Latest / The Adults In The AI Room / The Earley AI Podcast with Seth Earley - Episode #56 David Marra on AI's Impact on Financial Services and Investment Strategies

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The Earley AI Podcast with Seth Earley - Episode #56 David Marra on AI's Impact on Financial Services and Investment Strategies · The Adults In The AI Room

• 0:00
  SPEAKER_03: So welcome to the AI uh early AI podcast.
• 0:04
  SPEAKER_03: My name is Seth Early.
• 0:05
  SPEAKER_01: And I'm Chris Featherstone.
• 0:07
  SPEAKER_03: And today we're really excited to introduce our guests to discuss how AI is revolutionizing the landscape of investment strategy as well as business performance evaluation.
• 0:18
  SPEAKER_03: We'll talk a little bit about some of the common misconceptions around AI tools, such as LLMs, the importance of data hygiene and purpose-built systems, the use of AI for extracting data from business databases and accurate information retrieval in corporate settings.
• 0:34
  SPEAKER_03: And we'll talk about utilizing algorithmic investment strategies to improve portfolio performance.
• 0:40
  SPEAKER_03: So our guest today is a seasoned expert who has been in the AI space since the late 90s.
• 0:46
  SPEAKER_03: He sold his first venture in 1999, which focused on internet search.
• 0:51
  SPEAKER_03: He spent time implementing quantitative prediction systems at large enterprises with Boston Consulting Group, BCG.
• 1:00
  SPEAKER_03: His academic background is in finance.
• 1:02
  SPEAKER_03: And in 2010, he focused, he uh founded an investment research firm building quantitative investment strategies for institutions.
• 1:11
  SPEAKER_03: He brings a unique perspective with a background in coding, systems implementation in large enterprises, and the use of AI in financial modeling and in the investment space.
• 1:20
  SPEAKER_03: His current venture is focused on algorithmic-based investment strategies for institutions, wealth managers, and retirement advisors.
• 1:28
  SPEAKER_03: David Mara, welcome to the show.
• 1:30
  SPEAKER_02: Well, thanks very much, Seth.
• 1:31
  SPEAKER_02: It's nice to join you.
• 1:33
  SPEAKER_03: Terrific to have you.
• 1:34
  SPEAKER_03: So tell me, give me your thoughts, tell me your perspective on the state of the industry.
• 1:39
  SPEAKER_03: How well are organizers, uh organizations in your sector leveraging AI and what is their level of understanding?
• 1:47
  SPEAKER_03: The people that are doing the stuff you're doing in terms of sort of quantitative analysis, qualitative analysis in the investment uh space?
• 1:55
  SPEAKER_02: Yeah, sure.
• 1:56
  SPEAKER_02: So just um, you know, for the benefit of your listeners and your viewers, so my day job uh uh uh as today is as a portfolio manager, an investment portfolio manager.
• 2:09
  SPEAKER_02: So um in this role, where we have long-only strategies, equity growth strategies, and a hedge fund, those are the things, those are the investment funds and strategies that I that I manage.
• 2:23
  SPEAKER_02: And my job in this role is to, you know, obviously find opportunities to invest in that we think are gonna uh perform well, you know, in excess of the risk that we have to take with them, to build diversified portfolios of AI companies and AI-related companies from the tech sector and um uh and then to manage to manage risks, right?
• 2:49
  SPEAKER_02: So on the one hand, one part of my job, there's really two aspects that are to it that are relevant for our discussion today.
• 2:56
  SPEAKER_02: On the one hand, we're looking at AI as an investment opportunity.
• 3:00
  SPEAKER_02: That's that's one aspect of it.
• 3:02
  SPEAKER_02: And the other aspect of it is that we're using the tools as a way to invest as well.
• 3:09
  SPEAKER_02: So we're both a kind of consumer of it and a producer of AI.
• 3:14
  SPEAKER_02: So I just wanted to set the stage back to your question uh about artificial intelligence and how, you know, I think your question gets at kind of how how we view it from how it's affecting our industry.
• 3:27
  SPEAKER_02: For our industry, I would say that our industry is probably one of the financial services, is one of the slower industries to adopt AI.
• 3:37
  SPEAKER_02: And this gets at the importance of data for implementing really enterprise-wide, robust AI systems.
• 3:45
  SPEAKER_02: The more data you have, combination of publicly available data that's relevant to your business and private, the more quickly and more broadly you can embrace AI and start to generate either new business models or exit or efficiencies off of existing business models.
• 4:04
  SPEAKER_02: Financial services is a weird thing in that a lot of the data is proprietary.
• 4:07
  SPEAKER_02: That doesn't always lend, that's not the fastest way to take off in this industry.
• 4:13
  SPEAKER_02: So I don't really view, again, as a quantitative investor, we're a little bit different than financial services in industry because you know, we deal with more diversified data sets and more of them.
• 4:25
  SPEAKER_02: But outside of our niche of quantitative investing, just more broadly speaking, to the industry, which is what your question was about.
• 4:30
  SPEAKER_02: Yeah, financial services is definitely embracing AI, particularly on the customer service side and the banking side.
• 4:36
  SPEAKER_02: On the investing side, I think it it will come later.
• 4:39
  SPEAKER_02: It's really other industries, frankly, that you know I'm more excited about and see a lot more opportunities, particularly to invest in.
• 4:46
  SPEAKER_03: And so just for our audience, because there are companies that are either early stage, mid-stage, some startups, what kinds of companies are you looking at?
• 4:55
  SPEAKER_03: Are you looking at large established companies that are doing a good job with AI to change their business models or evolve the business models?
• 5:02
  SPEAKER_03: Or are you looking at earlier stages, mid-tier?
• 5:05
  SPEAKER_03: Where what are you what are you looking at?
• 5:06
  SPEAKER_03: What's your investment profile for Google?
• 5:08
  SPEAKER_02: Yeah, so our investment universe is anything that's listed, or anything that's listed that's heavily investing in unlisted things.
• 5:15
  SPEAKER_02: So the the Googles of the World, for example, is an example of a company that's listed, but certainly a certain percentage of their market cap and their growth in their market cap can be attributed to a lot of their investments and things that are unlisted.
• 5:28
  SPEAKER_02: Microsoft's probably the best example having a stake in in open AI, for example, right?
• 5:32
  SPEAKER_02: But but primarily our uh uh our universe is anything that's that's that's listed.
• 5:37
  SPEAKER_02: So for your listeners, any company that's looking to go IPO or on an IPO timeline, as well as anybody who who's listed would be the kinds of things that that we're looking at.
• 5:48
  SPEAKER_01: So yeah, I was just gonna ask you.
• 5:49
  SPEAKER_01: So Dave, so clarify the quantitative investment strategies.
• 5:53
  SPEAKER_01: What does that mean?
• 5:55
  SPEAKER_01: Is that just in terms of of how you just described the companies you're investing in, or is that something specific?
• 6:00
  SPEAKER_02: No, no, that that actually, yeah, just so there's no confusion.
• 6:03
  SPEAKER_02: That quote that's what we do every day.
• 6:06
  SPEAKER_02: We use a lot of quant and compute to evaluate all these companies in the AI ecosystem that we're looking at for investment purposes.
• 6:14
  SPEAKER_03: So one of the things you you discussed, and I think you're alluding to this now, are AI beneficiaries, right?
• 6:20
  SPEAKER_03: So these are the companies that are doing a good job using AI.
• 6:24
  SPEAKER_03: They're either achieving growth or reduction of costs using AI moving forward.
• 6:29
  SPEAKER_03: So give us a sense of, and you and you said you mentioned that that finance is a little bit behind in the quantitative investing side of things, but they are using it for certain for banking and analysis and portfolio analysis and predictive analytics in all sorts of ways, customer service, but not necessarily in the quantitative uh investing, which seems a more specialized area.
• 6:52
  SPEAKER_03: But talk a little bit more about the um AI beneficiaries that that you're seeing.
• 6:58
  SPEAKER_03: You know, how well are the organizations that you're evaluating for investment doing in their execution and operationalization of AI?
• 7:05
  SPEAKER_03: And how do you really separate what they say they're doing from what they're actually doing?
• 7:10
  SPEAKER_03: I think I we talked a little bit about in our prep call.
• 7:13
  SPEAKER_02: Yeah, so AI beneficiaries is something uh, you know, I'm particularly excited about.
• 7:18
  SPEAKER_02: Um, because it's really where the rubber hits the road with AI, and it's certainly where all you know the valuation gains is gonna come from.
• 7:29
  SPEAKER_02: If if companies don't adopt it, everything that the hyperscalers and the data center people of the world are building is all gonna be for naught, right?
• 7:37
  SPEAKER_02: You there has to be efficiencies generated by from all industries for this, this, this to work out from an investment perspective.
• 7:44
  SPEAKER_02: So um, and we're very bullish on it.
• 7:46
  SPEAKER_02: I mean, we think this is a secular opportunity, secular, very long-term opportunity to invest in a revolution that's gonna play out over multiple decades.
• 7:56
  SPEAKER_02: So uh I'm glad you you kind of mentioned beneficiaries.
• 7:59
  SPEAKER_02: We also, you know, for the sake of your your your listeners, at Markin Asset Management, where we're you know, um, you know, we're we have an AI beneficiary strategy.
• 8:08
  SPEAKER_02: So we're invested in 50 firms across industries that we think are well positioned to benefit from um artificial intelligence in terms of how it's going to drive its earnings and drive its growth.
• 8:20
  SPEAKER_02: I mean, what I see in the beneficiary space is first the big picture that we are at this um tipping point in time.
• 8:29
  SPEAKER_02: Machine learning came along, and again, you you alluded to my my life at BCG and other management consulting firms that I used to be with.
• 8:37
  SPEAKER_02: Like you, Seth, I was in the trenches there implementing these these types of predictive modeling systems using machine learning.
• 8:45
  SPEAKER_02: And up until large language models came along, it was a slog.
• 8:49
  SPEAKER_02: It was very, very hard to find companies that had everything you need, which is you had to had pristine, clean data sets that are constantly being updated.
• 9:00
  SPEAKER_02: You had to have the data pipes that that combine those data sets, because you've got all these data sets sitting in all parts of your organization geographically in different places, all with different permission structures and the maps that have to go into making that a single, easy way to query, et cetera.
• 9:16
  SPEAKER_02: All that infrastructure needs to be in place.
• 9:18
  SPEAKER_02: Then once you had that, which most companies didn't, uh you then had to build these very kind of highly specialized prediction models that could be very difficult to maintain, very difficult to create, a lot of PhDs involved, a lot of data scientists involved.
• 9:34
  SPEAKER_02: And and then the the payoff, so the so the number of use cases, every time you got something like this, it's filtering down to a smaller and smaller number of use cases that delivers less and less return on investment, right?
• 9:45
  SPEAKER_02: That was up until the large language models came along.
• 9:49
  SPEAKER_02: And so we're really talking up until about a year ago.
• 9:51
  SPEAKER_02: You know, that was the world of machine learning.
• 9:53
  SPEAKER_02: And then, you know, I really large language models comes along and it's a much easier implementation use case, where because a lot of firms did put in the effort for machine learning type of algorithms to build all those data pipes and those data uh uh database assets that they needed, they were kind of they're kind of now ready for the LLM revolution.
• 10:17
  SPEAKER_02: And then LLM itself being sort of a thing that, again, to oversimplify, because I think you have a very sophisticated audience, everybody knows, but you can just take a mass of data and sort of throw it at it, and you'll start to get reasonable results.
• 10:30
  SPEAKER_02: We're gonna we're gonna have to go beyond reasonable results to drive, you know, big ROIs.
• 10:35
  SPEAKER_02: But LLM becomes something that's much, much easier to implement and start to drive return because it's so general, generalizable to many functions, many different types of queries.
• 10:47
  SPEAKER_02: It doesn't require so much kind of quirky, very narrow use case type of fiddling that you had to do with the machine learning stuff.
• 10:55
  SPEAKER_02: So that's you know a very colloquial way of explaining why we think we're at the beginning of a very generalized revolution that's probably got a multi-decade run to it across many industries.
• 11:07
  SPEAKER_03: So one of the things that you mentioned was that you have about 50 firms across um industries that uh you are investing in.
• 11:17
  SPEAKER_03: And obviously, I'm not asking if you're a secret sauce, but what are some of the commonalities that you're seeing um generally in terms of organizations that are doing a good job?
• 11:27
  SPEAKER_03: Do the are they the ones that have already made that investment in their data cleanliness integration, uh, the data pipes and so on as kind of a precursor because we still need new good data, even though LLMs will help us deal with bad data.
• 11:44
  SPEAKER_03: And, you know, you know, we can use LLMs to cleanse data, right?
• 11:49
  SPEAKER_03: But what are the what are some of the characteristics you're seeing in companies that you believe are kind of at the cusp or going down this path?
• 11:57
  SPEAKER_03: Is it that they've already deployed this for certain applications?
• 12:00
  SPEAKER_03: Is it that they're they have uh new business models that they are uh enabling?
• 12:06
  SPEAKER_02: What are some of those ways of looking at those companies that maybe so on the one hand, I think, and I'll just speak, I'm not gonna speak to our portfolio specifically, but speak to the kinds of firms, you know, that I think are the characteristics of the kind of firms that really kind of understand this revolution and then are starting to show evidence that they get it and they're gonna be able to derive something from it.
• 12:29
  SPEAKER_02: I think on the one hand, they've they've done that uh basic data asset work that we just discussed.
• 12:37
  SPEAKER_02: That's something that, you know, that's not something you can do quickly, right?
• 12:42
  SPEAKER_02: That type of work to get your data assets in order often takes years to get right in large global organizations.
• 12:50
  SPEAKER_02: Most of the firms who are going to be able to move quickly have that piece largely in place.
• 12:55
  SPEAKER_02: There certainly may be work to do, but they've they've done that already.
• 12:58
  SPEAKER_02: That's because they were already participating in kind of the machine learning decade or two leading up to the present moment.
• 13:06
  SPEAKER_02: The second aspect of it is that they perform the kinds of activities, they're intensive in the kinds of activities that AI is good at.
• 13:16
  SPEAKER_02: Because AI is not good at everything, right?
• 13:18
  SPEAKER_02: AI today is not good at physical activity.
• 13:21
  SPEAKER_02: AI applied to robots is way behind just AI relative to like query-based activity.
• 13:28
  SPEAKER_02: Right.
• 13:28
  SPEAKER_02: We don't have C3POs walking around doing stuff yet today.
• 13:31
  SPEAKER_02: Right.
• 13:32
  SPEAKER_02: So things that involve physical work, construction, a lot of services, businesses like delivery and logistics, a lot of businesses like, you know, uh hotels and things like that, where it's about cleaning a room, like thousands of rooms a day, you know, that kind of anything that involves physical work is not the type of thing that's ideally AI's positioned to do today.
• 13:54
  SPEAKER_02: Earlier, the things that where AI is very good at are things like accounting, tax, even legal, right?
• 14:02
  SPEAKER_02: It's it's work that's often data retrieval oriented.
• 14:07
  SPEAKER_02: AI is not very good at reasoning.
• 14:09
  SPEAKER_02: Reasoning is not a strength of it today.
• 14:12
  SPEAKER_02: Human beings are much better at reasoning than AI is.
• 14:14
  SPEAKER_02: That may change, but the kinds of industries where data retrieval, you think something like tax, for example, a lot of it is just knowing the rules, right?
• 14:23
  SPEAKER_02: It's a rules-based industry.
• 14:25
  SPEAKER_02: So where you have rules-based industries that you can, you know, that lends itself to query and being able to, and in fact, where there are simple queries is even better for AI today, where AI is struggling and a lot of startups are starting to kind of focus in on trying to improve, is the multi-step query.
• 14:44
  SPEAKER_02: And in fact, what's even more difficult is the multi-step query that has an answer that come from multiple domains or multiple databases.
• 14:55
  SPEAKER_02: These are things that even today AI struggles with.
• 14:58
  SPEAKER_02: So things where there are simple queries from a single domain, that really gives you, you can get you know 30, 40, 50% uh productivity increases.
• 15:08
  SPEAKER_02: And those are some of the industries, you know, I just mentioned accounting, tax, legal, those kinds of things, as just some some some uh ready examples.
• 15:16
  SPEAKER_02: There are others as well.
• 15:19
  SPEAKER_01: So when you guys get into you know some of this from the perspective, because you know, a lot of the investing used to go with like definitely a ton of education and also with your gut.
• 15:29
  SPEAKER_01: Right, like and I think that's where you're alluding to this, where the AI models are really good at binary, you know, classification of yes and no, right?
• 15:39
  SPEAKER_01: Here's the outcome, here's the input, here's what we're driving towards.
• 15:43
  SPEAKER_01: But we still need, I'm assuming, that human and the loop to do some of the exception handling.
• 15:49
  SPEAKER_01: Right.
• 15:50
  SPEAKER_01: Can you talk a little bit about that and how you think about that from the investing side of things?
• 15:54
  SPEAKER_01: But you know, and then also maybe what companies are maybe missing as they implement some of these because we have all the patterns and already using the reasoning.
• 16:04
  SPEAKER_02: Yeah, and I think what you're getting at there, and it's a great question, is you know, when you're transforming an ERP system and an enterprise system, um, the first thing you're gonna, you're always gonna look for low-hanging fruit, right?
• 16:20
  SPEAKER_02: You're in a new industry, you're in a new age, you're dealing with new technology.
• 16:24
  SPEAKER_02: To minimize the risks and maximize the return, what are you gonna do?
• 16:28
  SPEAKER_02: You're gonna go for low-hanging fruit.
• 16:29
  SPEAKER_02: What's the low-hanging fruit gonna be?
• 16:30
  SPEAKER_02: It's gonna be doing existing things better, taking existing workflows and doing them better.
• 16:36
  SPEAKER_02: An example would be like an ADP type of workflow, where um, you know, ADP is a payroll company, right?
• 16:43
  SPEAKER_02: Payroll involves a lot of rules, right?
• 16:45
  SPEAKER_02: Think of the kind of workflows that some that a payroll expert is involved with today on an ERP system, it's like that.
• 16:51
  SPEAKER_02: It's like somebody gets hired, a whole bunch of workflows are going to come from hiring.
• 16:56
  SPEAKER_02: Somebody gets fired, somebody gets laid off, a whole bunch of workflows.
• 17:00
  SPEAKER_02: Somebody moves from location to location, tax implications, all kinds of pay and pay implications.
• 17:05
  SPEAKER_02: The government changes its rules about how something is accounted for, whatever a whole bunch of it.
• 17:10
  SPEAKER_02: Each one of these workflows today involves a person who is skilled in that particular workflow working on an ERP system.
• 17:20
  SPEAKER_02: Now, if you can make that ERP system so that somebody with less skill in the hiring process, for example, and they've got to go through, you know, 40 steps in the ERP system.
• 17:35
  SPEAKER_02: If somebody's skilled, they can go through all 40 steps and they know how to how to answer all the questions and plug everything into the model.
• 17:41
  SPEAKER_02: If somebody's not skilled, you could have a query-based system and say, I'm stuck here.
• 17:45
  SPEAKER_02: This is what I'm trying to do, and have a chat bot that answers specific to your firm exactly what you're looking for.
• 17:53
  SPEAKER_02: So you can plug in the right data into the ARP system.
• 17:56
  SPEAKER_02: This is going to reduce errors that you were talking about before, right?
• 17:59
  SPEAKER_02: This is gonna allow more people who with less training to do more work in a day and be able to process more hires, to be able to process more transfers to different locations.
• 18:12
  SPEAKER_02: That's kind of an example of how the rubber hits the road.
• 18:14
  SPEAKER_02: And like the ADP CEO, for example, has actually talked about this on conference on conference calls, for example, quarterly conference calls.
• 18:22
  SPEAKER_02: That's, I think, to me, that's a very illustrative way to show how you know you can take an existing ERP system and existing workflows, and all of a sudden you can make it.
• 18:33
  SPEAKER_02: And the average here with these kinds of types of innovation is about 30%.
• 18:37
  SPEAKER_02: But the big gains will be from you know, new workflows and actually new new things, which we could also talk about.
• 18:43
  SPEAKER_03: Yeah, we we are uh we're we're with one enterprise right now that is in the medical device uh manufacturing space, and the number of uh knowledge sources, data sources, information sources that they need to deal with that uh field service tech needs to access.
• 19:01
  SPEAKER_03: We did this of applied materials years ago as well.
• 19:04
  SPEAKER_03: You know, there's about 14 different uh information sources, and so just that consolidating that, getting them the answers, it's the holy grail of you know, knowledge management, the right information of the right person at the right time, or personalization and the right information of the right person at the right time, or recommendations, right?
• 19:23
  SPEAKER_03: These are all things that we've been striving for, or usability.
• 19:26
  SPEAKER_03: Anytime we're trying to improve that user experience, we're trying to reduce the cognitive work, the cognitive load of the human, right?
• 19:35
  SPEAKER_03: And be able to present information from people who need it.
• 19:39
  SPEAKER_03: And and instead of going to a colleague or not being able to find the answer or having a lot of other manual processes to be able to surface that information.
• 19:48
  SPEAKER_03: So one of the things you talked about was the knowledge and and the access to that knowledge.
• 19:54
  SPEAKER_03: And of course, LLMs are very good at understanding information, they're very good at understanding language, and they're good at making things conversational.
• 20:03
  SPEAKER_03: They do hallucinate.
• 20:04
  SPEAKER_03: So, you know, are these organizations that you're seeing be more successful, are they paying attention to knowledge as a foundation, knowledge management, knowledge engineering, those types of things that are a little bit in the weeds, but are foundational and fundamental to some of the improved workflows that you're talking about?
• 20:23
  SPEAKER_02: I think that the I think we're in the early stages.
• 20:28
  SPEAKER_02: I think that 2023 was the wake-up call year.
• 20:33
  SPEAKER_02: 24 wake-up call year means we've got a new technology.
• 20:37
  SPEAKER_02: It could be very useful.
• 20:38
  SPEAKER_02: We need to start looking at this.
• 20:40
  SPEAKER_02: 2024, right, yeah, exactly.
• 20:42
  SPEAKER_02: 2024 is okay, we've we we started running pilots in 2023 and the first half of 2024, and now we're starting to roll out production systems.
• 20:54
  SPEAKER_02: 2025 is really the year when I think the the results will start to show up for these more leading edge companies that are heavily investing in it.
• 21:06
  SPEAKER_02: Um, uh, because they've they've the the the pilots, for example, a number of the firms with the early pilots were were literally starting to transition from pilot to production um, you know, three months ago.
• 21:19
  SPEAKER_02: You know, this is a rough, kind of, kind of rough kind of time frame.
• 21:25
  SPEAKER_02: And so those results won't really start to hit bottom lines in an appreciable way until early next year, right?
• 21:32
  SPEAKER_02: That's when those results will start to start to show up.
• 21:35
  SPEAKER_02: Um, but this is the time when people are creating competitive advantage by being out there running more AI pilot projects to figure out what what the priority priorities are for production, then you you start to you start to realize the benefits uh starting next year.
• 21:52
  SPEAKER_03: Yeah, and part of it is also the data.
• 21:54
  SPEAKER_03: I mean, when content is data, knowledge is data as well.
• 21:58
  SPEAKER_03: So when people are looking at remediation.
• 22:00
  SPEAKER_03: And getting their house data house in order, it also has to be around content uh and knowledge, right?
• 22:05
  SPEAKER_03: The unstructured information, which is what large language models are good at.
• 22:10
  SPEAKER_03: And so when you think about these organizations that are making some progress, uh what what are they missing?
• 22:18
  SPEAKER_03: So they're getting some things right in terms of their understanding the workflows that need to be uh enhanced.
• 22:25
  SPEAKER_03: They're looking at the heavily rules-based, unambiguous outcomes, right?
• 22:29
  SPEAKER_03: Like you know whether it works or not, right?
• 22:31
  SPEAKER_03: Right based on a use case, based on testing, you know, it's not ambiguous.
• 22:35
  SPEAKER_03: So they understand the workflow, they understand the process, they understand what the outcome needs to be, they understand the rules.
• 22:41
  SPEAKER_03: What are they not getting, or what are they missing, or what are they still struggling with, would you say?
• 22:46
  SPEAKER_02: So on this, I think the I think the total addressable market here for kind of the I think this comes down to the application layer.
• 22:53
  SPEAKER_02: And the application layer, you know, you get you get the chips first, right?
• 22:56
  SPEAKER_02: You get the LLM second.
• 22:58
  SPEAKER_02: Well, I guess you get the data centers, the data centers and the chips first, you get the LLM second, and then third, what do you get?
• 23:04
  SPEAKER_02: You get the applications, right?
• 23:05
  SPEAKER_02: And it's really the applications that explode the industry.
• 23:08
  SPEAKER_02: I think the total addressable market right now for the application layer is just under a trillion dollars globally, 900 and some billion.
• 23:16
  SPEAKER_02: So just market at 1 trillion.
• 23:18
  SPEAKER_02: I think that where this goes uh is probably to two or three times that.
• 23:23
  SPEAKER_02: So we could be looking at a$3 trillion application layer market, because that's where the action is.
• 23:28
  SPEAKER_02: Um, I mean, that's and that's huge, right?
• 23:30
  SPEAKER_02: You know, triple, triple, triple a very already very large industry.
• 23:35
  SPEAKER_02: And that's the thing that's missing, Seth, right?
• 23:37
  SPEAKER_02: It's the thing that's gonna come, it's but it's the big, big driver.
• 23:40
  SPEAKER_02: You know, we talk about those kind of the low-hanging fruit will never be the transformative fruit, as you know, right?
• 23:47
  SPEAKER_02: The low-hanging fruit is simply let's do existing things better.
• 23:51
  SPEAKER_02: And we can get a probably a 30% bump on that.
• 23:54
  SPEAKER_02: Then we can get another 30% bump by kind of let's do more of everything we do better, right?
• 24:00
  SPEAKER_02: Right.
• 24:01
  SPEAKER_02: But the 300% bump that I'm talking at 1 trillion to 3 trillion, that's gonna come from doing new things that we didn't even knew we could knew we could do today.
• 24:11
  SPEAKER_02: And there are certainly lots of, and that's where, you know, you coming back to like startups and things that are um uh uh uh in the in the in the in the in the research centers of the listed companies, those are the really uh interesting things that uh are gonna explode this market, and that's why I called it a secular growth opportunity, you know, over the next few years.
• 24:34
  SPEAKER_01: I think of you know, you went back to you know, in terms of 2023 being a marquee year for a new introduction of technology, 2024, POC, you know, health, right?
• 24:46
  SPEAKER_01: 2024 is also I would argue part of the secular market is not only you know the advancement and innovation of the of the LLM, you know, LMs themselves, but all of that infrastructure that's sitting around it because you outlined it.
• 25:01
  SPEAKER_01: You know, you have all the data centers and chips, and then you've got you know the other infrastructure pieces, but we're also seeing a proliferation of how many different technologies now are there to help with architectures to help with, you know, um, you know, you know, like ML ops, but now LLM ops, you know, and architecture centered around what's the most cost-optimized way in order to put an LLM up and also facilitate you know the ability to switch out l language models at will, right?
• 25:30
  SPEAKER_01: Things like that.
• 25:31
  SPEAKER_01: So the part of that I'm excited about is to see this ground swell of all this supporting cast of characters that's also being raised and forced to be raised because we're just barely getting to optimal infrastructure or optimal architectures for these types of applications, which will then, to your point, facilitate the two to three times growth that's happening, right?
• 25:52
  SPEAKER_01: So just anyway, go, go, yes, yeah, yeah.
• 25:55
  SPEAKER_02: I mean, on that, Chris.
• 25:56
  SPEAKER_02: So, so, so uh, you know, the one strategy that we that you know Seth asked about is you know, what are gonna be the beneficiaries?
• 26:03
  SPEAKER_02: And I talked about you know the 50 companies, and then we chat had a chat about that.
• 26:06
  SPEAKER_02: But the other is, you know, we have uh another investment strategy, which is an AI technology strategy, right?
• 26:13
  SPEAKER_02: Here, you know, we're invested in like another 65 companies, completely different companies, which is purely on the technology side, which is what you're talking about.
• 26:20
  SPEAKER_02: The you know, who are gonna be the beneficiaries within the technology space?
• 26:24
  SPEAKER_02: And I mean, it's for for a portfolio manager like me to have so many opportunities to look at um many good opportunities uh where I want to keep a portfolio that really doesn't have more than 75 things in it.
• 26:39
  SPEAKER_02: Um, it it's just it's a fantastic time to be managing a portfolio like that.
• 26:44
  SPEAKER_02: So let me give your your your listeners some examples, and you gave some examples.
• 26:48
  SPEAKER_02: Like, you know, I was there at the um NVIDIA uh uh uh uh GTC conference where uh uh Jensen you know got on stage and introduced the new Blackwell chip.
• 27:02
  SPEAKER_02: And and one of the points he made during the introduction is that it's not a chip.
• 27:08
  SPEAKER_02: The entire uh server, the the entire data center is a chip, is basically his point.
• 27:15
  SPEAKER_02: The whole data center is a chip, right?
• 27:17
  SPEAKER_02: So what you're to to think about it, you know, in simple terms, and the way they kind of express it is you can think that we had to we had to start to put chips on top of each other, but don't think of you know a thousand chips sitting on top of each other as a chip, as the thousand chips, it's a chip, right?
• 27:34
  SPEAKER_02: So your whole rack becomes a single chip, and then your rack is now a row, and the row is really a single chip, and now you've got 30 rows, and really the 30 rows is a single chip, right?
• 27:46
  SPEAKER_02: What enables that?
• 27:47
  SPEAKER_02: And there's there's technical reasons why they have to go that way, right?
• 27:50
  SPEAKER_02: There's physical limitations um when you get down so particles are so small and all this kind of thing.
• 27:56
  SPEAKER_02: The the the what enables making an entire data center work essentially as a single extraordinarily fast chip with extraordinarily big throughput is networking.
• 28:08
  SPEAKER_02: Right?
• 28:09
  SPEAKER_02: And I can and one of the one of the one of the big networking companies is a company called InfiniBand, for example.
• 28:14
  SPEAKER_02: I remember buying my first Infiniband server at my investment research firm now a decade and a half ago, right?
• 28:21
  SPEAKER_02: And Infiniband has just constantly improved the amount of throughput that can go through a cable, right?
• 28:28
  SPEAKER_02: Because now if you've got 30,000 chips that are all acting as one chip, right, they're all communicating with each other, and they've got to communicate with each other very, very quickly.
• 28:39
  SPEAKER_02: And Jensen made the example during the during the the conference.
• 28:43
  SPEAKER_02: He was like, Let me show you the the the cabling, the wiring, right?
• 28:47
  SPEAKER_02: And he was showing essentially what I think is uh don't quote me on this, but I think it was InfiniBand, all InfiniBand wiring, and it was just a bunch of silver cables wrapped around each other within one rack.
• 28:57
  SPEAKER_02: And he's like, There is more throughput in just these cables you're looking at here than there is data passing over the entire internet in the same uh global internet in the same point of time.
• 29:11
  SPEAKER_02: That is so and that's what you need because you're you've got a lot of data coming in, it's being spread across so many chips, all these things got to communicate with others, all got to be synced, and that's what that so this was just one simple example to ill illustrate what Chris is talking about is that all of a sudden you had this kind of sleepy area of networking, which a lot of people didn't care about.
• 29:32
  SPEAKER_02: Now, people who like me who want to invest in this stuff really care about it, right?
• 29:35
  SPEAKER_02: Right, um, because it's essential now to this new revolution.
• 29:38
  SPEAKER_03: That's so essential.
• 29:40
  SPEAKER_03: Fascinating, especially when you start thinking of the physics and uh and the uh you know all of those pieces behind it and the the volume of communication.
• 29:50
  SPEAKER_03: I mean, that that boggles my mind, what you just said, and I I'll have to dig up that that recording to to hear that and see what that cable looks like.
• 29:58
  SPEAKER_03: But it is it never I love the science behind it.
• 30:01
  SPEAKER_03: I'm a scientist at heart, you know.
• 30:03
  SPEAKER_03: My degree is in science.
• 30:04
  SPEAKER_03: I I love to read about science, so that's really fascinating.
• 30:07
  SPEAKER_03: But let's see.
• 30:08
  SPEAKER_03: So I wanted to ask, so people when you when you start looking at the organization, yes, there is this big ecosystem, uh, resources and technology firms and smaller technologies and boring, sleepy technologies that was that that are now exciting and leading edge, right?
• 30:24
  SPEAKER_03: Like networking, as you said.
• 30:25
  SPEAKER_03: Um and and and there's lots and lots of different, like a billion flowers blooming, right?
• 30:31
  SPEAKER_03: There's so many organizations that are just building on top of foundation models, that are building different applications, that are integrating with other systems.
• 30:40
  SPEAKER_03: You know, one of the companies I was talking to, and I get approached by a lot of these startups who you know want to be on our podcast or want to partner or whatever, was talking about agentic technology flows.
• 30:51
  SPEAKER_03: And, you know, and we've been using agents with LLMs, even when you're doing a lookup, when you're doing retrieval augmented generation, you're using an agent, you're using an API to call something else, right?
• 31:03
  SPEAKER_03: So that's getting more attention now, you know, as simply being a mechanism by which you can do other things with an LLM.
• 31:10
  SPEAKER_03: You know, it seems to be growing up as its own space, agenc workflows or agent-powered LLMs or LLM-powered agents, however you want to say it.
• 31:19
  SPEAKER_03: Do you have a thought about that?
• 31:20
  SPEAKER_03: Because again, what we're talking about is having uh uh an autonomous agent that can go off and do certain things, make certain decisions, be focused on a goal, and then find ways to achieve that goal.
• 31:35
  SPEAKER_03: And I just read or started to read a research paper, I didn't read all of it, about LLMs learning what tools to use, you know, kind of autonomously, you know, and I again I don't know how big that is or how valid that is, but it was talking about how these LLMs are able to start looking at research.
• 31:53
  SPEAKER_03: And I of course with APIs, you know, you you can have a lot of descriptive information and what all those integrations should look like, and potentially an LLM could look at that and say, okay, this is something I need to do.
• 32:04
  SPEAKER_03: What are your thoughts about you know multi-agent workflows and you know this idea of orchestrating specialized agents that will go off and do things and work with other APIs either internally in the organization or externally?
• 32:18
  SPEAKER_03: Do you have some thoughts about that?
• 32:20
  SPEAKER_02: So I think that um where we're gonna see innovation first in that space is actually gonna be in the consumer space.
• 32:29
  SPEAKER_02: I think uh I'm thinking Google, I'm thinking Apple.
• 32:32
  SPEAKER_02: So Apple demonstrated this agent uh activity, agent action type of use cases in their in their you know when they announced Apple intelligence.
• 32:46
  SPEAKER_02: And we'll see.
• 32:46
  SPEAKER_02: It seems like some of the features are gonna be delayed, and you know, this is the usual stuff in tech.
• 32:51
  SPEAKER_02: But this is this is definitely coming.
• 32:53
  SPEAKER_02: But I think it comes, and the reason I said I think it comes first in consumer is because you're not gonna let at such an early stage of you're not gonna let it do take autonomous actions if they have big consequences.
• 33:11
  SPEAKER_02: It's okay if it takes an autonomous action to remind you about you need to do something or buy something or go somewhere or whatever, whatever that, you know, that's okay.
• 33:20
  SPEAKER_02: It can kind of screw up and get it wrong, and you know, we're not, we're not, we're not, we're not gonna drop a bomb in the wrong location or whatever we're gonna do.
• 33:29
  SPEAKER_02: You know, so I think the autonomous actions will start to come in places where the the the cost of getting it wrong is low.
• 33:39
  SPEAKER_02: And that's why I say consumer.
• 33:41
  SPEAKER_02: And certainly Google has announced they have a whole big set of a lot of projects working on autonomous agents, and Apple demonstrated it actually in their in their their launch videos.
• 33:51
  SPEAKER_02: So I think that's that Seth, that's where we'll first see those actions.
• 33:56
  SPEAKER_02: And as they become more reliable and dependable, those things will migrate toward ERP systems and things like you know, enterprise systems and things of that nature.
• 34:05
  SPEAKER_03: So, David, when you when you say that we want to be safe, obviously you need guardrails, right?
• 34:11
  SPEAKER_03: You need to have governance and you need to understand what your processes are and what the limitations are and what triggers a workflow and what triggers an exception and when a human needs to decide.
• 34:22
  SPEAKER_03: But it sounded to me like the the whole uh ADP workflows could be agent-driven, right?
• 34:29
  SPEAKER_03: So, so or portions of them, right?
• 34:32
  SPEAKER_03: So you could speak, and I imagine this is what they're already doing.
• 34:35
  SPEAKER_03: And then the question is when you do have that exception and the human in the loop, then you're also argumenting that with that ready accessibility of that knowledge.
• 34:45
  SPEAKER_02: Definitely on on that that that that journey from a$1 trillion industry to a three trillion dollar industry, that this happens along the way, right?
• 34:53
  SPEAKER_02: You know, that's part of creating all that value.
• 34:56
  SPEAKER_02: But um, I just I just think it takes some time, Seth.
• 34:59
  SPEAKER_02: Yeah, that's all I'm saying.
• 35:00
  SPEAKER_02: It it definitely comes in it, and it sort of has to come to go from one to three, so to speak.
• 35:05
  SPEAKER_02: But I do think that that the you know, people are going to be very cautious about making sure they implement that in a way that it doesn't cause bad things to happen in a costly way.
• 35:16
  SPEAKER_03: Right.
• 35:16
  SPEAKER_03: And and as these things get more interconnected and as you have more dependencies and more APIs, it becomes more and more challenging to know for traceability, for audit trails, for troubleshooting, for optimization.
• 35:29
  SPEAKER_03: When something does go wrong with you know, Joe's third-party agents in your ecosystem, right?
• 35:36
  SPEAKER_03: You know, what's the liability?
• 35:37
  SPEAKER_03: So there's a lot of governance uh issues around this as well.
• 35:41
  SPEAKER_03: How are you seeing some of these leading edge companies doing on the government governance side of they must have to get that fairly locked down in terms of how they're using things, how they're making decisions, allocating resources, measuring results, you know, making assigning accountabilities and so on.
• 35:57
  SPEAKER_03: So that's a big piece.
• 35:58
  SPEAKER_03: What are your uh observations there?
• 36:00
  SPEAKER_02: I'll have to be honest with you there, Seth, as a as a more taking the lens of a financial analysis of these ferns, that's probably at a layer below which I really look and get involved, and I don't feel like I'm the right person to answer that question.
• 36:13
  SPEAKER_03: Yeah, yeah.
• 36:14
  SPEAKER_03: Well, you know, it's something that we're finding there's a lot of demand for and appetite in the industry because people are trying to figure this out.
• 36:21
  SPEAKER_03: They're trying to understand well, how do we make these decisions at the board level so that we can have guidance and policies and procedures on the ground in order to make this work.
• 36:30
  SPEAKER_02: I will say that certainly I've seen analysts comment on that.
• 36:34
  SPEAKER_02: And, you know, the big issue there seems to be that, you know, obviously when you have a query, you have a query and it involves multiple databases and multiple layers of data within databases, all of which have their own sort of permission structures, right?
• 36:56
  SPEAKER_02: Um, this becomes very, very complicated because I might be asking a question, and if I'm the CEO, I can get all the databases involved to give me an answer.
• 37:06
  SPEAKER_02: But if I'm somewhere else in the organization, only can answer from these databases and only from these sections of these databases because of permissions.
• 37:14
  SPEAKER_02: So obviously there are all kinds of, you know, there are startups working on these issues to kind of you know figure out in a very automated way, okay, for a particular query, depending on who you are, and that's part of the context, right?
• 37:27
  SPEAKER_02: Not only who's asking and what department are they in, but are they allowed to see, you know, right?
• 37:32
  SPEAKER_02: You know, so this becomes very, very complicated very quickly, as we all can imagine.
• 37:36
  SPEAKER_02: Certainly that's part of the one to three trillion dollar journey, is companies need to be able to figure this stuff out because we can't be calling somebody up to say, hey, does he have permission, right?
• 37:46
  SPEAKER_02: It's all got to be built within into the system.
• 37:49
  SPEAKER_03: Yes, the permissioning, the provisioning, the respect of privacy and even confidential communications within the organization.
• 37:57
  SPEAKER_03: There's one company we're working with that has a uh an orchestration agent and you know it's monitoring all these conversations that are going on to give some provide some memory to the individuals having those conversations.
• 38:10
  SPEAKER_03: But some of those conversations are privileged.
• 38:13
  SPEAKER_03: So while the orchestration agent knows about it, it also knows that when Joe and Bob spoke about you know potential layoffs or terminations or whatever it is, that is a privileged communication and that cannot be serviced to others.
• 38:28
  SPEAKER_03: So it is, as you say, it's a comp a set of problems and provisioning.
• 38:32
  SPEAKER_03: It's it's kind of analogous to an organization working with IBM and Apple on semiconductor fabrication, certain things they can share, certain things they can't share.
• 38:41
  SPEAKER_03: So all those security models have to be very well thought out and and so on.
• 38:46
  SPEAKER_03: I was just joking laughing a little bit because I find that there's always SharePoint databases in my company that I should be able to access that are telling me I need to ask for permission.
• 38:55
  SPEAKER_03: Theoretically, the C CEO has access to everything, but in reality, I don't know why that's happening.
• 39:01
  SPEAKER_03: I should be super adamant.
• 39:03
  SPEAKER_03: Hey, I know this is a little bit off topic from the web podcast itself, but I'm really fascinated by the uh GLP1 ecosystem that we had discussed, right?
• 39:13
  SPEAKER_03: And just because I'm sure people are fascinated by that stuff, but we know that those are being used for uh controlling appetite and hunger.
• 39:21
  SPEAKER_03: But you had also mentioned that this receptor and the drugs that target it have huge potential beyond weight loss.
• 39:27
  SPEAKER_03: I I I would just love to hear you riff a little bit on that, just just uh uh because I think it's a fascinating topic.
• 39:33
  SPEAKER_03: Sure.
• 39:34
  SPEAKER_02: If there's an AI connection, great, but if not, then we'll Yeah, I mean, just again for the benefit of your listeners.
• 39:39
  SPEAKER_02: As I mentioned earlier, you know, I manage uh a uh a portfolio that is um, you know, technology stocks.
• 39:47
  SPEAKER_02: Um but another portfolio brings in, keeps that portfolio, but for people who want to also invest in the healthcare innovation sector.
• 39:55
  SPEAKER_02: So it's it's AI-driven innovation and healthcare-driven innovation.
• 39:59
  SPEAKER_02: We have a another strategy which adds on the the the the GLP one.
• 40:04
  SPEAKER_02: GLP one for the benefit of your your listeners is the the the weight loss drugs.
• 40:09
  SPEAKER_02: And the the GLP one refers to the actual mechanism by which it's that it's accomplished within your body.
• 40:14
  SPEAKER_02: Um, but it broadly speaking, it's it's um it's it's it's weight loss drug innovation.
• 40:20
  SPEAKER_02: But the interesting thing about weight loss drug innovation is not just about weight loss, and that's that's what you were getting at.
• 40:25
  SPEAKER_02: Um, it's turned out that these drugs um uh are delivering outcomes, health outcomes, across uh a variety of of diseases.
• 40:36
  SPEAKER_02: So from liver disease to diabetes to um uh heart disease and even to Alzheimer's.
• 40:44
  SPEAKER_02: And it seems like not a quarter goes by where I don't see some study come out showing that there's promising applications in some other area of health.
• 40:53
  SPEAKER_02: So we think that the ecosystem, just like as we've been discussing, is huge on the AI beneficiary side, the AI technology side, that there's a whole ecosystem there to invest in in um in GLP ones.
• 41:08
  SPEAKER_02: And we think that's another secular trend, meaning when you start to be able to show efficacy and promising outcomes, health outcomes in an area like heart disease, in an area like um uh diabetes, in an area like Alzheimer's.
• 41:26
  SPEAKER_02: I mean, these are massive, massive markets on their own.
• 41:29
  SPEAKER_02: Even just a hit in one area would be massive.
• 41:32
  SPEAKER_02: And when you're talking about a drug that could potentially do it across these multiple areas, that's an innovation stream that a lot of investors, you know, want to be invested in.
• 41:40
  SPEAKER_03: So it's so, and that's interesting because it's not just about the weight loss, it's about another mechanism that is impacted by that receptor and by that uh mechanism of action and those biochemical pathways, not just weight loss.
• 41:53
  SPEAKER_03: And it's not just the consequence of weight loss, but it's a separate impact as well.
• 41:57
  SPEAKER_03: That's right.
• 41:58
  SPEAKER_03: Yeah, that's facts.
• 41:59
  SPEAKER_02: That's right.
• 42:00
  SPEAKER_02: And and it and it seems to, again, we don't know because there are there are completely separate mechanisms going on.
• 42:05
  SPEAKER_02: And then there are overlapping related mechanisms.
• 42:08
  SPEAKER_02: So just think about someone who loses weight becomes just more healthy, right?
• 42:13
  SPEAKER_02: Um, they're closer to where their natural body uh weight should be.
• 42:18
  SPEAKER_02: Part of it is is benefits related to that, but as you're pointing out, but then there are these kind of specific mechanisms that can be more targeted, and the drug can be modified to be more targeted and get an even better outcome.
• 42:30
  SPEAKER_03: Wow, that's great.
• 42:32
  SPEAKER_03: I know we have uh just a couple of minutes left.
• 42:34
  SPEAKER_03: And again, fascinating aside, because I'm a chemistry geek.
• 42:38
  SPEAKER_03: I have my degree was chemistry, I love biochemistry, biology.
• 42:41
  SPEAKER_03: I still read, you know, all sorts of publications and research just for my benefit.
• 42:46
  SPEAKER_03: So I love hearing about that stuff.
• 42:48
  SPEAKER_03: Um, I just want to learn a little bit more about you for our audience, and you would tell us about your time in Japan.
• 42:54
  SPEAKER_03: What was the catalyst and interest and and how did those experiences impact your career and your personal growth?
• 43:01
  SPEAKER_03: You want to talk a little bit about that?
• 43:02
  SPEAKER_02: So, for the benefit of your listeners, I lived to I lived on sort of multiple tours of duty.
• 43:07
  SPEAKER_02: I lived uh in Japan for about 14 years.
• 43:09
  SPEAKER_02: Uh, two of those years were were education, learn the language, study economics, things of this nature.
• 43:15
  SPEAKER_02: And then uh, and then uh I worked there for a large multinational uh for a few years.
• 43:22
  SPEAKER_02: And then I started that that internet search uh company that we talked about.
• 43:26
  SPEAKER_02: I started that in Japan as a VC backed uh you know startup um back in 1999, as you alluded to earlier.
• 43:34
  SPEAKER_02: And it was, I mean, that change which happened in Japan um was was you know a seminal event in my life because that was what, you know, having not really been a technology person, been more of an analytic finance kind of uh you know, MBA type of person prior to that, that sort of woke me up to the power of how data, when you bring together data.
• 44:00
  SPEAKER_02: When you bring together compute and when you bring together algorithms, these three things, man, magic can happen.
• 44:07
  SPEAKER_02: Things that just weren't possible before.
• 44:09
  SPEAKER_02: At that time, it was searching the internet, searching this global thing, which I know.
• 44:14
  SPEAKER_02: Um that just gave me a bug for the rest of my life.
• 44:17
  SPEAKER_02: Was you know, that we could do things with at the nexus of those three things that would be exciting and bring people benefits, uh, and just would be an exciting place to be.
• 44:30
  SPEAKER_02: And so that's where I spent the rest of my life at the nexus of those things.
• 44:36
  SPEAKER_03: That's wonderful.
• 44:37
  SPEAKER_03: That's very exciting.
• 44:38
  SPEAKER_03: Another question I like to ask people.
• 44:40
  SPEAKER_02: And just last thing to add there.
• 44:42
  SPEAKER_02: Sorry to interrupt, Seth, but I will say Japan, obviously, Japanese markets have done very well in the past year.
• 44:47
  SPEAKER_02: And a large part of that is not just to do to the macroeconomic background, which is certainly part of it.
• 44:52
  SPEAKER_02: The other part of it is Japan is Japanese companies are right at the center of the AI ecosystem.
• 44:58
  SPEAKER_02: I mean, there's a lot going on in Japan, particularly on the hardware side of AI, um, that's absolutely essential to everything that goes into the data center and chips that that go that are powering the industry, but also that are going into your phone as well, right?
• 45:13
  SPEAKER_02: I mean, Japan is a very, very well positioned on the hardware side in particular of the industry to grow and benefit from AI.
• 45:22
  SPEAKER_03: That's wonderful.
• 45:22
  SPEAKER_03: That's very interesting.
• 45:24
  SPEAKER_03: Uh, and you have some very unique insights from a the language and cultural perspective.
• 45:29
  SPEAKER_03: Absolutely.
• 45:30
  SPEAKER_03: Another question I like to ask people when um when we have them on the show is you know, if you could go back to the time when you were graduating from college and you could you could give yourself some advice.
• 45:42
  SPEAKER_03: What might you say to yourself?
• 45:44
  SPEAKER_03: Is there anything you would tell yourself with this kind of perspective?
• 45:48
  SPEAKER_02: Well, I think when you're my age and you look back on yourself in college, you think, man, geez, that guy knew nothing.
• 45:54
  SPEAKER_02: Right?
• 45:54
  SPEAKER_02: Absolutely nothing about the world.
• 45:56
  SPEAKER_02: I mean, I at least I I hope we all say that about ourselves because you know, if we didn't, we wouldn't have learned anything along the way.
• 46:02
  SPEAKER_02: But um, yeah, I mean, there's a question what I know about the world.
• 46:05
  SPEAKER_02: Yeah, I mean, you know, on on the one hand, maybe I would get if I were to talk to myself at that age today, I would say, first of all, realize you know very little.
• 46:16
  SPEAKER_02: So, you know, which is a good place to be, it's a good mind to have because then you're open to constantly learning, right?
• 46:23
  SPEAKER_02: You know, to um trying to, you know, ingest as much as you can, learn as much as you can, you know, move along the learning curve as fast as you can, um, and you're inquisitive, you're curious.
• 46:36
  SPEAKER_02: I think that's always a really good place to be in life and kind of stay that place and stay inquisitive, stay knowledge hungry, stay skill hungry.
• 46:45
  SPEAKER_02: These are the kind of things that benefit you at at every at every age, but it's better to learn it younger and sooner.
• 46:52
  SPEAKER_03: Get outside of your comfort zone and be comfortable, comfortable with that.
• 46:56
  SPEAKER_03: And that's it.
• 46:56
  SPEAKER_02: Yeah, I mean, I will say the last point on that is you know, to any any young people uh heading off to university or parents of people heading off to university, I did read a very interesting study recently on this.
• 47:09
  SPEAKER_02: So there's there's empirical academic work behind what I'm about to say.
• 47:12
  SPEAKER_02: But it's been shown that, and in at least one study, it's been shown that people who did dual majors have better lifetime earnings outcomes and actually happiness outcomes.
• 47:23
  SPEAKER_02: And they speculate why is because um, you know, there's a lot of volatility in the world these days, right?
• 47:30
  SPEAKER_02: The world is technology is making the world change at a at a much more rapid rate, not just technology, other factors, but that's one big one.
• 47:36
  SPEAKER_02: And in a volatile world, you really don't know what the skills are going to be in demand over the work your your future working life.
• 47:44
  SPEAKER_02: So it's better that you have a few pockets of skills to pull from.
• 47:49
  SPEAKER_02: I personally recommend left brain, right brain, right?
• 47:52
  SPEAKER_02: Analytic, liberal arts, right?
• 47:54
  SPEAKER_02: Do a dual major and or you know a major minor, whatever is the case, but pick up something that's analytic, uh uh kind of oriented, uh scientific analytic kind of oriented.
• 48:05
  SPEAKER_02: Pick up something perhaps more liberal arts kind of oriented, which is what I call critique oriented, uh critical thinking oriented.
• 48:13
  SPEAKER_02: Thinking, yeah.
• 48:14
  SPEAKER_02: Uh uh, I think anyway, the study says do a double major, it's better than doing a single major.
• 48:20
  SPEAKER_02: I would add to that, if you're gonna do a double major, do a left brain one and a right brain one.
• 48:24
  SPEAKER_03: That's one of the things.
• 48:25
  SPEAKER_01: Well, I mean, from a portfolio manager, you know, it's it seems very, very in line with diversify your education, right?
• 48:32
  SPEAKER_01: Diversify your portfolio, diversify your education.
• 48:34
  SPEAKER_01: Diversify your brain spot.
• 48:36
  SPEAKER_01: Yeah, exactly.
• 48:36
  SPEAKER_01: Exactly.
• 48:37
  SPEAKER_01: I like it as well.
• 48:38
  SPEAKER_01: It's a good company.
• 48:39
  SPEAKER_03: Well, guys, this has been great.
• 48:41
  SPEAKER_03: David, I've really enjoyed this.
• 48:43
  SPEAKER_03: Uh, I wish we had more time, but maybe we can have you back uh for this conversation.
• 48:48
  SPEAKER_03: Thank you so much for sharing your your insights and joining us today on the early AI podcast.
• 48:54
  SPEAKER_03: We really appreciate it.
• 48:55
  SPEAKER_02: Well, thank you, Seth, and thank you, Chris.
• 48:56
  SPEAKER_02: Uh, it was it was a delight to join you both today.
• 48:59
  SPEAKER_03: And you will have your LinkedIn profile in the show notes.
• 49:02
  SPEAKER_03: Your company website is Markin M A R K-I-N Funds, one word.com.
• 49:08
  SPEAKER_02: Markin Asset Management.
• 49:09
  SPEAKER_03: Oh, I'm sorry.
• 49:10
  SPEAKER_03: We had uh Market Funds.
• 49:12
  SPEAKER_02: Markinfunds.com is the the URL, but the name of the company is Markin Asset Management.
• 49:16
  SPEAKER_03: Oh, I'm sorry.
• 49:17
  SPEAKER_03: Okay, Markin Asset Management, but Markin Funds is the URL.
• 49:19
  SPEAKER_03: Okay, great.
• 49:20
  SPEAKER_03: All right, very good.
• 49:21
  SPEAKER_03: Thank you.
• 49:22
  SPEAKER_03: And uh again, thank you so much, and thanks to all of our listeners and uh Carolyn for doing all that work behind the scenes.
• 49:29
  SPEAKER_03: And again, David, thank you uh so much for thanks, David.
• 49:32
  SPEAKER_03: Thank you.
• 49:33
  SPEAKER_03: All right, take care.
• 49:35
  SPEAKER_03: And we'll see you all next time in the next Early AI podcast.
• 49:39
  SPEAKER_00: Visit early.com to find links to the full podcast on all audio platforms to listen on the go.
• 49:45
  SPEAKER_00: Thank you for watching.
• 49:47
  SPEAKER_00: Contact us today at info at early.com.