ChatGPT, AI, Systemic Risk & The Case for Dividends

[00:00:00] Speaker 1: This is the Insight is Capital Podcast.

[00:00:07] Speaker 2: The views and opinions expressed in this podcast are those of the individual guests and do not necessarily reflect the official policy or position of advisoranalyst.com or of our guests. This broadcast is meant to be for informational purposes only. Nothing discussed in this broadcast is intended to be considered as advice.

[00:00:21] Pierre Daillie: Welcome back. I’m Pierre Daillie, Managing Editor at advisoranalyst.com. My very special guest today is Sri Iyer, Lead Portfolio Manager and Managing Director and Head of i3 Investments at Guardian Capital LP. Sri, welcome back. It’s great to see you again and catch up with you at this pivotal moment in the economy and monetary policy and markets. What’s new with you? And how are you?

[00:00:48] Sri Iyer: Pierre, again, glad to be back. Well, it looks like every time we talk to each other, something big is happening in the market. So-

[00:00:55] Pierre Daillie: For sure.

[00:00:56] Sri Iyer: … [inaudible 00:00:57] own good, and I think since we last spoke, a lot of the things we discuss have come to fruition. So, it’s kind of a very good point of time to kind of reconnect with you and try to get you some ideas as to what we’re thinking and what we’ve seen where things are going. So, really looking forward to our chat today.

[00:01:13] Pierre Daillie: Yeah, um, me as well. Very excited, because I, you know, I think we’re, you know, as I mentioned, we’re in this, we’re at this, what seems to be like a turning point or a pivotal moment in markets and, you know, this meltdown of SVB. It’s really highlighted, you know, the, the, the, the story that people tell that the Fed is almost always responsible for breaking the economy, once, you know, we, we, once it, it engages in a rate hiking regime.

[00:01:41] Sri Iyer: Mm-hmm.

[00:01:42] Pierre Daillie: And that’s exactly, that’s exactly what we’re starting to see the signs of, right?

[00:01:46] Sri Iyer: Yeah, I, I would say the average person sees a bipolar response in the market right now by the Fed. Um, my take is the Fed, in my opinion, actually is having a very measured response to the market cycle. Um, the real risk of market happen in the blockchain Bitcoin side. If there was no Bitcoin, things could be much, much worse and things could have precipitated much more, because a lot of the risk capital went into that side of the market-

[00:02:21] Pierre Daillie: Right.

[00:02:22] Sri Iyer: … um, response. So, with the unwinding of the Bitcoin phase, and you saw the meltdown in that phase with, with, with a blow up in a couple of exchanges on that side. The market got a taste of what, uh, de-risking really looks like. Despite that, as we say, don’t fight the Fed is the common adage on the statement, um, I think the Fed is still committed to reducing inflation and I don’t think so the market should construe this bailout however ad hoc it has been to be a signal towards any kind of pivot to say that there’s going to be a backstop to a measured response to the market.

So, about three months ago, the standard response to the market was the subconscious bailout was going to come up from a pivot. Now, with persisted inflation just because CPI just came out today and you saw that the inflation has been persisted across virtually every category you can imagine-

[00:03:25] Pierre Daillie: Right.

[00:03:25] Sri Iyer: … other than used car, used car sales is down about 2%, big D. So, what you’re seeing here is persistent inflation we should want the Feds targeting. What the Fed is saying on one side is that we’re going to keep going at that target at any cost, but the definition of costs is not a bailout for some kind of failing institution, but more about protecting the average investor because of a contagion response to consumption and the consumer in general. So, I don’t think so the Fed has taken a bucket to a tsunami yet. That’s my two cents. I think this response to SVB and anything else is actually a good decision by the Fed because you want to take the air out of the balloon in a measured way as much as possible and not pop it.

[00:04:28] Pierre Daillie: Yeah.

[00:04:28] Sri Iyer: So, the narrative of a hard landing versus a soft landing and all of those kinds of, um, conversations in my world are not really relevant. What I’m trying to say here is that do not assume the Fed is in a bipolar mentality here. It’s quite determined and measured in keeping interest rates high and the fallout of high interest rates for longer, high inflation for longer is a compounding effect on the economy, and so the Feds got a much more longer horizon than the average human being as right now, and we’re gonna understand the Fed doesn’t look at it from a day-to-day basis or a month-to-month basis, but looks at it as a structural change in the way free money in the market needs to be taken out. Then I think the action that the Fed did is quite measured, in my opinion. I could be contrarian-

[00:05:19] Pierre Daillie: Yeah. I, I, I, I… I mean, my, my thoughts on it is you, you really have to applaud the speed with which they reacted to this matter and so let’s, let’s, let’s take a pause on that part of the conversation, because I want to change gears just for a moment. Um, I’ve been dying to ask you about your take on ChatGPT and generative AI in general. I mean, when, when that happened, actually, you were one of the first people that I thought of, because we’ve had so many conversations about, um, you know, surrounding artificial intelligence. You must have been beside yourself when, when AI took center stage in the tech race this year. What’s, what’s your take on it?

[00:06:05] Sri Iyer: Oh, it’s, it’s… In my opinion, it’s very real. For us, we’re really excited in the sense. Obviously, ChatGPT came out in November, and it took the world by storm, right? It’s, it’s, I mean, it’s, it’s the, it’s the, what I call it, the poster child for artificial intelligence for all the wrong reasons. I’ll put it that way, um, but it’s basically a LLM model, large language model, and the biggest thing that ChatGPT did is to shake up the world beyond the abstract sense of what AI is, is because suddenly the average person is actually touching AI in a very, very cognitive way. So, the evolution of what you call humans and bots merging, ChatGPT is the first instance of that, which could be kind of overt versus being covert.

[00:07:01] Pierre Daillie: Right.

[00:07:02] Sri Iyer: So, even an average person, my, my, my dad, who’s 80 years old, was asking ChatGPT a question, which is an 80-year-old when who was around when there was no computers is now chatting to the bot and trying to get an answer. So, you could tell how profound his response is. And so if you look at it from a philosophical sense, the 1800s is all about mechanization, water and steam. Then you saw the 1900s, where you saw mass production, electric power, and turbines and assembly lines, and in 2000, you saw computers, automation, electronics all emerge. 2000’s not that far away. Then it was like, 2010 you saw Internet of Things, networking and machine learning, which is what we got into in 2017, 2018, and now you’re looking at ChatGPT, regenerative AI, human bot reaction, collaborative AI, cognitive AI, prescriptive AI.

So, it’s going at a pace that guys like us even can’t keep up with it. So, as someone who implement artificial intelligence in investment decision making, um, I think the emergence of regenerative AI, which is more prescriptive predictive type of model that is using natural language processing, as a, as a core base, um, is quite profound. We, we use NLP data for new sentiment analytics, and-

[00:08:28] Pierre Daillie: Right.

[00:08:28] Sri Iyer: … uh, gauging the tone of a news article and aggregating the tone of the market. We do all of that, but regenerative AI is, is, is, is, is kind of the next, next level of I would say, uh, inferencing, for lack of words, and let me just kind of, um, simplify it in the sense that most machine learning the guys like what we do. By the way, I don’t profess any knowledge in the ChatGPT space. I’m just giving you a context of what data scientists like us do. Most data-

[00:08:57] Pierre Daillie: But you do you understand… I mean, but Sri, you do understand how artificial intelligence works.

[00:09:01] Sri Iyer: Oh, very much so. Very much so. So, if you look at what we do with machine learning and AI, uh, deep learning and all aspects is a lot of the thing is about processing data, curating data, what you call feature engineering data, feature selection, um, and that’s where 90% Of all the work happens. Once you train the models, and then you give it today’s information, it can infer-

[00:09:26] Pierre Daillie: Right.

[00:09:27] Sri Iyer: … a solution from what we have trained it with. ChatGPT have made this whole thing fully upside down. It’s crazy. Now, you go to see society use ChatGPT to infer, while they speed up processes, the number of GPUs being used and TPUs is being used. GPUs are graphical processing units. TPUs are tensor processing units. The size that this has scaled out to now, you’re going to see an upside down response in the world where 70 to 80% of AI is not going to be used now’s going to be used for inferring, and only 30% is going to be used for training.

[00:10:04] Pierre Daillie: Wow.

[00:10:05] Sri Iyer: So, that’s a monumental juxtapositioning in a matter of two years. So, guys like us look like a carpet has been pulled out of our feet right now to figure out where’s the trade-off between compiling and inferring a training and inferring? So, the ratio of training data and inferring solutions has been put upside down in the world of ChatGPT. So, it’s open source. You got programming languages like PyTorch now that are coming out with a new generation that teenagers can use now where it’s going to be a common language that you can use for AI training across any processor, whether it’s the GPU, CPU, TPU.

So, you have got to have a common language of training data and inferring knowledge. So, it’s been a little bit of a dramatic shift in the world of Democratization of artificial intelligence. That innate feeling is what is creating such a big buzz in ChatGPT right now.

[00:11:03] Pierre Daillie: Well, sure, I think I think a lot of people are looking at ChatGPT, you know, especially in the content creation world, um, you know, to, to make them better writers, provide you know, like, like, if you’re writing an essay, you can ask ChatGPT for an outline for that essay. You know, you can, you can… you know. It, it, it certainly, uh, helps as a creation, you know, as a creativity tool as well, because it, it, you know, it has the ability to go into areas of thought that you might not have thought about yourself if you’re using it.

But I, I think, I think it’s interesting what you said though about how, you know, people are using, you know, u… Content is being created and inferred using ChatGPT and then it’s being published, but does that mean, and, and so, but I think what you meant was that because I heard, I, I did hear some, um, rumblings about this in, in interviews that I’ve watched about it, but I think one of the points was that, was that if the future if, if in the, as things progress, obviously, in the future ChatGPT or the AI behind Chat, the ChatGPT is training itself on, on, on bad information. Then that bad information will trickle into new information down the road, right?

[00:12:25] Sri Iyer: Absolutely. Training data is everything. If you, if you look at the source data for ChatGPT, it’s the digital universe that’s been around for 30 to 50 years now, and we can all make our own personal judgment as to what the digital universe looks like. So, ChatGPT learns from the narrative of digital data and digital data always has a narrative-

[00:12:47] Pierre Daillie: Yeah.

[00:12:47] Sri Iyer: … because news is always intertwined with opinions and facts.

[00:12:51] Pierre Daillie: So, Sri, sorry, does that mean that, does that mean, ultimately, that ChatGPT will wind up training itself?

[00:12:59] Sri Iyer: ChatGPT already trained itself.

[00:13:02] Pierre Daillie: [laughs]. Yeah.

[00:13:02] Sri Iyer: So, that’s what regenerative AI is, in the sense-

[00:13:05] Pierre Daillie: Yeah.

[00:13:05] Sri Iyer: … it is using its data-

[00:13:06] Pierre Daillie: Re… Yeah. Regenerative. That’s right. I mean, it’s generative, which is, which is from scratch, but then regenerative is where that stuff is being recycled.

[00:13:15] Sri Iyer: Absolutely.

[00:13:15] Pierre Daillie: Right?

[00:13:16] Sri Iyer: So, it, it is detecting false positives through a feedback loop of humans. So, when it gives you a question, when you give it a question, and it gives you an answer, and if you respond to ChatGPT that your enter is biased. That’s when you get the true answer from ChatGPT, not the first answer. The second answer from ChatGPT is much more accurate than the first answer because it is regenerative. It, it, it… Once you question its bias, it will go back and recheck to see it has bias or not, and if it has bias, it will come back and respond to you saying that, yes, that could be bias, but so that gives you that caveat, so that you as a human being can be intelligent enough to not take it at face value, but use it as an opinion piece or information response that you can then make your own opinion upon.

[00:14:03] Pierre Daillie: Interesting. That’s very, very interesting.

[00:14:06] Sri Iyer: Versus just, just reading the first thing it sends tells you. Reading the first thing it tells you while it is quite accurate I don’t think so involves a degree of intelligence. Probing ChatGPT to get a more detailed response is where the real learning comes in and ChatGPT appreciates it because your feedback loop is what it’s using to learn and make it better.

[00:14:27] Pierre Daillie: Right, but what… I mean, if you, if you just consider the simple adage of garbage in, garbage out. Um. You gotta be, you gotta be careful.

[00:14:38] Sri Iyer: Yes.

[00:14:38] Pierre Daillie: You do have to question the results you have, but the fun… But that’s interesting that you can actually question the AI issue itself.

[00:14:45] Sri Iyer: You sure can. You sure can.

[00:14:46] Pierre Daillie: In it, in its… Right?

[00:14:47] Sri Iyer: Yes.

[00:14:48] Pierre Daillie: And so, so users are going to have to become increasingly careful about where the information is coming from, and, and I mean, it, it, it’s almost like… Well, I don’t want to say It’s… It’s in a way, it’s human-like. If all, if all we’re caught in is a feedback loop, you know, of bad information like, for example fake news. There are entire cohorts of civilization that are, that are, you know, caught up in the fake news, thinking that that’s reality or because they’re visiting the same sites every day. They’re visiting the same, they’re visiting the same sources. They’re, you know, they’re seeing it in their feed.

[00:15:28] Sri Iyer: Well, it’s on, on both sides, right? It’s both on the left and the right and that’s where the bias kicks in, right? When, when the narrative is only right or only left, that’s where the problem is and ChatGPT, uh, right now will be biased on the narrative in the market per se, as to whether much more about left is being written know much more of our right’s being written. It doesn’t have that cognitive intelligence yet to say that I want to stay unbiased unless you probe further, but I would say that we’re missing the big point here.

This naive conversation about the bias that ChatGPT bring completely takes away the revolutionary aspect of ChatGPT. Where ChatGPT really comes into handy or comes… It’s going to be a dramatic changes is in software development. You’re going to have software development where code is going to be written for training data. That means semiconductors will be getting more and more powerful. It’s going to be used where you’re going to have such massive big data because of ChatGPT. It’s going to change data centers where storage is going to become a massive, massive growth area in the market. Where, where’re you going to store all this data? It’s got… It… When people say it’s in the cloud. It’s not really in the cloud. It’s in hardware, sitting somewhere.

cybersecurity, right? ChatGPT can now generate lifelike emails with content to do cyber crimes and cyber attacks. It can write codes and viruses codes that you can implant somebody and it has all that because virus code is available on the World Wide Web. So, it can learn from that and build your code to destroy something.

[00:17:09] Pierre Daillie: Right.

[00:17:09] Sri Iyer: You know, you’re looking at, basically, search engines in the future. Um, right now, we are doing some basics like Google searches, but tomorrow, it’s going to replicate the Google where you’re going to have a chat bot. So, you’re not going to really type anything. You’re going to chat with ChatGPT or Google/Bing, or BERT or anything like that. You’re just going to have a conversation and ask a question, and it’s going to give you an answer. Not only, not in fact in typing. It might even give you a voice response to, to… Boy, Space Odyssey 2000 comes to mind.

[00:17:42] Pierre Daillie: [laughs]. Yeah.

[00:17:44] Sri Iyer: It’s fiction becoming reality in front of our eyes in our conversation. Another area’s communication, your space, right? Uh, writing. It can write a full textbook on any topic now.

[00:17:54] Pierre Daillie: Right.

[00:17:54] Sri Iyer: Um, media, content generation, news articles, social media posts, um, marketing, story writing, script writing of movies. All of this can be done by ChatGPT in a very precise, concise, closed form solution. If you as a domain expert can give it some basic parameters and tell it exactly what you need. Then you’re looking at music, right? You’re looking at generate melodies, critique music. It could become a critic. It can even write music if you tell it that these are the topics or these are the kinds of parameters. The legal, big highly disruptive ChatGPT, you’re gonna see it can write legal documents. It could write dissertations. It could even pass board exams for legal exams. So, you’re looking at a disruptor response there and I’m not stopping there.

[00:18:44] Pierre Daillie: Wow.

[00:18:44] Sri Iyer: Look at healthcare.

[00:18:45] Pierre Daillie: Yeah.

[00:18:46] Sri Iyer: Healthcare, telemedicine, you’re talking to a doctor. Tomorrow you’re going to be talking to a bot, giving you prescription medication. You’re going to look at it as a remote monitoring system for a doctor. The doctor will know through the chat bot is if this guy taking his medicine or not and it’ll give a prescriptive response to the patient. Pharmaceuticals, drug screening, pre-drug, pre-discovery, drug discovery, process analysis, probability of success in drug discovery. All that is going to go through these kinds of frameworks.

You saw what happened to banks, right? Predictive Analysis, credit default, consumer default. All these things could be used in a more regenerative sense on a predictive basis based on what’s being written or what could be solved and aerospace engineering, fuel optimization. Unfortunately, defense contractors could use it for very bad things, ChatGPT.

[00:19:37] Pierre Daillie: Yeah.

[00:19:37] Sri Iyer: So, you’re looking at much, much more that ChatGPT or regenerative AI can impact than just the common very myopic response of, uh, politics or left, right opinion or anything like that. That’s, in my opinion, a very, very small component of the ecosystem that ChatGPT exposes human civilization to. Again, this is my opinion.

[00:20:02] Pierre Daillie: Amazing, amazing, truly amazing. I, I, I… Thanks for sharing that. I hadn’t even… Uh. You know, I haven’t even gone that far in, in my, my own reading, as far as, you know, all the, that, that laundry list of, of, um, both, I think both blessings and, and, and dangers. Um, you know, you can see where we’re we could, we could use or our children could use ChatGPT to stand on the shoulders of giants, but what do they do with it once they’re there? And what, what what’s, what, what are the, what are the ranges of outcomes that are possible, um, from that? Because you know it’s going to be both used for a very productive means and on the other side, it’s also going to be used for, for probably destructive means as well.

[00:20:59] Sri Iyer: Like anything else in society-

[00:21:01] Pierre Daillie: Yeah.

[00:21:01] Sri Iyer: … over a thousand years. So, as humans, we can’t question the future nor should we live in the past, nor should we live in the moment. We just have to accept inevitable when it happens. If you do that, then you will not see confrontation, but if you always are in all, all defense up in front of you, and you always see everything as a threat, then you’re going to confront things. Rather than confront things, if you accept the inevitable, I think society will evolve much more. You’re going to have some bad things, but you’re going to have much, much more good things come out and bad things, but there’s never been a situation that any evolution in civilization always resulted only in good.

There’s always been bad with good, but that’s, that’s nature and that’s the way evolution works. So, I would say accepting the inevitability in the world of artificial intelligence should be the baseline norm that will help civilizations and people evolve in a better way. In our world, in our investment industry, artificial Intelligence is still nascent, but guys like us-

[00:22:13] Pierre Daillie: Not, not, not, no… [laughs]. You know what? Now you know why I was dying to ask you. I was dying to ask, like specifically, I was dying to ask you about, about ChatGPT about AI because, because-

[00:22:26] Sri Iyer: I think AI’s very overrating. It’s very liberating for me and my team especially. It’s because investment decisions are now being made with a lot more objectivity and a lot more sense of inevitability rather than just prognosticating the future and having a lot of error. So, the acceptance rate of decision making exponentially grows if you accept technology and the evolution of what’s going on in the market right now.

[00:22:52] Pierre Daillie: Before we get to talking about investing, uh, I think it’s only fitting to introduce you to those listening who don’t know you. So, bear with me.

[00:23:04] Sri Iyer: No problem.

[00:23:06] Pierre Daillie: Sri joined. Now you’ll understand why I wanted to talk to Sri about AI. Sri joined Guardian Capital LP in 2001. As well over 25 years of experience managing quantitative investments in risk management, he was instrumental in the development and implementation of Guardian Capital’s proprietary systematic strategies, which subsequently led to the creation of the i3 Investments team. Sri’s expertise in guiding the overall development and implementation of systematic strategies for the firm is unparalleled. Sri and his team manage multi-billion dollar investment portfolios using a combination of AI based predictive models to make investment decisions.

Sri has through his leadership pioneered the application of machine learning and deep learning models at Guardian Capital LP since 2018 and continues to research and innovate in the space of feature engineering, selection, while managing an all star team of data scientists. Sri, in my opinion, is one of the most experienced investment professionals in systematic investment strategy in the business. If Sri wasn’t in investment management, I suspect in an alternate reality, he would have his own tech startup. So, [laughs], so Sri, let’s, let’s set the table for our conversation about, uh, investing, um, because your, your area of specialty is in the, uh, is in dividend investing. Um, how can investing in… By the way, was that, was that a fitting introduction?

[00:24:48] Sri Iyer: That was a… It’s a humbling introduction, I would say. It’s funny you mentioned dividends in the sense. Obviously, we don’t do only dividends. We do growth dividends we recently launched a hedge fund to, but you’re right. The relevancy of how you present, it was amazing. I mean, it’s like my whole career of 35 years just flashed in front of my eyes very quickly. So, thank you. It was-

[00:25:15] Pierre Daillie: Well, you’re welcome, Sri. I think one of the things that that we’ve, that I love about you is that you’re, you’re very, you’re, you are humble. You’re modest. You’re humble, especially given the things that you’re doing. Um.

[00:25:30] Sri Iyer: Totally. I manage a team of all-star data scientists. They humble me every day.

[00:25:34] Pierre Daillie: Well, and you’re so-

[00:25:35] Sri Iyer: [inaudible 00:25:37] in the team I can imagine.

[00:25:37] Pierre Daillie: Yeah, you’re so quick to, you’re always you’re always very quick to, to call it a team effort, which is also admir- admirable, so, um, I’m sure your team loves it as well. Um, so, let, let, let’s get to the discussion about investing because I think, I think in the context of what we’ve talked about at the very beginning about SVB, about, you know, the… some of the changes that are taking place in the economy, uh, whether or not, um, you know, whether or not rate, you know, the rate hiking regime is going to continue or pause or well, and we’ll get to that. How can investing, like it’s a turbulent period. I mean, this past year has been turbulent for investors. Um, how can investing in dividend paying stocks help investors defend their portfolios in a turbulent market?

[00:26:33] Sri Iyer: I’ll keep it brief some of these answers because the practicality of your questions are very profound here. So, to answer your question, how do dividend paying stock help investors in turbulent markets, what comes to mind is low volatility, downside capture, cashflow visibility, and most important, opportunity to improve yield at cost. These are some basic responses that could be tagged and achieved by dividend investing during turbulent markets. So, clearly, when markets are undecided in their direction or the magnitude of the variance of the direction and path is violated, people look for some kind of downside protection, some kind of a lighthouse, as to how to moor their boats do, and I think dividend investing is that lighthouse.

Now, one of the biggest things that people do not perceive is the opportunity to improve yield at cost. That is, if you’re buying a security that pays a coupon at X price, and over the years, you have held that security, then whatever the coupon is, today, whatever the payout on that stock is today, if you divide it by the original price of the stock, your coupon is significantly higher than taking what it’s paying today by the price it’s today. So, when you see a turbulent market, and you see weakness in the stock market where the baby gets thrown out with the bathwater, it is a great opportunity to buy dividend-based portfolios, because you can lower your cost basis and increase your yield at cost. That is true wealth building for the long term.

[00:28:24] Pierre Daillie: Amazing. That’s a great point. I think, I think, you know, we saw, we saw on Monday of this week, and today’s March 14. So, we’re talking about March 13, ’23. We saw the regional bank stocks in the U.S. take basically take it, you know, get a kicking because of the, the uncertainty surrounding the Silicon Valley Bank meltdown and, um, maybe it’s not a meltdown, but collapse. And so it’s the, it’s the old cockroach analogy, right?

[00:29:05] Sri Iyer: Mm-hmm.

[00:29:06] Pierre Daillie: There’s never just, there’s never just one, right? And, and so, so some people have pointed to Janet Yellen basically saying that, that we’re going to do whatever it takes to guarantee all depositors their, their deposits. So, suddenly the $250,000 FDIC limit went out the window and now what’s, what’s been implied through that statement is that 100% of deposits will be guaranteed by the, by the, you know, at the U.S. government, whether it’s the Fed or the treasury, I’m not sure. The Treasury, I guess.

Um, so, uh, you can see why… I mean, I can see why like in a in a turbulent period, just to circle back to your answer, investors would immediately start looking for companies that have, you know, wider moats, companies that have steady or growing dividends and they wouldn’t, they would also, at the same time look for companies whose moats have shrunk and say, "This company might cut its dividend," right? But, and that those are qualitative measures of businesses, but so it’s very interesting. The same way depositors were feared that they were they would pull their money out of lower tier banks, and, and put them into tier one banks yesterday. I don’t know if the… You know?

There’s, you know, there’s been throughout the day, there’s been a string of interviews with, with regional bank CEOs, um, you know, and they’re not all suffering, you know, runs on, on their, on their deposits, but you can see that same mentality happens in the market where where investors, um, default back to the highest quality companies that they can… I mean, depend on how you define quality, of course, but dividend paying stocks obviously have always held a very dear position in the market. So, why is it important? I mean, I don’t know if I asked. I’m answering the question that I’m about to ask with, with my chatter, but why is it important to focus on finding consistent companies with proven track records of growing their dividends year over year? And did I, did I… I think I might have just answered the question I just did.

[00:31:34] Sri Iyer: You did, but let me give you a slightly different context to that in the sense, what dividend growth does is it signal secure viability of a company’s business. That’s all it does. It gives you the secular viability tag. Consistent cash flow personifies a consistent company. So, when somebody says management is consistent or a company’s consistent, it has to be reflected in the cash flow. It allows for a clean evaluation. When I’m valuing companies to own through our big data DCF models, if I have secular cash flow and clean cash flow, I can value the company better so it leaves less error for me. It also allows for better market imbalance detection. That is if I can value a company on secure cash flow, when the market overreacts or underreacts, I could then analyze the true valuation of the stock.

So, when you when you look at it in that perspective, um, one has to be very, very careful in not throwing the baby out with the bathwater is what’s happening is if you look at what happened at SVB in the market and why I have a contrarian response to actually supporting the Fed’s intervention is because you have the asset side and the liability side. The deposits are now getting have to be paid much higher at about what? Four, four and a half percent on the short term deposit side.

[00:33:01] Pierre Daillie: Right.

[00:33:02] Sri Iyer: And in those days, you are paying, you’re getting paid 25 pips or 50 pips. So, that’s been a dem- demonstrative jump in the liability asset mismatch right now. And so what happened here is to create net interest margin and to be viable as a, as a profitable business, most of these banks went on the asset side into very long duration assets, and when I say long duration assets, you can go from 10-year treasuries all the way to some kind of private equity-

[00:33:32] Pierre Daillie: Right.

[00:33:33] Sri Iyer: … agreement on a lending agreement. Now, what’s happening here is in the last six months to nine months, with the short end of the yield curve, and the rates starting to go from 25 pips to a target rate of around 5%. A lot of these 10-year duration assets are now under the water, but my question is, just because a 10-year bond is under the water doesn’t make it a bad bond. It doesn’t-

[00:33:59] Pierre Daillie: Yeah.

[00:33:59] Sri Iyer: … because that yield to maturity, you’re gonna get your money back.

[00:34:02] Pierre Daillie: Right.

[00:34:03] Sri Iyer: So, what’s happening here is the Fed intervention was not to secure bad assets. Fed’s intervention was actually to secure good assets, which are underwater. So, this is almost like a REPO in the sense that the Fed is saying that we’re going to guarantee customer deposits so that the customers don’t make a run on the bank because if we take the money out of the bank, the banks will be forced to sell 10-year bonds when they don’t want to be selling it.

[00:34:36] Pierre Daillie: Yeah. Yeah. So, I mean, that was… That, but that’s good, right?

[00:34:39] Sri Iyer: That is very, very good.

[00:34:40] Pierre Daillie: It’s a great plan, because it, it… By, by protecting depositors, you’re protecting everybody.

[00:34:45] Sri Iyer: You’re protecting the duration of an asset by protecting short-term depositors from running, making around in the bank. So, this is not a bailout.

[00:34:55] Pierre Daillie: Yeah, so a bond-

[00:34:56] Sri Iyer: This is a measure of intervention for protecting the duration of an asset class. Now, if you went and gave your money to some private equity, which is not going to do any, make any cash flow for the next 30 years, that’s a risky asset that should never have been on the balance sheet beyond a certain percentage was a deposit. That kind of risk should not be bailed out, but the problem here was happening was that people were making a run on mid cap, small cap banks, and a lot of the assets are sitting in long duration, fixed income bonds, and that could create a liquidity problem in the market, and to avoid that, the Fed guaranteeing deposits, in my opinion, is a very, very good thing. It’s not a bailout. It’s, it’s a, it’s stopping irrational behavior.

[00:35:41] Pierre Daillie: I think, I think they’ve actually they’ve actually avoided using the word bailout, haven’t they? I mean-

[00:35:45] Sri Iyer: Correct. Well-

[00:35:46] Pierre Daillie: It’s, it’s more of a stop gap or… Yeah.

[00:35:50] Sri Iyer: Well, banks that have massive liability mismatches will fail.

[00:35:55] Pierre Daillie: Yeah.

[00:35:55] Sri Iyer: But not at the cost of clients making a run out of the banks, but because eventually the mismatches will play out in the earnings and the liability. So, let bad banks fail, but not let all good banks fail. That’s the main thing and the separation of mega cap banks to mid cap, there’re a lot of good banks in the mid cap, small cap space too. Unfortunately, this contagion had moved further down the pipe. You can see a lot of small good banks also get destroyed and that’s extremely unhealthy for a economy like the United States, which has thousands of banks, not like Canada, which has-

[00:36:32] Pierre Daillie: Well, I said… That’s one of the… Uh. That’s really one of the huge differences between, between us and them, right?

[00:36:40] Sri Iyer: Correct.

[00:36:41] Pierre Daillie: Is the, is the regional, you know, all of the regional and smaller banks that they have.

[00:36:45] Sri Iyer: The lifeblood, the lifeblood of micro financing into small businesses is done by regional banks, not by large banks.

[00:36:54] Pierre Daillie: Interesting. So, just to put things, just put things back into perspective, um, what, just so that investors… I think you touched on it at the beginning, at the beginning of our conversation about dividends with regards to the coupon, and the coupon from dividend stocks, and correct, correct me if I’m wrong, but what share of total return of the S&P 500 is attributable to dividends over the last 20 years?

[00:37:30] Sri Iyer: You have said 30 years since 1970s.

[00:37:33] Pierre Daillie: Yeah.

[00:37:33] Sri Iyer: Between dividends, buybacks, which is money being taken back, and debt reduction, or what we call dividend yield plus buyback yield plus debt reduction, which is called shareholder yield. That is the shareholder gets his yield and his cash back. This represents about two-thirds or slightly more than two-thirds of the compounding of equity returns are since the 1970s. Two-thirds.

[00:37:58] Pierre Daillie: Yeah.

[00:37:58] Sri Iyer: So, when you’re capturing dividends, which is a very large part, um, almost 70 to 80% of that two-thirds with some very strong buybacks and very little debt on balance sheets, you’re able to capture a very significant part of the very reason why you should be owning equities in the first place.

[00:38:20] Pierre Daillie: Yeah, and that seems to be really an under, underappreciated aspect of, of investing in stocks is, is the dividends, but I think, I think… I want to, I don’t know, I think I wanted to make a quick point, because we went straight to dividends and I know that’s not all you do. But, but it really, it’s kind of like synonymous with our conversation about how the treasury has promised depositors that their, their deposits are safe. Um, that looks after everything that comes after it. Everything that’s related to those deposits, such as the duration of the bonds in the held to maturity portfolio or the available for sale portfolios in the banks.

[00:39:11] Sri Iyer: Correct.

[00:39:12] Pierre Daillie: But I’m not quite exactly that way. Maybe it’s I’m making a terrible connection there, but, but the, the connection I see with, with dividends is that if you, if you, if you focus on dividend investing as a core equity strategy, um, by focusing on the quality of dividends and dividend growers, companies with, with a long dividend history and a history of growing their dividend, aren’t you in effect taking care of some of the more important aspects of building a quality portfolio of equities?

[00:39:51] Sri Iyer: Absolutely. So-

[00:39:52] Pierre Daillie: Yeah.

[00:39:52] Sri Iyer: … if we can go to finance 101-

[00:39:55] Pierre Daillie: As a core principle.

[00:39:56] Sri Iyer: Absolutely. If you look at CAPM, capital asset pricing model, look at what the Fed did here in guaranteeing short term deposits. When you start with capital asset pricing model, you start with money market or money, a three month. A three-month T bill or money market is the most sacred instrument you can own. If you start making a run on banks and the mid cap bank, you cannot even be comfortable owning a three-month T bill at a local bank, then anything beyond that is suspect for you.

So, what’s happened as interest rates started to rise is, in general, people have dramatically reduced their duration, gotten out of fixed income, gotten out of Bitcoin and in general, even equities, and are sitting with copious amounts of cash in the asset allocation right now.

[00:40:50] Pierre Daillie: Right.

[00:40:50] Sri Iyer: And on that, you’re giving headlines that people are making a run on banks. Can you imagine the spike in implied risk premium, in short-term bonds if you start making run on bank, bank deposits? That is extremely risky, because the short end of the CAPM line has to have an implied safety mechanism always built into it. Otherwise, it has a dramatic cascading response to risk further up the CAPM curve. So, this approach by the Fed actually kind of stopped a tsunami, a credit risk mounting up the, the, the CAPM curve in a very aggressive way.

Um, the correction in, in fixed income last year would look like a picnic if we start seeing long duration, duration assets start to default. You’re seeing it in private equity right now. There’s no bid for exits. So, there’s a lot of, um, uh, underwater private equity exits now sitting on big firms and big conglomerates right now that cannot get out and so… and they don’t want to market these products.

[00:41:57] Pierre Daillie: Right. Right.

[00:41:58] Sri Iyer: It gets marked to market only when you go to market to exit. So, these unrealized losses sitting on balance sheets right now, uh, could have a devastating effect on capitalism in general and it all starts with the short end of the CAPM line and so what the Fed did here is to make sure that the cash is protected. The problem with cash at 4% is when inflation is at 6%, you still have a negative rate of return, and I’m trying my best and our job as investment managers is to make sure that people should not get afraid of duration, but embrace duration the right way and so jumping from cash to dividends, which is still equities, not a bond, gives you a very good conduit to capture some duration visibility on McDonald’s, or Johnson Johnson or an AstraZeneca, um, gives you that long visibility of secular cash flow with a three to three and a half percent coupon, and that’s as good as it gets in this market when you’re afraid that you’re not getting your deposit back tomorrow from a bank local around your corner.

So, there is a true value added to make sure that the short end of the CAPM is protected at any cost and give people a viable response to move away from cash because at 6% persistent high inflation, um, it’s actually giving you a negative rate of return. So, you do need some duration debts to get appreciation along with a payout and dividends play a very, very important role in that transition between safe deposits and risky duration. Dividends play the mid space extremely well in this market. I cannot explain it any further than this. It’s as-

[00:43:49] Pierre Daillie: You did a great job. You, you did a terrific job there. Um, so, uh, does, does systemic risk does or does the potential for systemic risk in the financial system make dividend payers and growers more attractive?

[00:44:05] Sri Iyer: I, I will only answer it in one word. Yes.

[00:44:11] Pierre Daillie: Okay, great. So, um, how does inflation, assuming that it remains sticky for the foreseeable future, how does it impact fixed income and equity portfolios? And what is the difference in how they’re affected?

[00:44:29] Sri Iyer: I think, again, these are my opinions in my analysis of data. I think consistent inflation is significantly worse than inflation and people do not differentiate between inflation and persistent inflation. If I have to ask an economist a question, I’ll ask him, "Please define inflation to me as being persistent or not. If inflation is not persistent, I actually like it," because it allows us to… It acts as a transfer mechanism from the company to the user and you could still make money if inflation is not persistent. The problem with persistent inflation is it eats into real income.

[00:45:12] Pierre Daillie: Right.

[00:45:13] Sri Iyer: And the compounding negative aspects of high inflation is never really perceived by the market or by the individual. So, when you’re not perceiving the compounding of persistent inflation, um, you start seeing risk. Show me inflation, and I’ll show you rising rates. Show me rising rates and I’ll show you credit risk. So, that’s how I see inflation, if it stayed sticky, impacting the market. So, just credit risk, perception that’s it volleying in the market right now and ended with the Fed intervening to a degree is all a logical culmination or a cumulative response to persistent inflation.

[00:46:03] Pierre Daillie: Yeah, that’s the key. I think, I think the what I got from, from your response is that it’s persistent inflation that the Fed is hell-bent on tackling.

[00:46:16] Sri Iyer: Correct.

[00:46:17] Pierre Daillie: It’s not inflationary spikes or-

[00:46:20] Sri Iyer: No.

[00:46:21] Pierre Daillie: Events. Interesting, very interesting. I, I… You know, it’s the sticky components that they’re, that they’re really focused on, right?

[00:46:32] Sri Iyer: There were… At the peak of COVID, in March, March 2020, there were accumulative 1,000 rate cuts around the world since subprime and we expect all that to go away in a matter of two years.

[00:46:49] Pierre Daillie: [laughs].

[00:46:49] Sri Iyer: There’s not enough liquidity in the market that is driving prices up, does enough. Lack of labor force participation that is keeping wages high and unemployment low. These are all structural issues that just don’t turn on a dime after giving 1,000 rate cuts for 10 years. It takes time to bleed excess out of the system and what the Fed is trying to do is to make sure it’s done on an orderly basis.

[00:47:19] Pierre Daillie: I think, I think they have established that. I think, I think, you know, because investors at large are not very, not, not pleased, obviously, with, with the outcome of Fed intervention, you know, that, that that’s why you have this. You know, on one side, you have folks who are accepting of what the Fed is doing and understand it like you, uh, and on the other side is, is the other, uh, other cohorts of, of opinion on the, on Fed policy that that are in disagreement with it.

[00:47:58] Sri Iyer: Well, if I may phrase it, I rather be wrong and save my money than right and lose all my money.

[00:48:07] Pierre Daillie: Did you find it interesting before… I find it interesting that we went from there is no alternative to what almost feels like the opposite and not that there… I mean, now, there’s lots of alternatives. Of course, I don’t think that… I don’t think that that we have to stop investing in equities and start strictly investing in short duration, but um, in a, in a, in a strange way, like this, you know, in… You know, people for example pulling, and let’s, let’s let’s, let’s, let’s say this X, this SVB thing that just happened. But when people know that they could get, you know, 4.5 to 5% on a treasury bill versus a bank account, um, then, then did the Fed not… has the Fed or the bond market not put investors at odds with deposit-taking institutions even though-

[00:49:25] Sri Iyer: I agree with you to a degree, yes, but if I may see the positive in this, perhaps you’re going to see the bank’s work much harder in raising their interest on their deposits to entice clients to stay around and perhaps-

[00:49:43] Pierre Daillie: That’s the reason, right?

[00:49:44] Sri Iyer: Perhaps not buy the T bill. So, this is gonna-

[00:49:45] Pierre Daillie: Yeah.

[00:49:46] Sri Iyer: … make banks in my opinion more honest going forward to the extent that if you’re going to sit with a bank and get a, a, a deposit rate higher than the T bills, and by the way, T bills is a sophisticated context. The average John Doe person doesn’t know what a T bill is. So, while we-

[00:50:07] Pierre Daillie: You’re absolutely right.

[00:50:08] Sri Iyer: While we’re gonna have a nice conversation about T bills, the average John Doe person doesn’t even know what that is. They’re looking at a bank deposit and income on a bank account including myself and my bank.

[00:50:18] Pierre Daillie: Let alone a, let alone a money market fund, right? So-

[00:50:20] Sri Iyer: Exactly. I’m not buying… With my free cash, I’m not buying cables. I’m still owning my bank’s savings account. I’m hoping they bought the T bill and not something crazy, but they-

[00:50:31] Pierre Daillie: Well, there’s a whole degree, there’s a whole degree of separation involved in that. I mean-

[00:50:35] Sri Iyer: Exactly.

[00:50:35] Pierre Daillie: … accessibility being one of them.

[00:50:37] Sri Iyer: Exactly. So, banks will compete in raising their deposit rates to attract high quality assets. It’s as simple as that.

[00:50:46] Pierre Daillie: Yeah.

[00:50:46] Sri Iyer: And the larger banks will be able to do it better than smaller banks. That’s also true.

[00:50:52] Pierre Daillie: That reduces their net interest margin.

[00:50:54] Sri Iyer: Correct. It does, which means earnings for banks could get a little bit volatile and our AI systems have been showing for last six months that bank earnings don’t look good.

[00:51:07] Pierre Daillie: Well, we just saw, we’re starting to see some reporting from banks now. Right? Of, of decline in earnings.

[00:51:16] Sri Iyer: Canadian banks in general look significantly more stable because we have a large repository of global banks around the world. Uh, relatively speaking, Canadian banks are much, much, much more stable, but when you see some of the predictions from AI, with respect to a one-year fraud earnings estimates or even dividend risk, uh, probabilities, you could clearly see a spread between, say, mid cap, small cap banks and large cap banks around the world clearly.

[00:51:45] Pierre Daillie: Interesting. So, sure, yeah. How does, how does the recession affect the demand for near term predictable and attractively priced cash flows and why does this make dividend payers valuable?

[00:51:59] Sri Iyer: Well, I would say recession amid persistent high inflation is deemed as stagflation. The mistake the market is making is I think it feels the Fed can engineer a soft landing. However, the principal components of inflation are what? Supply chain driven problems, labor costs, persistent high labor costs, because of lack of participation in the labor force. Remember, I can’t get a 25-year-old to come to work and I’m trying my hardest to keep a 60-year-old from leaving work and retiring. That’s my job today.

[00:52:35] Pierre Daillie: [laughs]. Yeah.

[00:52:36] Sri Iyer: So, I got both details leaving or not being there. So, I’m the sandwich generation at 53 years old trying to keep the high talent alive. That means more labor costs and persistent labor costs. So, when you look in at the global geopolitical problems with the Ukrainian war, and everything else, and now, the China aspect looming like a big, big Albatross right now, which leads to supply chain disruptions, labor cost driven. These are all very structural in nature. A mere rate increase here and there will not be enough to suffice a change. So, it’s going to take some time for structural issues to sort themselves out.

In the meantime, you’re going to see volatility in the market, and you’re going to see turbulent periods and so you have to be very careful right now, where beta and alpha should be separated, where it’s simply buying the market means you’re buying volatility. When you buy active management, or you’re buying responses to the duration and credit through active management, you probably are going to be far better off than just going passive right now. So, these are changes that the average person might feel, but does not comprehend yet and that’s how I see the recession if it does come around, which I think is in our opinion, Europe is already in a recession from an earnings standpoint and when it comes to United States, the earnings cycle is starting to slow down, are starting to decelerate at a reasonable pace. So, when you put all that together, I think my thesis might play out.

[00:54:13] Pierre Daillie: Interesting, I think, you know, some, some in the, some in markets, some people in markets are of the sort of simpler opinion that a recession would lead to lower interest rates and therefore, you know, that would benefit higher duration investments-

[00:54:32] Sri Iyer: To a degree.

[00:54:34] Pierre Daillie: Yeah.

[00:54:34] Sri Iyer: Mathematically, yes. Structurally, no.

[00:54:39] Pierre Daillie: Well, that’s, that’s the short sightedness.

[00:54:41] Sri Iyer: Right.

[00:54:41] Pierre Daillie: Is that… Yeah. So, we’re on the subject of events and, you know, systemic events or systemic… Sorry, not systemic events, but turbulence. A little bit of chaos this week. When these kinds of disruptions happen, how do you, how do you and like, how do you instruct or how do you teach your AI to recognize these types of situations? Um, and I guess the, maybe the relevant question is, do you want to?

[00:55:21] Sri Iyer: Well-

[00:55:21] Pierre Daillie: Or to what degree, to what degree do you want to-

[00:55:24] Sri Iyer: Good question.

[00:55:25] Pierre Daillie: … do you want to teach your AI to recognize something like SVB or, or a rise in, in systemic problems? Is it possible?

[00:55:36] Sri Iyer: It’s possible. So, if I give you an anecdotal response, um, our, our dividend strategy has added a track record since inception of having zero dividend cuts, and we’ve been running it for 15 years. So, when… Uh, so we had no dividend cuts during subprime because doing subprime, most dividend managers had about 40 to 50% in banks. Our systems allowed us to exit bank so we’re a non-bank dividend strategy doing subprime, if you could wrap your head around that. During COVID-

[00:56:08] Pierre Daillie: Impressive.

[00:56:09] Sri Iyer: During COVID, we had zero dividend cuts. So, people asked the same question here. Is your model learning from COVID? The answer is no, because COVID was not predicted. You have to train the model on COVID data. So, today, has the model learned from COVID data? The answer is yes. We’ve trained the model on the disintermediated responses of the market to the COVID crisis, but during COVID it learned from the subprime response. So, the source of the problem cannot be predicted, but their reaction to the problem is much more predictable and consistent across multiple regimes.

So, regime detection, in my opinion as a data scientist, is very hard to do in a very explicit way, but implied shifts are much more manageable. So, what we do is we feed macro data into the model in the form of features. So, all the lead economic indicators, we feed into the market. What happens is the stocks tend to react and interact with these features to develop patterns of behavior. So, a stock that are highly sensitive to rising rates, some are very less sensitive to rising rate, some are very high sensitive to rising oil prices, some are less. Some are very highly sensitive to manufacturing water PMI cycles, some are not. Some are high, highly sensitive to the steepening of the yield curve while some are not. These kinds of things have to be thought or trained into the model.

And once they are trained in the developed patterns and behavior recognition, then when you feed it today’s data, whether it’s SVB, or whatever, the model can use that training data to do what? Infer.

[00:58:02] Pierre Daillie: Infer. [laughs]. Thank you.

[00:58:05] Sri Iyer: So, that’s what we are building, um, in our model context is to allow the model to infer and it can infer from the past to predict the future with a degree of certainty and we use probability estimates. In some cases, we use random forests techniques where we’re using the average of 2,000 or 20 founder predictions, because we do not rely on one prediction. We’re doing 2,000 predictions and taking the average. So, there are different techniques, a supervised machine learning for certain features and certain parameters for decision making, and there are certain very advanced deep learning type responses we use for a market neutral hedge fund where we actually labeling stocks as when to short and when to go long.

So, there are a lot of complex variables, where we start with machine learning all the way to deep learning and now you got to regenerative AI, which I have not really ventured into just yet, but perhaps someday my all-star data science team might come back to me and saying, "Sri, you’re an old hag at this. Let me get to take a crack at it," and I’ll be like, "Go for it."

[00:59:09] Pierre Daillie: So, you’re not yet at stage, at the stage where you would consider adding regenerative AI to your processes yet?

[00:59:17] Sri Iyer: No, we’re not.

[00:59:18] Pierre Daillie: Yeah, it’s, it’s, it’s, it’s, it’s a whole other ballgame. Right?

[00:59:22] Sri Iyer: You, you… It’s hard. If I, if I use ChatGPT as a subscription, enterprise subscription, and use it to make decisions it, to a degree, it’s a black box, and in the world of investment management, where we have fiduciary responsibilities, I rather build a glass box than a black box and so us being data scientists, our feature engineering, feature selection, we have an innate understanding of how our artificial intelligence frameworks work. So, as we do our academic research, and our readings and theory as to how regenerative AI works, uh, we could probably start to learn from it.

One of the things that we do very unique, which is kind of similar to regenerate AI in the sense regenerative AI uses transformers, um, in its deep learning framework that it uses ridiculously multiple layers of neural nets, um, and any kind of data coming out of the neural net is very abstract in nature and regenerate AI can create recognitions of vectors and clusters of words and all that stuff. So, it’s very abstract in nature. We are now actually feeding the output of one AI model into a another AI model. So, one AI model of learning from the predictions of another model.

So, things have progressed quite significantly in the world of artificial intelligence. Um, it’s somewhat proprietary, but we’re having a chat, conversation. Hopefully people can get some knowledge out of these things, but that’s what, um, transformers and abstract learning is all about. It’s how the human brain reacts to certain relationships between abstract datasets, and we’re doing that right now, but as far as ChatGPT goes, the size of data… You know how many GPUs we use? We use about three to four GPUs. You know how many ChatGPT uses?

[01:01:11] Pierre Daillie: How many?

[01:01:12] Sri Iyer: Over 100,000 per query.

[01:01:14] Pierre Daillie: [laughs]. That’s mind blowing.

[01:01:18] Sri Iyer: It is.

[01:01:20] Pierre Daillie: I, I can see where where you don’t want to introduce, uh, what’s potentially, a, a… I mean, maybe I’m not using the right term, but it’s a wildcard. It’s too many wildcards into your process by, by if you were to try to do something like introducing regenerative AI, but thank God you’re not because it’s, it’s, it’s a… It, it… You know, your, your, your situ… your AI is by design.

[01:01:45] Sri Iyer: Yeah and that’s a large language model. So, a new sentiment data specialist might use that to create better perspective or projected sentiment for a stock. So, not only are they telling you what the sentiment is today based on news media. They might be able to tell you that we could regenerate the sentiment that is going to happen tomorrow or the next minute based on it. That could happen in the future and that could be a very positive feature set for us, because we currently use new sentiment data as a momentum feature set because we don’t read anything. The machine reads for all of us. So, can sentiment become more intelligent with ChatGPT and regenerative AI? My opinion is yes, but that’s not our domain. We actually subscribe to that data. So, hopefully the data scientists are doing that for us who work on that.

[01:02:31] Pierre Daillie: [laughs]. Speaking of sentiment, um, what are your expectations for market volatility or equity markets for 2023?

[01:02:40] Sri Iyer: Um.

[01:02:40] Pierre Daillie: How do you, how do you see things unfolding for the balance of the year and, and, uh, and even ahead?

[01:02:46] Sri Iyer: I have mentioned in the recent past that the only macro risk I worry about is liquidity and credit risk. If inflation stays sticky, impact on technology, in general, I think goes into warp speed. Technology’s gonna go into warp speed if inflation says sticky, because productivity and labor force participation will create opportunities and tech that me and you haven’t really comprehended yet. Human and robot interaction becomes a norm much earlier than one expects. It already has become.

[01:03:24] Pierre Daillie: Right.

[01:03:24] Sri Iyer: Market wall stays elevated, but not in any panic state, in my opinion. The four-letter word hope, which are H stands for housing, O stands for orders, P stands for profitability, and E stands for employment are all shifting and an economic bottom at least in our opinion is not in sight yet. So, there’s still more fog on the road. So, don’t speed up, slow down. That’s the overall expectation I have for market volatility in 2023. So, there’re a lot of nuggets here I put in that I will not explain-

[01:04:03] Pierre Daillie: I love it. I love it. No, that’s perfect. That’s I think, I think it’s prudent to be cautious, and uh-

[01:04:14] Sri Iyer: But also, I put in some components of where the optimism is also there. So-

[01:04:19] Pierre Daillie: Yes.

[01:04:20] Sri Iyer: … don’t invest in beta, invest in alpha. Invest in areas where the market will continue to evolve and grow.

[01:04:29] Pierre Daillie: Sri, thank you so much. I, I, uh, you know, love talking to you. We love you. It’s always a mind-blowing conversation with you. You’ve always got a world of, of intelligence to, to share with us, and um, you have, you’re doing so much. I think, I think if anybody actually got to, to see what you’re actually doing, I know that’s proprietary, but I, I… It’s mind-blowing to. To me, it’s mind-blowing. I think what you’re doing with, with investing is, is, uh, on another scale that that… and it’s not something you just… You didn’t just start it this year. This is something you’ve been doing for your whole career, um, working your way up to this, and, and then since, since 2018, which doesn’t seem like that long ago, but that’s five full years of full implementation-

[01:05:25] Sri Iyer: Correct.

[01:05:25] Pierre Daillie: … that you worked up to in the previous decade of your career or decades. And, and um, I, I… It’s astonishing is all I can say. I think that, that, um, you know, you’re, you’re, you’re a pioneer in, in AI. I think that’s why I was excited, particularly excited to talk to you about AI today because it’s been such a big year for, for that and uh, I… I imagine it’s going to be… It’s making… You know, it’s making the work that you do even more, uh, profoundly satisfying, gratifying.

[01:06:05] Sri Iyer: Thank you very much. These are exciting times, indeed.

[01:06:08] Pierre Daillie: Thank you, Sri. Thank you so much for your incredibly valuable time and, and your thought.

[01:06:12] Sri Iyer: Thank you very much.