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- [00:00]
Host
This is an Iheart podcast.
- [00:04]
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- [00:56]
Host
Can I get you a refill?
- [00:57]
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- [01:08]
Joe Weisenthal
I'm Joe Weisenthal.
- [01:09]
Tracy Alloway
And I'm Tracy Alloway.
- [01:10]
Joe Weisenthal
Tracy, we're doing another live show and it's right here in New York City.
- [01:14]
Tracy Alloway
Yeah, this one should be our biggest yet. And we're gonna have a bunch of Odd Lots favorites and do something maybe a little different to some of our previous live podcast recordings.
- [01:25]
Joe Weisenthal
When the guests are revealed. The show is gonna sell out right away. So you should really just go get your ticket right now. And it's June 26th. It's at Reckitt NYC and you can find a ticket link at bloomberg.comoddlots or bloombergevents.com oddlotsliveny we hope to see you there.
- [01:45]
Tracy Alloway
Bloomberg Audio Studios Podcasts Radio News hello, and welcome to another episode of the OD Podcast. I'm Tracee Alloway.
- [02:05]
Joe Weisenthal
And I'm Joe Weisenthal.
- [02:07]
Tracy Alloway
Jo, does it feel to you like every day when you wake up in markets, there is a new sort of dominant narrative, and often it is the exact opposite of the narrative that was dominant a day or two ago.
- [02:22]
Joe Weisenthal
Go on, say more. What you mean, where are you going with this? I mean, I agree in some sense, but I think you have something specific in mind.
- [02:28]
Tracy Alloway
Well, no, I actually have a bunch of things in mind. So, for instance, you know, recently there was a lot of talk about the Bond vigilantes back and people worried about the fiscal deficit. And then this week, that seems to have suddenly gone away, or at least it's not the only thing people are talking about tariffs. You know, obviously we had the big market freak out in April, and then we had subsequent delays and extensions of the deadlines. I don't hear people talking about tariffs as much as they were in April. So.
- [02:59]
Joe Weisenthal
And then through it all is, you know, on days when we don't have talk about tariffs or bond vigilantes, then we immediately turn to AI.
- [03:07]
Tracy Alloway
Well, this is what I was going to say.
- [03:09]
Joe Weisenthal
So this is the thing. There's this default thing that's in the background, which is AI. And then that, of course, leads to things like power and electricity and so forth. So, yes, I now see what you're saying, which is that on any given day, it's like you spin the roulette wheel or the wheel of fortune, and then it's like, okay, it's an AI day. Actually, we're recording this May 28th. By the time people are listening to this, Nvidia earnings are going to be out.
- [03:33]
Host
Oh, yeah.
- [03:33]
Joe Weisenthal
And so today is an AI day, or tomorrow might be an AI day.
- [03:36]
Tracy Alloway
What I was going to say is one of our previous guests, Victor Schwetz, over at Macquarie, he framed it really well where he was basically saying, if, you know, if you can't gauge what the dominant narrative is going to be every day, or if everything that's happening is so volatile and so dramatic and we're talking about really big changes, then you kind of have to go back to doing the normal thing. And the normal thing before all of this kind of started in the new year, was worrying about AI, right? And thinking about AI and whether or not, you know, all of that AI spending is going to be backed by revenue. So on that note, that's what we're going to do today. We're going to go back to normality, although we are going to talk about some of the current stuff as well. And I am very pleased to say we have someone who I have wanted to get on the podcast for a really long time, and he's finally sort of been let out into the wild. So we are very excited. We're going to be speaking with Michael Cemblest. He is, of course, the Chairman of Market and investment strategy for J.P. morgan Asset Management. He writes a great, great piece called Eye on the Market, which he's been doing for a while, which has a.
- [04:42]
Joe Weisenthal
Cult following on Wall Street. Every time a new semblance piece comes out, everyone talks about it and reads it.
- [04:47]
Tracy Alloway
Yeah. And I think it's been going on for something like 20 years now. And he himself has been at JP Morgan since 19, so, you know, a real witness to financial market history. So I'm very excited. Michael, thank you so much for finally coming on the show.
- [05:02]
Host
Thank you very much. Good to be here.
- [05:04]
Tracy Alloway
I guess my first question is, can you explain what you do over at JP Morgan Asset Management? Eye on the Market is a phenomenal publication. I've been a fan for a long time, but I always wonder like, is there a difference between writing investment outlooks for asset management clients versus writing them if you're on the sell side?
- [05:23]
Host
Well, yeah, there's a huge difference. First of all, you know, thank God for the Chinese wall, ERISA rules and things like that, because I have the independence and what our clients want is for me to share what I'm thinking. The best example of that is in February 2021, the investment bank was making money hand over. All investment banks were making money hand over fist in the SPAC market. And I published, I forgot about that. I published, you know, two really scathing pieces on SPACs. The first one was called Hydraulics Backing and the next one, because it was during COVID was called Spaccine Hesitancy where I kind of pointed out that the sponsors were the only ones making money and that their break even threshold was something like an 80% decline in the merged stock price was their break even. So anything better than that, they would make money and everybody else was getting slaughtered. So being behind the fiduciary wall allows me to kind of call it like I see it. And the eye in the market is the external presentation of our thoughts and views. And you know, on an internal basis, I'm participating our investment committees dealing with asset allocation and security selection and, and everything else that goes along with managing money for endowments, foundations, pension plans, insurance companies, sovereign wealth funds and individuals.
- [06:38]
Joe Weisenthal
It does feel like on the media side we are at the whims of narratives and all the things that Tracy talked about at the beginning. Whereas if you talk people who are engaged in the art of security selection, it feels that actually might be liberating because on some level, whatever is going on, you could still look at an instrument and say, is this a well priced instrument or not? And not having to get caught up so much in just, you know, movement, you know, whatever the headline of the day is, Right.
- [07:10]
Host
And actually, you know, if you're managing money other than kind of a fast money macro Hedge fund.
- [07:16]
Joe Weisenthal
Yeah.
- [07:16]
Host
You don't have the luxury of changing your asset allocation every time a new narrative comes along. Right. So you, to some extent, you have to kind of choose your poison, make your asset allocation decisions, and then be either rewarded or punished based on the outcome.
- [07:31]
Tracy Alloway
So one of the reasons we wanted to get you on the show was because you've been writing in great detail about AI. So let me just ask the basic question. If we're not going to worry about, you know, Trump and tariffs and bond vigilantes and all the sort of headliney stuff, if we're going back to considering, I guess, the macro and market importance of AI at the moment, how dominant is AI when it comes to market valuations at the moment? I guess how fundamental is AI when it comes to the wider stock market?
- [08:01]
Host
I don't think you can overstate the importance of this stuff. Wow. When you decompose the evolution of S and P profit margins and you strip off the Mag 7 versus the rest of the market, it's like east and West Berlin. I'm dating myself. But that's how different, that's how divergent those are. If you look at earnings growth, I think earnings growth for the Mag 7 over the last 10 years is close to 20%. It's 6% for the S&P 493. So it's almost like two completely different universes of companies. And so they are extremely profitable. They are spending somewhere between, depending on the quarter, 25 to 40% of their revenues on capital spending and R and D. Those numbers are unprecedented. And in the history of the markets, even going back to the 1960s mainframe era and things like that, we've never seen anything quite like this. And what the hyperscalers are doing, I would describe. I know we're only 25 years into the century, but this is the bed of the century that you can spend this much on AI infrastructure, depress your free cash flow margins for some period of time and wait for the ultimate payoff.
- [09:11]
Joe Weisenthal
Even prior to the AI, you mentioned this extraordinary gap. I mean, there are two things about hearing you describe the earnings picture within the S and P 500 that strike me. One is the absolute gap. But then there's also the fact that it's the biggest companies growing really fast. In my mind, when I was younger, assumed that as big companies got bigger, they grew slower and smaller companies grew faster. When you look at stock market history, how rare is the type of earnings growth the year on year, the huge numbers that they put up earnings growth wise, how Unusual is it for stock market history for the biggest companies to also just be galloping past everyone else like this?
- [09:52]
Host
You can't find it. You can't find it. I mean, stock market history on a security level basis goes back to around 1980. There's a variety of databases through FactSet, through the University of Chicago. That's about as far back as you can really.
- [10:03]
Joe Weisenthal
You can't go much back.
- [10:04]
Host
It's hard to go back further than that. There's some Wilshire data, but it's difficult. It's very spotty. So if you're looking for things like earnings growth, if you just want stock.
- [10:12]
Joe Weisenthal
Price growth, sure, yeah, of course.
- [10:13]
Host
But if you're looking for earnings and margins and things like that, it's hard. So we can't find any comparable period where the bigger companies are growing faster. And even within the private equity universe, your theory holds true. The generally smaller and mid sized funds usually are going to outperform over long periods of time. The larger funds, there's negative size bias. So this is pretty unique. And over the years I've always thought that the biggest risk to all of this was going to be some reinterpretation of the Sherman act and antitrust. And while Epic Games has won a lawsuit here or there and the Department of Justice is picking away at Amazon, Apple and Google for different reasons, there really hasn't been a fundamental rethinking of antitrust law that would get in the way of this.
- [10:58]
Tracy Alloway
Can you talk a little bit more about that? What are the drivers or the circumstances that are allowing Mag7 to continue to grow at a phenomenal pace, but also just become these giant cash flow monsters?
- [11:10]
Host
Yeah, well, it has to do with the digitization of the economy and how much technology is at the core of how companies run. You know, JP Morgan right now is a firm. We've got 300 different language model and AI projects going on in this firm. And all of that requires, in one way or another, a lot of these Mag 7 companies. I think we have to put Tesla aside for a minute for reasons that we should discuss. But technology is really at the foundation. And I think a lot of times when people compare and they say, oh, US stocks are expensive relative to the rest of the world, they're missing a couple of important things. They're missing the fact that the US has a massive weight to technology. And in Europe, you know, basically it's a value market, right? It's a lot of energy, financials and consumer staples. And so the US is really the dominant player in Large cap tech. There's an amazing chart. Mario Draghi, you know who he is, obviously wrote a piece at the beginning of the year. What can we possibly. I'm paraphrasing. What can we possibly do to reinvigorate entrepreneurship in Europe? And he had a chart in there that we recomputed in our own way. And it's a bubble chart that shows the number of new companies created in the US versus Europe since the year 2000. And again, it's like east and West Berlin. They are completely different.
- [12:27]
Joe Weisenthal
Yeah. One of the things that struck me recently, we did an episode with the CEO of a women's clothing company. M.M. laFleur. We're mostly talking about tariffs, but the thing that I keep thinking about is she said that when she started the company a little, I think. When was it? Over ten years ago. Tracy?
- [12:44]
Tracy Alloway
Yeah.
- [12:44]
Joe Weisenthal
Her customer acquisition costs on a platform like Instagram, she was like $12 a customer, and now it's like over $200. And it just struck me that one of the things that must be going on in the economy is everybody's margin becoming Facebook's profits, or everybody's margins eventually becoming Alphabet's profits. That part of this gap that we're seeing is there is this digital real estate that exists. It's where everyone is moving. And they could collect massive rents from owning that real estate from anyone who wants to do commerce there.
- [13:21]
Host
Yeah. And I think rent is the right word here.
- [13:24]
Joe Weisenthal
Yeah.
- [13:25]
Host
And for reasons both good and bad. You know, Amazon requires. If you want to be. If you want to sell to prime customers, you have to agree to use Amazon shipping. If you make an in purchase app in some kind of a game like Fortnite on an Android phone, you have to make that purchase through the Google Play store. These are some of the issues that have been litigated in some of the. And so I'm not 100% in favor of some of the tying that essentially they get. If JP Morgan said, if you want a credit card, you gotta do your mortgage with us, we get slapped down pretty fast and for good reason. And so there's a lot of product tying that takes place in big tech, you know, that a lot of companies are beginning to challenge and the DOJ is looking at as well.
- [14:21]
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- [14:23]
Host
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- [14:31]
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- [15:23]
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- [15:52]
Tracy Alloway
So we've been talking about the dominance of US Tech and you know there are a lot of numbers to support this and you've been laying some of them out. But before we had all these tariff related market freakouts, the freakout that everyone was focused on was deep seek of course, and the idea that oh suddenly China has invented a low cost AI model that is very competitive with what we've seen from the giant companies like the Microsoft's and the Googles of the world. Looking back at that, was that freak out overstated or just. It was okay. All right.
- [16:25]
Host
For a couple reasons. One, I think it's become clear and I wrote a piece at the time they were not completely transparent about how they accomplished some of their goals. Imitations, the cirrus form of flattery. They actually used I think OpenAI models to train their models and so they did a lot of piggybacking that isn't necessarily a sign of some kind of productivity breakthrough. Also, the price at which they are willing to sell something is not necessarily reflective of its true cost and so there is some loss leading that's going on in there. That again is different. That said, cheaper AI models, if that's where we're going, is probably going to make adoption even easier. So if this can all be done with less energy at lower token cost, that's a bullish story for AI Not a negative one.
- [17:19]
Joe Weisenthal
It also occurred to me when that happened that if there is this existential battle between the US and China for AI supremacy, we're probably going to spend more on it. If it becomes a nuclear bomb type race, then the impulse is going to be to spend more on it. But actually you mentioned things like energy costs per token and so forth. And I'm curious from your perspective or you know, you've been doing this for a long time, how did you learn about that? How did you educate yourself on understanding the sort of the key economic variables that you have to get wrap your head around in order to say informed things about this new topic?
- [17:59]
Host
Well, there's a bunch of things because.
- [18:01]
Joe Weisenthal
We'Re trying to figure that ourselves. You know, we probably should be doing more about AI and talking. So we need to learn about how you taught yourself about it.
- [18:07]
Host
I think you have to budget your time. And my most important thing is reading about 2,000 to 2,500 pages of research each week. So that comes first. Other than spending time with my wife and my dog, that's the most important thing I do because that's where whether it's on energy topics or politics or healthcare or biotech, it's an information laden world. And the great thing about JP Morgan is I can call just about anybody and say, hi, I'm the chief strategist on the asset management business, J.P. morgan. I'd like to talk to you about something. Would you talk to me? I can count on one hand the number of times that somebody has just flat out said no. And one of those people was the bundler for Bernie Madoff. And that's the great thing about the halo effect that Jamie has brought to JP Morgan. And remember, I worked at JPMorgan for many years when there was no halo. And that halo effect is very powerful and it benefits me and a whole bunch of other people at the company. And I recognize that every day. So I spend a lot of time working deeply on some of these topics. And energy as an example, I needed to learn about energy from somebody that understood energy better than anybody else. So 15 years ago, I made a pilgrimage to Manitoba in February, which was can you prove yourself? Because it was cold. And I established a relationship with Vaclav Smil, who for over a decade was my personal technical Sherpa on understanding and learning about energy.
- [19:39]
Joe Weisenthal
That's someone we haven't had on the podcast.
- [19:41]
Host
He is a hermit. He's Czech, he's over 80 and he's a hermit.
- [19:47]
Joe Weisenthal
That's our excuse.
- [19:48]
Host
And he hates the political dynamics in the United States, so he will never get him here.
- [19:54]
Joe Weisenthal
We'll go to Manitoba.
- [19:55]
Tracy Alloway
We'll try. I'm still stuck on the idea of the Madoff guy answering the phone and being like, oh, we would prefer not to talk in depth about why our line just keeps going up in a very smooth sense.
- [20:07]
Host
Remember, it was an 11% compound return with 2% volume. It was an unprecedented return history. Like in the history of hedge funds.
- [20:17]
Joe Weisenthal
Well, also, Tracy. I don't expect you to comment on this, Michael, but, Tracy, if I recall, one of the people who was initially raised alarms about Madoff was actually a guy, Matt Zames, at J.P. morgan.
- [20:27]
Tracy Alloway
Oh, yeah.
- [20:28]
Joe Weisenthal
So perhaps there was a reason. I don't know. We don't want to talk to these J.P. morgan fellows.
- [20:32]
Host
Actually, if you go back even further, in either 2001 or 2002, there was a guy named Harry Markopoulos in Boston, and he wrote a piece basically saying, Bernie Madoff is a fraud, it's a Ponzi scheme. And he sent it to the sec, and they kind of poked around and did nothing.
- [20:49]
Tracy Alloway
Did nothing. Yeah. Okay. So when it comes to, I guess, learning about new sectors like AI, I think one of the things that everyone, us included, is struggling with at the moment is to understand, like, how much actual corporate spend there is going to be on this. And for obvious reasons, companies, you know, they're not necessarily that vocal about how much they're spending on this technology, although they do like to talk about the importance of AI in a sort of very nebulous theoretical manner. But what are you doing to understand the actual spend on this tech?
- [21:24]
Host
Right. So there's multiple stages to this. The first stage was understanding how much the hyperscalers were spending. And once we looked at those numbers, we were astonished and for the reasons you're mentioning, concerned. Right. I mean, there's not a lot of examples. If you look back at cable and airlines and casinos, when you start seeing R and D&Capex as a percenter of revenue, go to 20 to 30% for a sustained period. You know, usually that's turned out bad. So we said here, okay, what's going on here? The next thing to look at was signs of corporate adoption. Now, there's nobody that hates McKinsey surveys more than me. But, you know, we had McKinsey surveys, you have Bain surveys, you've got census surveys, a lot of which were pointing in the same direction, which is that corporate adoption has been accelerating, particularly over the last six months, and CEO surveys. I mean, there's too many of them that are all telling you the same thing for them to be wildly off the mark. But again, that doesn't tell you how much they're willing to spend on it. Right. Everybody might enjoy lollipops, but that doesn't mean they're going to spend a lot of their personal income on the. Then the question is how much are the big hyperscalers starting to earn on AI? And so far, Microsoft is really the only company that's disclosing it very discreetly. And we had a chart, I published a piece on this on May 13th. And Microsoft's the only company so far that's really explicitly telling you how much they're earning. And those numbers are growing at a pretty rapid pace from a low base, but they're earning a lot.
- [23:09]
Tracy Alloway
150% over the past year is what I see in the net.
- [23:12]
Host
Right, right. The other companies are still burying it in cloud revenue.
- [23:15]
Joe Weisenthal
Yeah, there's a lot of questions just on this. So get to them. It's one thing for a company to spend on AI, you know, a lot of pilot projects on there. When you ask companies in terms of. Yes, but is this actually saving money? Is this actually profitable investment? It starts to get cagier. And sometimes when you hear people talk about the returns that they've got from AI spending, it actually sounds like they're talking about more traditional machine learning algorithm stuff that has been going on for a while. But for this spend to continue, it's going to have to flow through to the bottom line in some way. And I'm curious what you're seeing on that in terms of like here is a company that's not a tech company, maybe it's an airline company, maybe it's a chemicals company, whatever. Where it's like, you know what, they can point to something that's not just algorithms, that is actually generative AI that is making money for them or saving money for them in some way. And what are you seeing on that?
- [24:11]
Host
Yeah, we're seeing anecdotes.
- [24:13]
Joe Weisenthal
Anecdotes.
- [24:14]
Host
We're seeing anecdotes.
- [24:15]
Tracy Alloway
Anecdote, yeah.
- [24:16]
Host
And usually it has to do with faster throughput and call centers with less people, stuff like that, faster customer acquisition, fraud reduction. Right. For big banks like JP Morgan, generative AI, one of the huge potential payoffs is identifying serial defaulters and mortgages and credit cards and things like that. So what is interesting is among the best surveys that are done. The surveys are asking them explicitly for generative AI, how much money are you Saving. And most of the answers are still in the lowest category, 0 to 10% by far. And then it's the above. Those levels have fewer responses. We're still in the exploratory phase here and I don't think we're out of the woods. And there may be a day of reckoning at some point. I think because of how absurdly profitable these companies are, the markets are going to give them another 18 months at least for the proof statement to bear out.
- [25:12]
Tracy Alloway
This was going to be. My next question was like how much of a leash are investors allowing some of these companies?
- [25:18]
Host
As long as, as long as the cloud revenue and the AI revenue specifically are growing at 50% plus and as long as the free cash flow margins don't fall below some critical level, I think the markets will still feel okay about it.
- [25:35]
Tracy Alloway
I wanted to also ask a question about your overall career. As we said in the intro, You've been at J.P. morgan since 1987, which is a phenomenal amount of time. Looking back, what was the hardest or most challenging sector? Wrap your head around over that period of time because as we've been discussing, you jump from thing to thing and you basically become an expert on whatever is important to investors or the market at that particular moment in time.
- [26:03]
Host
Yeah, and I usually get airlifted into things that nobody, you know that, that are a pain. Like I became for the asset management business and even for the broader firm. I ended up being our Covid point person and I, I put together a medical advisory committee of biophysicians and mathematical epidemiologists and people like that from La Jolla Institute of Immunology. And so I get airlifted into things. I would say, oddly enough, of all the sectors and sub sectors I can think of, the one that drives me nuts the most is healthcare and managed care. Because you know how they snuck this provision into the energy bill three years ago where the government can negotiate drug prices finally after never be able to do that. On the face of it, that should be negative for the big pharma companies. But because they can play around with all sorts of discounting mechanisms and distribution arrangements, a lot of which aren't super public, the actual impact on their margins so far has been somewhat negligible. So there's an impenetrability to parts of the way the healthcare system function that, that drives me a little bananas. That said, large cap pharma and biotech right now are among the cheapest sectors. If you compare price to book to projected ROE or something like that, we have this Giant map of all the sectors and subsectors and industries and where they trade relative to price to book and ROA and roe. And you know, health care is cheaper, but it would normally make me want to dive in and kind of come up with an, an asset allocation recommendation for our investment process. And it's just very difficult to do.
- [27:44]
Tracy Alloway
This is how I feel about US health insurance, by the way. As someone who did not grow up that much in the us I still struggle to wrap my head around how all of this works. It is truly impenetrable to me.
- [27:56]
Joe Weisenthal
I think many people would agree with that assessment. I want to go back to actually AI for a second because you're talking about the hyperscalers and this booming business for them. It's still actually a little bit hard for me to understand because for some companies their AI revenue is very easy to understand. Right. So Microsoft sells various things that go along with its suite that whatever, whether it's coding tools or so forth. Okay. They're selling an AI service. Maybe Google one day figure out how to do that too. For a company like Meta, their model is an open source model. They don't sell enterprise llama as far as I know. Yet they spend like crazy on AI and they talk about how much they spend.
- [28:40]
Host
Yes.
- [28:41]
Joe Weisenthal
Can you articulate for me what Meta's AI business model is? Because all I hear is about their spending and I don't even know what revenue they're deriving from.
- [28:51]
Host
Yeah. When you, when you try to understand that, what you learn is that Meta basically considers itself a generative AI company.
- [28:58]
Joe Weisenthal
Yeah.
- [28:59]
Host
And that everything they do and the success of all of its algorithms and social media tools maintain their market dominance because of how well they function. And the way that they function is because they're driven by AI. And I agree. I think they thought that the open source llama model would become an indirect profit center as people built tools and applications on top of it. And it seems like they're stepping away from that right now. Like they that a year ago that was the kind of message you were hearing. And not so much right now, but they, you know, I think they would.
- [29:35]
Joe Weisenthal
Say, Aaron, but somehow all of this spending which they talk is AI spending specifically, even though they're selling AI, here's how somehow it's accruing to their fundamental business of selling ads or selling content or selling time.
- [29:50]
Host
And here's how I would articulate that. If you go back three, four years, there were these really interesting sell side presentations that talked about, well, Google's going to try to compete with Apple here. Apple's going to try to compete with Nvidia here. Nvidia is going to try and compete with OpenAI here. And guess what? A lot of those moats are still in place, and a lot of the encroachment that people were looking for hasn't happened. So Meta's AI spend is primarily focused on maintaining an impenetrable moat for other companies that might otherwise want to compete with their core business. And that's how they see it. Which means that you basically will want to look at top line free cash flow and other profitability metrics for the company as a whole to understand the benefits of all that spend. I think investors are right to be skeptical. It's only three years ago that they took a ton of money, put it in a pile and set it on fire.
- [30:49]
Joe Weisenthal
Yeah, with the.
- [30:49]
Host
With the Metaverse. Right. So they. They have had a history of misreading where things are going.
- [30:56]
Joe Weisenthal
Tracy, by the way, I actually really admire how quick they were willing to pivot off that. Because you think, you know, you make this big bet, it's embarrassing. You rename your company Meta off of this, and then you're like, we're an AI company. Actually, that raised my admiration for Zuckerberg as a CEO that he didn't, like, succumb to some big sunk cost fallacy there. Anyway.
- [31:18]
Tracy Alloway
I changed my business model. That's my version of the Keynes quote.
- [31:21]
Host
He has demonstrated a mental agility to pivot as well earlier this year. And I'm not going to make a value judgment on this, but I was struck by him saying that he needed to re. Inject a dose of masculinity into.
- [31:34]
Joe Weisenthal
Oh, yeah, yeah.
- [31:35]
Host
I think that's a sign of somebody that. Yeah, this is audio, so you can't see my eyes rolling. But that's. But, you know, that's a sign of somebody that is willing to kind of change horses when necessary.
- [31:48]
Tracy Alloway
Oh, dear. I have thoughts on AI as a masculine endeavor. But anyway, one thing I wanted to ask is. Okay, so at the beginning of this conversation, you described AI as the bet of the century.
- [31:59]
Host
It is.
- [32:00]
Tracy Alloway
And when I hear, you know, when we're talking about the dominance of the big US Tech players and the moats and the billions, if not trillions of dollars that people are spending on this now, it feels like almost certainly it's the big players that are going to emerge as the winners from this. It seems very, very hard for smaller entrants to get into this. Is that right? Is it just. You know, fate accompli. I guess that the big guys are going to wait.
- [32:26]
Host
I think, I think it's a hyperscaler level, yes, but in a whole bunch of other industries, not necessarily plenty of room and. But look, you're seeing these unicorns all the time that are chipping away at different aspects of, you know, machine learning and generative AI. One of the most fascinating ones is look at Palantir, right? I mean, Palantir is taking a run at the large defense contractors primarily through its nimbleness and better application and understanding of the intersection between generative AI and defense weaponry. And for everybody that thinks about data centers, the massive concentration of data centers in Virginia and in the PJM ISO region more broadly is a very clear indication of just how the national security and defense stuff is tied into generative AI and who their big users are, right? If that didn't matter, there would be a lot more data centers built in the Midwest where 20 to 30% of the time electricity prices are actually negative. Right? Think about this. You have certain applications that are so reliant on not there being a millisecond of lag that they'd be willing to pay more for power and proximity than to have cheaper power that has just a tiny bit of lag built into it.
- [33:57]
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- [34:58]
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- [35:27]
Joe Weisenthal
One company where there's some question about the moat is Alphabet. And you know, people are like, well, could OpenAI just become people's default homepage or the first page that people go to when they want to learn something? And you've seen Alphabet shares underperform in some of the rebound. I think from a multiple standpoint, it's trading at some of its cheapest levels in a very long time. There's a lot of questions about the degree to which it can productize its own models. Historically, it's not so good at that. It makes great tech. The models are very good. I'm still discovering new Alphabet models that I never knew existed because they're buried under some URL. Is there a risk to their business model specifically that perhaps their distribution, their ownership of digital real estate, risk to the rents that they can extract from it?
- [36:20]
Host
Yeah, but this is the kind of thing where I would continue to just look at their market share. You know, for a while, Bing was taking a run at them and then nothing really happened. And okay, so in Europe, when you buy a device, Apple's no longer able to make Google the default engine.
- [36:38]
Joe Weisenthal
Oh, yeah.
- [36:38]
Host
So you have to set it up yourself. People pick Google anyway. So you could actually argue that Google is overpaying Apple for the default status on their devices. Oh, that's interesting because people would be willing to pick it anyway. I don't know. I think obviously the tools. OpenAI. I personally love perplexity, but I use them when necessary. And I wouldn't bet against Google's generic market share for search until you actually saw them beginning to lose market share. It reminds me, for the last 15 years, people have been talking and screaming about the end of the dollar as the world's reserve currency. And every year, I love this analogy. We look at the data on the share of foreign exchange reserves invested in dollars, the share of corporate and equity debt issuance in dollars, particularly offshore, the share of Swift transaction payments, and the dollar share of all these things. Bis intercompany bank loans, cross border. The dollar share is kind of unchanged. So it's, you know, you have to wait until you see stuff.
- [37:40]
Tracy Alloway
Network effects are definitely a thing. One thing I wanted to ask you, or one of the reasons we wanted to have you on, is, as you say, you read a lot, you talk to a lot of people. A lot of clients. And I'm curious, what's the sort of time split or the question split between I Trump and policy versus other market questions like AI?
- [38:05]
Host
It's hard to say. Right? I mean, the whole tariff drama was. Took an enormous amount of time because I would say that a lot of CEOs, and I've described this recently as I went to college in the Boston area, and every time it snowed, some of the local kids would come out, and when there was a lot of snow and ice on the road, they would hold onto the back of trucks and go skitching. And I had never heard of skitching until I saw people doing it. That sounds good. I never did it because, like, after you stop skitching, you tend to slam face first into the ice. A lot of CEOs are sketching right now on this administration because they thought that what they were gonna get was deregulation, oil and gas pipelines and lower corporate tax rates. That's what they were supporting this guy for. And they come out of the gate with deportations and tariffs and the productivity shock supply side agenda got put in the back seat for a while. They appear to be wanting to get started on some of that stuff. And I think it's interesting we can talk about it. It'll be interesting to see if they can get the Constitution pipeline reinvigorated, which would bring Marcellus Shale gas to the Northeast. But in the beginning, because so much of the first stuff was tariffs and deportations, a lot of focus on that. And tariffs in particular require a lot of work. There's 12,000 HTS codes across 200 something countries. And they came up with a patchwork of legislation that differentiate. And so there's just a lot of math involved in really understanding what the impacts are.
- [39:33]
Joe Weisenthal
By the way, I see you went to Tufts. And so I think you're the first person to ever say you went to a Boston area college. And that not being code word for Harvard, you actually did go to a Boston area college. Excellent university.
- [39:45]
Host
There's lots of them.
- [39:46]
Joe Weisenthal
No, I know there's lots, but it always, you know, you weren't being coy when you said Boston area College. As you mentioned, another huge theme over the years, and you write a lot about it, is energy. And you've learned a lot from Falklof's Mill. And we could talk hours just about energy. I would love to talk hours about energy at some point. But in the interest of time, how much has the AI story over the last few years changed how you thought about the future of North American energy needs and energy production and just the energy story in general?
- [40:23]
Host
Not until very recently, because it was a negligible part of overall electricity consumption sub 1% until a couple of years ago and has been growing slowly. There are three big components to this electricity story. One's AI, another one's electrification of transportation, you know, EVs. And the third one that people should focus on because it's just as big, is the potential for electrification of winter heating in homes, office buildings and industrial locations. One of the hardest things when you break down energy consumption, an enormous amount of it, enormous amount of fossil fuel consumption is used for heat. Some of the heat around, around a third of all the heat that's used is used at temperatures below 200 degrees centigrade. And that's where heat pumps are extremely efficient and measured by something called the coefficient of performance, which is the units of heat you get per unit of electricity you put in. And so there's three components roughly equal. Right. So data centers, electrification of heating, electrication, transport. The wide eyed, you know, kind of fairy tale projections of the Rocky Mountain Institute and Green tech media people.
- [41:38]
Joe Weisenthal
Yeah.
- [41:39]
Host
Are looking at 20 to 25% growth in electricity demand over the next 10 years. I think that's way too high. Adding up those three components, the lower end is closer to like 5 to 7% BNEF, which I think does very good work on these topics is somewhere in between.
- [41:56]
Tracy Alloway
Thank you. Thank you for the plug.
- [41:58]
Host
Yeah. Again, I know all of the sources, rigor of their work. And so I think we will need, you know, 7 or 8% additional electricity capacity for generation over the next decade or so. Electricity consumption in the United States has been roughly flat for 20 years, but before that it went up a lot. The issue back then was we were meeting it with nuclear and large natural gas plants. It's a lot harder today because most of the country is going to try to do it with renewables, which is just very difficult. And I'm also, for reasons I wrote about in our annual energy paper, I'm a skeptic of small modular reactors. Great idea on paper. Very complicated in terms of execution.
- [42:39]
Joe Weisenthal
By the way, Tracy, it's interesting, the heat as one of the big consumption drivers of energy. You know, there's a lot of northeastern chauvinism where they say people shouldn't be living out in the areas out in Arizona and all these like air conditioned places. But it really is heat. Like really if we want to be more energy efficient, my understanding is we should all leave the Northeast and go to the desert and live in air conditioning instead of heating our home? No, for real.
- [43:04]
Tracy Alloway
As someone with an old house in Connecticut, I will push back on that narrative. You know, I have a personal bias on this, but I was very excited about heat pumps. But then we looked into it for a home that was built in 1850 and it turns out you would have to like re insulate the entire house for this thing to have any effect. So I oh yeah, no.
- [43:22]
Host
The biggest problem with heat pumps is the the fact that while it's more efficient, you're buying electricity instead of natural gas. And per megajoule of energy it can be, depending on the state you live in, three to four times more expensive. So it's great for the climate. It's just economically, you know, challenging. And that's why I think some of those Rocky Mountain Institute and Green Tech Media forecasts are way too aggressive. Because I think these transitions are going to take place over a much longer period of time.
- [44:04]
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- [45:06]
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- [45:36]
Tracy Alloway
Since you've been in the business for almost four decades now, If I was reading one of your notes or one of your research pieces in, let's say 1987 or 1988, I'm not sure what exactly you were doing back then.
- [45:47]
Host
I was an analyst.
- [45:48]
Tracy Alloway
Oh, okay.
- [45:49]
Host
And I was sitting someplace behind a pile of pizza boxes and nobody was talking to me. I don't know.
- [45:54]
Tracy Alloway
So how different would your research then be versus your research now?
- [45:59]
Host
I'm definitely somebody that has taken advantage of massive quantities of information and research. Right. It was a lot harder. I used to have to go to the Mid Manhattan Research Library to find things and I used to go, and you pull out microfiche and then you would go and you would try to, you know, you put in a quarter and they would let you print it. I mean, it was hard to get historical data back then. And I'm a data hound. And so the way the world works today is much easier for me to draw parallels. I've also learned a lot of lessons in investing for all these years. I was the chief investment officer just in the private bank for a long time. And so I've learned a lot of lessons. And we're in the process of putting together a 20 year on the market retrospective. And it's amazing to go back and publish some of those pieces and see what we learned at the time.
- [46:47]
Tracy Alloway
Oh, well, okay, give us a preview. What were the big lessons?
- [46:50]
Host
The number one thing more than anything else, and we have 30 topics we're going to show and some of them are in there kind of for humor or sarcasm. But the number one investment lesson was equities bottom before everything else. And so does credit, and so does high yield, and so does real estate. In other words, asset prices bottom so far in advance of the related fundamentals. For instance, during the financial crisis in 2009, bank stocks bottomed when only 10% of the ultimate bank failures had taken place. So if you were listening to the really bearish voices at the time, they would say, too soon to invest. There's a giant tidal wave of bank failures coming. But the KBW or whatever index you want to look at bottomed in advance of all of that. And the same thing happened during the SNL crisis. Stocks bottomed way before all the defaults eventually took place. And if you then take the seven post war recessions in six out of the seven of them, equities bottomed before you even saw an upturn in payrolls, industrial production, housing, credit card delinquencies. And so as an investments person, I have to acclimate our clients and our Investment committee to be willing to take risk when you look out the window and everything looks terrible.
- [48:14]
Joe Weisenthal
Yeah. Oh, this is a great lesson because people say right now, oh, the shortages from the tariffs haven't even hit. Maybe we won't have them because the tariffs have been dialed back so much. But the idea that maybe the market already bottomed for this cycle on everything related to tariffs. Sometimes I like to say hope is the only strategy because if you wait until all of the data confirms that things are back, it's clearly going to be priced in at that point. But I actually wanted to ask something else. I am an SMR skeptic too, but I form my opinions based on seeing five tweets that are like, okay, yeah, those sound good to me. I'm an SMR skeptic. I'm. You don't. You have a much more rigorous process. Can you explain to us why you are a skeptic of small modular reactors?
- [48:58]
Host
Okay, so the easiest one is nobody's built one.
- [49:03]
Joe Weisenthal
That's a good. That's a good answer.
- [49:05]
Host
In the United States.
- [49:05]
Joe Weisenthal
Yeah, yeah.
- [49:06]
Host
One hasn't been built. Point number two, there are three or four of them, depending on how you want to define what small is. That have been built in China and Russia, and the costs have been multiples of the original projections and the timeframes have been much longer. So failure even in places that have success building regular.
- [49:27]
Joe Weisenthal
Here's what I figure. If there's no good, China would have built 100 by then.
- [49:30]
Host
Third point, there's no industrial project on Earth that is more capital intensive than nuclear power. And so when nuclear power first started to become popular in the late 60s, early 70s, the goal was, how big can we make them? Because we want to spread some of the sunk costs over the most megawatts we can build. The notion that you want to shrink a capital project to make it more efficient makes no sense to me unless you can completely commoditize them. The mistake I think people are making is that there has been an incredible learning curve in solar polysilicon panels in, to lesser extent, in wind turbines, both onshore and offshore. And the best example is lithium ion batteries. Best example of a learning curve you've ever seen. But that's because millions and tens of millions of units are being produced. I don't believe that you can, from going from one reactor to four reactors or six reactors, develop the kind of learning curve that would result in exponential cost declines. And so, you know, I'll wait until I see it. And I don't think we will. But I would love to eat my hat if OCLO or some of the other public companies or private companies that are working on this can deliver a small modular reactor in anywhere of the zip code of the original projections. And just as just one last thing, as a reminder, the new scale originally said $3 million a megawatt. When it hit 20, the plug was pulled. So the most recent example of such efforts in the United States failed when the budget hit 6x.
- [51:08]
Joe Weisenthal
That was a fantastic answer. Very intuitive answer too. Real quickly, what about geothermal? You think that could be a meaningful contributor to our portfolio?
- [51:15]
Host
And I do. You know, it takes effort. Chris Wright, who's the sector of energy is a big fan.
- [51:19]
Joe Weisenthal
Yeah.
- [51:20]
Host
And you know, there's different kinds of geothermal. The most interesting kind is where you're going really deep and you're accessing like supercritical fluids whose heat is comparable to nuclear power. But you know that instrumentation has to be able to withstand very high heat. You have to use plasma drilling bits. I think there's opportunity for expansion of geothermal, but they're long term industrial projects. It'll take a long time to do, not a quick fix.
- [51:46]
Joe Weisenthal
Yeah.
- [51:47]
Host
You know, and the other thing too is, and this is amazing, I've been very focused on the rising costs of wind and solar power and I should have been paying more attention to it. I turned my head the other day. The cost of a natural gas combined cycle turbine has gone from 1200 per kilowatt to 2,500. Like more than double just in the last two years. Supply chains. And mostly a reflection of the fact that ge, Vernova and Siemens and the other companies involved don't believe the long term demand story enough to expand their production facilities.
- [52:21]
Tracy Alloway
Interesting.
- [52:22]
Host
So that's why.
- [52:23]
Joe Weisenthal
Classic energy phenomenon, right?
- [52:25]
Host
So that's why you go to GE for NOVA today. We're happy to deliver you a plant. It's going to cost two times more than it did a couple of years ago and we'll give it to you in five years. That's the AI bottleneck.
- [52:35]
Joe Weisenthal
This is how falling demand can result in higher prices or expectations of falling. Right.
- [52:39]
Host
Because the two to three entities, Mitsubishi as well that are involved in this sector don't feel comfortable enough to go out and do a mega capital raise to double and triple the size of their production facilities for these kind of turbines. And that I think is going to end up being a binding constraint for AI at some point. I just don't know when.
- [53:00]
Tracy Alloway
So one of the reasons I'm a Big fan of your research is because it is so wide ranging and again, you have a very diverse diet when it comes to what you read and the information and data that you consume. Last question for me, but give us one under the radar thing that you are watching at the moment. Basically. What should we do an episode on?
- [53:24]
Joe Weisenthal
Yeah, what should we do an episode on?
- [53:26]
Host
On, let's see. I mean, not in any kind of order or importance. I'm just going to give you a stream of consciousness sometimes I heard you in the introduction talk about narratives that come and go and I agree with that totally. And one of the narratives that came and went but might come back is there are still five to $700 billion of unrealized interest rate related losses on bank balance sheets. That has not gone away. Now it only goes away if you go back to 3% or sub 3% on the 10 year. But we still have a banking system where many of the participants went hog wild extending duration on treasuries and agencies at a time of historically low interest rates. Because you had this deposit surge from all the Fed bazooka money money. And that hasn't gone away. Now the Fed has proven itself willing to provide whatever emergency facilities. I actually was personally against bailing out the VC depositors in Silicon Valley Bank. The average deposit balance in that bank was two and a half million dollars. I mean it wasn't really a bank, it was something else. But they decided for systemic reasons to do it. They'd probably do it again. But that issue hasn't gone away. And the higher 10 year rates go, the more those losses get pronounced. So that's an issue that's kind of still out.
- [54:49]
Joe Weisenthal
I'm glad you brought that up because I remember we trace and I did a bunch of episodes in like 20, 21, 22. It's like, why isn't the rate rise having more of an effect? And I was like, oh, you know, they got termed out, everyone locked in low rates, etc. And then it's like all right, but now rates are higher at some point. Like they only locked them in for so long.
- [55:04]
Host
I'm only, I'm almost tempted to believe that the Fed essentially created this problem through financial repression. Tempted some bad asset liability managers at these regional banks to load up at low rates and feel some sense of actual responsibility for this mess. Because it was Fed monetary and congressional fiscal policy that led to this mess.
- [55:26]
Joe Weisenthal
So the cause explosion.
- [55:28]
Host
Yeah. So I think they feel somewhat beholden to try to manage around some of the risks. Another thing I would Say is I don't think the tariff story is done.
- [55:37]
Joe Weisenthal
Okay.
- [55:38]
Host
You know, the average tariff rate at the end of last year was something like 2 1/2 percent. I'm using round numbers.
- [55:42]
Joe Weisenthal
Yeah.
- [55:43]
Host
If you did all the math and you don't assume any import substitution. At the peak of the kind of, you know, Lutnik cardboard cutout, you know, presentation, we were looking at A, a 25% all in rate. Again, assuming no import substitution and things like that. Now we're back out down at like 13%. So the market's like, wow, we dodged a bullet. It went from 20 something to 14. But 14 is still very high compared to 2.
- [56:11]
Tracy Alloway
Isn't it like the highest since the 1930s or 40s or something? Yeah.
- [56:14]
Host
Now he may backtrack again. We'll see. But historically, whenever there's been a disconnect between hard data and soft data, it takes about 90 days for that to resolve itself. So some of the shocks that are seeping through the system are going to take until July or August to play out. One of the things I wrote earlier this year that resonated with a lot of people is the markets are the one that you can't deport them, you can't intimidate them, you can't arrest them. At the end of the day, the markets are going to be a binding constraint on what this administration can do and can't do. And that's actually a plus for investors because it means at some point they're going to have to adhere to some generally acceptable level of policymaking.
- [57:01]
Joe Weisenthal
I just have one last quick question going back to AI. You hear a lot about inference versus training and people wondering, could there be some alternative to Nvidia for some of these services? Because right now on the chip side, all the profits seem to be accruing to Nvidia is that moat. You know, people talk about the CUDA ecosystem. Is that an impenetrable moat? Or are there opportunities for other chip companies to gain some real profits? In the AI world?
- [57:25]
Host
There are. You know, in the piece that we did last year, we listed all the companies that are trying to pick away the Nvidia moat. But in the asset management business or when companies hire underwriters.
- [57:39]
Joe Weisenthal
Yeah.
- [57:39]
Host
If an underwriting goes bad and you picked JP Morgan, Goldman and Morgan Stanley, nobody's going to question you. If you pick a ranked 8 or 11, you know, underwriter and something goes wrong, people will question you. So to some extent, the network effect and the moats that you guys are referring to, nobody's going to get blamed for spending extra money to buy Nvidia products and wait for a long time until these other products have proven themselves. And so I think it's going to take a long time for that to play out. But yeah, eventually, if this AI story works, we should be looking at 80% inference and 20% training in two to three years. Right? @ that point, corporate adoption, which drives the training models are supposed to be the overwhelming amount of what's going on. But again, Amazon claims to have been working on a foundational model. Haven't seen it yet. Microsoft has actually kind of hinted that, oh, we've created our own foundational model. There was this great story recently, if you like AI. The head of the Microsoft AI group was basically told to pound sand by the OpenAI people when he was asking them to fully disclose how some of the new reasoning models worked. So Microsoft is working on their own foundational model hasn't been released yet either, even internally within Microsoft. So again, so some of these moats, I wouldn't underestimate the amount of time that Nvidia is spending defending that moat and improving its products and its MP energy efficiency so that they don't give up market share prematurely.
- [59:12]
Tracy Alloway
All right, well, by the time this episode comes out, Nvidia earnings will have been published, so maybe we'll get a little bit more insight. But Michael Sembalist, thank you so much for coming on. Yeah, that was a real pleasure.
- [59:23]
Joe Weisenthal
There's a crime that we hadn't had you before, but I guess the fact that you couldn't you.
- [59:27]
Host
I think I've been put out. Back in the cage after this.
- [59:43]
Tracy Alloway
Joe, that was a real treat to get Michael finally, finally on the podcast.
- [59:48]
Joe Weisenthal
It was so good. And it's like, I've always liked his notes. Yeah, I mean, a. I think everyone who hasn't read them will now understand why he has sort of this cult following on Wall street where everyone devours his note. But then even beyond that, hearing him like, in my mind, it's like, okay, these notes are great, but there's a difference between comfort at, like being able to put together this big research product or this big note product. But also just being able to talk about all these things extemporaneously absolutely is really impressive.
- [60:18]
Tracy Alloway
There's so much interesting stuff to pick out there. But one of the things that struck me was, I guess, the importance of geographical location for data science, because I hadn't thought about that. But he's absolutely right. You know, you could locate a bunch of data centers in the middle of nowhere where maybe there's cheaper energy costs, but people so far have not chosen to do that.
- [60:38]
Joe Weisenthal
Yeah, I hadn't really thought about that either because it was like, you know, we all know that Northern Virginia is this huge data center cluster, but why the data centers really need to be that close to the government and defense industry and so forth. It's like, come on, it's like, like, is it really that big of a.
- [60:56]
Tracy Alloway
Deal to like your extra millisecond of latency or something?
- [61:00]
Joe Weisenthal
But I mean, you know, they're obviously there for a reason. I also just thought like this idea of the moats, you know, obviously that the big tech companies have built up and the amount of effort they have to sustain those moats and distribute through those moats and so forth and like how powerful that is and how much that is. The story of the fact that the biggest keep getting bigger at literally the expense of anything else. It's such an important perspective to have on all kinds of dimensions, whether you're thinking us versus international, the price of US equities and so forth. You just have to remember like how extraordinarily unusual these companies are.
- [61:36]
Tracy Alloway
Absolutely. And the fact that they continue to just throw off cash even while they're spending billions and billions of dollars on AI tech, which we're not entirely sure, like the degree to which it's actually gonna be able to be monetized, but they still managed to do it while generating cash.
- [61:53]
Joe Weisenthal
The clarity of the way he spoke about energy was really impressive. And I really liked what he said about small modular reactors.
- [61:59]
Tracy Alloway
You know, no one's done it yet in the U.S. yeah.
- [62:02]
Joe Weisenthal
And when we talked to Jigar Shaw about it, he used an analogy which I thought was fantastic. You know, he's more optimistic about them. He used an analogy that I thought was great, which is that the nuclear industry has to be in the business of building airplanes instead of airports, because airports are all one offs, but airplanes are obviously they're built on a very complex assembly line, but after you've built several, the cost gets cheaper. And this idea that there's so much capital intensity in building nuclear and the idea that there is no insight prospect for that learning curve where you do it over and over and over again for the cost to fall dramatically, that they haven't even done that in China. Maybe you're not gonna ever really get the sort of learning curves that you get in solar or lithium ion batteries. Super, very clear, intuitive explanation of why the opportunity may not be there.
- [62:55]
Tracy Alloway
Yeah, we have to have them back on.
- [62:57]
Joe Weisenthal
Yeah. Oh, so much more we could talk about.
- [62:59]
Tracy Alloway
All right, shall we leave it there?
- [63:00]
Joe Weisenthal
Let's leave it there.
- [63:01]
Tracy Alloway
This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.
- [63:06]
Joe Weisenthal
And I'm Joe Wiesenthal. You can follow me hestalwart. Check out all of Michael Sembelis notes. They're all online. Search for them. All of them are a must read. I'm going to go back and read a bunch of them. Follow our producers Kerman Rodriguez at CarmenArmen, Dash O' Bennett at Dashbot, and Kale Brooks at Kalebrooks. For more Odd Lots content go to bloomberg.com oddlots we have a daily newsletter and all of our episodes. You can chat about all of these topics 247 in our Discord, Discord, GG.
- [63:36]
Tracy Alloway
Oddlocks and if you enjoy Odd Lots, if you like it when we talk to market veterans like Michael Sembelas, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg Channel on Apple Podcasts and follow the instructions there. Thanks for listening.
- [64:02]
Joe Weisenthal
SA.
- [64:30]
Tracy Alloway
Hiscox Small Business Insurance Knows there is no business like your business. Across America, over 600,000 small businesses, from accountants and architects to photographers and yoga instructors, look to Hiscox Insurance for protection. Find flexible coverage that adapts to the needs of your small business with a fast, easy online', @hiscox.com that's his cox.com there's no business like Small Business. Hiscox Small Business Insurance this is an I Heart podcast.