Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
Speaker 2: So here's the latest this morning, Apollo noting a lack
Speaker 2: of profit margin gains due to AI outside of the
Speaker 2: tech sector. Torston's slock of Apollo, writing, there's a mismatch
Speaker 2: between current earnings expectations and the actual time firms need
Speaker 2: to generate ROI on AI investments, and it could have
Speaker 2: significant implications for many AI company valuations. Towston joins us
Speaker 2: now for more. Torston, good morning, good to see you.
Speaker 2: Let's build on that quote. What are you track in
Speaker 2: right now? What do you think?
Speaker 3: Well, what's really really important to this discussion is, of
Speaker 3: course profit margers have been phenomenal in the Magnificent seven,
Speaker 3: but what really is critical is that now we need
Speaker 3: to see profit marners grow up outside the Magnificent seven.
Speaker 3: In other words, what's going on with this in p.
Speaker 3: Four ninety three becomes very very critical because at this
Speaker 3: point profit margers is in P.
Speaker 4: Four ninety three have just not gone up.
Speaker 3: So therefore, one very important concusion, and one very important
Speaker 3: place to look for signs of AI beginning to have
Speaker 3: an impact is to look at what's going on in
Speaker 3: earnings growth, profit margins, and overall the health of this
Speaker 3: in p. Four ninety three, as a result of the
Speaker 3: technological improvements we're seeing at the moment.
Speaker 2: Mats are also making a call potentially as well that
Speaker 2: the ROI on selling access capacity might be hard in
Speaker 2: using it internally. Does that reinforce some of this message
Speaker 2: for you?
Speaker 3: Well, The issue, of course is that there is a
Speaker 3: huge of course built out of capacity and compute, and
Speaker 3: there will literally be unlimited demand for compute. The question
Speaker 3: is just at what price and who will the buias
Speaker 3: be and where is that capacity coming from? And the
Speaker 3: big picture still remains that AI is a very revolutionary technology.
Speaker 4: Everyone agrees on that.
Speaker 3: But the key question now is how long time is
Speaker 3: it going to take before this shows up, especially in
Speaker 3: profit margins outside the Magnificent seven, Because if there's in p.
Speaker 3: Four ninety three, let's say that it takes several years
Speaker 3: before profit margins begin to go up. The question is
Speaker 3: whether the implicit earnings assumptions in the Magnificent seven are
Speaker 3: too high or too fast relative to what's actually going
Speaker 3: to happen, so that mismatch between are we going to
Speaker 3: see profit margins earnings growth go up? Outside this the
Speaker 3: Magnificent seven. Is that going to come slower? Is it
Speaker 3: going to come faster? Is absolutely critical for a conversation
Speaker 3: about what should the value be of the Magnificent seven.
Speaker 1: Today, in exactly one week's time, as John was reminding us,
Speaker 1: the big banks begin reporting earnings and we have the
Speaker 1: kickoff of the earning season, there's going to be a
Speaker 1: lot of discussion on AI and what it means for
Speaker 1: jobs in the banking sector. How do you parse through
Speaker 1: what the companies say to really understand what it means
Speaker 1: in terms of whether they're going to cut jobs or not.
Speaker 3: What's really challenging about this is to talk about AI
Speaker 3: exposure because there's a lot of different studies already that
Speaker 3: look at what is the exposure meaning AI exposure in
Speaker 3: different occupations. Two these margets, These two studies fall into
Speaker 3: different buckets of one saying what is the actual exposure
Speaker 3: in terms of actual AI usage? So this is trying
Speaker 3: to measure, say, what are people using clode for what
Speaker 3: tasks and what requests did they get and therefore measuring
Speaker 3: and quantifying the actual usage of AI. Another bucket is
Speaker 3: studies that look at, well, let's theoretically assume what is
Speaker 3: the economistic exposure to AI and then try to match
Speaker 3: that with occupations and figure out what is employment in
Speaker 3: those sexes. And those studies, of course, are what you
Speaker 3: call more theoretical. So the challenge is that there is
Speaker 3: not an agreement about what does AI exposure mean, So
Speaker 3: that's raising all these questions around, well, what does it
Speaker 3: even mean when we say that a certain part of
Speaker 3: the economy is exposed to AI, because that's just not
Speaker 3: at this point in the studies that look at this
Speaker 3: a really good way to quantify what AI exposure means.
Speaker 3: And that means also for the financials, that it means
Speaker 3: for legal services, that means for consultants that we're having
Speaker 3: some challenges figuring out how do we even quantify what
Speaker 3: is the impact of AI. We all know that it's
Speaker 3: going to make a big difference, but to speak with
Speaker 3: which this difference comes along? How many people is impacting today,
Speaker 3: next month, next year. It becomes absolutely critical when you
Speaker 3: again think about that company valuations today are the net
Speaker 3: present value of the cashflows that these companies get in
Speaker 3: the future.
Speaker 1: We're also hearing a changing story, a changing narrative from
Speaker 1: AI companies themselves. There's a Wall Street Journal story about
Speaker 1: how the narrative has shifted, at least from open AI
Speaker 1: and anthropic from these doomsday scenarios to a future where
Speaker 1: workers actually keep their jobs but just do it better
Speaker 1: thanks to AI.
Speaker 4: What does that tell you?
Speaker 3: Yeah, and RAMP had a really interesting study over the
Speaker 3: last week that they put out where they basically look
Speaker 3: at what has been the cost and the spending on
Speaker 3: AI among different companies and what they did that they
Speaker 3: looked at, well, what was the job growth in those
Speaker 3: companies that had more spending on AI? And they did
Speaker 3: indeed find that more spending on AI, it actually resulted
Speaker 3: in more job growth. So one way of looking at
Speaker 3: this is one dimension of saying, what's the actual spending
Speaker 3: relative to the more theoretical matching of occupations with what's
Speaker 3: been going on with job growth in those sects that
Speaker 3: also have been spending on AI.
Speaker 2: Tostened to your points, put this all together, this is
Speaker 2: a potential risk of valuations, which is an obvious market risk.
Speaker 2: Is it a macro risk as well? In Central Portugal,
Speaker 2: I believe you were there big conversation about this. We've
Speaker 2: got a story in the market right now that feels
Speaker 2: like one trade and increasingly a story in the economy.
Speaker 2: The Fiells lie with firing on one engine, how much
Speaker 2: is it one and the other?
Speaker 3: Absolutely, it's actually three different things. First of all, it's
Speaker 3: of course everywhere in markets because the concentration to the
Speaker 3: AI story is so strong.
Speaker 4: Think about it.
Speaker 3: For the last fifteen years, the main lesson in finance
Speaker 3: is factor investing, and now we're staring at one factor
Speaker 3: that's driving all financial markets. In equities, the concentration is
Speaker 3: very high in AI. You also look at IG issuance,
Speaker 3: high yield issues, even venture capusal.
Speaker 4: The concentration in AI is very very strong.
Speaker 3: So in markets, let's just agree the AI exposure or
Speaker 3: the AI factor plays a very very critical role at
Speaker 3: the moment when it comes to the economy. Also, if
Speaker 3: you look at actual spending on data centers and energy,
Speaker 3: if you add up what that contributes to GDP this
Speaker 3: year is roughly about zero point seven percent out of
Speaker 3: a two percent GDP growth this year.
Speaker 4: If you add about zero point three.
Speaker 3: Coming from the wealth effect because of high stock prices,
Speaker 3: that's also very important. And finally, let's also not forget
Speaker 3: that the hyperscalers are issuing so much IG corporate debt
Speaker 3: that this is crowding out demand for US treasuries because
Speaker 3: if you are bunt manager and investment great credit, you
Speaker 3: could either buy sovereigns US treasuries, you can buy financials.
Speaker 3: That's what you've been doing for a long time. But
Speaker 3: now you can also buy seven hundred billion hyperscalers. So
Speaker 3: it's not only that it has an impact on markets
Speaker 3: overall and has an impact on GDP, but it actually
Speaker 3: also has an.
Speaker 4: Impact on demain for treasury.
Speaker 3: So yes, the conversation and the panel Amazon in CenTra
Speaker 3: was exactly around this risk that AI is indeed now
Speaker 3: and more and more prominent factor basically not only the
Speaker 3: economy but also in financial markets.
Speaker 2: What's the consensus on how to manage that risk?
Speaker 3: Well, the challenge is page one in your finance textbook.
Speaker 3: If there's one factor you're trying to avoid is to
Speaker 3: try to pick another factor. But the question is what
Speaker 3: is that other factor? Momentum is a growth is a value?
Speaker 3: Value has some opportunities because it's not growth. So that's
Speaker 3: why ways of looking at parts of the private markets,
Speaker 3: public markets, that is value investing is indeed one place
Speaker 3: to hide, but it has to be value investing. That's
Speaker 3: indeed protecting you against the downside risks that come along
Speaker 3: if Ai does not deliver in the.
Speaker 2: End, Towston's Slock of Apollo toaston fantastic no great rate
Speaker 2: in the last week orse, so I appreciate it. Towson'slock
Speaker 2: of Apollo. There On, Ai and tag