The Multifamily Innovation® Podcast
Patrick Antrim, Founder and CEO of Multifamily Leadership, Producers of the Multifamily Leadership Innovation & AI Summit, the Multifamily Women® Summit, and the Best Places to Work Multifamily® will bring you success strategies for Multifamily CEOs, executive leaders and aspiring leaders that want to drive high performance results for their portfolio.
The Multifamily Innovation® Podcast
AI for Multifamily Innovation and Data-Driven Success
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In this episode of the Multifamily Innovation® Podcast, host Patrick Antrim engages with Mukund Chopra, a serial entrepreneur and early investor in companies like Groupon and Slack. Mukund shares his insights on leveraging technology and data to enhance business efficiency in the multifamily industry. The conversation delves into the core purpose of a company, emphasizing the importance of maximizing return on equity through efficient resource deployment.
Mukund explains the significance of data ownership and its transformative potential when combined with AI, highlighting that the most valuable companies today are those that can access and unlock their data effectively. He provides a nuanced perspective on when not to use AI, stressing that its application should be strategic and not just a trend-driven decision.
Mukund recounts his experiences in various industries, including his leadership role at Groupon during its historic IPO and his work in big data and AI. He discusses how conversational AI and machine learning have been integrated into business processes long before the current hype, advocating for a thoughtful approach to adopting new technologies.
The episode covers practical frameworks for identifying areas where AI can add value, emphasizing the importance of starting with first principles thinking. Mukund also talks about the cultural shift required within organizations to embrace efficiency and innovation. He shares examples from his current venture, Blue Lake, which helps multifamily operators monetize customer data without invasive practices, illustrating how data can be a significant asset beyond traditional real estate operations.
The discussion wraps up with reflections on the evolving nature of companies, comparing traditional resource-based models with modern data-driven enterprises. Mukund underscores the need for multifamily leaders to view technology as a critical component of their business strategy, not just an IT function.
Overall, this episode offers a masterclass in understanding the intersection of technology, data, and business efficiency, providing valuable insights for multifamily executives looking to innovate and stay ahead in a competitive market.
About the Multifamily Innovation® Council:
The Multifamily Innovation® Council is the executive level membership organization that makes a difference in your bottom line, drives a better experience for your employees, and allows you an experience that keeps demand strong for your company. The council is uniquely positioned to focus on the intersection of Leadership, Technology, AI, and Innovation.
The Multifamily Innovation® Council is for Multifamily Business leaders who want to unlock value inside their organization so they can create better experiences and drive profitability inside their company.
To learn more or to join, visit https://multifamilyinnovation.com.
For more information and to engage with leaders shaping the future of multifamily innovation, visit https://multifamilyinnovation.com/.
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Multifamily Innovation® Council: https://multifamilyinnovation.com/
Patrick Antrim: https://www.linkedin.com/in/patrickantrim/
AI and Conversational Interfaces in Business
Speaker 1Welcome back to another episode of the Multifamily Innovation Podcast. I'm your host, patrick Antrim, and I have a very exciting guest for you today. Mukand Chopra is a serial entrepreneur and investor. He was activated early with Groupon as a chief revenue officer, also an early investor in Slack, and you know he brings this really interesting fundamental approach to investments and the application to business efficiency for multifamily.
Speaker 1We discussed what really is a company right. What is the primary purpose it serves. He breaks this down in the simplest form and we explored questions like do executives even want to find efficiencies in the business? And, if they do, what are those roles? How do those roles play out today? Should they exist the way that they exist? So, for example, do I need a head of marketing or do I really just want leads? And so, using first principles, thinking through some of those things and he goes into later in the episode around how the most valuable companies in the world today have data, own their data, can access their data and can unlock their data with AI we go into really the turning data into cash and then where not to use AI. Right, that's an interesting perspective and it's just really more than just a conversation. It's a masterclass on how to build and think through enterprise value in a business today. So with that, have a listen.
Speaker 1Great interview with Mukan Chopra. All right, so here we are. We're talking about something different when to not use AI. Should we start there? Yeah, actually, you know what? Let me pause, pump the brakes a little bit, because some of our audience our viewers, listeners may have not had the opportunity to share time with you. Let's pull back and talk about your background a little bit.
Speaker 2Sure background a little bit, sure, um. So, patrick, I um am a recent uh, I've had a recent entry to sort of the multi-family industry. I've been an entrepreneur um, kind of an entrepreneur born out of hubris. I think of some some probably poor career decisions I made when I was younger. I'm some canadian of indian origin. I went to school there, I did a couple grad degrees, um and uh, in canada and in paris, and I ended up doing a lot of work around econometrics, measurement and sorts of things that were more management science oriented. Um, this led me to some some really cool experiences in tech, including.
Speaker 2I was really fortunate to be part of the senior leadership team on the revenue side of Groupon when that company right in and around when that company was going public which people forget about now was the largest tech IPO after Google at the time. So we were actually before Facebook was 20 billion, and then I had a bunch of businesses in between something in the healthcare space and a bunch of businesses in between something in the healthcare space and a lot of stuff that was called at the time big data, right, sure. So you know AI as we call it now, and, I think, a lot of the excitement around AI. When we look at the likes of ChatGPT and the large language models, a lot of it is really an interface layer or sort of a UI change, and I think the actual like machinations of how to use models to make decisions consistently have actually pre-existed for a super long time, and it's often been useful to have programmatic decision-making functions whenever you're dealing with large volumes of data, short decision-making windows and things that are repetitive enough for it to make sense. So I was a business that we just sold Well, not we. I was chief revenue officer of maybe 2012 called Granify, and granify was venture funded in canada, um, I'm now an lp and a number of these venture funds sold for 80 million cash. What granify did was granify did um e-commerce uh checkout, um optimization. So it would determine when somebody was coming to an e-commerce store and then not likely to buy because they got pants in their cart or whatever, but now they're like, oh, these look expensive. So then you shoot them an offer at the sort of right time. So that's the sort of stuff that's been termed AI in the past.
Speaker 2I think that right now there's quite a lot of hype in the media because the chatbot interface has made this stuff more accessible, so it's in many cases a solution sort of looking for a problem. So I've been working in this space and actually, you know, my day-to-day operating business has been using conversational agents as well. But suddenly people think AI is great at making decisions. When you look at the fundamentals of what a large language model actually is, a large language model is just a token input-output mechanism. Right, it just says the cat jumped over, and then you leave it blank and it says the dog. It just looks for the most logical sequence. That doesn't mean it's reasoning, it just means that it's making something come out that looks plausible.
Speaker 2So these conversational interfaces, left in isolation, are prone to error, prone to hallucinations. Many of them, unless they're attached to a knowledge repository that is relevant in your business, can actually be pretty damaging. If you're sort of like going off of what chat gpt says in a in a raw sense. And I'm, you know, since I've been working in this space and there's a lot of excitement, I'm getting 10, 20, um probably requests a month, without exaggeration, from you know, folks that we work with or connected to, or multifamily operators who are looking for a reason to use it, and probably nine times out of ten I.
Speaker 1My response is please do not use ai for that sure, or do not try to yeah, and speaking of the conversational ai and and what is um, where you, like you, mentioned it's been, you know, we've been doing machine learning and big data and running programs way, way back Now that the UI ChatGPT made it easy to converse and basically program, because that's what they're doing when they're putting something in there. Essentially a programmer yes, I'm always I'm really fascinated, even in the work that we do. It's like how the people that are doing the work now have the ability to basically program with conversation. But going back into your background, you were involved in slack and and they had some big initiatives around conversations and data and things like that.
Speaker 2Take us, take us through that around conversations and data and things like that. Take us through that, yeah. So I think the insight that it's not much of an insight, right, like it's like way easier to talk to someone and be like, hey man, hand me a burrito than go on to an e-commerce website or download the Grubhub app or the Groupon app or whatever is going to deliver it to your Uber Eats. So I don't think it really takes a genius to figure out that people want to converse for requests rather than looking in knowledge repositories. So Slack was an interesting one where I was very fortuitously kind of a fairly early shareholder. We had a company that we'd sold to them and Slack actually you know the full name, the, the acronym or take us through it. Yeah, I know we talked about it at lunch, I thought just for the viewers, um, but slack is the um searchable log of all conversations and knowledge, um, so slack's entire premise was that it was going to be some sort of central database or brain and they've done basically all the things that you would call. You know that now, if you look at the assistance api and you look at tokenization and pine cone and vectorization and vector databases and vector search, slack was doing that stuff in a more plot along sort of way, which was okay, let's take all of the stuff that's in this word document and let's make that queryable as well as metadata. So if you're looking for employee leave policy, if Slack can find that that's mentioned, or stuff similar to that is mentioned, into any document using direct keyword lookup plus fuzzy logic, et cetera, et cetera.
Speaker 2That's why people use Slack Because people found that people wanted to chat back and forth and share info and the. You know, if I WhatsApp you and I, you know, I can, you know, work with some of my teams and WhatsApp and I WhatsApp you a file, um, the rest of the company can't see it Right, correct Um, if they come in and want to sort of use it. Um. So Slack always had a vision and actually a roadmap that in the s1 was pretty like heavily communicated um, where they really wanted to become um kind of an ai assistant, like that was really the intent of slack bot, which sits in here which gives you all the alerts um for like approvals and this and that, and workflow automation was basically to become a knowledge repository, that search for that stuff automatically being like oh, it looks like you've just joined, just so you know this is our medical policy, or whatever it is.
Unlocking Value in Real Estate Industry
Speaker 1Yeah, it's interesting because a company may have things in their documents if we're talking AI, maybe things that they're putting into retrieval libraries and, to that point, those are things that have been documented and the large language models have documented things as well but there's this invisible stuff that happens in private meetings and conversations and it's really interesting. You mentioned the WhatsApp message. Like, the rest of the company can't see it because it's invisible. Sure, one-to-one, one-to-one right, and there's opportunities, I think, in business that are invisible to leaders.
Speaker 2Yeah.
Speaker 1You see the invisible.
Speaker 2Yeah.
Speaker 1Take me through. How do you spot these things?
Speaker 1Seeing the invisible Well yeah, because think about it, In multifamily, it's the development process. We're very patient, understanding like we have to plan ahead. We have to think. We're really good at integrations. We have multiple disciplines, from plumbing to architects. We have plans, we follow those plans, we have regulation and we're patient with that capital. We have to think about the customer who we're building for all that stuff. But when it comes to technology, we rush in some decisions and that's why we're talking about what you know, when to not use AI. Yeah, you know, because you know you have to unwind things and stuff like that as business rolls out. But I'm curious to talk to you about how, when you see a multifamily portfolio, you're seeing what others don't see in many cases. And what is that? Knowing first principles, knowing what's possible?
Speaker 2I think so. I think the multifamily industry is interesting for a number of reasons. I mean, obviously it's the it's residential real estate is the biggest industry on earth and I think it's enormously capital intensive. So when it's an industry is that enormously capital intensive and has that many stakeholders, enormously capital intensive and has that many stakeholders, um, there gets to be a lot of um, a lot of competitiveness, a lot of loss aversion, a lot of like focus on, um, uh, visibility throughout the value chain, and I think that rigidity and visibility and sort of almost territorial habits that's begin to sort of come into play can be a disservice to our industry at various points in time.
Speaker 2This is another thing we were talking about in lunch.
Speaker 2You know, when you're talking about, like the PMS landscape and building a potentially truly open source sort of PMS. You know, you know the the advent of Yardi, you know to, to my knowledge, in terms of how it got to be so pervasive in the industry, was it started as an accounting shop? So it was, from what I understood used to be a CPA who was working in the US and worked predominantly with multifamily owners and was doing well, then built software around automating some of those processes and the big institutional funds got used to sort of the reporting frameworks that he was doing and there you've got this sort of there you've got this sort of like you know this accounting software that people are really relying on, which then snowballs into it, turns out a you know ERP and a CRM and yada, yada, yada Um, even if it's not, you know, perhaps the absolute best solution for each one of those things and has chosen to be very, very sort of closed off Um. And I think that closeness and rigidity, which is a function of sort of the capital stack and the capital intensivity um of the industry, you know, sometimes hurts a pragmatic person who might walk into the industry, who is sort of newer and I think you know newer entrants and, more generally, more pragmatic people, would see things very differently from a technological lens because they're not sort of caught into the monolith.
Speaker 1Yeah, you know, the more and more time I spend in the AI community talking to some really bright people, I notice the collaborative approach where I mean developers understand that they build on top of sometimes other developers' work and that makes for a greater marketplace network effect product. And you mentioned that sometimes as leaders we end up staying with what's familiar, what's safe and it makes sense. I mean you know you're fighting fires, you know you're dealing with what's urgent and important. Um, and you know, back way back in the days of where the internet came along and it was print, it was like you know, your phone number was an asset, right, and so you had printed it on certain things and so, like, changing a phone number would be problematic because you'd have to then do all this other stuff that can feel like a distraction to the business, right, so you're kind of stuck or you feel sticky in some way.
Speaker 1And to those providers that have built those businesses, I mean good for them for creating a. I mean you know what a wonderful founder story to be able to build demand and retention and all that stuff. But I think where we go next is really interesting because you know for an executive to truly unlock value within the organization, they need to see data. You can't do AI without data. Correct Right, and so that's a conversation that's not solved in one meeting, but it's where.
Speaker 2Yeah, absolutely, and it's interesting because it isn't you know. So, when we were a group on back in the day I don't know this for sure, but I heard it said colloquially so don't quote me on it as for sure true, by number of records, because we had um in chicago, six hundred west chicago, we had three thousand sales reps who were cold calling every spa and restaurant and whatever. We sped up a ton of data and a ton of ton of records and we did a. I remember us doing a, a migration taking from like leads to accounts and like changing the disposition of how it's working. And we took down salesforce, like global, uh, took down their servers, like it. It was catastrophe, not just for us but for Salesforce globally.
Speaker 2But interestingly, you know, it isn't unusual for CRM providers in all industries and Salesforce used to be like this to, once they've got your data, say this is ours. So Salesforce at the time had like very limited ability to export. They had very limited ability to do APIs, all this sort of stuff. They had rate limits that were super heavy. They'd cite, you know, vagaries like oh, compute is expensive, blah, blah, blah, blah, blah. But really it was like we want to lock you in, yeah, we want to lock you in and I think that's like very much um for better or for worse.
Speaker 2Like I don't want to, I don't want to hurt anyone else's businesses, but um seems to be the case with with sort of the large pms providers here. Yeah, and it really has been quite um cost prohibitive and you know, I know that entrata recently introduced a fee as well. Um, but sometimes it's 20 to 50 000 a year to be integrated with the rd. Sure, try to are like one of these groups. And it's not even about the cost. You know the the api. The process of being approved for the api firstly requires you to have a customer, a joint customer right, who is aggressively advocating for you yeah, um, also could be a competitive environment too, because some of the other thing they they ask for your use case, they ask for your financial, they ask for, like, a lot of stuff that's pretty invasive, to be blunt, and that stuff is reviewed by the corp dev teams.
Speaker 2Sure, that's known to be the case because and then they say, okay, well, we'd like to let you on the platform, but like that may be influencing their own sort of product roadmap.
Speaker 1Sure, yeah, no, absolutely. And and you know as we talk through those things, what it really comes down to is, when you pull the lens back, you know you're trying to create value for the end customer, the resident, right, and so if we want to unlock innovation I believe in the industry then you know we can get more efficient with many things in the business. That allows these real estate operators to not only depend on one source of income Right, because with limited units you just can't. I mean you could build more if you found land and all that stuff, but on your site you have 300 units. Yeah, you have 300 rental checks that you could potentially get.
Speaker 1Yeah, you have 300 rental checks that you could potentially get, and that's where, I think, where you're finding alpha in other ways. That gives relief to be able to, you know, fulfill obligations that these real estate companies have made to investors in ways that could be significant, I think, material.
Speaker 2Yeah, yeah, I mean, let's get into it. You know. So, with, with blue lake, what we're doing is we really we're a alternative income partner for, um, multi-family operators. Um, we're we're saying to them you know, get a 10 000 foot level. Basically, people uh, multi-family investors are asset driven, hard asset driven people and they have their assets, which are their land and their brick and their mortar and their elevators and whatever's in there. But they have another asset that they tend not to value, which is data, customer data and insights and the contact information people are interacting with their properties.
Speaker 2Fortunately, and though real estate operators themselves don't value this data, it's very clear that the financial markets do. You'd only look at the valuations of Facebook and Google, who own no real estate but own customer data. Sure Right. So should you have the ability to just sort of like monetize customer data? That's obviously something that puts an enormous sort of multiple in your business. Now, most real estate operators obviously don't come from that background. You know, it's a totally different way of operating. It's totally different technological stack and skillset required. We've been partnering with multifamily operators in the most non-invasive ways possible to help them to generate income streams from customer data that they're housing at no cost to them.
Speaker 1They're actually more like vendors to us and we revenue share with them. Yeah, and in your network you have access to some of the brightest minds in AI and intelligence around this stuff. So it's like you know, if you have 2,500 units, you know ideally that data scientist or you know the engineer is likely not going to want to come into that organization and reshape it and reimagine it, because they're going to be working for teams like yours and doing big reshaping industries and and seeing greater upsides. And that's why I think it's interesting what you're doing to provide a lot of value, to sort of be this outside arm that comes in and value engineers financial opportunities that otherwise people don't even know is possible yeah, certainly.
Speaker 2I mean, the fact of the matter is, anybody who's going through a moving event, um is going through a major major life transition. You know, if you're, if you're moving, you're moving because you're some plans, some unplanned. Some plans, some unplanned, unfortunately, yeah, um, and hopefully positive, but some also not so positive. But you know, let's, let's start with the positive ones. You know you're getting married or you're having a child or you're. You know you're starting a new exciting job and in Chicago, from New York city, where rent is one third Um and uh, and you know you're, you're keen to getting moving forward with life. But, like you know, when somebody is going through a moving event, um, people know that this is when all new purchasing behavior is born, right, so I'm moving to downtown Chicago from Manhattan.
AI Use Cases in Business Operations
Speaker 2I need to get an apartment, but not only do I need to get an apartment, I need to find a gym, I need to find a supermarket. I need to. I might get a new credit card. I might do, you know, like, quite a lot of things. I might find a romantic partner, I might want to be on tinder, I might, because those things, once I've lived in that unit for long enough, maybe I meet someone there. Maybe now I need a bigger place, maybe I need to purchase a place, maybe, you know, maybe I need a mortgage.
Speaker 2All these sorts of intents um customer potential, customer purchasing intents that exist, sure, um in and around a moving event, um, but the difficulty is, how do you become that trusted guide or that trusted person that takes them through? So, um, blue Lake's use of conversational AI is basically by being um a conversational front end um, which is a concierge Um, the core concept of which is empathy, like an extremely empathetic concierge connected to um what I'd call SKUs or SKU databases, which have various different types of products for different things that people could need based on what they're doing. We roll up that income stream and we do it at very good margins and we give a chunk to our operators. I know you're about to ask how much?
Speaker 1Well, no, no, I mean, the thing is, this is happening already anyway. Right, so it's just a matter of you mentioned the enterprise value of, like the bigger companies, like companies that do get the data right Yep, there's, and we're talking about AI too, and when not to use it. In some cases, not having the data makes it very hard to you know, not use AI or leverage it in the in the right places, and so I start to think about you know, as you, as you move through these opportunities for companies, you know what, what, how, how should they think through them? Because they they're not coming with this knowledge to the table, right, they're, they're, they're looking at their cap rate, they're looking at their budget. They're looking at their cap rate, they're looking at their budget, they're looking at their day-to-day operations. What are some of the questions that they should be asking when going through something?
Speaker 2Yeah. So I think there's what we do, which is Blue Lake, which is taking these lost cause, renter inquiries or move-ins that we're not monetizing anyway, and attempting to monetize it. I think that's a great use case for AI. The reason I think that's a great use case for AI is it's very, very high scale. It's very repetitive, responsiveness is extremely important and the stakes are pretty low, meaning if I get it right it's worth something, but if I get it wrong, I mean the thing was already worth zero, so it's not like something is catastrophically gone wrong. What concerns me the most is when we see multifamily operators talking to us about using AI in their core operations and they're either picking something that is not repeatable enough to warrant it Sure, it's not scalable enough to warrant it or, and here's the scariest one if they put in ai and it hallucinates, something could go catastrophically wrong.
Speaker 2Yeah, um, I had a developer um approach me recently.
Speaker 2Um, who is who? Uh had a team that did you sort of stuff, taking B minus and turning him to B plus or anything, and what he was concerned about was codes, building codes and building code stuff, so sort of trying to build a conversational agent that you vectorize or put in pine cone or something and all the codes and the rules and documents, and then said, hey, like can we build this X? And like get a yes or no or get a, you know, get feedback on it and get citations. And I almost had a meltdown. I was like, yes, but like, how are they doing this so far? He's like, well, they would, you know, it's a lot of documents, but they know the similar terms and my, you know, I have a team that does this and they do keyword look, they basically do control F and do keyword lookups. Sure, I was like, please still do the keyword lookup, right, because LLMs are not, they're not meant to return correct answers, they're meant to given an input of tokens, given an output of tokens that look similar.
Speaker 1Sure, Generate If you literally asked it.
Speaker 2you know, am I allowed to? You know, do I need to have any low-income housing here? It could say it would say no, you're not allowed to have any low-income housing. And then if you write to it back and say actually I am, it'll say oh, yeah, you're correct, it's actually yes, because it's not trying to factually answer the question. It's trying to give you a token of text. That is logical given the preceding token of text, and the preceding token of text is a yes or no question. It's going to give either yes or no, depending on what it thinks you wanted to say. You know, it's kind of right, kind of like um, I'm sorry for for married people out there, like I I sometimes do this, you know.
Speaker 1Uh talk about she's like can we go to dinner? Yeah, yeah, right, you know what I'm saying.
Speaker 2Short answer so it's sort of like you're not listening but you're trying to you're not really listening to it or give an actual answer, but you're trying to you're not really listening to it or giving the actual answer, but you're trying to give a agreeable sounding response. So that's kind of what, like, large language models are doing. And, yes, with vectorization of documents and knowledge repositories, certainly, like the risks have, like it's become more accurate. I would say it's definitely moving in the direction of more accurate. I would say it's definitely moving in the direction of more accurate. But you know, when we are in an enterprise multifamily context and you're talking about a hundred million dollar physical asset and you know somebody wants to save, you know, five hours a week of time for like a well-trained staff member who knows how to do this stuff and has a lot of it says I don't know. This doesn't feel right, like I feel like yeah, we did talk about low income in that meeting six months ago when we were looking at the plans. The AI is not going to have that knowledge.
Speaker 2And I think my major use cases are like when people are trying to replace AI, use AI to replace basically our search function, a control F or a keyword lookup, I'd say vector databases are not great for that. Um, and god forbid, it's not even relying on a vector database and it's just doing like some sort of google search or bing or something like that on the back end. Yeah, um, then you really can be in trouble. So, um, I think ai is effective if you can constrain it to really mimicking the human right, if you can say, hey, like I don't want to run a vector search, like you can actually if you were using Lanczain or Lanczmith appropriately, instead of saying, hey, here's this 300-page document, answer this question.
Speaker 2You could say come up with the 10 words that I need to use to answer this question. I could use a potential low-income or Section 8, and then run a Control-F function for each of it and then respond to me the chunks of text. That's really what your admin person is doing, right? Sure, that's the safer way to go and I think people get. They think these are sort of god models and they get, you know, a little bit overzealous yeah, you need to start with your process right, how, how, how is work getting done?
Leveraging AI for Business Efficiency
Speaker 1I always say that there's a difference between automated and autonomous. Yes, and I think it's a little uh ambitious to to be autonomous in some of these situations that you're talking about, because then you're you know, you know people begin and end the process. There's some level of review. Then you can automate the stuff in the middle, yeah, but it's still the. The leader is still making a decision from. You know it's more assistant than artificial at that point, but, um, yeah, it's very interesting.
Speaker 2Yeah, and there are human in the loop, frameworks, right so, where you can do that, and then it has to go to someone for approval and then that person sort of says yes, but I just get really. I get really worried with this stuff because so many people think this is how they're going to operate their business and it like there's a reason that you have that person who's been reviewing building codes with you for 20 years and costs $100,000. It's because they've been with you for 20 years and they've seen all these things and there's all of this stuff that they're intuiting around. And offloading this stuff when it's super high stakes if something goes wrong is a horrendous idea Right, and let's talk about frameworks shall we.
Speaker 1I mean, what are the frameworks we should be thinking through?
Speaker 2So I yeah, I think it's how to say it, it's just that the use case has to be correct. So I think that first things first, don't try and skip what the labor is, what the person who's doing it today is doing, don't try and leapfrog them, try and mimic them, right. So if the person is doing a control F search, program it to do a control F search.
Speaker 1I will tell you. What's interesting is just this is where I think AI can be a benefit without even using AI. The idea of using AI is you first need to look at your current process. Who's doing it, where's the data, what tools they use? You know all that stuff, right, and we did this. We have this framework, we take people through.
Speaker 1It's like a sort of like a time assessment thing, right? And you can't do that until you understand, okay, well, who's doing the work, how's it done, what steps are they taking? And then there's the actions that you'd want to have happen. And it was interesting that just by getting the just journaling what was done for the week on these things and what tools were used allowed us to realize like, wow, we've got four people we're paying high dollar amounts sitting in a 60-minute meeting every week, and so originally we just said, well, let's just stop doing that meeting. So, without even using AI, to the fact of getting ready to use AI, we found money. We didn't even use AI yet, right? And so when you talk about mimicking the work, like there's value, because sometimes these things creep into the organization, people love to surround themselves with work and Busy work, yeah.
Speaker 1Yeah exactly, and the CEOs don't always know that. Sarah, three departments down when they say do the thing, that they've got to go into four different systems and pull out and download and upload and do the thing. They don't have visibility over the work. That's what I love about this is that because we went from a leadership responsibility where you could see work like you had an office and you knew when they were there and you saw them working and they were in the boardroom with you and you saw them on calls we visually see the work. But now, with even remote work and people in different areas, we don't often see where work gets done. But when you get these things into tables and into databases, into systems, we have a whole nother visibility of how work gets done. And I think if executives knew they could be trained to find value within the organization.
Speaker 2Completely. Yeah, I mean, if your need to use AI forces you to first do this pre-work, that's a win enough. You know what I'm saying. Right, versus you to first do this pre-work, that's a win enough. You know what I'm saying? And this sort of like what you're describing is to what extent some extent. Recently it's called first principles, thinking, just data-driven, operating in general. I think it's probably more valuable. And then AI is sort of like a tool set right, sure, Kind of. At the end of it, that like may or may not be relevant, but probably 98% of the win is just adopting a culture that's looking for efficiency and that's really breaking things away from roles and into sort of accountabilities right.
Speaker 2And sort of saying like we do we need a head of marketing or do we need leads to apartment buildings? Right, Like do we need a head of marketing or do we need leads to apartment buildings? Because a head of marketing may result in leads to apartment buildings, they also may not. It might be, you know, a performance marketing person who's not a head of marketing. It might not even be a performance marketing person, it might be an agency.
Speaker 1Yeah, and we have our biases coming into these conversations because we grew up and this is the other problem that you may be bumping into more than I is as people are considering these things, they're quite successful.
Speaker 2Yeah.
Speaker 1And they've been cashing million-dollar checks for a long, long time, and they did that in a world that AI didn't exist in some cases I mean obviously in later years here and so it's tough to make change when you have so much certainty around how the wealth was already created, Like what's the incentive?
Speaker 2right, yeah, the incentive is that Jerome Powell just continues to raise rates you know that we're all going to get pretty creative. Yes, Out of necessity, right Out of necessity. I mean, necessity is the mother of invention, right. Flow while being very liquid, yeah, um, and so, yeah, necessity is the mother of invention, right. So, like the second, you um, things get tough.
Speaker 1You gotta go to the mat and you gotta find a way yeah, I, I love the the conversation around uh well, self-driving, autonomous vehicles, uh, obviously very data-driven. I recently uh went and toured one, or actually went on a ride down here in Phoenix, sort of a Waymo. Yeah, the Waymo, have you been in one?
Speaker 2No, not yet. I'm going to do that later. Yeah, you will.
Speaker 1You will. It's pretty interesting, and so here's the deal. At first, I'll tell you a quick story. We were in a sort of we upgraded to uh in a ride share uh, current ride share, uh, vendor or product. We summoned our ride and halfway through the ride, um, we realized, uh, it got scary, like literally, like you know, aggressive driving, okay, and this was a, like a five-star driver, and I was like I wasn't expecting that, oh, this was with a five-star driver. And I was like I wasn't expecting that, oh, this was with a human. This was me in the ride, yeah, so with the, exactly. And what happened was that driver got a new ride and wanted to finish my ride to be able to time and accept the other one, sure, so the interests weren't aligned to our safety, and so it's funny because we were having brunch. And then we're like, oh, let's try the Waymo, right? So we got the. I'm like, well, we'll fix that problem.
Speaker 1First principle is thinking right. Elon says you know, engineers often optimize something that shouldn't exist. Well, in this case it was the driver. So I'm like we can do this. We got the app summoned, a Waymo came and immediately when I hit the app, it said and this was brilliant marketing, it said. The most experienced driver is on the way and I thought you know what. This thing is probably driven more than I have. Yeah, you know what I mean. Probably millions, millions of miles or something, right, because it's just always driving and it's probably paying attention more than me and stuff like that, and I know that there's a lot involved in that. But I start to think about when you look at what feels safe, because we're familiar, like there's a steering wheel. I would say the Waymo Nex would not even have the steering wheel, right, because why would you need that if right? So, but right now it's there.
Speaker 1And I always tell our teams and people I'm sharing time with, like, in the state of transformation we talk about things as they used to be, for us to even have context for what they are. So, cordless phone, you know right now, self-driving car it was horseless carriage, I mean everything, motion pictures for cinema and all that. And so at first it was a picture, then it moved and now, well, what is wait, it's a video. No, we don't know, we're not even to video. It's like it's a motion. Then it moved and now, well, what is wait, it's a video? No, we don't know. We're not even to video. It's like it's a motion picture. Yeah, and in the states that you're moving people through, I got to imagine that there's a level of context to what it was and what it can be. Yeah, do you feel like transformation is as important as AI?
Speaker 2More, yeah, yeah, I mean you feel like transformation is as important as ai. More, yeah, yeah, I mean like it's, like it's always, it's crawl, walk, run right like you have to. You have to start small, you have to get it right, you have to build the trust. You gotta have the steering wheel there for some period of time when you start seeing that the thing's not going to run into the road. You know that's when you start doing it. So I, I think it's, I think the it's great, that it's exciting and everyone's like ai, this, ai, that, whatever, but it it really is, I think, a mentality, and you know that mark suckerberg's been saying it and all these people like look, facebook cut 10, 20 000 employees. What happened to revenue? Nothing. Earnings exploded right, like you know, in Elon's words, like removing things that most people are optimizing, things that shouldn't exist. Probably many of the roles don't need to exist. But that's not an AI statement, that's a cultural statement.
Artificial Intelligence and Decision-Making
Speaker 2The fact of the matter is that the majority of the world is doing something that I would state whether that's a head of leasing, whether that's a leasing agent, whether that's even asset manager to some extent are doing something that can be summarized as a conversational agent who looks at a database, right. So if I'm ahead of leasing, I see a thousand leads coming in. I was trying to see how many went to tour, trying to see how many applied. And I call the agent who has a low tour to close rate and say what are you doing, you know, or how, why didn't you get this many people to tour? And I talked to him, right, that's a conversational agent with a database, had to ask the management.
Speaker 2I say and rent rolls good, rent rolls not good. Renewals are good. Renewals are not good when compared to the 10 other properties. Because I'm running a spreadsheet. You know here's 50 of my assets and this one's got extremely high NOI and this one has lower NOI and this one the least velocity is this and that one's the least velocity, that this one's doing comparably worse. And then I reach out to the person and I say, hey, you know Green Gables apartments. You know you guys aren't doing so great, look at these guys. You know in another state, but they're outperforming in least velocity, they're outperforming in occupancy, they're outperforming in a.
Speaker 2Y, what's going on, and I try and have a conversation about that. I think most of those things are ripe for at least first step automation, right In terms of like-.
Speaker 1We don't get the defensive behavior. Sorry, you don't get the defensive answer. You get the right answer, you get the right answer.
Speaker 2So I think they're ripe for at least first stage automation where there's no reason every dashboard, dashboard, every spreadsheet that everyone's looking at shouldn't be, instead of them having to log in to look at it. On the flip side of an alert based system that says you know, anytime one of my properties is doing super well, shoot me a note. Right, and it just sort of, and you know the llm can shoot like sure, this one's good. We recommend you reach out to Catherine, who's over there and and here or whatever you know. Would you like me to do it, yes or no?
Speaker 1Yeah, here are some good questions.
Speaker 2Here's some good questions, and as long as you're doing taking any majorly consequential decision like that, you know we're talking about a positive scenario. Let's talk about a negative scenario, where katherine's actually an underperformer and the algorithm says let her go and reaches out and terminates her. That's a catastrophe, right, right, sure, um, because maybe there's a bunch of extenuating circumstances. Forget that. Maybe you don't want the algorithm you know involved in it to like that extent let's lean into the human in the loop.
Speaker 1Uh framework, what, how do you, how would you share um?
Speaker 2reflect on that. We can show you. We can show you in our systems. But I mean, you're familiar with um lang chain lang smith, right? So lang chain lang smith, a lot of these things have um what we're talking about. Are you familiar with agentic frameworks? I'm not.
Speaker 1No, okay are we talking about?
Speaker 2embeddings of no um agentic frameworks are born out of processor vision. You're familiar processor vision, no, okay, so the the reason that large language models um, are so exciting isn't because it's a chatbot that you can shoot. The shit with which I mean, of course it's cute and whatever you know, you can make jokes to it and say, hey, give me this song and the style of jay-z and whatever, and like all that stuff is great, but it's that you can use it to become a decision engine and the reason that you can use it to like an actual end-to-end, close to autonomous agent, and the core reason for that is because of sort of chain of thought prompting. You feel the chain of thought Mm-hmm.
Speaker 1So basically, the way you, it's just like a thread for yeah, where how it came to. So, like you and I remember what we said to each other yes In how it came to this. So, like you and I remember what we said to each other yes, in the previous context and we coax each other.
Speaker 2So, for example, let me give you an example where it says you know what is the what's two times 10, right? So there's two ways of making the model work. When it says you can say what's two times 10? And it'll say 20. So if it says two times 10 is 20, what that's doing is that's just doing token extension. Saying two times 10, 20 looks like plausible. It might give you, might just as well give you 30 or 40 or 50. But it says I would like to calculate two times 10. Um, please give me the steps to get there and I'll say two plus two is two times Two is four, and two plus two plus two is six and two plus eight.
Speaker 2So this is what we call sort of chain of thought, which is like getting it to explain how it is deriving and it's changing, it's getting to what it's. What it's saying is ultimately the answer and ultimately, all you know, this is some of the stuff that well, now is exposed in the media but like wasn't openly talked about before. You know, people look at opening eyes. It's sort of a tech company and it is very much a tech company, but like that tech was trained by tens of thousands of humans, principally in kenya, actually in nigeria, some markets where I actually have some offshore staff as well who are paid like two bucks an hour to like rate the appropriateness of answer. So it's sort of like two times two, two, you know, two times ten is 20, correct, yes or no? Now what they've done is now we've started moving from two times 10 is 20 to two times 10 is 20, explain your thoughts and says well, two plus two is four, two plus two plus two is six, and like kind of do the breakdown and it says yes, I agree with the reasoning process you took to come to this. So that second type of thing where we've gone from input output to showing the process of reasoning and then supervising that process is called process supervision. So process supervision is about changing reward models from being token input to token output, to being like what is the process through which you took this token input and ultimately came to that token output? And many times that reasoning is taking place in the background and you're not seeing, you're not and OpenAI is not showing you those steps. Sure, right, like, you can coax it to show you the steps as well as a user if you want, but like, in actuality it's doing it without you those steps. Sure, right, like, you can coax it to show you the steps as well. As a user if you want, but like, in actuality it's doing it without using those steps.
Speaker 2So we're seeing sort of like tool selection, like this sort of stuff happening as well as where it's like, hey, like, what's the?
Speaker 2When was Nelson Mandela born? You know, was Nelson Mandela a good guy? How old is he? So, like he'll realize, nelson medela is a good guy, sort of a subjective thing. If lm tokenization okay, yeah, and generally people speak about him positively, he's a good guy, great, and it says and how old is he? He's no longer around, obviously, but you know it'll go like okay, well, now that is a factual answer. For that I have to go through tool selection, do I? And then I have an api for bing, because bing is the, the integrated api at the moment search api, go through this was the age. And then, okay, now that I have that, now I'm going to take you, take the birth date and derive it by today's date and say you know, he would have been this many years old, but unfortunately deceased in the case of nesmantella, um, so it it's about cultivating rules engines over periods of time.
Speaker 2So like, if I take that and I take a step back from that and let's come back to the person who's sort of doing the multifamily zoning stuff, right, like the developers looking doing zoning so it's sort of like, yeah, okay, it's coming out that this one says no low-income housing allowed, but is that consistent with what we've seen? Let me second-guess myself. So it's like, okay, yeah, that's the answer, but let's triple-check that answer. Okay, well, let's look at all comparable projects we have. Do we have many where no low-income housing is allowed? Do we have other buildings in the area as low-income housing allowed there or not? And that sort of like second third layer, like the model questioning itself. Inference is really where things are going to go.
Speaker 2But if you talk about that and you know there's a, there's somebody that I know somebody's come to my talks much more successful founder than me, guy named uh mike merchants and who's in canada. His company called ada. It's now valued at two billion. Um, he talks a lot about how to. He's been doing similar to us conversational ai in his case in customer service, for they handle like all of air asia, for example. But he was saying that one of the things that mike says which I think is great is that you really have to treat your ai as an employee so you know when we get back to the person who's looking at, you know those things for code, you know building codes, you know. You trust her blindly because she's been working for you for 15 years.
Speaker 1Yeah, you didn't trust her blindly 15 years ago right, because you've had many corrections, adjustments, investments, corrections, adjustments, knowledge repository.
Speaker 2Good things, she's had process supervision yeah, she's had. Like you are doing a good job of this. This is how you reason through this. If something looks a little unusual, you should double check, triple check. You should check with this knowledge repository. You you should look at four other comparables, you should use your sort of judgment and the question becomes like how do we inculcate judgment into AI agents if an AI agent is ultimately going to become basically your employee or is sort of like offsetting the work of what was historically done by an employee and that is going to be an arduous and continuous investment and the precursor to all of that? You rightly stated is cultural change.
Speaker 2So if you take that cultural view that you're looking for efficiency in your business, you know that's a precursor to any of this. Right, when that starts happening, uh, you may find that you never even get to the ai part. Because if you found a way to, yeah, you train people to do it and you find the ai is unnecessary, yeah, um, but I think it's really a function of like. Do we want to find efficiencies? Do we want to break things down into first principles and think does this role need to exist in this way? Right, um, do I really need a head of like zoning, you know, uh, zoning checks, is that like a, a role on an org chart that needs to exist? Or do I just need the zones checked Right? Same thing Do I really need a head of marketing? Or do I need leads to my properties? Cause, at least my properties may come from a head of marketing. They may not. They may come from, you know, a variety of different ways. Or maybe I don't even need leads to my properties, who knows, right?
Speaker 1Yeah, no, no. Those are really great points. So when I pull back as you reflect on this transformation, I think about like, well, what is a company? A company, if you think about it going back historically, had physical resources, buildings, things of that nature, and those that grew in scale, had the ability to bring together capital resources, financial resources, loans, debt, all that to really grow and scale. Then, as the physical resources leveraging financial resources grew we'll take a very easy example Blockbuster, right. More stores in more cities, more revenue, right.
Speaker 1So now you now have more what Employees, which brought the third piece, which was the human resources. So you had physical resources, financial resources and now human resources, and that was pretty much every company that existed. And now, as I speak to real estate owners and operators, they're good at the financial resources. They can bring JV deals together better than anybody in the world, right, they can see value, they can see a piece of dirt, have a vision for the future, make it happen, take the risks.
Speaker 1Great at people like building teams, leading people, all that stuff right, great at that piece of things. So they've got the physical resources, they've got the financial resources and they've got the human resources. And then this fourth part is this technology as leverage. What I hope to do is inspire people to realize, as good as a CEO knows how to do debt deals, cap all the things that pull leverage on a real estate deal that they need to speak to someone like you, right, or anybody that's really joining forces with bringing technology into a company not even AI, just tech and look at it like it's not something you give to IT, like this is part of building a healthy company.
Speaker 1That most companies are technology companies. They just don't know. And that's what goes back to my point seeing the invisible. You see that fourth piece. We were talking about oil and refining, and if you could just imagine all this value that's unlocked in your business as a CEO, where do you point people other than calling you up and working with you on these types of things? But where do you point people to accelerate the learning, to understand it's as important as understanding debt, equity, financial resources and these types of things that make up a company.
Automating Processes in Multifamily Operations
Speaker 2The number one thing is I think most people who are at senior levels, like CXO levels and multifamily operators, specifically institutional ones, come from finance backgrounds. And how I learned to this stuff and I, you know, don't have a formally coding background, econometrics background, um is from building financial models, um. So when I worked at city group and I worked in investment banks, we'd have to build large financial models where we would do things like, say, we were modeling multifamily. You know, here's this asset and our rent assumption is three bucks a square foot. But my MD is going to come in and ask me well, what if it's only two? And I need to be able to change one cell and it needs to flow through and I had to be able to test sensitivities. So most people who are, who can build a financial model that's basic in Excel, are coding. They don't realize they're coding, but they're coding it's a different interface.
Speaker 2It's a different interface and it's a simpler interface and it's algebra and whatever. If you can build a discounted cash flow model, you can use zapier. Are you familiar with zapier?
Speaker 2oh yeah, yeah yeah, zapier is like one of those most simple like if this, then that right conditional automation software is on earth and there are so many low code solutions out there to do things. Once again, you know, in my business I do one repetitive thing at extremely high scale, right, 30,000 renter inquiries a day coming through my you know my drive-through and I'm trying to service them and find them apartments and do things like this. So I'm the equivalent of sort of like McDonald's, right? So you know, mcdonald's is 100% a management science play. How the heck are they making money selling $1 McChicken? Sure, well, they're doing it because they built such scale and such efficiency, because they built a supply chain and a machine that was purpose built for one repetitive action taking place again and again, and again and again. That was purpose built for one repetitive action taking place again and again, and again and again. And they put thought into every inch of that place.
Speaker 2Like the burger buns, you know, should they be together? Do they reach to the left or right? Should there be sesame seeds? Should there not be sesame seeds? If there's sesame seeds or not sesame seeds, what are the consequences? Right, if it's sesame seeds? Should the bun be this way. Should it be this way? Should they be put together or should they be on the separate?
Speaker 2sides of it. You know we've got. We got to get the perfect French fry. How do we get the perfect French fry? You know this is a labor of love. You know what they've done right and they're. You know they must've had inconsistencies in the French fries back in the day because there was, you know, coming in and people would cut the potatoes different sizes. Now they're cut the perfect size. Now they put them in the thing. I'm sure at one point somebody was putting up and down the. You know the fryer. Yeah, that no longer happens. It's completely One third of a second. Yeah, it's completely automated because it needs to be exactly precise, it needs to be consistently done and they've still got humans and they're doing it and it's. It's a beautiful thing, it's a sort of symphony, you know, or that. So I think the, the it makes sense to automate. You know burger production. If you're mcdonald's and you sell 30, 000, you know burgers a day or whatever. You're selling millions, probably globally a day. But if you're, you know there's a burger joint that's up in Canada and Canadian called the Works, and the Works charges $27 per burger and they pride themselves on bison meat and avocado and they'll put two fried eggs in there and they'll put you're supposed to wait exactly and you can customize the
Speaker 2kinds're supposed to wait, like exactly, and you can customize. You know the kinds of like chili flakes you want, and this and that, and sauces and this and that, and like you know, if I start building that with an automated machine and I took away all the options, all the values eroded, sure. So I think, like a lot of the um, a lot of the tasks that, like a multi-family operator, an institutional level, is dealing with, he very well could use Zapier and things like that to automate some of them. But, like I'd say, you know, buying a hundred million dollar apartment building, it's a lot closer to the you know the $27 burger than the $1 Junior McChicken. Right, there's a few that don't do it.
Speaker 2So it's sort of like you got to find the Junior McChickens. So it's sort of like you got to find the Junior McChickens. Sure, what are the Junior McChickens in your business? What is the basic repetitive high volume, low margin. I don't really care about it, but they probably don't make very much money on Junior McChickens. It's fine, people come in and they get them, and that's volume, you're right, and they're able to amortize a fixed cost of running that operation and hopefully they'll buy some higher margin product while they're there. Yeah, right.
Speaker 1That's interesting.
Speaker 2So I'd say, find your junior McChickens and like that's what you should be automating. But like the, the majority of the stuff in your business probably isn't a junior McChicken, yeah, so don't go chasing the automation.
Speaker 1Yeah, you know it's funny, you, you mentioned Zapier and I'm going to show you while you're in town here. We kind of took that model and made it for multifamily, because there's no network effect in a marketplace like that, because it's mostly for freelancers or people that are familiar with how to connect APIs and things like that. But if you take a marketplace like that where you can connect system A to system B and start with everything outside the PMS right, you start to think about an email signature and if your turnover is 40%, you know that is the thing that occurs. And on and on and on, and you know you're going to be candidate experience resumes coming in at volume and things like that, resumes coming in at volume and things like that and those are the types of things that are the low-hanging fruit or the crawl, walk, run stuff that you can build a workflow around.
Speaker 1Yes, you know, and so it's interesting you say that because— that's your junior chicken yeah, it's like a low-stakes, high-volume thing, right. And what we've found is that people actually, when hired, don't want to do that type of work, right. So now you're, in a way, improving the employee experience and repurposing them into things that lead to either more revenue or other parts of the business Higher value, higher value, yeah, exactly, higher value things that you need humans for, exactly, exactly.
Speaker 2My mantra is aggressively automate, aggressively humanize. You have to be doing both, right. Right, if we have people in the organization and we're automating away their work, it's not so that they don't do any work, right, it's so that they apply the human layer, you know, to that work. So it might be saying, hey, give this customer this apartment, but then we ultimately expect, when that message is delivered, for it to be delivered with great care and empathy and attention to detail and contextualization.
Speaker 1We have some really great conversations in our Innovation Council and, for those of you listening or watching Multifamily Innovation Council, we watching multifamily innovation council. We talk to multifamily owners and operators on a weekly basis around the challenges they want to solve the priorities from fraud, centralizing the business, property automation, business automation, all these types of things and that's kind of how we end up in conversations like what we're having is like we first identify what's going to make the business better. That's the way we look at innovation, not the new tech. It's like what's going to make the business better either by driving more profitability, saving money, more revenue, whatever. That is yeah. And so when, when we have these conversations, we're like, well, okay, well, who's solving these things? That's kind of how we ended up bringing you in to have these conversations.
Speaker 1But we have some really good, healthy debates because everybody has this window by which they see the world, because of A how they were influenced growing up and then also how they worked. They get a good job, they do a good job, they get promoted, rewarded, they go to the next level. Now they're in charge of others, they tell others how to do their job and it sort of influences this. In some cases it could be bureaucracy, it could be waste in the organization, just from familiarity. And so that's why these first principles thinking kind of unlocks some of those things.
Speaker 1Well, what if we could do that? What if we didn't? You know what if we could lease without depending on a human we're not making the debate, you should have one or not. But if you didn't have to depend on it, what then? And so these debates are fun, exciting, and I would love to have you come in and we could facilitate some fun stuff there. But we have some that are completely nobody on site and some that are like how do I get to a new model, centralized, specialized, whatever they want to call it, like testing assumptions about how work is done or can be done.
Speaker 2Yeah, you got me thinking and there's something I want to share. So obviously, as you know, I was an early shareholder in Slack very, very fortuitous, and that created some wealth. Eventually, I was trying to be a real adult. I just had a kid, you know.
Speaker 1Growing up still.
Speaker 2So I just had a kid and that you know, having a kid and having to just sober up and look at the world and how real, responsible adults you know make money, it's what led me towards multifamily, um, but I've had the good fortune that I I keep, um, quite a bit of portfolio allocation. So I'm a partner to venture fund in canada, um, and I keep portfolio allocation towards venture and tech and one of the big reasons I get to learn stuff, um, and I'm a fortune being a JP Morgan private bank client. You normally have to have way more assets than I have to be there, but I don't know. They were nice to me. They seemed to be taking a bet on me, a young guy who may get there in the future. So they're like, okay, this is a smaller portfolio, we'll take it, but through that I got to meet Tiger Global. Have you heard of Tiger Global? No, tell me more. So, tiger Global is a New York-based hedge fund originally that became one of the most prolific late-stage venture capital investors now in the world. So they go neck and neck with SoftBank, for example, and they had a webinar and they were gracious. I got to go to it, as did all the JP Morgan clients and I got to hear from Chase Coleman, the Tiger Global founders, who were on that call. They said some really interesting things.
Speaker 2So I think you were coming back to sort of like what is a company? And I'm sort of like I have a master's in finance, right. So for me I'm a complete, I like to call myself. To my teams I say I'm a simple capitalist, meaning I don't understand rules or org charts, any stuff. I just understand like where's the money? Tell me where the money is. So a company has only one job really, which is return on equity maximization, right, like? We have equity in the company. We invested, working, you know capital, and now we're going to deploy those. In that argument, how do we deploy it efficiently to maximize value of the company? You can do that by building moats or things that you know building revenue, building free cash flow, building moats around that revenue and free cash flow, or perception of moats.
Defining Technology Companies and Cash Flow
Speaker 2Um, and I think there was a. There was a while, you know, when we look back 20 years ago in my dad's era of investing, where you know the biggest companies in the world most valuable were the ones that had oil and now they're the companies that have data. Um, and how did that happen? And why did that happen? Right, like, why do facebook and google and you know these things work? It isn't because, like, facebook is a cool app and everyone's excited about the app, large scale, long. Only, you know, a hundred billion trillion dollar institutional investors and sovereign wealth funds don't invest in Facebook because the app is cool. Those people only and only and only care about one thing, which is free class for a generation.
Speaker 2Right, and you know, for all the flack around meta and Oculus, and it hasn't gone so well, I think Zuckerberg was putting 10 billion plus a year into this AR bet that as yet, hasn't had any great positive results from a revenue perspective. Um, and the street couldn't say anything to him. You know why? That is because he produces 100 billion of free cash per year, so he can launch 10 billion, but he can put 10 billion bucks and light it on fire and the street can't say shit. So, um, when you're talking to chase coleman, these guys, the thing that they were saying which is really interesting about, like, the businesses they invest in and the thesis because they're hedge fund guys. So it's like, what the heck are these hedge fund guys, these serious, traditional late stage public markets investors, who are supposed to be like talking like Warren Buffett, what the heck are they doing investing in Flipkart, which is the Amazon of India, all these sort of speculative looking companies? And they had a really clear answer. And the really clear answer was they said look, I love Tesla, I have a Tesla, it's like I've got one in the garage. We invest in technology companies. Tesla is not a technology company, tesla is a car company. Do they use technology in all the wheels? Sure they do.
Speaker 2What defines a technology company? What defines a technology company is a company that maybe spends $10 million a year to generate $2 million a year, and then it grows its revenue from $2 million to $5 million and they're spending maybe $11 million a year. And then they grow their revenue from $5 million to $50 million and they're spending $12 million a year. Because what happens is, when you build a true technology company, you're able to support marginal revenue at no incremental cost, right? So if I get to you know $20 million revenue, I want to go to 200 or a billion or 2 billion or 10 billion or 20 billion. You can basically do it without adding any headcount. And there's giant private tech companies that people don't talk about. Do you know what the revenue of Craigslist is?
Speaker 1And the employee count very low employee count.
Speaker 2It's like 25 employees and it does like five billion of free cash flow and it doesn't even look.
Speaker 1And how many people come in and say let's redesign the site right? Yeah, exactly.
Speaker 2And it's just people paying that stupid $2 or $5 or whatever for a Craigslist ad and it is a monster, it's an absolute cash monstrosity. So Tiger Global were saying we get it Because their investor set are endowments and pension plans and, like major insurance companies and major, they're thinking three generations, in some three, four generations. And then how do we get this traditional investor set to understand why we're doing this? And they're like we're doing it not because the tech's cool, we're doing it because cash free cashflow is the goal. Right, and all technology you know should be oriented around free cash flow.
Speaker 2Like, if you're deploying you know technology in your business, there needs to be a very clear business case that this grows revenue, reduces costs, boosts income or protects maybe future reduction of you know. Sure, you know people can do preventative things too. Is what I'm saying like, make sure that this thing stays up and doesn't break? Yeah, but beyond that, like, business cases have to be really, really, really clear and I think there's too many things that happen because of with, you know, in all organizations and I'm very much to blame for this and it happens in my organizations as well but without clear, concise, coherent business cases and finance cases to support them right? It's really simple. Everything is a tiny little profit and loss. If I got that leasing software, what is it costing me and what is it making me? Period Sure Right.
Challenges of Managing Multiple Businesses
Speaker 1Yeah, or what is it saving me? Right? And the nice thing about and I'll leave this because we're coming up on the end of time and I'd love to obviously we'll have you back for more stuff in the future. But the P&L management is what these operators get, right. I mean, their job is that they're in the P&L all the time, and so when you look at technology, that's just another business case where you're talking about that free cash. That's. The purpose of this is to increase the revenue, cut the cost, which increases the net income, and so there's some. You know, if you're in finance and you're running P&Ls, you're well positioned to really think through this tech thing. But, like to your point, going back to these wealthiest companies on the planet, are data companies, right? So we have to ask where is our data? Who owns it? What are we doing with it? How are we unlocking it? You know all that stuff.
Speaker 2Yeah, and those are the partnerships we're working on right now is sort of saying like, hey, how do we get that data out of your system and turn it into a cash source? And I think it's like. I think everyone at a very high level knows that their data is probably worth something. Um, but knowing that your data is worth something and being able to monetize that data are very different things, right, Like I grew up in Saudi Arabia I've lived in 14 countries, by the way, a lot, a lot of the world. Um, I've lived in 14 countries, by the way, I've lived in a lot of the world, a lot of emerging countries, Africa and all these places. But one of the places I grew up was Saudi Arabia for some time, and Saudi Arabia I mean in the desert. They joke like literally, and I'm sure it does happen you can like just stick a sand in the, you know, stick in the sand and out, spritz oil. Like that's great, Like oil, sure, like theoretically valuable, but like if it's just spraying in your face.
Speaker 1Yeah, right it isn't super valuable yeah uh.
Speaker 2So you need to. You know, take it, capture it, structure it, store it, build a supply chain around you know, refine it, make sure it's usable, it's the right grade, get it to you know. Uh, golfer, sorry, pet, petro Canada or something, some retail across the world who's like, built a station? Yeah, so now the oil is worth something? Yeah, so I think it's it's. It's interesting and what we found, you know, in our partnerships, is that the majority of real estate operators have some concept that their data is worth something. But just because they think it's worth something doesn't mean that they have the wherewithal or necessary, you know, to be in all of the businesses that would make something from it, right?
Speaker 1And that's where I think when I mentioned my analogy to the development process, they trust the structural engineer. They know like I need this building to go vertical, but I also know like I'm not going to go out and be a structural engineer.
Speaker 2Yeah, it's hard.
Speaker 1So that's where you guys come in to help that refinement. We've already talked about how it unlocks the value, turn that data into cash, but really you need somebody that understands that space and keeps up with that space, because if they can't find people to be maintenance technicians or leasing agents or even VPs of property management, they're certainly not going to have the challenge of bringing in all the teams and disciplines and understanding to try and really unlock that type of value.
Speaker 2Look, you can ask my wife if she looks at me and other people. And I say to you, and we all say to each other running a business is hard, Running your core business is hard enough. And if you have the wherewithal to run your core business and five non-core businesses, please teach me how, Because I don't know how to do it. It's hard enough to bite off. You know one problem to solve.
Speaker 1Right, and there's all types of research on how that doesn't work for people, even in any kind of you can't. If you don't focus on it, nothing gets done. Well, listen, we're coming up on the end of our time. What any final thoughts you want to leave our listeners with?
Speaker 2Nothing specific, just thanks for having me Great, invigorating conversation. I'm glad I flew out here.
Speaker 1Wonderful. Well, it's great having you, and we'll be tracking all your success.
Speaker 2Yeah.
Speaker 1Well, our attempts, that's it. Attempt, that's a good point. We'll leave that Attempt. Something today that would be fun to do. All right, we'll see you in the.
