind

768
00:38:32,160 --> 00:38:35,319
Speaker 3: of older workers who are crowding out younger workers. Right,

769
00:38:35,440 --> 00:38:38,680
Speaker 3: like the kind of zoomers and millennials who feel like

770
00:38:38,719 --> 00:38:40,920
Speaker 3: they can't get a job, they can't rise within the

771
00:38:41,239 --> 00:38:45,160
Speaker 3: organization because because they're too many old people taking their jobs, and.

772
00:38:45,160 --> 00:38:46,720
Speaker 4: You frequently people won't retire.

773
00:38:47,200 --> 00:38:47,399
Speaker 1: Yeah.

774
00:38:47,480 --> 00:38:51,440
Speaker 3: Yeah. You frequently see these very smart op eds written

775
00:38:51,480 --> 00:38:54,080
Speaker 3: in like, you know whatever, the Atlantic or Bloomberg opinion

776
00:38:54,080 --> 00:38:56,920
Speaker 3: about the gar intocracy talking about Joe Biden being very

777
00:38:56,920 --> 00:38:58,359
Speaker 3: old or Donald Trump being very old.

778
00:38:58,520 --> 00:38:59,200
Speaker 1: It seems bad.

779
00:38:59,560 --> 00:39:02,719
Speaker 3: Now I have another example of this in an unlikely area,

780
00:39:02,719 --> 00:39:04,960
Speaker 3: which is professional football. I don't know if you're paying attention,

781
00:39:05,080 --> 00:39:09,759
Speaker 3: but last weekend, the new quarterback, the fresh faced quarterback

782
00:39:09,840 --> 00:39:13,440
Speaker 3: for the Indianapolis Colts, who is Philip Rivers, who is

783
00:39:13,680 --> 00:39:17,000
Speaker 3: forty four years old and who retired almost five years

784
00:39:17,000 --> 00:39:19,399
Speaker 3: ago ago for the NFL, who got into a game

785
00:39:19,480 --> 00:39:22,879
Speaker 3: last week essentially out of desperation, and although he did

786
00:39:22,880 --> 00:39:25,839
Speaker 3: not lead the team to victory, it was kind of

787
00:39:25,840 --> 00:39:28,680
Speaker 3: like an honorable defeat. And he's got another starting job.

788
00:39:28,680 --> 00:39:31,240
Speaker 3: He's going to be starting this weekend for the Colts

789
00:39:31,280 --> 00:39:33,200
Speaker 3: as a play I believe the forty nine ers. So

790
00:39:33,200 --> 00:39:35,040
Speaker 3: we're gonna have a forty four year old NFL.

791
00:39:35,120 --> 00:39:36,319
Speaker 4: It's great, is it?

792
00:39:36,360 --> 00:39:38,320
Speaker 1: Though? So this is what's.

793
00:39:38,080 --> 00:39:40,120
Speaker 3: Weird to me, everyone thinks it's great, and I guess

794
00:39:40,120 --> 00:39:41,920
Speaker 3: it is kind of great. There's something just sort of

795
00:39:41,960 --> 00:39:45,360
Speaker 3: fun I think about, like an out of shape professional athlete,

796
00:39:45,400 --> 00:39:46,520
Speaker 3: Like we all, why do you.

797
00:39:46,440 --> 00:39:48,880
Speaker 4: Say he's out of shape? He is a professional athlete.

798
00:39:49,080 --> 00:39:52,040
Speaker 3: If you watch him play, you will see like he is.

799
00:39:52,320 --> 00:39:53,640
Speaker 3: He is on the older side.

800
00:39:54,440 --> 00:39:55,600
Speaker 4: He's not a scrambler.

801
00:39:55,719 --> 00:39:59,160
Speaker 3: He's definitely he's lumbering at this point, I think in

802
00:39:59,239 --> 00:40:04,000
Speaker 3: his But you know, so there's this like interesting thing

803
00:40:04,040 --> 00:40:07,319
Speaker 3: happening where, yes, like the physical gifts aren't there, the

804
00:40:07,360 --> 00:40:10,719
Speaker 3: passes aren't great, he doesn't move very well, but a

805
00:40:10,719 --> 00:40:14,000
Speaker 3: big part of being a quarterback is is like understanding

806
00:40:14,040 --> 00:40:15,680
Speaker 3: the offense and making smart decisions.

807
00:40:15,800 --> 00:40:16,640
Speaker 1: And it's kind of like that.

808
00:40:16,760 --> 00:40:21,120
Speaker 3: The reason why the boomer is still in that you know,

809
00:40:21,280 --> 00:40:24,560
Speaker 3: middle management job and not a millennial or a zoomer,

810
00:40:24,560 --> 00:40:26,680
Speaker 3: it's because they they may be a little slower, they

811
00:40:26,680 --> 00:40:28,560
Speaker 3: may not be as hip to the kids, but they

812
00:40:28,680 --> 00:40:30,840
Speaker 3: understand kind of the ins and outs of the organization.

813
00:40:31,360 --> 00:40:33,239
Speaker 3: But anyway, what I think is just funny is that

814
00:40:33,280 --> 00:40:35,800
Speaker 3: everyone is like rooting for Philip Rivers.

815
00:40:35,719 --> 00:40:37,240
Speaker 1: And it feels like a weird.

816
00:40:37,600 --> 00:40:40,560
Speaker 3: It feels like kind of discordant with our with the

817
00:40:40,560 --> 00:40:43,680
Speaker 3: rest of the conversation on on the gerontocracy. How come

818
00:40:43,719 --> 00:40:46,239
Speaker 3: no one is rooting for Joe Biden when he's like

819
00:40:46,280 --> 00:40:48,759
Speaker 3: slurring his words or whatever during a debate, or Donald

820
00:40:48,800 --> 00:40:51,080
Speaker 3: Trump is he's falling asleep in the Oval office. Like,

821
00:40:51,120 --> 00:40:52,640
Speaker 3: I feel like we need to kind of get on

822
00:40:52,640 --> 00:40:55,680
Speaker 3: one page and whether it's cool to have elderly people

823
00:40:55,719 --> 00:40:56,240
Speaker 3: in jobs.

824
00:40:56,719 --> 00:40:59,520
Speaker 2: I think, actually, this is a big conversation that we

825
00:40:59,560 --> 00:41:02,640
Speaker 2: should have. And I think we will be working later

826
00:41:02,640 --> 00:41:05,640
Speaker 2: and later and longer and longer because our health has improved,

827
00:41:05,680 --> 00:41:09,640
Speaker 2: our healthcare is improved, and also like retirement is just

828
00:41:09,680 --> 00:41:11,239
Speaker 2: a much different thing than it used to be.

829
00:41:11,960 --> 00:41:14,840
Speaker 4: I think it's a good thing that the workplace is not.

830
00:41:15,040 --> 00:41:17,040
Speaker 2: I mean, I feel like the US were just so

831
00:41:17,320 --> 00:41:19,560
Speaker 2: youth obsessed. It's always like, what's the next thing? What's

832
00:41:19,600 --> 00:41:21,200
Speaker 2: the next thing? Like what are the eighteen to twenty

833
00:41:21,239 --> 00:41:24,319
Speaker 2: four year olds buying? But the reality of like our

834
00:41:24,400 --> 00:41:27,880
Speaker 2: demographics is that things are should be more holistic. I

835
00:41:27,920 --> 00:41:29,920
Speaker 2: think there is a place for zoomers and a place

836
00:41:29,920 --> 00:41:32,600
Speaker 2: for boomers in the same workplace. Like I don't think

837
00:41:32,640 --> 00:41:35,439
Speaker 2: one precludes the other. I don't think it's a zero

838
00:41:35,560 --> 00:41:39,960
Speaker 2: sum game. I think I think we should honor older workers.

839
00:41:40,000 --> 00:41:41,640
Speaker 2: I think older workers have a lot to contribute.

840
00:41:41,760 --> 00:41:44,480
Speaker 3: Well exactly, like, so I'm forty three, when you're younger

841
00:41:44,520 --> 00:41:45,200
Speaker 3: than Philip.

842
00:41:44,960 --> 00:41:47,680
Speaker 4: Rivery got, you've still got some years in your career.

843
00:41:48,000 --> 00:41:50,800
Speaker 3: My kids they think, like, I'm pretty good at you know, baseball,

844
00:41:50,800 --> 00:41:52,759
Speaker 3: I'm pretty good at kicking the soccer ball. They think

845
00:41:52,920 --> 00:41:54,719
Speaker 3: I could be a professional athlete. And they're always tell

846
00:41:54,800 --> 00:41:57,800
Speaker 3: me like, oh, well, like you could be in you

847
00:41:57,840 --> 00:41:59,760
Speaker 3: could be a baseball player. You could do this because

848
00:42:00,000 --> 00:42:02,400
Speaker 3: they bring up one example or like my son watches

849
00:42:02,440 --> 00:42:05,080
Speaker 3: American Ninja Warrior and he's always telling me like, Dad,

850
00:42:05,239 --> 00:42:07,479
Speaker 3: there was a sixty year old on American Ninja Warrior.

851
00:42:07,480 --> 00:42:09,840
Speaker 3: So yeah, fine, see so yeah, so this is like

852
00:42:09,880 --> 00:42:12,200
Speaker 3: another data point, I guess in that case, but I

853
00:42:12,239 --> 00:42:12,600
Speaker 3: don't know.

854
00:42:13,160 --> 00:42:15,240
Speaker 1: We'll see if he what happens this weekend.

855
00:42:15,560 --> 00:42:18,000
Speaker 2: I do have an interesting data point that kind of

856
00:42:18,080 --> 00:42:21,840
Speaker 2: backs up what your kids are saying. In the business world,

857
00:42:22,080 --> 00:42:25,600
Speaker 2: there was this really interesting study done about the startups

858
00:42:25,800 --> 00:42:28,719
Speaker 2: and like the most successful startups, and as it turned out,

859
00:42:28,760 --> 00:42:32,000
Speaker 2: the most successful startups were started by people in their forties.

860
00:42:32,680 --> 00:42:36,279
Speaker 3: Obviously, in the business world, the kind of like youth

861
00:42:36,320 --> 00:42:38,319
Speaker 3: doesn't give you the same advantage as it gives you

862
00:42:38,400 --> 00:42:40,839
Speaker 3: as it gives you in the NFL, where like you know,

863
00:42:40,880 --> 00:42:42,600
Speaker 3: you need to be able to cut or whatever, and.

864
00:42:42,560 --> 00:42:44,800
Speaker 4: The feel faster and yeah, and.

865
00:42:44,680 --> 00:42:49,839
Speaker 3: The fact that even like physically limited, his sort of

866
00:42:49,920 --> 00:42:54,520
Speaker 3: experience and decision making is enough to make up for that.

867
00:42:54,560 --> 00:42:56,960
Speaker 3: To the point they can't find any quarterback out there

868
00:42:57,000 --> 00:42:59,880
Speaker 3: anywhere who anyone who just graduated from college who can

869
00:43:00,080 --> 00:43:02,680
Speaker 3: pete in the NFL. Like, it tells you something that

870
00:43:02,680 --> 00:43:05,319
Speaker 3: that like these skills, these like maybe these soft skills.

871
00:43:05,360 --> 00:43:07,440
Speaker 4: I'm sure they could find other people, but he might

872
00:43:07,520 --> 00:43:08,080
Speaker 4: just be better.

873
00:43:08,239 --> 00:43:09,000
Speaker 1: He's better general.

874
00:43:09,120 --> 00:43:11,680
Speaker 3: I think it's I mean, I think there's something with

875
00:43:11,719 --> 00:43:14,040
Speaker 3: where the coach knows him or whatever. But still I

876
00:43:14,080 --> 00:43:15,919
Speaker 3: think there is something to the fact that to the

877
00:43:16,040 --> 00:43:17,719
Speaker 3: fact that like, once you get down to your third

878
00:43:17,760 --> 00:43:21,560
Speaker 3: string quarterback, which is where they were your bait, you

879
00:43:21,719 --> 00:43:23,759
Speaker 3: might be better off just getting a guy who was

880
00:43:23,840 --> 00:43:26,879
Speaker 3: coaching high school football in Alabama for the last five years,

881
00:43:26,880 --> 00:43:28,319
Speaker 3: which was what Philip Hurbs was doing.

882
00:43:35,000 --> 00:43:37,840
Speaker 2: The show is produced by Stacy Wong. Magnus Hendrickson is

883
00:43:37,840 --> 00:43:41,000
Speaker 2: our supervising producer, and Amy Kean is our executive producer.

884
00:43:41,120 --> 00:43:44,120
Speaker 2: Sam Roganic handles engineering and Dave for self fact checks.

885
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Speaker 2: Sage Bauman heads Bloomberg Podcasts. Special thanks to Jeff Muscus,

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Speaker 2: Julia Rubin, Charlie Gorivin, and Maria Ling. If you have

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Speaker 2: a minute, please rate and review the show. It does

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Speaker 2: mean a lot to us, and if you have a

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00:43:55,800 --> 00:43:59,120
Speaker 2: story that should be our business, please email us. Everybody's

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Speaker 2: at Bloomberg dot net. That is, everybody's with ans at

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Speaker 2: Bloomberg dot net.

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Speaker 4: Thank you for listening and we'll see you next week.

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Speaker 8: M