Hello, and welcome to the Life By Design podcast, brought to you by Strategic. Each episode will feature insights from our Chief Investment Officer, Doug Walters.

Hello, and welcome back to the Life By Design podcast. Joining me again this week is Doug Walters.

Fantastic. Great.

So, this week, we really wanted to touch on technology over the years- Mm-hmm. uh, and how from an investment planning we've used it, and, and kind of bringing it to current, and maybe, uh, ending it on AI and, and that.

And, and so, you know, I thought it would be fun, and Doug, Doug and I thought it would be a good idea to kind of go back and talk about-. you know, when Doug first started, how, how he used technology, and what the technology was at the time.

Okay. Yeah, that'd be great, and could be a lot of fun.

Uh, so I think, you know, it's, when I think about technology through the years, it was really about 2 things. And even though you said, you know, let's go back to when Doug started in the industry, I'm gonna go a little bit further back.

All right, yeah. Because this predates me, uh, slightly.

But it wasn't that long ago when data would come in paper form. And there was this thing called the Ibottson Book, and some people around the office talk, uh, you know, lovingly about it, but it had, that was the way.

If you wanted to see, you know, the historical performance, the historical, uh, earnings, you know, sharp ratios, all the, you know, the metrics you might be interested in of a company like GE over the years, you would get your annual book and you'd be, have all the history there, and you could, um, you know, use that for your analysis. you know, look through.

If you're looking for value in companies, look through this data set. So, it was, it was paper form.

It's not that computers didn't exist at the time, but that was, you know, the main form of dissemination. Yeah.

I think, so just a little anecdotal here. Uh, it's funny, because I think we all know in our lives, books are still, still a big part of life.

Uh, but one of my kids is going off to college, and we were talking, like, for the intro, and we're like, Okay, what, like, you're gonna need books. Like, we're, we gotta really think about this.

They don't need to buy books. Like, You're gonna take that money and you're gonna spend it on a computer, and then they're gonna use the computer- Right.

to access the, the information. Right.

Right. Exactly.

Yeah, so. So, you know, I think books are still around, and definitely, definitely good, and have uses, but it's just funny.

I wonder how far in the future when we're on this podcast, or whatever the technology is at the time, that we, books aren't even, like To some of our listeners, they're gonna be like, What is a book? Right.

Exactly. Paper?

Yeah. So, yeah.

Uh, yeah. So, so books- So, yeah.

and then, and then what was the transition from there? Yeah.

And it wasn't, you know, that was, even back when this book was coming out, and I'm gonna, you know, we'll call this the, you know, the 80s. Mm-hmm.

There were spreadsheets. There were early spreadsheets, so there was the potential to be analyzing this data on, you know, in Lotus 1-2-3 or Quattro Pro.

And, uh, but it was, you know, it was taking that data, putting it into your spreadsheet. And I can remember putting toget- together models- Yeah.

in, it was Quattro Pro at the time, and running out of columns. Like And the, the spreadsheet wasn't that big, but there was a, you know, there was a defen- you know, a finite number of columns you could use.

You ran out of it, sorry, tough luck. Man, that's wild.

So- Yeah. one of our, one of the em- our people here, Bob, was telling me that he would do the calculations by hand, put them in the spreadsheet, then have them printed out, then have to th- or have, or have the, someone else put them in a spreadsheet, then print them out, then he would have to check them again by hand- Right.

to make sure all the calculations were correct, and that it got transferred over correctly. Right.

So Yeah. As someone, you know, like, it's, it's weird being in the generation that's highly lived in both worlds, where, like, I feel like part of my life was kind of in that world, but then all of a sudden, I had to adapt very quickly to this, to the new world.

And it's, it's interesting how much you forget about how the old technology and the old ways were. And you're, and then when you start thinking about it, you're like, I can't even believe- Yeah.

we survived that. Like, how did that even Yeah.

Yeah. Yeah.

If you told me there were a limit on the number of columns I could use in Excel, I would- Yeah. I don't know what I would do.

Yeah, like- Yeah. I'm sorry, what did you just say to me?

Yeah. So, but in about, you know, in about this time, uh, like Bloomberg was being developed.

So, Bloomberg Terminal is one of the main sources of, of financial information. Mm-hmm.

And this is sort of, you know, a step up from, from the Ibottson Book. You can access it directly.

It was primarily for fixed income and, and trading at the time. FactSet, which is another big platform, also, uh, evolving back at this point in time.

And that was more towards the buy side, so fund managers, um And these were like, um, computers that, that would have access to this information? Right.

Kind of, like, some sort of database that was being updated and- Yeah. Yeah.

Data compilers, so right there, pulling data from various sources, giv- giving you tools within that plat- platform to be able to manipulate that data. So, that was, you know, if we're going back sort of, again, uh, back to the 80s, 90s, these-These, uh, products were just evolving and developing.

Um, they're still around today. Mm.

Still very much in use today, but with a lot more functionality than they- Sure. had at that time.

Sure, I'm sure. And quicker- Yeah.

access to information, I would assume. Quattro Pro I don't think is still around, though.

Hm. Or maybe it is.

Who knows? Yeah.

I have not seen- As far as you're- Yeah. concerned, it's not around.

Yeah. Um, yeah.

And then, I'm, I'm betting the internet probably changed a lot of this as that started to become a little bit more popular. Yeah.

Yeah, the internet really, uh, democrasi- democratized access to data. It started to move us much more towards that, that digital world.

These platforms, Bloomberg and FactSet, were really developing, you know, pre-internet. So, they were that way to pull all this data together.

The internet made access to data a little bit, again, more democratized. Think about, you know, Yahoo Finance- Mm.

which is still around today, was around, uh, quite a while ago, early days of the internet. A way to get easy access to financial data that would have just been, you know, inaccessible to the average person historically.

Yeah. Yeah, and it st- and I think that too, you know, changed things into As that technology grew, that access to data grew, you know, from, from like my side I, I've spent, you know, the last 20 years on the web and on websites and h- and building out tools, you know, that do different things.

And, and I think because the data comes d- so easily, that allows people to focus on the tool in which is processing and, and, and delivering that data. Right.

Right, exactly. And I would say the other thing that has changed over time as information has become more and more readily available is where the alpha comes from, where the potential outperformance comes from.

Mm. So, there was a time when you could do very well stock picking, right?

F- fundamental analysis. If you had some sort of information asymmetry, you didn't have the Ibbotson paper book, but somehow you had access directly to, to live feeds of data, you're gonna have a lot more information than the person that's waiting for the annual book of- earnings.

So, you're going to have an ability to extract, um, some outperformance out of that. That obviously has shrunk over time and has, you know, required investment managers like ourselves to look for different sources of outperformance.

So, as data becomes more readily available, that changes the way that we use the data as well. Right.

Yeah. And, and I think that kind of starts to bring us into the now, right?

Yeah. As, as we continue and I, I Everyone knows, it's a, it's a news item, it's hot, is artificial intelligence, AI.

And really, you know, I think one of the things that gets missed a lot in the news and, and kind of this f- fever of AI is what it actually is at its core. And it's not this like, it's not like this being that can do everything, right?

It's, it really, in the, in the way we use it, you know, and I think, I think you can say the same, is more of like augmenting what I'm doing now as a technology to just allow me to process that data even better. Right.

Yeah, I think of AI use being 2 different forms. One is searching for information, right?

I send out a request, I want some information, uh, to get back. And other times, I am giving it information and saying, How would you interpret this?

Yeah. So, there's 2 different uses that we have.

So let's say I'm building, you know, I'm writing some code for, uh, a spreadsheet that, that we're working on and trying to think, Well, geez, how do I, uh, automate this part of the analysis so that it runs faster? I can now ask AI, you know, Here's my code, or, This is what I'm trying to do.

What code should I use? And it will give me, you know, a pretty good first pass.

Yeah. It takes a little bit of manipulation to get it exactly where we want it, but that is saving us you know- Right.

a significant amount of analytical time. Yeah, it's, uh, it's funny.

As we're talking about this, a couple of things, uh, came up in my thoughts is, uh, for anyone who has gone to Disney World, at Epcot, they have this ride called Spaceship Earth- Mm-hmm Phew. which basically you're going through and you're watching the whole, this, uh, basically what we're talking about play out, but from, like, papyrus- Right.

to, like, the Library of Alexandria all the way up, you know. And it's just, it's so interesting to think about, uh, really at its core, you know, I think sometimes technology can be nebulous, like this podcast and how we're getting this to you, right?

And really all it is, is for us, from, from the moment we could start doing this stuff is just, how do we get information - Right. to other people quicker- Right.

and, and more accurately? Mm-hmm.

That, I mean, at its core, that's all technology is doing for us- Right. is, is, like, bringing information from one person to another person in a faster way.

And, and I think that this, this tool, AI, is helping us both use the tools we have better. It also is helping us develop new tools- Yeah.

and be more efficient in the way we do things. I am, you know, I grew up in the spreadsheet era.

to do everything. You know, Python can handle, uh, i- it, large data sets a lot more efficiently- Mm-hmm.

than an Excel spreadsheet can. You know, AI has helped us bridge that gap, right?

Yeah. We are doing a lot of our analytical work now through programming as opposed to in spreadsheets.

So we have people, um, you know, like Matt Johnson on our team, who is, you know, becoming a wizard in Python, taking, you know, the ideas that were always already there, right- Yup. Like, sure, you could, you can nail a, a nail into a, a wooden plank with a brick or a stone or anything else- that you can find, right?

But we make that more efficient because we're, if we wanna build the house faster and better, right, the tools that allow us to build that house, uh, uh, you know, make, make it more efficient for us. It's not building the house for us.

It's just helping us to do it more efficiently, you know? And so we went from a stone to a hammer to, you know, now a- now there's air, you know, air gun hammers.

Like, uh, I mean, it's just how can we make this process more efficient, faster, and, and so that we can be better at our jobs? Yeah, yeah, and it, AI is developing very quickly.

Yeah. So it is quickly developing, uh, and we are, you know, constantly keeping an eye on those developments to see how we can better leverage it to benefit our clients in the end.

Right. Right.

Yeah. All right.

Well, I think that was a great talk, Doug. Thank you.

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