John Fitzpatrick, Sr. Managing Director & CTO AAMT – Blackstone

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Terms apply. That's bill.com/invest. Hello, I'm Ted Seides and this is Investment Management Operations. This show explores the inner workings of the most sophisticated institutions in the industry. Through conversations with executives across operations, compliance, legal and finance, you'll hear how key operating partners run their businesses in an ever-changing and complex investment landscape. You can join our mailing list and access Capital Allocators content at capitalallocators.com. I'm Scott McDonald and I'm your host. My guest on today's show is John Fitzpatrick. John is a senior managing director and chief technology officer of alternative asset management technology at Blackstone. These are exciting times in technology with a lot of moving parts at speed so I was excited to sit down with John to get his perspective on where we are today and where we're going. John shares his thoughts on the big picture with trends in data acquisition, automation and process improvement. Whether you are a technologist or someone accountable for technology, there are some great takeaways. We also discuss common challenges of technology selection and adoption, budgeting and how one can effectively manage expectations with non-technical leaders. Please enjoy my conversation with John Fitzpatrick. So John, it's great to see you today. I'd love to go back and hear a little bit about your background and how you found your way to Blackstone. First of all, Scott, thank you for having me on the podcast. My career journey started post-college. I worked at Merrill Lynch in an analyst level role for about a year and a half. And then decided I wanted to do something different from a career perspective like a lot of young individuals. As I went through that particular search, assembled upon the time and still to this day, a relatively innovative company called Capital IQ, which is now part of the McGraw-Hill S&P enterprise. The attraction there was really the ability to bridge finance and technology, they now called Fintech. At the time, Fintech wasn't really a cool thing, but for me, it was pretty neat applying technology to the field of finance, which I really enjoyed. So it took a leap of faith. I was a finance major of college, always thought I was going to do finance, always had a real affinity of love for technology and said, how can I use technology to essentially create more efficiencies in finance? So it took a job as an entry-level business analyst at Capital IQ, which was probably one of the best career decisions I've ever made. And given the timing there, it was a very small company. So I was afforded the opportunity to work on some really neat and innovative projects and components of the application. S&P was a great organization, still is, in the sense that they gave us a lot of leadway to really innovate in this sector, and it was a really interesting ride. Fast forward from entry-level business analysts all the way to the head of product when I had left several years later, I decided to embark on sort of a new chapter of my career. The startup world of Capital IQ was a lot of fun, really interesting, and I wanted to challenge myself to the next level with a large organization that had a global footprint. So canvassing the market, I stumbled upon Blackstone. The thesis for coming to Blackstone was large alternative asset manager sits across almost all avenues of the economy globally. How do we take the data that Blackstone has, predominantly proprietary data, just given the nature of our business, and how do we apply that data in a way that we can make the most sense of it and be a creditor to our investment decisions? So at the core of my job here at Blackstone is really how do I look at this Blackstone data, apply it to a bunch of Euro-syncratic public data sources that we have, and how do we make really good investment decisions? Because at the end of the day, in the most simplistic manner, investing is really about pattern recognition and connecting the dots, and it all starts with data. So that was a really early and interesting thesis from leadership here at Blackstone when really at the time most alternative asset managers weren't thinking that way. And what year was this when you started? I came to Blackstone in 2011, so it's been a pretty substantial journey now. Data is paramount to what all shops do. AI is the hot buzzword now, but again, battle starts with data. If you don't have interesting and applicable data as the input, your output is just not going to be that, and that was our thesis here at Blackstone. How big is your team today? Blackstone's technology team is just north of a thousand individuals, so very sizable operation, globally dispersed, and it comprises of basically five buckets. We have an engineering bucket, that's your traditional software engineers, business analysts, and the likes. We have an infrastructure bucket, that's your servers, storage, DevOps, SRE. We have a cybersecurity would be the third bucket. Fourth bucket of individuals is quality assurance, and then finally a service desk. We keep sure our Blackstone professional has the tools and training to be as efficient as possible. Always given that surface area, what's a day in the life for you? A day in the life varies wildly. Core principle for me on a daily basis is just how do I empower Blackstone to be the best at what they do? How do I surface information to our investment professionals? How do I power them with new, either data or tools to make them more efficient? How do I assist our investment professionals in diligence on deals, specifically the technical elements of deals? And how do I continue to empower us to have a competitive advantage? At the end of the day, our competitive mode is the data. That's really what drives the business forward. We're not Coca-Cola, we don't have some magic formula to build a soft drink. It's really about intellectual capital IP. That's what drives Blackstone. Our understanding, the data advantage we have, and how do we bubble that up to the investment professionals who make those key decisions in the most efficient and accurate manner? How do you think about things that you want to acquire and keep an eye on internally versus potential investments that maybe some of your funds are making and bringing you in as the expert? It's the age old buy versus build question. The way that we think about buy versus build is pretty trivial. We tend to be buyers of software, now we do heavy customization on that, it does not just buy and keep it, it's buy and integrate it within our ecosystem, so through the evaluation process on those systems, we look at sensibility of said solution, what is potential lock-in risk for us, how does it apply in an overall Blackstone technical ecosystem? And then where we build solutions, it's usually for a number of factors, but boiled down simplistically. Does this exist in the marketplace today, and does it create a competitive advantage for Blackstone? Overall, I don't know the exact numbers, so don't quote me on it, but I would say it's somewhere like 90% buy, 10% build, and we're building for those particular reasons. We're not just a bunch of cowboys that want to just build everything, we want to be really efficient with our capital and treat it like ROI on what we're actually doing. What's your view on new technology? So you guys are trying to create leading-edge competition, how do you think about the opportunity and the risk of new technology that may be a little bit early for Blackstone? I think when it comes to new technologies, a couple of things. Stock and some of the controls, you have to go through that. When you look at it today, the buzzy technologies are some of these AI companies, and a lot of times they're just wrappers on a generic LLL. So the question is, what's the value of that? And I think I saw something the other day at a company cursor, which helps software engineers write code faster. It's effectively a wrapper on top of open AI. It's really good. Our engineers like it. Our engineers in general like it, but what is the lasting power of that? The next cool one that comes out, they're probably going to want to move to the next one. So when we are looking at these solutions, we really want to identify what's the moat and which solutions do we want to be our core solutions within our ecosystem. We don't want everybody just buying the new flashy toy and have this whole hodge podge technology environment because it brings a lot of maintenance across and it slows you down. So when we look at any new technology, we're certainly not opposed to new and modern technologies and smaller players. It's just really assessing what is the moat, what's the advantage, what do they bring to the table, and then how does that plug into our ecosystem because we're really methodical about that in the sense that if it doesn't plug into the overall ecosystem and we don't have more of that longer-term vision, it's probably not going to make a lot of sense. There's some exceptions at times, sure, but for the most part, that's just how we think of it. I'd love to zoom out and get the big picture. Just what's your sense of where we are today in the market with AI technology, especially with regard to the investment management space in particular? I think it's one of the most exciting kinds to be a technologist and technology gentleman right now, just giving AI what it's doing. My general view is this personal view is the next two years, it's probably going to underwell in terms of what some of the expectations are. Though I think over the next 10 years, I think it's going to overachieve what people are giving it credit for. As a little bit older than I guess most, we've seen a lot of cycles in the past. We've seen this with the internet. We've seen this with mobile. We've seen this with cloud, and in all of those particular cycles before it, it was always a lot of hype in the beginning, a lot of doubters. Maybe some of the early entrants didn't pan out, but they were real macro trends which changed basically how we live, how we work, et cetera. I don't think this one's going to be any different. Look at this as a platform that's going to allow us to grow and scale. We're going to do things differently. The factor I look at it there is, when you look at companies today and how they build their businesses and utilizing these tools to build their businesses faster, to scale cheaper, that usually tells you this is a new application layer. You're going to see that with the LLMs. On my view, they're going to move towards that application layer. They're going to be critical to what you're doing. Now, does that mean that the LLMs in the current form and fashion are what's going to win in the future? Maybe not. You're going to see the buzzword of 25 has been agents or a genetic workflow, and that's certainly what's coming. I think it's a little overhyped in terms of the reality today. I have a lot of folks that claim that. The hype's probably a little bit ahead of where reality is right now, but it's certainly coming. I think if you wait a bit too long, you're going to be on the wrong side of history. You need to be their first mover and try every brand new thing. No, but do you have to have a good knowledge of this and understand where technology is going? Could be prudent technology leader? Absolutely. How do you balance market awareness with an actual need? When to demo, when to just be curious, send me information and just managing expectations? I would say when it comes to the R&D element and understanding the market, the good news is our technologists here are super inquisitive, so they spend a lot of time understanding what's going on, because I think they're just intellectually interested in that. We're constantly looking at that. We have carved out ties to do R&D, but to be totally frank with you, Scott, we're really ROI-driven here. We look at these particular use cases. The question is, is generative AI the right use case for the job that we're looking to do? Again, it goes back to what is the right tool for the job? Generative AI is a wonderful technical solution, but it's not for everything. If you're looking for 10-point accurate numbers or investor documentation that requires a high fidelity of accuracy, today it's really not the right solution. If you're looking to ask questions and generate content, it is pretty good at that particular function when applied properly. Again, as a technologist, you really have to think about what is the problem we're trying to solve? What is the particular issues with it today, and can this make us more effective? When you think about your team and think about your journey, what makes a great technology higher today? There's no right or wrong answer, and there's no real clear answer on there. We like a lot of diversity in terms of the talent we have, in terms of what technical skills they have, backgrounds, etc. I don't like to really paint a picture. The thing I would say is, you see a lot of folks in the industry that are all specialists in things. I'm a data science specialist, I'm an AI specialist, I'm this, I'm that, etc. The reality is, building a technology team, let's look for great technologists. I think if you have a great technologist, they're going to learn these traits. If you're an AI expert and you really love AI, maybe you come to an institution like Blackstone, but more likely you're going to work at a research lab, because you want to be on that bleeding edge of AI, and vice versa. We look for the best athletes, I guess, is the analogy that you look for there, or a full-stack engineer, because I don't really believe in full specialization. There are certain functions where that's required and we have that, but for the most part we look for really good technologists, because those folks are hungry, they want to learn the new skill sets, and with the tools out there, the large hyperscalers, and the training, etc., like focusing out the speed really quickly, identifying the use cases. That's where our technologists are wonderful with technical skills, but they understand the business case as well, not just simply being great at understanding LLM or the intricacies of that. You have to understand how that works, how that ties to solving the business case from soup to nuts in terms of that workflow. I want to get your thoughts on just key trends, automation, the process improvements. I mean, there's a lot of activity in the digitization of the fund operating platforms. What we're doing is we're looking at the use cases for which the technology works well today. Let's say something like a DDQ. Folks ask questions in various forms. We also have different document types when they're asking those questions, but they're essentially asking 80 or 90 percent the same question. Why does that come in? We have an information base that's approved by our legal team that's informed by our HR team, our finance team, our valuation teams for said answers. How do we need people to go and email folks around there, come into our said solution, drop the document. AI will parse the question and will give you the answer from a approved database. It'll go back to the user so they can go and review it because they always have a human in the loop there and go out and that just saves an astronomical amount of time. That's just a great use case. Again, when you look at AI today, there's two or three good use cases. There's a lot of conversational use cases which folks are familiar with with your chat GPT, the world of perplexities, deep-seek, ask the particular question, get your answer to that question. Then there's the document base. How do I carry the one to many documents? How do I use a DDQ document? You just be a concept. How do I translate documents, ask questions, how do I get a deal room and quickly understand what's going on in this deal room so that I can process it faster. And what we're doing is really attacking the use cases where the technology makes a lot of sense. Everything from beginning to end right now doesn't really exist. It's not creating some of the Excel modellate, though it's getting better there, and it's not creating a lot of high-fidelity financial data-driven metrics right there. It'll continue to improve without a doubt if folks are building solutions on top of that, but that's just not where it's the strongest today. Is there anything in particular you're excited about? I'm excited about a lot of things, but I think as it relates to fund operations, I think as the agentic frameworks become more and more advanced, I think it adds a lot of value into our processes, specifically some of the more commoditized roles. If you could put particular agents and build a web of numerous agents, which are just effectively steps in a workflow there, you can cut out a lot of time on those commoditized roles and have our valuable employees working on a more high-value task. And I think that's where the future is going. We're spending a decent amount of time on that, where that's predominantly middle and back office functions. Additionally, on the front office functions, again, it's how do you make investment professionals more efficient? How do I do my research tests in a more efficient manner? On the sales side, specifically in this industry, as it's moving more to the retail channel, how do we empower sales professionals to get a greater understanding of the client, of the relationship there, too, of sales metrics to client appetite for said product, et cetera, all in real time? I think there's a lot of interesting things coming down the pipe. It's interesting because it's one of those things where everyone talks about replacement, but I think it's really enhancement and enrichment of root processes to make everyone a little bit better with time. I think that's right. Maybe replacement is the long-term thing. My general view is it's more of a co-pilot to help folks be more efficient, at least in the mid to short term. What about selection? I want to focus more on how you guys think about really selection of technology and adoption. Are there any elements of success that you've seen? I think when it comes to selection, we're pretty selective. We don't want to have a ton of tools in the toolbox. We want to keep it as user-friendly and easy as possible because that's how you drive adoption in there. Obviously, if you're building an accounting system, there's going to be a lot more complexities, but users are used to that. But when you're rolling out AI today, it's about what it's good for. We have what we call BXAI here, which allows folks to go and run those functions with a really simple interface or tailored how our business users work, both on their desktops, the mobile devices, et cetera. When it comes to point solutions, it's really about what is specific with those point solutions to make them more valuable. How do you build consensus with selecting of technology? We're highly centralized here at Blackstone. We have so much to select a piece of technology in your full process where it goes to our ARRB or our Architectural Review Board right there. It goes through PAC, a product advisory committee, so that we get it here. To your prior point about solutions, the one challenge we have, versus most, and the reason we built this whole underlying ecosystem, was we're a regulated industry. We're a financial institution, so we've got to think about things that maybe others don't in terms of permission, in terms of set up times, in terms of data loss prevention. Et cetera. When it comes to AI or any solution, it's no different than buying a regular SaaS platform. The same level controls are 100% required before we roll that out to our users and our ecosystem. I want to turn now to really the implementation. Any advice that you would give to maybe a smaller organization and they're implementing it and how do you avoid the uncommon pitfalls? You sort of hit on this earlier in the conversation. Everybody wants to just try a new solution as whatever the new flavor of the month is or business user heard from their friend that this thing's going to solve X and Y. I think as a technology, you have to have discipline. You have to follow your process. No different than any other software solution. To give you some context, what we did here at Blackstone, specifically, I have a generative AI part. We probably started that about two and a half years ago. The first thing we built at Blackstone is our SDK, our software development kit, basically our base layer. Those are foundational services for our engineers to go and build on top of. Think of things like permissions, monitoring, the vectorization of data, the orchestration of data, et cetera. Our thesis when we tackled the how do we deploy AI or generate AI and Blackstone was simple. Instead of buying a bunch of solutions, how do we empower our thousand plus technologists to build the solutions that our business users are asking for? Instead of them having to build all that base layer functionally every time, we'll go and build that for them. When we build foundational services like a chat service that they can go call and build applications on top of, so think of, "Here's a bunch of board books. Let me go and summarize those particular board books." A relationship service or like a graph database that they can build on top of. Who knows who, who knows which company. Then we built controls. If you were a third party, these are the controls and the vendor onboarding process you have to do to make sure that it has a legal element of it, the data loss and cybersecurity element, et cetera. What we also do with some of our tools to add value to our portfolio companies, we intersource it so they can basically have stripped out of data. They could have most particular tools to get them up to speed faster. We spent a lot of time in the beginning coming up with this ecosystem there. How do we want to take this forward? We knew this was going to be a big mega trend. We knew this was going to be a major technical shift and our view is spend the time up front, do the blocking and tackling work. No different than if you migrated to the cloud and the likes of the past to really set us up on a good foundation to move fast, longer term. So I think sometimes what folks do is they want to just move so fast and they do a bunch of quick solutions. Then what happens is you end up tripping yourself up and making a big old spaghetti mess of your technical ecosystem, whereas you have some poor thought in the beginning, you could do that. Now, if you're a firm that's not quite as big as Blackstone, the hyperscalers have plenty of solutions for you, vis-a-vis a Microsoft stack, vis-a-vis Amazon's bedrock stack to build a lot of those components that I was talking about there for a cost. And I would really look for folks to really think about it, think of it holistically versus try to chase the next shiny object. What about process and roles and responsibilities? Do you retool who does what whenever you're onboarding something new and when tearing apart and implementing, enhancing that tech? I'd say for the most part I follow the standard technology process. Why are we retooling it? It's the ROI, runs through our ARBs of tax and base teams to make sure that it integrates properly in our ecosystem, runs through all of our onboarding and procurement processes, runs through our legal compliance team to make sure it hits all those particular checks. We have full guidelines of the firm that we spent a bunch of time writing to ensure that we're right there, technical perspective, from a legal perspective, a GAI perspective, et cetera. And while it sounds heavy, it sounds like quote-unquote big company process, it actually makes it much more efficient in the long run. And it moves pretty quick where you run these particular tracks, allows you to move fast without making mistakes. What's your view on data extraction, PDF, still rules in the fun world? Are we ever going to crack the code on that? At the end of the day, everything comes down to data. And getting your data canonically modeled in the proper manner is really the competitive advantage for almost any organization. Now doing that is the hardest yard. Everybody say, "Hey, I can go buy this whole tool that's just going to extract everything. I know this perfect data." Well, if that was the case, everybody would do it. I think we all know that's not the case. It's hard yards and you don't necessarily see the results right away, but lower term you do. This is something that we spent a considerable amount of time at Blackstone. This is by core competency, from a career perspective, bringing the data element here from Capital IQ, then bringing that data element here to Blackstone is just crucial. If you have that data right, you have it standardized. You can make sense of the data using your decision-making process. Here in a really good competitive advantage relative to most. Now, to do that, it's very expensive, very timely, requires folks with the proper skill set. But if done right, I think it's materially changes your business. I always give the example of Amazon, when they were collecting a ton of data back in the day, folks would say, "Well, why are they spending so much money doing that? What's the quote unquote ROI?" And then you see what they become because they'd have that data. They train on that data. They use it properly. Very similar to what we do as we're collecting data from our portfolio companies, we're learning historical trends, we're seeing things faster than others, and we're able to spot trends quicker. And then action upon that, just give it the data if it's modeled in the right way. But the converse there is garbage in garbage out. If your data is not in the right spot, you're going to get improper signal right there. Might not be in the best footing. The example I give to most here at Blackstone, major bet we made several years ago was buying a bunch of industrial warehouses. At the time, if you just looked at data and the real estate space, you would have saw that retail malls looked very, very cheap. Historical trends, if you looked at it on your Excel sheet, conventional wisdom would say, "Hey, it's time to buy it now as a dip here. Any time it's ever dipped like that in the past, it always bouts back up." We looked at our data and said, "We don't believe so." If you look at this e-commerce trend, if you believe it, which we did, then there's no way it's going to bounce back up, so why not start buying industrial warehouses and then more data showed us that last mile industrial warehouses are the most profitable ones? Why don't we just make a big bet there because that's what the data was needing us to versus buying retail malls. It's just a cost of capital, a dollar in, where you're going to get the best return dollar out. That made a lot of sense for us because of the data platform that we had built to identify that trend and very similar to several years back when we started making data center investments. We started seeing the particular trend, the digitization. Even before AI would be content creation, et cetera, this just made a lot of sense as to where the world is going based on data, and again, not your traditional real estate din. The whole data platform that we built here at Blackstone, both our internal and idiosyncratic data sources were telling us this is what you should be thinking about potentially deploying capital and then our investment professionals did the rest on top of that. Then from there, where do you go with power and how do you supply the power to the data centers and all these knock-on effects? That's where the data maps that whole ecosystem. Once you have that thesis and you have conviction on that, which are the used to describe there is what our investment teams do. How do you hit all components of that? What's the proper way of doing? I want to turn to budgeting, at least on the smaller end of the spectrum. I think a lot of organizations look at their spend as my budget is what I spent last year. How do you balance running the firm versus actually pushing out process improvement? We use zero-based budgeting every. We start bottom-up first, so what is the absolute minimal to run the firm? Software licenses, the basics that you need right there, and then what are the particular projects that we have in correspondence with our business users? At the end of the day, they're the ones paying for the technology budget. What is it there? What fell off from years past? We really focus on fall-off because then we can recycle those dollars into the newer projects there. That means sunsetting something that you had previously? That means sunsetting. That means there was a one-time build-up cost there, and too often what you see in technology teams is they do a particular project, there's this one-time cost, and it's not really a one-time cost. It's a recurring cost. A lot of times, when you look at it, it's because folks haven't went back and really examined that. When we do a project, if we say it's a one-time cost, we look, okay, we'll do this one-time implementation fee, and how are we going to get those dollars back, or how are we going to take those resources, typically your engineering resources, and move them on to another project when the mission is done? The other thing we do, we look across our organization, is what are the big projects we want to get done, and let's make sure we have heavy focus and heavy talent attributed to those projects. When you see the drag, typically in most organizations, if someone works on a project, and they're the workday expert for the next 10 years, maybe that's right, and you need some folks to manage it, but should your top talent just be continuing doing maintenance work on a particular solution like that? I would say no. That's not what we do here. We'd rather move that top talent to the next largest project right there and really think holistically. That's where I think the zero-base budgeting comes into play, because it really calls on everything into question. Is it safe to say that these decisions are actually front and center at the top level of the C-suite? 100%. Effectively, how the technology budgeting process works here. For my particular role, I have three of the four major business units here at Blackstone. I have those conversations with the senior most folks in those groups, typically the COO and the heads of those groups talking about, "Here's your technology budget. Here are the asks from the various verticals with your group. Let's take the real estate group. Here's what the folks acquisitions have been asking for. Here's what the folks in asset management, portfolio management, finance, legal compliance. We frame that out. We talk about directly what the budget is there. Those projects in the most simplistic manner are broken down into the must do, should do, nice to do, so we can calibrate right there. Then we come up with a particular budget. Once that's done, myself and my fellow colleagues will go up to the higher level. Our boss, John Stecker, will show them that budget and we'll ultimately have that conversation with Michael Chair of CFO, who's managing the budget across Blackstone. We'll have some hard conversations in terms of scoping and scale right there. Because at the end of the day, we're at an investment shop and we treat it like an investment here. Certain dollars out there. What are those dollars getting you in terms of new innovative features, risk reduction, compliance regulatory features, competitive advantage, et cetera? It's just the math equation to balance all that out. People have different needs. Maybe the real estate group has a very single specific need, but there might be something else that might be a little bit more generic that other people can cross leverage. How do you balance those two pieces? Typically when someone asks us for something, they'll go the route you say, "Hey, I want this particular tool or solution." What we quickly do, we say, "Okay, that's great. What problem are you trying to solve?" We really want to understand the problem that's for what they're trying to solve. Once we understand what the problem is, we'll say, "Here's what we have right now in our toolbox." For the few today, it might get you eight of the 10 requirements you have there, but you get it. I would say it's free, but it's generally preconcerty built. You need this custom solution, and let's have a conversation, and there are some times you need to go that extra mile. Other times we have it in the particular tool kit. Other times we don't have it in our toolkit, but it's an interesting thing, and we've heard it tangentially in other businesses, so we go and deploy it in a way that other business groups can go and get the value from that. It's really a risk-based decision there, and actually everybody wants the greatest and shiniest tool, but it's really understanding, "Is that really worth it from an ROI perspective?" I liken it to, "Sure, I could take a Ferrari and drive it to the grocery store, it'd be cool, it could be fast, it could be great. Is that the most efficient for that?" From a cost perspective, from a practicality perspective, same kind of concept right here. What fundamentally is the issue that you're trying to solve? Oftentimes, we also see, as we're going through that process, they'll tell you the particular problem, but you could probably solve 10 other problems right there when you understand that workflow. To leave you on one note, I always liken it, we asked that to the old Henry Ford quote, we said, "If I asked my clients what they want, they'd say a faster horse." People are very good at giving you a problem. Typically, when it comes to technical solutions, business users, they're not great at that. That's our job to educate them on that and then have a collaborative conversation to come to the right solution. Thinking about really managing expectations and working with upper management, non-technical leaders. That's what people don't have a fuller appreciation of the Lyft involved. When it comes to conversations with senior leaders, I think where most technologists fail is they get too technical to quit. I guess maybe like a superpower, good technical leaders is being super technical with your team to make sure you make the right technical decisions, but when having conversations with business slash non-technical users, distilling these complex terms into a more digestible matter. In a more plain English matter, here's what it is. Here's why this makes a lot of sense. Here's what it is at its core and why it works. If you run into particular issues that you're describing there where we tend to see that or I've seen that in the past is when there's not full buy it. When we're doing a technology project, it's not the technology team goes and delivers this project. The group is going to deliver the project jointly. Let's say, for example, we do a finance project to automate X. My team has to go and build the technical solution and deliver that. The finance team typically has to change their workflow, their business process in conjunction with the new piece of technology. If both teams aren't doing that, there's joint accountability there, we're either going to succeed or fail as a team. I could build the greatest piece of technology, but if they're not going to change their process and use it, it's a complete waste. We have a dive adoption and in the beginning, we talk about what is our KPI in terms of if we go and deliver this, what is the ROI that the end users are going to get, et cetera. I really do think when you're talking to the business users, you need to do that and effectively how I do it here is in a particular business group, we have a steering committee, which is typically comprised of four or five of the most senior people in the business group. They're not making all the decisions. That's a quarterly basis where we have that conversation. On a monthly basis, we have leads per vertical, so we'll stick on the real-estate example. Someone in asset management, someone in portfolio management, finance legal, we'll bubble all the ass that folks told them in those verticals. Those group heads will say, "Okay, yeah, this is the most important thing." They'll have a steering committee where we'll go and go across the board. I think that might be number one illegal, might be number four on the overall. There might be two acquisition ones that make more sense, and then we'll have a fruitful conversation. We have limited resources, limited dollars. We could scale up when necessary, but we have to make scoping decisions. What I've always found is this five or so big things that really matter, a bunch of little things you're going to do here and there. What are the real things that are going to move the business? When you really focus on that and you're excellent at that, where it's getting spread really, really wide, you're going to deliver a lot more value. I can always just do another little feature that makes an accountant 2% more effective or so-and-so wants this little thing for their workflow. Great, I can do that, but engineering resources are valuable resources. They're a scarcity. When we think about what are the big things that are going to move the firm, that's what you want to focus on. It's not too different than Blackstone's core business. We can invest in a lot of things, but what are the big macro bets we should be making? That's how we've been as successful as we have over the years. Same kind of principle when it comes to technology investing. Does that force you to think about where we're going over the next year, maybe two years, that near term to push out your need is X, but really what you need is X plus two, because that's where we need to spend? Always anytime they add that, it's always there because too often, sometimes folks are just too narrow minded and they're saying, "Okay, you do this product great," but probably the other business groups are going to do that too. Think of example at Blackstone, be perpetual capital vehicles. It just made me think about innovation and you have innovation, you have collaboration. How do you think about is this something that you do on your own initially and then surface in ID8 with others, or does it really come bottom up from a larger group? No, it's come bottom up. Our ethos, when it comes to innovation, everybody should be innovating. We have a lot of times and forums for folks to do quick POCs on things that they want to do. Those POCs are not going to be brought to a production environment. They'll be shown and then if we think there's merit in interest there, we'll go ahead and we'll put the right resources to coordinate with the enterprise grade functions that I talked about earlier. I'm not a fan of having an innovation team or whatnot, because I think if you do that, you basically don't have a culture that promotes innovation here, but it doesn't matter if you're the most senior individual or most junior individual. We want to promote everybody to be innovative because that's where the best ideas come. Not to keep going back to the Blackstone values and ethos, that's just how our investment process works. If you're a senior or junior, bring that particular idea. We'll have fruitful debate on it and we'll make a decision. The same thing applies to technology. Somewhat sort of neat solution or there's a new technology or they want to do a lunch and learn with the broader technology team to explain, here's a new technology coming out here, so you should potentially think about it and how it could potentially impact what you're doing. Why not let them come in and go ahead and do that? I think you have to have a culture that rewards that. Sure, when it gets to that production state, you got to go through all those checks, but when you're that ideation state, allow them to go and bring up their thoughts, they might be dead wrong, which is totally fine, but at least they had the foresight to go and do that and you have to have a culture that rewards that. Folks should think outside the box that they bring it up and then to do it right, my view, we seem to have a culture like ours that has a lot of gates before something gets a production, so you don't make mistakes. It makes a much better organization, it allows the best of both worlds. This is more of a cyber question, but what's your view on quantum computing and quantum encryption? We get that question a lot. If and when quantum is mainstream, it's going to break current encryption. How are you thinking about that Blackstone? The way I think of it, my view is the cyber providers are going to get on top of that as well. Now, it's a big, big topic right there, it's something we look at quite often. It's not there today. Is it going to get there in the future? I don't know. If I was a betting man, I would say yes, and then it's just going to require a cycle to go and re-upgrade. The faster that the hackers get it, it's going to require us all to upgrade, but there's a whole OS and ecosystem for quantum that needs to be in place for all that to occur. It's definitely something that's top of mind on our radar. We have a lot of it indexed and mapped out in terms of our ecosystem should that occur, but really getting and applying solutions and whatnot today, less select cases probably doesn't make a ton of sense because it's not there yet. It's been 30 years in the making or something like maybe longer, but maybe no different than converting to mobile. It's just another version of it. That's how I look at it. Whenever the next threat is, it's a chicken and egg game with threats and we're pretty robust in terms of how we think of it. I'd say a lot of things that we do, you would say are on the bleeding edge and Adam Fletcher and the team there, I mean, they do a phenomenal job, but you have to think of these things that are coming in the future. There's probably 20 others like that that we have top of mind that you can look at, are you 100% protected today for where to occur tomorrow? Probably not. But can you adapt pretty quickly? Do you really understand your state and are there other ways to get around it? Yes. Well, John, this has been really insightful. I'd love to turn with two closing questions and one is what advice would you give to an emerging manager from a technological perspective? If I say it's an emerging manager from a technological perspective, it's really understand what you want to achieve from technology. Too often, folks, they don't really have a strategy and where you're getting a decent leader in place, have a real tech strategy there. Do you have to spend as many dollars and have the complexity of Blackstone? No, but it's the size of the organization in our view that warrants it. But if I was at a smaller manager, you could apply a lot of the things that we do pretty simply. And instead of getting a bunch of point solutions that you talked about earlier, I think utilizing some of the larger players and have a holistic view of how that ties together, you could do it in a real cost-effective manner and getting nearly the same level of scale if the right thoughts there. I think what happens too often is these emerging managers, they view technology as core IT, i.e., the help desk person that makes sure your email works and your Excel works, etc. That's not the individual that's going to have a broad technology strategy as it relates to data, AI, analytics, etc. And it's an ROI decision. Should you spend a couple of dollars more and get the right leader? My view is it pays dividends and spades. Now that might be a biased view as a CTO, but I see it time and time again. I looked at small managers that tend to do quite well and the technologists that run that are pretty impressive individuals. So that would be my view for them, twofold. Get the right leader. And then two, really think about your overall architecture and ecosystem. You don't have to spend a ton of money on a ton of disparate solutions. Start with the big base. Figure out if you get good enough for there and then spend your extra dollars on really things that are going to move the needle as opposed to slight nice behaves because you're just going to be having a lot of dollar drag there. Rico, and the other question I have is what book, article, other resource you commonly refer to people? I read quite a bit where the advice that I have for folks is read disparate sources. I like a lot of technical sources, which I think would for most financial professionals to death. But I like to read a lot of sources and listen to a decent amount of podcasts, but I also like to talk to a bunch of folks. And my train of thought might just be like the engineering train of thought is really try to get down to the source and understand what's real or what's not. As I've gotten older, I understand when it comes to technology or anything for that matter is the mass of sales components to a lot of things, which is great. But really understand, okay, what is it actually solving? What's there today? What's the aspirational element of it? Where's the world going? Because I mean, it's a fascinating time. Fitz, thanks for the time. This has been really fun and insightful and look forward to staying in touch. Thanks, Scott. Pleasure. Thanks for listening to the show. If you like what you heard, hop on our website at CapitalAllocators.com, where you can access past shows, join our mailing list, and sign up for premium content. Have a good one and see you next time.