Brian Dally: Right.

Stacey Schacter: So, what I always tell people is, there is always distress. There's always distress. There was distress in the oil and gas market. Now oil and gas is going [00:03:00] through the roof, and so that's really helped those industries. So who's being hurt? You can always find... it doesn't matter if it's boom or bust. There's always companies or sectors booming, and there's always those that are busting.

One area you could say has been talked about a lot is the education area. And you have candidates saying, "Hey, we don't need a four-year college system. We need one that's faster and that will help train for AI."

I'm not saying that that's the right answer. But you just now have people looking differently at the economy than they used to. I was listening yesterday to, I don't remember if it was CNBC or another station, talking about how the Trump administration and others want [00:04:00] companies that are responsible for AI to give something back to the people. I don't know what that means, what it looks like.

So, now you bring it down to our sector. How does that impact things? Well, I think money is tight. It really is two worlds. And I don't know if, unless you're involved in that world of people trying to live paycheck by paycheck, if we truly understand it.

Brian Dally: Right.

Stacey Schacter: But that divergence I think is certainly increasing between the haves and the have-nots.

Brian Dally: I mean, certainly, if you own assets, life has been great, and by all accounts could continue to melt up, become even greater. And if you don't own assets, financial assets in particular, it feels like people are being left behind. I think that's the observation that leads people to talk about this K-shaped [00:05:00] economy, right?

Stacey Schacter: That's right. And you nailed it on the head. It is a true K-shaped economy, and people who have 401(k)s feel rich. And I know there's been a couple of down days here recently, but for the most part the market is doing really well. But not everybody has that safety net. We joked about Social Security and, yeah, that's going to run out of money in 2038. Is that what I saw?

Brian Dally: 2031 is the year that they project the amount of money coming in will be outstripped by the money going out. And, no, that's already the case, but by then they won't be able to afford, they'll have depleted the trust fund such that they won't be able to afford the balance and they're going to have to cut, some people say as much as 20% or 30%, of benefits.

Stacey Schacter: We've been talking about [00:06:00] this issue for decades now, and it would've been a pretty simple fix. Another fix is, well, people are living longer, shouldn't the retirement age be moved up?

Brian Dally: It's another dial they can turn and have.

Stacey Schacter: But imagine the protests. So it's a complicated world, and it's made all that more complicated by an economy that is sort of topsy-turvy and erratic. But I don't see recession coming, but that doesn't mean it's certainly a rosy picture. We added more jobs than people expected, but are they the right kind of jobs?

Like with all data, you have to dig deep into it. The Fed puts out something called a Beige Book. Interesting read if you ever read it. I say that somewhat tongue in cheek. Now I guess you could take it, you drop it in Claude, Claude will spit something out to you and say, [00:07:00] "Oh, this is what it says."

Brian Dally: Much better. Much more efficient.

Stacey Schacter: I don't think so. I don't think so. Just because Claude says it, or any of these, doesn't make it true. Look, I use AI a lot. I built my own OpenClaw machine. But you have to understand how it comes up with the answers in order to properly utilize the data.

Brian Dally: Yeah, for sure. So we're late cycle, we're fragile. I like that word. I think, there's maybe less room for error. There are more things that can break and cause problems, second and third world problems. But if you zoom out and look ahead 5 to 10 years, let's assume we get through late cycle, what do you think is on the horizon, that'll have the biggest impact on these markets? Whether it's consumer or [00:08:00] commercial or both. Presumably we'll get through whatever the late cycle resolution is. What do you think is beyond that horizon?

Stacey Schacter: I think, rather than midterm, if you talk long term, and so let's call midterm 5 years and longer term 10 years plus, and I think they're two different markets. I do believe that we're going to see a lot of dislocation with AI, but oddly, when we've seen similar things in the past, the invention of the car and many other things, people thought a whole bunch of jobs would be lost, and in fact, the opposite turned out to be true. With the invention of the computer, people thought all sorts of jobs would be lost, but in fact, companies had to hire all those people to run and understand those computers. [00:09:00] So yes, we will see a great loss of jobs, at least probably in the beginning, and a great retraining of the world. But 10 years from now... I'm a pro AI guy, in the fact that I believe it is actually going to do wonderful things in medicine and other areas that will stimulate the economy and make the world a better place. Though there is a decent chance of significant harm, including, and this is coming from the people who invented AI, or not necessarily invented AI, but who have capitalized on AI, whether it be OpenAI or some of these others. There's a greater than zero chance that it annihilates all of humanity.

However, the stoic response is, "Oh, there's nothing I can do about that, so I [00:10:00] focus on that which you can control and accept that which you cannot." So, if you look 10 years in the future, I think you're going to see actually a huge employment boom brought about by AI being used as a tool instead of as a replacement. For the very reason that AI, like I said before, you have to understand it. If you're just using something, it's like, "Well, this is what it says, I guess it's right."

Brian Dally: Yeah. You have to understand how it got there so that you can assess for yourself, and ask the follow-up questions. If you ask enough follow-up questions, you start to realize some of the weaknesses in the initial perspective that maybe your model is producing, right?

Stacey Schacter: Yesterday I was using AI for something, and this one was chat. And it gave me a response, and I looked at it and I said, "Well, this isn't right." And I typed in, "I think you did this." And it came back and goes, "Oh yeah, you're absolutely right. I made a mistake."[00:11:00]

Brian Dally: It reminds me of when I was an analyst right out of college, working in management consulting, and there'd be these sophisticated models to predict all kinds of things, financially or operationally. And, the way I was taught is, yes, the Excel model is wonderful, but the Excel model is built on a stack of assumptions that if you don't understand, you've got to look at what the model tells you and you've got to question it, then understand how it's getting there, and that's how you debug it, and that's how you improve the model, actually. Is by going back to Claude and saying, "Wait a minute. Based on my experience, you may have been thinking about it this way." It's the kind of thing that a partner would tell an analyst or an associate at a firm.

Stacey Schacter: No, you're right. And if you remember when fintech first came out and was a thing, I was going to conferences that go, "We have a proprietary model." They always use the word proprietary. "We have a [00:12:00] proprietary model, and we have 50 different attributes that we put in to analyze whether or not a customer is going to be a good credit risk." And I wouldn't do business with those companies because when you have 50 parameters, switches, think about on and off, then you have 50 things that can go wrong in that model that can greatly distort the result. If you get one of those things wrong, it throws off the entire model.

I like the people that said, "Yeah, we have ten to twelve things that we look at primarily." Are they employed? We do employment verification, whatever. And yes, you use the data, but you use it in a way that's smart, and then you end up being almost too smart. You bring in anomalies that you [00:13:00] didn't need to bring in. Like, okay, we go beyond the zip code, we look at the street they live on. Okay.

Brian Dally: It reminds me of when we were trying... I was not a real estate person before I started Groundfloor. I wasn't even a finance person. And we needed to come up with a model to assess risk, in some sort of repeatable, quantitative way. And having little experience in that, I was dumb enough just to go talk to people, who had been underwriting credit in the space to just find out, how do they do it? And I learned, I think our first model had eight data points that we looked at.

Stacey Schacter: That's pretty good.

Brian Dally: And eight was great, and then maybe we added a couple more, and then we did some refinements as one does over time, as you learn from it. But I'm glad we started from eight that we could understand.

And there were some elements in there that I wouldn't have thought of as immediately [00:14:00] relevant that turned out to be highly explanatory in our early algorithm. And so I'm glad I didn't take that scattershot 50, to try to sound impressive and flex our muscle about what we know because, well, the truth was we didn't know that much, so we had to simplify it.

Fortunately, I think it's probably why we survived through these different interest rate cycles, and in our space, a lot of change in the last decade.

Stacey Schacter: I'll tell you, whenever people ask me, though, what are the signals that I look for, for a problem, you have the things that signal ahead of a problem. Of course, credit card, auto delinquencies, consumer loan charge-off rates, small business default rates, and then, C&I loan [00:15:00] demand, bank tightening. And even in private credit, is it covenant light or are they beginning to add more covenants into it? You can watch yield curves as well.

Most of the information we get is stale, but there are certain signals, like the New York Fed is really good at putting out some information, and they do it in charts, so it's really nice. I'm a picture guy myself. And you can look at things, you go, "This just doesn't look right." And then you can compare it, and that's where you can take that stuff, drop it in AI, and say, "Hey, do you see anything here that I'm missing? This is what I think. Tell me why I'm wrong," is a better question oftentimes.

Brian Dally: I definitely want to go back to your medium-term and long-term view, because [00:16:00] it's intriguing, I think I'm in line with you. I do think this K-shaped economy has to be resolved somehow. It sounds to me... these are not the only two ways to solve it, but a lot of people talk about political solutions, universal basic income, some sort of redistributionist politics, or political solution. Jeff Bezos was recently quoted as saying he thinks the bottom half of earners just shouldn't pay any income tax at all. Why bother? There's a political aspect to this, and it feels like the politics will be significant over the coming decade.

I like what you described about the impact of AI because it also points the way to there being a market-based solution. That, if you're a techno-optimist, you think, "Well, technology will save us eventually," right? Through some disruption. But it sounds like you would cast your lot more with [00:17:00] the market and technology evolving in a way that solves some of the K-shaped issues more than the political. Or do you feel like it has to be both, or maybe more political?

Stacey Schacter: Yes, it absolutely has to be both, because all you've got to do is let a politician get involved and... I majored in economics as well as accounting, and the market is overall pretty darn efficient, unless you do something to screw around with it. Now, there are times when you have a natural monopoly that might need to be broken up, and we may end up with that with AI.

But AI can be a great equalizer because now somebody who doesn't have a lot of resources doesn't have to spend a lot of money to get access to the world's knowledge. However, if you think about places, in Africa or [00:18:00] Eastern Europe, that don't have as much resources as the United States and other countries have, I could see where the ownership of this technology will push those countries further ahead and other countries further behind.

But the use of the technology I think can really help a lot of people, from starting a business, and I think that is the one thing you're going to see. You're going to see a lot of people who were dislocated becoming entrepreneurs, and using this technology for things that people can't even imagine, and I can't imagine what they are. You don't need a huge stack of money to start a business now with this technology.

So, I think you're going to see this rotation of people moving into new fields that, "I'm an AI [00:19:00] consultant." Okay, fine. There's going to be plenty of those. But an AI consultant who focuses on all these different sectors, and then you'll have people coming in and rolling all those companies up. Right There's this flywheel cycle that goes on.

Brian Dally: It certainly happened in the late '90s and early 2000s when the web itself was coming of age.

Stacey Schacter: That's right. That's right. But the biggest issue I really see is debt, that's sovereign debt. One of the reasons we're as strong as we are is because of the dollar.

Brian Dally: Right.

Stacey Schacter: But if the dollar weakens or is no longer needed, then there's a lot of pain and suffering that could put us into a great recession. I'm sorry, a depression, rather than even a great recession.

Brian Dally: People don't think about the impact of that. Since 1971, at least, and arguably earlier than that, we've lived in such a favorable [00:20:00] environment for the dollar. Dollar dominance has been powerful.

Stacey Schacter: So if they can, and I don't know who Lei is, by the way, so if the dollar is no longer the currency of the world, and I'm not saying it's going to be Bitcoin, but then that is the most serious threat probably to the US economy, because then our cost of borrowing will go through the roof, and we will no longer be able to pay our obligations. There's a whole rollercoaster of things that would happen. I would encourage your readers to go to Ray Dalio's site and read his commentary about debt.

Brian Dally: Yeah, the big debt cycles are fascinating.

Stacey Schacter: We're just being irresponsible at this point.

Brian Dally: For sure. This has been great. I really appreciate you taking the time to do this, Stacey. So we like to wrap up these conversations with a rapid-fire Q&A. Let's see if we can knock a few of these out in one [00:21:00] sentence. Whatever comes to mind is perfectly fine. What's the most misunderstood concept in investing?

Stacey Schacter: Oh, God. One sentence, huh?

Brian Dally: Yeah, if you can. Whatever comes to mind.

Stacey Schacter: That return equals wealth.

Brian Dally: Return equals wealth. Solid.

Stacey Schacter: So in other words, another way of saying it is, you can't eat IRR. You have to have absolute return.

Brian Dally: Yeah. Multiple on invested capital, for example.

Stacey Schacter: Yes, that's right.

Brian Dally: So what's the biggest mistake you see investors making right now?

Stacey Schacter: You already mentioned it. It's the FOMO. Jumping into something after the smart people have jumped in, and then you have all the followers, by then it's typically too late.

Brian Dally: And I know [00:22:00] you've been patient, so now I have to ask, when has your patience been a regret? Is there a deal that you've passed on that you still think about?

Stacey Schacter: All the time. There's not just one deal, there's several deals. Especially during the Great Recession, there was another bank we had an opportunity to acquire. We thought, "Well, it'll be there," and it wasn't. We've had several opportunities pass us by because we were doing diligence. Others were jumping in, and we just, we try and be thoughtful, and sometimes speed does win, and we were not fast enough in those cases.

Brian Dally: So, we live in a little bit of a hype cycle. What's your favorite, most over-hyped trend today in finance?

Stacey Schacter: Hmm. The most overhyped trend in finance? I think, subprime [00:23:00] credit cards.

Brian Dally: Subprime credit cards. It's too many people piling into that, eh?

Stacey Schacter: Right. Too many people piling into that, and there's only so much that that cohort can actually absorb.

Brian Dally: All right, and then I've got one more to wrap up. If you weren't in finance, if you hadn't been led down this path through the story you shared last episode, what do you think you'd be doing instead?

Stacey Schacter: Oh, one of three things. One, continuing to practice law. Two, I actually started a farm.

Brian Dally: Awesome.

Stacey Schacter: So, maybe actually just leaving law altogether, being a farmer. The third one, this is absolutely true, when my wife and I had, we had two girls and they were very young, my wife worked for Chiquita, and she used to go to Central America all the time. And she said, "Costa Rica was so beautiful, we should just sell everything, move down [00:24:00] there, and start up a boat business that does parasailing, and just throw it all to the wind."

Brian Dally: Literally.

Stacey Schacter: And I do sometimes wonder what if we had done that? Where would we be now?

Brian Dally: That's always fun to think about alternative paths.

Stacey Schacter: I call them sliding door moments.

Brian Dally: Sliding Doors, I love that movie. Was that Gwyneth Paltrow? Do I have the right actress?

Stacey Schacter: I'm not sure. I just remembered the door closed, she went back, saw her husband having an affair, and the other time she walked through and she knew nothing about it.

Brian Dally: I think that was in the '90s, or maybe 2000s.

Stacey Schacter: Yeah, and people have sliding door moments all the time that define their life.

Brian Dally: I agree. It's fun to think about that. All right. Well, let's wrap up. I really appreciate you joining us on the podcast, Stacey. For our audience, you can visit vioninv.com, V-I-O-N-I-N-V.com, to learn more about Stacey's firm. We'll also share Stacey's LinkedIn in the show notes, and I'm sure you can reach him that way.[00:25:00]

You can check out all of our podcast episodes at groundfloor.com/podcast. Thanks everybody for tuning in. ​