AI in Practice: An Inside Look at Apollo's Approach

Transcribed by qwen3-asr-1.7b with a schedule prompt. Possible prompt artifacts, names heard more often here than in the unprompted Voice Memos transcript (Qwen vs Voice Memos): Michael Manapat (1 vs 0), Rowspace (11 vs 0), Runway (1 vs 0). Check them against the audio before quoting.

00:00:00 — Introducing Rowspace and Apollo's Redding Ridge Credit Business

I'm Michael Manapat. I'm a co-founder of Rowspace. Rowspace is an adaptive platform that uses AI to help enterprise service partners like Apollo deal with messy, fragmented data. We essentially use agents to do the work of data engineering, right information or reconciled data from across a variety of systems to the 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 Redding Ridge, what about Apollo's credit units. And maybe to kick it off, Jack, we'll take hear a little bit about you and what your business does.

Yeah. Thank you, Michael. I'm Jack McDonald. I am a principal at Apollo in Seattle, Running Ridge Capital, which is our structured credit and CLO manager, as captive to Apollo. We're a separate, fully separate entity, operating company that manages about 50 billion in assets, and all across kind of the structured credit ecosystem, primarily CLOs. I was been in Apollo since two thousand twenty, where various different hats throughout the my time there across our financial institutions group, our asset back group, and now currently at Redding. So yeah, happy to be here.

00:01:19 — Fragmented Private Credit Data in Active CLO Management

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 NVIDIA 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 firm operates. I'd like to hear some examples from you on the kinds of work your part of the business doing what made it complex, we're just seeing now with AI. Yeah, so at Apollo and specifically Runway, you know we manage many different fund entities, investments. We're getting thousands and thousands of data points on a like 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 important. So at Apollo, what we're doing is, and at Reddit 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, 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.

00:03:24 — Automating CLO Portfolio Construction and Weekly Trade Reconciliation

I think we have a bunch of engineers in our audience, and we have spoken a lot to you and I on fire drills that the firm does. It used to take a team working a week, if not if not more. Can you make a concrete for us and maybe walk us through an example of something that was painful, time-consuming, hard to do before, that now you can do more easily with rotation and AI? Yeah, happy to. And I'll give kind of two examples that we that I like to look to.

The first being is just on the portfolio construction side. So, all those are very test specific. They have a bunch of — they're very rules based. They have like a very specific waterfall of where money flows. So those all live in deep contenter documents and multiple different legal agreements that — 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 Collet anywhere from 200 to 400 single credit names to create a portfolio, which would typically take a PM before kind of adopting growth space, 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 eighty, ninety percent completion, and then kind of use their judgment and their thought to further refine that portfolio, that last 10 percent, to make it theirs. That's one of the most tangible examples that's kind of pulling in various forms of data and you know information from across multiple different things, whether it's in doc libraries, you know our trade and compliance systems, and real-time prices like that. The other one that I think I would look to is our kind of like warehouse purchase activity, so how we run our business operates with having a bunch of bank financing. You can go out and buy loans in the fixing market at any given point.

Historically, we would kind of do a trade rec and trade activity on a weekly basis that would go around: here's your sales 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 writing that out, and then sending it as a reported email to 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 looks right. And now it's a much more rinse and routine type process, which historically takes many hours of the week for two to three individuals on our team to actually wreck that out.

00:06:32 — Earning Trust Beyond Chatbots Through Golden Data Sources

Yeah, love those examples. So you know you aren't just reading out of a chatbot to be informed, your team are making trading decisions, procurement decisions. To 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 cloud and track the keys? See what happens. For someone like you, how does your team get the confidence in or part of our rowspace, or other AI tools to say, we are going to make business decisions on this, make trades, build portfolios, all that? How do you get that level of trust?

Yeah, I would say Claude and the Open AIs of the world, and like the Chat GPTs, 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 from what we've kind of taken our approach to this — the AI buildout at Redding has been around owning your own data and having some type of platform and technology that sits on top of 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 third boots on the ground. So like Michael sits with our team on a daily basis, learning and understanding our information flow, because 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 understanding calculations, understand what data is actually correct, and override other sources of information, and kind of aggregate.

So to that end, we're able to kind of have trust in the system, and you know appreciate that okay, we've had our team go and validate that this information is correct. 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, you know, call it systems throughout even our own company, it's very fragmented, and we have to kind of lift one piece of information 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.

00:09:07 — Apollo's Early Platform Mindset and Openness to Change

Go ahead. We were talking about the firm at a higher level. It seemed to me for a long time, even early last year, before Claude became a thing, when Krasura was still in the tab completion era, Apollo was among the most AI filled of enterprises. And that was everything from the firms to you on how to invest in an AI world, or operational. We began talking to you a long time ago, and you were in the: we must rebuild the firm in the AI way, must have a platformal point solutions. So I'm just curious, where did that come from, and what is about Apollo's culture that made you so AI-pilled, even before things that took off?

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 you know discredit it or not utilize it. So we've been very forward and you know 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 you know, 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 the question. Sorry. Ah.

No, I guess I might.

00:11:07 — Where Finance Stands on the AI Adoption Curve

Well, maybe we could keep going. So, I think one thing that we've been thinking about is software engineering that has completely changed as a discipline, like the goal is to defend your technology company, like Ros Space, 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, got into the bottom for the first thing? And what do you think is the question again — remember the second part — if we're early on, what what's necessary to really change the shape of finance and AI?

Yeah, I think this is a very good question. So I think finance broadly, these banks and large organizations are massive. They have a lot of, you know, backend, what I'll call middle back office support, and I think there's a lot of room to grow and enhance their. I think, probably as you appreciate now, working with us, that we are extremely complicated and have a lot of complexity and 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 on some company quite quickly and pulling and aggregating that type of research, but I think that's lower value add, at least 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, or we see from at least my perspective at running, 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 because we kind of have like endless things to do and enhance to our company, and that's just giving us a lot more flexibility to adopt that.

00:13:10 — Commodity Productivity Tool Versus Durable Competitive Edge

Yeah, yeah. So maybe, on that note, I'm curious. Those are world in which AI just becomes something that helps speed up tasks, saves you on opex, things like that, and being the same way, no given firm now has an advantage because it's Excel. Our music Excel, and it's just if I could be tool. There's also a role in which AI actually materially makes competitive advantage, or helps unify, follows lead in structuring creative fields and so forth. Do you have a take on whether AI is going to be just a useful but a general commodity, or it's going to be something that actually structurally make a business more competitive in a durable way?

I think if it's adapted and utilized properly, it will make you more competitive. I think at its core, especially around asset management, investing type companies that are and 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 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, they know their teams know that it's accurate and like stuff is not misaligned, because the last thing you want to do is, you know, do something, make a decision that was misinformed or had misinformation, and then that streamlines down the chain to both the enterprise and to, you know our investor base today. Yeah, yeah, that makes sense.

00:15:10 — Near-Term Priorities and Lessons for AI in Finance

So I wanted to ask you what your prediction was for what the world will be like when we're talking five years and surveying all the change. I think that's unfair. I can't even predict if my team's going to use code next week. So I want to ask — I want to ask about priorities in the future. Nikhil, Harithas, your job team, how AI use evolves, what conditions have to hold for the technology to actually be truly valuable. Yeah, what are you? How are you thinking about things over the next, you know, next few months?

Yeah, I think it comes back to some of the stuff I touched on before, of just one, having just, you know, 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. We all spent 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 because we do have just a wide investor base, you know, over four hundred global investors. I would also say being much more tech-enabled and mindset. I think our team is not lack that today, 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, you know routine tasks, you know, 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 is not lack 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 spot, is they're tackling different problems specifically. This is specifically running our day to day and kind of scaling our organization. If you 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, but human and AI to get things to work. Of all people in this room, who either want to work on technology side of AI and finance, or perhaps finance side, is there anything that you would mention to them, from here's something that you should know or keep in mind when trying it to apply AI to domains like yours? 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 roadmap and expectations has been, you know, due to the fact that Michael and the team have spent ample time sitting on the ground with our team, pretty much every single day, and you know understanding our workflows, which I think is crucial.

Awesome. Well, it's something I'll say is that shameless plug, both Rowspace and Apollo Technology are hiring people who want to apply to finance. So could be either of us if you are interested.

Nah itu jangan salah sebenarnya kita itu. This is great. Thank you.