#89 - Brock Sutton: Adapting to the AI Era

24:57So the new country of kind of capability actually unlocked within this. And so one of the things I think you could potentially see a hypothesis out there that I've heard is maybe that human element just becomes more and more valuable. So things like empathy, things like connecting with other humans, that in-person component. I think things like client acquisition, being able to tell stories, being able to sell, I think those things become more and more valuable. They're inflationary. And then you have other things that can be automated by AI become that kind of deflationary. So long-winded way of saying, I think we're super early on with most kind of teams in that one and two phase, one and two.

25:35But I think those are some of the dynamics maybe to monitor on how it could permeate throughout time and actually change some of the value within financial services overall. A lot of people may feel intimidated by AI. What parallels would you draw to earlier technologies and how should people think about maybe overcoming some of the overwhelming feelings they may have towards AI? It's interesting to think about like that earlier technology, because I think one of the things to think about is like, did you use it at home first or did you use it at work first? And I think things like the personal computer, things like that, you used it work first and then that bled into the home.

26:16And I think what we're seeing here is it's kind of a mix of both, but I think a lot of it is in person, more like the internet. Maybe you're using it outside of work and then it starts to bleed into work where, you You know, I think they blocked the internet early on, you know, at workplaces, and then they allowed for people to access the internet in their day-to-day jobs. And so I think that's kind of one parallel to see. And so one thing I think then the parallel across all of this is to experiment with it. Like the number one guidance we give is to build in time to play around with it. So what we recommend is everyone's busy, just take an hour a week.

26:51Just take one hour, block that hour, and then take two things that you're going to do. This could be a personal hour, it could be a work hour, two personal things or two work things potentially that you wanna leverage a tool for and allow yourself to try to actually get those things done with the tool or with the technology. So what we really recommend is to just start because to your point, you can ask questions. You can say, hey, is there a better way that I should be doing this? It is more and more like a conversation. And so we really think that getting started, building in time to play around with the technology and then making sure you're not time bound and time constrained is incredibly important.

27:27I guess to add some sense of urgency, the technology is rapidly evolving and you're much better off being early rather than trying to catch up later. And it's really easy to start learning because as we both talked about, it's more of a just engaging in a conversation with somebody who is available to you. Absolutely. Yeah. It's, it really is that conversational piece, right? You know, you think about the history of kind of compute, right? Going back and forth. This is the next evolution of software. It's the next evolution of how you or I, anyone listening, you know, engages with a computer. So that natural language interface is great.

28:06And the other thing is it's actually super helpful too. So I think people would be surprised by the number of use cases, you know, that they could potentially leverage it for. So I really think that experimentation is the best part. Right. And it's actually, that's the whole point. It used to be that you have to have programming expertise to communicate with a computer, right? And then it got a little bit easier with the internet and search engines and so on. And now you're at the point where you can actually just have a conversation like you would with any individual. So even though it may be intimidating, it's actually much easier for the user.

28:38Absolutely. Totally agree with that. So you talked about the different layers in the AI stack. Where are we seeing bottlenecks or maybe even the other way, where are we seeing the biggest innovations within those layers? Again, not an expert in the chip or the cloud layer. So, and I don't want to sell the advancements of their short. I pay attention to it, but I don't want to speak on it because it's so complex. It's really interesting, super nerdy though. There's tons of advancements going on there. And I think the big interesting advancement there that I would talk about is like the power component of it.

29:09And it's specifically in, you know, maybe a country like the US that struggles to build out like all of that power. Some of the power advancements that they're doing, basically doing more with less is really compelling there. But I think for this group and for kind of the broader audience, the really interesting stuff I think is more happening kind of at that model layer. And so there I think what you're seeing is you're seeing, again, the technology's doubling every seven months on kind of what it's able to do. but you're also seeing this really interesting kind of balance from what you experienced with that original chat GPT in November of 2022, where it's kind of responding, you know, immediately to you.

29:49And it's just kind of like text back and forth. You're seeing a combination of that with, with tool use, which is super interesting. And when I say tool use is what I mean is the same way, you know, that you or I would use a tool to solve a problem. We may use an Excel file. We may use Python, right? We may use a search engine to answer a question. AI is now able to use some of those similar types of tool uses. So I think this is a really interesting space where you're seeing more and more kind of work put into that tool usage. So it's allowing the capabilities to really advance. The other tension that I think that we're seeing within those two layers, within the model layer, the platform layer, and the application layer, those three layers is really around like the capability of the application layer and of that model layer.

30:40So the model layer is advancing so quickly that a lot of times these applications will build very specific use cases that the models can't do yet. But by the time they release it or, you know, six months after they release it, the models catch up and they're able to actually kind of advance on that use case. So I really think that if you think of within the stack, there's a lot of really great work going on there. On the application side, they're much more focused on kind of very vertical, very specific use cases. So you've got a lot of companies within some of the disciplines and the industries that you mentioned that are focused on, we are going to be the AI for law, we are going to be the AI for finance, et cetera.

31:21And I think it's an open question for how that market plays out versus a general purpose kind of model component versus these application layers that are using those core leading edge models, but then they're building all this really cool, you know, infrastructure on top of it. So I think that's one way to kind of like pay attention to the tension between those two and see kind of what develops first, what wins, etc. Could a potential bottleneck be humans? And what I mean by that is technology is rapidly evolving, but sometimes the technology could be ahead of where society is comfortable. So could the technology be growing too fast for society where it starts pushing back on that growth and is slow to catch up?

32:05I think that's actually what you saw with the original chat GPT. Because I think OpenAI, they had been building out these kind of models and they thought that their business model was going to be an API where they were going to build the core model and then everyone was going to build amazing products on top of it. And I think there was just a lack of you know, products like within that space. And so I think this is when you had open AI kind of release chat GPT and that actual product, because they said, hey, we need something that's going to capture this. And so to your point, I think this is like a, how does do organizations, people start to actually use and adopt this technology?

32:45These labs can run as fast as they want, but to be useful, we have to actually embed it and go through workflows. And that's challenging. Like, it's really challenging at a big organization. You know, it's a massive ship, you know, it's hard to get those things to kind of like rethink those things. So it takes a long time. So one is, I think we're at the early stage. I think the bigger the change, the longer it typically takes. So this is going to be kind of a longer thing. The other thing that's interesting too is if you think about the workforce, you know, you or I are not AI native. We didn't grow up, you know, at 12 years old using ChatGPT.

33:22And so I think there's also kind of this osmosis of younger folks who grew up using the technology. They're kind of AI first, right? AI native, and they're starting to leverage it. So as they kind of enter the workforce more and more, I also think like potentially this is what you've seen with some other technological evolutions is you start to see a bit more of that kind of growth or that value created as they start to enter the workforce. So I think it's an absolute tension that we're dealing with there. So obviously AI is incredibly productive, but one thing that is interesting that I'd like to know if you can comment on is you don't really see it in the stats yet, meaning economic growth or productivity gains aren't really showing up in current stats or even projections of the future.

34:09Why do you think that is? Well, so I think one interesting thing is the employment workforce, right? Maybe that takes time. That's one thing. Two is I think we are super early on. Three, it would be interesting to see the world would look like if AI was never invented. What would our GDP, growth rate, things like that look like if we didn't have this CapEx? I think that would be an interesting thing. And then maybe the other one would be the consumer surplus versus value capture type equation. And so I think right now there's kind of this consumer surplus within AI. And so one of the funny comments that I stole from someone.

34:49I forget who said it, but, you know, the 2010s, millennials had, you know, free Ubers and free, you know, food delivery subsidized by the VC market, right? Maybe in the 2020s, we have unmetered, you know, super intelligence in our pocket subsidized by, you know, tech companies and VCs. And so right now, I think that there's kind of this mismatch between like value being created and maybe value being captured by these organizations. And I think one of the big things, the reason that you're seeing that is the actual ad kind of monetization. So if you look at like just look at ChatGPT, the biggest consumer kind of company within this space, 80, 90 percent of their users are on the free tier.

35:27So they're costing open AI money. And then you have the paying customers actually creating that revenue component. And typically what you see in a consumer company is you see monetization through ads over time to reach the entire market, etc. So I think one thing there is it'll be interesting to see how that develops, how they kind of figure out the ad dynamic, how that changes from what search was in the past, and how they start to capture more of that value. The other thing is, if you think of that physician example that we talked about, that number isn't really coming through of all the times that I'm going to these tools when it may be billable hours for the physician.

36:12So in some areas, it may be deflationary until that catches up and then maybe pushes value, and it just takes a long time to kind of digest through the system. So those are some theories on why it's not showing up, but my guess is it's going to take some time. We touched on this earlier, but how should people think about collaborating with AI as opposed to competing? In other words, how should they think about what parts of what they work on and what they do is better served by computer rather than them? That's a really interesting question. I don't have a strong answer for it. I think my bias here is I default to AI is better than me at everything.

36:53And I start there with that assumption. And then I try to go to the tool first. Like I've really become, you know, the action button on my eyes. If that was true, it'd be very scary. It would be, it would be, it would be. But what's interesting is, you know, I think like the expansiveness of what it allows like you to do. And it falls short in many areas. Like there's a lot of things, you know, it absolutely can't do, but it does continue to kind of grow. And so I think the more that you can kind of default to trying to use the tool, I think that's helpful because I think it gives you a really clear sense of like, where is that exact line at?

37:31And so what I've found right now is like more discrete tasks. So like think of a more, you know, maybe a junior individual, you know, someone that, you know, you give a very specific kind of task to a specific thing that you want them to do. The less variables and kind of overall complexity of it, the better like the output you're going to get. If you add in, you know, too many steps and too much complexity, it will fall short. So one of the things that we recommend when people start to use this technology is like a lot of times people are asking really complex tasks. So they're saying, hey, I want you to do this 10 step thing for me across finance, across health care, etc.

38:12Like solve this problem. Go out in the world and find a reservation, book a reservation, book my flight, all these things. And it can't handle that much complexity yet. And so what we recommend is to cut it into chunks. So really say, okay, instead of 10 steps, let's go one by one. Let's actually then do step one, validate the answer, move to step two, validate the answer. And we've seen a lot of success with people developing that and leveraging that in their personal life, as well as within their business. And that's how we've kind of solved that problem. And then as the technology is getting better, then you can start to combine those.

38:49You can say, hey, maybe we can do one and two together, as opposed to, you know, just one on its own. And so I think that's a good way to kind of think about it as really discrete, kind of concrete tasks. And then I really like to lean on it in areas that I'm not an expert in because I think it really expands, you know, kind of your overall education or understanding of different concepts. Are there any AI tools or platforms that you think may be most helpful for people to focus on? Yeah. And I think how we think about this and what we're seeing folks do is really come up with this kind of like core versus satellite type of approach to the technology.

39:27So think about the core as in general purpose, something that you could apply across your day-to-day life or your entire business. And so this is where we're seeing access to those platforms and those leading edge models. So maybe it's from Google, maybe it's from Microsoft, maybe it's from OpenAI. What we're seeing is making sure that you have a general purpose tool. Doesn't really matter which one, test them out, right? See what you like. We don't give advice necessarily on that. And then also think about these satellite solutions. So let's say you have like a really specific problem that you just need solved and you just want an easy button.

40:05That's where some of these existing software solutions with AI kind of added on to the side is really, really helpful. So the number one use case we see there with advisors is around meetings. So it's, hey, on my client meetings, can we get a summary and can we get next steps and can we get next steps to the right people? So there's a bunch of products within that space, but that's kind of the number one satellite use case we're seeing, but we're seeing that pop up more and more. So as folks kind of start to dabble in this space, that's how I would think about. I think about which core, you know, experiment, but if you have a core you're using, keep using that and use it across everything.

40:39But then if you have something that you're just like, hey, I just need to solve this. Let me see if there's an AI featured, you know, product out there. Then let me leverage that in that specific space. I'd be interested, does this mirror kind of like how you're thinking about it and how you're using it? I think so. And then I think the interesting part of what you just described is the core tool that you're using can help you find those specialized tools. Yes, absolutely. Yeah. One of my favorite use cases is actually on vendor selection. So, you know, tell it a little bit about your business, give it all the context.

41:07You know, one of my favorite tips there is to flip the roles. So say, hey, I am a financial advisor. I am looking for the different satellite solutions for this specific problem. What 10 questions can you ask me about my business that then I can go ahead and answer that then will help you do a better job? So you give it a little context about your business. Then you tell it about the problem. I want, you know, meeting notes and next steps. Can you go research these couple of vendors, find them, research them? And then based on what you know about my business, give me a pros and cons list of this.

41:40So really kind of accelerate, you know, typically what it would take your time to understand that market, make a purchase decision, et cetera. It's really that kind of helpful analyst, which is a lot of the use cases that we've been seeing. It sounds like an important part of getting the most out of this new technology is knowing how to use it. So, so think of it like I think of it as, cause I use it just about every day. I think of it as I have a really smart friend that has a PhD in every discipline in human history, but they don't, they may not know me that well yet. So I have to tell them what I'm looking for.

42:14You almost have to walk in towards looking at the world through my eyes and describe exactly what I'm looking for. and then they can help you find the answer better than if you just give them too generic of a question. They may not know you that well. Yeah, absolutely. And it's definitely like context is key in all of this. If you think you've added enough context, think again and ask what questions you may have not answered. Hey, do you have any other questions about my situation that I could help answer that would allow you to do a better job? It'll come back with some very thoughtful questions that then you can respond to.

42:50So I completely agree with that. And then for businesses, how should businesses think about starting to use AI to maybe offload some tasks and allow them to do the things that they should spend more time on? So this is an interesting space. And one of the things that we're working with and helping different advisors in their businesses do, and this applies across all businesses though, is really take a kind of top-down approach to this. Because what we see, and I think a lot of people are doing, is this kind of reactive. This is how I act too. It's, hey, I've got a problem. oh, I could use AI for this, right?

43:22I go to the tool, I use it, I get a better response. But really what you see some of those more advanced organizations do is come with this top-down kind of framework for how to do it. And so this is where we're helping them actually go through this. So there's five steps in it, nothing revolutionary, but a systematic process for it. So the first one is to understand your business operations today. So what are all of the things that you do to run your business, right? What are those big bodies of work that you do weekly, monthly, quarterly, et cetera? The second thing then is based on that first list, let's prioritize where you're going to get the most business value.

43:57So where does it have the highest business value and match to AI and that you're also spending a lot of time, money, resources against that. And then let's break that down step by step. So if it's something like a client review or a portfolio construction process, what are the 10 steps or so, right, that you do to leverage that process? Let's then record the actual data within that so that we know where the data is at, who typically does it, how long does it take, et cetera. Step three then is matching whatever technology solutions you have available to your existing process. So it's saying, okay, based on what you're trying to do, let's make sure you're using the right tool, right?

44:33And then step four is where you take that plan, you take that plan to your team and your organization, and you actually have them start to implement that plan. And you start to record, like how much time is this actually saving you? So we get a better sense of where those kinds of gangs are coming from. And then step five is to analyze that time lift and then go back to step one. So as you're thinking about this from an organizational standpoint, I think those are kind of the main process that I would take to this. Now, the other thing we could dive into is the type of tasks to approach with this.

45:07And I guess first, does that mirror how you guys are approaching it? Like that process? Does that make sense to you guys? It does make sense. I think in order to do that, you need to have some experience and familiarity with AI and its power. because it may be difficult to envision what it can do until you have some back and forth and some successful experiences. So I feel like you have to start there. Then once you have an idea and you get a glimpse into what its potential is, then to me, it does make sense to say, let's start with a blank slate. We're in this business. This is what we're trying to achieve and almost forget everything that's gotten you to that point because you almost have to think about there's a new tool that didn't exist when we built our current systems.

45:57So if we start from this, how else would we design this? Yeah, that's a really good point. Yeah, because if we think of those four phases that we commented on before, really phase one and even starting phase two, it's like you just have to understand how to use the technology and tools. And then this is really where you get to that end of phase two, kind of phase three. And then the other thing, and I'd be interested to hear if these are some of your use cases that you guys are using it as well, is to make sure you're using the right tool for the right kind of AI capability. So what's been really interesting over time is like how these models have developed.

46:28So October of 2022, we had the original chat GPT. And the analogy we like to use here is that PhD intern, PhD across every discipline in human history, they come to work for your business. You ask them a question and they just respond immediately off the top of their head. And this is great for things like content creation. So writing, things like summarization, things like fact retrieval, And so making sure that you're using conversational AI for those types of use cases. So that's maybe step one is start with those kind of use cases. Step two, and kind of the next evolution of AI was reasoners.

47:03And so reasoners is what we got into actually about a year ago now with OpenAI. they came out with their O series of models, which were these reasoning models. And the example here is, you know, same PhD intern, you know, they come to work for your business, but now you ask them a question and they actually think about it for a week, you know, they plan it out, they break it down step by step, and then they come back to you with an answer. So really good things with a binary right or wrong answer. So think math, science, coding. So this is where like data analysis becomes a really one. Decision-making, you know, what-if scenarios.

47:37This is where kind of that next level of AI. So after you've mastered kind of those basic use cases, think about those reasoner-type use cases. And then level three, where we're at today and we're just starting to see cool products come about is agentic AI. And so this is that same PhD intern, but now they're actually going out into the world and they're executing on your behalf. So they're dealing with the ambiguous world around us, they're taking action, et cetera. And I think the best, you know, use case here is that kind of that analyst, you know, type role. So there was one advisor that we were working with that they were getting, you know, questions every week from a client on a specific like angle.

48:12And it was taking them, you know, a couple hours to like research this topic, get a really good perspective. And so we worked with them to show them to use the tools that they have access to to say like, hey, use this as your research analyst and let's cut those couple hours down to 10 to 15 minutes where you're just, you're directing it in the right direction. and then you get a really thorough research report that then helps you quickly answer that type of client question. So as we're thinking about the kind of evolution of use cases, I would maybe use that framework for folks, start with that conversational, then move to the reasoners and then finally the agentic kind of phase there.

48:48Are there certain industries that you feel are more exposed to disruption from AI and maybe on the other end, the industries that are less exposed and maybe more easily can adapt to it? Yeah, this is a tough one. And I don't have like a clear answer on it. But I think the one thing to look for is business model changes and business model kind of like disruption overall. So that makes me think of, you know, there's a lot of areas in the economy that are kind of high costs, kind of flat fee, right? You pay for it kind of with a fixed set cost. And I think what AI potentially allows is this transition to actually pay per outcome.

49:27So you're paying based on the results. And so like the beautiful example here is advertising and what happened with the internet. So prior to the internet advertising, we'd go out here to Sunset Boulevard, we'd get an ad, right? We'd go to the New York Times, we'd place an ad there. It was a fixed fee and we had no idea exactly how much revenue we were going to generate from that. We hoped it would be profitable, but we weren't certain every time. And then came along the internet, came along companies like Google, companies like Meta, and they were able to transition that to a paper outcome. So we could say, hey, we sell$10 widgets.

50:00We're willing to spend up to$5 for every customer that you can find me that's going to purchase this$10 widgets paying based on an outcome. So I think what will be interesting to see what types of functions, jobs to be done, things like that within the economy start to transition to this kind of paper outcome or can benefit from this paper outcome. So it's hard to say like what industries have the most disruption associated with it. But one place I may start to look is that with kind of very high fixed, you know, flat fees for some type of, you know, good or production or something like that. Well, we know data is fuel for AI.

50:38Should companies be focused on generating their own proprietary data in order to build a competitive advantage? Absolutely. I think this is a key. So if we go back to the framework of thinking about technology has the same impact, it commoditizes something. The beneficiaries are the complements. So you want to know what this technology is good at and you want to position yourself as the complement to that. The first one we talked about, Winter, was ideas. Where are the questions you ask? Where do you point this technology? I think the other one that's really interesting is this data component.

51:12So if you or I, if analysis, again, and execution is free, if we can ask any question or execute on anything across the web, then what matters most, and we can do that across all the data that's publicly available, what matters most is the data that you have that I don't have. because then your analysis is going to be different, right? And so I think data, private data, becomes a real competitive advantage in this type of universe. So one thing I think about in the financial services space is like client data, client information, client preferences. How are you thinking about collecting that data, keeping that in a way that it's going to be accessible and actually useful to your business?

51:51And then how do you actually then start to deliver a better experience on top of that? So that's absolutely kind of a component. Is that something that you guys have started to think about and incorporate within your business? It is, for sure. Yeah, I mean, we live in a world full of data. So any data that you can collect while maintaining privacy that's to the benefit of clients is probably a good move. So as AI becomes more pervasive, what do you think are the biggest risks that we should be mindful of? I'm not a good person to answer the biggest risks. I'm not smart enough to do that. I'll tell you what I'm thinking about, maybe.

52:30One of them is, I'm a terrible speller. And I'm a terrible speller because, you know, I never had to learn how to spell. I mean, we did spelling bees and things like that in school, but typing out, I never, directionally, if I got the word correct, it would correct it for me. And so I outsourced that function completely to computers, essentially. I'm never going to be a good speller. My concern, one of them, is a little bit around do humans outsource thinking and reasoning and making decisions? And I think that's a potential risk. I think we need to make sure that humans are able to think. They're able to reason through different problems.

53:10They're able to make decisions on their own. absolutely use AI as a helpful tool, but I think, you know, actually learning how to do that for yourself is incredibly important. And I think you're seeing some of this with the education system right now where they're kind of, you know, because the younger individuals are going to be the first users and are the first users of this technology, they're wrestling with some of the tensions before everyone else. And you're seeing this in higher education and things like that. How do you give a test? How do you, what does a course look like in a post AI world?

53:41And so I think that's a really important thing to keep an eye on as a society is make sure that people are able to reason, think for themselves, and use this technology to help them and extend them, but not necessarily replace that function. The other interesting one to think about is that's become really kind of compelling use cases around the relationship side. I think human relationships are incredibly important. And there are studies that's been done that show people that have longer lives and higher levels of cognitive function, one of the commonalities, their diets could be different, their exercise and the climate could be different.

54:15But one of the commonalities that they have between them is human relationships. They have a lot of human relationships, and they care about those relationships, and they work hard at those relationships. And so I think that's super important for humans in general. And so I think one of the things you see with some of these tools that are coming out is a little bit of that, you know, having relationships kind of that are more personal with AI and with this type of technology. So I think that's fine. And there's probably tons of benefits within that space. I'm not smart enough to know the exact ones.

54:44But I think keeping the human relationships and keeping those really crisp, I think, is another thing that's probably going to be important for us to keep an eye on. Do you have any others that you worry about to keep you up at night? Well, I'm a worrier. I'm a professional worrier, so I'm always thinking about, and the thing that I think about the most are the things that I'm not thinking about. And that universe is obviously vast. So one thing that I'm thinking about, and I'm actually curious to get your take, is if we take current trends of this technological innovation, and it's rapid, and you fast forward five to 10 years, what does the world look like?

55:21And what do all the humans do if computers are taking over so many of the tasks? Obviously, humans are innovative as well. They're not going to just sit around and just rely on computers to do everything. What does that world look like to you? I think the first thing is like, what is the time horizon question? And that question I struggle with. You hear the predictions from folks, and I'm not sure on when this will get absorbed into the economy, into the world. But if we just say, hey, forget the timeline and kind of extend this out to when it might become, you know, pervasive. I think I don't have an answer for what humans do.

55:59I think that's that same framework of saying, what do people do 100 years ago? And what do they think of our jobs maybe persists. And then one of the places that I might look is on kind of the, you know, client acquisition, customer acquisition side that becomes even more important, more valuable. So things I I think like storytelling, you know, things like empathy, things like human connection, being able to direct this technology, communicate clearly, I think is going to be kind of incredibly important within this. And so I would err on the side of the human, the things that make us kind of uniquely human that are a bit different than maybe some of the historical kind of hard quantitative type skills.

56:43And so I don't know if that's how it will play out, but that's definitely something like I'm paying attention to. and trying to understand how those dynamics persist. And that's one area where I'm looking for that inflationary price pressure, where as it becomes more costly, as you think about it, like let's say you automate more and more of your backend, right? Then that cost kind of drives down overall and then it pushes the value or the cost, that inflationary pressure on that front end, on that client servicing, customer review, client acquisition side. And so I don't know if those will be more and more of the jobs of the future.

57:20I think another interesting trend that we've seen is more and more of the U.S. economy in general are getting some type of income from creators or influencers type concept. And so I'm paying attention to that trend early on because I think that's an early signal if that would persist that this was playing out in that type of way. But I'm not certain. Do you have any ideas on what you think? No. To me, it's fascinating because you know humans are innovative. They also have some control over how this plays out. So if the technology, we alluded to this a little bit earlier, but if the technology is expanding too rapidly, humans have the ability to hold it back a little bit until they can adapt.

58:10And then at the same time, you have the younger generation that's born into this that is going to adapt more quickly than the older generation that is going to adapt slower because they're used to the way things were, not the way they're going to become. So you have all of those cross currents happening at the same time. and how it nets out is really difficult to foresee. Just like if you go back and you look at technological innovation in the past, I think oftentimes what has happened, this is just my brief study of it, is there's a lot of excitement and enthusiasm in the beginning. And it seems like it actually transpires much slower than people expected, ultimately, because there's also many layers of it.

58:53So you can look at the electricity and you can look at the internet You can look at AI. Railroads. Yeah, yeah. And it's because that's an interaction between technological innovation in different forms and human innovation. And you kind of, they both have to work together. Yeah, and overall, I completely agree and I would bet on humans, right? Like, I mean, we're incredibly, you know, we can adapt to a lot of things and get a lot of things thrown out at us and handle. So, you know, I think I would bet on humans. are there any types of skills you talked about empathy and and others relate to that that in general if you have to generalize humans should really focus on you know like like just really quick one thing that i've noticed is there's a swinging pendulum right you go from one extreme to the other extreme and i could see a world where anytime you want to get something done you have to talk to a computer and at some point you'd be thrilled to talk to a person yeah Right.

59:54And you're seeing a little bit when you call customer service and you got to hit six buttons before you get to a real live person. And you could see a world where people would just love to talk to somebody that is a human, not a computer. So you could see the pendulum going one way and then potentially going the other way. I completely agree. I think it's like a supply demand problem, right? Where historically, the scarce supply was maybe great digital experiences. and more and more that's going to become the kind of oversupply. And if you're going to have infinite, amazing digital choices, et cetera, it's going to be like so much, right?

1:00:29And so the scarce resource becomes this, becomes kind of the in-person component. And so that I think is a true kind of future that we could be headed towards. The other one that I think is interesting is like just general curiosity, right? If you think about the amount of kind of access you have, you know, via your phone today to expertise in all these areas, like you can almost get any question, you know, answered in a fairly intelligent way. I was just watching the movie Limitless again and wouldn't recommend, by the way, didn't hold up. I can't. I turned it off after 30 minutes, but I remember the premise.

1:01:05And I was like, this guy is, you know, supposedly they're a genius, right? They take this pill, et cetera. And I was like, this is AI. Like this is an AI, right? In movie form to some extent. And so I think that curiosity, like follow what you're interested in, being curious about some things, like ask questions because the individual now and today, and it has more capability than ever before. Like one person can do more today than they could ever in the past. Because they have this tool. Yes, and all these tools around us, all technology, right? It just amplifies humans. So I think that kind of curiosity driving towards your interest, I think that'll lead to good places for people.

1:01:47So that would be another one that I would throw in the ring. Well, Brock, this has been fascinating for me. I've learned a lot just having this conversation. It's put new ideas in my head, and I hope it's done the same for our listeners. So thank you. Thanks for having me. Thanks for listening. We hope you enjoyed this episode. Please visit our website at insightfulinvestor.org to access past shows and learn more about our podcast. If you have questions, feel free to email us at info at insightfulinvestor.org. And if you enjoyed the discussion, please subscribe to this podcast to ensure you don't miss future episodes.

1:02:25And don't forget to forward today's conversation to others you think would enjoy listening. This podcast is provided for informational purposes only and should not be relied upon as legal, business, investment, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of Evoque Advisors, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations, nor reference past or potential profits. and listeners are reminded that securities trading, commodity trading, and alternative investments are complex and carry a risk of substantial losses.

1:03:04As such, they are not suitable for all investors.

1:03:11Listeners should be aware that guests featured on The Insightful Investor may have current or past associations with Evoke advisors or the host, including as an investment manager of a private fund opportunity by Evoke, or access through an affiliated Evoke fund, or as a client. Participation as a guest on the podcast should not be perceived as an endorsement or testimonial with respect to Evoke Advisors, the podcast host, or their services. Similarly, the inclusion of a guest on the podcast does not imply that Evoke Advisors or the host endorses the guest or any company with which they may be affiliated or employed.

1:03:50Evoke has neither paid nor received compensation from guests for their participation.