The Atomic Bomb Changed Everything. Could AI Do the Same for Real Estate? | Ep 149

I'm going to attempt to do the impossible here and I'm going to merge real estate with AI and the atomic bomb into um some sort of topic. that all works together because believe it or not they do because I just finished this really cool book it was called The Making of the Atomic Bomb by Richard Rhodes And I think there's a lot of lessons that we can pull from it that are applicable as real estate investors.

Today on the Furlo Capital Real Estate Podcast, where we dive into the intricacies of passive real estate investing. And our mission is to invest wisely so that we can improve, um, improve housing while also um building wealth. I think I don't actually have my notes, so did that out of the top of my head. Awesome. So normally I would this is where I would introduce my wife, but she's taking a nice little break.

And so you got just me and we're gonna be talking about a book. So if you've heard any previous podcasts, which is totally cool if you haven't Um I have been reading history books that span US history in celebration of the 250th birthday of the United States And so I've been slowly working myself up and I finished a World War I book, was the previous one, which was one of the saddest and most frustrating books to read. And so this time I am now focusing on World War II, but specifically the making of the atomic bomb, which ultimately spanned a ton of history.

but was specifically focused on that one little piece. And then after that, I'm gonna move into a book about Martin Luther King Jr. Which I've already started and is kind of amazing. So I'm excited to talk about that one in a future episode. But for now, we're talking about the atomic bomb. And it's so interesting How things just rhyme.

History rhymes. You know, you hear that and it has become so clear to me just listening to all or yeah, listening, reading all of these books. That uh yeah, that is a hundred percent true. So we're gonna dive into it with a story and it starts back in 19 33, there was a guy named Ernest Rutherford.

He made the statement that um that anyone expecting power from the transformation of atoms is taking moonshine. It's like they're drunk, they're drinking, there's no way that this could happen. The Times reported it. It was kind of like this is it. And I love stories like that. We're like, it can't happen. Now, this wasn't just some random dude.

He was, he discovered the atomic nucleus, okay? And he won a Nobel Prize in Chemistry in 1908. Not physics, but chemistry, because at the time, like that's where it was at. So like we're not talking about some random dude making that proclamation. And just super funny how sometimes It's the people who are closest to it struggle to see the forest from the trees because they are just so focused on those little pieces.

So here's what's funny, is there's another guy named Leo S uh oh my I got this Sillard who he saw the article and was crossing the crosswalk and he was thinking about it along with uh a previous book by um Um by H. G. Wells, who had kind of already semi-propose this thing.

Anyways, he's crossed on the crosswalk thinking about this article, how it can't be done. thinking about a little HG wells and is like, you know what? This can be done. I think I know how to do it. And so he got after it. And he started running some some different experiments to try to figure it out and ultimately did.

So again, I think it's just I think it's just funny. And I want you to remember this curb, this idea that one person says no and someone else goes, uh, maybe I think it can. And it's not necessarily that it's a yes-no type of thing. But that it is instead a maybe let's run the experiment and let's see.

So that's kind of how I want you to to think about where we are in real estate and with AI today.

So it was, um, as I said, it was the people who were closest to the technology, they forecasted the worst. Okay. And so the problem is that there's a ton of people out there who are trying to forecast what's going on with AI. And I mean, I'm sure you've read some of the articles where it's like, man, this is either going to doom humanity or it's gonna take all the jobs or it's gonna enable Amazingness.

Not very many people have seen that one. It's going to enable business owners to do stuff. It's going to enable real estate investors to to like do unimaginable things, to do underwriting that levels that we've never seen. Though we're gonna talk about that because I actually think there's yes, you can have very sophisticated sophisticated underwriting models, but it turns out back of the back of the envelope napkins are like Pretty darn good.

If it doesn't make sense for that, the chances of an AI sophisticated underwriting tool having it suddenly make sense are pretty low. But Um but again there's there's a whole bunch of different stories there. And so what you don't want to ask is is AI gonna matter? The answer is yes, of course. So instead, you want to ask what happens to my property when someone connects those two ideas like real estate and AI.

What do those two things look like together? Like that's the question that you should be asking. There's an inevitability about it, which is totally okay. And And for me personally, uh I have I have a lot of units that are under management that I own, and I have been in the process of building my own tools. And so I've seen that that gap between where it works and where it doesn't. Um I'll give you a very small example. Uh I ran some experiments with doing a voice AI agent.

So when people call into the office, if no one picks up, they they leave a voice message. Which is fine and it works. But it seems to me like there was an opportunity to do something better. So I spent a lot of time building it out, working on the prompts, getting the technology right, connecting the right models and things like that And ultimately, I just shelved the project because I couldn't get that balance of, okay, this thing can hold all the information and it can respond reasonably fast. And it just it just caused a problem.

So that's an area where I'm like, okay, there's something here. And again, the idea was like, hey, if there's a maintenance request that someone's calling in about. Can I have this thing do essentially a verbal intake form instead of them just telling me what it is? Can we prompt with some more questions, maybe get a little more details to save our team around? Like that's what we were going for. Essentially. And then once it's taken that intake that once it's done the intake form, can it then actually put it into our system automagically?

So that, okay, it's not captured. And again, saving us time so that we can actually just get down to doing the thing, which is scheduling someone to get over there to actually fix the issue. That's a thing. It didn't work. Yet. I'm sure it will in the future. Just wasn't quite ready for me when I was looking at it. And here's the thing too. Um when you are thinking about the future, what you don't want to do is just make a prediction, right?

You just pick one future. And yeah, bet on it. Uh I recently, um earlier this year, I think, yeah, I read a book called Thinking and Bets. Super good book. Uh it's it's It's about poker, but then how it's applied to business and just life decisions. And it was so good. I'm highly recommend it.

But one of the things is that there's never a hundred percent probability of something. And so you don't want to talk in that like absolutist type of language and you don't want to think in that term as well. Instead, what you wanna do is you wanna put yourself in a position to see how it's going to like so that you can So that you have optionality, so that you can try multiple different things.

Like that's that's the goal how we want to do it. And um, oftentimes we talk about taking asymmetric risk. on this podcast that idea of you want to do something where the downside risk is a lot less than the upside. And so that's you do that like by you just buy smart, right? If you're able to buy at a cheap price that gives you a lot of different options in terms of doing it for cash flow or doing it for uh the appreciation, investing in it, holding on to it, getting rid of it. Like it gives you all sorts of choices if you Um if you buy right.

That's an idea of asymmetric risk. And and you also want to look at survivability, right, which is kind of very similar. Right. At what point can I can I survive it if rents stay flat for a few years, which they're currently in the process of doing, or if interest rates double, which did happen a few years ago? Like, can you survive those types of things? And you want to apply that same type of thinking to different systems and tools that you put into your life. For example, and this is one that we're currently wrestling through, like a bunch of our tools now are dependent on like this one piece of software that I have.

Man, if it goes down, are we in trouble? Like, does this create some weird um uh key man or key software risk type of thing. In my opinion, I actually think it's a lot less now that I'm not dependent on some third party software. For a lot of things that we're doing. So I just think it improves that situation.

And maybe that's one of the thoughts that you have. Um, you know, I know a few years ago uh we were using a piece of software to collect rents and they got bought by someone else and they just shut it down. Like, oh Cool. Okay. Now what? And um so now um I'm on to a different one.

And and and I and I I think it took me like three iterations before I finally found the one where I was like, okay, this is the one that's gonna work for what I want. It's the thing, and I felt bad for my tenants because I kept jumping, but I was like, I just not good. I'm sorry. And I don't know how to tell that without doing it. So, um, anyways. Uh those are those are the things that you want to think about when you're deciding what's the future going to look like, right?

It's not about picking the right future. It's about positioning yourself so that no matter which way the future goes, you're gonna be okay. And there's some futures where you're like, man, I'm gonna do really well, but at the very least I'm gonna be able to survive. So um that's how you want to do it. And that was the difference between Rutherford who was like, it'll never be able to do this.

He was making a single prediction. And um I can do it. Sillard, where he was like, well, maybe let's run some experiments. Let's see what happens. It could work. And we want to have the Sillard type of mindset. Okay.

Cool. Moving on.

That's right. There was this um there's some other cool things that happened in the book that that I liked. Another one was just the idea of x-rays was not something that they were planning to to discover. Like it was totally by accident. So it was November 1895.

There was Wilhelm Ronteg. Oh man, I'm so bad with names. I'm so sorry. In Wozburg, he was working on cathode rays, and he noticed that there was this coded screen glowing across the room. Which is awesome. And then it was within weeks he has these images of the bones of his wife's hands. And he's like, wow, this is really weird.

Again, he had nothing to do with medical imaging. Like that wasn't what he was going for at all. Another situation for radioactivity where there's some guys in 1896, they're messing around with some messing around with some uranium salts and some photographic plates. They're left in a drawer. Oh my gosh, they have another accident where it's like, oh my word, we got even more information coming in on this x-ray piece. And then you have fission. which is December of nineteen thirty-eight, where there's Hahn and Strassmann, they're bombarding uranium, expecting heavier elements.

But instead they find barium. Like, oh, what is this? And so they start like trying to figure out what is this, how can we use it And it was at that point in time where there was a guy named Frisch. He borrowed the word fission from biology, which was that idea of Multiplying, splitting, multiplying, I think.

And um, because again, they were like, we don't, we don't really like, we weren't looking for this, we weren't trying to do it. So how do we describe it? Let's borrow from something else, okay? And and the point is, is that nobody was planning to like for the edge, for that innovation, for that thing that they were seeking. And instead, what they did was they just stayed in the lab and they stayed close to the mess, right?

They just kept working it. They kept working it, okay? And and I think you can get that kind of thing inside of real estate as well. I know we've had a couple of deals where we're actually looking at one Well, we have one. It was a piece of land that we bought where we were actually make trying to make an offer on one across the street. And we saw the land and we're like, huh, I wonder what's up with that. And so we just called randomly.

For my lion apartments. That was one. It's 11 units. And the story that I like to tell is that when I bought it, it was 50% vacant. And Um a hundred percent of the occupants were uh registered offenders of sorts.

And so not good. I wasn't looking for it. I actually got a phone call from another owner who heard that I had been doing informational interviews with other owners just learning And so he was like, hey, thought maybe you could help me with this. And that was how I got this amazing deal of a lifetime property. It wasn't one that I was actually looking for. Some it it came upon me because I was just kind of doing my thing.

And so you want to be a tinkerer, not a spectator. Be someone who gets your hands dirty. You know, it's like when I'm talking to new investors. Oftentimes I tell them like dude just do some underwriting, like pick some deals, run the numbers, get good at doing a spreadsheet, get good at making assumptions. And then for a few of them, like, yeah, just like walk through the units. Make offers. Just get into the process of doing it. Don't don't just be like, man, it'd be really nice to invest in real estate or I'm waiting until I get a certain amount of money.

I'm like, dude, just start running numbers. Like get good at it It's okay. Build out your tools and your spreadsheets because they're going to be bad at first. It's all right. You know, it's funny. I was actually looking at a deal recently where I have a very I have a very sophisticated spreadsheet at this point.

And ultimately for this deal, I was like, you know what? This is too sophisticated. It's too much. There's too many knobs. I actually, for this particular deal, it made sense to bring it, but just back down the basics. And that was mostly because there were no other investors involved. It was a very straightforward deal. And my spreadsheet was like it had too many knobs for what was going on. And I was like, honestly, like if this thing doesn't make sense for the simple underwriting process, dude, it just doesn't make sense.

And so um So again, you want to be like you want to be that tinker, okay? Um yeah. All right, here's the second part to this story. Uh the discovery of of fission was actually the the easy part and it was the physics of it all took years and this is where Rhodes the author spends most of the time on the book is talking about that that like that process of making it and Man, it took it took a it was a well it took an entire country, honestly, in order to make this weapon.

You had General Leslie Groves He ran the overall project. You had Oppenheimer at Los Alamos. He was the one who actually designed the bombs and they did more than one. You had Oak Ridge, enriched in uranium. There was Hanford, who made the plutonium. And it was like this this is like 1940s numbers. It was like over two billion dollars to do all this.

I mean we're talking like these are massive, massive numbers. Okay, just crazy. Um and and I think that's the thing. It's like, yeah, AI is amazing. You can use the tools Great, but then you actually have to build them and you have to develop and you have to figure it out. It's funny when I started doing software for our company, I was like, yeah, I started with something super easy, which is making invoices.

That was it. And then eventually it was like, okay, invoices are cool. Now I want to do maintenance. And now it's now it's just blown up into this entire workflow. thing um that just gets increasingly more and more automated uh for us, which is which is really nice. Um but it's it's taken It's taken a tremendous amount of effort.

It's funny. I think I started it um I think I built the first tool in like April. Might have been May And it was just a little thing and and again now we're sitting in October and um yeah, this will this will publish in October.

Yeah And uh and it's like I feel like I'm only halfway through building it. All if I'm honest, I have I have a massive roadmap of all the things. And it has just been Every day, every week, continuing to build, to tinker, to do stuff, to refine systems.

And every once in a while you break something and go, oh, I gotta I gotta fix that. My bad. Um so uh So again, they had the idea and then it was a whole bunch of engineering and builders and managers made it real. And honestly, like I think Um yeah, that's uh and that's how AI feels right now with real estate, where it's like, yeah, there's a bunch of stuff that you could do.

It's super interesting. But you gotta be willing to invest the time and energy to figure that out. Or hire someone like myself who is willing to take that time and energy to figure it out. But even then, I don't think you just want to fly fly blind and be like, hey, you know, whatever he thinks, it's good. Uh I think it's it's good to understand the process and the difference.

Um, or at least what they're doing um you know with it so that you at least understand what's going into all of it. Um I uh I already shared one story of one that That didn't work.

The invoices one was super easy, um, but was nice. And I mean, and we've done little things now where it's like, honestly, we have very little that actually um that AI, I don't want to say it. AI helps with the development with building the capital. It's like capital expenses, but in terms of the operation side of it, I have very little of that that actually happens on where it's somehow taking something in and logicing it out and then Like thrown at us outside of for every single message that we receive, whether that's an email, a text message, a phone call.

Um we actually have AI go through and summarize all of that stuff so that we can then put together summaries for our owners so that they understand what's going on. Because we just get we get so many messages and we get inundated by it. We had to kind of have a We had to have a system to help us bring it all together. And then I actually built something super cool. It's an AI analyst that sits on the side. So it can actually pull all the financial data. the conversation history it can pull from maintenance and other files and so we can ask it a question and it'll pull from all of that and and answer it for us essentially That's been pretty helpful.

That honestly has saved me hours of time. And one of the things that I have it that I set it up to do was it was like, don't just don't just tell me what the answer is. Like Show me the research. Tell me how you got to this answer. You know, I want all that. And that actually is like that's like three to five times as long as the actual answer because it has all the here's all the stuff.

Because I want to be able to verify it all. Um so anyways, those are some tools that um that we have built. I kind of want to get back to this idea of of thinking of the experiments and it's that idea of parallel bets. So there was Groves, um, guy in charge of it all. He didn't just pick one method, he picked several ways to enrich uranium.

And he ran them all at the same time. And um and the bomb itself, they actually had two different types. They had one that was like a um it was like a like a gun type thing where the it it shot the uranium bomb And like within the core of it, it shot it at a super fast speed, which then set off the reaction. And then they had an implosion plutonium bomb as well.

And and that was one that they they initially tested, and like that was the first one to go off, and it was nuts. Um and and again What Groves was going for was position, not prediction. Okay, like that's you want to have that optionality. You want to have parallel bets.

I love that idea. of parallel bets. And they were okay with some of them failing. Like that's all right. Man, that's something that I keep trying to tell my kids. Like it's okay to fail and to be wrong and to mess up. And we try to watch videos like we saw one Watched it the other night. It was uh Casey Neistat was talking about this big failure that he had done with Mr. Beast trying to make this video and how it didn't work.

And through that, he could see the success of Mr. Beast. And and I think like And that's what we're seeing here. It's like, yeah, we know they're not gonna work out. So like let's place bets on all of them. And I think we can do that same thing in real estate. We can diversify across asset types. Uh we can take on different operating approaches to it. And we can run small-ish experiments.

Again, we don't need to bet the entire portfolio on a single AI claim. You can do little things. And that's okay.

Alright, so I think this is important to talk about what AI changes and then what it doesn't. So AI, I think it it definitely changes the leasing response speed. For sure. It allows us to process a lot faster because we can build tools that are just fully integrated with it. It helps with maintenance triage significantly. And As I said, I've got a failed product that I'm working on that'll help us triage even faster.

Um, but it definitely helps us like right now what we have is set up where it's like And we schedule a maintenance call. Now our system will automatically send a message to the tenants the morning of just say, hey, friendly reminder, we're doing this. Super helpful. Again, we've talked about we use it with some owner reporting. We started using it with some bookkeeping, though we definitely still have a human in the loop for that one. But it helps with uh Um, you know, just like quickly grabbing receipts and whatever stuff for us so that we can so we can get it into the system.

Um it's super nice because I used to because our system I used to have to go into my email. and grab the receipts and then save it to the system and tag it and all that stuff. And now what what I have AI doing is it will, it goes into the system, it sees what receipts are missing.

And then combs through all the emails and goes, oh, this is the one. And then if it's missing or I can't find it, it lets me know. And I can go to the website and grab it. Super helpful. But here's what it doesn't do, right? It doesn't impact the dirt. It doesn't help with the tenants, like it doesn't create tenants. It doesn't help create local code, though it helps interpret it, which is super nice. And it doesn't help create relationships, right?

So that's things that AI doesn't do. And so your question is like And and I think the way to to think about it is that what AI is enla enabling us to do is it's letting us migrate from labor over to judgment. And I think that's that's super help.

um for us to think about. You don't have to be the one typing into the keyboard, doing the work, um, creating the documents anymore. You just need to be the one that's directing it. And I think that's true for a lot of things, whether that's Talking with your property manager or your syndicator or um, you know, when it comes to underwriting, understanding financials. Like I do, I do think you should learn how to read financial statements.

But you can also throw that in the AI to help you understand what's going on and to train you and educate you about um how to read it and what it says. Super helpful. But at the end of the day, like you're the one who needs to apply that judgment to it. And so um Yeah. Um I oh yeah, I I had the side thought here where um in Oregon it is a very landlord tenant.

um friendly friendly towards tenants for sure. And I think AI does a good job of help surfacing what the rules are. And there's times where we'll use it as a very fancy search engine. Like, ah, what are the rules on this thing again? What are the statutes on it? Give me the ORS number.

But at the end of the day, like it's still, so that's a task that AI helps with. But the judgment, how to interpret it, how to apply it. That is still reserved for for me to do. And I think that's very important. Um, and uh yeah, and we don't have it Um part of our screening process is we do phone calls with tenants.

And um and so what we don't do is automate that part at all. Like we still want to get on the phone, we still want to talk to people. and understand whether or not just kind of hear their answers, like I still think that's um really important.

Okay, let's talk a little less signal versus noise. I love talking about that kind of thing. Um so you want to make sure that you're getting signal from your property manager or your investor, right? You 're thinking about response times, cost per work order, vacancy days, tenant retention, just like all the numbers and stuff. Okay.

Um, but what you don't need is the noise of just saying it's AI powered. Oh my gosh, how many websites have you come across where it's like and now AI this and that like Is it though? I feel like you've just realized like this is the word, the buzzword, so you use it and it's not real. So you gotta try to figure that out. And so what you as owners can do is you can ask what changed in your operations this year because of AI.

Show me the numbers, right? Like that's what you can do. And if they can't answer it, or they're just like, whoa, we have this cool tool, like All right, like that should be a semi-red flag for you. And so, you know, like for us, um, geez, I don't actually know if I have any metrics off the top of my head. Um but what I well what I do know is that um I was going to hire another person and I didn't have to.

Because we were able to leverage and automate a bunch of workflows through AI. So that that reduced for me one headcount. one potential headcount, um, which was nice. I definitely another thing is um making owner reports was an onerous task and it has become less so because now I can use AI to help me pool all that data together so that I can focus on the final piece and the interpretation side of things.

So that's one where it went from, all right, maybe we'll do like quarterly reports. Like, no, we can do monthly reports now. No big deal. And we're now doing the same thing where we're having weekly, we recently started this, weekly leasing reports that are going out to our owners as well. Because again, we have these systems and tools. They're pulling everything together and so we can quickly get a get a view of the funnel and talk about where we're at. Super helpful. Um, so Rhode, I thought this was really interesting.

Um, the way that he decided to write his book. Um, the first half, it just reads like a project story. Honestly, it's really cool. They got a problem. They're doing experiments. It's the next problem. And the mission just kind of carries you along. Super cool. And you're like, yeah, here we go. But then.

They actually use the bomb. And and it's just like they take a look at Hiroshima and Nagisaki and the survivor accounts and All of a sudden you're like, oh yeah, they were working towards this thing that kills people and a lot of people at once, ah.

So you have this inside view where you are. You're thinking about the mission and momentum and you got the camaraderie, and it's the belief that they're saving lives, which actually was true. But then you have this outside view, which is, man, it's a weapon of enormous destruction. And there are people who didn't fully take that in until it happened. Um Yeah, again, I think many people would say that it was justified, but that doesn't mean that it was costless.

Okay. I thought this was really interesting. I read a book a bunch of years ago. This was probably like six years ago now. Um it was booked Evicted by Matthew Desmond. It was actually interesting. He described the same problem at a much smaller scale. There was a trailer park in a community.

And he was in there and learning about it. And people were supporting each other, helping each other out, like being like, oh wow, what a great little community, you know? But then He flipped it and talked about it from the neighbors from the outside view. And it was like, yeah, they're all supporting each other with their drug habits. And like, and they're not actually helping them out. They're their um th they're allowing these these bad things to perpetuate.

And um it was just kind of an interesting twist. And I felt like the book kind of did a similar thing. We're like, wow, this is a really cool project. We're getting after it. We're building something really neat. And it's like, oh wait a second. We're building something very destructive. Which, like, you know it, but it just kind of feels weird.

And and I guess the question is You know, for you as an owner, investor, you're on the inside track, right? You know the stuff going on with your building, you know the decisions and the reasons why you're doing things. But what does that necessarily look like from the outside? From a tenant perspective, the things that you're doing, would they also interpret them the same way? Okay. And so I think it's um I think it's important to to ask that and um not to say that let's like go against your mission, but you know, just to do that outside audit and be like, yeah, how How do people perceive what we're what we're doing?

Okay. Like for example, um you might have some sort of blind spot that you're not aware of. Like if you fix up buildings, right? Rents raise. That has an impact on the community. Or tenants can be displaced. That's that's a huge one.

Um if you invest in low-income housing, that also shapes people's stability. and what's going on there. Um if you're using AI to screen tenants, which we don't, but if you did, um you know those decisions Well, also like they could potentially have a bias or um something that's hidden inside the score.

Like things to look out for, I guess. And so so you wanna pay attention to uh what um you know what those things do And again, saying that it's justified isn't the same as saying that it's costless.

Like there could be a cost, and it might be okay. We just want to be just be aware of those things. And that's all right. Okay, um let's go back to that crosswalk, right? Um you got the guy, he's just like he's out, he's reading It's a morning, it's a headline, and he's able to connect those two ideas. Like, man, I think we can do that.

And I think that same kind of headline is running today about AI. I really I can't get over how much at the beginning of doing the f the physics research. They were just grasping. They were just trying to figure it out. Dude, they didn't know what they had their hands on at all.

And honestly, even when they made the bomb, they were kind of like, we pieced this together. We're figuring it out. They didn't know And it really feels like we're in that same stage for AI where it's like we made the if the the first version of the bomb. We're like, oh man, we actually have something here. It's kind of cool.

And we are now starting to head down that route of like, okay, like Now what do we do with this? What does this look like? What are the implications? We've started using this thing and we still don't fully know what the impact of it is. And I do think there will come a point in time where AI does something we're like, oh, that's really not good But just like learning about the atoms and splitting it, it has also led to some amazing things like power plants, which again there's been some learnings there.

Um, I also think it's helped with figuring out the current um computers and electronics, like really understanding atoms and how they work. That all flowed out from this. It's amazing stuff. And I think we're gonna have that same stuff happen from AI. What's it gonna look like? I don't know. And that's the question, right? Which side of the curb are you on? Are you the guy who says this is all moonshine, it's bad, moonshine, it's bad, we shouldn't use it? Or Are you the one who crosses the street and says, man, I don't know.

Like, I'm gonna run some experiments. I'm gonna see what I can learn from it. I think there is some good, and we've probably got to keep our eyes out for some bad as well. What side of the street are you on? Okay, so here's five things for you to do uh this month, um, or at least pick one of them. Um first one Is um you want to just have well the overarching theme here is you want to have better thinking, not just better tasks.

Okay. So ask your property manager or your investor, like What AI, how has AI changed to their operations? You know, have a serious conversation about it. Ask for numbers, not just buzzwords. And then you want to stress test some of these ideas. Does the portfolio work if the AI hype is wrong?

And then conversely, what if it's right? You know, that's the, yep, some will fail, but some won't. Can you survive that? And is there a small experiment that you can do this year, the remaining part of this year, something that you can do just to see? What its impact is going to be. And I think it's important to have that outside view as you are running those things. How do other people perceive what we're doing? How is that going to come across? And so you want to ask either a tenant or a neighbor.

Cool. Um

So, um I thought this was this was kinda fun Um just kind of getting back to the bet idea. So the bomb went off at 5. 29 a. m. And there's a guy named Enrico Fermi.

He He kind of is a dark humor kind of guy. Um he was taking bets from other scientists on whether or not the explosion would not only just work, but if it would ignite the atmosphere, and on whether or not it would destroy all of New Mexico or the entire world. Yeah, one of those guys. Um, super funny. Uh kind of funny. Dark humor type of funny, right?

Um, but like again, I just think it's that idea of like they were taking bets. Either way, they're gonna run the experiment, they're gonna find out, they're gonna see how it works. And I think that's the big takeaway, the mindset that we want to have in all of this. So, my final question is if the experts are wrong, Again, okay, if they if the experts are all saying AI is doom and gloom, and they're wrong about that, um, which direction does your portfolio lose? You know? Um Can you afford to ignore the AI?

Can you afford to hire someone who is? Or someone and conversely, right? Can you even afford to hire someone who's like a hundred percent all in building an AI-only somehow company? I think the answer is a hybrid somewhere, but that's that's what you want to think about for your property. So anyways, a really cool book. Um I was shocked by how much it related to what we're experiencing right now with AI and how much of it was actually applicable to what we're doing in real estate.

Um highly recommend it. It's probably top three book that's up there. Lewis and Clark, uh definitely number one for sure. I thought Wright Brothers would be number two. Turns out I like like Turns out I like the adventurous people doing things um book.

So um this one's kind of the invention one as well. So it turns out I like it. It just was more than two guys. Um but yeah, a lot of good lessons and um so I would love to hear what you thought about um you know what your thoughts are for AI in the future and what you think the implications will be for rental real estate as well. So and if you are interested in doing this crazy journey with us, you can check us out online at Furlo.

com. So with that, thanks for listening. Have a great day!