In this episode of Narratives, we’re joined by Byrne Hobart to discuss finance, bubbles, reflexivity, George Soros, the EMH and a whole lot more.SHOW
William Jarvis 0:05
Hey folks, welcome to narratives. narratives is a podcast exploring the ways in which the world is better than in the past, the ways that is worse in the past, or it’s a better, more definite vision of the future. I’m your host, William Jarvis. And I want to thank you for taking the time out of your day to listen to this episode. I hope you enjoy it. You can find show notes, transcripts and videos at narratives podcast.com.
Will Jarvis 0:38
Well burn, how are we doing this afternoon?
Byrne Hobart 0:40
I’m doing great. Thanks. How about you? Doing great,
Will Jarvis 0:43
thank you so much for taking the time to come on the show today. Do you mind giving us a brief bio and some of the big ideas you’re interested in?
Byrne Hobart 0:51
Yeah, absolutely. So I have a kind of eclectic background, when to college briefly and then dropped out, moved to New York and sort of bounced around a couple different things, but ended up in online marketing. And then through that, I started writing online about how different companies were marketing themselves online, there was this crop of companies that were going public in the 2010 2011 period was really the first time that internet companies could start going public again, after, after the disaster of the.com collapse. And so through some of that writing, I ended up getting in touch with a hedge fund, join that fun to work on analyzing internet companies. And then, since then, I’ve done some finance stuff and some crypto stuff. And then a couple years ago, I basically got a little bored and decided to start writing for a while and figure out my next thing. And it turned out my next thing was writing. So I’ve been doing a newsletter. Since pretty regularly since early 2020. It’s the diff, go to the diff.co. And check it out. And I cover a couple different things. And I guess, I think part of it is I do have this general attitude, which is partly a result of reading newsletter, that the rule is just really complicated. There’s a lot of interesting stuff to learn about a lot of different companies about how a lot of different countries structure their economies, and how different technologies have impacted the world. So I’d like to do deep dives into that whether it’s looking at I don’t know there’s supposed to did a while ago on a bank called Synchrony Financial, which is sort of like, sort of like a firm and the rest, except it was founded in the 1930s. And it’s been doing store cards ever since then. So they’re like, you know, they’re things that people learn after almost a century of doing unsecured consumer lending to help people buy stuff that, that yeah, other companies will think you’re out in, in good time. I am a big fan of the reading of Ronald Coase, the economist who is he has this wonderful framework for thinking about how how transaction costs are such a limiting factor, and especially how just the lack of the existence of asymmetric information makes it really hard for the economy to be perfectly efficient. And then a lot of things that look like they don’t make sense or look like they’re just done imperfectly in the economy, they they start to make more sense when you think about things from a transaction cost lens. So I would say that the I think the big thing is our transaction costs matter that financial bubbles are underrated, that they are actually a coordinating force, that when there’s when you’re in the middle of a bubble, there are a lot of things that if you think of the bubble is like this definite vision of the future, you know, borrowing from zero to one, this idea of definite optimism, often that definite vision of the future requires a bunch of specific things to go right. And if nobody’s working on any of those things, then it’s very unlikely that future will come to be, but if everyone is really excited about those things, and if they’re sort of betting that they will happen, and they have to happen for that future to work, then it actually makes sense to build some of the things that have to be built for that future to happen. So you can do a lot of bubbles as like this positive feedback loop. Like, I think Moore’s law in software is a really classic example where Microsoft was always building software that would be able to run on the next generation of Intel chips, Intel is always building chips that will be able to run the next generation of Microsoft software. So they just keep pushing each other forward, you go back half a century, and you have the same thing with cars and oil, where we had to drill for oil because people are buying cars, people were buying cars, because there’s lots of cheap oil and the the feedback loop just made both of those industries really huge.
Will Jarvis 4:20
That’s great. That’s great. I love that I want to talk about a couple different things and tie these threads together. You know, trends I think about transaction costs and Ronald Coase and how a lot of things make more sense when you when you think about them under the lens of transaction costs, things that may look like they’re actually inefficient are efficient under under these constraints, you know, how does your thinking around something like that which sounds a lot like the efficient market hypothesis is that you know, the EMH and bubbles, you know, how real are bubbles? I have a friend who is a researcher for Scott. Oh, man, Scott sunder you know the money illusion blogger. And, you know, Scott, in his book, he talks about how you know, up asset prices could have this random walk, and they can look like bubbles. But really, it’s just like all random at the end of the day. Can you just talk a little bit about about that, like the EMH? Bubbles, how they interact, and maybe how they don’t interact? Yeah, sure.
Byrne Hobart 5:13
So I think one thing to say on on the efficient market hypothesis is that it does come in varying strengths. And the strengths can vary from the statement that you probably can’t beat the market because they were easy. Everybody would do it all the way to the like, the the strongest version of it is, even if you have perfect insider information, you cannot actually beat the market prices always perfectly reflect reality. And I think those I mean, one way to think about it is that it’s a model, and that models are useful for two reasons. One is that they tell you how something should behave. So like a lot of models of physics have been incredibly accurate at doing this kind of thing. But the other thing is that models tell you that, here’s a series of constraints you can apply. And within those constraints, this is exactly what has to happen. And therefore, given that it doesn’t happen, we have a limited list of reasons that that can be. So you can go back to EMH. And say, well, one of the things they assume is that it is costless. For companies to change their financial structure, one thing they assume is perfect information. And, and so when you see the markets, if you believe that markets are not perfectly efficient, then you can say it’s probably because of things like imperfect information and transaction costs. Which is, given that those are both really broad categories doesn’t really narrow things down too much. I think the other thing to say is that a lot of EMH practitioners or EMH, believers will say they don’t mean that the price is always exactly perfect to the penny. They mean it’s generally roughly correct. So you know, there’s still room, I think you can be like a believer in wheat EMH, who says it’s hard to beat the market unless you have an advantage if I have an advantage, and therefore I beat the market. So I think that’s that is semi compatible. Where I think things get hairy is is that claim that markets are efficient, they are discounting new information. And so you know, when prices seem to be crazy, it’s because there is a lot of new information, because you do sometimes have feedback loops that are just improperly discounted by the market like there are market participants are not fully informed about why things are the way they are, like I mentioned these positive feedback loops with, say cars and oil. There’s a wonderful Paul Graham essay where he talks about what happened with Yahoo and with the stock market bubble cuz he was at Yahoo for a while during the peak. And one of the things he says is that there was this cycle where companies would raise money, and they would say, we are going to build a profitable business, just looking at Yahoo, they would have money, they would have to market themselves, the only way they can market themselves internet users is buying banner ads, they’ve got to buy those ads on Yahoo. So Yahoo reports more revenue. And then the next round of companies can say Yahoo is really profitable. So we’re going to be the next Yahoo, and so give us money. And so that can go on for a while. But eventually people realize that that’s where all the money’s coming from. And you can have there’s, there was similar stuff with the real estate and subprime mortgage bubble in the 2000s, where a lot of people who would have defaulted on their loans didn’t because they could refinance, because their house housing prices have gone up because of the availability of lots and lots of mortgages. So yeah, for a while it was kind of self sustaining for a while you had a lot of these cities where jobs were booming, because a lot of jobs were in construction or in mortgage financing. And suburbs kept expanding. And eventually, we just ended up with a lot of excess housing stock. Now, there is a there is a sort of counter argument there, which I think is somewhat true that part of the housing bubble was just that we lived in a housing supply constrained environment where we just don’t build as many houses as people want to have. And so the price of that limited resource goes up. So to some extent, you can, you can sort of argue against the bubble hypothesis that way, but then you look at what happened to the housing prices. And it is it is clear that there was at least something that looked a whole lot like a market panic. And I guess that’s, that is another place where you can argue that bubbles happen is just that. If you have a financial system that allows people to borrow money, you can end up with this cycle, which the economist Hyman Minsky wrote about and did a really good job of articulating this. And it shows up in a couple other places where what happens is, when the economy is not doing great, and asset prices are depressed, they’re a lot of good investments to make. A lot of stuff is cheap. So people buy it, those people make money. And then the next round of people or those people when they’re reinvesting their money, they see that the the expected future return to these assets is not as good. So you know, maybe stocks were trading at 10 times earnings at the low and now they’re at 12 times earnings or 15 times earnings. But things look a lot safer, like the economy is doing better, you can be a little bit more certain. It’s easier to borrow money. So people are would have money, you can get to the same return that you were getting before. And potentially, if things appear to be getting safer, faster than they’re getting more expensive. You can actually get better returns if you just lever up more. And then what we end up with is a situation where the amount of leverage in the system is growing. That leverage is actually making asset prices. says look better, like when there’s more money circulating more people have more money to spend, while the facts mean that they’re spending more money on just about everything. So the economy looks like it’s doing well. But it’s, it’s probably doing well, because asset prices are so high. And so if anything disrupts that, then things get really bad really fast. So that, you know, I think there will there will always be this semantic argument about bubbles. And I, I think it’s, it’s kind of an unwinnable one. And I do because I have, I’ve had these debates many, many times over whether or not bubbles exist, whether the markets are actually efficient. And I think some of it just comes down to sort of a matter of taste or like, you know, a matter of what you think you can know, and whether or not you wait, anecdotally and, you know, there’s, there’s always anecdotal evidence that people seem really, really excited at the peak, they seem really, really depressed to the bottom. So I don’t think that, you know, they’re they like, did some complicated Excel model and decided I’m gonna be really, really depressed today, because bitcoins under 20k No, I think that they actually probably were actually feeling that and that those feelings affected their behavior. I think one last thing on EMH. There’s another critique, which is sort of like the the critique of positivism, where EMH explains everything except EMH, where if markets are actually efficient, it means people are working really hard to price things correctly. But if markets are actually efficient, you shouldn’t work so hard, you should just buy an index fund. And so Andrew Lowe at MIT actually has sort of, sort of tried to synthesize this into what he calls the adaptive market hypothesis, where prices are getting more efficient in the ways that we are thinking about, and then they’re getting inefficient in other ways, like they’re always tail risks accumulating somewhere, there’s always something new happening, that we don’t quite understand how to price. But at the same time, everything that’s been around for a while, whether it’s a trading strategy or a particular company, every quarter every year, we get better at understanding how it works, and better pricing it more efficiently.
Will Jarvis 11:49
I love that a little bit. I’ve got a question going off of that. We had Mike Green on the show a while back of logic of capital funds, believe is fun. And he has this big idea about passive indexing kind of distorting the market in some way. And it’s kind of like talked about with like this critique of positivism, where, you know, if you have these kind of like automatic inflows and people’s 401 K’s that you know, I have one that automatically buys you know, this like index of, you know, every stock in the entire world do you think that does, at some level has some bad distortionary effects in the long run?
Byrne Hobart 12:22
I think so I’ve been really impressed with with Mike Greene’s theories on this, I think that it is like he has I think he has like the the high level theory of just, you know, they’re they’re blindly buying and they’re valuation insensitive, and that has issues. And then there’s a market microstructure side to this of how they buy what impact that has on the probability distribution, different prices, and that I’ve just less well equipped to, to really understand that one, in part, because you need a lot of data and a lot of theory on how how, what the market impact is of these things. And markets tend to be really, really adaptive, especially when you when you get to the market microstructure level, you’re looking at this system where there are people who are constantly tweaking their market making models to predict and take advantage of exactly this thing. There’s a really good point made in the book in August on the wrongs, the laws of trading, where he says that any source of alpha cannot last forever, that people will find it, they will exploit it. And the points out this doesn’t just mean positive Alpha doesn’t just mean that you find a market inefficiency, and you’re making money that eventually you’ll make less money. It’s also the the inefficiencies of things like there used to be an inefficiency in the treasury market where I think it was something like, there was a Japanese giant Japanese pension fund or all the Japanese pension funds, they all got money from workers on a one month cadence, they would always buy at the same time, they would always buy the same treasury bond. And so you knew that you buy it the night before you sell it the morning of and you make easy money. The problem is eventually everyone knows that. And then eventually, the price just reflects that. And really, instead of you making money, because you know about this big trade beforehand, you you sort of you make money if you are able to accurately predict both the size of the trade and the size of the traders who are trading ahead of the trade and the behavior of those traders after the trade is over. And that’s a harder way to make money. So eventually, those inefficiencies, they tend to go away. And then when the inefficiency is less about the current price and more about like the probabilities, the distribution of possible prices, then it gets really hard to talk about because you don’t really know if you’re right or wrong until there is some tail event that causes crazy things. And, you know, I think if you are if you’re trading deep out of the money options and constructing strategies based on this, you can you can make a lot of money. It’s just you have to be really, really patient and expect to often not often lose, lose money for a while or at least make a lot less than you could otherwise. Until that happens. I think the there are interesting distorting effects from just the fact that there are a lot of investors who are benchmarking to the s&p 500 and a lot of investors were directly investing in it were companies that are right on the edge of index inclusion do tend to be They seem to be a little bit under priced companies get added to indices immediately go up in price. On the other hand, I think if you if you’re EMH believer, you can say that one of the factors that affects the value of any security is liquidity. And anything in the s&p is going to be more liquid than something that’s not in it. And so maybe I think the strongest EMH advocates would just say that as a measure of the value of liquidity, slightly weaker ones would say that is, maybe that’s an exaggerated measure of liquidity, like maybe a company is not worth 5%, more 10% more just because it’s being traded by index funds. And just because it gets a little bit more investor attention, but it is, that is also a factor. I think there’s, there’s there are other kinds of similar inefficiencies where there’s historical patterns. So you can you can divide bonds into high yield and investment grade, and it’s a pretty arbitrary categorization is basically below investment grade means that the rating agencies think there’s a meaningful chance that this bond is not going to pay off 100 cents on the dollar. And then there are all sorts of gradients of that, and so triple B rated as the lowest rate of investment grade, so you’re probably gonna get your money back. But something bad could happen, double B is highest of the high yield. So you are probably going to get some most of your money back. But it’s really likely that at least for some of these bonds will default. And historically, returns for risk adjusted returns for triple B bonds have been a lot lower than for other investment grade grade bonds are a bit lower. And then risk adjusted returns for the Double V bonds have been a lot higher than for other junk bonds that are high yield bonds. And it’s because a lot of investors have a mandate of only high yield or only investment grade. And so when they’re looking at those bonds, they want to do something that is exciting, that is value added where they can actually make an impact. And one of the things they do is they try to buy riskier stuff that’s going to move around more, whether that’s because they like the volatility, they love gambling, or it’s because you can add a lot more value and there’s more uncertainty, but either way, they tend not to touch certain bonds in those categories. And that actually does slightly distort their performance.
Will Jarvis 17:03
Gotcha. No, that’s really that’s really fascinating. Bert, I’m curious, you don’t have to talk about this if you don’t want to, but but what is your personal strategy, kind of the macro level look like? Do you just buy the index? Do you like actively try to trade things? You know,
Byrne Hobart 17:15
I do? Yeah, I do. I do some active trading, I try to sort of separate that for the newsletter, because I think there’s, you know, there’s the inner conflict, conflict of interest when you’re talking about things and also investing. And so what I’ve typically done is that there are a handful of big companies that I’ve invested in, and I like, will hold for a long time. And I just disclose, I mean, I disclose whenever I have a position and I write about a company involved, or company I have a position in. And then for smaller companies, my thinking is, it’s actually just tough to manage the fact that if it’s, the newsletter is big, and if people are reading it, then if you mentioned some tiny company, and you say, here’s what’s great about it, and I like it, there’s a chance the stock will react. And if that happens, then you can be in this awkward position where you liked it because it was $4 a share. And now it’s $6 a share. And so it’s much less attractive. So you want to sell and then you’re sort of telling people that Well, I liked this, and that’s why you bought it, and then I sold it, basically to you so so basically, for smaller companies, I have to just choose like is this something that is more interesting to write about, or better to own for short. And so that’s, that’s what I do. It’s tough to manage, it’s just tough to do the time management thing of having a portfolio and also writing about companies. But there is there’s a lot of synergies, I’m paying attention to the general macro situation, I think macro is just really interesting. And you get to pull in a lot of threads. And in Tech has fallen into companies and falling macro. But they do kind of scratch the same itch, where you have these really complex systems, you have these equilibria that can last for a while, they feel like they’re gonna last forever, and then they get completely blown up and something crazy happens. So kind of similar, similar mindset, similar, really high level mental models, and then totally different low level mental models.
Will Jarvis 19:03
How would you rate yourself as an active investor? Do you think you’ve done well? Or do you like, you know, reflect on these things?
Byrne Hobart 19:09
Yeah, it’s it’s a tough question. Because my, my overall track record is good, like, going back to when I started active investing, but it is very skewed to a very small number of decisions that turned out really well. So you basically take away like, two or three trades, and then I have a really poor track record, and so are a poor track are gonna really mediocre track record. And so that, I guess, you know, that depends, I think it would be, it’d be tough to actively manage money on that basis. I think if I were trying to actively manage money, you know, it’s either, I’d have to find people who really had a strong understanding of, there’ll be lots of kind of lumpy, mediocre times and then some really, really good times, but it’s easier for me to just myself, because there’s there’s no one to explain things to other than me, so Yeah, but I’m, I’m happy with my track record. It’s it’s been good, but it’s also when the sample size is small, you never really know how much of that was luck and how much wasn’t on the other hand, that’s, that’s going to be true for a lot of people. And actually, just given that there’s a parallel distribution with a lot of this stuff, the better your track record is, the more self doubt you got to have, because the more it’s likely to come down to just one or two big decisions. Right. Right.
Will Jarvis 20:23
That makes a lot of sense. I’m curious. When we look at some of the really good investors, I just finished George Soros and his book and I listen to a couple of lectures and and they were fascinating. And we mentioned this on an email exchange with me and I did have the sentiment as well. It seems like the best traders don’t really know like, exactly how they did it, if that makes sense. I remember Tyler Cowen asked Ray Dalio this recently and Ray Dalio is like, I don’t know, like, you know, he kind of puts out an answer, but it’s really make any sense. And he clearly can’t really self reflect. Do you think there’s some sense in which the best investors they you know, it’s just it’s kind of like parasympathetic or something, it runs the background, they’ve got this like feeling about it, but they couldn’t really like write it down on a whiteboard and tell you how they did it.
Byrne Hobart 21:10
So I think some of that depends on the strategy. So Buffett has been really, really good at explaining what he did and how he did it. He explained some of it, you read the explanation, realize this is really hard to replicate. So I think my favorite Buffett interviews will be the ones where he goes into the unit economics of different companies. There’s, I think there was some some, I think an MBA class went to visit him and someone asked him, I think the question was something like, couldn’t Costco make their own version of Coca Cola, and he walked through the unit economics of, okay, here’s how, here’s how much aluminum you need for Ken, here’s how much corn syrup you need, etc, like, here are the costs, here’s the cost to transport it. And then here’s the margin. And here’s what RC Cola costs, here’s what Coca Cola costs. So you’re somewhere in the middle of that. And he basically makes the case that it’s, it’s very hard to do that and actually turn a profit. I mean, maybe it can’t be done. But it’s like, it’s not a huge competitive threat. It’s just one part of the market is that for price sensitive customers, you sell them something without a great brand name. But what I suspect about that is that you can walk through this economics for a lot of other industries. And that, you know, if you ask him something like, why, and I don’t know if this is true, but like, Why did copper mines outperform iron mines that he’ll be able to tell you? Well, it’s this much per tonne. And, you know, this is your unit economics. And it’s how long the mines last. And this is, what the swing producer does, et cetera. So if you have this huge library of just infinite knowledge on all sorts of businesses, and you’ve been accumulating it over a 90 year period, then it’s tough to compete with and you can, you can have a lot of that knowledge that is just on the shelf, and eventually you deploy it and actually invest in something. And you may have been keeping track of an industry or keeping track of a company for your entire career and never pulled the trigger. And so I think, like part of the, you know, part of the Buffett technique is just spend all of your day either reading about companies or talking to people about companies, and then once every couple of years, pull the trigger and buy something. And if you can have that discipline, and you have investors who will let you do that, then you can do really well. But it’s it’s hard to duplicate. Now, Soros is a he’s a really interesting case, because he The striking thing, to me from reading his autobiography is that he’s been his autobiography and biographies of him is that he’s been trying to quit since the 60s, like he got into grad school, and then he’s working on this working for a long time trying to leave Yeah. And I think he may have finally done it, he may have finally successfully retired one, his 60 years struggle to retire. And so you know, one level, maybe he has a hard time introspecting. On the other hand, people who never have those kinds of struggles can’t really introspect. So maybe, maybe you actually learn more from someone who has, you know, knows, knows what they want to do, and can’t quite do it, because they’re actually running into problems, and you can learn from them about what those problems are. I think the other thing is that some of the sources, techniques seem pretty impressionistic, you know, but it’s, it’s also kind of similar to the buffer thing of if you’re constantly absorbing information, you have these mental models, you know, and cirrhosis case, it’s more like mental models of interest rates, and commodities, prices and currency prices. And you are just updating that model continuously, then every once in a while you find something really interesting. They do. They do act really differently. So Buffett can be very, very careful, very cautious and only buys in size when it’s exactly the right time. And then Soros. He has these lines about how when he hears about a bubble, he’ll just buy immediately and then start doing research and see what’s going on. I think those are just totally different mindsets, and I think some of that is that you’re always engineering things around your own psychology. So if you are capable of making decisions and immediately cutting losses when you’re wrong, then then it’s okay. To do that, in fact, it might actually be helpful. There’s this this term tracker position that I like that some investors will use, or it’s like they buy enough of something that they will notice they’re losing money on it. And that is a reminder to them to actually figure out what’s going on. I’m not like that could probably get overwhelming, either just in a practical sense of, if you have 50 Tracker positions, you’re probably not going to research all of them. And also, in this mental sense of you add up all the money that you have lost from basically something that could have been a sticky note that you put on your monitor saying, provider, look into company XYZ, then maybe you start to feel like that’s a bad, bad decision. But you know, a lot of people find they find ways to, to play mental games with themselves to keep on performing. And so I think some of it is like, it’s the equivalent of, you know, if you’re if you want yourself to work out tomorrow, you have your gym clothes already by the bed, and you know that you’ll see them first thing, and then at least you have to affirmatively choose not to do it, rather than choosing to do it. So yeah, sometimes sometimes that kind of middle management is is what they’re doing. And I think,
like, like going back to the question of how well do they understand themselves? How well can they tell you what they do? And how well can you copy that? You would want to be careful that you’re you’re not copying the mental coping strategy for one person’s particular mindset, rather than the practical approach to how do you pick things that will go up and not things that will go down?
Will Jarvis 26:24
Definitely. No, no, that’s, that’s quite wise. I’m curious for Berkshire, in particular, in Warren Buffett, I haven’t looked at this in the last year. So this could be it could have changed, but it seems like Berkshire’s performance is kind of, you know, come back to this, like median returns for the s&p 500. Is that just a consequence of the size of Berkshire? And it’s just more difficult to find, like, you know, alpha, or, you know, is Buffett, you know, just like lost some fluid intelligence over time. Is it just, you know, I don’t know, is it something weird like that?
Byrne Hobart 26:53
So I suspect that, for investors, crystallized intelligence probably matters more, in most areas that they, so I think fluid intelligence matters a lot for, for things that are closer to the market making and swing trading and day trading end of the spectrum that just if you if you see a headline, and you can immediately figure out what the first order and second order consequences are. That’s, that’s really good. But it can be really valuable to have that huge databank and have lots of different examples. And to remember that this is just like, I don’t know, the the crash Bolivia or whatever. Yeah, so yeah, so I think crystallized intelligence does pay or pay good returns. And there have been investments have been able to persistently perform for a long time, although they do have to adapt, they do have to choose different strategies. And a lot of them, like a lot of investments. If you look at their early, early career, they’re doing something that is lucrative, but non scalable, like one of the first things that Soros was doing was, I think, betting on the difference between things like copper gold mining companies that would be traded in London and in Johannesburg. And sometimes there’d be a little discrepancy. And if you act fast, you make money. And I think the like one of the things you learn to do is just really quickly handicap your odds, and really quickly do the mental math. And I think one of the techniques that are one of the talents that he said he had to develop was just, if someone calls you at two in the morning, New York time, and they say, Hey, we have 2000 shares of XYZ, do you want them or not? You have to be able to wake yourself up enough to answer that question. So that kind of thing can be useful. But then a lot of those investors move into more scalable markets over time where the percentage returns are gonna be worse, but the absolute returns are a whole lot better. So you know, Buffett, he would be, I think, like a beloved contributor to micro cap investing message boards, if he were still investing in the kinds of companies he did in the 50s. But he’s, he’s not. But it does mean that yeah, he’s, I think, you know, 15% is a really good return to be getting on hundreds of millions of dollars in capital, especially if you are like Berkshire, and you keep a lot of cash on hand. In case of emergency, I think if you, if you looked at Berkshire Hathaway and backed out how much cash they actually need, you’d see a business with better performance, and just a little more volatility. And they’ve, so he’s been warning investors since the beginning, like, literally, since the investor letters in the 60s that, hey, my performance is not going to be what it was when I was running less than a million dollars. And he’s actually told people at Berkshire annual meetings that if you were running a million dollars, that he’d be earning 50% a year. And he’s confident he could do that. But he’s also, you know, Buffett has a lot of ways to make 500k in a year, and it’s probably not the best use of his time.
Will Jarvis 29:29
Got it? Got it. What do you think about sources idea of reflexivity? You know, do you think it’s a it’s a good model to help them model? Is it interesting, or is it just like, I don’t know. Yeah, yeah.
Byrne Hobart 29:41
Yeah, no, I think I think it’s really powerful. So the source model is that when you would think that what happens is fundamentals change, and then asset prices reflect this. And his view is no asset prices change that actually reflects fundamental that actually affects fundamentals. And so fundamentals will improve when when stock prices go up, and that’ll keep happening until sentiment changes. And then both of those go down again. And that you can make money on both sides of this equation, I think so his first example of this that he’s given was real estate companies. And the model works really well in that case, because these companies would borrow money buy properties, and then they would pay a dividend and keep on levering up as they made more returns. And in that case, when the stock price goes up, they actually can borrow more cheaply. Some of this is that the broad perception of the company is there, they’re gonna grow, there are solid investment. Some of it is just that the the lender can say, if this company runs into trouble, they can issue stock. And if they use your stock, they can definitely pay this loan back. So we should lend to them. And so you had with real estate trusts, they were, stock prices started going up, he bought, and then their earnings actually started growing faster. And so prices went up even more. And then at some point, he, for whatever reason, I realized it was time to sell. Because in the real world, you do eventually run out of good real estate investment opportunities, especially if tons of capital is flowing into you and all your competitors. So eventually, you run out of good things to spend money on, but you’re still compelled to borrow and grow. And then things start to get bad. And reflexivity also works nicely for currencies, where if a country’s economy is doing well, money starts flowing in the money flowing in helps the economy. But you end up with these countries that this happened a lot in Southeast Asia in the 90s, where they sort of get, it’s kind of like they overdose on capital, because what happens is, they were growing because they were manufacturing because they had a cost advantage. And then as more money flows in the banking sector gets bigger, the real estate sector gets bigger, eventually, the eventually people start quitting their jobs in factories, because they can make more money building the building, the tower that houses the bank, that is making all the loans to other property developers or making belongs to the airport company that is letting more people fly in to make more investments. And so yeah, so eventually, you end up with an economy where a lot of the growth is and handling growth. And then if that growth goes away, for whatever reason, then the rest of the economy has his kind of languished, and then you’d have a pretty severe crash. So it works really well for those. It doesn’t always work well in other industries. But I think an interesting place where it works well, I wrote a piece on this a while back was in, in Tech because of equity compensation. So if tech companies, if the stocks are doing well, then people start to figure out that you are getting a better base salary and be making a ton of money on your equity if you work at a big tech company. And so those companies get their pick of talented people. And then if you have an industry where everyone in your supply chain is pretty smart and ambitious, then things happen really fast. So you know, the fact that Amazon did well meant that AWS could hire a lot of good people. And that meant that if you were using AWS, they were shipping a lot of features that you really wanted, because everyone there was really on the ball. And and if if that equation stops working, if that’s not where all the smart and ambitious people, that’s not their first choice, then then it it kind of feeds on itself for a while. And this is you know, it’s been different industries have been that have had that status at different times. So 80s, a lot of people wouldn’t finance and finance really well. And there’s this proliferation of new products. So if you were a trader, you got a lot more things to trade and a lot more people to trade with. So for a while, everyone’s doing really well. But then we sort of ended up over finance for a while. And you can look earlier than that. And in the 60s, a lot of big companies had that status. So you know, Procter Gamble, really desirable place to work, you know, you joined when you’re in your mid 20s, and your assistant brand manager for a brand your parents have heard of. And then after a couple of years, you get promoted to brand manager. And now it’s your job to make sure that the world uses lots of lots of Tide detergent. They got a lot of really, really smart people who did well later on like in the I think in the 70s. Jeff Immelt who ended up running GE and did not do especially well. It’s impressive that he became the CEO, and Steve Ballmer, who over his entire track over his entire career at Microsoft had a really good track record. Although people tend to focus on the last couple years, they were actually office mates cubicle mates in the 70s working for Procter and Gamble. So that’s where ambitious people went, then. And then you can even go back to like, 1940s don’t really count because whether whatever your level of ambition was, you were probably getting conscripted, or you were working at a factory because someone else got conscripted. And then 1930s, a lot of those people went into government. And so you’ve actually had a really competent government that was getting a lot of stuff done, because that’s where all the smart people works. It’s not like anybody else is hiring. So you can look at those cases where there’s a big flow of talent and money in one direction. And you can view that as reflexive process where the talent means that there are more useful things to do with the money, the money means more talent gets attracted. And then eventually, you just run out of all the useful things that that entity can do. And then everything has to flow out.
Will Jarvis 34:54
And it can kind of work the other way too. I’m assuming we’re like this process can run backwards and things and get worse for quickly.
Byrne Hobart 35:01
Yeah, although it’s it’s kind of I wonder how much of that how much of it sticks around, you know how much because there’s, there’s gonna be some inertia like, I think this with with US government, the government was accomplishing really impressive things in the 50s. And in the 60s, you know, Paulo might be the peak of that. And then by the 70s, I think a lot of the a lot of super competent people who had joined in the 30s, they were getting to a point where they could retire. And so they did. And then they there wasn’t a new cohort of the same caliber of people to take things over after that.
Will Jarvis 35:35
Is that kind of what we’re seeing right now with tech? I know Noah Smith had this tweet a couple of weeks ago, where he said, you know, although there are a lot of people who thought they made $40,000, who now make, you know, $200,000, or something like that, because the RSUs for tech, equity compensation had collapsed a lot. Do you see that as a problem for big tech, you know, over the next decade? And is there any, anything they can really do about it at this point?
Byrne Hobart 35:58
I think that there’s there’s still a secular bull case for the big tech companies in that a lot more human activity, a lot more economic activity will be mediated through software over time. There’s just a lot of there are a lot of people whose job revolves heavily around copying and pasting or answering emails in a fairly formulaic, straightforward way, a lot of that can and will get automated away. So there’s, there is still room for that industry to grow. But yeah, it’s, it is harder for it to grow when asset prices are not going up. So they have a harder time recruiting people, I think, maybe the positive news is that it’s hard to name another industry that is going to get all of that talent. I mean, maybe some of them are moving into, you know, going back to school and doing biotech or learning a ball about fusion or something. Certainly, some of them are going to quantitative hedge funds, because those funds were already at the point where they were trying to match offers from Microsoft or Facebook or whoever. So maybe, maybe now those those funds get all the talent they can handle. But yeah, there’s there’s probably not, I can’t think of an industry that would use the same kinds of people and be able to employ that many of them.
Will Jarvis 37:08
Got it. Got it. So it’s an interesting challenge. What do you think about the macro environment? The US it right now, I think about the, you know, inflation in context of, you know, perhaps lower growth, peers, a lot of talk back in 2013, about how, you know, if we don’t have enough technology, underlying technology driving, he’s talked about this since then. But we don’t have enough underlying technology driving economic growth, it’s like, you know, what are you going to do like, there’s just gonna be less coming from? What do you see coming in the next decade? Are you bullish on us building enough tech to kind of drive economic growth in the US? Or do we face like a real challenge?
Byrne Hobart 37:41
Yeah, I’ve, I’ve really gone back and forth on that, because I think there’s, there’s this interesting analogy, you can draw between factory electrification and AI, where in both cases, it just there was a really lengthy rollout. And part of it was that it took a long time to figure out why it took a long time to figure out how to run a an electrified factory differently from a factory that used a more centralized power source. And eventually people realized that you could have a totally different architecture, like, it doesn’t need to be right next to a river, it can be somewhere else. And it can spread out in just two dimensions, instead of being this big three dimensional structure where there’s like a central power source, and everything is just ropes and pulleys attached to that. And that actually, it eventually fed into even how companies are financed, where if a factory is something you build around a specific discrete power source, then you build them one at a time. And they’re really big, which means that you issue a bond or you issue stock to do this one thing, and then you’re paying your investors a pretty fixed return based on what that one asset generates. But the factory is this two dimensional thing that can just expand indefinitely in any direction, because you can just always add more equipment or you can replace it with more efficient stuff, you are constantly trying to keep this power balance exactly right. And you don’t have to worry that as it expands, you know, the pulleys gets stretched and something breaks. Once that happens, it actually makes sense to retain more earnings and grow continuously. And so you started to actually see, you can you can look, it’s there’s a there’s some data source, it’s all compiled in I think the 1930s of basically, every industry is price, earning price and earnings and dividend performance going back to the mid 19 century, then you can actually see the dividend payout ratios going down as more companies retain earnings and start to fund their growth internally. Where I think the analogy fits with AI is that there are a lot of tasks that can be partly automated can be definitely enhanced through that kind of technology. A lot of it requires human input for now. And then there is a period where it actually requires more human input to fully automate it. So there are a lot of people out there who are doing image labeling. I think a lot of us don’t don’t realize the extent to which we are training, very elaborate language models to produce the things that we produce, but I’m like, I assume that I’m an input into a lot of these models, and they will eventually be able to do roughly what I do. But in the meantime, there is this period where any kind of symbol processing work, especially if the client that is not just map this table of symbols to this one according to a defined set of rules, where if the rules are more implicit and harder to write down, you actually just want human beings to follow the rules a bunch of times and train the computer in how to do it. So there can be a period where that actually increases the demand for white collar work until it eliminates the white collar job category entirely. You know, hopefully, that’s a nice, gentle process. Like it’ll be, it’ll be kind of funny. If we reach something, if we reach like the singularity for white collar workers where, you know, you have like GitHub, copilot, 5.0, that can just write you a new Facebook or can actually solve Twitter spam problem in a week. And it’s just done. But then we still need human beings for things like actually assembling iPhones because there’s a there’s a wonderful article in the information a couple years ago about how Foxconn keeps trying to automate things that keep her to automate their assembly. But it’s really hard. And one of the things that the article clarified for me was that the human hand is actually just this amazing piece of technology, where it has little motors, it has little sensors, it’s actually hard to get motors and sensors that are that sensitive, and that can, you know, start screwing a screw in and then stop screwing before they break the screw robots, I have a hard time doing that humans can actually do that pretty well. So yeah, maybe maybe newsletter writers will all end up working as electronic electronics assembly in the future. And you know, hopefully, hopefully, that’s a world where GDP per capita is like $500,000. A year, right? It’s we’ve we’ve had this super abundance of information goods. And then you know, you hope that almost cost disease kicks in. And there’s still a middle class lifestyle available to the assembly workers and yeah, other people doing non scalable, either, like light manufacturing, or service drops or whatever. But yeah, it’ll be it’ll be an interesting world regardless. But yeah, so that’s, that’s a case of maybe a scary case. But a case for being a techno optimist is that we have a lot of cases where human intelligence is non scalable, applied to problems that machine intelligence could be scaled li applied to, and we don’t really know, the total extent of that space in the same way that would electrification came on, we didn’t really understand how many cases there could be were just add power, and you know, have it come in a little wire that you could plug into anything, we didn’t realize how many applications there would be for that, and how many things would just turn into electric into electrical appliances.
And then I think the the negative case is that if you if you view the the great stagnation, as either we’d picked all the low hanging fruit, like then we there was a lot of low hanging fruit. So the the existence of hydrocarbons, aside from environmental impacts, has been this incredible blessing for humans, because they are very energy dense, and they’re all around the world. And you can transport them easily. And they’re useful for just generating a lot of energy. So you know, maybe like, if we’re really optimistic, we can imagine that there is some oil like resource we haven’t discovered yet. And that’s going to have a similar effect and give us another give us a third industrial revolution. But probably not probably, we would have found it by now. So maybe we picked all the low hanging fruit. And then what we have left is increasing efficiency, offset by increasing resource depletion, and that hopefully, that evens out with just we continue to find efficiency gains over very long periods, which is possible, like there are industries that have driven very long term efficiency games, you know, Walmart is an incredibly well run operation, and continuously gets better. So there’s, there’s room for that kind of growth. There’s a there’s a book by an economist, I think his last name is Bill Rath called fully grown, where he argues that the great stagnation is just the natural end state for an economy that early on. So very early on, you’re agrarian, you start manufacturing, you eventually reach the limit where everybody has all the stuff that they really want. And more and more of the growth is in services. And those are naturally hard to scale. But I think the current argument to that is, we don’t really know if those are hard to scale yet. We don’t really know whether you know, whether teaching scales whether there is some kind of enough variance in teacher skills such that one teacher remotely teaching to 20 million people is actually better than a million teachers, each teaching 20 people in a classroom, you know, if you if you make some assumptions that you can quantify teacher skill, and that you can quantify the information loss of zoom versus in person, maybe you get to the point where you say, actually, we can replace a lot of teachers with a small number of superstar teachers who do an incredible job. Or maybe we partially like we replace some subset of education with that, and then some of it remains not scalable. And if you take out big chunks of a non scalable business and turn them into a product that’s continuously improving, then You do actually free up a lot of human time for other activities, you do actually create a lot of growth. Something similar might happen in healthcare. So you know, healthcare, it’s a very labor intensive job. Like, if you’re dealing with patients, you have to actually move them around, you know, roll them over and things like that. And you can, there’s there interesting promising obesity treatments that could just significantly reduce the a the amount of chronic illness and country and be just the physical effort required. And if you, if you can slightly increase, you know, just five or 10%, the number of patients who could be served by a given number of hospital personnel, then you have just made the entire healthcare system that much more efficient. So I think that there’s there’s a an optimistic possibility that we will find these cases where something is less labor intensive than we thought, or there’s a, there’s a part of it can be made capital intensive, instead of labor intensive. And then then we can move back to pessimism. So one of the pessimistic cases is demographics, that when people are having kids, that tends to drive a lot of consumption and tends to produce a lot of demand. So you know, they, they’ll want houses, they will want appliances, they’ll want cars, they will buy a lot of stuff. When people get older, they tend to consume a lot less, and they tend to have a lot of savings. It used to be that they would get older and spend on their savings. But actually, if you look at data the Fed does I think the New York Fed does a survey every three years. And they look at wealth by demographics. And it used to be that wealth peaked for people in their 50s. And I think like 50s, through 60s, and then decline after that. But now there’s, there’s a cohort of those people who have basically no money, yeah, living off of Social Security. But there’s another cohort where they, they own a house, they have retirement savings, and they are they’re earning money faster than they’re spending it. And so they actually get more and more wealthy over time. And if that happens, then you just have a big supply of capital and not a lot of demand for it. And, and then you end up with a low real growth world with low interest rates and low inflation, but just not not much is really happening. And since since that cohort is outbidding young families for housing and things, it ends up delaying family formation, so it actually exacerbates the problem. So that’s and also the older demographics are way more likely to vote. So even though like the baby boomers are not the biggest generation, it’s actually my generation, this sort of echo of the baby boomer is a slightly bigger generation in numbers. But in terms of votes, baby boomers matter a whole lot more, so policy is going to wait, their interests a bit higher. And those interests are just, you know, less if they’re less growth oriented, more stability oriented. And so that’s, that’s probably how calls is gonna tilt.
Will Jarvis 47:45
That’s quite the black belt. That’s great. But But, but on the positive note, I wanna get your take on this something you mentioned a little earlier in that meta calculus, I think has the odds. It’s a prediction market. And I might have pronounced it wrong. I’m sorry, I’ve only read it for the audience. But did they have the date at which we’ll have AGI you know, the average bed is around I think, like 15 to 20 years at this point, which seems like really close to me. Do you think that number that’s a good number? Is that way too close? Is that you? What are your thoughts there?
Byrne Hobart 48:19
So I have been increasingly impressed by AI advances over the last eight years, they seem to be coming faster and faster. Yeah, I just got my got my Dalai access. days ago. It’s it’s really fun. And so, and my understanding is that there’s a lot of those prediction markets, they shift materially when one of these events is happens. So yeah, I feel like probably the confidence interval is getting wider now. Because there’s one possibility, which is just that there is something just ineffable about this stuff, we can’t actually build general intelligences, we can build things that are really good at copying some salient parts of it, but not not all of it. There’s there’s also the possibility that AGI matters less than really tight integration between humans with agency and tools that can offload a lot of computing. And that, you know, it’s we can you can hit that intermediate stage I mentioned that I’m I am, by by accident training the language models that will replace me in my job, there could be a period where I am instead, training a language model that I own and instead of writing a newsletter, or writing one newsletter that goes out to 35,000 people, I’m actually writing 35,000 different herbs seeding the algorithm that writes 35,000 of them each one, that is exactly what one person is interested in, and maybe that gets premium for a while until again, they get replaced. That’s, that’s a definite that definitely feels really valuable. And, you know, anytime you’re talking about AGI, you can imagine that event horizon where you just can’t model any thing that happens after we get to sufficiently powerful general intelligence, as long as it’s sufficiently good at persuasion, which I think is one of the things that the AGI people, Ajay, researchers will potentially overestimate, maybe because they are very persuadable by by sufficiently rigorous logical arguments. So it feels like if something is, you know, like me argues like me and is way smarter than me and can can make a counterpoint any point that I make, then I should actually do what it says. Right? Or at least if I shouldn’t do it, it says, at least everyone I know would be easily persuaded by this thing. So they’ll all do it. It says, it’s entirely possible that AGI just ends up being a nerd who gets ignored and has a bunch of really good ideas that don’t actually get implemented. So there’s there’s that as maybe slight handbrake. But AGI is a tool where it’s very smart, but doesn’t have agency or can’t really act. But sufficiently smart people who realize that smart can apply it to things they want to get done. That’s, that’s probably just for the average person functionally equivalent to malevolent AI. It isn’t quite malevolent AI, but it’s just, you know, someone who is sufficiently indifferent to them that they might as well be malevolent. And they also have AI. That’s, that’s a risk that has to be closer than actual AGI because it’s, it’s easier to implement than AGI. And so that’s, that’s one that I really worry about. But, yeah, AI is one of those topics where I pay more attention to the incremental advances, because it just, if you buy the singularity argument, and you buy the possibility of AGI modeling, what happens afterwards is almost meaningless. You can think about what your mental model is for when we are when we actually hit the hockey stick. And what do you do in the hopefully, weeks, not like hours, before everything completely changes. And that’s kind of that’s kind of worth doing. But again, it’s sort of like if you’re planning around a revolution, but you’re in a country that you can’t leave, then you sort of just want to be kind of ready for things to get weird and unpleasant.
Will Jarvis 52:14
Definitely, definitely. This next question, it’s been a lifetime turn, but I am curious, your take on it. On average, should we have more cults in the world? And why?
Byrne Hobart 52:23
Sure. So I think that there’s no, there’s been a general decline in social trust. And there are a lot of big institutions that are just hard to hard to trust, because they have a lot of visibility, and there’s a strong incentive to highlight their mistakes. And I think what’s nice about cults and other small groups that they just don’t have enough visibility to have added haters, and they might have enough visibility to have, you know, socially proximate haters. But that’s actually more of a powerful unifying force where you know that these people think you’re the out group, and therefore the other members are the in group. And, you know, solving coordination problems is really hard. And when when groups can do it, they can accomplish great things. And so I think that having more of these Lucas’s of coordination can be really, really powerful. And yeah, we just have a, we have a shortage of civic engagement in the US, at least compared to where things were historically, it’s something has to fill that gap. cults are obviously dangerous, and the cult term it has, it has broad applications. And there’s, there’s a whole spectrum of saltiness where, you know, one of the things is like, if a cult wins, it’s not a cult anymore. It’s something else. And it often gets, a lot of the weird stuff just slowly dissipates or gets ritualized and made a little bit more harmless. And you can sort of see echoes of it in in whatever that group practices but it’s, it’s not quite as crazy as it was initially. So when you’re betting on culture, you don’t you don’t want to bet on cults that make you think of the word cult, you want to bet on things where you you look at this high functioning stable institution, and you go back in its early history, and you realize wow, that sounds a lot like a cold good thing. They’re not like that anymore. So it’s like it’s a it’s a good launchpad for other things. I wrote a piece a while ago, when when peloton was going public, were one of the lessons I asked was, is Palantir a cult, because I lived in the bay area for about six months. And I noticed that people I do a Dropbox, sometimes that Dropbox wax sometimes did not. And people I knew at Facebook sometimes did sometimes did and usually didn’t buy them. But people who worked at Palantir always had their Palantir stuff. And it seemed like they did not actually own non Palantir clothing. So and they have kind of unusual corporate culture they spend, they work really hard, which means they spend a lot of time with other talented people. You know, it has a lot of these cult like things. And part of what I realized is that that is a very powerful motivating force that you have these groups of people who have a shared objective, and everyone else thinks they’re weird, but they they know what their mission is. They’re trying to accomplish it. And that seems like a powerful force.
Will Jarvis 54:50
Gotcha. So should more businesses, particularly startups, you know, perhaps try to be called?
Byrne Hobart 54:56
Yeah, yeah. And I think it’s it’s hard to fake on the other hand cults They can, they have to exist at that really uncertain stage where it, it could be cringe, it could be non cringe, and you’re actually not sure yourself. Like you, you ideally want it to be the case where everyone is thinking to themselves, I bet other people think this is cringe, but I’m really into it. And that’s not actually an observable trait. It’s not observable that anyone that has that mental state, but that’s kind of what you want to aim for. Because you do want, you want there to be enough enough stuff that define someone as a member of the group, that it’s a sharp, meaningful distinction. And then not enough that it’s actually self destructive.
Will Jarvis 55:36
Got it? Got it. No, that’s wise. burnup got one last question here for you. What have you learned about business from the success of the death? And do you have any advice for someone who is like producing content or something like that on the internet? Yeah, so
Byrne Hobart 55:49
I would say in one sense, the diff was actually a failure, because the goal was right to do better for a while in order to stay in touch with people and find another job somewhere else. So in that sense, it did not work it it worked better than they thought it would. But yeah, it’s, it did not accomplish its original goal. So I guess that’s one thing is, don’t be wedded to that original goal. You know, I think if someone starts a newsletter, trying to build a big subscriber base, and have ads and things, and then that ends up getting them a job at a company that never heard of, but that is really cool. And doing is like what they want to do, then that is also you know, a failure by its original metric, and then an ultimate success. So that’s, that’s one thing is, don’t be don’t be afraid of that kind of pivot and newsletters do have, they have this wonderful surface area expanding trait where you’re in touch with a lot of people. At the point where I don’t think there’s anything I can write about where I know more than the best informed reader on that topic. So I’m always getting useful corrections, amplifications clarifications and things. And you know, sometimes, occasionally, that is just I got something totally wrong. And that’s always very embarrassing. But there’s often something where I’m speculating on why a company did x, and someone tells me Well, I was, you know, I was in the room when that decision was made, it wasn’t X. But that was a concern, it was actually why. So that kind of feedback can be really interesting, and really powerful. And I’ve met a ton of people through the newsletter business, just through through writing about different companies and covering different topics. And so it’s, it’s a really good way to have a social network that is very closely attuned with your personal interests. So I highly recommend it as a just a part time side project for that reason that if you do one good post a month on something you’re deeply interested in, then the right people will find you. And I think, within that category, there have been, it’s very hard to predict success. This is one of the reasons I write a lot of posts. It’s not it’s not quite that the strategy is just to have an overwhelming amount of content, it’s more like I am very bad at figuring out which posts will take off and which ones will not. There have been cases where I thought something was, you know, I would be interested in nobody else would be interested in turns out a lot of people were interested, like I recently wrote a piece on the Canadian economy and how Canada is structured a lot more like a developing country where you have these big oligopolies, and lots of capital flows from other places that are distorting the real estate market and lots of dependence on natural resources. And it actually turned out to be really popular on Twitter, and I got a lot of good feedback from it through email. So that was one that I was pleasantly surprised on. And then I’ve been unpleasantly surprised sometimes where I spend a ton of time writing about something that I think is just really deeply interesting, and should be known by a whole lot more people and nobody cares. But what I have found is that for those kinds of posts, where it’s something you really care about, you’re not sure if anyone else cares about Google is a wonderful thing. And people who are Googling a really odd collection, it will find keywords, they will find it and so there’s a there’s a piece that I that I wrote about voulons philosophy treating treating a lot of political movements as the Gnostic heresy, and I wrote about parallels between that and bubbles. And it not a lot of people read it, it’s very long. Not a lot of people read it, but I think in terms of percentage of people who read it, and then told me that they want to get lunch with me the next time I’m in town, or they’re in town, it’s it’s by far the winner. So that and some of that is is due to industry specific factors. So a fair number of people will go they major in philosophy, and then work and finance sources, not the only one and it’s I think part of it is that if you look at GRE scores by major philosophy is actually second to physics usually or or third after math, but it’s highest on verbal by far. So the subset of finance that is understanding the rules and understanding the loopholes and rules so things like there’s a contract you know, CDs contract that says if a company defaults on its debt, you get X amount of money, if you can figure out something that is a default for the purpose of that contract, it is not a default for some other purpose. And the CDs contract is not price things as if that is a life possibility giving a lot of money, but you have to be strictly verbal, and you’d have to be the kind of person who is thinking a lot about the difference between what reality is and what can be expressed as words on a page, you know that these like deep metaphysical questions that also turn out to generate alpha. So finance does select for some of those people. And I don’t know that you can play quite that game in other fields, in terms of writing things that hit that Venn diagram. But yeah, finding, you know, writing, I would say, if you are, in whatever industry you’re in, if you write something about a company, that industry that is no longer in business, but was interesting, 30 years ago, is totally forgotten today, that kind of thing that people will eventually find it. And there are people who are researching that company, and if you can demonstrate that you really care. And you You found it as fascinating as they did, then those are probably people you wanna hang out with.
Will Jarvis 1:00:49
That’s good. That’s good. And do you have a 50 year plan for the Deaf? Is there? Is there you know, do you have great ambitions? Or what do you think?
Byrne Hobart 1:00:55
I don’t have a 50 year plan I have I have nearer term plans than that. And a lot of them, you know, there’s a business like this accumulates a lot of potential energy. So there are, you know, a lot of people who read it, and some of them subscribe. And so that is that is one source of revenue. But there are a lot of other things that you can do with a large contact list of people who are really interested in tech and finance. And if you have that, that group of people and a brand and a balance sheet, then there’s a lot of really interesting stuff you can do. So yeah, there’s there. I’ve thought a lot about different ways to make that happen.
Will Jarvis 1:01:34
Gotcha. Something to come. Well burn. Thank you so much for coming on the show. Where can people find you? Where should we send them?
Byrne Hobart 1:01:40
Sure. So best place look for me would be on the diffs site, which is the diff.co so please check it out. I’m also on Twitter. Awesome. Burn Hobart.
Unknown Speaker 1:01:51
Byrne Hobart 1:01:53
You bet. Thanks.
William Jarvis 1:01:58
Special thanks to our sponsor, Bismarck analysis for the support. Bismarck analysis creates the Bismarck brief, a newsletter about intelligence great analysis of key industries, organizations, and live players. You can subscribe to Bismarck breeze at brief dot biz market analysis.com Thanks for listening. We’ll be back next week with a new episode of narratives. Special thanks to Donovan Dorrance, our audio editor. You can check out documents work in music at Donovan dorrance.com
Transcribed by https://otter.ai
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