Capital Allocators: AI in the Investment Office – Abby Barlow, Laura Hill, Brian Sugrue, Jenny Heller, John Lawrence, Matt Bank, Kristin Kallergis Rowland, Jon Webster (EP.515)

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Show notes (from RSS)

AI is top of mind for everyone in the investment business. Our Summits are abuzz with curiosity about what others are doing.

I asked 8 CIOs to share how they're using AI today, including what's working and what isn't, the tools they've adopted, and where they're headed next. They range from a single-family office with one investment professional to one of the largest pension funds in the world with thousands.

What emerged is a range of use cases — from using AI as a personal productivity tool, to changing investment workflows, organizing institutional knowledge, improving decisions, and ultimately trying to generate alpha.

You'll also hear some consistency in the tools currently used and different views on how far AI should go in the investment process.

Featured in this interview: Abby Barlow, CIO of Westwood Management Laura Hill, CIO of Advocate Health Brian Sugrue, CIO of Shannonbridge Jenny Heller, President and CIO Brandywine Group Advisors John Lawrence, President of Rice Management Company Matt Bank, CIO of GEM Kristin Kallergis Rowland, Global Head of Alternative Investments for J.P. Morgan Asset & Wealth Management Jon Webster, Senior Managing Director and COO of Technology & Operations at CPP Investments

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Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)

Transcript

Ted Seides: Foreign. I'm Ted Seides and this is Capital Allocators. AI is top of mind for everyone in the investment business. Our summits are abuzz with curiosity about what others are doing. So I asked eight chief investment officers to share how they're using AI today, including what's working and what isn't, the tools they've adopte and where they're headed next. They range in available resources from a single family office with one investment professional to one of the largest pension funds in the world with thousands. What emerged is a picture of the current state of AI in the investment office, including preparing for meetings, conducting analysis and leveling up decision making. You'll hear some consistency in the tools used alongside different views about how far AI should go. Taken together, there's a progression of opportunity in the investment office that awaits everyone in the seat. So let's get started. First up is Abby Barlow, CIO of Westwood Management, a single family office at which she is the sole investment professional. Why don't you tell me about your role and how you're using AI in it?

Abby Barlow: I am the CIO for a single family office. I started here three years ago. I have not hired yet on my investment team. Part of the reason I have not hired is because I happened to start at the same time that AI was becoming a thing and have leaned into it to support me and become my analyst. I joke with my team here in the family that my analyst's name is Claude. He works all hours of the night and he's amazing. I'm going to put him on our org chart. I mostly am using Claude. We feel comfortable that it's not training on our data and it's walled off in terms of privacy and security. We also have a copilot subscription. It is not as good at this moment.

Ted Seides: Why don't you walk through the different ways that you're using Claude?

Abby Barlow: A better question is almost how we're not using it. We are using it in every step of the process. From the first time a fund manager sends us a deck and we agree to take a meeting. I'm feeding the prep materials into Claude as a project and I'm saying summarize, help me prep, help me ask smart questions. It's amazing at that all the way to the point where we're making an investment. You're at the 10 yard line. They are sending you legal document revisions in the final stages. I will take a red line. Got one. Recently there were 185 tracked changes in this document which historically I would skim them. If anything seemed relevant, I would probably send it to council. We would pay them to review it. It would take a day or two or three. Now I can just take that, put it in Claude and in one minute get back a good summary of what's relevant. It is better than what I could do on my own. We're saving a lot of time and money doing that. We ask better questions to the fund manager than we otherwise would.

Ted Seides: Beyond the meeting notes and legal, what are some of the other ways you're using it in your process?

Abby Barlow: We're building a lot of tools. Think of this as an app or a software program that historically you would maybe go buy from the market. Benchmarking or a pacing model or portfolio company look through to understand what you own. But instead of paying someone to host that service and implement a tool, I said I'm going to see what I can build first. This benchmarking tool is a great example where I used to in Excel build vintage year comparison across funds and peer groups. It was messy. I would end up with 60 tabs in Excel. It took a long time. I got halfway through with one of those projects and I said, hey Claude, look what I'm trying to do. How would you approach this in five minutes? It thought about that. It spit out an app. I iterated it with Claude to dial it into exactly what I wanted. It probably took me half a day to say, I have this amazing tool that I can now use. I've used it eight or ten times since.

Ted Seides: What have been some of your other large use cases?

Abby Barlow: Investment committee decks. I've been going through this strategic review of our equity program, Public equities. There's a 30 year history and it's complex. I'm trying to make a recommendation. I used Claude as a thought partner in laying out the setting. This is what we own. This is the history. These are 20 things I'm thinking about. Help me organize my thoughts. Recommend an agenda for a deck. Claude is available all the time. I'm laying in bed one night on my phone with Claude. What about this and what about that? Can you show me a draft of a PowerPoint? You can say if that's good or bad that I'm doing that night. But it felt good to get it out of my head. The next morning I come in and it's done stuff.

Ted Seides: How about tools you've used beyond Claude?

Abby Barlow: I use ChatGPT for lots of questions. Mostly I know others are using perplexity. We went with Claude. It is more than we even know how to use at this point, something that's worked well is I've asked AI to interview me in the goal of creating a context document for it to always have. I say, hey Claude, let's take an hour. I want you to ask me everything about my organization, my business, myself, my working style, the history of this organization, the family. Ask me anything. I want the output at the end to be a document that is shared for any new project that you start working on. We want to create an enterprise level context doc that everyone on the team is using consistently. We just hired a new person. He's been here three weeks. How valuable would it be for him to have some master context document to put into Claude before he asks it to do any little project? It's going to know more about where he sits, what he's working on, what

Ted Seides: are examples of things that you thought you could use AI for that didn't really work.

Abby Barlow: It does make mistakes, especially when you're dealing with numbers and complexity. It gets me to a solid first draft. I need to thoroughly scrub it like a junior analyst would do. You would never take something the analyst would give you the first time and be like, this is perfect. I'm showing it to the family now. It was double counting some of our commitments in our pacing model I was building. It's hard to catch that sloth. I have gotten in a habit where I will ask it at the end of any deliverable. Go back to the beginning of this conversation and what we were trying to accomplish. Review the deliverable, triple check all the numbers again. Find your own errors. It will be like, oh, I missed these three things.

Ted Seides: How did you determine what data you're comfortable sharing with information that might be confidential? Coming from managers.

Abby Barlow: I have been asking managers, how do you feel about LPs taking your legal docs pitch decks and putting them into AI? The answers I've gotten have been all over the place. In general, they say we just expect it's happening. We would assume that those LPs are treating it like we would want them to.

Ted Seides: As you look out a year from now, how are you thinking about continuing to lean into AI alongside of potentially bringing in humans to do some roles?

Abby Barlow: I'm trying to push it as far as I can. As these things are being built, it will get to be too much for me to handle on my own. When I hire someone, I would love someone who also wants to embrace these tools and keep building things. Claude will always be on my team. Whatever model is the one that is best. Someday maybe we'll switch. The next frontier for me is I want to try to build a virtual EA for myself, an executive assistant. I would love to build an agent to help me with email and calendar.

Ted Seides: Abby showed just how far an individual can push AI. Laura Hill, CIO of $26 billion Advocate Health introduces an important boundary to protect human judgment. Laura, how are you and the team thinking about your AI strategy?

Laura Hill: We challenged the team to come up with a one sentence sound bite. The sound bite that they came up with was we will use AI to augment human expertise, accelerate routine work and improve decision quality while preserving human judgment, confidentiality and accountability. I prompt the team a lot. How will we not use AI? A lot of allocators are quick to say, oh, it can draft an investment memo. What's the purpose of your investment memo? To me, the purpose of an investment memo is to sit down and make yourself think, what are the bets I'm taking? What would make this blow up if we come back for a re up in three or four years? What would make us pause or what could have gone wrong? I absolutely do not want AI doing that. Let AI help you format it or make it look pretty or add some graphics. It should not be helping you answer that core question of looking around the corner. The team is excited for some of the time savings, but we all want to make sure that we're mindful of not using it in ways that would take away from our edge.

Ted Seides: How are you using AI in your office right now?

Laura Hill: Three categories. We have the AI embedded in existing vendors. This is pretty nascent, pretty low evidence of vendors incorporating AI much in existing software. The second category, which is where we're using it the most, is the Copilot suite. This has come a long way in the last year across SharePoint emails. One of the unique aspects that we have is being part of a large healthcare system is we have an internal data science team. We've created an investment brain. The private markets team has started it. They've named it General Gist. We've spent a lot of time working on how to set up the groundwork with uniform document titling categorization. We already have IT handling some routine work. Right now it's ingesting all of our private markets statements, capital calls, nits, extracting the data, which is different across every manager. It's spitting it out into a uniform Excel template that our analysts can then review. Then they get an email every Monday that shows which documents have been extracted. We process it with human eyes. It's a perfect example of elevating analysts to work at the top of their license because they're progressing from data entry to actually reviewer status. Healthcare is good at KPIs iterations, metrics and safety. Think about where is our edge? How can we use AI to get the other stuff out of the way so that we can really focus on where our edge is?

Ted Seides: When you've looked at your tools, how did you decide to do it internally

Laura Hill: versus externally Agnostic to whatever vendor may sell this the principles are that we want to own our data, we want to be able to own the output. The flaw with a lot of third parties is there's a barrier to re extracting your information. There's going to be rent extracted. We want to maintain autonomy here. We're in a unique place that we recognize a lot of other allocators wouldn't have the resources we have. We're going to be able to customize, tweak and iterate as we find out what works and what doesn't.

Ted Seides: Is there anything you've explored with your team where you came away thinking the hype around AI surpassed the reality of what you could do?

Laura Hill: The external vendor solutions so far for document routing has not met expectations. We have chosen to build that internally. A lot of this is the heterogeneous nature of how gps send documents. I'm extremely skeptical of the solutions that claim to be able to manage your inbox, manage data rooms. People are also selling LP portfolio insight. I haven't talked to a whole lot of LP colleagues that have had any external vendor success.

Ted Seides: What do you plan to work on over the next 12 months?

Laura Hill: The boring hard part that's going to lay the foundation is continuing to get the data in order. Consistent nomenclature, document descriptions, prompting. This is all stuff that we have to walk before you run the definition of success. Expanded use of AI agents to help with meeting prep note prep, document drafting, data analysis big picture those massive queries. When you get a question about the portfolio of where do we have exposure to xyz theme or company? That's a lot of hunting and pecking. That's where our investment brain will be able to help then monitoring the legacy funds. I would love minimal time spent on those legacy funds and way more time spent on our core competencies. It's not a better PowerPoint, it's not a better model, it's not a better memo. I can do all of those, but it's speed to decision, differentiated relationships, differentiated viewpoint. The next year will be telling because there's a lot of promises but not yet a lot of traction.

Ted Seides: Laura drew the line around preserving human judgment. Brian Chagru, CIO of Ireland based single family office Shannon Bridge Investments, picks up that thread with a practical look at using AI to create more space for that judgment. Brian would love to start with how you're currently using AI.

Brian Chagru: There's three categories of things. The first is getting us to a point where a diligence process, a portfolio monitoring or management process can check all of the boxes in a timely manner, fill out our investment file on something we have said yes to and is in our portfolio, or something that we've said no to. But we can build out the reasons and the investment memo as to why we've said no in a much more fulsome way without building a huge backlog of work. The second is the standardization that we have found AI tools extremely helpful for we know the output that we're looking for, but it becomes a growing backlog of work to make sure that memos reporting performance can move everything into our standard house format in a much more efficient way manner. The third is around preparation. I find it to be a great foil if I'm preparing for a diligence meeting with an existing manager. Portfolio update I can have a red team debate with AI based on here's our view of the portfolio, here's the update we've received, here's where I think there are issues. Take the other side of the argument. I get to the meeting with the nuanced, slightly richer version of the conversation that I would otherwise had. The one thing we have not used it for is as a decision maker or a recommendation tool, because I'm not that comfortable with the idea of that. Maybe we'll get there. We haven't crossed that hurdle yet.

Ted Seides: What are some things that you tried that you found AI didn't get to where you were hoping to.

Brian Chagru: There's a broad category of things where it continues to disappoint, and that's where you venture into areas you maybe don't understand. What it can produce if I ask mediocre questions is something that seems to form, fact checked, footnoted and probably fictional, but I'm maybe not expert enough to recognize that. I see it from the team as well when they've leaned on their own experience. It produces garbage in the three areas

Ted Seides: where you are using it actively. What's your tech stack?

Brian Chagru: We are primarily using Claude as a secondary tool, ChatGPT. We do have a portfolio management tool called Asura and they have built a huge amount of capabilities into their ui. They have brought some of their own engineering expertise to help with some customized applications that they can now deliver extremely quickly for private market.

Ted Seides: As you look out over the next year, what are some of the projects you're hoping to take on using AI that are not in place today?

Brian Chagru: The Big One we talk a lot in our organization about the hit by a bus risk LPs ask managers all the time, but what's your key man? Who's going to run the show? It's true for us as well. If I disappeared in the morning or if my principals were not around or available to share institutional memory what is our process for how we can transition things smoothly? AI as a tool for us to examine where there are gaps to help us generate some of the content that's required to take my and the team's institutional memory today, commit it to paper so that hopefully it's a positive transition in some number of years, I'm handing everything off to another experienced CIO who can pick up the ball and continue to run, not go. Oh my God, I've been left with a total dog here. I don't know where to start. Try to bring the operational side up to a grade A and above is the one big goal for us. A couple of practical pieces Cash Forecasting I would like to see us primarily leaning on AI for pointers on exposure risk inefficiencies on cash holdings, what rates we're exposed to on the borrowing side, what rates we're capturing on the cash deposit side. Mirroring that with our expectations on capital calls and distributions, capital commitments made to private markets. There's a lot that can be done there. The second will be on manager and portfolio monitoring. It's maybe a little easier in public markets. There's more readily available fluid information. I'd like to automate our process a little better on that. We have an organized calendar on manager updates. Still a huge backlog of well, I'd love to pull all my notes together from the last 10 meetings with that manager. When I have a few hours and think about how their narrative has evolved or why don't I take this firm's fund two, three and four decks. They tell me their process has not changed. What is their marketing materials telling us about their process and how it's evolving? These are questions that anybody who sat in a CIO seat has always wanted to answer. These tools are grease to the wheels, making it easier to go out and find those answers.

Ted Seides: What does it take for you to get there compared to where you are today?

Brian Chagru: In theory, one of the great things about AI is that it can draw from unstructured and disparate data sources and put one narrative together. If I could standardize the documentation that I have on a manager, that's what it takes. There's a broader point here which is how much time does using AI really save? What I'm describing is a time consuming preparation process to get to an answer. It's not two or three or four times faster than it would be otherwise. It's a little bit faster and it facilitates the final steps. But all of the preparation on getting data quality right and getting the information organized, that's still true today.

Ted Seides: What do you think it would take for you to start using AI to improve your decision making?

Brian Chagru: The way I have the framework in my head, if I was to sum up where AI is for us today is that it's not about producing analysis that we could not do before. It's about creating a little more space for us to use judgment in analysis. There's only so many hours in the day if I can save 5 or 10% of those by letting AI consolidate data, analyze a transcript from a discussion, put all of that into some keynotes so that I can sit back and take a little more time to think about what my next steps following a diligence meeting or a manager update. That's where it's opening our aperture a little bit right now. It has not opened our eyes to new sectors we're not exposed to. It hasn't helped me make investment decisions yet, hasn't helped me size positions better. I don't know if we'll get there. I'm maybe a little bit of a skeptic on whether it's really ever going to overtake that judgment piece. Where we're getting benefit today is carving out a little more space for judgment, taking up a little less time on producing analysis.

Ted Seides: Brian highlights both the power and limitations of using AI as thought partner. Jenny Heller, President and CIO of multifamily office Brandywine Group Advisors, takes us from individual experimentation toward building AI across an investment team with a particular focus on trust verification and turning a collection of one off use cases into a shared operating system. Jenny, how are you using AI in your office now?

Jenny Heller: A principle that underlies how we're using AI today and will continue to use it is building layers of trust. One is trusting in the boundaries of AI, having confidentiality in the perimeter of where information goes. Everything we use is enterprise grade. Nothing trains on our data. If we ever use agents, which we're not doing yet they'll be walled off and sandboxed before we let them loose. And this is what earns us permission to use it at all. Another thing we think about is having trust in the output, thinking about accuracy as a primal part of any of the work we do. Which means that we have to have a verification discipline, keeping a human involved in any consequential decision as we get there, finally trusting each other, thinking about what the provenance is of anything that's produced over the long term. Thinking about what's human and what's AI is important in ensuring everyone has transparency in any piece of work. Today we've spent a year plus in undirected play inside of our office. We're using AI and manager meetings and diligence prep. It could be running investor letters. We've connected ChatGPT, AmpCloud to our Slack channel. We can pull manager data. We can pull meeting notes together to come up with thoughtful updates before we enter a meeting, generating questions that I think are grounded in a longer history of data than you can consume independently to draw on our accumulated history with the manager. Everyone on the team has different use cases now, but we do these manager assessment frameworks where we grade our managers before deciding whether they go in the portfolio or whether we're going to engage in a re up. I will pull a ton of information together beyond the memo. I can bullet point every part of the framework and ensure that I'm leveling out my own bias by looking at broader history. The team is using it for portfolio and position analysis, actively using Claude for Excel to recreate manager models and pressure test assumptions. One of the cool use cases has been around HR and mentorship. Whoever's responsible for a meeting will pull together meeting notes prior to a meeting. If I read through the notes and feel like an area needs to go deeper, I'll start using Claude as a thought partner to have a back and forth on how can we deepen this line of questioning and then pull from that dialogue to then give that feedback back to the team? Everyone on the team filled out a sheet called How I Operate that focused on how they like to communicate what some of their pet peeves are. Before I was giving reviews to anyone on the team, I wrote the reviews and then I paired them in Claude with the How I operate language and said how could I reframe some of what I've written so that this person can better hear it?

Ted Seides: What have you tried that haven't met your expectations in the hype of what AI might be able to do we

Jenny Heller: tried to pull together mid diligence updates and this would sit between our issue memo, which is early stage diligence sharing with the team and our investment memo. The thought was that the lead or associate director could pull together this update using AI in an hour. It was a failure. We got some great diligence updates. The team was finding that they were spending hours and hours pulling it together because the context was too big, the questions weren't specific enough or the outputs that they were getting weren't conducive to the way they wanted to share information. So then we put it more on the team to say we're still going to do the middle agence updates but it's on you to be informed as opposed to creating another layer of paperwork trying to use AI as a cover. I'm hesitant about how AI is used in memo writing. It can be helpful in producing a robust quality of returns analysis, something that's concrete and specific. There's real limitations. If the problem set gets too big and too unstructured, the LLMs start to lose their potency and their accuracy. The more structured and specific the question, the better the answer.

Ted Seides: How have you managed the trust of

Jenny Heller: all of your data using enterprise grade systems? Was step one ensuring that the systems have a robust compliance structure built into them. The model isn't training on the data, at least that's what they say. Another component of it has been that we have been deliberately slow in turning on cowork code, any of the use of agents because we want to understand how we can ring fence what we're building. We know we want to get there and we have a path. I would love to have Claude be able to connect with my email. I'm just not at a place yet where we can be sure that that's safe and secure until we get there. We're approaching it slowly.

Ted Seides: Where do you hope to be a year from now?

Jenny Heller: The beginning of the ARC was having this unstructured play. The next step will be level up the team. We're structuring a number of teach ins. We're going to have learn to code through cloud day that one of our wonderful managers, Sam Lesson is going to teach a number of teams in the New York area. The goal is really to build flexibly so that we have an owned layer that sits above any model that can have all of the skills of our process built into it where we can take all these one off use cases that the team has and turn it into something that's more of a shared language. And a shared ecosystem that the whole team can use. What we'd like to do is start letting the LLMs selectively read data, whether it's tying into data rooms or tying into the underlying documents. That will create a powerful knowledge layer. I want AI to be at the core of our operating system as an investment team, doing things like every time someone on my team writes a memo, the system can prompt them and say, well, instead of writing this for you, here are some questions that I can ask to help you hone your own thinking. It can become more of a dialogue that we have a system that knows us well enough that it can gently flag anchoring bias or help pull in information historically that could be relevant that we may not be thinking of. To strengthen a thesis, Jenny describes the

Ted Seides: challenge of turning experimentation into an investment team operating system. John Lawrence, CIO of Rice University and president of Rice Management Company lays out the next step, a roadmap from productivity to investment Alpha Why don't we start with how you're thinking about AI in the investment office?

John Lawrence: We're using it for everything. AI is such an amazing technology that can not only help us from a productivity perspective, but from an investment perspective. We're ultimately hoping to get to a point where we can drive Alpha out of utilizing AI. We're not there yet, but that's our ultimate goal. First we have to set up the infrastructure, which is important. We have to empower the team as well and have to have buy in from the team, the university. There's hurdles we have to get over. We certainly have to do the right thing. There are risks with regards to data integrity and some of the output. We have to be mindful of that. I think it can be a meaningful productivity enhancer. It can be meaningful from an investment decision making perspective and then ultimately drive alpha generation.

Ted Seides: How are you using AI today in the office?

John Lawrence: I'll walk you through a few examples. I probably have a dozen for you. How we're already utilizing AI. One that's a very easy win for us was we have a daily news summary delivered by AI. We see news from our investment partners, our key investments as well. So that's helpful for me as a cio. One thing more helpful is a weekly summary of all the work that our team has done. We have multiple asset classes, multiple folks out traveling all the time. All this comes in one weekly summary. All of our manager meetings, all the manager letters. That's been tremendously helpful for our team as far as staying on top of the portfolio. Another thing that's been Helpful is preparing for manager meetings. We have a tremendous amount of historical data with our current partners, then new partners. We also have some data as well preparing pointed questions and due diligence. It's been helpful on the meeting prep side, on the front end, on our board memos. It's been tremendously positive beneficiary. We save about a day of time. That's a very onerous process. It consolidates the work that our team has already done and puts it into a template for us analyzing financial statements. AI has been great, particularly when we look at co investments. AI has been a fantastic set of eyes for us building valuation models and building financial models. Claude is good at this and it's been helpful when we start from scratch from an operations perspective, processing incoming documents. The tool Canoe. That's been helpful. Automating a lot of the integration of those documents into our current Systems saves roughly 30 hours of time for our operations team. The culmination of all this is when you have 10 or 12 data points. It's a tremendous efficiency enhancer. It allows our team to spend more time on the value added work that hopefully helps us make better investment decisions. We had an AI day for the team internally. For our team we prepared a demo for our board and we prepared a podcast. One thing that's fascinating. With the podcast, we decided to use an AI generated voice. We put about two hours of previous podcasts that I've done for our board. AI took that. I found it remarkable as far as the likeliness of my voice as well.

Ted Seides: What are some of the areas that you dove into hoping AI would help but hasn't?

John Lawrence: The real challenge is aggregating our data into one centralized data lakehouse, which we're working on. It's easier said than done. That has taken more time than I would have expected. I was hoping we would have had this six months ago. We're still working on it.

Ted Seides: I'd love to hear what your tech stack looks like on the front end.

John Lawrence: Canoe has been fantastic for us as far as automating processing when we have incoming documents and financial statements. We're building a centralized data lakehouse. You can use Microsoft or Google. On top of that we can utilize any platform. ChatGPT Quad, Google Gemini, Microsoft Copilot. We also use a tool called Hebia, which has been productive for our team on the back end. Assuming we have our data lakehouse in a good position, then we can utilize any one of the LLMs to access our data. I'm hoping we can utilize our data to identify patterns at scale. It's hard to connect all the data that we have. Allocators have tremendous amount of access to managers, to letters, to peer conversations, to conferences, to podcasts. Listen to your podcast Ted Aggregating all that data together can be a challenge and then making better decisions, that's ultimately where we want to get what have

Ted Seides: you found have been the most effective uses of the various LLMs?

John Lawrence: I found ChatGPT helpful from a qualitative perspective. Claude when we build financial models helpful from a quantitative perspective. Using Hebby has been helpful building presentations for the investment discussion, tying some of the analysis together, putting this into our MC Rice Management Company template, which we have in hevia. Tremendous efficiency gains across the board for all three of those platforms.

Ted Seides: As you look out over the next year, what are the steps you're hoping to take to get to the point where you're impacting alpha generation?

John Lawrence: It's going to be proven at some point in five years or not. If we're successful, I'm certainly hopeful we get there. A 10 basis point increase in annual returns would be worth it. I'm hopeful to find patterns and information that can allow us to make one or two better decisions a year. Minor increments in improvement can have major compounding effects in the future.

Ted Seides: In case you're wondering about John's AI generated presentation for his board, here's a short clip from the introduction. Good luck Telling the difference from John's

John Lawrence: Real voice Before we begin, I want to acknowledge that a considerable amount of this content and discussion you are about to hear was generated with the assistance of artificial intelligence. In fact, this is not the real voice of John Lawrence. The narration for this presentation was also generated by AI.

Ted Seides: John explains why getting proprietary data organized is essential to improving decisions. Matt Bank, CIO of $14 billion Ocio Gem, is rethinking the investment workflow itself. He also describes how dramatically cheaper software development is allowing GEM to build customized tools internally instead of relying on outside vendors. Matt, how have you thought about using AI strategically at jemp?

Matt Bank: Phase one for us was building out a foundational enterprise tool set some internal development capabilities we needed to enable secure, decentralized experimentation across teams. Because everybody has a slightly different workflow, we're encouraging everybody to think like a shop floor four person deconstruct their workflow, sourcing diligence, monitoring managers, portfolio management. Every stage has some bottleneck or limiting factor. You start with the lowest order stuff and that has been time saving on data aggregation and retrieval, note taking apps that saves junior team members time and cleaning and parsing, particularly for introductory calls we have with managers every week. For our marketables team, there's a lot of meeting prep that goes on, reviewing old call notes, letters that might have been written over the past several years for a quick refresh. We want to be able to pull all that together in a thoughtful way. For our private team, it's sector research. We do a lot in the independent sponsor market. Trying to get a baseline on a new sector is important. We're not so arrogant to think we're going to know more about geofabrics extrusion relative to what a GP or the owners of that business know. Can we get smart enough to ask better questions? In my seat, the bottleneck is consuming research, strategists, papers, podcasts. My commute is only 20 minutes. The feasibility of getting through all of the ones I want to get through in a week, it's not a reasonable exercise. The next level is doing things more quickly and deeply. How do you cut to the chase through data faster for current manager meetings? That's monitoring theses through time and space. How have the stock positions evolved? Are there any inconsistencies in the way a manager's talking about that? If you're on a biotech call and someone's talking about a royalty stream on a drug, let's get some comps pulled together within the next five minutes and figure out how we might think about valuing that. Where we've stopped short so far is decision making and agency. We want to make sure everything we're doing is intentional. That last piece amplifying the pace of decisions we've not executed on in part because we're cautious about what the bleeding edge looks like and making sure we're being extremely thoughtful with client capital.

Ted Seides: As you look across that order from sourcing all the way to decision making, what tools are you using?

Matt Bank: Claude, which has been a critical driver of internal development Granola, is the note taking tool that people have congealed around as most useful. We have an internal search tool called Glean, which is model Agnostic. It has basically driven the cost of search across the firm's various data sets to zero. Despite everybody's best efforts to save things in a file structure that's useful that you can retrieve information from. Inevitably, over 20 years things get hard to find. We've been able to stitch our SharePoint in with our Microsoft Teams, in with our CRM BipSync in with Chronograph, which monitors PE related cash flows. And you can just go onto this thing, ask it to pull various information. We continue to use a number of things that GEM alumni have helped build over time, OWL being one of them. Campbell Wilson's firm is terrific. They pull from a lot of interesting data sources. They are using AI in thoughtful ways that has plugged in nicely with our map of GPs and LPs. We get a spit out every week from them in terms of position level changes that they're seeing at the managers we have. However, if you rewind the calendar a year, about 90% of our spend and our tool set was externally developed. We were buying the shelf things trying to integrate them. Today that has flipped. 90% of the things we're developing are internal. We've got a team of three developers working full time on building tool sets that are specific to our particular use cases and objectives. One example would be they're in process on a sourcing tool that will instantly identify when title changes have happened at various firms. When deals have gotten done by certain people, people have left firms. It will plug that into a relationship map that helps the sourcing team get out in front of prospective manager relationships in the future. That's been helpful because you're not relying on the marketplace to invent this stuff or working with third parties. We're doing it directly. Our team generates about 30,000 lines of code per month. I'm told that's a lot. I don't know for a firm of our size, but it's up 10x from where that was last year.

Ted Seides: What are aspects that you've been hoping you could use AI to enhance that haven't worked?

Matt Bank: There's agentic chatter about the capacity of the tool to take workflows end to end and execute on them. We haven't really seen that in a cost effective way work. We've had to meter some of our token usage because of things that were way out of bounds with respect to what the benefits were. We're deploying capital to primarily third parties. There's so much unstructured data still that we can capture that will be time saving and enhance the quality of our insights that we haven't really spent a ton of time on. Some of this. Well, let's solve this particular workflow. That'll save us three FTEs. We still have all the same people. In fact, we've added folks. We will continue to add folks who will be capable because of this tool, but it is by no means a panacea for operational expense. Part of the challenge is we've got a few folks on the team that feel strongly that the junior team members need to dig through things, learn how to prosecute the process in a way where we haven't been eager to make their lives that much easier. The core function is building out the dashboards, understanding the pattern recognition, making sure you're in the flow of the information in a way where you understand where all the pieces fit together, then advance

Ted Seides: the ball as you look out. What do you think you'll be using AI for a year from now that you're not?

Matt Bank: Today, there's a ton of room between where we are and optimal in terms of our ability to capture information. We are effectively in the mosaic business. We are responsible for taking in an evolution of facts and circumstances and trying to parse out whether we think they're predictive of the future or not. Splitting that up is a critical task of manager selection. What if instead of the numbers, you could capture all the data? You could get the team out in the field, traveling more, meeting with folks, all of the subtle cues. If you could somehow organize those, get them into a system that allowed you to draw conclusions, see patterns, anything that can continue to push the flywheel faster. That's the thing that if we could do quicker and better, we'd be more effective. We want to see everything that's worth seeing out there. That's a fantastic ambition. The fun thing here, I tend to think of this more in a cosmic sense. Ultimately, you want a firm brain. We don't want to let the team and the thought processes that go into this atrophy. As long as we have sufficient agency. The flywheel is beginning to spin a lot faster and we're confident it's going to continue to.

Ted Seides: Matt takes us to the edge of your using AI in actual investment decisions, but deliberately stops short. K.K. rowland is embedding AI across the investment life cycle. K.K. is global head of alternative investments for J.P. morgan Asset and Wealth Management, where $250 billion of the private bank's $500 billion in AUM is under her watch. Her team is currently using agents to create manager profiles, engage in red teaming and generate signals that influence capital allocation. Kk, how are you using AI inside the team?

Kristin Kallergis Rowland: Alts in particular runs on documents. Public markets are structured and normalized decades ago. Alts never were. A fund arrives with a private placement memorandum, then side letters, performance commas as a gp, reports in whatever format they choose. At every stage, we've had this skilled person reading documents, retyping to one it says work was slow, it didn't scale, doing fund research. How we Collect data from fund managers. How we move it into a diligence phase. We pre populate the first draft of an investment review committee memo. We do what's called a renting. Before we go to invest review committee, we use an agent alongside us to play devil's advocate in that process. The ongoing diligence, making sure that we're staying in check with what the initial investment thesis was. AI is typically taking that first path. We get that data in on a monthly or quarterly basis. It's also structuring and onboarding, negotiating terms. Doing the compare and contrast is really helpful for us. We have a lot of portfolio construction tools where we've embedded AI in terms of how we generate simulations. The last piece of the process is all the servicing and the operations. It's taken off probably a third of the workload of people just in the last six to nine months. AI is embedded into every aspect of our work stream. It's not bolted on the side. It is a part of everything.

Ted Seides: Let's dive into some of the specifics.

Kristin Kallergis Rowland: Within JPMorgan we have this go slash LLM suite. We keep all of our client data in house. We're using AI to organize the data around the tracking the themes. So that could be everything from what's going on in healthcare and biotech right now. Mixing across public and private information that we have available all the way to Japanese corporate governance reform. Using AI agents to ingest the news that we're seeing externally as well as the information that we have internally. The other places in AI is in these profiles. We create internal notes when we onboard a manager to say what the profile of that manager is. We have these living profiles to make sure that when we get call notes as words that they use change to describe the same thing as before. Or an investment thesis changes how that's relating to the risk parameters that we put around a manager. We'd use a lot of AI when it comes to coaching growing professionals as we bring on new people or as we try to educate our investment committee, our advisors and ultimately our clients.

Ted Seides: I'd love you to jump into that profile of a manager. Walk me through how that works.

Kristin Kallergis Rowland: Every manager looks somewhat different within an investment review committee. We have a lot of the same initial criteria of what we put out there. What's their philosophy? What is the process by which they've generated that? What you want to make sure is that what a manager does is repeatable in an investment review committee deck it's setting up the agents to know a thesis that you originally underwrote whether it was a year ago, three years ago, five years ago matches the outcome of where that return is. When there's outsized winners or outsized losers, you make sure that it was in line with their original risk expectations. AI is able to help us do that because they're matching data to what we originally underwrote versus be swayed by the headlines that are coming in and out.

Ted Seides: You mentioned using AI for your red teams. We'd love to hear how that works.

Kristin Kallergis Rowland: We use AI to help us compare and contrast. We're on a fund 6. We ingest the first five funds to help us preempt some of the questions that we're going to have about is the firm growing faster than the people have grown? There's been a shift between funds 3 and 4 versus funds 5 and fund 6. It starts preempting some of the questions that we should be thinking about so that when we do go on site with a manager, we can highlight specifics around some of these attributes. The second thing we'll do is strategy mandate. We'll do private funds as an example. They'll say, had the average hold changed, how quick they are to put the money out to work? It goes through that level deeper so that we can get quicker to the key points that we think are driving the wins or the losses. The third thing in our red team that we look at is an investment process from an AI perspective. A lot of it uses some of the call notes that our operational diligence team has or our investments Diligence team has compares and contrasts over prior calls to see if there's a shift in tone, shift in people, shift in how they talk about value creation. AI is picking up on a lot of those transcripts that we're using to then preempt the question about is this something we should be worried about? It's amazing because we can create these agents and we can share them across partners and people so that we can see if there's something that someone on the growth equity team did that the private credit team wants to think about. We're using our agents across all those things to create synergies across creating red Teams and Devil's Advocates before we go to an investment committee, so that we're best prepared to answer any questions that

Abby Barlow: might get thrown our way.

Ted Seides: What tools are you using to integrate that?

Kristin Kallergis Rowland: There's a couple vendor tools that we use AlphaSense on the back end. We use all of the models. We have a system within our LLM suite that helps us understand which one's the most efficient. We have what's called Connect Coach, which connects us internally to each other and then our data to advisors and clients. We're using everything in our closed loop ecosystem. There's not a model we haven't used or haven't tried.

Ted Seides: How have you thought about using the different tools to improve your alpha generation? Your decision making?

Kristin Kallergis Rowland: A lot of it is still to be seen. What's pure and measurable is the LLM signals that we're getting. Where we get sentiment from a transcript from our own transcripts, where we're questioning a manager, rethinking forward looking risk factors. It's allowing us to better manage portfolios. In the world of hedge funds right now, there's a lot of features that can be generated from tech sources as well as the numerical data. There's many more managers that are not buying or analyzing the raw alternatives data out there. In general, the alpha generation from us is more of the signals that we're seeing to recognize that there's an emerging risk or a confidence factor in a manager. Something that might make us lighten up exposure or use that next dollar elsewhere. A lot of it is in our uncorrelated hedge fund portfolios where we can look at macromanagers, quant and relative value signals that we're seeing and incorporate that. We're seeing some alpha generation already because we've built Spectrum IQ and some of these other systems that are able to help us identify some of these things.

Ted Seides: What were some of the obstacles you had to overcome to deploy these tools into the investment process?

Kristin Kallergis Rowland: Three things. One is the walls of JP Morgan, our own cybersecurity. Two is that because we have our own applied AI lab, we have a pretty high standard for the things that we want to use. I have over 50 engineers that work on $250 billion of asset that we oversee. Everyone's been AI engineer. The third thing is the complexity of being a business that had over 40 years of data to ingest to understand the nuances of the things that we had from the 90s up until today. That took us almost two years. Every time we go to create what our tech agenda is for where we want to deploy AI next it would take us between two and a half to three months to implement that idea. Now that we have AI embedded in every aspect of what we do, it takes us two to three weeks to get something mocked up.

Ted Seides: What's on the agenda for the next year?

Kristin Kallergis Rowland: Taking what we've created from how we've built our own portfolio tools and Allowing our clients to see that there's a lot that we're doing in terms of agentic portfolio management. We are going from delivering something within 45 days to four to five days. That's what I'm looking forward to is making sure that it's clean enough to get as quickly as possible.

Ted Seides: We finish with John Webster, Senior Managing Director and COO of Technology and Operations at CPP Investments, the largest of the Maple Lake Canadian pension funds with $580 billion US under management. John brings all these ideas together at institutional scale. Using AI to read everything, remember everything and challenge everything. John, tell me how you're using AI at cpbib.

John Webster: The test for AI is does it make us a better investor, Better underwriting, better risk, taking better decisions on behalf of 22 million Canadians. AI now touches every stage of our investment lifecycle. If there was a tagline, it's trying to get AI to help us read everything, remember everything and challenge everything. The intent around read everything is across all of our investment teams, across Total fund manager, the office cio bring as many eyeballs to bear on everything that we would have wanted to look at in the past but could never reach. Natural language processing with LLMs allowing us to read filings and earning calls transcripts across a much broader universe than any team could have historically covered in places like secondaries and private equity. We've scaled our capacity to price and underwrite LP portfolios with tens to hundred plus underlying companies in a single transaction. So we evaluate more deals. Our entire deal history is now queryable. So that means for things like comparing governance rights in a new deal against the existing book GP intelligence. We can now draw on demand relationship briefings from our own CRM pipeline and performance data. So every GP conversation is backed by our full institutional knowledge. We've put in place an investment committee copilot which encodes senior investors decision heuristics plus provide your deal history into diligence and investment committee prep. Even at the individual IC member level. Trying to make sure that we bring our entire institutional memory as an asset as much of the individual investors institutional memory into everything that we do. We've got a lot of places where we're using AI to challenge everything we do. We have a memo coach which is a multi agent reviewer that stress tests draft investment committee recommendations through a whole range of distinct lenses, logic, risks, scenarios, value creation, even how the case is communicated with the intent of bringing forward a prioritized set of questions and actions before the memo reaches the investment committee.

Ted Seides: I'd love to tackle how you went from zero to where you are today.

John Webster: When ChatGPT 3.5 popped out of OpenAI three and a bit years ago, we made a deliberate choice to put the best frontier platforms in front of all colleagues in the organization as quickly as possible. We tried other products before, but we felt the integrated work surface that you got in an OpenAI or an Anthropic suited very well the way our investors think, work and make decisions. So a lot of those things came out of the real understanding of our investors on the front line making decisions, working with our partners, and they could then start to see how they could use the technology to scale and change parts of the investment decision making process. We're not saying there's the portal where you go to use AI. There are workflows and existing applications that we're building underpinned by AI. So we're trying to meet users where they do their work today and bring the best tools and skills to them in a way that is accessible. Getting people over the mental hump of being brave enough, courageous enough, confident enough to keep moving into the leading edge is part of what we're trying to do as well. People are going to have to be flexible and willing to lean in and use it in a variety of different ways. Wiring it together across 2,000 people in a way that reflects the heterogeneity of how people want to look at the world and their specific skills and judgment in a way that does bring the institutional perspective is non trivial, but this technology is giving us the best chance of doing it.

Ted Seides: If you look at the tools that you're using that are most effective, what are you using inside the organization in

John Webster: order to read everything? You have to get your information into order. We've spent a lot of time across the last couple of years making sure our structured data is searchable, traceable, can be leveraged across the organization. And we put a lot of time into making sure all of our investment recommendations of the past are old fashionedly indexed and available. Then we've made the unstructured data available via Knowledge Platform Rag and Knowledge Graph. You can now connect the knowledge graph into Claude. You can connect it into OpenAI, you can connect it into Copilot. We do use OpenAI, we do use Anthropic. We make those available in web based chat form. We also make them available in the desktop form so people can use CLAUDE code and codecs. In the next nine to 12 months we will start to converge more on a consistent application landscape. Some of that will be waiting for the market to figure out what's going to win. There's a lot of choice out there at the moment and the choice is good. It also means that as a buyer of enterprise technology, you're having to keep your options too open at this point in time. Most of what we're doing is making sure people are feeling fully conversant, very literate past the 20 hours of working with AI, where they then start to understand how to get the best out of the machinery.

Ted Seides: What applications have you found effective when it moves towards the challenging and decision making?

John Webster: If you look at the IC Memo coach, we have a privates platform that we've developed over the last five or six years. We found that the ability to embed lots of different challenge perspectives, being able to quickly understand the skew of upsides and downsides, or quickly extract the beliefs on which the thesis is set out, where the evidence for that is strong, where the evidence is sparse, what metrics you might want to further go after, what things you want to monitor. Bringing lots of different perspectives, which would be computationally hard in the past to do. No one lens lets you see everything. If you can look at it through 10 different lenses relatively quickly, you can start to get a different sense of where the strong parts are and the weak spots are.

Ted Seides: As you have dove into all of the internal deal making and public investing, curious what you found effective for the teams that are tasked with external manager selection?

John Webster: I've probably just got a strategist's view on that. Technology never confers a lasting competitive advantage because whatever is available to you is available to everybody else. If you believe that as a base proposition, then what this technology can do is either amplify or undermine your existing competitive advantages. Our competitive advantages are organizational eq. It's not to dismiss the IQ half of the equation. We are very bright people. Think carefully about investment thesis. Our advantages are ultimately in the trust, the relationship, the standing, the patience. Clayton Christensen Law of Conservation of Attractive Profits when one part of the value chain is attacked, then value accrues to adjacent parts of the value chain. If IQ is under attack, value is going to accrue to eq. Whilst it's really important we are forward on the use of the technology. One of the things all organizations have to do is work out. Are they really competing on the IQ side or are they competing on the EQ side? Lots of our partners compete on the IQ side. They're leaning heavily into the technology. We are leaning appropriately heavily into it. But I hope with an understanding that what we do brilliantly might not be to become the next smartest investor, but make sure we continue to work with the smartest and best investors in the smartest and best way.

Ted Seides: What are some of the things you have tried using the LLMs that haven't been as effective as you would have thought today?

John Webster: If you roll back three years, I would have looked at the machinery as a way of bringing many different challenge perspectives to a problem. The more ways you can challenge a problem, the more ways you can find insights that others haven't thought about. I don't think that's been true. You've got to be far more deliberate about what are the lenses you want to bring to a problem? How do you codify those lenses? How do you make those lenses consistent across the organization? The machinery and the platforms haven't caught up with doing that well at this point in time.

Ted Seides: What are some of the risks you've encountered?

John Webster: The big risk is that we put a lot of effort into this and it does not make us a better investor. That's the one we're focused on most continuing to push ourselves to understand how we can judge whether we are making better investment decisions. It's still early in the technological revolution. We are getting to the point in the next 12 to 18 months where you've got to feel confident that you are getting a return on investment of the technology. In our case, that means we can really feel confident we are making substantially better investment decisions commensurate with the investment we're putting into it. The areas I outlined earlier, those people feel confident are real benefits, real opportunities. Now, the test case is always in a long term investment opportunity which might play out over seven years. How do you know that it's helping you make a better decision that turns into a better outcome? We do need to figure it out.

Ted Seides: Thanks for listening all the way to the end. If you're made it this far, how about one ask for me Tell one friend about the show. It's the best way to keep growing this incredible community. Thanks so much. And until the next one, stay curious and keep compounding knowledge, relationships and capital a little bit at a time.

Laura Hill: All opinions expressed by TED and podcast guests are solely their own opinions and do not reflect the opinion of capital

Kristin Kallergis Rowland: allocators or their firms.

Laura Hill: This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of capital allocators or podcast guests may maintain positions in securities discussed on this podcast.