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- [00:00]
A
Kevin, we're at record highs right now in the stock market and coming into the year there was this story of broadening out, but my understanding is that that has sort of dwindled and now we're getting back into this top heavy situation. What's going on here?
- [00:13]
B
Yes, you're right to point that out. I think that if you segment the market into really just sort of two phases so far this year, you could sort of take everything up until the war in Iran, obviously. But I would actually extend that to the low that we had on March 30 for the S&P 500. So since then it has been this sort of tale of two markets, as I've been calling it, where you do have the really big companies and just a few of them at this point in the S and P that are, you know, continuing to rip higher. Of course we've had a little bit of weakness in the past couple of days, but the rest of the market, the equal weighted S&P 500, if you want to use that as the proxy, has failed to keep up. So yes, in some ways we're sort of back to almost regularly scheduled programming, you know, before the, before the beginning of 26. And you sort of flipped that whole consensus call at the beginning of the year, which was international outperforming the U.S. you know, big tech, ceding some leadership to small caps. But what I do find interesting, and I think it sort of goes against the narrative that it is just a few big tech stocks that are leading. There are still meaningful pockets of outperformance in the market. It's just harder to find and sort of harder to come by and maybe lost in the headlines. So just as a, you know, as an example, small cap tech has outperformed large cap tech significantly over the past year. Doesn't get covered a lot. And that's a little bit, admittedly that's a little bit of a cherry picking, I'll admit on my side and just picking that one year time horizon because you could extend that out maybe a couple more years. And of course large cap tech has done really well and outperformed. But I think that is an instance where you do want to see that it's in performance terms, it's not just a couple of companies that are up. But I will say moving forward, if it does continue where you get this split, not just in terms of a couple of stocks lifting the S and P, but in terms of underlying breadth, you know, materially weakening and if it does break down historically, that is Consistent with more, you know, more corrective phases and corrective activity at the index level. So that's where you could potentially sort of open yourself up to, you know, maybe a bigger drop in, a bigger correction.
- [02:20]
A
Well, I think that gets so hidden beneath just like news coverage and the news cycle because we see every day, the trade pretty much accelerating, record highs every day. But you have this stat here. On a rolling three month basis, the percentage of members that are outperforming the index has dropped to 20%. 23%.
- [02:37]
B
Yeah.
- [02:37]
A
And we've only seen that in 24, 23 and 1973. So to me there's this tension here where generally in a bull market you want that breadth, but then also we're in this generational change with AI and like how do you resolve that conflict? Because I think AI could fuel things on its own. But you also historically want this broader rally here.
- [03:03]
B
Yeah, it's an interesting stat because I mean when you point to the, to the years that I mention, you know, you look at 2024 and 2023, of course not consistent with epic bear markets to any extent. But if, but you had, you know, significant corrective phases in the market in 2023. The fall of 23 was sort of that classic example, a little bit like we're living now in terms of rates really spiking and then you know, big tech in particular driving a good portion of the correction at that point. And then you also had some corrective activity in 2024. So and then of course you bring it back to 1973, that was a very different scenario of a pretty epic bear market and then a pretty, you know, a pretty tough recession. But the economy looks so different back then. So I think that if you looked at in the instance of the past few years when you did have that really low share of companies that were outperforming, we know now with the benefit of hindsight that it wasn't the end of the bull market, but it didn't mean that you couldn't get some of that corrective activity at the index level. So I think it's, it's more an instance of understanding that when you do have such a large const. Concentration or a large portion of the market cap being explained by fewer names, that in and of itself is not a bad thing. We shouldn't point to that. I think we shouldn't point to that and say, oh, that that's a negative in and of itself. I think it just means that you need to be more aware of how much weight and punch and power they have both to the upside, but to the downside. And, you know, I think that to your point earlier, about the beginning of this year, when we were seeing that shift away from the largest names and towards the average stock, the. We were getting a lot of questions at the time of, well, the market's not really doing well, not doing much this year. And we were saying, no, it is. You just have to go under the surface and look at that average stock that was meaningfully outperforming the indexes. And again, that was almost purely driven by the weight that the biggest companies have in the S and P. Well,
- [04:58]
A
I spoke with a lot of investors in December who were saying they were going overweight into the equal weight index instead of the market cap weight. And I think that's totally shifted in the last couple months. But because, because AI and technology in general is becoming more and more prevalent in the economy, to me, it makes total sense that it would continue to, like, overwhelm the market, essentially. Like, I don't see that as necessarily a negative thing. I think it's kind of just inevitable, right, that tech is going to be the thing that drives the entire market.
- [05:29]
B
I think that, you know, as a driver, again, in contribution terms, in terms of market cap, I think that that could certainly be the case. The one thing that I would say, and I think this does sort of speak to the benefits of being properly diversified across all sectors to the extent, you know, whatever you're comfortable with as an investor. I think that the distinction between the contribution to returns for an index and performance is very important at this point. And just to give an example, you know, and I don't do any coverage on individual stocks, so this is not a recommendation. This is just using it as an example. If you look at Nvidia, which is the largest company, you know, in the s and P500 by, by market cap, the contribution to the s and P500's return this, this year is, is. It's ranked number one because of its size and it's got, you know, positive performance. So therefore you have, you know, a pretty significant contribution. If you look at, if it's at its performance, There are almost 90 names in the S&P 500 that are seeing stronger gains this year, you know, in front of Nvidia. So again, not, not to say that it's a good or bad thing, but I think it's to point out that, you know, there's often this conflating with the biggest stocks and the best performers, not necessarily the case. So I think two things can be true where you can have a significant contribution from those big AI names, but you can also have meaningful performance outside of those of those areas. So that's why I think, you know, it sounds boring to sometimes to people to, to talk about diversification, but I think it's an important topic because it does sort of speak to how strong some of the gains have been across the market and across many sectors so far this year.
- [07:04]
A
Well, I think that reiterates the call from before about equal weight exposure. And I do think there's value there. To me, I have a very long time horizon as an investor. I'm going long tech right now as I know a lot of my same age group peers are. But. All right, let me ask you about this chart. You have this idea of a convergence trade between the Mag 7 and the S&P493, essentially when the rest of the market's earnings are going to catch up to big tech. Walk us through what's going on here.
- [07:33]
B
Yeah, so this is, and admittedly we've been writing about this for a while where we've sort of been waiting for not a convergence in terms of the growth rates have to surpass each other in terms of earnings growth. So you take the Mag 7 and then you take the 493, you know, split them in two. You look at their earnings growth trajectories actually coming into this year, they were expected to almost at this point have already crossed or at least the estimates but, you know, by the middle of the year were supposed to have crossed. I don't look at it that way and saying that has to happen, you know, for the market to do well. But we've kind of been waiting for that convergence for a while to maybe, you know, provide some validity to the fact that the average stock could continue to do well, that it wasn't just a few companies holding up the S P in price performance terms or in earnings terms. So I think the good news is that, you know, so far in this reporting season for, for the first quarter, which we're essentially, you know, done with the exception of one big company. But, you know, so far the beat rate has been quite strong both on the revenue and the earnings side. And you've seen this pretty remarkable upward revision TO S&P 500 earnings for the rest of the year. Some of that is concentrated in, in some sectors, but I think broadly when you do look at tech and communication services, you know, their weight in the S and P in an earnings sense not just in a, in a market cap sense. It does, it does provide some, you know, validity to the idea. And it just sort of underscore this idea that, that the earnings picture as of right now has been relatively strong and relatively resilient. So when we think about that sort of convergence trade, yes, the growth rates may not be close to each other right now, but I think directionally it matters more for the 493 to continue to see growth estimates improve. That to me, I think the trajectory is more important than just looking at the two percentages and comparing them against each other.
- [09:21]
A
Yeah, I totally agree with you. And everything in the stock market is about expectations. And as long as expectations don't break, I think we're on a pretty steady path here. If we think about how there's a small number of giant companies that are driving the AI trade. To me, it also makes total sense that as they spend the most and they also allocate the most to different parts of the economy, they attract the most talent. Right. In the employment sector, traditional metrics of like breadth or even valuations seem to be getting less and less relevant because there's such a, you call top heavy, but just like a power concentration across the whole economy and these few companies. What do you make of that?
- [10:07]
B
Well, I, I think I would first say as an employment driver, it certainly has looked different this time because you, you know, traditionally in a, in a, in a strong capital spending cycle, you would see these pretty powerful ripple effects where you would go through, you know, a strong business investment cycle that would, would eventually filter through to labor and you would see a pickup in hiring. And then the idea is that you create this vir, a cycle of more people spending money because they have, you know, more income growth or they, they achieve, you know, they actually obtain, obtain an income if they get hired. But we haven't seen that yet specifically in the tech sector, which is why productivity has looked so strong in that sector because output has been quite strong, but employment growth has been lacking. And really the tech sector, it's, it's named information in, in the BLS data. So, but it's essentially the tech sector, but that hiring has looked incredibly weak. It's actually one of the worst charts right now out of all of the industries in terms of employment levels. But I think a lot of that has to do with how strong productivity has been in that sector. Could be a cost story as well because a lot of that spending from companies is going towards, you know, just sort of that pure investment piece of it. But I think stepping back beyond that, we have seen some stronger ripple effects in the broader economy where, you know, hiring has largely stabilized so far this year. I'm not sure how much I would put that on sort of the AI story other than maybe companies just finding a little bit of, relative to stability, you know, after pretty significant disruption geopolitically over the past year. But regardless, you know, it's a good sign to the point on valuation, you know, I think that the, the difference over time and when you look at the construction of the market, at least in the US how much it has changed over time, if you compare it to, you know, in the, in the 70s or the 80s or the, even the 90s, the early 90s to now, you look at the, the weight of areas like tech or communication services and they've grown significantly, but we've also grown in our, in our economy and the economic, in economic terms, we've grown significantly in terms of their weight in output. So to some extent I think it does justify why multiples have been more elevated and not even in the past several years. This is taking it back to to some extent the early 2000s, but especially the late 2000s. And you look at a long term chart of any price to earnings metric, it is elevated. And we've sort of had this higher floor in the past several decades versus the couple of decades that preceded that. So I do think to some extent it has been consistent with how the economy has evolved over time. We've become more services oriented, we've become more tech oriented. And at the end of the day, you know, to me valuation is really just a gauge of investor sentiment. That's, that's how I was sort of trained to look at it. You know, there, there is a, there's a PE that somebody is willing to pay sometimes, I would argue today they're willing to pay a lot for stocks. Sometimes in, you know, times like the, in portions of 2020 when we were deep in the sell off or 2009 or even 2022, to some extent in October, people are not willing to pay anything and they're waiting for multiples. They're willing to wait for multiples to even go lower. So I use it more as a gauge of sentiment than anything else, especially a market timing tool because it is not strong as a market timing tool. So long winded answer, but I think important context around why valuation metrics have changed, but also why sometimes investors get tripped up in thinking that they are these perfect signals for what the market is going to do. In any given time frames out six months, out one year. There's just not enough historical data to support that.
- [13:50]
A
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- [15:07]
B
Yes. By definition, one dimensional. Yes.
- [15:09]
A
Yes.
- [15:09]
B
Picking one metric.
- [15:10]
A
Yeah, it's. Well, you'd be surprised. Some of these guys are very successful too. But let me ask you this. You said PE is not a. You said it's not a good predictor of market timing, it's not a good predictor of performance.
- [15:25]
B
So if you take a sort of a standard price earnings multiple for the S&P 500, you do it a modern era. Look back so around to the 1950s as you're starting point and we often show this as a scattergram. So just a bunch of dots on a chart. There's basically no statistical significance and essentially no relationship. So if you kind of looked at a linear relationship between the two, between where multiples were a year ago and what performance looks like today, you really don't find any relationship. And there are times on that chart, there are times when you've got sky high multiples and yet the market has done well a year later. There are times when multiples have been, you know, at the lows and the market has still done poorly a year later. So it's a little bit shorter term, you know, thinking in terms of just a year out. But I think it's a good exercise because oftentimes especially, you know, in, in the current time, you will see a lot of charts put up with pes being very high and, and a lot of warnings around. You know, there's an imminent correction or crash coming. And it's not that it's right or wrong. I just think that using only that variable leaves out a lot of context of, you know, what the macro environment looks like and what the earnings trajectory looks like of a particular company or a particular sector.
- [16:43]
A
Well, there's the chart of the Shiller PE ratio. Everyone uses that to make the dot com comparison saying the stock market's more expensive or frothier than it was dot com. But I think that's an amazing stat that there's no connection to performance. I haven't verified this, but that sounds, I mean, sounds about right.
- [17:02]
B
And you know, I think.com is a, is an interesting thought experiment or exercise because yes, you could look at that moment in March of 2000 when everything was essentially at its peak. It was sort of that perfect storm. I mean, price to sales, price to earnings, price to free cash flow, the Buffett indicator. You could, you know, market cap to GDP or gnp. I mean you could literally take whatever metric you want and everything was sort of screaming, this is ridiculously expensive. But there are also several metrics that were saying that for, for several years in the late 90s leading up to that moment. So if you had, you know, sat out of that rally really early on, you would have lost out on, on a lot of those gains. So again, it's, it's a, it's sort of a way I think to underscore that nobody could have perfectly seen that in the run up and that kind of parabolic move and then vice versa. Nobody could have necessarily seen that right at the, right at the peak and then perfectly predicted, okay, this is going to be a two year long, you know, bear market, almost two and a half year long bear market process where the NASDAQ gets, you know, chopped significantly, you know, upwards of, of 70 to 80% depending on, depending on the sector.
- [18:09]
A
Are you seeing anything in today's data that makes you think the comparisons are warranted?
- [18:15]
B
Yes and no. I mean I think that on the, on the market breadth side and I like looking at market breadth because it's, it's kind of like math. It just is. There is no opinion, there is no, there is no other way to look at it. It's just an objective metric and for me, one thing that I've been keeping my eye on recently, especially with some of the, you know, before the weakness that we've seen in the past couple of days, some of the recent highs, all time highs in The S&P 500, that was happening at the same time that you were seeing this steady tick lower in the percentage of companies that were trading above their 200 day moving average. So you were getting this cluster of moments and days and weeks when you were hitting all time highs, yet you were, you know, sub 60% on that breadth met. And the last time that we had that, that much of a cluster was in the late 90s and early 2000. So in that sense, yes, you can point to a lot of comparisons in performance concentration and market cap concentration. But on the flip, I think that there are also differences, of course, in the macro backdrop. I mean this post pandemic cycle has really been sort of unlike anything we've ever seen relative to history. But even in terms of, you know, some metrics of investor sentiment, everything was screaming euphoric in, in 99 and 2000. Today that's not necessarily the case. And I think I attribute a lot of that to some of these many washouts that we've gone through over the past several years. Back to the point about 2023 and 2024, some of those rolling corrective phases have actually wrung out sentiment and frothy sentiment in kind of this rolling fashion. So it hasn't led to this kind of epic, you know, melt up moment for the market where you get every metric screaming euphoria, ultra bullishness, max bullishness, and then you see an epic bear market unfold after. So whether it's healthy or not, we won't know until we know. But I think so far up until this point, that's why there has been a little bit of a difference in the character of the market relative to the late 90s. Not to mention the fact that, and I know this gets discussed and covered a lot, but the earnings profile of The S&P 500, I would argue today is healthier. Does that mean that you can't have bubbly activity? No, because I think one of the more important aspects of, of the early 2000s was that even for the biggest companies in The S&P 500 that did have, you know, real earnings and they were seeing earnings growth, it's not that all of a sudden their earnings just flipped negative and that they were losing a bunch of money. It was just that the expectations couldn't be met consistently and eventually sort of ran out of that, you know, ran out of breath. And that's when, you know, to your point about expectations, that's when the market kind of reassessed things and said, okay, things don't seem right here. And that's what sort of led to that toppling over of the stock market. So I think that's an important thing to keep in mind, especially when, you know, earnings are growing and look relatively healthy.
- [21:05]
A
You know, you have this great chart here that is essentially following up, right, what you said. We've seen amazing earnings and yet the penalty for companies missing on earnings has been essentially as severe as we've seen in recent years. I have this stat here. Companies that miss earnings have seen stocks average excess returns of around -3.9%. What do you make of this?
- [21:31]
B
So I think anytime you're looking at that, that chart, you know it or that statistic, that metric, I think it really just kind of helps explain where investor sentiment was, you know, heading into earnings season and what people were expecting. So the fact that you are seeing, you know, more of a miss or more of a punishment on the part of the market just tell. Tells me that there, there are greater expectations for companies to provide, you know, not only a beat on earnings, but I think beyond that, you know, what does your forecast look like, what does your guidance look like for the rest of the year? You know, if you are seeing a beat on earnings, you know, why is that the case? Or if you're seeing a miss and there have actually been some instances this year or in this reporting season of, you know, pretty significant misses, yet the stock has reacted favorably, that might be in part because of cost cutting or, you know, any, any number of things. So maybe the market treats that in a good way. But there have also been a lot of use cases or examples of people using AI. Companies using AI as a reason that they've maybe cut their workforce, that they're saving on costs that way. So maybe it makes sense to some extent. But I just think, yes, at a more simple level, anytime you do see that Ms. Bar getting much wider and much deeper and negative territory, it's just that I think the context around the earnings miss is just not treated favorably specifically as it relates to guidance.
- [22:56]
A
Well, I think so much of this can be tied to sentiment. Like as you've talked about this whole time, sentiment is for investors. I think it's pretty high. Like it's very bullish. Even if a lot of the survey data suggests otherwise. The people I speak with who are managing money are super stoked right now. They're very excited. They see a lot of opportunity. And just because we're not seeing that necessarily in the traditional sentiment data, I think there's a gap there. But I want to ask you about this other chart here. Profit margins have exploded, right start the year. But you see this as potentially a risk in the market, which I don't understand. So help me, help me figure this out.
- [23:36]
B
Actually, it ties into the point I was sort of ending on in that, in that last answer about why, why are profit margins expanding? So I do think at some point, if it becomes a labor centric story where margins are expanding for the so called, you know, wrong reasons of laying off more people, isn't that isn't a, you know, a risk that shows up right away, you know, in the next quarter? To me, I think of that as a medium to longer term risk where eventually if you do have enough people that are laid off and not all at once, if it's, you know, gradually happening, but if that job finding rate is low and hiring is lower, then that's where I think it becomes more of an economic risk, but not a market risk. Well, by, in, by virtue of that, it does become a market risk. But again, I think the sector skew is, is important in that you kind of have to look at margins. You know, unfortunately, because of, of the concentration that we do have in the market, sometimes you do have to look at it on the equal weighted versus the cap weighted basis. So similar to market performance and breadth. Same thing with margins. If you do see a split in that tail of two markets and it is the, the rest of the, of the market, you're sort of failing to catch up up, then I think that you would run into a little bit of a bigger issue. But again, I don't think it's something that you can time and I don't think it's something that we sort of face imminently, you know, at the end of the day, all else equal, higher margins and strengthening margins is a good thing. And so far we're not seeing that mass layoff cycle take hold in the labor market. So I think you combine that all together and it's still a relatively constructive setup for the market.
- [25:09]
A
Well, I think you're the only person who's pointed out the potential risk of higher margins. Because if a, you know, I see it and I say, okay, stocks are definitely going to keep ripping for a while, margins keep going up. But then it's like, yes, the second and Third order effects on the macro side I think gets pretty interesting. Kevin, I have to ask you though, the last month and a half or so we've seen a ridiculous rally in semis and chips. What do you make of this?
- [25:37]
B
Oh, it's been amazing. You know, the very frothy. The statistics are amazing because I was, I was looking at the semiconductor performance recently on a rolling 30 day basis and if you, if you look, most recently, the strongest gain that we had for, for that industry was just slightly above 50%. And the last time that you had that kind of return was in 2001. 2001, we were in the middle of the bear market that started in March of 2000. So that was a, you know, a bear market rally where clearly it didn't last and you had more weakness to go. So then I looked at the different context of okay, when have we had that outside of a bear market? And last time was 1998. Even in that instance in 1998, that rolling 30 day percentage change was about a percent percentage point lower than the most recent aspect. So. So, you know, you can kind of nitpick all of the details, but I think regardless, the message is that it's rare to see these kinds of moves. But I think, you know, to this whole conversation about sentiment, I think it kind of just explains number one that the sentiment backdrop today and I think the sort of short term attention spans around markets and trading is quite powerful. It's much easier to trade, of course, than it was back in the 90s. You still kind of have a little bit of this retail cohort that gets very excited at times and piles into names if it's sort of the Reddit effect to some extent. But I also think that it speaks to the fact that you're still seeing, you know, relatively durable in earnings growth and an earnings trajectory around certain areas in that space. Because even, and then I would, you know, when I was looking at price performance for semis, I was also looking at valuations. And it's interesting for some cohorts of just tech, the sector, you know, of which semis are a part, but a lot of earnings estimates continue to go up faster than price. So, so that puts downward pressure on the multiple. And I think for some people that sort of draws them in and saying and thinking, oh, this is a relatively inexpensive, expensive or this is getting less expensive over time. So it's a, it's a little bit of a mind bending, you know, exercise to go through and think about. But that's just sort of where we find ourselves that in some instances the run up in earnings estimates is, is frothier and more exciting than the run up in price. And to some extent semis are fitting that bill a little bit recently.
- [27:51]
A
I mean, it's so unusual because typically the, I would guess price leads earnings. Is that right? Yeah.
- [27:58]
B
Yeah.
- [27:58]
A
Okay.
- [27:59]
B
Yeah.
- [27:59]
A
Yeah. Right now things are pretty topsy turvy and tilted super bullish. So that's been very interesting to watch. And I want to ask you about the. There's been this divide in the hard data, what we're seeing across the market and the macro side with the soft data. A lot of the sentiment survey surveys don't match the hard data.
- [28:23]
B
Yeah.
- [28:24]
A
Which side do you trust more right now?
- [28:26]
B
Well, I trust both, but for different reasons. I think to start with the hard data. Yeah, I mean by definition you can't really object to things like GDP and ultimately payrolls. Of course they get revised over time. But I think even something like the household survey that still tells a relatively constructive story of the US labor market in terms of unemployment remaining low relative to history. Household employment has rolled over in the past several months, which is a bit concerning because it's at odds with payrolls. But it's not, you know, I don't think it's flashing any Armageddon warning. But yes, the hard data collectively generally have been relatively solid, especially on the CapEx side, which has been a big driver of GDP on the, on the sentiment in the survey side. And I've spent a lot more time in the past year really thinking through this and what it means for the economy. And of course, you know, you could point to any sentiment metric, University of Michigan Consumer Sentiment index, the conference board, anything coming out of, you know, polling, whether it's Gallup or any other, any other, you know, body that's conducting a poll, all of that is, is pretty much inconsistent with where, where the broader economy is. So I think it reflects a couple of things. Number one, the inflation backdrop for the average person has just not gotten better over the past several years. You know, people tend to think in price level terms. They just don't think in month to month, year over year. It's, it's, it's not even that it's academic. I think it's just nobody measures their life that way, at least you know, in Main street, in the Main street world. So we all obsess over it when we, when we get the data, but that's just not how the average person thinks about it. So I think that's the One thing, the second thing has been, I think, you know, more related to labor. I have been doing a lot of client events recently and it's starting to be the case that the first question I get, and sometimes it pops up second, third, fourth time is about AI in the labor market and whether people are going to have jobs in, you know, five years. And I think that has kind of become the next phase of this where you haven't solved the inflation problem from the, you know, average consumer's mindset perspective. But, but also you add on the labor aspect and it's this just tangible fear that people have around, you know, employment prospects in the future, particularly as you skew younger. And I think when you get into that younger cohort, that's where the pressure is exacerbated and made even worse because younger people face the housing issue, housing affordability issue. The idea that, you know, they're not going to be able to build wealth that way and they're going to have to do it via other channels. And I think that a lot of that is happening in the stock market. So that's kind of what's causing this, this angst. And I think what, why it's caused the split between the two. So I think it would be a mistake to IGN the sentiment data in terms of what it means for people, you know, longer term and how they respond to the economy or how they invest. But at the same time, you know, again to the two things can be true. I don't think that you can necessarily use it as a forward looking indicator for economic activity because clearly it hasn't been at least in this cycle. And I think that's become sort of this important distinction over time and understanding that it just hasn't been indicative of where the economy is going because it kind of gets back to that old saying of at least, you know, American consumers love to spend when they're happy and they love to spend when they're sad. So in this instance, even if they're feeling pretty sad, you know, they're still spending month to month. But I just chalk that up to the labor market being relatively resilient each month.
- [31:58]
A
Wow. You know, it's amazing. If we were to only go by the survey sentiment over the last couple years, we would have had like 10 recessions by now if the hard data had no say in it. One idea that I've been thinking about a lot. If AI is really this mega productivity tool for everybody, companies are using it as an excuse right now to lay people off. Yes. But if it really was that powerful of a tool. Shouldn't they want more people to be able to deploy more productive people across their companies?
- [32:30]
B
Yeah, I do, and I think that's one of the reasons that I'm relatively constructive, that at least it won't be a massive driver of unemployment. But I think it will definitely shift, you know, job roles and maybe create some new industries, maybe destroy some, some industries or at least, you know, whittle them down a lot. But that kind of happens with every technology. And if this is going to be as transformative as. As it's being, you know, sort of marketed, then I think that the same would sort of ring true, true this time. So, so do I think it will be a massive employment driver? Perhaps, but that's different than saying it's not going to be this massive employment, you know, killer. So I think that the distinction there is important. And so far you can look at, you know, a couple of metrics that, that are measured via what's called a dissimilarity index, which is really just sort of looking at, you know, the change in jobs over time and how many people have to change jobs throughout, throughout a particular time period. And so far, that hasn't spiked meaningfully in this current AI phase relative to other major releases of technology, whether it was the Internet, whether it was the computer. So I think that's a relatively good sign because so far the data have told us that it hasn't been this major disruptor at the broad economic level. Has it been at an industry level like tech? I think yes, it's hard to. It's hard to argue against that, but I do take that as a relatively positive sign. But again, it's not that that's going to solve a lot of the fear that especially younger people have today around what they're sort of confronting right now and what companies have been reticent to do, which is higher. And I think a lot more probably has to be solved on the geopolitical stability front for companies to get back into that posture of saying, okay, we're ready to initiate a, you know, a big hiring, big hiring campaign or effort. So unfortunately, I think it'll take a while. But I think right now, at this point of the cycle, what's more important, especially for the longevity of, you know, the bull market and the expansion, is companies not shifting into layoff mode. So I know it probably sounds weird from the standpoint of somebody who is looking for a job, but when you think about it in the broader context of, of the economy, it's still the case that, you know, it's just more of a hiring recession than it is, you know, a layoff initiated recession. But even on the hiring front, things again have stabilized to my point earlier over the past couple of months.
- [34:57]
A
You know, Kevin, this is why you come on the show, because you have such a nuanced take on very complex issues that people usually take a black or white view on. Where can people find your work online?
- [35:07]
B
Well, schwab.com learn is where all of our research lives. It's free, so you don't even have to be signed in to read it. But everything from what we cover in the stock market and the economy to fixed income to crypto, it all lives there. And as you know, because you're my good online pal also in person, but on X, I'm on there quite a bit and putting a lot of charts out. Maybe too many charts.
- [35:32]
A
Sometimes impossible, Never too many charts. Kevin, thank you so much for your time and we'll do this again.
- [35:37]
B
Yeah, it was good. Thanks.