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The "manual era" of capital allocation is in its final chapter. The firms still relying on manual data extraction and analysis aren’t failing overnight, but they are falling behind one week at a time. While most of the industry continues to "white-knuckle" through 200-page documents and legacy databases, and manual Excel extraction, a new breed of Agentic Allocators is quietly rewriting the rules. They aren’t just using AI to summarize emails; they are leveraging AI-augmented workflows that intelligently automate parts of their investment and operational processes that were previously impossible to automate.
Hosted by Victoria Sienczewski, CEO and Founder of AuumAI, The Agentic Allocator is the "behind-closed-doors" look at how the world's most sophisticated Limited Partners (LPs), allocators and General Partners (GPs) are actually deploying AI, and the hard-won lessons from those building the systems.
This isn't a series about high-level theory or technical gibberish. Each conversation features industry leaders, forward-thinking LPs, GPs and experts who are rewriting the rules of capital allocation through agentic AI. Expect real-world case studies, tactical frameworks you can actually use, and moments that challenge outdated norms. You'll come away with a clearer understanding of the critical questions every allocator must ask - about data privacy, team adoption, integration, and governance - before investing in any AI solution. If you're tired of the "black box" and ready to evolve your investment office for what comes next, you're in the right place.
This AI moment isn't necessarily a crisis in the markets. It's sort of a an existential crisis for a lot of pension funds, which is like, what what do we do here? If you don't have an answer to how you're changing your organization, you're in trouble with your board.
Victoria Sienczewski:Welcome to The Agentic Allocator. Today, we're joined by Dr Ashby Monk, executive and research director of the Stanford Research Initiative on long term investing. For over two decades, Ashby has worked closely with some of the world's largest sovereign wealth funds and pension funds on governance, organizational design, technology, and investment strategy.
Ashby Monk:Governance should be like a steering wheel rather than a brake. They're both about guiding the organization safely, but the steering wheel allows you to move forward. That's its job to avoid crashes. But brake is the wrong mindset because this is happening.
Victoria Sienczewski:Few people sit at the intersection of institutional investing, technology, and entrepreneurship the way Ashby does. And today, we're digging into what AI adoption really looks like inside LP organizations, where the ecosystem is headed, and what it will take for the industry to get this transition right.
Ashby Monk:You gotta surf the wave or survive the wave. And sometimes, the people who survive the wave are the people who know how to surf it.
Victoria Sienczewski:I hope you enjoy the conversation. Ashby, great to see you again. Thanks for joining.
Ashby Monk:Great to see you again. It's been too long. I'm looking forward to the chat.
Victoria Sienczewski:Ashby, before we dive into our topic today, which is AI and LPs, would love to take a step back and ask you to paint the picture of LP pain points that have existed long before AI was on the horizon, if you don't mind.
Ashby Monk:Absolutely. There are many LP pain points. You know, these are large understudied organizations. And I say understudied because my day job is academic researcher, and it can tend to feel pretty lonely to be one of those researchers focused on pension funds and sovereign funds. And so, really, when you ask, like, what are the ongoing challenges, there's many we could list.
Ashby Monk:But the basic problem is that, the complexity of investment portfolios has far out outstripped the capabilities to manage those portfolios. If you go back to the original construct of pension funds and sovereign funds, you know, we were talking about sixty forty. 60% equities, 40% fixed income. You know, these these were the models of institutional investment that really, dominated the industry. A lot of passive investing, not a lot of privates.
Ashby Monk:All that has been transformed into what we see today, which is, you know, funnily, this this word alternative investments, has almost become the more conventional form of investment rather than public equity or fixed income. Many organizations are now past 50% of their portfolio being in alternatives. And so what's missing is an organization that can really, handle that complexity, that opacity through technology, through process, through governance, and all the rest. So that's the basic.
Victoria Sienczewski:Fantastic. And, obviously, within that landscape and those challenges that you've laid out, LPs are also grappling with how to adopt AI in a prudent and a responsible fashion within their organizations. And you've had an incredibly unique vantage point Ashby in terms of your research but also your investment portfolio and co founding a number of different companies within the ecosystem and landscape. And so I'd love to ask you, when you think about how the industry has gone through the adoption curve of AI and we're obviously still earlier on, what have you seen? How is the industry evolving so far?
Ashby Monk:Yeah. It's a good question. AI has motivated asset owner investors like I've never seen before. So, there was a time when, the threat of climate really motivated investors to adopt net zero portfolios. There's been a few crises like the two thousand eight financial crisis, the two thousand one, what we call the perfect storm financial crisis.
Ashby Monk:I'm sort of dating myself here by by going that far back, but I was in the industry back then. But so this AI moment isn't necessarily a crisis, in the markets. It's sort of a an existential crisis for a lot of pension funds, which is, like, what what do we do here? Are we gonna be the ones to oversee the management of this corpus of assets, or are we going to ultimately rely on, you know, new forms of intelligence knowledge to to manage? And I'm really thinking about a a five, ten year project, but it is a project that starts today.
Ashby Monk:And so most CEOs or CIOs are being asked by boards of directors, what are we doing about this? And so when I say, like, I've never seen the, you know, anything motivate people this much, it's because if you don't have an answer to how you're changing your organization, you're in trouble with your board. You know, most boards are really asking hard questions about organizational readiness for AI, And it could be as simple as cybersecurity. Are you ready for, you know, chat agents that jump their box and come and try to hack your organization? You know, these are the the basic things that CEOs are being asked about.
Ashby Monk:But then it is, you know, how do we make investment decisions? How do we add value to portfolios through AI? How do we understand what we own through AI? We talk a lot about AI, but, ultimately, AI is a use case and a solution to a problem. And as we just said in the first question, there are many problems we could talk about.
Ashby Monk:And so, actually, pension funds, sovereign funds, endowments are actually really ripe environments for deployment of AI if we can get the, you know, the culture and the governance right.
Victoria Sienczewski:It's such important framing because it really is a transformation for these organizations, not just an implementation of a use case for a specific pain point. As I've personally observed different LPs and allocators exploring AI adoption and experimenting, I've seen also the commoditization of what I would say early use cases around productivity. And curious if you're seeing within the organizations that you advise whether they've started to move, let's say, to value add use cases as well.
Ashby Monk:Yeah. It's a really it's a it's a fun question. We I have this like on ramp that I try to talk to pensions about, you know, how to get going. And, like, making investment decisions is, like, the expert level. Okay?
Ashby Monk:It's like like, let's not start with, like, the core thing that your organization was designed to do. Let's start with some, like, very basic things. And so, look, I admit that, like, what I'm about to say is is kind of hypothetical because I would say most organizations right now don't have an organizational strategy. There's a few that do. But, really, like, the wave one of AI that we see is about individual productivity of specific team members using Claude, using ChatGPT to make beautiful PowerPoints or synthesize documents.
Ashby Monk:And that first wave, is useful, you know, getting meeting notes quickly, you know, in into a format that can be stored. But, really, like, the power of AI is collective knowledge, and what those individuals are tapping into is the collective knowledge outside the investment organization. Those organizations that are doing a good job of this are beginning to use AI to manage their collective knowledge and to mobilize that knowledge and history and, dare I say, belief systems, cultures, for the benefit of the individual that's opening AI. So collective knowledge is the power of AI, but a lot of the collective knowledge isn't from inside the organization. And so going back to the on ramp, you know, we often say, look.
Ashby Monk:Start by using AI just to understand what you think today. Like, who who's thinking what? How are they thinking it? Like, if somebody's doing a project on autonomous vehicles, make sure everybody in the organization knows about it so that you can tap into that expertise. From that, like, understanding what you know, what are your capabilities, what are your portfolios, you can start to do fun things like re underwrite past deals.
Ashby Monk:It's a little bit embarrassing for some people because AI is very good at pointing out what you missed. So you have to have some thick skin as you get into an exercise like that. But reunderwriting deals allows you to begin to understand how AI thinks. And then from there, you can begin to think, alright. Well, let's use the AI for a red team analysis on a deal that we're doing.
Ashby Monk:You know, point out things that we might consider. And then from there, we don't even jump in our thinking to, well, if you can do a red team analysis, let's do an investment decision. We kinda deviate from that thread of investment decision making to value creation. So how do we help add value inside a portfolio? Low stakes, generally, where you're saying, how do we help a fund manager be better?
Ashby Monk:How do we help a portfolio company solve a problem? It's a great use case for AI that's sort of lower stakes than, hey. We're about to make an investment decision at the investment committee. What does AI think? And then once you get past that, you know, re underwrite past deals, do red teams, do value creation, then that fourth final phase starts to be, alright.
Ashby Monk:Well, let's bring this thinking into our current decision on a deal on a manager. This is a little bit like managing the trust that investors have with these tools and building that trust.
Victoria Sienczewski:Absolutely. And one of the value add use cases that I think is particularly interesting as well is how to break down, let's say knowledge silos within Allocator and LP organizations. So Allocator organizations, you'll have let's say a client team or an client investment team. You'll have a research management team or manager selection team and then you'll also have an operations team and historically they've had three different systems that don't speak to each other and sharing that collective knowledge or wisdom within an organization is very difficult. So lots of exciting things on the horizon for the industry.
Ashby Monk:Couldn't agree more.
Victoria Sienczewski:Ashby, wanted to speak about KDX as well. And KDX has a very, unique investment philosophy and thesis around backing founders who are founding and building companies within the AI and also analytical tool space for institutional investors. When you think about generalist VCs, right, on the opposite end of the spectrum, often time they have misconceptions around institutional investors. I've heard things like, okay, institutional investors are slow to adopt technology or once they do they need a lot of customization. What do you think that they're missing?
Victoria Sienczewski:What don't they understand about this specific industry?
Ashby Monk:It's a great question. I I bump into those generalist VCs all the time, and they are generally brilliant technologists. And then they have some connectivity into an industry or a vertical. But, you know, generally, the the point of a generalist VC is they understand the technology being deployed just about better than anybody. And so they can get into these use cases and and begin to back founding teams or companies that even already have, you know, revenue and things like that.
Ashby Monk:Most VCs back companies that are going. That's kind of a a misconception. Very few venture capitalists are willing to back a person with an idea. And so there's there's a time piece that's a little bit different from the work we do, and there's a vertical piece, which is we go early. And in a specific vertical, they go a little later, and they're sort of more agnostic to which vertical thereafter.
Ashby Monk:But what do they, you know, what do they not know about LPs? I think they don't see LPs as a user of technology, not generally. They see LPs as a source of funds to go back technology. So, many VCs think of themselves in the service business, but the service is to the founders, to the entrepreneurs, not necessarily to the limited partners, the pension funds. The pension funds need the VCs to make money.
Ashby Monk:The VCs need the LPs to invest. But, generally, the GPs are focused on founders. I can think of one very famous, venture capital GP that has a hard rule that LPs can only get in touch once a year. And and so it just creates a different dynamic. And I'm not trying to fault the GP.
Ashby Monk:It's simply for their investing. They're not investing in the domain of pension tech. You have to be a crazy person to invest in pension tech. And yes, you are looking at a crazy person because this is what I do. So look, I think what they don't understand is, you know, I am crazy enough to go after, Like, I would build a pension tech VC fund because I think these investors, they may be bureaucratic, they may sit inside government, they may sit inside departments of universities, but they manage trillions of dollars.
Ashby Monk:You know, at at last count, we're up above a 140,000,000,000,000 with a t. And the way they make decisions is by processing data, turning it into information, building models from that information into knowledge, and then applying that knowledge as intelligence. All of that is affected by technology. The entire investment industry is really about processing data and who you know, to put it, like, very simply. And so my belief is, like, there are almost no industries more prone for tech disruption than the investment industry.
Ashby Monk:I mean, you just need to go look at the way hedge funds operate. You know? It's very tech enabled. It's very well governed technology applied in specific domains. I know you know this.
Ashby Monk:So, look, I hope to bring more, more generalist VCs into the space or convince more specialist VCs to launch. I don't think there's anything about invest tech that is so crazy, that we can't expect to be like defense tech. You know, if if defense tech has fifty, hundred specialist VCs in it, You know, it has similar scale and similar budgets on IT and tech spend as, you know, the global investment industry. And so we'll see.
Victoria Sienczewski:And, Ashby, in your answer, you laid out a current structure, right, of the industry. LPs, GPs, how they interact, how information flows, how available GPs are to LPs. I firmly believe that AI will change how the industry is structured, how LPs and GPs will interact and we can get into let's say the futuristic sense of that. But would love to hear your vision for how the industry will change when LPs adopt more technology, when it's easier to process GP data, when some of that opacity of the industry maybe, you know, is removed with AI as well.
Ashby Monk:It's dangerous to give somebody whose job it is to look over the horizon a chance to describe what they think is over that horizon. What I think is over the horizon, and and let's call the horizon ten years just to just so that we, like, really get it outside this moment. Because this moment is so wild. Like, the amount of tech disruption that is even happening on my own systems is kind of nuts. I mean, just making a PowerPoint presentation, like, used to take so long, and now you get a more well written PowerPoint than you've ever had in your entire life in seven minutes.
Ashby Monk:It's it's really kind of shocking, the the scale of things coming. So ten years out, I I have a lot of questions about organization, asset allocation, and implementation. So every investor operates kind of a gearbox of interlinking gears. The organization is your governance. It's your capital base and its preferences.
Ashby Monk:It's your culture. It's your tech stack. It's your people. That's the organization. It's sort of set up by a sponsor to go and invest in financial markets to achieve some goal.
Ashby Monk:That organization has a target asset allocation. That's the next gear. And then you have a way of implementing that asset allocation. That's the next gear. And the the gear out here is kind of the global market that you're sort of operating with to try to power the outcomes for the sponsor.
Ashby Monk:You can think about implementation and asset allocation as likely being transformed by AI, whether it's, you know, do we set the asset allocation related to some target return, or do we actually unravel the cash flows we're expecting to flow out of the organization and optimize for the perfect asset allocation to meet those cash flows? So right now, the standard is we optimize around a return target, not a cash outflow. Now the return target is meant to reflect a return that allows you to meet that cash outflow, but it is not exactly the cash outflow. So that's one of the things I expect will change in the next ten years To simplify it for, you know, those that don't speak academic jargon, I'm really saying the goals we set will become much more precise for investors. The implementation gear is also profoundly gonna change.
Ashby Monk:I do wonder what role for asset managers in this industry. So where we delegate investment decision making to a manager and we send our money to that manager to be managed. You have to imagine a world in which the money sits in an SMA in your own custody account, and asset managers simply have a right to engage in your SMA. And asset managers look and feel much more like tech companies selling a signal, selling a custom index, where you are engaging in a mass customized portfolio where each of your managers is providing you something that is slightly unique because, really, you're just tapping into their knowledge and their understanding of the markets and the intelligence that connects knowledge to action and applying that in the context of your own portfolio. Now I'm not saying venture capital goes away or private equity goes away, and those are very bespoke strategies.
Ashby Monk:But I do think the implementation gear will shift ten years from now from a delegated asset manager driven, mode to something that feels much more like tech outsourcing, where we're bringing in dashboards, we're engaging in technology, and it's all happening in our own SMAs at our own custodian. And there's there's other things I think like who are the people that are gonna have jobs in this industry, I think are gonna really change. But we can get into that if you want.
Victoria Sienczewski:Fascinating view of the future, Ashby. And wanted to dive back into the present a little bit more Sure. In terms of the on ramp that you often advise LPs to adopt. What are some examples of successful AI implementation that you've seen? What are some best practices?
Ashby Monk:Boy, it is about building a coalition of the willing because the general mode here when you bring AI into an investment organization. So I show up at a pension fund and I have a great new tool. The reaction of the pension leadership is to take this tool and send it to the person whose job it is is to do the thing that this tool does and to say to this person, what do you think? Now I can tell you the very first reaction from that person is highly negative. Now I've earned I've earned that knowledge.
Ashby Monk:I'm now sharing it with you, but I have earned that knowledge over ten years because what you realize is that person is very threatened. I'm not trying to put that person down, by the way. They might actually see the weaknesses in governance process, etcetera, better than anybody else. But the reality is embedded in that is a threat. This piece of technology is coming to do the job you've spent your life dedicated to, and it's called valuations.
Ashby Monk:It's called pacing models. It's called sizing transactions. It's compliance. It's legal. And and they don't want necessarily to give an endorsement to something, but it's gonna come into the organization and put their job at risk.
Ashby Monk:And so finding alternative pathways into organizations is often what I try to figure out on behalf of our startups, which is a little bit like saying you're trying to avoid the person you're gonna insult and go find the people who can take a broader vision of what the organization should look like three to five years from now and build programs where that person's job isn't being replaced, but that person is not working on the mundane, boring tasks that they used to have to do to run their spreadsheets. And it's usually spreadsheets, by the way, still to this day. We're usually talking about replacing spreadsheets with agents or software or some governed AI. So that's really one of the things I've learned. And so best practices is about, finding safe spaces to experiment, not relying on the single person whose job is gonna be affected to make the decision on that piece of technology.
Ashby Monk:You you don't wanna have them not involved in the discussion, but you can't give them the type of authority that I see given all the time where you've invented a new way to do pacing models and the first thing you do is ask the person who does pacing models, you know, what they think about this tool. 99 times out of a 100, this is not gonna be a positive response just for the fear of their career.
Victoria Sienczewski:No. Absolutely. And I've I've experienced that personally as well. For example, from a risk perspective. Right?
Victoria Sienczewski:That individual say, I am highly fearful, right, of sharing specific information, right, or specific documents that are under confidentiality agreements even though they're comfortable sharing it with other Lawyers. Rules that may yeah. Or lawyers. Accountants. Absolutely.
Victoria Sienczewski:Other intermediaries. Absolutely. And so it's a it's a learning and an adoption curve for every single role and title across these organizations. So, fascinating to hear. And Ashby, wanted to close with two last questions if you don't mind.
Victoria Sienczewski:The first one would be focused again around if you were to give advice to an LP or Allocator organization that is earlier on within their adoption journey, what would you say?
Ashby Monk:So get the governance document written in a way that, how do I put this? Governance should be like a steering wheel rather than a brake. K? They're both about guiding the organization safely, but the steering wheel allows you to move forward. And, you know, actually, that's its job to avoid, you know, crashes.
Ashby Monk:Steering wheel. If you're just driving straight, there's a crash inevitably that's gonna happen in front of you. But but brake is the wrong mindset because this is happening. This is a wave that, like, you can't dodge. We're all gonna have to survive this wave.
Ashby Monk:And, oh, here's another fun analogy I say to my students. I say, you know, you gotta surf the wave or survive the wave. And sometimes the people who survive the wave are the people who know how to surf it. We do a lot of surfing analogies in our writing, but but we actually you know, that's a really because it's you're out there. You're in the water.
Ashby Monk:It's you're in a new environment, and that is like AI. Like, you're just not gonna dodge this. It's already you can pretend like you're not in the ocean right now, but your team is using AI. Your scheduling is AI. AI is trying to attack your organization right now with phishing requests.
Ashby Monk:You know, you're in the ocean. You can't dodge it. And so teaching people how to surf gives them confidence when that wave starts coming that they know how to survive it.
Victoria Sienczewski:Amazing. And Ashby, last question. And this really comes actually from the Agentic Allocator's listener base. But oftentimes, I get inbounds from younger professionals who are saying, I feel stalled within my organization. I'd like to adopt AI but, you know, senior folks are let's say not supportive.
Victoria Sienczewski:What advice would you give to them in terms of trying to, let's say, make a change within their organization or advocate for the use of AI internally?
Ashby Monk:Wow. That puts it directly into this, like, really practical moment, which we see all the time. So so the it's so funny. It's like most of the time I get the question from the person who's that person's boss being like, how do I control these young people who are constantly after me for for AI adoption? And so I'm I I'm accustomed to answering the question of the person with gray hair, not in you know, helping the younger person talk to the gray hair person.
Ashby Monk:But let me try. I'll I'll give it a shot here. I I think reminding that, this is happening, and it's it's not taken hold in our industry in the way that it has in others yet because our industry doesn't have the same artifacts as a legal industry or a medical industry, we have been allowed to go a little bit slower because we don't have all of this training data. Like, we don't write everything down the way that doctors do because of insurance. And so whatever you think, is happening in our industry is actually much slower and much more guarded or gated or governed.
Ashby Monk:You know, in a car, there's a governor that, like, slows it down so that you can't drive 200 miles an hour. Like like, we kind of have that happening because of our opacity. And so if I'm a young person talking to their boss about AI, I would try to explain, like, look. This may feel like it's happening fast, but in other industries, it's happening faster, and we are gonna catch up at some point. We are gonna have AI that captures the key decision artifacts, and then we're gonna train the models off those artifacts.
Ashby Monk:In a way, like, we are being given an extra period here because of our bureaucracy and our, know, administrative bent to have a little bit more time. And we need to use that time to build safe spaces for AI experimentation. So make a plea to your boss to build the guardrails around that safe space. Don't make a plea to your boss to just let you do it. That's how they get fired, just letting you run wild with AI.
Ashby Monk:The plea is, let's build a capability to use AI. That's your job. Don't use AI. So there's a slight nuance there. Your bosses wanna hear that you're gonna build the capability.
Ashby Monk:They're gonna have an approval right over that capability, and the capability will provide visibility into what's going on. It will provide safe spaces when failures happen, which they will. And that's all part of innovation, by the way. And so that might be my pitch. Pitch your boss on building the capability rather than pitch your boss on a new piece of AI.
Victoria Sienczewski:Ashby, phenomenal. Thank you so much for your time. It was such a pleasure speaking again.
Ashby Monk:So fun to see you.
Victoria Sienczewski:That's a wrap for this episode of The Agentic Allocator. If today's conversation gave you a clearer vision of where the industry is headed or helped you pinpoint exactly where your own process is stuck, go ahead and follow or subscribe wherever you get your podcasts. Your firm's judgment took years to build. Stop letting it reset every time a team member walks out the door. Visit auumai.com to discover the structured digital intelligence foundation your team needs to leverage Agentic AI to its fullest potential.
Victoria Sienczewski:Until next time.