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OpenAI declares ‘Code Red’ as Google threatens AI lead · The Financial Exchange Show

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  Speaker 2: Good morning, Welcome back to the Financial Exchange. Hope everybody's
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  Speaker 2: having a nice snowy Tuesday out there, for those who
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  Speaker 2: are north and west of the city of Boston and
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  Speaker 2: staying safe and warm out there for our first first
• 1:29
  Speaker 2: major winter storm of the year. So good luck to
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  Speaker 2: everybody this winter. We've got markets making a recovery from yesterday,
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  Speaker 2: specifically crypto markets clawing back some of their gains from
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  Speaker 2: the prior trading session.
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  Speaker 3: Well, there's not really a.
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  Speaker 2: Trading session for crypto, just kind of trades twenty four
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  Speaker 2: hours a day, so that might not be relevant, but
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  Speaker 2: equity markets clawing back as well. We had some stories
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  Speaker 2: about the Federal Reserve upcoming meeting on December tenth and
• 1:58
  Speaker 2: the likelihood of a rate cut. Air also a fair
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  Speaker 2: bit of just ajuda about crypto winter and whether or
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  Speaker 2: not we are heading for another shakeout of amateur crypto
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  Speaker 2: investors in that space. But I've been given a lot
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  Speaker 2: of thought to concentration in the US stock market recently,
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  Speaker 2: and mainly because I you know, my day job outside
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  Speaker 2: of the Financial Exchange is to describe investment portfolios to
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  Speaker 2: people and help them better understand what it is they're
• 2:28
  Speaker 2: doing currently and how it can be improved upon. And
• 2:31
  Speaker 2: when I describe to them, you know, their US stock exposure,
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  Speaker 2: it's different than what I used to say, right like
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  Speaker 2: ten years ago, it was, hey, this is this diversify.
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  Speaker 2: If you're on the S and P five hundred, for example,
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  Speaker 2: you owned this diversified thing where yeah, fifth of it
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  Speaker 2: is tech exposure, but you know it's a bunch of
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  Speaker 2: different industries, all of which are participating in profits.
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  Speaker 3: For the overall index.
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  Speaker 2: This year, about a third of the S and P
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  Speaker 2: five hundreds gains are made up by alphabetan and video alone.
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  Speaker 2: So important to frame all that that, Hey, when I
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  Speaker 2: describe people to people like, hey, you know where did
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  Speaker 2: your investment gains come from this year? Well, largely it
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  Speaker 2: was from big tech companies. Furthermore, when I look at
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  Speaker 2: the S and P five hundred today, nearly half of
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  Speaker 2: it is comprised of the top ten companies in that index.
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  Speaker 2: That's I think the most concentrated the index has ever been.
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  Speaker 2: It's been around since what nineteen forty something, nineteen sixty something.
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  Speaker 2: I know we muddled together data going back further than that,
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  Speaker 2: But the S and P five and then.
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  Speaker 4: The invest the idea of an investable index fund dates
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  Speaker 4: to like this seventies, but those will run as separate
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  Speaker 4: accounts for banks, right, yeah, So in terms of investment on.
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  Speaker 2: The market index eighties nineties timeframe, maybe, yeah.
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  Speaker 4: When was the first, like in I should know this,
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  Speaker 4: it was sometime in the eighties, but people had been
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  Speaker 4: running S and P portfolios since A bunch of researchers
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  Speaker 4: pointed out in the nineteen sixties that a market gap
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  Speaker 4: weighted portfolio is efficient and that's all you really need.
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  Speaker 2: So I think the important thing for people to just
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  Speaker 2: understand if let's say you're running your own money or
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  Speaker 2: having somebody run it for you, and a major portion
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  Speaker 2: of your exposure is the S and P five hundred, A,
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  Speaker 2: you're a lot less diversified than you were a decade ago. B.
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  Speaker 2: You're effectively making a bet on artificial intelligence right now.
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  Speaker 2: Right Like, if you look at the majority of US
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  Speaker 2: stock gains recently and the risks to that US DOC portfolio,
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  Speaker 2: it is largely the exposure of what can artificial intelligence
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  Speaker 2: do for these companies or on the other hand, what
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  Speaker 2: can it detract from those companies' performance in the future.
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  Speaker 2: And I think it's just I'm not saying that people
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  Speaker 2: should be changing how they're doing it. But you know,
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  Speaker 2: I keep hearing investment managers talk about diversification, and they're
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  Speaker 2: kind of coming to this point where they're like, well,
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  Speaker 2: crypto doesn't diversify as much as it used to. US
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  Speaker 2: stocks are all one AI bet. I guess I can
• 4:55
  Speaker 2: go foreign and get some divers fied exposure, but if
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  Speaker 2: I go to China, it's also an AI bet.
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  Speaker 3: And so so there's.
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  Speaker 2: Some acknowledgment here that Okay, Yeah, if you are investing
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  Speaker 2: in stocks, especially US stocks, yeah, you're making a pretty
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  Speaker 2: serious bet on that next dollar going into largely big
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  Speaker 2: tech companies today.
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  Speaker 4: Yes, but if you're averaging in if you're a long
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  Speaker 4: term investor with a horizon of I don't know, ten
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  Speaker 4: years or more, that's a little arbitrary.
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  Speaker 5: But let's just purpose as of an example, say that.
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  Speaker 4: The complexion of the index could be as you pointed out,
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  Speaker 4: it was different, it will be different, no, da I
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  Speaker 4: suspect tech will not be a third of the index
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  Speaker 4: as it is today in ten years, not because I
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  Speaker 4: think I'm prescient or a profit, but because historically those
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  Speaker 4: weights have been unstable as investors decide that companies in
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  Speaker 4: different sectors for whatever reason, are worth more or less.
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  Speaker 4: That's the beauty of a market cap weighted index. Funn
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  Speaker 4: and I'll say again it comes out of theory theory
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  Speaker 4: that everybody accepts who works in finance, and whether you
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  Speaker 4: know it or not, if you're in finance, everything you.
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  Speaker 5: Do is based on this.
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  Speaker 4: As you start with a market cap weight that's a
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  Speaker 4: fancy way of saying, you wait, companies in proportion to
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  Speaker 4: the value of all their stock. You start with that portfolio,
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  Speaker 4: and everybody can own a little slice of that. And
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  Speaker 4: as it turns out under certain conditions and they're not
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  Speaker 4: very stringent, that's an efficient way to invest. You start
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  Speaker 4: with that. And again, this is the way all the
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  Speaker 4: software and the portfolio construction models, whether you go to
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  Speaker 4: Fidelity or Vanguard. I shouldn't start ticking off names because
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  Speaker 4: I'm going to leave somebody off the list. But no
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  Speaker 4: matter who you go to your local bank, they use
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  Speaker 4: mot what's called modern portfolio theory, the name of the
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  Speaker 4: whole body of thought that motivates this to start with,
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  Speaker 4: and they start with a market cap weighted index. That's
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  Speaker 4: why everybody's got to s and p five hundred or
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  Speaker 4: Russell one thousand or Russell three thousand. You can market
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  Speaker 4: cap waight just about any equity segment. That's why everybody
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  Speaker 4: has that as the core of their portfolio.
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  Speaker 2: So the tag Alonge piece to this is one of
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  Speaker 2: the clear beneficiaries and innovators in the artificial intelligence space.
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  Speaker 2: Open ai, which is not publicly traded. You cannot go
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  Speaker 2: invest in this in public indices. You can go invest
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  Speaker 2: in companies that are somewhat related like Microsoft and Nvidia,
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  Speaker 2: but to open ai is not investable in of itself.
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  Speaker 2: It was also the first publicly available large language model
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  Speaker 2: tool that was out there, with chat GPT CEO Sam
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  Speaker 2: Altman declaring quote code read to improve chat GPT. This
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  Speaker 2: is a follow on story to something we covered a
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  Speaker 2: few weeks ago, which was Gemini owned by Google recently
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  Speaker 2: released an updated version and came out to some pretty
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  Speaker 2: big fanfare and people who are far more qualified to
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  Speaker 2: test this stuff than I am did it ran a
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  Speaker 2: bunch of tests and found it generally containing features and
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  Speaker 2: usability that was a lot better or i shouldn't say
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  Speaker 2: a lot better on the margins, better than what chat
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  Speaker 2: GPT had most recently released and so Altman kind of
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  Speaker 2: putting it out there as, Hey, this is a big
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  Speaker 2: threat to our overall success. I'm going to start off
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  Speaker 2: by saying that I have no means of measuring the
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  Speaker 2: usability or accuracy of either of these tools. I'm relying
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  Speaker 2: on outside experts to tell me which one is better.
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  Speaker 2: What I am comfortable saying is that early indications seem
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  Speaker 2: to be that every company that either creates artificial intelligence tools,
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  Speaker 2: provides the energy or infrastructure for those companies to create
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  Speaker 2: those tools, or uses any of those tools in their
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  Speaker 2: businesses are being treated as winners from a stock perspective
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  Speaker 2: Right now? Am I oversimplifying things there? That, with few exceptions,
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  Speaker 2: most everyone who's playing in the AI space is being
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  Speaker 2: treated from a stock perspective as though they are going
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  Speaker 2: to win the AI race in some way.
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  Speaker 4: If you claim your company has something to do with AI,
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  Speaker 4: it will attach some ridiculous multiple to your stock price,
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  Speaker 4: is the way I would put it.
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  Speaker 3: What I think we are. Yeah, you just just.
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  Speaker 4: Claim you're doing AI. That's good for one hundred percent
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  Speaker 4: pop in your share price, or at least that has
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  Speaker 4: been and I true have passed.
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  Speaker 5: By the way.
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  Speaker 4: This is true of the electronics revolution of late sixties
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  Speaker 4: and the Internet graze among them.
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  Speaker 2: And I feel very comfortable saying that not every company
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  Speaker 2: is going to be a winner in this space.
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  Speaker 5: Very comfortable. They say that, yeah, they.
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  Speaker 3: Can't be, And so what does this look like to me?
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  Speaker 2: Well, it's another version of the deep Seek story from
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  Speaker 2: January of this year, where some unknown Chinese startup came
• 9:16
  Speaker 2: out there and said, oh wow, we were able to
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  Speaker 2: create a pre darn good large language model with a
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  Speaker 2: fraction of the cost of what chat jpt is doing.
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  Speaker 2: Google didn't do it for a fraction of the cost.
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  Speaker 2: They did it for quite a bit of money. But
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  Speaker 2: the idea that a technology that's three years old can
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  Speaker 2: already be substantially leap frogged by those with deep enough pockets.
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  Speaker 2: I think further is that overall truism that we know,
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  Speaker 2: which is not every company is going to be a winner.
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  Speaker 4: This is why you can't trust anybody on TV who's
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  Speaker 4: talking about this space.
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  Speaker 5: They don't.
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  Speaker 4: They're mostly carnival barkers. They're promoters, they have skin in
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  Speaker 4: the game. They don't try to get it themselves. If
• 9:51
  Speaker 4: you did get this, if you were a coder and
• 9:54
  Speaker 4: you understood just generally speaking, how these models work, and
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  Speaker 4: I don't claim to, by the way, what I do
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  Speaker 4: is a little.
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  Speaker 5: Bit of a s learning.
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  Speaker 2: But oh that's how I started the segment was I
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  Speaker 2: have no means of testing which one's better.
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  Speaker 5: Yeah, I can't.
• 10:04
  Speaker 4: Yeah, So I assume that anyone who's talking about this
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  Speaker 4: is trying to sell me something, and unless they are
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  Speaker 4: coders themselves, they don't know what they're talking about, and
• 10:14
  Speaker 4: so whatever they're saying is unreliable hype propaganda designed to
• 10:19
  Speaker 4: get me to buy whatever it is they're selling. All
• 10:21
  Speaker 4: I know is that historically speaking, the first movers in
• 10:26
  Speaker 4: some domains, the first movers because of so called network effects,
• 10:30
  Speaker 4: they become the winner that takes all. Google is an example, sure,
• 10:33
  Speaker 4: but they weren't the first mover. They were an early mover.
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  Speaker 4: Their browser eventually displaced others. And if Gemini becomes too successful,
• 10:40
  Speaker 4: they're gonna want to break Gemini off of Google's browser.
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  Speaker 4: Google's browser has well like sixty seventy percent I don't
• 10:46
  Speaker 4: even know off the top of my head more percent
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  Speaker 4: of the market. They can't allow the same company that
• 10:52
  Speaker 4: owns the browser to capture the AI market too. They're
• 10:54
  Speaker 4: going to be breaking Google. If Gemini gets too successful,
• 10:57
  Speaker 4: they're gonna want to break it. Some anti trust person
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  Speaker 4: who wants to put a notch in herb or his
• 11:02
  Speaker 4: belt is going to want to break Google. A Gemini
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  Speaker 4: off of the browser. It'll happen if it becomes too dominant.
• 11:08
  Speaker 4: But network effects suggest that it could be. It's very
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  Speaker 4: easy for me to And by network effects, I mean
• 11:13
  Speaker 4: we all use the qwe r t y keyboard because
• 11:17
  Speaker 4: early on a few people adopted it. I don't want
• 11:20
  Speaker 4: to be on a different standard. We all use Microsoft Word,
• 11:23
  Speaker 4: which wasn't, by the way, compatible with Apple Word. Early on,
• 11:26
  Speaker 4: you had to translate your file. Once it became usable
• 11:30
  Speaker 4: on any operating system, everybody got on word. That's network effects.
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  Speaker 4: Think of it as think of it as a snowball effect.
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  Speaker 3: And by the way, this only answers like you know.
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  Speaker 2: This begins to answer one of the questions, which is
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  Speaker 2: how difficult it is to create a successful model. The
• 11:46
  Speaker 2: probably more important question is does any business know how
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  Speaker 2: to take that model?
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  Speaker 5: Doesn't want to be the fastest.
• 11:52
  Speaker 4: It has to be capable of exploiting those network effects.
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  Speaker 4: Attach it to your browser. Make Gemini easy for me
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  Speaker 4: to use. Oh, it can see the Google page I'm
• 11:59
  Speaker 4: looking at. Can you put that end of day market
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  Speaker 4: data in a table for me?
• 12:03
  Speaker 5: Gemini?
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  Speaker 4: And it does it. It's looking at the page. I'm
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  Speaker 4: that's the type of combination that's going to give it
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  Speaker 4: a lethal advantage potentially over something I have to toggle
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  Speaker 4: away to. And it also happens to be a little
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  Speaker 4: bit clunky. I refer obviously to open AI. Sure, maybe
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  Speaker 4: anthropics having similar issues. I don't know these well enough.
• 12:21
  Speaker 4: All I know is is a user Gemini is really
• 12:23
  Speaker 4: convenient for my kind of mundane Can you do this
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  Speaker 4: really simple task for me?
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  Speaker 5: Type uses?
• 12:28
  Speaker 2: Say a quick break when we come back, we're playing
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  Speaker 2: a little bit of trivia here. Next on the Financial Exchange.
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  Speaker 7: for trivia here and the Financial Exchange, And on this day.
• 13:59
  Speaker 7: In two thou one, Enron filed for Chapter eleven bankruptcy
• 14:03
  Speaker 7: in the Southern District of New York. CEO Kenneth Lay
• 14:07
  Speaker 7: was heavily involved in Enron's accounting scandal that unraveled in
• 14:11
  Speaker 7: two thousand and one. Lay was indicted by a grand
• 14:15
  Speaker 7: jury and was found guilty of ten counts of securities
• 14:18
  Speaker 7: fraud at trival trial. So our trivia question today, how
• 14:22
  Speaker 7: many years did Kenneth Lay spend in prison? Once again,
• 14:25
  Speaker 7: how many years do Kenneth Lay spend in prison? Be
• 14:29
  Speaker 7: the third person today to text us at six one
• 14:32
  Speaker 7: seven three six two thirteen eighty five with the correct
• 14:36
  Speaker 7: answer along with the keyword trivia, and you win a
• 14:39
  Speaker 7: Financial Exchange Show T shirt.
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  Speaker 5: Once again.
• 14:41
  Speaker 7: The third correct response to Texas to the number six
• 14:45
  Speaker 7: one seven three six two thirteen eighty five with the
• 14:48
  Speaker 7: correct answer in the keyword trivia, We'll win that T shirt.
• 14:53
  Speaker 7: See complete contest rules at Financial Exchange Show dot com.
• 14:56
  Speaker 2: The editorial board for Bloomberg wrote a piece yesterday that
• 15:00
  Speaker 2: Washington must break its promise on Social Security and here's
• 15:05
  Speaker 2: what they lay out. Basically, the Trust Fund, as we
• 15:07
  Speaker 2: all know, is going bust by twenty thirty four because
• 15:10
  Speaker 2: elected lawmakers have chosen to ignore the issue. Any of
• 15:14
  Speaker 2: the fixes that could have been implemented a decade ago
• 15:17
  Speaker 2: that would have been relatively minor and you know, slowly
• 15:21
  Speaker 2: paced in are no longer doable because we waited until
• 15:26
  Speaker 2: nearly the depletion of the funds to deal with the problem,
• 15:29
  Speaker 2: and so now if you go and fix it, it's
• 15:31
  Speaker 2: going to have to be more significant. They lay out,
• 15:34
  Speaker 2: you know, a number of different solutions, from raising the
• 15:36
  Speaker 2: full retirement age from the currently sixty seven to sixty
• 15:39
  Speaker 2: nine and then indexing it for life expectancy. They propose
• 15:43
  Speaker 2: changing the inflation assumptions when it comes to cola adjustments,
• 15:48
  Speaker 2: which most retirees already find inadequate because it doesn't fully
• 15:52
  Speaker 2: account for the rising cost of healthcare. They talk about
• 15:56
  Speaker 2: increasing taxes on Social Security in order to fund the
• 16:00
  Speaker 2: trust fund better. But the point they make is a
• 16:03
  Speaker 2: good one, which is the solutions to actually fist fix
• 16:06
  Speaker 2: the existing the existing program would have to be substantial
• 16:12
  Speaker 2: because we've waited so long. But I go back to
• 16:15
  Speaker 2: what I think is the more likely scenario. Washington's gonna
• 16:18
  Speaker 2: have to break their promise one way or another. Either
• 16:20
  Speaker 2: they're gonna have to break their promises on Social Security
• 16:23
  Speaker 2: and rewrite the program, likely some combination of raising taxes
• 16:27
  Speaker 2: plus cutting benefits, either for current recipients, future recipients, or both,
• 16:33
  Speaker 2: or they're just gonna have to break their promise. When
• 16:35
  Speaker 2: it comes to what I view as a key job
• 16:38
  Speaker 2: of every elected official, which is maintaining some degree of
• 16:43
  Speaker 2: economic stability for our country's finances and spend the money
• 16:49
  Speaker 2: out of deficits. And in my opinion, if you're looking
• 16:52
  Speaker 2: for the much more likely solution in terms of how
• 16:54
  Speaker 2: lawmakers deal with the trust fund issue, they write a check.
• 16:59
  Speaker 2: They don't ever come up up with taxes to fund it,
• 17:01
  Speaker 2: and there is no compromise, there is no.
• 17:03
  Speaker 3: Solution to it.
• 17:04
  Speaker 2: It's just, oh, we've got the money, let's just write
• 17:07
  Speaker 2: a blank check for funding Social Security, even though the
• 17:09
  Speaker 2: program's completely bankrupt.
• 17:11
  Speaker 4: I guess this was inevitable with increasing life expectancy. We
• 17:15
  Speaker 4: all know that the program was designed and implemented nearly
• 17:19
  Speaker 4: one hundred years ago when life expectancy basically didn't extend
• 17:24
  Speaker 4: much beyond retirement. So it was meant to be truly
• 17:27
  Speaker 4: a safety net. That's changed, and our expectations of it have.
• 17:32
  Speaker 2: Changed, as have demographics as society. And that's what I'm
• 17:34
  Speaker 2: getting healthier life expectancy changes, but birth rates changed too,
• 17:38
  Speaker 2: and so like none of the formula works anymore, and
• 17:41
  Speaker 2: it's going to get worse probably before it gets better.
• 17:44
  Speaker 4: It's a little tedious to have to talk about the
• 17:46
  Speaker 4: things that could be done to fix it, because obviously there's.
• 17:49
  Speaker 3: No nobody would be happy about any of them.
• 17:51
  Speaker 4: And I got to say again, for what it's worth,
• 17:52
  Speaker 4: I've never seen such bad fiscal and monetary policy at
• 17:56
  Speaker 4: the same time in my life, nor have I ever
• 17:59
  Speaker 4: and I'm not the oldest person in the world, but
• 18:01
  Speaker 4: at fifty three, I'm not the youngest. I've never read
• 18:04
  Speaker 4: about such bad and don't tell me well Andrew Jackson
• 18:06
  Speaker 4: veto the Second Bank of the United States, and that's
• 18:08
  Speaker 4: I'm not talking about the eighteen thirties. I'm talking about
• 18:10
  Speaker 4: the modern era. Hohmever, go ahead, no no good.
• 18:13
  Speaker 2: If you are evaluating decisions about your social security, it
• 18:18
  Speaker 2: is a pretty important time to do so because of
• 18:21
  Speaker 2: this looming concern. Twenty thirty four is right around the corner,
• 18:24
  Speaker 2: and I know a lot of people are making fear
• 18:26
  Speaker 2: based decisions when it comes to social security. I'm here
• 18:30
  Speaker 2: to tell you that even in the face of this,
• 18:32
  Speaker 2: you can't make your decision on social security out of fear.
• 18:34
  Speaker 2: It is a critical part of many retirees future. And
• 18:37
  Speaker 2: if you have questions about it, about the strategies for
• 18:40
  Speaker 2: claiming social security and how it will affect your future.
• 18:43
  Speaker 2: Call eight hundred three nine three for zero zero one.
• 18:46
  Speaker 2: That's the number for the Armstrong Advisory Group. You can
• 18:48
  Speaker 2: book your free call at Armstrong Advisory dot com or
• 18:51
  Speaker 2: eight hundred three nine three for zero zero one.
• 18:54
  Speaker 1: The proceeding was paid for by Armstrong Advisory Group, a
• 18:57
  Speaker 1: registered investment advisor. Nothing in the ad or in any
• 18:59
  Speaker 1: Armstrong guide to specific financial, legal, or tax advice. Consult
• 19:02
  Speaker 1: your own financial, tax into state planning advisors before making
• 19:05
  Speaker 1: any investment decisions. Armstrong may contact you to offer investment
• 19:08
  Speaker 1: advisory services.
• 19:10
  Speaker 3: Do you think I'm wrong about that?
• 19:11
  Speaker 2: By the way, I seem to have these blinders on
• 19:14
  Speaker 2: where I just refuse to believe that lawmakers are actually
• 19:17
  Speaker 2: going to solve the problem when it comes.
• 19:19
  Speaker 5: To social that's what I was talking about.
• 19:21
  Speaker 2: Or we're just going to spend a whole bunch of money, right,
• 19:23
  Speaker 2: We're just gonna blow out deficits.
• 19:24
  Speaker 4: It's not our nature to do something about a problem
• 19:28
  Speaker 4: the magnitude of which is uncertain years in advance, whether
• 19:32
  Speaker 4: it's climate change or this. Democracies don't lend and I
• 19:36
  Speaker 4: would defend them, but they don't lend themselves to proactively
• 19:40
  Speaker 4: years in advance. When the consequences of not doing anything
• 19:43
  Speaker 4: are uncertain, they don't lend themselves to imposing sacrifices today,
• 19:46
  Speaker 4: when the reward is uncertain, they just if.
• 19:48
  Speaker 3: Your job is to get reelected, you wait until twenty
• 19:51
  Speaker 3: thirty three.
• 19:51
  Speaker 5: Much easier to do nothing.
• 19:54
  Speaker 3: Quick break full market update next with Wall Street Watch.
• 20:10
  Speaker 1: Bringing the latest financial news straight to your radio. Every day.
• 20:14
  Speaker 1: It's the Financial Exchange on the Financial Exchange Radio Network.
• 20:21
  Speaker 1: Time now for Wall Street Watch. A complete look at
• 20:24
  Speaker 1: what's moving markets so far today right here on the
• 20:28
  Speaker 1: Financial Exchange Radio Network.
• 20:29
  Speaker 7: Oll markets are now flattening after initially seeing a tech
• 20:33
  Speaker 7: rally in early trading.
• 20:34
  Speaker 1: Traders are also.
• 20:35
  Speaker 7: Monitoring Bitcoin after its recent pullback. Today, Bitcoin is rebounding
• 20:42
  Speaker 7: and trading right around ninety thousand dollars at the moment.
• 20:46
  Speaker 7: The Dow is up two tenths of a percent or
• 20:48
  Speaker 7: ninety five points. SMP five hundred is completely flat, NASDAC
• 20:53
  Speaker 7: up only a tenth of percent after seeing a nice
• 20:57
  Speaker 7: rally in early trading. RUSSA two thousand is a tenth
• 21:00
  Speaker 7: of percent as well. Tenure treasure field is flat at
• 21:03
  Speaker 7: four point zero nine two percent, and crude oil off
• 21:07
  Speaker 7: about a half a percent, trading just above fifty nine
• 21:10
  Speaker 7: dollars a barrel. According to sever reports, Comcast, Netflix, and
• 21:14
  Speaker 7: Paramount all submitted second offers for Warner Brothers Discovery assets,
• 21:19
  Speaker 7: while Paramount and Comcasts have made improved offers. Netflix reportedly
• 21:23
  Speaker 7: submitted a primarily cash based offer. Netflix shares it down
• 21:27
  Speaker 7: about one percent, while Warners Warner stock is up about
• 21:31
  Speaker 7: one percent. Meanwhile, Apple is shaking up its AI leadership,
• 21:35
  Speaker 7: where the IFL maker landed a Microsoft executive after announcing
• 21:39
  Speaker 7: the retirement of its top AI leader. Apple shares up modestly. Elsewhere,
• 21:44
  Speaker 7: developer data platform Mango dB is seeing its stock serge
• 21:48
  Speaker 7: twenty three percent after posting solid third quarter results. The
• 21:52
  Speaker 7: company also hiked its full year guidance in a push
• 21:55
  Speaker 7: against rivals Google and Nvidia. AWS or Amazon Web Services
• 22:01
  Speaker 7: announced the public launch of its Trainium three custom AI chip,
• 22:05
  Speaker 7: which it says is four times as fast as its
• 22:08
  Speaker 7: previous generation of AI chips at Amazon stock is edging
• 22:12
  Speaker 7: higher and Boeing stock is soaring nearly eight percent after
• 22:16
  Speaker 7: it's CFO said at a UBS conference this morning that
• 22:20
  Speaker 7: the company expects deliveries for both its seven thirty seven
• 22:23
  Speaker 7: jets in seven eighty seven jets to be up next year.
• 22:28
  Speaker 7: I'm Tucker Silva and that is Wall Street Watch. And
• 22:30
  Speaker 7: in the previous segment, we asked you the trivia question
• 22:33
  Speaker 7: how many years did Kenneth Lay spend in prison? That
• 22:37
  Speaker 7: would be none. Kenneth Lay died three months before his
• 22:41
  Speaker 7: scheduled sentencing. Jason from Kingston, New Hampshire's our winner today
• 22:46
  Speaker 7: taking home a Financial Exchange Show T shirt. Congrats to Jason.
• 22:50
  Speaker 7: We play trivia every day here in the Financial Exchange
• 22:52
  Speaker 7: See complete contest rules at Financial Exchange Show dot com.
• 22:57
  Speaker 2: Just one more thing that I was running across when
• 22:59
  Speaker 2: it came to that AI story. I finished one of
• 23:02
  Speaker 2: the last segments with the fact that we still don't
• 23:05
  Speaker 2: really understand how these tools are going to be monetized
• 23:08
  Speaker 2: properly and who's going to actually pay for them all,
• 23:11
  Speaker 2: which is somewhat concerning. But Amazon rolling out to exclusively
• 23:16
  Speaker 2: to their AWS customers. So let's say you are a
• 23:19
  Speaker 2: business that stores your data in Amazon's cloud. What they
• 23:23
  Speaker 2: are offering is the ability for those business customers to
• 23:26
  Speaker 2: go in and customize a generative AI model. What does
• 23:31
  Speaker 2: that mean so that in the midst of training this thing,
• 23:33
  Speaker 2: you let's say I'm going to take this to our
• 23:36
  Speaker 2: industry for ease of you. Now, let's let's use a
• 23:39
  Speaker 2: different industry. Let's say you are a marketing agency that
• 23:44
  Speaker 2: works specifically with small banks I don't know, and you
• 23:49
  Speaker 2: write campaigns and design campaigns for them. This offering would
• 23:54
  Speaker 2: allow you to basically take all of your content that
• 23:57
  Speaker 2: you have written over the years and feed it right
• 23:59
  Speaker 2: into a model so that that model is more customized
• 24:02
  Speaker 2: and designed to help you build campaigns for those small
• 24:07
  Speaker 2: banks as opposed to just building it all right in chat,
• 24:10
  Speaker 2: GPT or another large language model. They're charging one hundred
• 24:14
  Speaker 2: thousand bucks a year for that ability.
• 24:16
  Speaker 5: See how it goes?
• 24:17
  Speaker 3: I don't know.
• 24:17
  Speaker 2: I mean, you don't think think about the previous employers
• 24:20
  Speaker 2: you've worked for, and granted they've been a lot larger
• 24:22
  Speaker 2: than a small marketing agency.
• 24:24
  Speaker 4: But actually I've worked for mostly small companies. Okay, so
• 24:26
  Speaker 4: I've only had three, so it's like a ton.
• 24:28
  Speaker 2: So you know, imagine you are one of those companies
• 24:30
  Speaker 2: and you have a whole bunch of analyst reports and
• 24:33
  Speaker 2: models that you've built on all sorts of sorts of
• 24:36
  Speaker 2: different analytics. You don't think it'd be useful to feed
• 24:38
  Speaker 2: that into an AI model and you know, building around.
• 24:41
  Speaker 4: Using it today to do mundane stuff like here's some data,
• 24:44
  Speaker 4: put this in a spreadsheet and do this other cleanup
• 24:46
  Speaker 4: stuff for me, stuff that might take me fifteen or
• 24:48
  Speaker 4: twenty minutes. The engine can obviously do a lot faster.
• 24:51
  Speaker 4: But when you ask it to explore relationships, plot date, chart,
• 24:56
  Speaker 4: data used, it has no judgment.
• 24:58
  Speaker 5: It may someday.
• 25:00
  Speaker 2: That's my question is maybe by training it on Well,
• 25:03
  Speaker 2: that's actual work that you've done.
• 25:04
  Speaker 4: That All models are trained on stuff that people have already.
• 25:08
  Speaker 4: By the way, training just means fit a model to data.
• 25:11
  Speaker 4: We say training, We give it data, find a relationship.
• 25:14
  Speaker 4: That's training a model.
• 25:16
  Speaker 5: This is this.
• 25:16
  Speaker 4: Stuff's been going on for a long for as long
• 25:18
  Speaker 4: as computer computing power has been available. It's AI is
• 25:22
  Speaker 4: just a different layer of complaining.
• 25:23
  Speaker 3: Guess my point would be at one hundred thousand bucks. Yeah,
• 25:27
  Speaker 3: that's a.
• 25:27
  Speaker 5: Large spend, like how much you spend a night. That's
• 25:31
  Speaker 5: a question.
• 25:31
  Speaker 2: Yeah, I mean to me, one hundred thousand bucks for
• 25:33
  Speaker 2: a pretty small business to throw at training its own
• 25:36
  Speaker 2: large language model does not seem.
• 25:38
  Speaker 5: Like never gonna happen.
• 25:38
  Speaker 4: Now, Well, what's your ad spend. Now, if it's two
• 25:40
  Speaker 4: hundred o K, then maybe this makes sense. But I
• 25:43
  Speaker 4: want to see the proofs in the shooting show me
• 25:45
  Speaker 4: that this isn't just some homogenized PAP that every other
• 25:49
  Speaker 4: little bank gets. It can have a hard time proving
• 25:52
  Speaker 4: value added. I mean, if I can have somebody come in,
• 25:55
  Speaker 4: do a little song and dance for me, present some
• 25:58
  Speaker 4: you know, present some ad stuff on an easel, the
• 26:01
  Speaker 4: old fac I know it's not mad men, but you
• 26:03
  Speaker 4: know I expect to be wooed for my one hundred K.
• 26:08
  Speaker 2: Yeah, I agree, but but I don't.
• 26:14
  Speaker 3: I would want that.
• 26:15
  Speaker 2: But I think there are plenty of bustesses that know
• 26:17
  Speaker 2: nothing about AI who are feeling behind the eight ball
• 26:19
  Speaker 2: because they don't have the expertise nor the money to
• 26:22
  Speaker 2: spend on developing their own internal tool like Goldman Sachs does.
• 26:25
  Speaker 2: Who say WHOA six figures? And I can customize my
• 26:28
  Speaker 2: own AI model and Amazon's gonna help me do it.
• 26:31
  Speaker 2: I think there'll be plenty of customers to pick.
• 26:32
  Speaker 4: Okay, maybe maybe the chief marketing officer needs something to
• 26:35
  Speaker 4: add to burnish their year end stuff so they get
• 26:37
  Speaker 4: the bonus they will want, and so they come in
• 26:38
  Speaker 4: and say, well, okay, my big idea is we pay
• 26:40
  Speaker 4: Amazon another hundred k a year to do this. Yeah,
• 26:42
  Speaker 4: Ceo says, I don't want to deal with that. Sure,
• 26:44
  Speaker 4: let's give it a try.
• 26:44
  Speaker 3: Yeah, yeah, maybe yeah.
• 26:46
  Speaker 7: Uh.
• 26:47
  Speaker 2: Mark Financial Times asks how sticky is you in US inflation?
• 26:50
  Speaker 2: We're gonna get some sense of this on Friday, as
• 26:52
  Speaker 2: inflation figures for September get published, which, again, if you're
• 26:56
  Speaker 2: the Federal Reserve trying to make interest rate decisions in December,
• 26:59
  Speaker 2: I don't know how useful September data is other than
• 27:01
  Speaker 2: to tell you that directionally inflation usually it kind of
• 27:06
  Speaker 2: goes in the same direction. But how do we answer
• 27:09
  Speaker 2: the question of how sticky US inflation is? Because well, ultimately,
• 27:13
  Speaker 2: in twenty twenty two we were getting readings of nine
• 27:15
  Speaker 2: percent on its night, and we've come way down from there.
• 27:18
  Speaker 4: I think when they say how sticky is it, they
• 27:20
  Speaker 4: mean how we introduced the concept of correlation and the
• 27:22
  Speaker 4: for we talked about it.
• 27:23
  Speaker 5: We didn't introduce it. Sure, we talked about.
• 27:24
  Speaker 4: Correlation in the first hour. The way you measure you
• 27:26
  Speaker 4: persistence is how correlated is something with its most recent observation,
• 27:30
  Speaker 4: then it's next most reason. Then it's next most recent
• 27:32
  Speaker 4: fancy term word of the day. If you want to
• 27:34
  Speaker 4: impress your boss. Is it auto correlated? Self correlated? Is
• 27:37
  Speaker 4: it serially correlated? Is another way analysts might say that
• 27:41
  Speaker 4: inflation is persistent, it's highly auto correlated. I don't that's
• 27:45
  Speaker 4: not what they mean here, though, I don't think so.
• 27:47
  Speaker 4: Why did I just go off on that tangent? I
• 27:49
  Speaker 4: don't really know what they mean. I think yet lost.
• 27:52
  Speaker 5: How hard is it going to be?
• 27:53
  Speaker 4: Yeah, I don't know where I am, but what I'm talking.
• 27:55
  Speaker 3: About I just blacked out what I'm aging and I'm confused.
• 28:00
  Speaker 4: I think what they mean is how hard is it
• 28:02
  Speaker 4: going to be to get inflation down? And all modern
• 28:06
  Speaker 4: macro whole economy models assume that prices are there's some rigidity,
• 28:13
  Speaker 4: some stickiness, if you will, in prices. That's why they're
• 28:16
  Speaker 4: that's why monetary policy works. It's also why inflation is
• 28:21
  Speaker 4: uh is difficult to get down. So the short answer
• 28:26
  Speaker 4: to this is, well, prices are rigid, prices are sticky
• 28:28
  Speaker 4: on average, That's why you get inflation in everything. When
• 28:32
  Speaker 4: oil why the average rate of inflation goes up when
• 28:34
  Speaker 4: oil prices jumped, Because you might ask, well, if oil
• 28:38
  Speaker 4: prices or tariffs are only going up on certain things,
• 28:40
  Speaker 4: why don't they go down elsewhere? The modern macro answer
• 28:43
  Speaker 4: to that is, and I'll just be technical, say nominal rigidities.
• 28:47
  Speaker 2: And to humanize this a little bit, and not that labor,
• 28:50
  Speaker 2: not that labor is the only piece of this. But
• 28:53
  Speaker 2: if your boss came to you and said, hey, you know,
• 28:57
  Speaker 2: prices have been moderating a little bit, so I need
• 28:59
  Speaker 2: to take your salary from ninety thousand to eighty eight
• 29:01
  Speaker 2: thousand dollars, you're not going to say, yes.
• 29:04
  Speaker 5: Yeah, wages are rigid to wages.
• 29:06
  Speaker 3: Are extremely rigid.
• 29:07
  Speaker 2: That is a big portion of what contributes to overall
• 29:10
  Speaker 2: price levels. And nominally, yes, and so yeah, the idea
• 29:16
  Speaker 2: that prices they have the pace that there increases have
• 29:22
  Speaker 2: calmed down quite a bit. But the idea that it's
• 29:24
  Speaker 2: going to be some easy feat to get inflation from
• 29:27
  Speaker 2: three percent down to two percent is a big question
• 29:31
  Speaker 2: mark in my mind. And at this stage I'm not
• 29:33
  Speaker 2: entirely sure that it is an actual goal of those
• 29:36
  Speaker 2: who are going to lead the FED.
• 29:38
  Speaker 4: We were getting there, but the Fed, the FED is
• 29:40
  Speaker 4: chickening out essentially, And I'm not saying I wouldn't do
• 29:42
  Speaker 4: the same thing, by the way, because it's well known
• 29:45
  Speaker 4: that I'm a coward. The Fed does not. Everybody's just nodding.
• 29:49
  Speaker 5: Everybody in the office is yep, his whole family.
• 29:52
  Speaker 4: He shaped me, he turns tail. The Fed stopped trying
• 29:57
  Speaker 4: to get inflation down. It's turned to fighting weakness in
• 30:01
  Speaker 4: the labor market. Because getting inflation to two percent would
• 30:05
  Speaker 4: require reducing demand. The Fed is doing the opposite right
• 30:08
  Speaker 4: now by easing monetary policy. It's goosing demand. To get
• 30:13
  Speaker 4: inflation down to two would require probably a recession, which,
• 30:16
  Speaker 4: by the way, is what everybody said in twenty twenty two.
• 30:20
  Speaker 4: We all acclaimed the immaculate disinflation, but we never really
• 30:24
  Speaker 4: got to two percent because getting finishing that last mile,
• 30:28
  Speaker 4: or whatever analogy you want to use, would have been
• 30:30
  Speaker 4: economically painful. Now it's too late.
• 30:33
  Speaker 2: Let's take a quick break. As we head towards the
• 30:36
  Speaker 2: noon hour. Here, markets are wavering a little bit.
• 30:39
  Speaker 3: We've got the S.
• 30:40
  Speaker 2: Andp up now only a little bit more than a
• 30:42
  Speaker 2: tenth of a percent. We'll recap it for you and
• 30:43
  Speaker 2: play some stack roulette here.
• 30:45
  Speaker 5: Next.
• 30:47
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• 30:50
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  Speaker 7: This segment of The Financial Exchange is brought to you
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• 32:13
  Speaker 2: There it is, Ah Tucker. Are you fed up with
• 32:18
  Speaker 2: spot me talking slop bowls?
• 32:23
  Speaker 3: Also?
• 32:23
  Speaker 2: When did we start calling Chipotle bowls slop bowls? Because
• 32:26
  Speaker 2: that seems.
• 32:28
  Speaker 7: Chipotle That term come out maybe six months ago. Is
• 32:32
  Speaker 7: the first time we put it on.
• 32:34
  Speaker 2: They never heard of a garbage plate. No, it's an
• 32:37
  Speaker 2: upstate New York thing when I was in college. Basically
• 32:40
  Speaker 2: just French fries, ground beef, cheese, like, basically whatever they
• 32:44
  Speaker 2: could find they're just toss into a garbage plate.
• 32:46
  Speaker 3: And to answer her, that's a slop plate.
• 32:49
  Speaker 2: I don't think that a Cova Mediterranean bowl is a
• 32:54
  Speaker 2: slop bowl. I think I think we're doing that that
• 32:56
  Speaker 2: branded disservice.
• 32:57
  Speaker 7: I mean to answer your question, am I toired of
• 33:00
  Speaker 7: the said slop bowl? I would say no, because I
• 33:03
  Speaker 7: think it's the most customizable lunch out there. And I
• 33:07
  Speaker 7: still don't understand why I'm continuing to see these headlines
• 33:10
  Speaker 7: of diners pulling back because they're over the slop bowls.
• 33:15
  Speaker 7: I think it's more of a money thing than anything.
• 33:17
  Speaker 2: Tucker, you're old man. You're not the target demo anymore. Right,
• 33:21
  Speaker 2: sixteen year olds here, I mean don't want slop bowls anymore.
• 33:24
  Speaker 7: So what are they going to back to?
• 33:25
  Speaker 2: Well again, I think if he stopped calling them slop bowls,
• 33:28
  Speaker 2: I think it would do a lot for the brand image.
• 33:33
  Speaker 3: To me.
• 33:33
  Speaker 2: I like you said, I think that this is a
• 33:36
  Speaker 2: cost issue. If I go to Chipotle, it's going to
• 33:37
  Speaker 2: be sixteen bucks. If I go to Cava, that new
• 33:40
  Speaker 2: Mediterranean startup chain. Yeah, you're talking, it's all in the
• 33:44
  Speaker 2: same about the same, yea. So maybe McDonald's needs to
• 33:47
  Speaker 2: put together a slop bowl and prove them all wrong.
• 33:49
  Speaker 2: Just a disgusting bowl field.
• 33:51
  Speaker 7: I mean they've done the salad.
• 33:52
  Speaker 2: Fried chicken chunks, you know, French fries, and whatever else
• 33:57
  Speaker 2: they've got, you know, big Max sauce.
• 33:59
  Speaker 3: I think that I would work for them.
• 34:01
  Speaker 4: Well, big Mac is a slop bowl between two slice.
• 34:04
  Speaker 3: It's a sandwich.
• 34:05
  Speaker 5: Mark, I love McDonald's.
• 34:06
  Speaker 3: It's a sandwich. Yeah.
• 34:08
  Speaker 2: So apparently some of these brands pulling back on some
• 34:11
  Speaker 2: of that stuff and focusing more on there. I mean,
• 34:14
  Speaker 2: you know, Chipotle was made famous by burritos. I think
• 34:16
  Speaker 2: that's largely what they still sell. But those those brands
• 34:20
  Speaker 2: that are focusing on just salad, you know, not just
• 34:22
  Speaker 2: sout what's the what's the sweet greens and others are
• 34:26
  Speaker 2: having a tough time attracting.
• 34:28
  Speaker 3: Again. I go back to.
• 34:30
  Speaker 2: These are all sixteen to twenty dollars lunches, and I
• 34:34
  Speaker 2: think people are just feeling like they should pinch some
• 34:37
  Speaker 2: pennies at this stage because they don't have enough money
• 34:39
  Speaker 2: to do a four to twenty dollars lunches.
• 34:41
  Speaker 7: If I'm Chipotle, I'm coming out with like eight ninety
• 34:46
  Speaker 7: nine burrito, like a smaller one man that's available now.
• 34:50
  Speaker 2: Yeah, I guess if you can be popular, if you
• 34:53
  Speaker 2: can do it without worsening your ingredients, then maybe that works.
• 34:57
  Speaker 2: But it's just a smaller brand nine dollars burrito. You're
• 34:59
  Speaker 2: gonna have to only after like ground beef.
• 35:01
  Speaker 7: Just have a smaller, tour less yeah, and basic ingredients.
• 35:07
  Speaker 5: That's a terrible idea.
• 35:08
  Speaker 7: It's not a terrible idea, Michael. It's a great idea,
• 35:11
  Speaker 7: and they should do it.
• 35:12
  Speaker 3: What do you got for us? Mark?
• 35:14
  Speaker 4: Bringing things back to finance, I like talking about earlier
• 35:18
  Speaker 4: on not this close to lunch, though very distracting. Earlier on,
• 35:23
  Speaker 4: we were talking about the S and P as being
• 35:26
  Speaker 4: composed of of sectors information technology, financials.
• 35:29
  Speaker 5: There are eleven of them.
• 35:31
  Speaker 4: Information technology is the biggest, Like we said, thirty five
• 35:34
  Speaker 4: percent of the S and P. So if every dollar ahead, Mike.
• 35:37
  Speaker 2: I just wanted to mention a confusing part of this.
• 35:40
  Speaker 2: The technology sector is comprised of a bunch of those
• 35:42
  Speaker 2: big names. Other sectors, though, are also comprised of what
• 35:46
  Speaker 2: we would consider to be big tech companies. The communication
• 35:49
  Speaker 2: services sector, for instance, is a big one, and you'd think, oh,
• 35:51
  Speaker 2: communication service, we're talking about AT and T. Well, yeah,
• 35:54
  Speaker 2: but we're mainly talking about alphabet We're mainly talking about
• 35:59
  Speaker 2: meta I think in the communications services sector, even though
• 36:02
  Speaker 2: largely we talk about them as though they're teching.
• 36:03
  Speaker 4: But you made the point that when you put a
• 36:05
  Speaker 4: dollar in the S and P, thirty five cents is
• 36:08
  Speaker 4: going into more narrowly defined tech companies, and in turn,
• 36:14
  Speaker 4: twenty percent of every dollar currently this changes every day,
• 36:17
  Speaker 4: goes into Nvidia fifteen percent of every so six percent
• 36:21
  Speaker 4: of your nearly six percent.
• 36:22
  Speaker 3: Twenty percent of the tech okay, twenty percent.
• 36:24
  Speaker 4: Of techs on multiplying fractions here or proportions point two
• 36:29
  Speaker 4: times point three five, blah blah blah. That's how I
• 36:32
  Speaker 4: get to that number. And Nvidia, I'm rounding up.
• 36:34
  Speaker 5: A little bit.
• 36:35
  Speaker 4: Your point was, well, it's it's never been more dominated
• 36:39
  Speaker 4: by one sector and that sector has never been as
• 36:42
  Speaker 4: large as it is today.
• 36:44
  Speaker 5: Did I get something wrong? You know you do? Okay, good? Good.
• 36:48
  Speaker 4: My point is, on a daylight today, you're glad that
• 36:52
  Speaker 4: I'm not saying you shouldn't consider something other than a
• 36:54
  Speaker 4: market cap weighted index. That's a personal decision. I am
• 36:57
  Speaker 4: saying that the whole investment for fashion starts with market
• 37:01
  Speaker 4: cap weighted and then makes bets relative to that. But
• 37:04
  Speaker 4: on a daylight today, infotech is, with the exception of
• 37:08
  Speaker 4: one other sector, industrials, infotech is the only sector in
• 37:11
  Speaker 4: the green. So as distended as that part of the
• 37:15
  Speaker 4: S and P may seem, there are times plenty of times,
• 37:18
  Speaker 4: like the year to date period, for example, when it
• 37:20
  Speaker 4: can pull the whole index along. And why if your
• 37:24
  Speaker 4: objective is just to match the market, not necessarily to
• 37:27
  Speaker 4: beat it, so you don't consider yourself to be a
• 37:29
  Speaker 4: savvy active manager, for example, why it can be dangerous
• 37:33
  Speaker 4: to tilt away from market cap weights.
• 37:35
  Speaker 2: Yeah, it most certainly can. I when I was trying
• 37:38
  Speaker 2: to pull up here, I'm going to see if I
• 37:40
  Speaker 2: can find it. Yeah, here we've I don't have the percentages, though,
• 37:43
  Speaker 2: what is it?
• 37:43
  Speaker 3: What are you looking on that I was looking up?
• 37:45
  Speaker 2: What are the what is the composition of the s
• 37:49
  Speaker 2: and P five hundred? So to your point, if I
• 37:51
  Speaker 2: invest a dollar, I believe something like not. No, I
• 37:55
  Speaker 2: want the company weights because I think if I invest
• 37:57
  Speaker 2: a dollars, something like nine cents is going into a video,
• 38:00
  Speaker 2: eight cents into Apple, and another seven into Microsoft or
• 38:03
  Speaker 2: something along those lines.
• 38:06
  Speaker 3: And so yeah, you take a look at that.
• 38:08
  Speaker 2: And so the top ten in Vidia, Apple, Microsoft, Amazon, Broadcom, Alphabet, Meta, Tesla, Berkshire.
• 38:15
  Speaker 2: I believe those ten are making up nearly fifty percent.
• 38:19
  Speaker 2: And you've got it. So between the top three, what
• 38:21
  Speaker 2: do we have.
• 38:22
  Speaker 4: Having seven six is twenty three three three? Called that
• 38:26
  Speaker 4: thirty ten now doesn't quite get you. They're more like
• 38:29
  Speaker 4: thirty five.
• 38:30
  Speaker 2: Yeah, but quite swesome. As we close out the show,
• 38:35
  Speaker 2: markets are still holding on too gains here, SMP up
• 38:37
  Speaker 2: one fifth of a percent, NASDAK up a half percent,
• 38:41
  Speaker 2: Dow Jones Industrial average up about a third of a percent.
• 38:43
  Speaker 2: We'll be back at it tomorrow. Folks, Stay tuned and
• 38:46
  Speaker 2: have a great rest of your day.