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What is DeepSeek and why is it crushing US tech stocks? · The Financial Exchange Show

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  Speaker 1: Chuck Zada, and Mike Armstrong.
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  Speaker 2: Chuck, Mike and Tucker with you here. And we've got
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  Speaker 2: a sell off happening in markets today, but really the
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  Speaker 2: sell off is concentrated primarily in big tech. And the
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  Speaker 2: reason why, if you have not heard of the AI
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  Speaker 2: platform deep Seek, you're going to hear about it right now.
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  Speaker 2: If you have heard about it, then you might know
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  Speaker 2: some of what we're talking about. But quite honestly, we
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  Speaker 2: think that we are very well versed on this topic,
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  Speaker 2: and so you're going to learn some new stuff from us,
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  Speaker 2: you better believe it.
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  Speaker 3: And if you had heard about deep Seek prior to
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  Speaker 3: five days ago, I'd be particularly interested because because nobody.
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  Speaker 2: Because you hadn't. Let's be honest. So what is deep Seek.
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  Speaker 2: It is an AI platform that is built by a
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  Speaker 2: Chinese company by the name of high Flyer. High Flyer
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  Speaker 2: is a hedge fund. And why did you Flyer go
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  Speaker 2: and build deep Seek? Now? The reason why They had
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  Speaker 2: been building internal AI processes for their investment platform, trying to,
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  Speaker 2: you know, find better ways to beat the market investing,
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  Speaker 2: and as part of that, they just had a natural
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  Speaker 2: interest in kind of the latest and greatest AI technology,
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  Speaker 2: and so they started building AI models a couple of
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  Speaker 2: years ago. The one that they released on January twentieth
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  Speaker 2: is called deep seek are one and the premise of
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  Speaker 2: this is that it competes directly with open AI's flagship
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  Speaker 2: open ai Chat GPT oh one product that came out
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  Speaker 2: I want to say, in like September or October, and
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  Speaker 2: now Chat GPT's been around for two years.
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  Speaker 3: Yeah, a little more than two years and it's public
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  Speaker 3: facing will but this latest iteration of their large language
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  Speaker 3: model is you said, one oh one.
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  Speaker 2: And the big thing that one claimed to be able
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  Speaker 2: to do and that it can do is it can
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  Speaker 2: handle more logical reasoning things. So you ask it, you know,
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  Speaker 2: logic based quests, and it can actually kind of think
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  Speaker 2: through them in certain respects. And this platform, the deep
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  Speaker 2: seek are one platform, does the exact same thing. So
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  Speaker 2: why does this matter? A couple reasons. The first is
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  Speaker 2: deep Seak claims that they built this using about five
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  Speaker 2: point six million dollars of what they call AI training,
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  Speaker 2: basically in putting all the data into the model and
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  Speaker 2: getting it to understand what it is. Sounds like a
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  Speaker 2: lot of money, Chuck, is it no, Because most AI
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  Speaker 2: models that are produced in the US right now cost
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  Speaker 2: anywhere from about five hundred million to a billion dollars
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  Speaker 2: to do the training aspect.
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  Speaker 3: Now keep in mind, this is the training part where
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  Speaker 3: they're feeding at internet articles and the entire history of
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  Speaker 3: the New York Times and Facebook and Twitter and all
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  Speaker 3: these different things. This is just purely teaching the thing
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  Speaker 3: how to read and think and do everything correct the
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  Speaker 3: AI models can do.
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  Speaker 2: And basically the training costs is a function of two things.
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  Speaker 2: How many computer chips do you need to train on
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  Speaker 2: and how long do you need to do that for?
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  Speaker 2: Those are the two things that matter, because you're you're
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  Speaker 2: basically paying for the compute time. What you are then
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  Speaker 2: doing on the the other end of that is after
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  Speaker 2: the training is completed, hey, you still need to pay
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  Speaker 2: for the processors to go and actually execute the queries
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  Speaker 2: that are asked of the model. So that's why if
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  Speaker 2: you're using open AI's enterprise platforms or they're you know,
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  Speaker 2: the their high end stuff for one and again, these
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  Speaker 2: numbers are going to be mean nothing to you. But
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  Speaker 2: don't worry about it. I'll make it make sense, I promise.
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  Speaker 2: If you need inputs from open ai, it costs fifteen
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  Speaker 2: dollars per million inputs for outputs, it costs sixty per
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  Speaker 2: million deep seek Remember open as fifteen per million inputs.
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  Speaker 2: Deep seek is fourteen cents per million inputs one percent
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  Speaker 2: of the cost. On the other side of things, outputs
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  Speaker 2: open AI is sixty per million outputs. Deep seek is
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  Speaker 2: two dollars and nineteen cents.
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  Speaker 3: Okay, So we've basically now understood from this company's own
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  Speaker 3: I wouldn't even say disclosures, but yeah, information that they've
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  Speaker 3: put out about themselves is that training and getting output
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  Speaker 3: out of this large language model costs one percent on
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  Speaker 3: the training side and three percent of chat GPT's own
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  Speaker 3: disclosed information on the output side of things. I would say,
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  Speaker 3: Chuck that this is one area that we probably have
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  Speaker 3: the least degree of certainty about this new model. Right, Like,
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  Speaker 3: it's a company held out of China, So could there
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  Speaker 3: be some accounting creativity outright lies a whole bunch of
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  Speaker 3: subsidies from the Chinese government. All of those things are
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  Speaker 3: entirely possible. But what they are claiming is that, yeah,
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  Speaker 3: the cost to both run and train this thing, we're
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  Speaker 3: one to three percent of what we've been experiencing in
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  Speaker 3: models here in the United States, and getting we'll get
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  Speaker 3: to the results side.
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  Speaker 2: So the training side of things is something we're for this.
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  Speaker 2: They trained on their cluster of a cluster of computers
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  Speaker 2: for about two months. So what I can tell you
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  Speaker 2: is that there are a ton of AI startups and
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  Speaker 2: even like big AI companies like open Ai or Microsoft,
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  Speaker 2: you better believe they're gonna be having all hands on
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  Speaker 2: deck today saying hey, can we do something similar. Let's
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  Speaker 2: try to get it out in the next couple months,
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  Speaker 2: And so we're gonna know in the next probably I
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  Speaker 2: would say by May or June, whether you can actually
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  Speaker 2: replicate the process for anywhere close to the cost that
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  Speaker 2: they've done, Because this is a cost thing. If you
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  Speaker 2: are a big you know enterpriser, if you're any company
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  Speaker 2: that's considering using you know, AI technology, and someone comes
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  Speaker 2: and says, hey, I can give you like ninety nine
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  Speaker 2: percent of the performance for one percent of the cost,
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  Speaker 2: you're like, where where do I sign up? Sign me
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  Speaker 2: up for it now?
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  Speaker 3: For maybe the average person we're listening right now, especially
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  Speaker 3: if you're not paying for chat GPT, this might not
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  Speaker 3: feel all that relevant to you correct the enterprise.
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  Speaker 2: But if you're not paying, they don't care because they're
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  Speaker 2: making no money off you if if you want to
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  Speaker 2: go and subscribe right now.
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  Speaker 3: Let's say you're a small business and you want to
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  Speaker 3: buy an enterprise license of chat GPT and their latest models.
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  Speaker 3: I think you're looking at between twenty five to thirty
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  Speaker 3: dollars per month per user, and they're not really making
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  Speaker 3: any money on that per se. But just to give
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  Speaker 3: somebody a framework here for if you're actually paying for
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  Speaker 3: this thing, what does it cost? And that's where the
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  Speaker 3: pricing is coming in right now for like an enterprise
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  Speaker 3: version of chat GPT.
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  Speaker 2: That's not even really where the money is though. It's
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  Speaker 2: really in Hey, like if you're someone who's paying in
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  Speaker 2: these you know, million input tokens and things like that.
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  Speaker 2: Again I'm using too much, I know, jargon and everything.
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  Speaker 2: The real money is, Hey, I want to pay you
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  Speaker 2: just to send a bunch of data away and have
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  Speaker 2: you send it back to me, not for the off
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  Speaker 2: the shelf license. So what we do know is bunch
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  Speaker 2: of companies offer benchmarks for AI models. How strightly can
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  Speaker 2: you do this? How well? How accurately can you do that?
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  Speaker 3: So maview on thousand math equations and how often do
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  Speaker 3: they get amound? Yes?
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  Speaker 2: Here is the LSAT for last year, what's your score
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  Speaker 2: and how long does it take you to do it? Basically,
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  Speaker 2: except for computers, what we have here is on the
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  Speaker 2: six biggest benchmarks that are out there. This is basically
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  Speaker 2: within one to two percent of the one model that's
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  Speaker 2: chat GPTs.
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  Speaker 3: Chat GPT's kind of best in class, right correct.
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  Speaker 2: And the other thing that's interesting about this deepseek they
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  Speaker 2: made it an open source model, so unlike chat GPT
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  Speaker 2: where everything is held proprietary, like you can't see how
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  Speaker 2: the black box works. This they published the code. They said,
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  Speaker 2: this is how it works. Here it is. And the
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  Speaker 2: fascinating thing so a couple things that they do differently
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  Speaker 2: based on what the actual code shows and how it
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  Speaker 2: actually works. First, traditional AI model generative AI. To this point,
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  Speaker 2: I say traditional. They've been around for two freaking years.
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  Speaker 2: What's traditional about that? They work? Each word is a
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  Speaker 2: different input the pause, cat, pause, saw pause, the pause
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  Speaker 2: dog open, Aye says okay, we're gonna read each of
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  Speaker 2: these in order to figure out what's going on this.
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  Speaker 2: They figured out a way to do it in one
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  Speaker 2: hole chunk the cat, saw the dog, and in doing so,
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  Speaker 2: instead of having to query your database, five times. You
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  Speaker 2: can do it once there. The other thing that they
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  Speaker 2: did is chat GPT is an example. It's got like
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  Speaker 2: six hundred and eighty billion parameters and it basically inputs
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  Speaker 2: that are there where if you throw something at it,
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  Speaker 2: it runs through six hundred and eighty billion things to
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  Speaker 2: figure out what to say back to you. Deepseek built
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  Speaker 2: what's called an expert system where if you ask a
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  Speaker 2: question about math, it says, Okay, here are the terms
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  Speaker 2: we've categorized as math. We're gonna pull from that subset,
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  Speaker 2: not from everything, because we don't need to access the
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  Speaker 2: whole database, and so it's more efficient.
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  Speaker 3: I don't need to access eighteenth century poetry if I'm
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  Speaker 3: trying to answer a physics.
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  Speaker 2: Correct Who cares about Dante? If you want to know
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  Speaker 2: why equals mc squared, he didn't know either. So ultimately,
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  Speaker 2: what this gets at is while this is running in
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  Speaker 2: the cloud on and you can pull up like the
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  Speaker 2: deep Seep deep Seek app on your phone, if you
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  Speaker 2: go and download the actual model, and like you got
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  Speaker 2: to do a little bit of work. You can't just
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  Speaker 2: like download this for a Windows computer and be fine.
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  Speaker 2: You can run this on a high end gaming computer.
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  Speaker 3: And but what makes that special, because that's confusing, Yes,
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  Speaker 3: because I've got chat GPT on my phone.
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  Speaker 2: If what makes it special is right now, if you
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  Speaker 2: put something into chat GPT or into the deep Seak app,
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  Speaker 2: it goes to those servers and it gets stored on them,
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  Speaker 2: and it goes out to the company they have what
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  Speaker 2: you are asking it. If you run it just on
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  Speaker 2: your computer, it doesn't go anywhere. It's private. It's held
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  Speaker 2: entirely internally. And you can see this because the code
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  Speaker 2: it's not sending anything back to deep seek you know,
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  Speaker 2: home base and everything.
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  Speaker 3: So let's say you work for a heavily regulated industry
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  Speaker 3: like the defense industry or the securities industry, where there
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  Speaker 3: are frequent questions asked about your cybersecurity, about what sort
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  Speaker 3: of tools you use, what the output is, where it
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  Speaker 3: gets stored. When you're using chat GPT, there may be
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  Speaker 3: a whole bunch of restrictions because you can't define to
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  Speaker 3: your regulator or to your company. Hey, this is how
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  Speaker 3: I know the information was processed, this is where it's stored,
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  Speaker 3: and I have certainty that chat GPT won't get hacked
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  Speaker 3: in my data won't go anywhere. Sure, you don't need
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  Speaker 3: otherwise you have to build this whole thing in your
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  Speaker 3: own mainframe and store it at raytheon fours.
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  Speaker 2: And you can't do that because you don't, like, you're
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  Speaker 2: not going to spend like ten billion dollars doing that.
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  Speaker 2: It doesn't make sense for you to the this you
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  Speaker 2: can run on a high end gaming computer. You could
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  Speaker 2: spend ten grand on it, and you can run it internally. Now,
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  Speaker 2: the DoD probably still gonna be like, don't use deepseek
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  Speaker 2: because it still as a Chinese you know, developed.
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  Speaker 3: So like, let's talk about the technology, yes, and the
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  Speaker 3: technology seems to be that I have a model that
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  Speaker 3: can be run with a fraction of the computing costs
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  Speaker 3: and therefore no need to have this massive processing power.
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  Speaker 2: And it can. By the way, there are people out
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  Speaker 2: there that have done this in the last week and
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  Speaker 2: are like, oh, bleep, this changes everything. And these are
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  Speaker 2: like smart people that actually know what they're talking about,
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  Speaker 2: unlike me, who's repeating what I've read from smart people.
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  Speaker 2: So why does this actually matter? Quick Break will fill
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  Speaker 2: you in when we return.
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  Speaker 2: All right. So during the first segment, we were talking about deepseek,
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  Speaker 2: which is an AI platform that just released its most
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  Speaker 2: recent iteration about a week ago and posted some really
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  Speaker 2: impressive results and claiming to do so at very minimal cost.
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  Speaker 2: Why does this matter and why are tech stocks getting
• 13:51
  Speaker 2: bludgeoned today, that's right, bludgeon Yeah. The answer is this,
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  Speaker 2: The entire AI industry in the United States is based
• 13:59
  Speaker 2: on one idea in one idea alone. If we build
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  Speaker 2: more powerful computers, we will get better models, and it's
• 14:07
  Speaker 2: willing to pay exponentially more in order to do so. Meaning, hey,
• 14:13
  Speaker 2: in order to get a model that's like ten percent
• 14:15
  Speaker 2: better right now, you need to spend fifty percent more.
• 14:18
  Speaker 3: So, for example, a company like Nvidia has benefited greatly
• 14:22
  Speaker 3: from the fact that people want to build out the
• 14:25
  Speaker 3: latest and greatest models and until today believe that they
• 14:30
  Speaker 3: need the latest and greatest chips in order to do so.
• 14:32
  Speaker 2: Soatnianadell at Microsoft says, hey, you know what, I got
• 14:35
  Speaker 2: to increase my AI capex from fifty five billion to
• 14:38
  Speaker 2: eighty billion this year in order to do so. Now,
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  Speaker 2: if it gets proven, hey you don't need to do this,
• 14:43
  Speaker 2: and there is a different path to building capable, powerful AI,
• 14:49
  Speaker 2: which again I'm gonna say, the jury is still out
• 14:51
  Speaker 2: on simply because this is like Daisier. What we have
• 14:54
  Speaker 2: right now is the product that is legit, whether the costs,
• 15:00
  Speaker 2: it is legit as one that we don't know yet
• 15:02
  Speaker 2: just because when it comes to the actual pricing and
• 15:05
  Speaker 2: cost of an AI platform built out of China, you
• 15:08
  Speaker 2: just have to look at it and say, am I
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  Speaker 2: really seeing everything? It's a black box, you don't know.
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  Speaker 2: But here's why the entire basically AI ecosystem is being
• 15:19
  Speaker 2: hit today. And by AI ecosystem, I don't just mean
• 15:22
  Speaker 2: in Vidia. If you look at this, it's in Vidia,
• 15:25
  Speaker 2: Microsoft Broadcom, who makes basically a whole bunch of memory
• 15:28
  Speaker 2: and other stuff that goes in there. Oracle who is
• 15:31
  Speaker 2: going to build out the stargate platform, Amazon, Google, utility stocks. Okay,
• 15:36
  Speaker 2: you got utility stocks getting absolutely crushed today. Why utility stocks, chuck? Well,
• 15:40
  Speaker 2: if you don't need to build giant or dinner centers,
• 15:43
  Speaker 2: Denner Center, I do like the word gianter. I'm gonna
• 15:46
  Speaker 2: start using that. If you don't need to build Gianter
• 15:49
  Speaker 2: data centers nuclear power plants to power them, then Constellation
• 15:53
  Speaker 2: Energy doesn't need to turn three mile island back on.
• 15:56
  Speaker 2: And so Constellation is down nineteen percent today. This, this
• 16:00
  Speaker 2: is the entire ecosystem that is getting absolutely whacked as
• 16:04
  Speaker 2: a result of this. So I think my view here is, Look,
• 16:10
  Speaker 2: I've wondered for a while Hey, is this AI boom
• 16:12
  Speaker 2: just a CAPEX boom in a different rapper is like,
• 16:16
  Speaker 2: is it going to have the same features? And the
• 16:18
  Speaker 2: answer today is now, for the first time in the
• 16:20
  Speaker 2: last two years, the answer is maybe instead of yes.
• 16:24
  Speaker 2: And so that's what's being priced in today is kind
• 16:27
  Speaker 2: of the first iteration of maybe if over the next
• 16:30
  Speaker 2: four to six months, we have other companies in other
• 16:33
  Speaker 2: parts of the world, the US, Europe, wherever that come
• 16:36
  Speaker 2: out and say yeah, we've been able to do something similar. Guys,
• 16:41
  Speaker 2: I got everything on its head. Then I got to
• 16:43
  Speaker 2: tell you the entire data center buildout model.
• 16:48
  Speaker 3: All that stuff we've been talking about about needing to
• 16:50
  Speaker 3: modernize and revolutionize America's electricity generation. How you know, I
• 16:55
  Speaker 3: was quite optimistic that, hey, yeah, this might result in
• 16:58
  Speaker 3: higher prices for electricity in the short term. Long term,
• 17:00
  Speaker 3: you can have massive capacity built out and all these
• 17:02
  Speaker 3: other features gone. Now that's the other thing's no longer
• 17:05
  Speaker 3: relevant if this is true. Other things to pay attention to.
• 17:08
  Speaker 3: Back in twenty fourteen through twenty sixteen, Russia decided, hey,
• 17:11
  Speaker 3: we don't want us fracking companies making a whole bunch
• 17:13
  Speaker 3: of money selling oil at ninety a barrel.
• 17:15
  Speaker 2: So what did Russia do? They said, Hey, we're gonna
• 17:18
  Speaker 2: turn on the spigots and just throw all the oil
• 17:21
  Speaker 2: at the problem. Oil prices went to thirty a bunch
• 17:23
  Speaker 2: of US frackers went out of business, and Russia said,
• 17:26
  Speaker 2: ha ha, gotcha. One question that I do have on
• 17:29
  Speaker 2: this again, the cost piece is the one that really matters.
• 17:32
  Speaker 2: Is China just throwing a bunch of money away saying, look,
• 17:35
  Speaker 2: we'll lose money on AI forever, Like, we don't care
• 17:39
  Speaker 2: if we make money on this, but we're gonna try
• 17:41
  Speaker 2: to torpedo USAI development through this very much possibility. And
• 17:45
  Speaker 2: if that's the case, then I think very quickly this
• 17:47
  Speaker 2: is gonna become apparent and it'll be okay, fine, We're
• 17:50
  Speaker 2: just not gonna pay for you know that that model.
• 17:53
  Speaker 2: And here's the really interesting thing. Remember the way that
• 17:56
  Speaker 2: most people access deep seek is not through setting up
• 17:58
  Speaker 2: their own you know, serve is running it, but they
• 18:00
  Speaker 2: downloaded in the app store. Mike, is there a law
• 18:05
  Speaker 2: on the books about national security as it relates to
• 18:08
  Speaker 2: Chinese based phone apps? Have you ever heard of the
• 18:10
  Speaker 2: application TikTok I have? And so I do wonder does
• 18:16
  Speaker 2: the US at some point try to ban deep Seek.
• 18:21
  Speaker 2: It like one of the first things that came to
• 18:24
  Speaker 2: It's like it follows most of the same framework. Right.
• 18:27
  Speaker 3: Do you have the ability to influence American's opinions on
• 18:30
  Speaker 3: large subjects? Do you have the ability to track information
• 18:33
  Speaker 3: that gets downloaded onto your phone when you use the
• 18:35
  Speaker 3: deep seak applet? All of these same things that we
• 18:38
  Speaker 3: were concerned about with TikTok would apply to deep Seak hypothetically.
• 18:41
  Speaker 2: One and so is it only a matter of time
• 18:45
  Speaker 2: before deep seat gets banned? The question that remains then,
• 18:49
  Speaker 2: if it does, what does that do to AI development
• 18:53
  Speaker 2: in the US? Do you still see the bigger is
• 18:56
  Speaker 2: better framework for how we have to develop? Or do
• 19:00
  Speaker 2: people still try to replicate what deep seek has done here?
• 19:03
  Speaker 2: I mean even without deep seek existing in the US.
• 19:07
  Speaker 2: They are real questions on this. So, ultimately, things that
• 19:12
  Speaker 2: I think we can take away from this because we
• 19:14
  Speaker 2: still don't know a ton. First person through the wall
• 19:17
  Speaker 2: not always the winner. Number two. In the tech industry,
• 19:22
  Speaker 2: generally hardware tends to get commoditized over time. That might
• 19:25
  Speaker 2: be the case here in the third one. Remember China
• 19:28
  Speaker 2: hasn't been able to buy legally a lot of these
• 19:30
  Speaker 2: chips for a while. Scarcity might force innovation. We still
• 19:33
  Speaker 2: don't know, because we don't really know how they developed it.
• 19:35
  Speaker 2: But we'll see quick break. Wall Street watches next.
• 19:41
  Speaker 1: Like us on Facebook and follow us on Twitter at
• 19:44
  Speaker 1: TFE show. Breaking business news is always first right here
• 19:49
  Speaker 1: on the Financial Exchange Radio Network. Time now for Wall Street.
• 19:54
  Speaker 1: Watch a complete look and what's moving markets so far
• 19:57
  Speaker 1: today right here on the Financial Exchange Network.
• 20:02
  Speaker 4: Well, markets are selling off as a result of a
• 20:04
  Speaker 4: significant drop in the tech sector. Wall Street's also readying
• 20:07
  Speaker 4: for the ramp up of earning season this week, a
• 20:09
  Speaker 4: FED meeting on Wednesday and a GDP reading on Thursday.
• 20:13
  Speaker 4: Right now, the Dow is off by only twenty three points,
• 20:15
  Speaker 4: SMP five hundred selling off by one and a half percent,
• 20:19
  Speaker 4: or eighty nine points, and the tech heavy NASDAC down
• 20:23
  Speaker 4: nearly two and a half percent, or four hundred and
• 20:25
  Speaker 4: seventy nine points of Russell two thousand is off by
• 20:28
  Speaker 4: about a quarter percent. Ten year treasure yield is down
• 20:31
  Speaker 4: by six basis points at four point five six percent,
• 20:34
  Speaker 4: and crude oiled down by two thirds of a percent,
• 20:36
  Speaker 4: trading just above seventy four dollars a barrel. Today's tech
• 20:40
  Speaker 4: sell off is driven by Deep Seek, a Chinese AI
• 20:43
  Speaker 4: company that has high performance ratings and is less expensive
• 20:46
  Speaker 4: than well known AI in tech names with high valuation.
• 20:49
  Speaker 4: So with that, several tech names are selling off today.
• 20:53
  Speaker 4: Nvidia down by thirteen percent, on track for its its
• 20:57
  Speaker 4: worst day since March of twenty twenty, AMD down by
• 20:59
  Speaker 4: four percent, Microsoft three percent lower, Amazon one percent lower,
• 21:04
  Speaker 4: Broadcom down by thirteen percent, and energy sector taking a
• 21:08
  Speaker 4: little hit as well. Constallation Energy down by twenty percent
• 21:12
  Speaker 4: at the moment.
• 21:13
  Speaker 2: Outside of tech.
• 21:14
  Speaker 4: Shares in US Steal down by one percent following news
• 21:17
  Speaker 4: that an activist investor is preparing to wage a proxy
• 21:20
  Speaker 4: battle seeking to oust the steelmaker's chief executive and drop
• 21:24
  Speaker 4: litigation to salvage emerge merger with Japan's Nippon Steel. Meanwhile,
• 21:29
  Speaker 4: AT and T shares up by six percent after the
• 21:32
  Speaker 4: telecom giant posted better than expected quarterly earnings and revenue
• 21:36
  Speaker 4: while also adding more subscribers than forecasted, and shares in
• 21:40
  Speaker 4: SOFI down by nine percent after the financial service platform
• 21:45
  Speaker 4: beat earnings and revenue estimates.
• 21:47
  Speaker 2: For the fourth quarter.
• 21:48
  Speaker 4: However, the company offered disappointing outlook for twenty twenty five
• 21:53
  Speaker 4: I'm Tucker Silvan. That's Wall Street Watch Mike.
• 21:56
  Speaker 2: I think we've done enough AI talk for today. We
• 22:00
  Speaker 2: might get back to it later, but yeah, there was
• 22:02
  Speaker 2: a lot, you know. Yeah, we embraced our inner nerd
• 22:06
  Speaker 2: for the better part of the last half hour, and
• 22:09
  Speaker 2: now it's time to talk about something not so nerdy,
• 22:13
  Speaker 2: Earning season. Oh wait, that's adequately aren't nerdy? Yeah? Okay, anyways,
• 22:17
  Speaker 2: Earning season really kicks into high gear this week with
• 22:19
  Speaker 2: some big big names reporting, including Tuesday is Defense Day
• 22:24
  Speaker 2: is what I've dubbed it, raytheon Boeing, Lockheed, Martin and
• 22:28
  Speaker 2: Royal Caribbean Cruises Striker. I was making a Royal Caribbean jo.
• 22:33
  Speaker 2: Starbucks two I appreciate, Yeah, Starbucks two Wednesdays where you
• 22:38
  Speaker 2: have the real heavy hitters, Microsoft, Meta, and Tesla all
• 22:41
  Speaker 2: in a day, so you get six trillion dollars in
• 22:43
  Speaker 2: market cap there. You also have T Mobile, IBM, Progressive.
• 22:47
  Speaker 2: Like again, there's a ton of companies reporting Thursday. You
• 22:51
  Speaker 2: got Apple and Visa, so it's another four trillion dollars
• 22:53
  Speaker 2: in market cap. Master Card as well, Yep, Caterpillar, Blackstone Ups.
• 22:58
  Speaker 2: So like a lot of names coming through there and
• 23:01
  Speaker 2: then we finished the week with some oil companies Exon, Chevron,
• 23:05
  Speaker 2: Phillip sixty six all reporting on Friday.
• 23:07
  Speaker 3: So all told, one hundred and four s and p
• 23:09
  Speaker 3: five hundred companies reporting this week. It's the second busiest
• 23:12
  Speaker 3: earnings week of the season.
• 23:14
  Speaker 2: By next week.
• 23:15
  Speaker 3: And shockingly, I I hesitate to say this, but let's
• 23:21
  Speaker 3: imagine for a moment here that Microsoft absolutely knocks the
• 23:25
  Speaker 3: cover off the ball in this earnings report. Yeah, I'm
• 23:28
  Speaker 3: not sure investors care, given the deep seaknowledge.
• 23:31
  Speaker 2: This is the question on This is back in the
• 23:36
  Speaker 2: bursting of the tech bubble. The first signs that it
• 23:39
  Speaker 2: was bursting in two thousand was you had companies, you know, Yahoo,
• 23:44
  Speaker 2: come out, hey we beat earnings by you know, a
• 23:46
  Speaker 2: couple pennies and the stock sold off five percent like that.
• 23:50
  Speaker 2: That was kind of the sign that you got. So
• 23:52
  Speaker 2: I think that just as much as the actual earnings
• 23:57
  Speaker 2: from the companies are going to be important, this is
• 24:00
  Speaker 2: one of those cases where the price action afterwards, to
• 24:03
  Speaker 2: tell you what investors are thinking about them, is going
• 24:06
  Speaker 2: to be even more important than normal. I know, everyone
• 24:08
  Speaker 2: obviously buys stocks for you know, they want them to
• 24:11
  Speaker 2: go up, So the price action matters, But I think
• 24:13
  Speaker 2: there's some real signal in the price action here depending
• 24:16
  Speaker 2: on how these stocks move after they report earnings. So again,
• 24:22
  Speaker 2: we're gonna know a lot more after the next two weeks,
• 24:24
  Speaker 2: just because basically most of the S and P five
• 24:26
  Speaker 2: hundred is done reporting. Then the only major company that's
• 24:29
  Speaker 2: left afterwards is in the video, which is always a
• 24:31
  Speaker 2: few weeks later. But this is, uh, it's gonna be
• 24:36
  Speaker 2: a meati week as far as earnings. And by the way,
• 24:40
  Speaker 2: have either of you looked at the economic data that
• 24:42
  Speaker 2: we're getting this week.
• 24:43
  Speaker 3: Well, we have a FED meeting on Wednesday, meeting on GDP,
• 24:46
  Speaker 3: GDP Thursday, PCE Friday, So I mean this has a
• 24:50
  Speaker 3: crazy week. Before we knew it about a Chinese AI company,
• 24:53
  Speaker 3: you could.
• 24:54
  Speaker 2: Live for like three years and not half a week
• 24:56
  Speaker 2: like this.
• 24:59
  Speaker 3: Yeah, So look, there is going to be plenty of
• 25:02
  Speaker 3: information to digest. But I think the question especially for
• 25:06
  Speaker 3: Microsoft and Meta as well as Apple, but to a
• 25:11
  Speaker 3: more limited extent because quite frankly, they just haven't done
• 25:14
  Speaker 3: anything themselves on artificial intelligence. But the question for these
• 25:18
  Speaker 3: two is what does this news mean to you? Right
• 25:23
  Speaker 3: beside all the earnings, Like, what does this deepseek news
• 25:27
  Speaker 3: mean to you and how are you going to need
• 25:30
  Speaker 3: to change your business models to adapt to it.
• 25:32
  Speaker 2: I'm sorry, I was just distracted by something on Bloomberg.
• 25:35
  Speaker 2: The New York City Taxi Commission just revised who's able
• 25:39
  Speaker 2: to provide insurance for taxis and they no longer require
• 25:44
  Speaker 2: the insured to prove that they're solvent. It's a good idea.
• 25:49
  Speaker 2: What's the worst that can happen? Which speaks to one
• 25:52
  Speaker 2: of two things. Either A, everyone solvent in the insurance
• 25:56
  Speaker 2: industry and you shouldn't have to worry about it, or
• 25:58
  Speaker 2: B you had real trouble attracting insurance money into the
• 26:02
  Speaker 2: New York City taxi insurance market and you're trying desperately
• 26:06
  Speaker 2: to get someone to ensure it, even if you don't
• 26:08
  Speaker 2: really trust their financials.
• 26:10
  Speaker 3: Yeah, I mean, I have to believe that in the
• 26:12
  Speaker 3: state of New York, you cannot issue insurance if you're insolvent.
• 26:16
  Speaker 3: Like maybe it's just duplicative, but I don't think Bloomberg.
• 26:19
  Speaker 2: Would be covering if it were just Anne. Why would
• 26:21
  Speaker 2: the requirement be there? Then? I have no idea wild Anyways, Yeah,
• 26:28
  Speaker 2: it's going to be a busy couple weeks, but yeah,
• 26:30
  Speaker 2: the earnings that we are going to be seeing this week. Remember, ultimately,
• 26:33
  Speaker 2: earnings are what drives stocks in the long run and
• 26:35
  Speaker 2: the short term. It can be a whole bunch of
• 26:36
  Speaker 2: other things, but ultimately it's earnings that drive them. If
• 26:40
  Speaker 2: you will get the long term performance of US stocks,
• 26:42
  Speaker 2: the vast majority of it, about seventy seventy five percent
• 26:45
  Speaker 2: of it is driven by the fact that earnings for
• 26:48
  Speaker 2: the last eighty years have grown at a compound annual
• 26:50
  Speaker 2: growth rate of almost seven percent, And so that is
• 26:54
  Speaker 2: what drives markets in the long run and the short term.
• 26:57
  Speaker 2: It can be I'm afraid about US, and so I'm
• 27:00
  Speaker 2: selling my stocks. I you know, China came out with
• 27:03
  Speaker 2: a new AI and so I'm selling my stocks the
• 27:07
  Speaker 2: shrimp er bat. So whatever it is can move it
• 27:10
  Speaker 2: in the short term. The long run, it's all about earnings.
• 27:13
  Speaker 2: And so I think on these calls, the tech ones
• 27:17
  Speaker 2: in particular, no one's really gonna care what Microsoft reports
• 27:21
  Speaker 2: for last quarter earnings. Tell us what you're seeing now,
• 27:25
  Speaker 2: what kind of demand are you seeing for AI? And
• 27:29
  Speaker 2: specifically what you're going to get is these are going
• 27:33
  Speaker 2: to be legitimate calls. Have you noticed any change in
• 27:36
  Speaker 2: the last ten days since deepseek was announced, right, and
• 27:40
  Speaker 2: since it seeks new version was announced, And like, how
• 27:44
  Speaker 2: quickly are you able to pivot?
• 27:46
  Speaker 3: You've made a massive investment in partnership with the leading
• 27:49
  Speaker 3: company open ai, the creator of chat GPT. You've already
• 27:53
  Speaker 3: done some pivoting Microsoft, that is over the last you know,
• 27:56
  Speaker 3: few months here in terms of more development in house.
• 28:00
  Speaker 3: So does that put you out of a competitive advantage
• 28:02
  Speaker 3: to some others out there to be able to shift
• 28:05
  Speaker 3: your thinking on how you are developing products with artificial intelligence? Again,
• 28:11
  Speaker 3: like to your point, what happened in the last quarter,
• 28:14
  Speaker 3: I'm not gonna say it doesn't matter, but what everyone
• 28:17
  Speaker 3: doesn't it's not all that important in comparison to Hey,
• 28:22
  Speaker 3: if this thing's real, how are.
• 28:24
  Speaker 2: You going to know that it's real? First? Microsoft? And
• 28:26
  Speaker 2: what are you going to do about it? Because the
• 28:28
  Speaker 2: big thing is, Remember, the whole AI race is not
• 28:32
  Speaker 2: just you know, can we build this? But ultimately it
• 28:34
  Speaker 2: has to make you money at some point. You can't
• 28:36
  Speaker 2: just spend it forever. Right, And we've called I.
• 28:39
  Speaker 3: At least have called Microsoft the low hanging fruit in
• 28:42
  Speaker 3: terms of who should be able to use artificial intelligence
• 28:45
  Speaker 3: to drive up prices for their consumers and deliver a
• 28:47
  Speaker 3: usable product. Right, there's so many stupid, silly things that
• 28:51
  Speaker 3: we do in all of those products that are annoyances
• 28:54
  Speaker 3: that AI should be able to do for you, that
• 28:56
  Speaker 3: companies would pay a lot of money for it to
• 28:58
  Speaker 3: work well.
• 28:59
  Speaker 1: And so.
• 29:02
  Speaker 3: Like, let me tell that's been the low hanging fruit
• 29:04
  Speaker 3: in my mind is Microsoft Develop and Excel program that
• 29:07
  Speaker 3: allows me to do what I do now, but at
• 29:09
  Speaker 3: a fraction of the time.
• 29:12
  Speaker 2: I'm someone I've had a Microsoft Copilot license for a
• 29:15
  Speaker 2: few months now. Yeah, it's been hot garbage for what
• 29:17
  Speaker 2: I understand. What's really interesting to me is that if
• 29:23
  Speaker 2: I have a summary of something, it can't summarize the summary.
• 29:29
  Speaker 2: Let's say that I have a bulleted list, like an
• 29:31
  Speaker 2: outline of topics and I say put this into PowerPoint.
• 29:36
  Speaker 2: It doesn't really do it well. It actually takes like
• 29:39
  Speaker 2: longer and it doesn't obey my instructions the way I want.
• 29:42
  Speaker 2: As an example, I had an eight to ten page
• 29:46
  Speaker 2: outline and I said, make me ten PowerPoint slides that discuss,
• 29:53
  Speaker 2: you know, the key topics that are in these this
• 29:56
  Speaker 2: word document. It made me thirty five I wanted ten.
• 30:01
  Speaker 2: I say again, no, do this again, but only with
• 30:03
  Speaker 2: ten slides. It made me thirty five. It's it's not
• 30:09
  Speaker 2: up there right now, and that's four hundred dollars a year.
• 30:12
  Speaker 2: So I'm very likely right now probably gonna be like
• 30:15
  Speaker 2: by Felicia, like I'm not, you know, gonna pay for
• 30:18
  Speaker 2: something that's not working. Who's Felicia? Just move on by
• 30:22
  Speaker 2: Felicia ice Cube? Got it? No? Sorry, okay? Is that Friday?
• 30:27
  Speaker 4: I don't I hate that saying.
• 30:29
  Speaker 2: Oh sorry, well, I won't say it anymore. I thought
• 30:32
  Speaker 2: this was pretty I was trying to be like all
• 30:34
  Speaker 2: hip and Edgy and Harry. I'm just alienating the It's
• 30:38
  Speaker 2: not rap, it's a movie. Oh okay, sorry, and I
• 30:41
  Speaker 2: think it's like two thousand and one. Thanks anyways, gosh
• 30:47
  Speaker 2: uh yes. So we have a whole bunch of earnings
• 30:49
  Speaker 2: that are going to be coming out this week, and
• 30:52
  Speaker 2: with the big tech ones, I do think it's gonna
• 30:54
  Speaker 2: be much more forward looking than just hey did you
• 30:58
  Speaker 2: beat on your earnings numbers? And you better believe a
• 31:01
  Speaker 2: month from now when in video reports, they're gonna have
• 31:04
  Speaker 2: some real questions. What kind of demand have you seen
• 31:07
  Speaker 2: in the last month? You know, are you seeing anyone
• 31:11
  Speaker 2: canceling orders? Are you seeing people downsizing orders? Yeah, that's
• 31:16
  Speaker 2: gonna be where the real signal is in terms of
• 31:20
  Speaker 2: you know, how activity might actually be shifting. Just take
• 31:23
  Speaker 2: a quick break. When we come back, let's see do
• 31:26
  Speaker 2: we want to do fed talker credit cards, draft credit cards?
• 31:30
  Speaker 2: Let's talk credit cards.
• 31:31
  Speaker 1: Right after this, find daily interviews and full shows of
• 31:35
  Speaker 1: the Financial Exchange on our YouTube page. Like us on
• 31:38
  Speaker 1: YouTube and get caught up on anything and everything you
• 31:41
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• 31:45
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• 31:48
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• 31:51
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• 31:57
  Speaker 1: business and financial news in New England and around the country.
• 32:01
  Speaker 1: This is the Financial Exchange Radio Network.
• 32:12
  Speaker 2: Wall Street Journal's got a piece of day title. Americans
• 32:14
  Speaker 2: are carrying bigger credit card balances and they are now.
• 32:19
  Speaker 2: Part of this is because of inflation, So on an
• 32:21
  Speaker 2: inflation adjusted basis, the difference is not that big. What
• 32:26
  Speaker 2: is really interesting to me is and there's data here
• 32:30
  Speaker 2: that this had some of it. But I was actually
• 32:32
  Speaker 2: looking at this last week so thanks Wall Street Journal
• 32:34
  Speaker 2: for looking at the same report I did. So if
• 32:38
  Speaker 2: you go back to the early part of twenty twenty,
• 32:42
  Speaker 2: just before the pandemic Q one to twenty twenty, about
• 32:46
  Speaker 2: just under ten percent of credit card holders were making
• 32:49
  Speaker 2: only the minimum payment. During the pandemic, it dropped down
• 32:53
  Speaker 2: to about eight percent because of, Hey, here's a bunch
• 32:56
  Speaker 2: of stimulus money. I go forth and do whatever. And
• 33:00
  Speaker 2: today you're closing in on about eleven percent of credit
• 33:05
  Speaker 2: card users are only making the minimum payment.
• 33:08
  Speaker 3: That's the more interesting piece to me, just because if
• 33:10
  Speaker 3: I'm doing the math here, So as of Q three
• 33:14
  Speaker 3: of twenty four, which is when this data was put together,
• 33:17
  Speaker 3: outstanding balances revolving credit card balances six hundred and forty
• 33:21
  Speaker 3: five billion dollars, which is up massively. That's up huge
• 33:24
  Speaker 3: from the first quarter of twenty twenty five hundred and
• 33:27
  Speaker 3: fifty three billion. That's a sixteen point seven percent increase.
• 33:32
  Speaker 3: Sure inflation's twenty right, So the balance piece is not relevant. No,
• 33:36
  Speaker 3: what is relevant is the fact that interest rates are
• 33:39
  Speaker 3: substantially higher now. And to your point, that other data
• 33:43
  Speaker 3: point of who is not paying off those balances that's growing.
• 33:47
  Speaker 2: I have one other data point that's not talked about
• 33:49
  Speaker 2: in this piece though. Do you know what percentage of
• 33:53
  Speaker 2: Americans in twenty twenty Q one, twenty twenty, right before
• 33:56
  Speaker 2: the pandemic, we're paying the full balance off each month? No,
• 34:02
  Speaker 2: it's about thirty one percent. Okay, today it's about thirty five.
• 34:09
  Speaker 2: K shaped recovery is back. Now. Remember K shape. Everyone
• 34:12
  Speaker 2: was talking about what shapes the recovery gonna be? Here
• 34:15
  Speaker 2: we go. The K shaped recovery says this. The people
• 34:18
  Speaker 2: at the bottom end of the wage spectrum doing worse,
• 34:22
  Speaker 2: evidenced by more a greater percentage of people making only
• 34:26
  Speaker 2: the minimum payments on their credit cards, but the high
• 34:30
  Speaker 2: end of the wage spectrum cranking, especially those that might
• 34:33
  Speaker 2: have investment accounts and things like that that have gone
• 34:36
  Speaker 2: up and you know, maybe they're making their they're retired
• 34:39
  Speaker 2: in their you know, spending is based on, hey my
• 34:41
  Speaker 2: portfolio is up. Yeah, they are paying off their credit
• 34:45
  Speaker 2: card balances and full. The remaining twenty eight percent of
• 34:48
  Speaker 2: people okay, they're paying more than a minimum but less
• 34:52
  Speaker 2: than full or whatever the percentages. I did the math badly,
• 34:56
  Speaker 2: but you get what I'm saying.
• 34:57
  Speaker 3: There's a lot of stuff that comes out of Dave
• 34:59
  Speaker 3: Ramsey's that I don't always agree with. The more and
• 35:03
  Speaker 3: more I've studied the credit card issue, the more I've
• 35:05
  Speaker 3: come to his way of thinking. His way of thinking,
• 35:07
  Speaker 3: he's never used any sort of data, you know, pay
• 35:09
  Speaker 3: off all sorts of debt other than your home. And look,
• 35:13
  Speaker 3: I have personally never carried a credit card balance, and
• 35:17
  Speaker 3: so credit cards are great for me. But his point is, look,
• 35:19
  Speaker 3: that's not the average experience. And even when people say
• 35:22
  Speaker 3: they're going to pay off the credit card balance, they
• 35:24
  Speaker 3: usually don't. And the fact of the matter is that
• 35:26
  Speaker 3: he's right on this. So over half of credit card
• 35:30
  Speaker 3: holders over the last twelve months had a point in
• 35:33
  Speaker 3: time where they couldn't pay off the full balance.
• 35:34
  Speaker 2: So could the Mike Armstrong corollary be, if you do
• 35:38
  Speaker 2: not have the money in a bank account to pay
• 35:42
  Speaker 2: off your credit card balance, then you shouldn't put it
• 35:45
  Speaker 2: on the credit card. Frankly, even then, I'm not sure
• 35:47
  Speaker 2: it's adequate.
• 35:47
  Speaker 3: Interesting, yeah, because how many times have we heard of
• 35:52
  Speaker 3: you know, I had the money in the bank, but
• 35:53
  Speaker 3: then my car broke down and I needed a three
• 35:56
  Speaker 3: thousand dollars a pair in there goes the money that
• 35:58
  Speaker 3: I had in my bank account, and now I'm sitting
• 36:00
  Speaker 3: on the credit card. What about credit balance? I don't
• 36:02
  Speaker 3: know that there's a thresh. I don't know that there's
• 36:05
  Speaker 3: a number. Because it comes back to human behavior, and
• 36:08
  Speaker 3: what we seem to know about human behavior is that
• 36:12
  Speaker 3: most of them run a credit card balance month after
• 36:16
  Speaker 3: month and eat away at frankly any of the benefits
• 36:19
  Speaker 3: that come with the credit card, which is disappointing because
• 36:21
  Speaker 3: there are a lot of benefits. Yeah, you've got a
• 36:23
  Speaker 3: bunch of cash back rewards, you've got a bunch of
• 36:24
  Speaker 3: protection from fraud, but those benefits seem to be outweighed
• 36:30
  Speaker 3: by the average credit card user experience.
• 36:32
  Speaker 2: So can we then amend Benjamin Franklin's famous saying, here
• 36:36
  Speaker 2: we go to be nothing is certain except debts and taxes. Yeah?
• 36:40
  Speaker 2: I like that? Is that where you'd like to go?
• 36:42
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• 37:36
  Speaker 2: Airlines are charging higher affairs and are confident that you'll
• 37:39
  Speaker 2: pay up. That's because there's minimal competition in the space,
• 37:41
  Speaker 2: and as such, if you want to fly on probably
• 37:44
  Speaker 2: like eighty percent of routes throughout the United States, you
• 37:46
  Speaker 2: have one or two options, and generally they come as
• 37:48
  Speaker 2: close as you can to price fixing without going over
• 37:51
  Speaker 2: that line.
• 37:51
  Speaker 3: And one of the famous low cost providers out there
• 37:53
  Speaker 3: is in the midst of bankruptcy, so they have a
• 37:55
  Speaker 3: fair bit of evidence that this model works. Yeah, monopoly
• 37:59
  Speaker 3: tends to, you know, like go, let's be honest here,
• 38:02
  Speaker 3: pipoly Chuck all right, triopoly, triopoly, quadopoly, there's there's four
• 38:07
  Speaker 3: big ones Southwest, American United Delta.
• 38:10
  Speaker 2: Right, monopoly. It's studied in economic text textbooks. Let's take
• 38:15
  Speaker 2: a quick break here, and when we return, we got
• 38:18
  Speaker 2: our two coming up.