AI Agents Cut 3-Day Portfolio Builds to Minutes — Jack McDonald, Apollo & Michael Manapat, Rowspace

Glad to be here. I am Glad to be here. I am Michael Manapat. I am the Michael Manapat. I am the Michael Manapat. I am the co-founder of Rose Base. co-founder of Rose Base. co-founder of Rose Base. Rose Base is an agent-based Rose Base is an agent-based Rose Base is an agent-based platform that platform that platform that uses AI to uses AI to uses AI to help financial help financial help financial partners like Apollo partners like Apollo partners like Apollo work with work with work with chaotic and chaotic and chaotic and fragmented fragmented fragmented data. We essentially data. We essentially data. We essentially use use use agents to do agents to do agents to do data engineering, data engineering, data engineering, direct information, and direct information, and direct information, and reconcile data from reconcile data from reconcile data from different systems so that different systems so that different systems so that portfolio managers portfolio managers , traders, and others , traders, and others , traders, and others can make can make can make decisions faster and more decisions faster and more decisions faster and more accurately. With me is Jack accurately. With me is Jack accurately. With me is Jack McDonald, McDonald, McDonald, COO of COO of COO of Redding Ridge, one of Redding Ridge, one of Apollo's lending divisions. Apollo's lending divisions. Jack, to start, Jack, to start, Jack, to start, tell us a little about tell us a little about tell us a little about yourself and what yourself and what yourself and what your your your business does. business does. business does. Thank you, Michael. I'm Jack Thank you, Michael. I'm Jack Thank you, Michael. I'm Jack McDonald. I am a McDonald. I am a McDonald. I am a director at Apollo and the director at Apollo and the director at Apollo and the COO of COO of COO of Redding Ridge Capital—our Redding Ridge Capital—our structured structured credit and CLO manager, which is part of credit and CLO manager, which is part of Apollo. We are a separate, Apollo. We are a separate, fully independent fully independent fully independent operating operating operating company that company that company that manages assets of manages assets of manages assets of approximately 50 approximately 50 approximately 50 billion. And we work billion. And we work billion. And we work across the entire across the entire across the entire structured structured structured lending ecosystem, lending ecosystem, lending ecosystem, primarily with CLOs. I have been with Apollo primarily with CLOs. I have been with Apollo primarily with CLOs. I have been with Apollo since 2020. During this time, I since 2020. During this time, I since 2020. During this time, I have changed several roles: have changed several roles: have changed several roles: worked in the worked in the worked in the financial institutions group, the financial institutions group, the asset management group, and now in asset management group, and now in Redding. So yeah, glad to Redding. So yeah, glad to Redding. So yeah, glad to be here.

be here. be here. Perfectly. We have noticed, Perfectly. We have noticed, Perfectly. We have noticed, speaking with speaking with speaking with many financial many financial many financial firms, that there is a firms, that there is a firms, that there is a significant gap between significant gap between significant gap between when AI is when AI is when AI is just a nice just a nice just a nice addition and addition and addition and when it becomes when it becomes when it becomes critical critical critical to the business. I think in the to the business. I think in the to the business. I think in the case of Apollo, we case of Apollo, we case of Apollo, we see that AI can see that AI can see that AI can really really really transform the way a transform the way a transform the way a firm works. I would like to firm works. I would like to firm works. I would like to hear examples hear examples hear examples of what of what of what your unit did, what your unit did, what your unit did, what made this work made this work made this work difficult, and what you are difficult, and what you are difficult, and what you are now seeing thanks to now seeing thanks to now seeing thanks to AI. AI. Yes. At Apollo and specifically at Yes. At Apollo and specifically at Redding Ridge, we manage Redding Ridge, we manage Redding Ridge, we manage many different many different many different funds and funds and funds and investments. We investments. We investments. We get get get thousands and thousands of thousands and thousands of thousands and thousands of data points every day on all of our data points every day on all of our data points every day on all of our investments. We investments. We investments. We manage probably manage probably manage probably a thousand loan pools and a thousand loan pools and a thousand loan pools and have loans in have loans in have loans in various businesses that various businesses that various businesses that we evaluate and invest in every day we evaluate and invest in every day we evaluate and invest in every day . . CLO management is a CLO management is a very active process very active process very active process carried out in the carried out in the carried out in the syndicated syndicated syndicated loan market. Therefore, having loan market. Therefore, having loan market. Therefore, having all this information at all this information at all this information at hand is very, very hand is very, very hand is very, very important. So at Apollo, and important. So at Apollo, and important. So at Apollo, and in particular at Reading, we are in particular at Reading, we are in particular at Reading, we are trying to trying to trying to improve and improve and improve and ensure the accuracy ensure the accuracy ensure the accuracy and consistency of all and consistency of all and consistency of all our data so that it our data so that it our data so that it is in a format is in a format that we can that we can that we can use, and that use, and that use, and that makes us makes us makes us much more much more much more practical and practical and practical and informed in our informed in our informed in our decision-making in a decision-making in a decision-making in a fast-paced fast-paced fast-paced environment. All of this environment. All of this environment. All of this information is information is information is private and, let's say

private and, let's say private and, let's say , not very widely , not very widely , not very widely represented on the represented on the represented on the market, unlike market, unlike market, unlike trading in public trading in public trading in public stocks or stocks or stocks or government government government bonds. It is bonds. It is bonds. It is very fragmented very fragmented very fragmented and contained in different and contained in different and contained in different documents. So we documents. So we documents. So we approached approached approached companies like Rose Rocket companies like Rose Rocket companies like Rose Rocket to partner with us, to partner with us, bring them on our bring them on our team to team to team to understand the calculations understand the calculations understand the calculations and the information that and the information that and the information that we need, and what we need, and what we need, and what really is our really is our really is our primary source of primary source of primary source of truth, and then truth, and then truth, and then continue to continue to continue to refine the AI refine the AI engines to understand engines to understand engines to understand and reproduce that, and reproduce that, and reproduce that, which makes us much more which makes us much more which makes us much more efficient. efficient. I think we have a I think we have a lot of engineers here, and you and lot of engineers here, and you and lot of engineers here, and you and I have talked a lot I have talked a lot I have talked a lot about the stress about the stress tests that tests that tests that the company conducts, which used to the company conducts, which used to the company conducts, which used to take the team take the team take the team a week, if not a week, if not a week, if not more. Can you more. Can you more. Can you give a specific give a specific give a specific example of what example of what example of what used to be a painful, used to be a painful, used to be a painful, time-consuming, and difficult time-consuming, and difficult time-consuming, and difficult task that has now task that has now task that has now become much become much become much easier thanks to Rose easier thanks to Rose easier thanks to Rose Rocket and AI? Rocket and AI? Rocket and AI? Yes, with pleasure. I Yes, with pleasure. I Yes, with pleasure. I will give two examples that will give two examples that will give two examples that I like I like I like to refer to. The first to refer to. The first to refer to. The first concerns the concerns the concerns the portfolio formation process.

portfolio formation process. portfolio formation process. So, CLOs ( So, CLOs ( So, CLOs ( collateralized loan obligations) have collateralized loan obligations) have collateralized loan obligations) have very specific very specific very specific terms. They have terms. They have terms. They have many rules and are many rules and are many rules and are based on them. There based on them. There based on them. There is a very specific " is a very specific " waterfall" of waterfall" of waterfall" of cash flow distribution. All of cash flow distribution. All of cash flow distribution. All of this is contained in this is contained in this is contained in deep legal deep legal deep legal documents and documents and documents and many different many different many different agreements. From the agreements. From the agreements. From the portfolio portfolio portfolio manager's perspective, when manager's perspective, when manager's perspective, when building a portfolio, one building a portfolio, one building a portfolio, one needs to process needs to process needs to process these documents, these documents, these documents, create models in Excel, create models in Excel, create models in Excel, and build test and build test and build test portfolios that portfolios that portfolios that include 200 to 400 include 200 to 400 include 200 to 400 individual credit individual credit individual credit instruments, which instruments, which instruments, which typically took the typically took the typically took the manager, before manager, before manager, before Rose Rocket, Rose Rocket, Rose Rocket, two to three two to three two to three days of manual work in days of manual work in days of manual work in Excel. Today, we Excel. Today, we Excel. Today, we can get the can get the can get the most up-to-date most up-to-date most up-to-date information in information in information in real time and real time and real time and build a portfolio that build a portfolio that build a portfolio that used to take used to take used to take days in days in days in minutes. This allows minutes. This allows minutes. This allows portfolio portfolio portfolio managers managers managers to prepare the foundation to prepare the foundation to prepare the foundation for 80-90% and then for 80-90% and then for 80-90% and then apply their own apply their own apply their own judgment and experience judgment and experience judgment and experience to refine that to refine that to refine that last 10% and give last 10% and give last 10% and give the portfolio a finished the portfolio a finished the portfolio a finished look. This is one of the look. This is one of the look. This is one of the most obvious most obvious most obvious examples. It's kind of a examples. It's kind of a examples. It's kind of a collection of different forms of collection of different forms of collection of different forms of data and, you know, data and, you know, data and, you know, information from information from information from many different many different many different sources, whether it's sources, whether it's document libraries, whether it's document libraries, whether it's our our our trading and trading and trading and compliance systems, or compliance systems, or real-time pricing, and so on.

Another example Another example I would point out I would point out I would point out is our is our is our warehouse warehouse warehouse purchasing purchasing purchasing activities. So, our activities. So, our activities. So, our business operates on business operates on business operates on bank bank bank financing, and we financing, and we financing, and we can can can go to the market at any time go to the market at any time go to the market at any time and buy loans in the and buy loans in the fixed income market. Previously, we would do a weekly Previously, we would do a weekly reconciliation of reconciliation of reconciliation of transactions and trading transactions and trading transactions and trading activity to activity to activity to summarize: here are summarize: here are summarize: here are your purchases and your purchases and your purchases and sales for the week. sales for the week. sales for the week. This is what we bought, this is what we This is what we bought, this is what we This is what we bought, this is what we sold, and it sold, and it sold, and it was all very manual. We was all very manual. We was all very manual. We literally went into literally went into literally went into different bank different bank different bank accounts to accounts to accounts to figure out figure out figure out where the where the where the payments were made payments were made , and there was a separate , and there was a separate , and there was a separate team that would summarize team that would summarize team that would summarize that data and then that data and then that data and then email a report to email a report to management with the management with the results. Today, results. Today, results. Today, we can take that we can take that we can take that same report and same report and same report and upload it to upload it to upload it to Rosebud for visualization Rosebud for visualization . This report . This report . This report is created and can now be is created and can now be repeated in a repeated in a matter of minutes. Now it's matter of minutes. Now it's matter of minutes. Now it's all all all automated, and from automated, and from automated, and from our team's side, we our team's side, we our team's side, we just do just do just do quality control and quality control and quality control and check it every day check it every day check it every day to make sure to make sure to make sure everything looks everything looks everything looks correct. This has become a correct. This has become a correct. This has become a much simpler and more much simpler and more much simpler and more routine process routine process routine process that previously required two or three that previously required two or three that previously required two or three people on our team to people on our team to people on our team to spend many spend many spend many hours each week hours each week hours each week putting it all together.

putting it all together. putting it all together. Yes, I like Yes, I like Yes, I like these examples. You these examples. You these examples. You know, you don't just know, you don't just know, you don't just read the read the read the chatbot's responses for chatbot's responses for chatbot's responses for information. I think information. I think your team your team your team makes trading makes trading makes trading decisions, decisions, decisions, portfolio management decisions portfolio management decisions . Therefore, accuracy and . Therefore, accuracy and . Therefore, accuracy and quality must be quality must be quality must be high. This is a high. This is a high. This is a two- two- two- part question. I think a lot of part question. I think a lot of part question. I think a lot of people are asking us people are asking us people are asking us or in general, "Why or in general, "Why or in general, "Why not just not just not just upload this to Claude upload this to Claude upload this to Claude or ChatGPT and see or ChatGPT and see or ChatGPT and see what happens?" And for what happens?" And for what happens?" And for someone in your someone in your someone in your shoes, how does your shoes, how does your shoes, how does your team gain team gain team gain confidence in a confidence in a confidence in a product like Rosebud or product like Rosebud or product like Rosebud or other AI tools other AI tools other AI tools to say, "We're to say, "We're to say, "We're going to make going to make going to make business decisions on this, business decisions on this, business decisions on this, execute transactions execute transactions , build portfolios, , build portfolios, , build portfolios, etc." How do you etc." How do you etc." How do you achieve this achieve this achieve this level of trust? level of trust? level of trust? Yes, I would say that Claude Yes, I would say that Claude Yes, I would say that Claude and OpenAI-like and OpenAI-like and OpenAI-like products like ChatGPT, all products like ChatGPT, all products like ChatGPT, all these big language these big language these big language models are very useful, models are very useful, models are very useful, but they work on but they work on but they work on general general general information, not on information, not on information, not on your own your own your own actual data. So actual data. So , it's great for , it's great for , it's great for research, research, research, generating new generating new generating new ideas, or quickly ideas, or quickly ideas, or quickly mastering a particular mastering a particular mastering a particular product or product or product or investment. What makes investment. What makes investment. What makes Rose Rocket unique and Rose Rocket unique and Rose Rocket unique and the approach we took the approach we took the approach we took to developing AI in Reading to developing AI in Reading is owning our is owning our is owning our own data and own data and own data and having a platform having a platform having a platform and technology and technology and technology that that that we are confident in. The we are confident in. The we are confident in. The Rose Rocket team, unlike Rose Rocket team, unlike Rose Rocket team, unlike other technology other technology other technology companies we have companies we have companies we have worked with, are worked with, are worked with, are specialists who work specialists who work specialists who work directly "in the directly "in the directly "in the field." Michael

field." Michael field." Michael works with our works with our works with our team every day, studying and team every day, studying and team every day, studying and understanding our understanding our understanding our information flows, information flows, information flows, because for one because for one because for one piece of data there can piece of data there can piece of data there can be 20 sources, but be 20 sources, but be 20 sources, but only one is correct only one is correct only one is correct . The Rose Rocket team . The Rose Rocket team . The Rose Rocket team spends time spends time spends time working with our working with our portfolio management and finance teams portfolio management and finance teams to understand to understand to understand the calculations, the calculations, the calculations, determine what data is determine what data is determine what data is correct, correct, correct, filter out other filter out other filter out other sources, and bring it sources, and bring it sources, and bring it all together. all together. Thanks to this, we Thanks to this, we can trust can trust can trust the system and be the system and be the system and be confident that our confident that our confident that our team has verified team has verified team has verified the accuracy of this the accuracy of this the accuracy of this information. We can information. We can information. We can rely on it, rely on it, rely on it, whether whether whether it comes from it comes from it comes from our our trading trading order management system, order management system, performance measurement system, performance measurement system, Bloomberg, or any Bloomberg, or any Bloomberg, or any price index, knowing price index, knowing price index, knowing that it is accurate. Whereas that it is accurate. Whereas that it is accurate. Whereas in many other in many other in many other systems, even in systems, even in systems, even in our company, everything is our company, everything is our company, everything is very fragmented: very fragmented: very fragmented: we have to we have to we have to manually move manually move manually move data from one place to data from one place to data from one place to another, Rose another, Rose another, Rose Rocket now Rocket now Rocket now automates all of this automates all of this automates all of this for us.

for us. Glad to hear that. Glad to hear that. Tell us about the company Tell us about the company Tell us about the company at a higher at a higher at a higher level. It seemed to me that way back at level. It seemed to me that way back at the beginning of last the beginning of last year, before Claude Code came along, year, before Claude Code came along, year, before Claude Code came along, when Crossover was in its when Crossover was in its when Crossover was in its setup era, Apollo setup era, Apollo setup era, Apollo was one of the was one of the was one of the most AI- most AI- most AI- focused focused focused hedge funds hedge funds hedge funds we talked to. This we talked to. This we talked to. This applied to everything applied to everything applied to everything from the from the from the company's views on company's views on company's views on investing in the world of AI investing in the world of AI investing in the world of AI to operational to operational to operational activities. We have activities. We have activities. We have been communicating with you for been communicating with you for been communicating with you for a long time, and you were of the a long time, and you were of the a long time, and you were of the opinion opinion opinion that you need to that you need to that you need to build a company build a company build a company as AI-native, with a as AI-native, with a as AI-native, with a platform, and not with platform, and not with platform, and not with separate point separate point separate point solutions. I'm solutions. I'm solutions. I'm curious where this curious where this curious where this came from and what in the came from and what in the came from and what in the Apollo culture made Apollo culture made Apollo culture made you such you such you such AI advocates before it all AI advocates before it all went mainstream. Yes. So, Apollo has always Yes. So, Apollo has always prided itself on being a kind of prided itself on being a kind of prided itself on being a kind of leader in leader in alternative alternative asset management asset management asset management and financial services and financial services and financial services in general. We pride ourselves on in general. We pride ourselves on in general. We pride ourselves on being very being very being very innovative and innovative and innovative and being at being at being at the forefront of all the changes and the forefront of all the changes and the forefront of all the changes and what they what they what they look like. So, I look like. So, I look like. So, I think from the very think from the very think from the very beginning, our beginning, our beginning, our management management management team's approach to work team's approach to work team's approach to work has been to constantly has been to constantly has been to constantly improve improve improve skills, expand the skills, expand the skills, expand the capabilities of our capabilities of our capabilities of our team, and team, and team, and be be be flexible and flexible and flexible and adaptable to adaptable to adaptable to market trends.

market trends. market trends. I think we have realized I think we have realized I think we have realized that the world of AI is here, and that the world of AI is here, and that the world of AI is here, and we need to we need to we need to adapt and adapt and adapt and implement it, implement it, implement it, not ignore it or not not ignore it or not not ignore it or not use it. So use it. So use it. So we are very we are very we are very progressive and progressive and progressive and open about open about open about implementing new implementing new implementing new technologies, technologies, technologies, integrating them into our integrating them into our integrating them into our workflows to workflows to workflows to increase increase increase efficiency and efficiency and efficiency and free up resources free up resources free up resources for more creative for more creative for more creative work, replacing work, replacing work, replacing manual operations. So, manual operations. So, manual operations. So, I think at our core I think at our core I think at our core we've always been a very we've always been a very we've always been a very innovative company innovative company innovative company with a flat structure with a flat structure with a flat structure and an open and an open and an open culture, ready for these culture, ready for these culture, ready for these kinds of changes, and now I kinds of changes, and now I kinds of changes, and now I forgot the second part of forgot the second part of forgot the second part of your question. your question. Sorry. Well, I guess so do I. Well, Well, I guess so do I. Well, maybe we can maybe we can maybe we can continue. So, I continue. So, I continue. So, I think one thing think one thing think one thing we've been thinking about we've been thinking about we've been thinking about is software engineering has is software engineering has completely changed completely changed as a discipline. The world as a discipline. The world as a discipline. The world becomes completely different becomes completely different becomes completely different if you are a technology if you are a technology if you are a technology company, like, say, company, like, say, company, like, say, in the aerospace in the aerospace in the aerospace industry. Perhaps industry. Perhaps industry. Perhaps jurisprudence and jurisprudence and customer service are also almost at customer service are also almost at this stage. Where do you this stage. Where do you this stage. Where do you think think think finance sits finance sits finance sits on the AI ​​spectrum: is it on the AI ​​spectrum: is it on the AI ​​spectrum: is it already here and affecting already here and affecting already here and affecting everything, or are we everything, or are we everything, or are we not even at the first not even at the first not even at the first stage yet? And what do you think? This is a stage yet? And what do you think? This is a two-part two-part question again, so that I question again, so that I question again, so that I remember the second remember the second remember the second part. If we are at the part. If we are at the part. If we are at the very beginning, what very beginning, what very beginning, what needs to be done to needs to be done to needs to be done to truly change the truly change the truly change the face of finance face of finance face of finance with AI?

with AI? with AI? Yes, I think that's a great Yes, I think that's a great Yes, I think that's a great question. I think the question. I think the question. I think the financial sector financial sector financial sector in general, these banks and in general, these banks and in general, these banks and large organizations, are large organizations, are huge. They have a huge. They have a huge. They have a lot of, what I lot of, what I lot of, what I call, internal call, internal call, internal middle middle middle and back office support, and I and back office support, and I and back office support, and I think there's a think there's a think there's a lot of room there for lot of room there for lot of room there for growth and growth and growth and improvement. I think improvement. I think improvement. I think you've already realized, you've already realized, you've already realized, working with us, that we are working with us, that we are working with us, that we are extremely complex, extremely complex, extremely complex, and everything we do and everything we do and everything we do has a high level of has a high level of has a high level of complexity. So it's complexity. So it's complexity. So it's not easy at all. not easy at all. not easy at all. I think that in finance I think that in finance I think that in finance in general, especially in in general, especially in asset management, asset management, investment investment investment banking, and the banking, and the banking, and the institutional world institutional world , it's still very , it's still very , it's still very early days. I think there early days. I think there early days. I think there is some value in is some value in is some value in building models or building models or building models or quickly learning about a quickly learning about a quickly learning about a company's operations, company's operations, company's operations, collecting and aggregating collecting and aggregating collecting and aggregating such analytics, but such analytics, but such analytics, but I see it as less I see it as less I see it as less valuable at this valuable at this valuable at this stage, although it will stage, although it will stage, although it will evolve. It evolve. It evolve. It seems to me that over time, seems to me that over time, seems to me that over time, the need for the need for the need for junior staff support junior staff support junior staff support for certain functions of for certain functions of for certain functions of this type will likely this type will likely decrease. But from decrease. But from decrease. But from where we are now, and from what where we are now, and from what where we are now, and from what I see, I see, I see, at least from my at least from my at least from my perspective in Reading, we're perspective in Reading, we're perspective in Reading, we're just trying to just trying to just trying to implement this implement this implement this technology and make technology and make technology and make it as it as it as efficient as possible for efficient as possible for efficient as possible for routine tasks to routine tasks to routine tasks to free up time for free up time for free up time for other projects, because other projects, because other projects, because our company has an our company has an our company has an endless amount of endless amount of endless amount of things to do, and it gives us things to do, and it gives us things to do, and it gives us a lot more a lot more a lot more flexibility.

flexibility. Yes, yes. So we can Yes, yes. So we can continue in the same continue in the same continue in the same vein. I wonder if there vein. I wonder if there vein. I wonder if there is a scenario is a scenario is a scenario where AI simply becomes a where AI simply becomes a where AI simply becomes a means to means to means to speed up tasks, speed up tasks, speed up tasks, save operational save operational save operational costs, and the costs, and the costs, and the like. Perhaps just like like. Perhaps just like no company no company today has today has today has an advantage just because of an advantage just because of an advantage just because of using Excel. using Excel. using Excel. Because Because Because everyone uses Excel, and everyone uses Excel, and everyone uses Excel, and it's just a it's just a it's just a productivity tool. But there is productivity tool. But there is productivity tool. But there is another scenario in another scenario in another scenario in which AI creates a which AI creates a which AI creates a significant competitive significant competitive significant competitive advantage or advantage or advantage or helps solidify helps solidify helps solidify Apollo's leadership in Apollo's leadership in Apollo's leadership in structuring and structuring and structuring and creative deals, creative deals, creative deals, etc. Do you have any thoughts on etc. Do you have any thoughts on etc. Do you have any thoughts on whether AI will simply become a whether AI will simply become a whether AI will simply become a useful, useful, useful, publicly available publicly available publicly available tool, or whether it tool, or whether it tool, or whether it will actually be able to will actually be able to will actually be able to structurally make structurally make structurally make businesses more businesses more businesses more competitive in the competitive in the competitive in the long long long run? run? I think if it is I think if it is adapted and adapted and used correctly, it will used correctly, it will make you more make you more make you more competitive. competitive. competitive. I believe that at the core, I believe that at the core, I believe that at the core, especially for especially for asset management asset management and investment firms and investment firms and investment firms operating in active operating in active operating in active markets, it all comes down markets, it all comes down markets, it all comes down to your processes and the to your processes and the to your processes and the information at your fingertips.

information at your fingertips. information at your fingertips. So knowing that So knowing that So knowing that you have a clear process you have a clear process you have a clear process for implementation, for implementation, for implementation, as well as understanding as well as understanding as well as understanding your available your available your available liquidity, liquidity, liquidity, pricing, and pricing, and pricing, and value at any given value at any given value at any given time is critically time is critically time is critically important. It simply important. It simply important. It simply gives you more of a gives you more of a gives you more of a competitive advantage competitive advantage . And I think that's already there . And I think that's already there . And I think that's already there in the actively in the actively in the actively traded markets, traded markets, traded markets, but but but it's just it's just it's just emerging in the private markets, and we've been emerging in the private markets, and we've been emerging in the private markets, and we've been one of the ones one of the ones one of the ones trying to push trying to push trying to push that across the that across the that across the Apollo structure. I think Apollo structure. I think Apollo structure. I think over time this over time this over time this will turn into a will turn into a will turn into a competitive advantage competitive advantage , allowing companies to , allowing companies to , allowing companies to get get get ahead by having access ahead by having access ahead by having access to better information. to better information. They know, their They know, their teams know, that it's teams know, that it's teams know, that it's accurate, and that nothing is accurate, and that nothing is accurate, and that nothing is distorted, because distorted, because distorted, because the last thing we want to do is the last thing we want to do is the last thing we want to do is do something or do something or do something or make a decision make a decision make a decision based on based on based on misinformation, which misinformation, which misinformation, which then spreads down the then spreads down the then spreads down the chain, both to the chain, both to the chain, both to the company and ultimately to company and ultimately to company and ultimately to our investor base our investor base our investor base . . Yes. Yes. Yes, that, that, that makes Yes, that, that, that makes Yes, that, that, that makes sense. So, I wanted sense. So, I wanted sense. So, I wanted to ask what to ask what to ask what you think Apollo will be like you think Apollo will be like you think Apollo will be like in 5 years, in 5 years, in 5 years, given all these given all these given all these changes. I think that's not changes. I think that's not changes. I think that's not entirely fair. I entirely fair. I entirely fair. I can't even can't even can't even predict whether predict whether predict whether my team will be my team will be my team will be using using using cloud code cloud code cloud code next week. So next week. So next week. So I want to ask, I I want to ask, I I want to ask, I want to ask you about want to ask you about want to ask you about 5 years in the future.

5 years in the future. 5 years in the future. But I'm curious But I'm curious But I'm curious for you personally, what do for you personally, what do for you personally, what do you hope you hope you hope to see in terms of your to see in terms of your to see in terms of your work, your team, how the work, your team, how the use of AI evolves at Apollo ? What conditions must be ? What conditions must be ? What conditions must be met for a met for a met for a technology to become technology to become technology to become truly valuable? You truly valuable? You truly valuable? You know, how do you think know, how do you think know, how do you think about things for the next, about things for the next, about things for the next, you know, the next you know, the next you know, the next few months? few months? few months? So, I think it So, I think it So, I think it goes back to what goes back to what goes back to what I mentioned I mentioned I mentioned earlier, which is, earlier, which is, first, to have complete first, to have complete first, to have complete confidence in confidence in any information that any information that any information that ultimately goes to ultimately goes to ultimately goes to our our our investor base that it's investor base that it's investor base that it's accurate, correct, and accurate, correct, and accurate, correct, and we don't have to we don't have to we don't have to spend hours spend hours spend hours manually checking to manually checking to manually checking to make sure it's make sure it's make sure it's accurate, and to have accurate, and to have accurate, and to have confidence that we're not confidence that we're not confidence that we're not passing on any passing on any passing on any issues and/or issues and/or issues and/or errors because errors because errors because we have a broad we have a broad we have a broad investor base, you know investor base, you know , over 400 global , over 400 global , over 400 global investors. Um, I would investors. Um, I would investors. Um, I would also say that it's also say that it's also say that it's important to be more important to be more important to be more technologically technologically technologically oriented in oriented in oriented in your thinking. I don't your thinking. I don't your thinking. I don't think our think our think our team has lacked that so far team has lacked that so far team has lacked that so far , but , but , but the issue of using the issue of using the issue of using technology has been technology has been technology has been more of a secondary issue. more of a secondary issue. more of a secondary issue. And I think the more And I think the more And I think the more we can we can we can develop and develop and develop and refine these refine these refine these tools that tools that tools that make routine make routine make routine tasks tasks tasks more efficient, the more efficient, the more efficient, the more that will free up more that will free up more that will free up resources to resources to resources to think about or be more think about or be more think about or be more creative about the creative about the creative about the question: why does question: why does question: why does this process exist? And how does it this process exist? And how does it this process exist? And how does it work today? And how work today? And how work today? And how can we actually can we actually can we actually improve it?

improve it? improve it? Should we change this? Should we change this? Should we change this? Should we Should we Should we introduce a new introduce a new introduce a new type of technology and type of technology and type of technology and use it use it use it in the future? Our in the future? Our in the future? Our team didn't team didn't team didn't lack this, but it lack this, but it lack this, but it was more of a was more of a was more of a secondary issue secondary issue , whereas I believe that , whereas I believe that , whereas I believe that ideally our ideally our ideally our employees should employees should employees should be set up to be set up to be set up to think about this in the think about this in the think about this in the long term, and it should long term, and it should long term, and it should become their become their become their primary thought primary thought primary thought when solving when solving when solving various problems. various problems. various problems. Specifically, this Specifically, this Specifically, this applies to the applies to the applies to the day-to-day work day-to-day work day-to-day work at Reading Ridge and at Reading Ridge and at Reading Ridge and scaling our scaling our scaling our organization. organization. organization. I think we have time I think we have time I think we have time for one more question. for one more question. for one more question. One of the One of the One of the lessons I learned from lessons I learned from lessons I learned from working with you was working with you was working with you was how how how incredibly complex incredibly complex incredibly complex and creative this and creative this and creative this work is and how much work is and how much work is and how much effort—both human and effort—both human and effort—both human and AI—it takes to make it AI—it takes to make it AI—it takes to make it all work. There are all work. There are all work. There are a lot of people in this room a lot of people in this room who want who want who want to work either in the to work either in the to work either in the technological technological technological direction of AI and direction of AI and direction of AI and finance, or finance, or finance, or perhaps in the financial perhaps in the financial perhaps in the financial sector. Is there anything you would sector. Is there anything you would sector. Is there anything you would advise them to know advise them to know advise them to know or remember when or remember when or remember when they try to they try to they try to apply AI in apply AI in apply AI in industries like industries like industries like yours? yours? Yeah, I would say it Yeah, I would say it comes down to the fact that comes down to the fact that comes down to the fact that we are extremely we are extremely we are extremely complex organizations, complex organizations, complex organizations, things are not so things are not so things are not so straightforward, and straightforward, and straightforward, and usually things may usually things may usually things may seem a bit seem a bit seem a bit new, strange, or out of new, strange, or out of new, strange, or out of place, but place, but place, but they probably have a they probably have a they probably have a reason or a specific structure to them reason or a specific structure to them . I'm sure that after . I'm sure that after working with us, working with us, working with us, you've realized you've realized you've realized how how how multifaceted and multifaceted and multifaceted and complex we are. So complex we are. So complex we are. So taking the time to taking the time to taking the time to really immerse yourself in really immerse yourself in really immerse yourself in the work, to sit down with the the work, to sit down with the the work, to sit down with the team or team or team or front- front- office specialist for whom you office specialist for whom you office specialist for whom you are creating or are creating or are creating or improving the improving the improving the tool—this is time well tool—this is time well tool—this is time well spent spent . And I believe the success . And I believe the success . And I believe the success and where we and where we and where we are now with are now with are now with Rose Space products, Rose Space products, Rose Space products, ahead of our

ahead of our ahead of our roadmap and roadmap and roadmap and expectations, is expectations, is expectations, is due to due to due to Michael and the team Michael and the team Michael and the team spending a lot of spending a lot of spending a lot of time on site with our time on site with our time on site with our team, practically team, practically team, practically every day, understanding our every day, understanding our every day, understanding our workflows, which I workflows, which I workflows, which I think is think is think is crucial. crucial. Perfectly. Well, probably the Perfectly. Well, probably the last thing I'll say last thing I'll say last thing I'll say is a little advertising: is a little advertising: is a little advertising: both Rose Space and Apollo Technology both Rose Space and Apollo Technology both Rose Space and Apollo Technology are hiring people who are hiring people who are hiring people who want to apply want to apply want to apply AI to finance. So, AI to finance. So, AI to finance. So, please please please contact contact any of us if you are any of us if you are any of us if you are interested. Jack, interested. Jack, interested. Jack, thank you very much, it was nice thank you very much, it was nice thank you very much, it was nice talking to you. talking to you. talking to you. It was wonderful. Thank It was wonderful. Thank It was wonderful. Thank you.