1 00:00:03,841 --> 00:00:08,601 It's great to be here. I'm Michael Manapat. I'm a co-founder of Rowspace. 2 00:00:08,921 --> 00:00:18,741 Rowspace is an agentic platform that uses AI to help financial services partners like Apollo deal with messy, fragmented data. 3 00:00:18,741 --> 00:00:25,681 We essentially use agents to do the work of data engineering, to reconcile data from across 4 00:00:25,681 --> 00:00:31,761 a variety of systems so that portfolio managers, traders, and so forth can make decisions faster 5 00:00:31,761 --> 00:00:33,041 and more accurately. 6 00:00:33,461 --> 00:00:38,121 I'm joined here with Jack McDonald, who is Chief Operating Officer of Redding Ridge, 7 00:00:38,681 --> 00:00:39,961 one of Apollo's credit units. 8 00:00:40,561 --> 00:00:44,441 And maybe to kick it off, Jack, I'd love to hear a little bit about you and what your 9 00:00:44,441 --> 00:00:44,981 business does. 10 00:00:45,501 --> 00:00:46,561 Yeah, thank you, Michael. 11 00:00:46,861 --> 00:00:47,981 I'm Jack McDonald. 12 00:00:47,981 --> 00:00:54,701 I am a principal at Apollo and COO of Redding Ridge Capital, which is our structured credit 13 00:00:54,701 --> 00:00:57,341 and CLO manager that is captive to Apollo. 14 00:00:57,841 --> 00:01:05,001 We're a fully separate entity operating company that manages about $50 billion in assets and 15 00:01:05,001 --> 00:01:09,881 all across the structured credit ecosystem, primarily CLOs. 16 00:01:10,281 --> 00:01:12,821 I've been at Apollo since 2020. 17 00:01:12,821 --> 00:01:17,421 wear various different hats throughout my time there 18 00:01:17,421 --> 00:01:19,861 across our financial institutions group, 19 00:01:20,241 --> 00:01:22,701 our asset-backed group, and now currently at Reading. 20 00:01:23,301 --> 00:01:24,641 So yeah, happy to be here. 21 00:01:25,001 --> 00:01:25,641 Awesome. 22 00:01:26,241 --> 00:01:31,601 So something that we've noticed in talking to a huge number of financial firms 23 00:01:31,601 --> 00:01:36,341 is that there's a pretty wide gap between when AI is nice to have 24 00:01:36,341 --> 00:01:39,501 and when AI can actually become mission-critical to a business. 25 00:01:39,501 --> 00:01:46,641 And I think in Apollo's case, we're seeing that AI can really be transformative in how the firm operates. 26 00:01:47,181 --> 00:01:52,281 I'd like to hear some examples from you on the kinds of work your part of the business was doing, 27 00:01:52,681 --> 00:01:56,381 what made it complex, and what you're seeing now with AI. 28 00:01:56,741 --> 00:02:04,001 Yeah. So at Apollo, and specifically Reading Ridge, we manage many different fund entities, investments. 29 00:02:04,001 --> 00:02:11,361 We're getting thousands and thousands of data points on a daily basis coming in across all of our investments. 30 00:02:11,601 --> 00:02:20,141 We manage probably 1,000 name loan pools and have credits across multiple different businesses that we're assessing on a daily basis and investing in. 31 00:02:21,521 --> 00:02:25,841 CLO management is very active, and it's traded in the broadly syndicated loan market. 32 00:02:26,001 --> 00:02:29,641 So having all that information at your fingertips is very, very important. 33 00:02:29,641 --> 00:02:51,721 So at Apollo, what we're doing is, at Reading 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. 34 00:02:51,721 --> 00:02:59,141 so all of this information is privatized and is not very what i'll say widely in the marketplace 35 00:02:59,141 --> 00:03:06,041 like how public equities or public fixed income trades it's very uh fragmented in various documents 36 00:03:06,041 --> 00:03:13,541 so we've looked to companies like row space to partner with to come in and actually sit with 37 00:03:13,541 --> 00:03:19,561 our teams work with us and understand the actual calculations and information that we're looking to 38 00:03:19,561 --> 00:03:22,161 and what's actually the golden source of truth 39 00:03:22,161 --> 00:03:25,981 and then continuing to enhance the AI engines 40 00:03:25,981 --> 00:03:27,521 to understand that and repeat that 41 00:03:27,521 --> 00:03:29,021 and just make us that much more efficient. 42 00:03:29,601 --> 00:03:31,521 I think we have a bunch of engineers in the audience 43 00:03:31,521 --> 00:03:34,021 and we've spoken a lot to you and I 44 00:03:34,021 --> 00:03:36,901 on fire drills that the firm does 45 00:03:36,901 --> 00:03:40,281 that used to take a team working a week, 46 00:03:40,461 --> 00:03:41,801 if not more. 47 00:03:42,521 --> 00:03:43,641 Can you make it concrete for us 48 00:03:43,641 --> 00:03:44,921 and maybe walk us through an example 49 00:03:44,921 --> 00:03:48,161 of something that was painful, time-consuming, 50 00:03:48,161 --> 00:03:51,241 hard to do before that now you can do more easily 51 00:03:51,241 --> 00:03:53,261 with road space and AI? 52 00:03:53,581 --> 00:03:54,521 Yeah, happy to. 53 00:03:54,641 --> 00:03:57,801 And I'll give kind of two examples that I like to look to. 54 00:03:58,501 --> 00:04:01,701 The first being is just on the portfolio construction side. 55 00:04:01,921 --> 00:04:04,021 So CLOs are very test-specific. 56 00:04:04,341 --> 00:04:06,281 They're very rules-based. 57 00:04:06,441 --> 00:04:09,641 They have a very specific waterfall of where money flows. 58 00:04:10,241 --> 00:04:13,181 So those all live in deep indenture documents 59 00:04:13,181 --> 00:04:15,861 and multiple different legal agreements. 60 00:04:15,861 --> 00:04:19,721 that, so sitting from like a portfolio manager's perspective 61 00:04:19,721 --> 00:04:21,281 in portfolio construction, 62 00:04:21,581 --> 00:04:23,661 they are reviewing those documents, 63 00:04:23,881 --> 00:04:25,001 building Excel-based models 64 00:04:25,001 --> 00:04:27,201 and building mock portfolios with, call it, 65 00:04:27,441 --> 00:04:30,221 anywhere from 200 to 400 single credit names 66 00:04:30,221 --> 00:04:32,001 to create a portfolio, 67 00:04:32,001 --> 00:04:34,181 which would typically take a PM 68 00:04:34,181 --> 00:04:36,601 before kind of adopting row space, 69 00:04:36,601 --> 00:04:39,701 anywhere from two to three days in a manual Excel. 70 00:04:40,061 --> 00:04:42,821 Today, we're able to kind of get the latest 71 00:04:42,821 --> 00:04:44,901 and greatest information that's real time, 72 00:04:44,901 --> 00:04:49,301 create a portfolio that would take somebody days to now do it in the matter of minutes. 73 00:04:49,941 --> 00:04:56,121 And then that allows these PMs to kind of build something to call it 80, 90% completion, 74 00:04:56,121 --> 00:05:00,881 and then kind of use their judgment and their thought to further refine that portfolio, 75 00:05:00,881 --> 00:05:02,661 that last 10% to make it theirs. 76 00:05:03,441 --> 00:05:09,061 That's one of the most tangible examples that's kind of pulling in various forms of data and, 77 00:05:09,061 --> 00:05:14,561 you know, information from across multiple different things, whether it's in doc libraries, 78 00:05:14,901 --> 00:05:20,601 our trade and compliance systems and real-time pricing and stuff like that. 79 00:05:21,461 --> 00:05:27,581 The other one that I think I would look to is our warehouse purchase activity. 80 00:05:27,881 --> 00:05:32,701 So how we run our business operates with having a bunch of bank financing 81 00:05:32,701 --> 00:05:36,101 and we can go out and buy loans in the fixed income market at any given point. 82 00:05:37,881 --> 00:05:42,781 Historically, we would kind of do a trade rec and trade activity on a weekly basis 83 00:05:42,781 --> 00:05:46,941 that would go around of here's your sale purchase activity from the week, here's what we bought, 84 00:05:47,101 --> 00:05:51,781 here's what we sold. And it was very manual. We were going into various, like literally going 85 00:05:51,781 --> 00:05:58,361 into bank accounts to understand statements, where things actually settled. And it was an 86 00:05:58,361 --> 00:06:02,401 individual team that was going and wrecking that out and then sending a reported email to 87 00:06:02,401 --> 00:06:08,761 the management team to say, here's the recap. Today, what we're doing now is we've been able 88 00:06:08,761 --> 00:06:13,641 to take that exact same report, drop it into row space from a visualization perspective. 89 00:06:14,121 --> 00:06:18,741 That report was then created and now repeatable in the span of a minute. 90 00:06:19,221 --> 00:06:21,461 And now all of that is automated. 91 00:06:21,801 --> 00:06:26,601 And then from our team's perspective, we are just QAing that and checking it each day to 92 00:06:26,601 --> 00:06:27,641 make sure that that looks right. 93 00:06:27,801 --> 00:06:32,441 And now it's a much more rinse and routine type process, which historically has taken 94 00:06:32,441 --> 00:06:37,101 many hours of the week for two to three individuals on our team to actually wreck that out. 95 00:06:37,761 --> 00:06:39,381 Yeah, I love both of those examples. 96 00:06:39,981 --> 00:06:44,841 So, you know, you aren't just reading the output of a chatbot to be informed. 97 00:06:45,121 --> 00:06:49,361 I think your team are making training decisions, performing management decisions. 98 00:06:49,541 --> 00:06:51,381 So accuracy and quality are high. 99 00:06:51,561 --> 00:06:52,921 This is a two-part question. 100 00:06:53,281 --> 00:06:56,221 I think a lot of people will ask us or ask generally, 101 00:06:56,821 --> 00:07:01,441 why can't you just plug this stuff into Claude or chat to your team and see what happens? 102 00:07:01,441 --> 00:07:03,381 and for someone like you, 103 00:07:03,701 --> 00:07:05,881 how does your team get the confidence 104 00:07:05,881 --> 00:07:08,001 in a product like Rowspace 105 00:07:08,001 --> 00:07:09,441 or other AI tools to say, 106 00:07:09,601 --> 00:07:13,301 we are going to make business decisions on this, 107 00:07:13,601 --> 00:07:15,741 make trades, build portfolios, all of that. 108 00:07:15,861 --> 00:07:17,961 How do you get to that level of trust? 109 00:07:18,261 --> 00:07:20,481 Yeah, I would say Claude 110 00:07:20,481 --> 00:07:22,261 and the open AIs of the world 111 00:07:22,261 --> 00:07:23,761 and like the chat GPTs, 112 00:07:23,901 --> 00:07:26,921 all the LLMs are very beneficial, 113 00:07:26,921 --> 00:07:28,861 but it's all general information 114 00:07:28,861 --> 00:07:30,081 that that's being worked off of 115 00:07:30,081 --> 00:07:31,681 and none of your own actual data. 116 00:07:31,861 --> 00:07:35,901 So it's great for research or kind of building new ideas 117 00:07:35,901 --> 00:07:39,421 or getting up to speed on some type of product 118 00:07:39,421 --> 00:07:41,081 or investment quite quickly. 119 00:07:42,261 --> 00:07:43,861 Where Rowspace is different 120 00:07:43,861 --> 00:07:45,921 and what we've kind of taken our approach to this, 121 00:07:46,101 --> 00:07:48,041 the AI build out at Redding 122 00:07:48,041 --> 00:07:50,361 has been around owning our own data 123 00:07:50,361 --> 00:07:53,181 and having some type of platform and technology 124 00:07:53,181 --> 00:07:56,841 that sits on top of that, that we know is to be true. 125 00:07:56,841 --> 00:08:06,361 So the Rowspace team, which has been much different from what we've seen from other technology companies that we've worked with, is their boots on the ground. 126 00:08:06,581 --> 00:08:12,481 So Michael sits with our team on a daily basis, learning and understanding our information flow. 127 00:08:12,641 --> 00:08:20,361 Because for one piece of data, that might come from 20 different sources, and there's only one that's actually right. 128 00:08:20,361 --> 00:08:22,761 So the Rowspace team is spending the time 129 00:08:22,761 --> 00:08:24,321 with our portfolio management team, 130 00:08:24,501 --> 00:08:25,481 with our finance teams, 131 00:08:25,521 --> 00:08:28,121 to understand the calculations, 132 00:08:28,661 --> 00:08:30,501 understand what data is actually correct, 133 00:08:30,661 --> 00:08:32,501 and override other sources of information, 134 00:08:32,721 --> 00:08:34,021 and kind of aggregate that. 135 00:08:34,241 --> 00:08:36,341 So to that end, we're able to kind of 136 00:08:36,341 --> 00:08:38,041 have trust in the system, 137 00:08:38,261 --> 00:08:40,181 and appreciate that, okay, 138 00:08:40,361 --> 00:08:43,081 we've had our team go in and validate 139 00:08:43,081 --> 00:08:44,341 that this information is correct, 140 00:08:44,461 --> 00:08:45,461 and we can rely on it, 141 00:08:45,561 --> 00:08:46,861 and it's coming from all of our, 142 00:08:47,001 --> 00:08:49,561 whether it's our trade order management system, 143 00:08:49,561 --> 00:08:51,461 whether it's from our performance system, 144 00:08:51,681 --> 00:08:55,101 whether it's from Bloomberg or any pricing index that we're pulling in, 145 00:08:55,261 --> 00:08:56,501 and know that it's accurate. 146 00:08:56,761 --> 00:09:00,361 Whereas in a lot of other, you know, call it systems, 147 00:09:00,641 --> 00:09:03,281 throughout even our own company, it's very fragmented, 148 00:09:03,281 --> 00:09:06,081 and we have to kind of lift one piece of information from over here 149 00:09:06,081 --> 00:09:08,881 and bring it back over here and kind of pull that together manually, 150 00:09:09,141 --> 00:09:11,701 whereas RowSpace is now just automating that all for us. 151 00:09:12,961 --> 00:09:13,961 Glad to hear that. 152 00:09:14,501 --> 00:09:16,701 You want to talk about the firm at a higher level. 153 00:09:17,281 --> 00:09:23,321 It seemed to me for a long time, even early last year before cloud code became a thing, 154 00:09:24,041 --> 00:09:30,581 when Kurser was still in the TAP application era, that Apollo was among the most AI-pilled 155 00:09:30,581 --> 00:09:32,221 of the financial firms we spoke to. 156 00:09:33,261 --> 00:09:37,641 that was everything from the firm's view on how to invest in an AI world. 157 00:09:38,021 --> 00:09:43,481 But operationally, we began talking to you a long time ago, and you were in the we must 158 00:09:43,481 --> 00:09:48,361 rebuild the firm in an AI-native way, must have a platform, not point solutions. 159 00:09:48,701 --> 00:09:50,941 So I'm just curious, where did that come from? 160 00:09:51,141 --> 00:09:58,601 And what is it about Apollo's culture that made you so AI-pilled even before things took off? 161 00:09:58,821 --> 00:09:59,261 Yeah. 162 00:09:59,861 --> 00:10:00,021 So 163 00:10:00,681 --> 00:10:09,821 Apollo has always prided itself in being kind of the leader of the alternative asset management space and the financial services industry broadly. 164 00:10:09,941 --> 00:10:16,001 We pride ourselves in being very innovative and being at the forefront of all things change and what that looks like. 165 00:10:16,001 --> 00:10:29,341 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 adopt to where the market's going. 166 00:10:29,341 --> 00:10:38,701 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. 167 00:10:38,801 --> 00:10:45,561 So we've been very forward and open-minded about adopting new technologies and bringing those into our workflows and how we can be more efficient. 168 00:10:46,001 --> 00:10:51,441 and free up capacity for people to be more thoughtful and think and replace manual processes. 169 00:10:51,701 --> 00:10:57,101 So I think at the core of its level we have always been a very innovative firm that is 170 00:10:57,101 --> 00:11:01,101 very flat and open culture to be open to those types of change. 171 00:11:01,821 --> 00:11:05,101 And then I'm blanking on the second part of your question. 172 00:11:05,601 --> 00:11:05,721 Sorry. 173 00:11:07,901 --> 00:11:09,921 Oh, so am I. 174 00:11:12,761 --> 00:11:14,081 Well, maybe we can keep on going. 175 00:11:14,081 --> 00:11:22,061 So I think one thing that we've been thinking about is software engineering that has completely changed as a discipline. 176 00:11:22,361 --> 00:11:26,701 Like the world is completely different if you are a technology company like Rowspace. 177 00:11:27,641 --> 00:11:32,901 And maybe arguably law and customer service are almost there. 178 00:11:33,301 --> 00:11:42,541 Where do you think finance is on the spectrum of AI is here, it's made a difference, or we haven't even gotten to the bottom of the first inning? 179 00:11:42,541 --> 00:11:45,621 And what do you think is a two-part question again, so remember the second part. 180 00:11:46,221 --> 00:11:50,761 If we're early on, what's necessary to really change the shape of finance with AI? 181 00:11:51,421 --> 00:11:52,741 Yeah, I think that's a great question. 182 00:11:52,901 --> 00:11:57,381 So I think finance broadly, these banks and large organizations are massive. 183 00:11:57,661 --> 00:12:02,121 They have a lot of, you know, back-end, what I'll call middle back-office support. 184 00:12:02,241 --> 00:12:05,581 And I think there's a lot of room to grow and enhance there. 185 00:12:05,741 --> 00:12:11,741 I think probably as you appreciate now of working with us that we are extremely complicated 186 00:12:11,741 --> 00:12:14,201 and have a lot of complexity in everything that we do. 187 00:12:14,321 --> 00:12:16,201 So it's not really straightforward. 188 00:12:16,681 --> 00:12:19,141 I think in finance broadly, 189 00:12:19,141 --> 00:12:21,281 especially in call it the asset management, 190 00:12:21,561 --> 00:12:25,581 investment banking, the bank institutional world, 191 00:12:25,741 --> 00:12:26,881 I still think it's very early. 192 00:12:27,121 --> 00:12:33,241 I think there's some value for some of the model creation 193 00:12:33,241 --> 00:12:37,081 or getting up to speed on some company quite quickly 194 00:12:37,081 --> 00:12:38,961 and pulling and aggregating that type of research. 195 00:12:38,961 --> 00:12:43,681 but I think that's lower value add, at least of where we stand today, 196 00:12:43,981 --> 00:12:45,021 and that's going to evolve. 197 00:12:45,281 --> 00:12:49,881 I think there's probably a need where there's less junior support 198 00:12:49,881 --> 00:12:51,801 over time for certain functions like that, 199 00:12:51,861 --> 00:12:55,241 but I think from where we are today and what we see 200 00:12:55,241 --> 00:12:57,301 from at least my perspective at Reading, 201 00:12:57,421 --> 00:13:00,401 I think we're just trying to adopt this technology 202 00:13:00,401 --> 00:13:05,881 and make it as efficient as possible for routine tasks 203 00:13:05,881 --> 00:13:09,581 that frees up capacity for other projects 204 00:13:09,581 --> 00:13:11,441 because we kind of have endless things to do 205 00:13:11,441 --> 00:13:12,481 and enhance at our company. 206 00:13:12,961 --> 00:13:14,581 And that's just giving us a lot more flexibility 207 00:13:14,581 --> 00:13:15,581 to adopt that. 208 00:13:15,921 --> 00:13:16,321 Yeah, yeah. 209 00:13:16,841 --> 00:13:18,741 So maybe on that note, I'm curious, 210 00:13:19,961 --> 00:13:22,761 there's a world in which AI just becomes something 211 00:13:22,761 --> 00:13:24,421 that helps speed up tasks, 212 00:13:24,761 --> 00:13:27,261 saves you on OpEx, things like that. 213 00:13:27,581 --> 00:13:29,141 And maybe in the same way, 214 00:13:29,561 --> 00:13:31,961 no given firm now has an advantage 215 00:13:31,961 --> 00:13:33,641 because they use Excel. 216 00:13:33,641 --> 00:13:35,961 because everyone uses Excel and it's just a productivity tool. 217 00:13:36,401 --> 00:13:41,721 There's also a world in which AI actually materially makes a competitive advantage 218 00:13:41,721 --> 00:13:48,061 or helps reify Apollo's lead in structuring and creative deals and so forth. 219 00:13:48,241 --> 00:13:54,921 Do you have a take on whether AI is going to be just a useful but a general commodity 220 00:13:54,921 --> 00:14:00,781 or it's going to be something that can actually structurally make a business more competitive in a durable way? 221 00:14:00,781 --> 00:14:06,241 I think if it's adapted and utilized properly, it will make you more competitive. 222 00:14:06,241 --> 00:14:11,141 I think at its core, and especially around asset management and investing type companies 223 00:14:11,141 --> 00:14:17,081 that are in the actively traded markets, it comes down to your process and the information 224 00:14:17,081 --> 00:14:17,821 at your fingertips. 225 00:14:18,301 --> 00:14:23,101 Knowing that you have a strong process in place to go out and execute and knowing your 226 00:14:23,101 --> 00:14:27,761 available liquidity or where things are pricing and what the value is at any given point is 227 00:14:27,761 --> 00:14:28,221 crucial. 228 00:14:28,221 --> 00:14:31,381 So that just gives you more competitive edge. 229 00:14:31,461 --> 00:14:35,901 And I think that exists today in the actively traded markets, 230 00:14:36,041 --> 00:14:37,941 but in the private markets, it's evolving, 231 00:14:38,101 --> 00:14:43,441 which we've kind of been at the forefront of trying to move on the broad Apollo machine. 232 00:14:43,701 --> 00:14:48,601 And I think having that will just turn into a competitive advantage over time 233 00:14:48,601 --> 00:14:55,581 where firms will be able to get further ahead as they're having access to better information. 234 00:14:55,581 --> 00:14:59,021 their teams know that it's accurate 235 00:14:59,021 --> 00:15:01,261 and stuff is not misaligned 236 00:15:01,261 --> 00:15:03,101 because the last thing we want to do is 237 00:15:04,081 --> 00:15:07,521 do something or make a decision that was misinformed 238 00:15:07,521 --> 00:15:10,461 or had misinformation and then that streamlines down the chain 239 00:15:10,461 --> 00:15:13,401 to both the enterprise and to our investor base 240 00:15:13,401 --> 00:15:14,001 at the end of the day. 241 00:15:14,581 --> 00:15:15,821 That makes sense. 242 00:15:16,441 --> 00:15:19,081 I wanted to ask you what your prediction was 243 00:15:19,081 --> 00:15:22,861 for what Apollo would be like when we're talking five years 244 00:15:22,861 --> 00:15:24,021 observing all the change. 245 00:15:24,161 --> 00:15:24,861 I think that's unfair. 246 00:15:25,581 --> 00:15:30,121 I can't even predict if my team is going to use Codex or Claude Code next week, so I want 247 00:15:30,121 --> 00:15:31,921 to ask you about five years in the future. 248 00:15:32,641 --> 00:15:38,541 But I'm curious for you personally, what are you hoping to see in terms of your job, team, 249 00:15:39,021 --> 00:15:45,601 how AI use in Apollo evolves, what conditions have to hold for the technology to actually 250 00:15:45,601 --> 00:15:46,441 be truly valuable? 251 00:15:47,501 --> 00:15:50,141 How are you thinking about things over the next few months? 252 00:15:50,521 --> 00:15:53,961 Yeah, I think it comes back to some of the stuff I touched on before. 253 00:15:53,961 --> 00:15:59,541 of just one having just you know full confidence in any type of information that's flowing out at 254 00:15:59,541 --> 00:16:04,681 the end of the day to our investor base that's accurate correct and we don't have to spend hours 255 00:16:04,681 --> 00:16:09,501 manually checking that to make sure that that's validated and having the confidence that we're 256 00:16:09,501 --> 00:16:15,581 sharing you know no issues and or mistakes um because we do have just a wide investor base 257 00:16:15,581 --> 00:16:17,701 over 400 global investors. 258 00:16:19,021 --> 00:16:21,701 I would also say being much more 259 00:16:21,701 --> 00:16:23,761 tech-enabled in mindset. 260 00:16:23,761 --> 00:16:26,561 I think our team has not lacked that to date, 261 00:16:26,581 --> 00:16:28,081 but it's kind of been an afterthought 262 00:16:28,081 --> 00:16:29,921 around the use of technology. 263 00:16:30,221 --> 00:16:32,641 And I think the more that we can develop 264 00:16:32,641 --> 00:16:33,961 and enhance these tools 265 00:16:33,961 --> 00:16:37,881 that make routine tasks more efficient, 266 00:16:37,881 --> 00:16:40,901 it frees up capacity to add more thought 267 00:16:40,901 --> 00:16:42,521 or think more creatively around 268 00:16:42,521 --> 00:16:44,381 why does this process exist 269 00:16:44,381 --> 00:16:45,441 and how does it work today? 270 00:16:45,581 --> 00:16:48,301 and how can we actually enhance that, or should we change that, 271 00:16:48,401 --> 00:16:50,781 or should we bring in a new type of technology 272 00:16:50,781 --> 00:16:52,721 and adopt that going forward? 273 00:16:52,941 --> 00:16:55,701 And our team has not lacked that today, 274 00:16:55,741 --> 00:16:57,461 but it's kind of been a secondary thought 275 00:16:57,461 --> 00:17:00,481 where I think ideally our workforce is positioned 276 00:17:00,481 --> 00:17:03,581 to think about that more on a go-forward basis, 277 00:17:03,601 --> 00:17:05,181 and that's kind of their primary thought 278 00:17:05,181 --> 00:17:06,701 is they're tackling different problems. 279 00:17:07,201 --> 00:17:09,481 This is specifically at Redding Ridge Day-to-Day 280 00:17:09,481 --> 00:17:11,201 and kind of scaling our organization. 281 00:17:12,261 --> 00:17:13,801 I think we have time for one more. 282 00:17:13,801 --> 00:17:19,961 I think one of my learnings from working with you has just been how unimaginably complex and creative the work is 283 00:17:19,961 --> 00:17:23,861 and how it takes a lot of effort, both human and AI, to get things to work. 284 00:17:24,361 --> 00:17:28,861 A lot of people in this room who either want to work on the technology side of AI and finance, 285 00:17:29,041 --> 00:17:32,521 or perhaps finance side, is there anything that you would mention to them from, 286 00:17:32,681 --> 00:17:38,361 here's something that you should know or keep in mind when trying to apply AI to domains like yours? 287 00:17:39,461 --> 00:17:45,561 I would say it comes down to we are extremely complex organizations and it's not as straightforward. 288 00:17:45,861 --> 00:17:54,541 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. 289 00:17:54,561 --> 00:17:59,821 I'm sure as you guys have come in, you appreciate that we're super nuanced and complicated. 290 00:17:59,821 --> 00:18:12,261 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. 291 00:18:12,261 --> 00:18:31,201 And I think the success and, you know, where we are today with the Rowspace product 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, like pretty much every single day and, you know, understanding our workflows, which I think is crucial. 292 00:18:32,081 --> 00:18:41,301 Awesome. Well, I guess last thing I'll say is that, shameless plug, both Rowspace and Apollo Technology are hiring people who want to apply AI to finance. 293 00:18:41,301 --> 00:18:44,661 So please reach out to either of us if you are interested. 294 00:18:45,241 --> 00:18:46,381 Jack, thanks so much. 295 00:18:46,521 --> 00:18:48,121 Yeah, this is great. 296 00:18:48,461 --> 00:18:48,841 Thank you, guys.