Latest / The Financial Exchange Show / Amazon’s $200B AI Bet, Software Selloff & The Hidden Risk in Private Credit

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Amazon’s $200B AI Bet, Software Selloff & The Hidden Risk in Private Credit · The Financial Exchange Show

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Speaker 2: It's a Friday here on the Financial Exchange. Chuck, Mike,
1:14
Speaker 2: and Tucker with you, and we're gonna kick things off
1:19
Speaker 2: with a discussion of Amazon, who reported earnings after the
1:22
Speaker 2: bell yesterday, And even though the vast majority of my
1:27
Speaker 2: board is showing green on it, there's a big red
1:31
Speaker 2: dot right in the consumer cyclical sector with the ticker
1:34
Speaker 2: AMZN down eight point nine to three percent. And it
1:39
Speaker 2: means one of two things in my mind, Michael. It
1:42
Speaker 2: either means that Amazon had a horrible earnings miss or
1:47
Speaker 2: something about their guidance upset markets. So let's talk first
1:51
Speaker 2: about their actual earnings, which were fine ish, Like they
1:56
Speaker 2: weren't great. They weren't amazing, they weren't horrible, they were
2:00
Speaker 2: were fine. They had a slight miss on earnings for
2:05
Speaker 2: the quarter.
2:06
Speaker 3: Road services growth wasn't as good as some of their competitors.
2:10
Speaker 2: Nope, revenue was fine, like they beat expectations on revenue.
2:14
Speaker 2: So ultimately, there's nothing in the actual earnings data that
2:18
Speaker 2: I think is hugely problematic. No, nothing problematic.
2:22
Speaker 3: But I will also say that the last couple of
2:25
Speaker 3: quarters when tech companies report, if they don't absolutely knock
2:29
Speaker 3: the cover off the ball. Their stock has been beaten
2:31
Speaker 3: up a little bit, and they did not, by any
2:32
Speaker 3: means not the cover off the ball.
2:34
Speaker 2: No, the more problematic issue that they have is the
2:39
Speaker 2: fact that they have now joined Google in back to
2:43
Speaker 2: back days in being like, oh, you think that we're
2:48
Speaker 2: gonna do X and capex, No, we're actually gonna do
2:51
Speaker 2: X plus like forty percent.
2:53
Speaker 3: So CAPEX guidance for twenty twenty six for Amazon now
2:58
Speaker 3: came in at two hundred billion dollars, which is the
3:01
Speaker 3: top of the list when you look at everybody out
3:03
Speaker 3: there between Amazon, Alphabet, Microsoft, and Meta. Amazon wants to
3:07
Speaker 3: spend more than any of them.
3:09
Speaker 2: And when you add all of them up, and again
3:11
Speaker 2: this is a moving target, but we're now at about
3:14
Speaker 2: six hundred and fifty billion dollars between the four of them.
3:17
Speaker 3: Yeah, that would be towards the top end of the
3:19
Speaker 3: ranges that they've all provided, which I thought an interesting exercise,
3:22
Speaker 3: like what do we as a country spend on spend
3:26
Speaker 3: money on in terms of a product that even comes
3:29
Speaker 3: close to the type of spending. I was looking at
3:31
Speaker 3: gasoline sales across the entire country. The latest date I
3:35
Speaker 3: could get was twenty twenty two. But in twenty twenty two,
3:37
Speaker 3: total US consumer spending on gasoline was estimated to be
3:40
Speaker 3: about five.
3:41
Speaker 2: Hundred and sixty billion.
3:43
Speaker 3: So just like for some scale there what we're talking
3:46
Speaker 3: because nobody can fathom what two thirds of a trillion
3:48
Speaker 3: dollars actually means.
3:50
Speaker 2: I can I can't, I don't. I can't do it
3:53
Speaker 2: all the time. I don't really understand what that means.
3:55
Speaker 3: And this was some helpful context that like, add every
3:57
Speaker 3: American out there in terms of their gasoline perch is
4:00
Speaker 3: that they do every other day every week, and you
4:03
Speaker 3: get to around close to what they're talking about spending
4:07
Speaker 3: on AI CAPEX.
4:10
Speaker 2: I mean, here, here's the way to phrase this. There's
4:14
Speaker 2: about two hundred million adult Americans. Okay, if every one
4:18
Speaker 2: of them spend three thousand dollars a year on AI,
4:22
Speaker 2: that would equal this, Yes, guys, like preposterous dollars. I'm
4:29
Speaker 2: really sorry that I finally like have gotten to this point.
4:33
Speaker 2: It ain't gonna work because remember this is just one
4:35
Speaker 2: year of CAPEX. They're talking about doing this year after
4:39
Speaker 2: year and continuing to agree. No one's gonna use this
4:42
Speaker 2: stuff to this extent.
4:44
Speaker 3: Well, and also I think it kind of raises the
4:46
Speaker 3: question of whether or not they could spend this much
4:49
Speaker 3: money if they actually wanted to. I don't know that
4:52
Speaker 3: they can spend it on enough chips and ram to
4:54
Speaker 3: be able to do.
4:55
Speaker 2: The part of the problem is they're trying to build
4:57
Speaker 2: so much that all they're doing is driving the cost
4:59
Speaker 2: up and not actually building more. Like, yes, there is
5:01
Speaker 2: more that's being built, but some of this is being
5:04
Speaker 2: driven by additional cost and so it also gets to
5:08
Speaker 2: the point where Amazon is not going to be able
5:10
Speaker 2: to cover this out of free cash flow. They're not.
5:14
Speaker 2: Amazon's free cash flow in the trailing twelve months then
5:17
Speaker 2: the quarter before was about ten billion dollars. This kind
5:21
Speaker 2: of increase here means they're probably gonna have to borrow
5:24
Speaker 2: or dip into their cash pile. They do have about
5:27
Speaker 2: one hundred billion in cash on hand, but either way,
5:31
Speaker 2: you're talking about somewhere between like forty and sixty billion
5:35
Speaker 2: dollars that they are going to have to dip into,
5:38
Speaker 2: either their cash pile or the borrowing markets. I really
5:43
Speaker 2: am at the point I don't know where this money's
5:45
Speaker 2: going to come from, and I think that's kind of
5:46
Speaker 2: what investors are saying here is you're starting to write
5:51
Speaker 2: checks that your body can't cash. And this is going
5:56
Speaker 2: to be a problem, especially because these models are increasingly
6:01
Speaker 2: looking like they're commoditized. There's not that much difference between
6:07
Speaker 2: all of them. They're all releasing new versions every couple weeks,
6:10
Speaker 2: Like this is what commoditization looks like when you see
6:14
Speaker 2: it happening in real time.
6:17
Speaker 3: I guess I want to clarify there. I'm with you
6:19
Speaker 3: about the commoditization of the models. I don't know how
6:22
Speaker 3: anyone would sit here and judge whether six months from now,
6:26
Speaker 3: chat apt Gemini, anthropic Anthropist Claude, or some new model
6:31
Speaker 3: is going to be the best and brightest out there.
6:34
Speaker 3: But am I wrong in stating that all of them
6:37
Speaker 3: will need compute power from the likes of what Amazon
6:41
Speaker 3: is attempting to do?
6:42
Speaker 2: So couple things. The first is that the general thinking
6:48
Speaker 2: as to why you need these bigger and bigger computing cousters,
6:52
Speaker 2: there's two reasons for him. The first is to train
6:54
Speaker 2: the models, and I think we've reached the point of
6:57
Speaker 2: diminishing returns there. Quite honestly, it's like, do I really
7:00
Speaker 2: want to pay eighty percent more for a model that's
7:03
Speaker 2: two percent better?
7:05
Speaker 4: No?
7:05
Speaker 2: Like what's what's the point, Like for the average person,
7:07
Speaker 2: you don't need that, yeah, sure. The second is you
7:11
Speaker 2: need it for what's called inference, which is the actual
7:13
Speaker 2: demand side, which is I'm throwing stuff into the model,
7:15
Speaker 2: spit it back out, and maybe the demands there. Maybe
7:20
Speaker 2: it's not like I don't know, but I do know
7:25
Speaker 2: that none of this is profitable at this point. And
7:28
Speaker 2: the reason that I know this is because, you know,
7:30
Speaker 2: open ai likes to be like, oh, we've got like,
7:32
Speaker 2: you know, X hundred million users. Yeah, like eight of
7:34
Speaker 2: them pay for your product right now, go go said
7:37
Speaker 2: the same thing.
7:38
Speaker 3: They've got three quarters of a billion users on Gemini.
7:42
Speaker 2: But and look it's an exaggeration because open ai, I
7:44
Speaker 2: do think their run rate for revenue is now up
7:46
Speaker 2: to like fifteen or twenty billion dollars a year. That
7:50
Speaker 2: doesn't cover two hundred billion dollars a year in capex. Sure,
7:55
Speaker 2: I've done the math. Twenty billion is less than two
7:58
Speaker 2: hundred and it's got in order of magnitude to go
8:02
Speaker 2: to make that up. And remember this is one year
8:04
Speaker 2: of construction for Amazon. So even if you think this
8:08
Speaker 2: has a useful life of ten years, now you've got
8:11
Speaker 2: twenty billion dollars in depreciation from this year, hitting your
8:14
Speaker 2: income statement going forward. Then you're gonna have twenty five
8:17
Speaker 2: billion from next year, then another thirty All of a sudden,
8:20
Speaker 2: you got like one hundred billion dollars in depreciation each year.
8:23
Speaker 2: That's hitting your financial statements. You have to at some
8:28
Speaker 2: point get the revenue. And I feel like the buildout
8:32
Speaker 2: is just happening so fast that it's happening without the revenue.
8:35
Speaker 2: And here's the other piece that I think is missing
8:38
Speaker 2: in this. Right now, the inference is being done on
8:42
Speaker 2: these central servers. What I am starting to hear from
8:49
Speaker 2: people in the AI space is that that might not
8:54
Speaker 2: be necessary as you get to more efficient models that
8:58
Speaker 2: are the next iteration. And what I mean by that is,
9:01
Speaker 2: right now, there's a ton of compute powder that is
9:03
Speaker 2: needed in order to power this. But some of the
9:07
Speaker 2: stuff that's being done with open source models and things
9:10
Speaker 2: that are you know, able to be modified and easily seen,
9:12
Speaker 2: Like you know, in terms of what people are doing
9:14
Speaker 2: are getting to the point where you can run them
9:16
Speaker 2: locally on a computer, and then you don't need a
9:19
Speaker 2: two hundred billion dollar data center commitment in order to
9:24
Speaker 2: do this stuff. And especially what's to stop let's say
9:28
Speaker 2: that I am company, Let's say i'm JP Morgan who
9:32
Speaker 2: said they're gonna spend you know, two billion dollars or
9:34
Speaker 2: whatever it is on AI this year. What's to stop
9:38
Speaker 2: JP Morgan from saying, Hey, you know what, instead of
9:41
Speaker 2: paying whoever to run this data center for us, Hey,
9:45
Speaker 2: we're just gonna put one in our building in New
9:48
Speaker 2: York and we don't need to worry like it's all
9:51
Speaker 2: self contained, it's all controlled by us. They have the
9:54
Speaker 2: resources to do it, and it probably is going to
9:57
Speaker 2: be a lot cheaper, faster, and easier for them to
10:00
Speaker 2: change what they want to on it. I'm not sure
10:01
Speaker 2: I buy that piece.
10:03
Speaker 3: I'm not sure if you're a New York bank, you
10:04
Speaker 3: want to run a data center in your New York headquarters.
10:06
Speaker 3: They already do, I know they do, But I can
10:10
Speaker 3: see the value in running it in Wyoming or Wisconsin.
10:13
Speaker 2: If you are JP Morgan, cho we don't put it
10:15
Speaker 2: in New York like you can understand wherever, but you
10:17
Speaker 2: don't need these companies to do it. Yeah.
10:20
Speaker 3: Here's where I kind of look at this is I
10:25
Speaker 3: think all of these CEOs genuinely believe that they are
10:28
Speaker 3: going to that the industry itself is going to need
10:30
Speaker 3: much more compute power over the course of the next
10:33
Speaker 3: five to ten years. And I think that they are
10:35
Speaker 3: right about that. What I also think could be very
10:40
Speaker 3: possible is they are overestimating how much compute power is needed.
10:43
Speaker 3: And I think, but I don't think there will be
10:46
Speaker 3: somebody out there that comes out and says, oh, we
10:48
Speaker 3: can do what Amazon's doing in terms of building data
10:51
Speaker 3: center capacity better than them. I do think there's actually
10:54
Speaker 3: a mote here in the way that there is not
10:56
Speaker 3: in the models themselves.
10:58
Speaker 2: But my point is you don't need data like the
11:00
Speaker 2: right that average person doesn't need. You can run a
11:03
Speaker 2: local model on your computer and it never has to
11:07
Speaker 2: touch any of those servers. Yeah, that would be My
11:10
Speaker 2: concern is that they overestimate it. And I don't know.
11:12
Speaker 3: I don't know how to estimate how much of this
11:14
Speaker 3: compute power is actually necessary. But historic tech buildouts have
11:21
Speaker 3: been defined by major powerful companies overestimating how much infrastructure
11:26
Speaker 3: is needed, and that would not be shocking this time around.
11:29
Speaker 2: But I'm trying to find it.
11:30
Speaker 3: You know, devil's advocate point of view on all this,
11:33
Speaker 3: and the devil's adate point of view would be maybe
11:38
Speaker 3: these aren't going to get much more efficient, and maybe
11:40
Speaker 3: this compute power is necessary, and if that's the case,
11:42
Speaker 3: then these four will be pretty much the only games
11:45
Speaker 3: in town to be able to provide it. But that
11:48
Speaker 3: does not really line up with how, for instance, the
11:51
Speaker 3: Internet was built out.
11:52
Speaker 2: Or railroads or airlines or radio or any other transformative
11:57
Speaker 2: technology that did change the world. Yeah, but ultimately resulted
12:01
Speaker 2: in huge speculative bubbles and massive losses like these. I've
12:07
Speaker 2: said it the whole time. These two things can be true.
12:09
Speaker 2: At the same time. We will be using way more
12:11
Speaker 2: AI ten years from now than we are today, and
12:14
Speaker 2: we're also likely to see a huge excess of capital
12:17
Speaker 2: flowing in that's going to be destroyed.
12:19
Speaker 3: We talked a little bit about the software blowout in
12:23
Speaker 3: markets this week. I want to take a step back
12:27
Speaker 3: next and really talk about what everyone's talking about with
12:30
Speaker 3: vibe coding.
12:31
Speaker 2: What the heck it is?
12:32
Speaker 3: Because we talked about it yesterday, I don't think we
12:33
Speaker 3: did a great job defining it.
12:35
Speaker 2: I want to talk.
12:35
Speaker 3: About the week that was destruction in the software market.
12:39
Speaker 3: That's next on the Financial Exchange.
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13:13
Speaker 2: All right, Mike, what do you got on software for me?
13:17
Speaker 3: Well, I wanted to talk just about what this release
13:20
Speaker 3: from Anthropic really was and why people cared about it
13:25
Speaker 3: so much. So we talked about the topic of vibe
13:27
Speaker 3: coding yesterday on the show. And I don't know if
13:29
Speaker 3: you can define it better than I can, but I'll
13:32
Speaker 3: take a swing at with Anthropics new tools. It attempts
13:37
Speaker 3: to make it so that the pretty much layman can
13:41
Speaker 3: go in there and build things fairly easily without needing
13:45
Speaker 3: a full coding background. From what I've heard, it's also
13:47
Speaker 3: very valuable for actual coders who know what they're doing.
13:51
Speaker 3: But in plain English terms, you can type in a
13:54
Speaker 3: prompt and say, hey, this is what I am attempting
13:56
Speaker 3: to design. Please write the code and design the website
13:59
Speaker 3: to do X, Y and Z, and so even you know,
14:02
Speaker 3: a complete and utter coding moron like me might be
14:06
Speaker 3: able to build a website that frankly probably looks like
14:09
Speaker 3: it was built in two thousand and five, but it
14:11
Speaker 3: is something that would actually be able to be created
14:15
Speaker 3: with these tools, and for those that actually know their.
14:17
Speaker 2: Stuff, it just makes them a lot faster.
14:20
Speaker 3: I know that that's kind of been used to describe
14:22
Speaker 3: AI generally, but this was seen as a bit of
14:25
Speaker 3: a breakthrough. So do you have something to add to
14:27
Speaker 3: it to explain why it was seen as more of
14:30
Speaker 3: a breakthrough than what I just described, or is it
14:32
Speaker 3: just another iteration of what other companies had already been doing.
14:35
Speaker 2: No, it's another iteration where apparently the latest version of
14:39
Speaker 2: cloud code is just really good at getting people at
14:43
Speaker 2: being able to generate productive applications with inputs that are
14:50
Speaker 2: basically just like you speaking.
14:52
Speaker 1: You know.
14:52
Speaker 2: It's yeah, think of it this way. Previously, Let's say
14:55
Speaker 2: that you wanted to build an app that Let's say
15:00
Speaker 2: you wanted to build an app that recorded like the
15:05
Speaker 2: time that you woke up every day. You had a
15:06
Speaker 2: camera in your room, and you just wanted to be
15:09
Speaker 2: like when did I wake up every day? Sure, you'd
15:11
Speaker 2: have to like figure out, you know, okay, what do
15:13
Speaker 2: I need? Like what language do I need to program
15:16
Speaker 2: this in? Learn the language and then be able to
15:17
Speaker 2: actually program it to do that. Today, you could say
15:21
Speaker 2: you could go into cloud code and say, I'm using
15:23
Speaker 2: camera XYZ, please build me an app for this operating
15:28
Speaker 2: system that you know can analyze the frames in that
15:33
Speaker 2: picture to determine when I get up in the morning.
15:36
Speaker 2: You'd have to put in you know, a little bit
15:37
Speaker 2: more than that, obviously, but that's the premise, and then
15:40
Speaker 2: it builds it for you. Now, where I think is
15:44
Speaker 2: what I think is completely hogwash is the average person
15:49
Speaker 2: isn't going to do this for all of their uses.
15:51
Speaker 2: Like I've seen people being like, oh, like the great
15:53
Speaker 2: thing about this is you can build like a custom
15:55
Speaker 2: application for something and then throw it away so you
15:57
Speaker 2: never have to pay for it. And it's like, guys,
16:01
Speaker 2: I'm filing my taxes like in a mon Sure, I'm
16:04
Speaker 2: not going to build a custom tax filing app for
16:08
Speaker 2: a few reasons, the first being that A Like if
16:12
Speaker 2: I mess this up, it's a real problem. B. I
16:16
Speaker 2: just don't trust myself to do that, and so I like,
16:19
Speaker 2: we're just not gonna do that. What I do think
16:22
Speaker 2: is possible is hey, someone can come along and basically
16:27
Speaker 2: build like let's say that someone gets laid off from
16:29
Speaker 2: into it who runs TurboTax Someone can an engineer from
16:33
Speaker 2: there can be like, Hey, I'm gonna get four of
16:34
Speaker 2: my friends together. We'll build a cheaper turbo tax alternative
16:38
Speaker 2: because we don't have all the overhead. And yeah, we're
16:41
Speaker 2: not gonna take all turbotaxes business. But TurboTax is not
16:46
Speaker 2: gonna have a twenty percent annual revenue growth rate anymore.
16:49
Speaker 2: It might be ten. And that has implications for how
16:52
Speaker 2: profitable they are in the future, and that's why the
16:55
Speaker 2: stock gets punished. And I really think that.
16:57
Speaker 3: You know, if you think through like what software applications
17:00
Speaker 3: do you use on a regular basis, I'm not sure
17:03
Speaker 3: which ones this doesn't apply to, right, I mean, even
17:06
Speaker 3: I'm thinking through video game design, does that become faster
17:09
Speaker 3: and easier for non established players to jump in there
17:12
Speaker 3: and say, yeah, we can build the next.
17:17
Speaker 2: Whatever. It might be faster than the established players. The
17:21
Speaker 2: reason why software and tech businesses have had high margins
17:25
Speaker 2: historically is because there's relatively low capex that's required and
17:31
Speaker 2: there are significant bearers to entry, like you have to
17:34
Speaker 2: know how to do X, Y and Z today. What's
17:40
Speaker 2: potentially being priced in is the commoditization of existing tech
17:47
Speaker 2: players software specifically, It's not that like, if you use
17:53
Speaker 2: Redtail as your company CRM, you're not going to go
17:57
Speaker 2: and hire a thirteen year old to vibe code a
17:59
Speaker 2: new CRM for you. But there might be a bunch
18:02
Speaker 2: of ex salesforce engineers that get together and say, hey,
18:05
Speaker 2: we can build something cheaper than any of these CRMs
18:08
Speaker 2: that are out there.
18:09
Speaker 3: We have the expertise, we previously didn't have the capital
18:13
Speaker 3: to be able to do this.
18:15
Speaker 2: We don't need any capital now. And here's the new
18:19
Speaker 2: enterprise level replacement or the other piece, if you want
18:23
Speaker 2: to get more insidious about it, is Microsoft. Is there
18:29
Speaker 2: any company that just loves getting its tentacles into everything
18:32
Speaker 2: more than Microsoft? Maybe Google, yah, one of those two.
18:36
Speaker 2: What happens if those guys come out and say, look,
18:37
Speaker 2: previously we couldn't justify the development cost of a CRM
18:42
Speaker 2: for businesses because it's just it's too much to throw
18:45
Speaker 2: at it. Hey, let's take ten engineers and have them
18:48
Speaker 2: build a CRM platform. And by the way, if you
18:50
Speaker 2: sign up for Windows right now, you're gonna get that
18:53
Speaker 2: CRM thrown in for free. Like that's the thread also
18:58
Speaker 2: is that the giant get gianter, which is a word
19:02
Speaker 2: we should use.
19:03
Speaker 3: Yeah, it's that does kind of suck to think about,
19:06
Speaker 3: because on one degree, you hope that there's a bunch
19:09
Speaker 3: of new disruptors, new small companies that get created as
19:11
Speaker 3: a result of this. But the other possible result would
19:14
Speaker 3: be Google saying, oh, we don't need our Gmail customers
19:18
Speaker 3: to use salesforce anymore.
19:19
Speaker 2: We're just going to create our own and look how
19:21
Speaker 2: fast we can do it. The next five years. The
19:23
Speaker 2: first three years of the AI boom have been everyone's
19:26
Speaker 2: a winner. The next five years we're going to actually
19:28
Speaker 2: see who the winners and losers are. And we still
19:31
Speaker 2: don't know for sure. By the way, quick break here.
19:34
Speaker 2: We got Wall Street Watch next.
19:40
Speaker 1: Like us on Facebook and follow us on Twitter at
19:42
Speaker 1: TFE show. Breaking business news is always first right here
19:47
Speaker 1: on the Financial Exchange Radio Network. Time now for Wall
19:52
Speaker 1: Street Watch. A complete look at what's moving markets so
19:55
Speaker 1: far today right here on the Financial Exchange Radio Network.
19:59
Speaker 4: Market seeing rebound today after this week's tech sell off
20:03
Speaker 4: as investors have been concerned about significant spending on a I.
20:07
Speaker 4: Right now, the Dow is up one point seven percent,
20:10
Speaker 4: or eight hundred and thirty points higher, SMP five hundred
20:13
Speaker 4: up one and a quarter percent or eighty five points higher,
20:16
Speaker 4: Nasdaq up one point two percent or two hundred and
20:20
Speaker 4: sixty five points, RUSS two thousand rallying two percent higher.
20:24
Speaker 4: Ten your treas reeled up one basis point at four
20:26
Speaker 4: point two to two percent, and crude oil up about
20:30
Speaker 4: one percent higher, trading just below sixty four dollars barrel.
20:35
Speaker 4: Amazon stock is sinking eight percent after the e commerce
20:39
Speaker 4: and tech China now is a near sixty percent increase
20:43
Speaker 4: this year on AI capital spending to two hundred billion dollars. Furthermore,
20:48
Speaker 4: Amazon web services revenue grew slower than its competitors Microsoft
20:53
Speaker 4: and Google's Alphabet. Meanwhile, jeep maker Stilantis reported chargers of
20:57
Speaker 4: about twenty six billion dollars as a scales back push
21:01
Speaker 4: into electric vehicles, to Lantis shares tanking twenty four percent. Elsewhere,
21:07
Speaker 4: Roadblock shares are rallying nearly seven percent after the video
21:11
Speaker 4: game companies said revenue could grow by as much as
21:14
Speaker 4: twenty nine percent this year, lifted by higher bookings and
21:18
Speaker 4: daily active users. Molina Healthcare stock sinking twenty six percent
21:23
Speaker 4: after the health insurance company missed earnings expectations as it
21:26
Speaker 4: was weighed down by premium adjustments in Medicaid and Medicare
21:30
Speaker 4: cost pressures. The company's revenue outlook also disappointed, and Reddit
21:35
Speaker 4: beat fourth quarter earnings forecasts. The social network also offered
21:38
Speaker 4: an optimistic twenty twenty six guidance and announced a one
21:42
Speaker 4: billion dollar share buyback program. However, shares are down by
21:46
Speaker 4: four percent at the moment. I'm Tucker Silva and that
21:49
Speaker 4: is Wallstreet Watch talk.
21:51
Speaker 2: A little bit about what we're seeing in private markets.
21:55
Speaker 2: There's a piece from the Wall Street Journal talking about this.
21:58
Speaker 2: I'll read you the important part. The end of last year,
22:00
Speaker 2: almost nine percent of private equity back to companies were
22:03
Speaker 2: in the software space, which, as we detailed the last segment,
22:06
Speaker 2: has some questions about you know, it's it's future. The
22:09
Speaker 2: exposure is even more significant on the loan side. Within
22:12
Speaker 2: the private credit universe. Tracked by ratings from KBRA, the
22:16
Speaker 2: firm classify seventeen percent of borrowers a software companies, representing
22:19
Speaker 2: about twenty two percent of the one trillion plus debt
22:23
Speaker 2: exposure in the universe overall. So for people wondering in
22:28
Speaker 2: the last you know, a couple of days. Hey, software
22:31
Speaker 2: is getting beaten up, and I'm just looking at like
22:33
Speaker 2: one week performance here, software is getting beaten up. But
22:36
Speaker 2: why is Blackstone down ten percent? Why is KKR down
22:39
Speaker 2: ten Why is Blue Owl down you know, fifteen percent?
22:42
Speaker 2: Whyse Aries down thirteen percent? The answer is, Hey, these
22:46
Speaker 2: are big asset managers who operate primarily in the private space,
22:51
Speaker 2: and they've got questions as to what their portfolios look
22:54
Speaker 2: like because they own a lot of this stuff.
22:59
Speaker 3: Good and these companies, if you listen to the program frequently,
23:04
Speaker 3: probably sound familiar to you because we started talking them
23:07
Speaker 3: about them last summer. Last summer, the issue that was
23:10
Speaker 3: plaguing them was a few very public fraud slash bankruptcy
23:16
Speaker 3: cases that plagued these companies, and everybody was laft scratching
23:20
Speaker 3: their heads saying, hmm, well, if three companies can rip
23:24
Speaker 3: you off and lie about receipts or you know, self
23:28
Speaker 3: deal and come up with finicky accounting maneuvers to get
23:31
Speaker 3: more loans, how many more might have done similar things.
23:35
Speaker 3: This is a completely separate issue. This is not any
23:37
Speaker 3: allegations of fraud on the side of software companies. It's
23:41
Speaker 3: a question of whether or not their business model is sustainable,
23:44
Speaker 3: but the private credit space has kind of worked its
23:48
Speaker 3: tentacles into every different area of the market. And so
23:52
Speaker 3: this again, until this week, did I know that private
23:56
Speaker 3: credit space is one of their biggest borrowers from those
23:59
Speaker 3: company but it is were software companies. I did not,
24:01
Speaker 3: I'll raise my hand. Didn't know, But we're finding out now.
24:06
Speaker 3: And once again, these companies are under a lot of pressure.
24:09
Speaker 3: Blue Oul Capital one that you mentioned, by the way,
24:11
Speaker 3: over the course of the last year, they've erased half
24:13
Speaker 3: of their half of their stock value.
24:15
Speaker 2: Is that bad? Usually you want to go in the
24:17
Speaker 2: other direction. Yeah, I mean you can go through like
24:20
Speaker 2: a lot of these Apollo Global Management has gone from
24:24
Speaker 2: you know, one hundred and sixty five bucks down to
24:27
Speaker 2: one hundred touched one twenty two earlier this week, so
24:30
Speaker 2: it's a quarter ares. Who's another big one that plays
24:34
Speaker 2: in that space. They have gone from one eighty seven
24:38
Speaker 2: down to one twenty eight. So it's again it's kind
24:41
Speaker 2: of across the board that you're seeing this. The other
24:44
Speaker 2: place that I got to tell you, and I hope
24:46
Speaker 2: we don't get to this point, But the other place
24:48
Speaker 2: that is dodgy is there is a ton of potential
24:53
Speaker 2: exposure in the insurance space.
24:55
Speaker 3: Yeah, I was gonna say, I wanted to bring this back.
24:57
Speaker 3: Most people probably don't own shares in any of the
25:00
Speaker 3: companies we just mentioned.
25:01
Speaker 2: Well, if you own the S and P five hundred,
25:03
Speaker 2: you do, you do.
25:04
Speaker 3: But by my point is, you know, most investors that
25:07
Speaker 3: are listening right now probably do not directly own shares
25:09
Speaker 3: in any of these companies that we just mentioned, unless
25:11
Speaker 3: they are you know, specialized individual stock investors. But that's
25:16
Speaker 3: the next point that you make, is Okay, these companies
25:19
Speaker 3: originate the loans, oftentimes package them and sell them off
25:22
Speaker 3: they're under pressure. But the people the companies that buy
25:26
Speaker 3: these packaged loans from these finance companies are companies that
25:31
Speaker 3: we all interact with very regularly.
25:33
Speaker 2: It's a lot of insurance companies, and some of them
25:35
Speaker 2: might even be captive insurance companies held by the private
25:39
Speaker 2: like they might be in the portfolio of the private
25:42
Speaker 2: equity firm where it's Hey, we own insurance company XYZ,
25:47
Speaker 2: and we're going to have insurance company XYZ buy the
25:51
Speaker 2: private debt from our private debt arm And this is
25:55
Speaker 2: potentially something to watch in the next six to twelve months.
26:00
Speaker 2: Uh I have no idea whether it actually becomes a
26:04
Speaker 2: problem or not, because you it's it's such an opaque
26:07
Speaker 2: market that unless you're actually holding this stuff and know
26:10
Speaker 2: what it is, you don't know where the problem could be.
26:13
Speaker 2: Have we seen insurance companies under pressure this past week? Uh,
26:17
Speaker 2: let me take a look one week. Uh, the publicly
26:20
Speaker 2: traded ones. Know you've seen like a couple Actually some
26:24
Speaker 2: of the life insurance ones have been a little bit dodgy,
26:29
Speaker 2: not like a ton, but you've got, you know, some
26:33
Speaker 2: mid single digit declines that you have there, whereas if
26:37
Speaker 2: you look at like the P and C guys, you
26:39
Speaker 2: don't have those those issues. Those guys are all up
26:41
Speaker 2: in the last week. Sure it's the life insurance companies,
26:45
Speaker 2: like that's where you're seeing the issues. Which makes sense,
26:52
Speaker 2: by the way, because like, let's say that you're a
26:53
Speaker 2: P and C company, Like if you are property and casualty,
26:56
Speaker 2: so yeah, sorry property. If you're a car insurance company,
27:01
Speaker 2: you're not holding that cash long term. It's a one
27:04
Speaker 2: year set of claims that you're dealing with.
27:06
Speaker 3: You're likely not investing a ton of the reserves into
27:09
Speaker 3: this type of thing.
27:10
Speaker 2: In the first you can't do a ton of it.
27:12
Speaker 2: If you are a life insurance company, you're sitting there saying, huh, well,
27:15
Speaker 2: my actuary has told me that I'm gonna need to
27:17
Speaker 2: have X ready to pay out in ten years and
27:20
Speaker 2: we just had a bond. Come do, Jimmy, Can you
27:22
Speaker 2: go and see what the private credit guys have to
27:24
Speaker 2: offer there? That's where the concern is on the life
27:28
Speaker 2: insurance guys. And again, it's not there yet because, like
27:34
Speaker 2: it's quite honestly, you're not going to know it's a
27:37
Speaker 2: problem until it's a problem. Because this is opaque stuff.
27:40
Speaker 2: It's not publicly traded. You can't see when it's going bad.
27:43
Speaker 2: All you can do is infer it based on what
27:45
Speaker 2: you're seeing from the companies that are publicly traded that
27:49
Speaker 2: may own it. But that's the kind of stuff that
27:53
Speaker 2: potentially causes problems in the life insurance market. I don't
27:58
Speaker 2: know if it will, but it's got the potential. To
28:01
Speaker 2: take a quick break. When we return, let's talk about
28:05
Speaker 2: this piece from the New York Times. It's titled It's
28:07
Speaker 2: time to rethink the standard investment advice but not too much.
28:12
Speaker 2: We'll talk about what that means after this.
28:14
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29:17
Speaker 2: Mike, we got a piece here. It's time to rethink
29:19
Speaker 2: the standard investment advice, but not too much. What does
29:24
Speaker 2: that mean? I don't know what the title of the
29:28
Speaker 2: article means.
29:29
Speaker 3: But they're running through something that I think we've tried
29:33
Speaker 3: to bring up here on the show before. And I'm
29:37
Speaker 3: not one hundred percent sure that people are fully aware
29:39
Speaker 3: of the S and P five hundred is a lot
29:42
Speaker 3: less diversified than it was ten years ago. Yeah, Like
29:47
Speaker 3: that's a critical piece of information to know. In fact,
29:51
Speaker 3: the index no longer actually meets the S and P. Sorry,
29:55
Speaker 3: the Security Exchange Commissions traditional definition of a diverse index
30:01
Speaker 3: based on how concentrated it is now, and so that
30:04
Speaker 3: is the piece that they are pointing out.
30:06
Speaker 2: They also they also go into.
30:09
Speaker 3: A lot of other things, But here is I guess
30:13
Speaker 3: one take that I would bring up. I frequently hear
30:15
Speaker 3: people talking about how doing nothing is oftentimes the best solution,
30:21
Speaker 3: and I'm I do believe that oftentimes doing nothing, especially
30:25
Speaker 3: in times of rapid change and panic, is the right decision.
30:30
Speaker 2: But it's based on a.
30:31
Speaker 3: Very important first premise, which is that you have built
30:36
Speaker 3: out that portfolio with intention prior to doing nothing. Right, Like,
30:42
Speaker 3: it's not good enough to do nothing if you have
30:47
Speaker 3: completely misaligned your portfolio with your cash needs.
30:52
Speaker 2: It's not okay, It's not.
30:53
Speaker 3: Necessarily the great idea to do nothing if you have
30:56
Speaker 3: spent all of your last three years bidding up a
31:00
Speaker 3: specific area of the market and betting on that continued success,
31:05
Speaker 3: and then you're saying, well, you know, suddenly volatility, maybe
31:08
Speaker 3: I should do nothing. You know, if, for instance, you
31:12
Speaker 3: have been investing all in one hundred percent in the
31:16
Speaker 3: S and P five hundred over the course of the
31:18
Speaker 3: last ten years and have been using your gains off
31:21
Speaker 3: of that to pay your everyday living expenses and that's
31:24
Speaker 3: worked out perfectly for you, guess what it might not
31:27
Speaker 3: next time, and doing nothing might not be a successful
31:29
Speaker 3: strategy there.
31:30
Speaker 2: And so I think people.
31:34
Speaker 3: Actually in times of crisis are oftentimes pretty good at
31:37
Speaker 3: sitting aside and understanding that, hey, every time in history
31:41
Speaker 3: something like this has happened, it's come back. But they
31:44
Speaker 3: oftentimes ignore the fact that if you're heavily concentrated, eventually
31:49
Speaker 3: coming back can take more than a decade. How long
31:52
Speaker 3: did it take the NASDAK to recover all of its
31:55
Speaker 3: losses after the dot com bubble?
31:56
Speaker 2: It was like fifteen years?
31:58
Speaker 3: Can you wait fifteen years? Most people can't. I mean, yeah,
32:02
Speaker 3: if you're thirty you probably can. Uh, do you want
32:05
Speaker 3: to if you're fifty, can you wait fifteen years for
32:08
Speaker 3: your portfolio to rebound to where it was prior to
32:11
Speaker 3: the next crisis.
32:13
Speaker 2: The other piece that I think is important to point
32:16
Speaker 2: out on this is just because something has behaved a
32:19
Speaker 2: certain way in the past doesn't mean it's how it
32:21
Speaker 2: will behave in the future. Yeah. And you know, when
32:26
Speaker 2: we talk about you know, markets and how long they
32:28
Speaker 2: take to come back, generally, with the S and P
32:30
Speaker 2: five hundred, you generally have gotten back to, you know,
32:33
Speaker 2: kind of where you've been previously within three to four years. Yeah,
32:38
Speaker 2: corrections are generally shorter than what you see in that
32:43
Speaker 2: Nasdaq tech bubble example. Yeah, S and P in the
32:46
Speaker 2: eight crisis took what five and that was one of
32:50
Speaker 2: the longer ones, right, right, worst recessions. Sin is a
32:53
Speaker 2: great depression. But you don't know that that's how it's
32:56
Speaker 2: going to behave in the future just because it's acted
32:59
Speaker 2: this way during the course of your lifetime. These are
33:02
Speaker 2: not immutable facts that can't change. If you go and
33:06
Speaker 2: look at different international markets, they don't behave in that
33:10
Speaker 2: way in any way, shape or form. You know, you
33:12
Speaker 2: take a look at what we've seen from European markets,
33:15
Speaker 2: take a look at like EM and what you see
33:17
Speaker 2: there where. Look, if you invested in EM for like
33:19
Speaker 2: the last twenty years basically until last year, you basically
33:24
Speaker 2: had no return. I mean you were making like low
33:26
Speaker 2: single digits per year just because the return was so bad.
33:29
Speaker 2: So I think it's important to understand every decision that
33:33
Speaker 2: you're making, from every decision that you're making on your investments,
33:37
Speaker 2: has a basis that you use to make that investment.
33:42
Speaker 2: You've got to identify what that basis is and what
33:44
Speaker 2: the risks are that could change it, because these are
33:48
Speaker 2: not fixed things that are set in stone, and they
33:51
Speaker 2: can change over time as markets evolved.
33:54
Speaker 3: Very clearly, this market is evolving. It's evolving rapidly alongside
33:59
Speaker 3: the technology that everyone is speculating about how it's going
34:02
Speaker 3: to change the world. Doing nothing in terms in times
34:07
Speaker 3: of dramatic market change, honestly can be the right thing
34:10
Speaker 3: to do. But it is built on that premise that
34:13
Speaker 3: you have started with a logical approach to your portfolio.
34:18
Speaker 2: To begin with.
34:20
Speaker 3: If you want some help developing that logical approach based
34:23
Speaker 3: on your particulars, not your neighbors, not your cousins, not
34:26
Speaker 3: your brothers, wives, brother's wife's situation not wives wife's then
34:35
Speaker 3: please call the Armstrong Advisor Group let us help you
34:38
Speaker 3: assess that for your specific circumstances.
34:42
Speaker 2: The number is eight hundred three nine three for zero
34:45
Speaker 2: zero one.
34:46
Speaker 3: You can check us out book time for us to
34:48
Speaker 3: call you back at Armstrong Advisory dot com as well,
34:50
Speaker 3: But that number eight hundred three nine three for zero
34:53
Speaker 3: zero one.
34:54
Speaker 1: The proceeding was paid for by Armstrong Advisory Group, a
34:57
Speaker 1: registered investment advisor. Nothing in the ad or in any
34:59
Speaker 1: Armstrong guide to specific financial, legal, or tax advice. Consult
35:02
Speaker 1: your own financial tax into state planning advisors before making
35:05
Speaker 1: any investment decisions. Armstrong make contact you to offer investment
35:08
Speaker 1: advisory services.
35:10
Speaker 2: According to data from AARP, in the past six months,
35:13
Speaker 2: seven percent of retirees have re entered the labor force.
35:16
Speaker 2: That's up from six percent who said the same in
35:18
Speaker 2: summer of twenty twenty five. The top reason they cited,
35:21
Speaker 2: with about half of them was that they needed money
35:24
Speaker 2: or had a poor economic outlook.
35:26
Speaker 3: This isn't useful to me unless you can tell me
35:28
Speaker 3: what percentage of people were re entering the labor force
35:31
Speaker 3: back in the twenty tens, right, Like, it's just that
35:34
Speaker 3: sounded abnormal to you to have less than one in
35:37
Speaker 3: ten people no re enter the labor force.
35:39
Speaker 2: I'm guessing that if again, I don't even think they've
35:42
Speaker 2: done this survey over time, but I'm guessing if you
35:46
Speaker 2: had this going back, I'm guessing it's probably ranged between
35:49
Speaker 2: like three and ten percent generally, and we're probably somewhere
35:52
Speaker 2: in the middle.
35:52
Speaker 3: I think we do know that the labor force participation
35:55
Speaker 3: rate for sixty five plus year olds is up over time, yep.
35:59
Speaker 3: But so is life expectancy, So I don't find that
36:03
Speaker 3: immensely surprising. I would imagine this data is not abnormal
36:08
Speaker 3: for any general economy outside of I would imagine it
36:14
Speaker 3: was a lot higher, for instance, in twenty ten, But
36:17
Speaker 3: I don't find this data to be surprising.
36:19
Speaker 2: Fifteen percent of those unretiring cited boredom. Well, that's bad, right, Like.
36:27
Speaker 3: We talk about this frequently, but I do try and
36:30
Speaker 3: point out to people when they're talking about retirement. It
36:36
Speaker 3: surprises people a lot because they expect me to ask
36:38
Speaker 3: them about the numbers. But usually my first question is
36:41
Speaker 3: not about the numbers. It's great, what are you going
36:43
Speaker 3: to your plans?
36:45
Speaker 2: What are you going to do?
36:45
Speaker 3: What do you have in mind, and they've gotten used
36:47
Speaker 3: to it at this point because I talk about it
36:48
Speaker 3: so much, But you do really need to put that
36:53
Speaker 3: thought process first. It also affects all of the financial
36:57
Speaker 3: stuff too. But like the unsuccessful retirees that I talk
37:01
Speaker 3: about all the time are not unsuccessful usually because they
37:04
Speaker 3: run out of money. They're usually unsuccessful because they retire.
37:07
Speaker 3: They retire because they're sick of their job, rather than
37:10
Speaker 3: the fact that they have a hobby they want to
37:11
Speaker 3: spend more time on and then they get bored.
37:14
Speaker 2: I gotta say, we got to work on some of
37:15
Speaker 2: the names of these companies. There's just a headline on
37:18
Speaker 2: Chiron on Bloomberg it was Novo targets Hymns we Go
37:23
Speaker 2: v Copy and like you're reading it because Hymns is
37:26
Speaker 2: the name of the company, but it's like, don't you
37:30
Speaker 2: mean his we go vy company? No, Hyms we go
37:32
Speaker 2: v Copy is what they're targeting and hers.
37:35
Speaker 3: By the way, also, you just named like three other
37:37
Speaker 3: companies in that statement that could have got me confused.
37:39
Speaker 3: Are we talking about Target or Nova Nordeskin here? Because
37:42
Speaker 3: I'm confused, did you see like what's going on here?
37:44
Speaker 2: By the way, No, so Hyms and Hers is a
37:48
Speaker 2: Internet based drug company. They like provide stuff. I don't
37:53
Speaker 2: really know what, but I guess some of it is
37:55
Speaker 2: weight loss uh, And there was novad Orders came out
38:00
Speaker 2: with their new oral weight loss pill instead of having
38:05
Speaker 2: to do the injections. HIMS apparently is saying, because we
38:08
Speaker 2: customize the amount to each person, we can make this
38:12
Speaker 2: even though it's not off patent yet and Novah's like, guys,
38:16
Speaker 2: I don't think so, We'll have to see how it
38:18
Speaker 2: plays out. Quick break here. Hour two coming up in
38:21
Speaker 2: a bit