AI Agent如何将3天投资组合构建压缩至数分钟：阿波罗全球管理与Rowspace的实战拆解

Michael Manapat: It's great to be here. I'm Michael Manapat. I'm a co-founder of Rowspace. And Rowspace is an AI cloud platform that uses AI to help financial services partners like Apollo deal with messy, fragmented internal data. We essentially use agents to do the work of data engineering, direct information, reconcile data from across a variety of systems so that portfolio managers, traders, and so forth can make decisions faster and more accurately. I'm joined here with Jack McDonald who is chief operating officer of Reddington Ridge, one of Apollo's credit units. And maybe to kick it off, Jack, I'd love to hear a little bit about you and what your business does.

Jack McDonald: Yeah, thank you, Michael. I'm Jack McDonald. I am a principal at Apollo and COO of Reddington Ridge Capital, which is our structured credit and CLO manager that is captive to Apollo. We're a separate fully separate entity operating company that manages about $50 billion in assets, all across kind of the structured credit ecosystem, primarily CLOs. I have been at Apollo since 2020, wear various different hats throughout my time there across our financial institutions group, our asset-backed group, and now currently at Reddington. So, yeah, happy to be here.

Michael Manapat: Awesome. So, something that we've noticed in talking to a huge number of financial firms is that there's a pretty wide gap between when AI is nice to have and when AI can actually become mission critical to a business. And I think in Apollo's case, we're seeing that AI can really be transformative in how the firm operates. I'd love to hear some examples from you on the kinds of work your part of the business was doing, what made it complex, and what you're seeing now with AI.

Jack McDonald: Yeah. Um so, at Apollo and specifically Redding Ridge, you know, we manage many different fund entities, investments. We're getting thousands and thousands of data points on a daily basis coming in across all of our investments. We manage probably, you know, thousand name loan pools and have credits across multiple different businesses that we're assessing on a daily basis and investing in. CLO management is very active and it's traded in the broadly syndicated loan market. So, having all that information at your fingertips is very, very important. So, at Apollo, what we're doing is and at Redding specifically is trying to enhance and ensure that all of our data and our data quality is accurate, consistent, and in a format that we can utilize and make us much more practical and thoughtful when we're making decisions in a much fast-paced environment. So, all of this information is privatized and is not very, what I'll say, widely in the marketplace like how public equities or public fixed income trades. It's very fragmented in various documents. So, we've looked to companies like Rowspace to partner with to come in and actually sit with our teams, work with us, and understand the actual calculations and information that we're looking to and what's actually the golden source of truth, and then continuing to enhance the AI engines to understand that and repeat that and just make us that much more efficient.

Michael Manapat: I think we have a bunch of engineers in the audience, and we've spoken a lot just you and I on fire drills that the firm does. Like it used to take a team working a week if not more. Can you make it concrete for us and maybe walk us through an example of something that was painful, or time-consuming, hard to do before that now you can do more easily with Rowspace and AI?

Jack McDonald: Yeah, happy to. And I'll give kind of two examples that I like to look to. The first being is just on the portfolio construction side. So CLOs are very test specific. They're very rules based. They have like a very specific waterfall of where money flows. So those all live in deep indenture documents and multiple different legal agreements. So sitting from like a portfolio manager's perspective in portfolio construction, they are reviewing those documents, building Excel based models, and building mock portfolios with call it anywhere from 200 to 400 single credit names to create a portfolio which would typically take a PM before kind of adopting Rowspace anywhere from two to three days in a manual Excel. Today, we're able to kind of get the latest and greatest information that's real time, create a portfolio that would take somebody days to now do it in the matter of minutes. And then that allows these PMs to kind of build something to call it 80 to 90% completion and then kind of use their judgment and their thought to further refine that portfolio that last 10% to make it theirs. That's one of the most tangible examples. It's kind of pulling in various forms of data and information from across multiple different things, whether it's in doc libraries, our trade and compliance systems, and real time pricing and stuff like that.

The other one that I think I would look to is our kind of like warehouse purchase activity. So how our business operates with having a bunch of bank financing and we can go out and buy loans in the fixed income market at any given point. Historically, we would kind of do a trade wreck and trade activity on a weekly basis that would go around of here's your sale purchase activity from the week. Here's what we bought, here's what we sold, and it was very manual. We were going into various like literally going into bank accounts to understand statements, where things actually settled, and it was an individual team that was going and wrecking that out and then sending a reported email to the management team to say here's the recap. Today, what we're doing now is we've been able to take that exact same report, drop it into Rowspace from a visualization perspective. That report was then created and now repeatable in the span of a minute. And now all of that is automated and then from our team's perspective, we are just QAing that and checking it each day to make sure that that looks right. And now it's a much more rinse and routine type process which historically has taken many hours of the week for two to three individuals on our team to actually wreck that out.

Michael Manapat: Yeah, I love those examples. So, you know, you aren't just reading the output of a chatbot to be informed. I think your team are making trading decisions, portfolio management decisions. So, accuracy and quality are high. This is a two-part question. I think a lot of people will ask us or ask generally, "Why can't you just plug this stuff into Claude and or ChatGPT and see what happens?" And for someone like you, how does your team get the confidence in a product like Rowspace or other AI tools to say, "We are going to make business decisions on this, make trades, build portfolios, all of that." How do you get to that level of trust?

Jack McDonald: Yeah, I would say Claude and the open AIs of the world and like the ChatGPTs, all the LLMs are very beneficial, but it's all general information that that's being worked off of and none of your own actual data. So, it's great for research or kind of building new ideas or getting up to speed on some type of product or investment quite quickly. Where Rowspace is different and what we've kind of taken our approach to the AI build-out at Redding has been around owning our own data and having some type of platform and technology that sits on top of that that we know is to be true. So, the Rowspace team which has been much different from what we've seen from other technology companies that we've worked with is they're boots on the ground. So, like Michael sits with our team on a daily basis learning and understanding our information flow cuz for one piece of data that might come from 20 different sources and there's only one that's actually right. So, the Rowspace team is spending the time with our portfolio management team, with our finance teams to understand the calculations, understand what data is actually correct and override other sources of information, and kind of aggregate that. So, to that end, we're able to kind of have trust in the system and appreciate that okay, we've had our team go in and validate that this information is correct and we can rely on it and it's coming from all of our whether it's our trade order management system, whether it's from our performance system, whether it's from Bloomberg or any pricing index that we're pulling in and know that it's accurate. Whereas in a lot of other call it systems throughout even our own company, it's very fragmented and we have to kind of lift one piece of information from over here and bring it back over here and kind of pull that together manually. Whereas Rowspace is now just automating that all for us.

Michael Manapat: Glad to hear that [laughter]. You know, talk about the firm at a higher level. It seemed to me for a long time even early last year before Claude Code became a thing when Cursor was still in the tab completion era that Apollo was among the most AI-pilled of the firms we spoke to. And that was everything from the firm's view on how to invest in an AI world, but operationally we've been talking to you a long time ago and you were in the we must rebuild the firm in an AI native way, must have a platform not point solutions. So, I'm just curious where did that come from and what is it about Apollo's culture that made you so AI-pilled even before things took off?

Jack McDonald: Yeah. So, Apollo has always prided itself in being kind of the leader of the alternative asset management space and the financial services industry broadly. We pride ourselves in being very innovative and being at the forefront of all things change and what that looks like. So, I think from the onset and the way our management team approaches how we operate is always being up to skill and enhancing our team's capabilities and being able to be nimble and adapt to where the market's going. I think we appreciated that the AI world is here and we need to adapt and adopt it not discredit it or not utilize it. So, we've been very forward and open-minded about adopting new technologies and bring those into our workflows and how we can be more efficient and free up capacity for people to be more thoughtful and think and replace manual processes. So, I think at the core of its level we've always been a very innovative firm that's very flat and open culture to be open to those types of change. And then, I'm blanking on the second part of your question. Sorry.

Michael Manapat: Uh I guess so am I. Uh

Jack McDonald: [laughter]

Michael Manapat: Well, maybe we can keep on going. So, I think one thing that we've been thinking about is software engineering that has completely changed as a discipline. Like, the world is completely different if you are a technology company like aerospace. Uh and maybe arguably law and customer service are almost there. Where do you think finance is on the spectrum of AI is here, it's made a difference, or we haven't even, you know, gotten to the bottom of the first inning. And what do you think is Two-part question again, so remember the second part. If we're early on, what's necessary to really change the shape of finance with AI?

Jack McDonald: Yeah, I think that's a great question. So, I think finance broadly, these banks and large organizations are massive. They have a lot of back end, what I'll call middle back office support. And I think there's a lot of room to grow and enhance there. I think probably as you appreciate now if working with us that we are extremely complicated and have a lot of complexity in everything that we do. So, it's not really straightforward. I think in finance broadly, especially in call it the asset management investment banking, the bank institution institutional world, I still think it's very early. I think there's some value for some of the like, you know, model creation or getting up to speed on some company quite quickly and pulling and aggregating that type of research, but I think that's lower value add at least of where we stand today. And that's going to evolve. I think like, you know, there's probably a need where there's less junior support over time for certain functions like that, but I think from where we are today and what we see from from at least my perspective at Redding, I think we're just trying to adopt this technology and make it, you know, as efficient as possible for, you know, routine tasks that frees up capacity for, you know, other projects cuz we kind of have like endless things to do and enhance at our company. And that's just giving us a lot more flexibility to adopt that.

Michael Manapat: Yeah, yeah. So, maybe to continue on that note, I'm curious, there's a world in which AI just becomes something that helps speed up tasks, saves you on opex, things like that. And maybe in the same way, no given firm now has an advantage because they use Excel. Cuz everyone uses Excel and it's just a productivity tool. And there's also a world in which AI actually materially makes a competitive advantage or helps reify Apollo's lead in structuring and creative deals and so forth. Do you have a take on whether AI is going to be just a useful but a general commodity or if it's going to be something that can actually structurally make a business more competitive in a durable way?

Jack McDonald: I think if it's adapted and utilized properly, it will make you more competitive. I think at its core, and especially around asset management and investing type companies that are in the actively traded markets, like it comes down to your process and the information at your fingertips. So, knowing that you have a strong process in place to go out and execute and knowing your available liquidity or, you know, where things are pricing and what the value is at any given point is crucial. So, that just gives you, you know, more competitive edge. And I think like that exists today in the, you know, actively traded markets, but in the private markets, it's evolving, which we've kind of been at the forefront of trying to to move on the broad Apollo machine. And I think having that will just turn into a competitive advantage over time where firms will be able to get further ahead as they're, you know, having access to better information. Their teams know that it's accurate and like stuff is not misaligned cuz the last thing we want to do is, you know, do something or make a decision that was misinformed or had misinformation and then that streamlines down the chain to both the enterprise and to the, you know, our investor base at the end of the day.

Michael Manapat: Yeah. Yeah, that makes sense. So, I wanted to ask you what your prediction was for what Apollo would be like when we're talking 5 years observing all the change. I think that's unfair. I can't even predict if my team's going to use Claude Code next week. So, I don't want to ask you about 5 years in the future. But, I'm curious for you personally, what are you hoping to see in terms of your job, team, how AI use in Apollo evolves, what conditions have to hold for the technology to actually be truly valuable. You know, how are you thinking about things over the next few months?

Jack McDonald: Yeah, I think it comes back to some of the stuff I touched on before of just one having full confidence in any type of information that's flowing out at the end of the day to our investor base that's accurate, correct, and we don't have to spend hours manually checking that to make sure that that's validated and having the confidence that we're sharing, you know, no issues and or mistakes cuz we do have just a wide investor base, you know, over 400 global investors.

I would also say being much more tech-enabled in mindset. I think our team has not lacked that to date, but it's kind of been an afterthought around the use of technology and I think the more that we can develop and enhance these tools that make routine tasks more efficient, it frees up capacity to add more thought or think more creatively around why does this process exist and how does it work today and how can we actually enhance that or should we change that or should we bring in a new type of technology and adopt that going forward. And our team has not lacked that today, but it's kind of been a secondary thought where I think ideally our workforce is positioned to think about that more on a go-forward basis and that's kind of their primary thought as they're tackling different problems specifically at Redding Ridge day to day and kind of scaling our organization.

Michael Manapat: I think we have time for one more. I think one of my learnings from working with you has just been how unimaginably complex and creative the work is and how it takes a lot of effort both human and AI to get things to work. A lot of people in this room who either want to work on the technology side of AI and finance or perhaps finance side. Is there anything that you would mention to them from a here's something that you should know or keep in mind when trying to apply AI to domains like yours?

Jack McDonald: Yeah, I would say it comes down to we are extremely complex organizations and it's not as straightforward and typically things might look a little bit novel or strange and out of place, but it's probably actually there for a reason or structured in a specific way. I'm sure as you guys have come in you appreciate that we're super nuanced and complicated. So spending the time and really putting boots on the ground to sit with whatever team or front office professional that you're working to build a tool for or enhance is time well spent and I think the success and you know, where we are today with the Rowspace products and kind of being ahead of our road map and expectations has been due to the fact that Michael and the team has spent ample time sitting on the ground with our team like pretty much every single day and understanding our workflows which I think is crucial.

Michael Manapat: Awesome. Well, I guess the last thing I'll say is that shameless plug both Rowspace and Apollo technology are hiring people who want to apply AI to finance. So please reach out to either of us if you are interested. And then Jack thanks so much and great chatting. Thank you.

Jack McDonald: This is great. Thank you guys.

(Applause)