Gabe Stengel - Building Investing Superintelligence - [Invest Like the Best, EP.492]

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You and I have talked many times about this basic question that I'll start with over the last couple of years. You are effectively trying to build investing superintelligence tools to help investors do their job much faster, better, cheaper, easier, higher quality. But it's starting to feel like, wow, we're really eating a lot of the core functions that even a very smart, analyst or even portfolio manager was doing a couple years ago. How do you think about that trajectory as you've seen it and lived it so far and where it's going over the next 2 years?

I think 2 years is actually easier to reason about than 10 years or 20 years, because in 2 years, the best investors are going to be figuring out how to reinvent their own firms and reinvent themselves. And if you look at what happened to market-making and quant trading, Jane Street took 15 years to build the dominant franchise. And the world's best investors today are going to spend the next 2 to 5 years figuring out how to integrate AI into what they do. And Dario has the great line about everyone's going to have a data center full of geniuses or a country full of geniuses in the data center. What would Goldman do? What would Millennium do? What would Citadel do if they had a country full of geniuses show up? It would probably take them a while to figure out how to change the way they work, how to take advantage of that, how to integrate it into their system. I think figuring out how to AI into the investment lifecycle is the biggest challenge over the next 5 years for every great investor.

There's been many companies on this trajectory where the product was— Cognition's very famous for like literally their ads now say, remember Devin? Like it's good now. So lots of now clearly great companies with great products had a stage of an AI business where the product stunk and Now it's excellent. If you think about the couple increases in capability that we've seen just from the raw models, could you do the same thing for the eras of Rogo and its product? Like you pick how many eras it is. I don't know how you frame it up, but like what it could do at each level up into and including today.

Yeah. I actually tried starting Rogo 2 times before we got started. So in high school, I had a friend whose dad was an investment banker who wanted an app for trying to track the basically equity exchange rate of 2 public companies as they were merging. And so we tried using really old AI techniques to do that. Terrible. And then in college, before GPT-3 came out, we published a paper on AI assistance for econometrics and financial econometrics. And we tried commercializing at the time and nothing worked at all. And then when we actually started the business, it was when GPT-3 came out, pre-ChatGPT. And so all the early days of Rogo, It was clear how kind of magical it was. You could demo things that were cool. Nothing worked at all. I mean, I would say since things actually started to work, the eras are very tied to the model eras, right? It was O1 Pro and then probably Opus 4 or 5. O1 Pro was the first time you got enough reliability where it was a good search tool. At the very least, you could say, help me calculate this financial metric for this business over the last 12 quarters, and it could do it reliably enough where it wasn't so annoying that you would just do it yourself. And then with Opus 4 or 5 and the end of last year, the end of 2025, end of the beginning of this year, the models just became capable of basically anything a junior investment professional or junior banker was doing, as long as you gave it the right instructions and context. I mean, I think there was a first mover's disadvantage for a lot of applied AI companies because you thought you knew where the world was going and you wanted to build a product for it, but the models weren't quite there. And so people would try it and go, this is terrible. This is garbage. You have a disadvantage. For us, we saw that too. But now what we've seen is, well, if you were right about the end state and where the models were going and you were building towards that, when they get there, it's magical. And for me, a great product is all the feedback we get every day. People all day are saying, hey, this is transforming the way I work. I'm saving hundreds of hours a month. I am doing things I never could have done before. And so I'm smarter as a result, and I'm able to make better decisions. And it's delightful and I like using it. And, you know, it brings me joy in my day-to-day because the UX and the attention to detail and the craftsmanship is so obviously built for me and who I am. And so that's been the best part of the product.

And you would attribute that to, you took seriously all the compliance, regulatory workflow, last mile hookup stuff, and then the models became good enough and all of a sudden that was super valuable.

Well, there's also small details of understanding how someone within one of these firms works and building for I'll give you an example. We make it so easy for a managing director at a bank to email a markup of a deck, which is how they're typically doing these workflows anyway, except sending it to an analyst and sending it to our AI analyst over email and then returning that markup in 20 minutes as opposed to 2 days. And at the same time, alert the junior analyst on the deal what's happening and show them the full auditability of all the little markups that were made in case they want to weigh in. And that whole UX, that whole flow just makes it so much easier for this financial professional who has not logged into a computer in 10 years, but does have an iPad where they know how to mark these things up to actually adopt and use AI. And there are these small details of how you build a product that's great for a specific end user that you only know if you have the kind of Spidey sense for what the job is.

What is like the bleeding edge of What it can do that impresses you the most? Like what kinds of jobs?

The coolest things that we're working on is taking these innovations like Maltbook. Imagine if every PM at a hedge fund had 10,000 agents that were just kind of fraternizing, talking about ideas, reading through the notes, pontificating, and then at the end of 24 hours of debate, just gave you one idea. And the reason you're able to do that is because investors are happy to pay $50,000 for one really good idea. Whereas there's very few other domains where you can expend that many tokens just for one simple insight. What we're seeing right now though is that the models are smarter than anyone I know, anyone I spend time with, and it's plumbing. Connect it to your context, inform it about your thesis, tell it the way that you work and try and integrate it into what you do. And so building out all the plumbing to actually collect that data, collect that context is what is cutting edge to me.

It's always interesting to me for a product like this that You're opinionated about what it should be used to do, but in some sense, people can be creative with how they use it. So you get to sort of reveal how people want to use it. If I adopted a God's eye view of, it's Monday morning here in New York City, lots of Roga users are probably fired up and using it right now. If I could somehow see into every instance of the product being used, what would I see? Who are the people? What are the predominant use cases? How varied are they? Give us a sense of how it's being used right now as we record.

Even though in so many ways I think public equities is the best application of AI because all the data is available. And so it's just about being as smart as possible. That's a very brute force framing. Our early users in ICP and kind of core market is actually what I would describe as dealmakers or people that are transacting, who are buying companies, selling companies, helping coordinate transactions. And so a lot of what we do is both make people smarter, but actually Do the dealmaking? How do you prepare a data room? How do you unpack a data room? How do you coordinate the call with the third parties to discuss the data room? How do you go through all of the initial steps through closing of a deal? And so if you took a bird's eye view of all the folks using Rogo, I mean, it's people who are either on the sell side of a transaction or the buying side of a transaction and are using it to basically prepare all the thoughts and materials to help execute that full deal, whether it's putting things into a data room. This is the company's model. This is the PowerPoint that describes their customers. These are the answers to the DDQ questions on what customer concentration is, or it's all the agents on the other side of that that are tearing through the data and mapping it to the firm's investment philosophy to say, oh great, you know, is it lower than the concentration risk profile that we would want for this fund too? And then the components of the system are people access it via all the classic channels you would access an AI tool. Email, chatbots, proactive alerts, and those kinds of things. But then Rogo's actually in a lot of the behind-the-scenes systems of these firms because when you're working on a deal, it's not just important for the human beings working on it, but you need to update your CRM, you need to update your portfolio monitoring system, you need to update the way that you distribute information to your LPs after the fact. And so half of our surface area is actually the underneath of the iceberg of interacting with these different systems of record based on what the humans are doing over the course of the deal.

So dealmakers today, when do you think you'll be able to give the same answer for junior analysts at a public equity hedge fund or something like this? There's a process to their workflow as well, but it's very, very different. You're right that it's interesting that my first intuition would be public markets are the best place to do this because there's so much data available. When do you think that transition happens?

I think that for our business, we need to have all the requisite domain knowledge of what it takes to be a great public markets investor. I don't know what it takes. I've never done it. I haven't spent nearly as much time as I should have with the folks that are great at it. And we need to both hire out that domain expertise and then figure out based on it how to apply the systems that we have built to that market. I have extreme conviction in the fact that the underlying systems and tools and infrastructure we have built will be invaluable to that market. But now we need the great chef who can figure out how to piece it together and create that kind of end-state product and that last-mile delivery for public equities investors. I get pushed a lot by our board to think about expanding the ICP beyond just core banking, but the reality has been is there's been so much depth and TAM in this dealmaker's vertical. And then for my end-state vision of actually being the full infrastructure for private markets where people can transact very effectively, that is far more important to the dealmakers. Whereas for public equities, all that infrastructure, all those exchanges already exist. And so I'd like to serve them because I want to serve the most sophisticated, smartest users who have inordinate amounts of knowledge on the companies they track and the industries they follow, and I'd like to make them even smarter because that sounds super cool, but I can build a huge, huge business just concentrating where I am today.

Interesting. So one takeaway from that would be a lot of the opportunity to build an AI business in a vertical is somewhere where there's lots of plumbing that's not yet built.

Yes.

And then applying your AI on top of that.

Exactly. I mean, part of the reason private markets are so attractive is because it's all done by humans, the coordination, the standardization, looking into things and the actual transacting. Whereas public equities, a lot of it has been automated.

Based on what you know, what skills do you think investment professionals, broadly speaking, public and private, should think about being or becoming more valuable as time progresses? And which skills become— it's kind of obvious the skills that become less valuable. What the hell are we going to do? I think can do the soup-to-nuts diligence and IC memo and objection handling and all this kind of stuff. Like that's a big part of a job for at least a junior. person in the investing world. So what skills do you think will still matter a lot in a couple of years?

I mean, I want to preface it all with, I worked in finance for 2 years, and so I am a student of these guys just as much as I'm fascinated by the technology and want to figure out how to use it. But the world's best investors have a way of figuring out what matters and exercising their own judgment across a range of topics. And I think the jury's still out on whether or not that is something that AI can eventually replace. When Move 37 happened and Lee Sedol saw something I could never see. If that starts to happen in public equities, yeah, it's going to really change what matters. And if that really changes all of how that works, I mean, I think the core skill set is folks who can go out and gather data and inputs into their model that no one else will have. If you can spend time in the field, if you can speak to experts, if you can develop a relationship graph of folks who can inform your model, Maybe you're not the one that needs to calculate what your MOVE 37 would be for a great public equities investment, but you can actually feed your model with data that no one else has too.

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Maybe it's a good time to talk about, I think about the assembly line, like if Rogo puts out some really useful output, I want to learn about each component part of this system that leads to that output. And I'm most especially interested in the data that you yourself have and use and buy and build and whatever, and how you do that, how you think about data, and then how you think about model. And these are both questions, one that I'm curious about your specific business for, but I also think that there will be some business like Rogo built in basically every vertical. And so I'm curious what can be abstracted to other professional services or other verticals that are interesting as well that will fall to AI progress. So talk us through, yeah, specifically like the data and model piece and how that's evolved over time.

The early days of Rogo was kind of this Rube Goldberg contraption where like you had 60 different model calls. A question comes in, you try to say, what companies is Patrick asking about? Now, what are their tickers? Now, how do I feed those tickers into an API call to Bloomberg or FactSet or some internal dataset? Now it comes back and I need to call a different model to pull it all together. And as the models get smarter, you kind of want to be less prescriptive, less of a Rube Goldberg machine, and just think about what are the simplest, best, highest quality tools. And the same way that if you have the world's smartest human being starting here tomorrow trying to be a great or an investor, what are the tools that they would need that are not just intrinsic to being smart, right? What are the data tools? What are the ways of going out and gathering information? What are the ways of auditing its own work? And then what are the ways of presenting it and pushing it back into the systems that it needs? And so for us, we spend a lot of time thinking through what are all the data inputs that a great banker or a great investor would need to actually do their job. Then we spend a lot of time thinking about, great, when you're doing that job, what are all the compliance and regulatory requirements? To make sure that if someday some Delaware court judge makes AI inputs and research discoverable, you have actually done it all the right way such that you're not intermingling information. Because the reality of AI investment judgments and AI banker outputs is that you're going to be able to see the full lineage of how those things are created. And then we spend a lot of time benchmarking these models and creating different evals and datasets so we can always decide what is the most performant, what is the cheapest from a token perspective, What is the lowest latency and route tasks to the appropriate type of models?

I'm sure your favorite question is, how do you ultimately compete with Anthropic and OpenAI who view finance as a big, one of the few categories that like you see them talking about and thinking about? What can you do in the long run that's counterpositioned against what they can or will do, do you think?

I mean, you want to build things that are perpendicular to what they want to build. And sometimes you might build a chatbot because that helps you go to market faster. But you should know there's a whole bunch of stuff underneath the surface that the labs are never going to build that we need to build for finance or for any other vertical. And when you think about financial services and capital markets, how many businesses are there that generate more than $5 or $10 billion by just going deep into those workflows, into the datasets and how work is done? There's a huge amount of TAM and spend on top of very messy, specific problems. And all of finance is a collection of different niches with different datasets, different definitions of good, different regulatory requirements. And we can get to $5 billion in revenue by going deep across those things and creating the systems, the systems of record that help manage them. That for Anthropic would kind of be like stopping on the side of the road to pick up a penny because they're on the pathway of trying to go from $100 billion in revenue to a trillion in revenue. And there's so much depth to these systems that actually need to be built beyond just intelligence.

Is there a favorite example of that, of some like pain in the ass thing that you've had to wire up, last mile things? Yeah.

I mean, think about If you are ingesting MNPI because you are working on transactions or you're working on deals that have an effect on the market, the compliance requirements you have just in the auditability or what you can flag and how it feeds into the internal systems of an investment firm or a bank so that if you ever get audited or a regulator ever wants to see what you did, do you have all that plumbing in place that is pixel perfect? That's one example. Another example is if you actually want to transact, Say you are a big public company buying another big public company and you need to send data back and forth. You actually need some sort of data room, something that is compliant, safe, and secure that coordinates. And ideally, it's not just some static kind of Dropbox folder, but something that's plugged into the way that you do work, your agents, your workflows. And I don't think OpenAI or Anthropic will ever want to build a data room business. If you actually want to be the exchange for all of high finance and all of capital markets, you not just need to own the intelligence, You need to own the transaction venue, the communication venue, the workflows, and all the data inputs that go into it.

It's interesting. I asked about data and models, but actually it sounds like harness or infrastructure is probably the most important of the 3.

Think about the fundamental difference between Claude Code when it came out and Claude Cowork versus OpenAI and ChatGPT. The models were actually fairly similar, but the harness, And the way that it was presented from Claude was far better. And it just allowed the models to exercise more of their long-running capabilities. And that's why they had a run-up in usage and a huge amount of expansion. And it just shows that the way that you harness these models is so, so important. And I think people underappreciate that intelligence, the reasons that humans are high agency and can do a lot is not just because we have high raw recall and IQ and knowledge. But there's all these different microservices in your brain. How do you put knowledge away? How do you retrieve it? How do you trigger things? Emotions are a way to trigger all these different microservices. That's why a great investor might have great judgment, is because they have good spidey sense for when they see this sort of thing in the market, it actually triggers the recall from this event that informs a creative decision. All those kinds of small microservices are things that need to be built out.

If I force you to become an investor and your only goal is to invest in robo-like businesses, like one of these businesses that's, let's say, a vertical application that's wiring up the capabilities of AI to an industry, what features would you look for that would get you the most excited either in the industry or in the founder builder and their approach based on what you've learned?

A few things. One is the industry actually does have to have enough complexity and depth in the types of data, the types of systems of record that people use, the types of deployment models. That you can spend a lot of effort solving those problems in order to have a wedge to solve everything else. Because if it's an industry that anyone can just walk into and sell the basic version of ChatGPT or Cowork or Copilot immediately, and you don't have to solve all these weird integrations, you're not going to have enough time to build all those things that are perpendicular to what you're doing. And so the industry itself needs to be adjacent enough to the core market. So that's one thing. The second thing I would look for is domain expertise. from the team and the founders. And I don't have unique domain expertise, but I had enough to get started. And then I was so curious about finance and money and capital markets. And I grew up in New York and I was surrounded by people that all they could think about, all they could talk about was high finance. And I was fascinated by it and I wanted to learn about it. And then we assembled a team that was uniquely passionate about it too. And so I have over 100 people that have spent time within investment banks or within investment firms across the world's best institutions. And so we can constantly take the models as they're released and harness them for finance. And our job is really to catch the change in the models and figure out how to apply it into these institutions. The final big thing I would look for is a business that's willing to constantly reinvent the core product and constantly willing to slash it to nothing. And I think anyone who their delivery method of their product is not something that they can fully cannibalize quickly, like a terminal, Or like a very specific UX or interface is not going to be agile enough to constantly reinvent every 6 months when there's a step change.

What's an example of that that you've done, like a tear down and rebuild?

The story, the anecdote I'm most inspired by is Max Levchin, who talks about how at Affirm they rebuild the fundamental ledger technology every year and they rebuild it for a few reasons. One is it's the most interesting engineering problem, and so all the engineers want to work on it. And so it's a good way to retain talent, to teach engineers about the core fundamental business of Affirm. But then number 2, it's a good way to make sure that system doesn't ossify and it's constantly improving. And so we do that same exact thing for our harness and the core agentic system. We are constantly looking at it, realizing we're not even at a local minima. It would be impossible if we were at a local minima because the models are changing so quickly and we need to redo the whole thing.

What's something that the models currently cannot do that if they could, would really change the nature of the product?

Compaction. So it's, if you have 100 conversations with a single agent, how does it make sure that it's actually remembering the right things and compacting its memory into an amount of tokens that it can use every time? And it has enough coherence and context on who you are and what you care about to make it feel like it's a true person that you're speaking to that, you know, learns more and more about you. That's a hard problem. And it compounds exponentially when you think about agents, not just as a one-to-one right now. Almost all agents are one-to-one. You use ChatGPT individually, you use Copilot individually, you use Gemini individually. As soon as these are actually things that consort with a lot of colleagues or in a Slack channel with 100 people have to work across an entire company, now the compaction problem just scaled exponentially because it's having conversations with 100 different people and needs to be able to coordinate across those things. And so being able to take all of that memory, all those interactions, and actually lodge it into the mental model or brain of that agent so that it can be persistent. So that it can actually maintain context over the course of a bunch of interactions. That's something that the models are not great at today.

What do you think the major kinds of AI software businesses there are? So we've got companies like a Cognition or a Cursor or something that can grow unbelievably quickly. And I'm especially curious for you to compare this to like the old classification system for software companies. I'm curious how people buy Rogo, what kind of category you would put it in. Is it usage-based? Is it seat-based? Is it something else like? Talk us through the how people want to buy this stuff and what the emerging models are for AI software business.

We are a classic enterprise software business. We price per seat right now because our buyers are used to pricing per seat. They think of us in a similar category to Bloomberg, to FactSet, to Capital IQ, to PitchBook. And so we have to build a very human business. Every time you sign a deal, it requires an AE and a solutions architect and a sales engineer going in and shaking a lot of hands, explaining how it works, explaining how to You can't sign one deal where the usage just rises 100-fold. I look at how hard it was for Anthropic to sell to us, very easy, and the amount that we pay them has risen exponentially without a human in the loop because it's a token consumption model. There's a lot of industries where riding the coattails of token consumption isn't going to work for enterprise sales, and we're one of those. And so it's actually pretty interesting because we have to build a go-to-market machine 5 times faster than most enterprise sales organizations ever have to build. And so I do think there's the category that's just typical enterprise sales, but using AI models as a tailwind to build 100 times better products. And then there are the token brokers, the token consumption businesses where you're selling in the parts of enterprise where they're used to buying usage-based tools, Cursor, Factory, Claude Code, and others. And so you can go much further commercially with fewer people.

What's your prediction for how or if that will change in finance?

I think that every business needs to go through 2 different pricing revolutions. You need to move to some sort of usage-based, and then you need to move to some sort of outcome-based. For me, if I can figure out a way to skip the token-based, skip the usage-based, simplify it for my users, and just wait until I can say, hey Patrick, what if I just charge you for every good investment idea I give you? Or what if I charge you for the quarterly report you send to LPs that I can do perfectly? Or what if I charge you for every sim that you create as a banker? I would much rather get there than have to figure out some random way of trying to assign dollars per token. That is something that we're not going to quite agree on because you're going to spend $100,000 on tokens and say, well, did I get $100,000 of value? And I don't really know. But you know what the value is to you of a good investment idea because you can actually see how much money did I earn. Or you know what the value to you is if you can produce a sim, if you're a bank, because you know what you charge these firms to actually sell the business.

Presumably you can't change your seed price on the fly dynamically, at least with the same customer. So how do you deal with the problem of like, in some ways, misalignment with the customer for your business, where if you do a great job and they use the thing way more, which costs you a lot of money, they become a worse customer?

The reality is, is we are 1% of the way into our product roadmap. 99% of the innovation for capital markets is in front of us. And so what matters is that we're a good partner, we're a good steward of their AI strategy, and they want to work with us in the future.

It's so interesting to think about the shape of this in the future. And you said you're 1% penetrated into the roadmap. Give us a sense of where you think this is all going from your product perspective, not industry-wide. If you have that much of the roadmap ahead of you still, describe that to us.

Think about the percentage of all capital markets workflows, investment workflows, investment banking jobs that are still completely human rate limited and done by human intermediaries. It's kind of similar to other parts of financial services where 15 years ago, 20 years ago, every mortgage that someone got, you went in, you spoke to a banker at a local branch. It felt like a very human decision. I'm buying a house, I'm taking out a loan. This is important. I need to speak to someone. No one ever thought that you wouldn't want a human in the loop for that. Now, 40 to 50% of mortgages are just delivered online by platforms like Rocket Mortgage. I think there's going to be a huge amount Of innovation and how companies transact, how companies raise capital, how companies raise debt. I think it'll be easier than it's ever been in 10 or 20 years for someone who's a business owner or someone who works at a company to go online and click a button and try to raise capital the way that someone can go onto Robinhood and click a button and buy an equity. I think it'll take 5 minutes for KKR to figure out, can I sell this portfolio company to another sponsor? Not 5 months. I think you're going to be able to price the assets in an order of magnitude less time. And as a result, markets are going to be more transparent, more liquid, more efficient, and there's going to be a whole bunch more activity.

I'm going to focus on the specific future of automated risk pricing, for lack of a better simple term. We're 3 years from now and everything you just said is true, where I can raise single-digit millions of dollars of better equity, kind of like filling out an online form, and the thing can just price the risk for me and give me an offer. It's like Opendoor for like everything or something. What do you need to build that you don't already have to enable that sort of capital market future?

It's actually a very similar strategy to Bloomberg's strategy. Bloomberg's strategy was, I'm going to omit a lot of details here, but offer a little bit of data to get in the door, build all the analytics and workflows on top that someone would need, and then provide the exchange and the communication platform where you can actually transact in a bunch of asset classes that before it was pretty opaque. Bloomberg Messenger. For us, it's use a little bit of AI to get in the door, build out the full workflows, go from Copilot chatbot to a full autopilot tool so I can make sure that I am 100% accurate on your IC memo or on the DBQs that you're doing, and then provide the communication channel between counterparties so that if I have agents that can autopilot do the work, I can actually transact for you. And the difference between what I need to build and Bloomberg Messenger is I don't need to build the communication channel for humans to transact. I need to build the communication channel for the agents to transact across these businesses, across these investment firms. And so if you think about what is the actual infrastructure that needs to get built, I mean, think about what is the system that would allow a large private equity firm to feel comfortable having an agent negotiate a deal on its behalf, correspond with all the third-party consultants in the transaction, the people doing the QOV, the legal advisors, and so on, and then actually run an auction process where you have a bunch of sponsors providing bids. There's a huge amount of software to be built out, and I think sometimes we talk about that as looking like an exchange for a lot of these asset classes that are not standardized.

Tell us a little bit about the customer base today, like how much of it is giant banks versus investing firms?

It's mostly large banks, and that's very simply because that was my background, right? I worked as an investment banker for just a handful of years doing buy-side M&A coverage, which was super interesting, And so we started targeting the banks pretty early on for a few simple reasons. One is the investment banks are kind of the distribution channel for the rest of finance. A lot of the folks who then end up as great investors started in their first 2 years as an analyst at Goldman in the TMT program or something of that nature. 2, they have the most seats by far. And so if you can land a bank like Bank of America, you can actually get in the hands of far, far, far more people than if you land the 10 best single portfolio manager public equities investors who each only have 10 investors.

If I think about a Bank of America or something, everyone's kind of wondering how deep into the adoption curve are we for enterprises using AI? You have a biased sample because your customers are using Rogo and they're using it a lot, but give us a sense of where you think we are. It seems really hard to pin down a good answer.

I would say the majority of firms are seeing a huge amount of individual productivity. And they're trying to figure out how do we parlay that individual productivity into firm productivity that we can measure. Speak to any individual banker at a bank we're deployed with.

They're like, life's great.

Oh, they're like, I'm 100 times more efficient than I used to be, right? You'll speak to an MD who will say, Gabe, I sent 5 pages to a client that before I would've had to go back and forth with an analyst on over 3 days to create, and I made it in 10 minutes myself. And these are bankers who haven't done any sort of analysis in 25 years. They haven't actually opened an Excel file in 20 years, and they're able to do it themselves. The problem is, where's that flowing through? Are you winning more deals? Are you actually transacting more? Are you servicing a part of the market that you haven't seen? And this is where it becomes not just an individual productivity tool problem, but a firm strategy problem. Like, what's your plan? Do you want to use this thing to cut costs? Do you want to use this thing to enter parts of the market that before didn't make sense to serve. You can look at a bank like JP Morgan. JP Morgan just announced that they're going to try and do a lot more M&A work for SMBs, for parts of the market that before they didn't think it made sense to go out and serve because the deal fees were probably too small, and so you needed too many people to staff them. Well, now if you have a banker that can be a deal team of one, well, maybe you can enter parts of the market that before just made no sense.

So in some sense, the bottleneck at some point will be the creativity of the customer. Like you can provision unlimited capability and you're soon going to be just relying on them figuring out the answer.

It's them figuring out what they want to do, right? If you had 100 great investors start here tomorrow working for you, how would you channel that productivity? It would take you a while to figure out what's the structure. Do they all work on different things? How much money do I give each of them?

What do we attack? 2 years ago, the most obvious question, and this would've been about accuracy and people to use the word hallucinations, which seems to have dropped out of the conversation. And I can't remember the last time that someone said hallucinations to me. What can you teach us about that problem, ensuring accuracy where accuracy matters a lot to the decimal? What's the nature of that these days?

It's still super important. I think it's actually more important to be auditable than it is to be accurate. And obviously those 2 things are conflated, but what's really important is that I give you an answer, you know how to use it. And if it's not accurate and you don't know how to check it and it's hard to see where it came from, You can't use it at all, whether it's accurate or not, because you don't trust it. If it's accurate most of the time, but even when it's not, it's very easy to see the assumptions that went in, where the data was pulled from. It's still actionable and it still saves you time. And then increasingly, as these things go from copilot tools that you're just using for information retrieval to autopilots where you're trusting them not just to gather the information, but to execute on it, to have agency and actually make an investment decision or send an email, you need to have the full confidence that if you were to go back in and see why it made the decision, you would be able to understand why, because you're going to need to debug it. And the same way that there's going to be individual investors that make horrible decisions and you need to go in and see what went wrong— was there an incorrect data input? Did someone lie to them? What was going on? You're going to need to do the same thing with agents. And then especially in parts of capital markets that have regulators that look at these things and need to make sure there's no foul play, if you can't explain why you made a decision and the data went in, that's not going to fly.

You were talking before about how you have to sell like a normal enterprise sales organization would, but you have to grow or can grow many multiples faster than the fastest growing enterprise SaaS companies of the last era. How do you solve that problem? Like you're rate limited by the speed of humans to some degree in enterprise sales. How do you hire enough people fast enough? Like, how do you think about being able to grow at the right rate when you don't have the Anthropic cognition API usage growth? It's so easy for them to grow 10x. It's much harder for you. How do you solve that?

The core problem we need to solve is how fast can you make a human being productive as a salesperson, salesperson, but anyone else as a marketer, as an SDR, as a post-salesperson, how quickly can they understand our business and help push us forward and push customers forward and help our end users? And so enablement and training people and constantly retraining people is the fundamental problem that we, and I assume other fast-growing enterprise startups, have to deal with.

So do you, like, use AI to build tools?

Oh, the internal tools we have are kind of magical. First off, every internal conversation that happens at Rogo is recorded. When anyone starts, we say, hey, just FYI, Patrick, you're always being recorded. It's always being filtered into the company brain. This is not in a kind of Big Brother situation. It's just Everyone you're going to speak to is going to have Granola or some meeting transcription running because they need to use it and they need to have excellent recall and they need to compound the knowledge that they have. And as a result, we just have this huge reservoir of information. And then we have all of these tools that people can use on top to say, oh, you know what, we're deploying with this sort of public equities firm in this sort of market. Have we ever served this kind of data before? Are there use cases that might be helpful? And you can pull in the conversation that a peer of yours had 3 weeks ago and you'd never even met that person because they're stationed in APAC. Being able to sponge all that information in and then get it out to people when they need it is the core problem of enablement.

So maybe describe the internal brain.

It's called Shrek for some reason, because my engineers thought it would be hilarious to call it Shrek. There's a dashboard where you can see the swamp of everything that people are working on at any given time, but it's connected to all of our different systems. It is very prescriptive about what it knows our company goals are. What are our values? What are the things we want to deliver to clients? What are our north star metrics? And so it can shape every answer and deliverable that way. And it's both proactive and reactive. Someone can go in and say, I'm trying to get up to speed on how I should talk about model routing and how I should think about the value prop there for a very large institution and what the savings will be. And it can pull out all that information for you. But it can also say, hey, Patrick, I see on your calendar on Thursday, you're meeting with this sort of private credit firm. Here's all the information you should know, all of the use cases that will resonate, and then all of the types of ROI metrics that firms we've worked with in the past would want to hear.

So what's the sales pitch to talent? Let's say there's somebody that if you landed them tomorrow would be transformative because they're so high quality or well-known or whatever. What is the pitch to them to come work at Rogo versus go somewhere else that's exciting right now?

Always depends on the person's motivations, so it's hard to give a generic pitch. But my pitch for Rogo today for talent is AI is going to completely transform the world. The place that is going to be the most interesting is applied AI, because that's where AI intersects with humanity. And so the companies that dictate how AI intersects with humans and touches humans are going to do the most interesting creative engineering and product work in the world. Finance is a domain that is the catalyst for all human progress and innovation. And capital allocation is upstream of the financing of every company, every idea, every economy. And so if you can make that more efficient, you can supercharge the world. And we're the category-leading player who has the best shot on goal to not just be the $100 billion business to do it, but the $500 billion business that completely transforms capital markets. And there's such a depth and a complexity and an amount of interesting problems that's so exciting. And we have a killer group of people that is super smart, hungry, curious, and low ego that's going to do it.

Good fucking pitch. But I got our investor. Say more about this capital markets piece. Historically, as markets get more efficient and liquid, their positive impact, in my opinion, grows a lot. You can chart this through market history, which isn't that long, 300, 400 years of proper markets. What do you think is possible? Where might this be going, and why do you believe that creating more Or less friction, I guess, in capital markets can be so powerful.

You're always at risk of sounding like the billionaire private equity guy saying that private equity is good for the world when you talk about how finance is good for the world. But I like to think about the origins of high finance. And when you think about a business like JPMorgan, some of the origins are J. Pierpont Morgan helping connect European investors with the entrepreneurs in an emerging market. The United States to finance railroads and the infrastructure buildout and everything that allowed the US to be a juggernaut economy. And that happened because there were intermediaries who helped connect folks who were risk-takers and capital allocators with the folks who were entrepreneurial and wanted to innovate. And that was something that had a profoundly great effect on the world. And now think about all the parts of the economy, all the parts of the US domestically, but also internationally that can't tap into capital markets. Every emerging nation where you would struggle to raise capital, to finance your idea, to raise debt. And then the 300,000 American businesses that couldn't even tell you what Goldman Sachs does, and JP Morgan doesn't have the time to go out and work with them because the business is too small. If you're able to speed up the rate at which entrepreneurs and company founders and individuals can tap into capital markets, you can accelerate all innovation.

What are you learning from your peers that are building companies kind of shaped like yours, but in other categories?

I am learning how much aggression it takes to grow this quickly. I'm learning how much chewing glass it is on a day-to-day basis and how much conviction you need to have in the long-term TAM and SAM to make sure that you don't fuss over all the things that are going completely wrong Every single day. And, you know, I'll call someone like Winston at Harvey and Winston's ability to just not worry about the 100 flesh wounds that are inflicted on him at any given time and just think about the kind of end state goal of where he's going to be 3 years from now. And the only 2 things that matter to get there is pretty amazing.

What is the glass like and what is the aggression like? What does that mean?

I worked during COVID so I didn't even get to see what an office looked like. John, my co-founder, and I used to joke that it's a very expensive business school education because we were just doing everything wrong. Didn't know how to hire, didn't know how to fire, didn't know how to mentor, didn't know how to manage, didn't know how to give feedback, didn't know how to set direction. And a lot of the people problems that arise with scaling quickly feel like eating glass to me anyway. And so when people quit, when you have retention issues, when you spend 6 months recruiting a candidate, they don't join, that's chewing glass. When you spend a bunch of time working on a product that gets completely washed over by the next model, that comes out and makes you feel like an idiot for spending all that time and capacity on something that was the wrong call, chewing glass. When you get rejected by 40 investors in a row before you're able to raise capital, chewing glass. And my experience of startup building is it's like a roller coaster where you have to feel the extreme highs and feel the extreme lows. And I'm a super emotional guy and I try not to let the team feel it, but I will feel on top of the world at the high and like everything is cataclysmic at the low. But then if I look back at the journey, the lows get lower, the highs get much higher. And I look back 3 months ago at the low I was dealing with or the high I was dealing with, I was like, I could do that in my sleep now. And I think it's just about modulating those things and channeling the emotion to push the business forward, but not letting it distract you.

What about aggression? Like that seems kind of like a trope, You've interviewed Pat Grady.

Pat Grady was at our board meeting on Wednesday and we presented what was an extremely aggressive plan for next year in terms of hiring goals, commercial goals, product goals. And one of the reasons I love Pat is because he boils everything down into like 2 bullet points and it's logically infallible. And, you know, he's just like premise 1, premise 2, this is the result. And he goes, Gabe, well, if everyone in finance is going to make a buying decision on AI, in the next 18 months, and they're definitely going to buy something no matter what, even if you're not there, then the only thing that matters is that you can blitz the market as quickly as possible to make sure that you are there. Given that, do you think this plan is aggressive enough or no? The answer was no. And the reason it wasn't aggressive enough is because I was being soft. And I think the reality is you need to be so, so, so aggressive and underwrite all that risk and know the game that you're playing. And my goal as a venture-backed business is to increase the tails of the distribution. It's fine if it gets 30% more likelihood that I fail if the odds that I become a $100 billion company also increase by 20%. But you actually have to be okay with raising both of those tails at the same time.

What's the most emotional low that you've faced?

When we were raising our Series A, we didn't have any star investors in our cap table yet. But there was a great investor, David Tisch at Box Group, who was an early pre-seed investor who introduced me to a bunch of all-time greats for the Series A. And I thought, wow, I've seen what it does to get a blue-chip investor in your cap table, what it does for recruiting talent, becoming more of a black hole for brand and customers and so on. This is finally the opportunity we're going to have to do that. We'd been building for 2 and a half years. And David introduced us to 40 investors and I met with everybody I met with. Sequoia, Kleiner, Benchmark, everybody. And 40 people passed. And it wasn't just like, you got the email with the deck and it's not exciting. It was like, oh, this is interesting. Let me meet Gabe. Oh, I kind of like Gabe. Let me spend an hour with him. Oh, Gabe, come to IC. Oh, Gabe, let's go to dinner. Oh, Gabe, come in for the weekend afterwards.

You know what?

We're going to pass. And it's so personal because at that stage, it has nothing to do with anything but you, right? It's like someone, it's like being broken up with by 40 girlfriends who like, Every time you fell in love and then every time they said, eh, not for you. And that was actually Thrive too at the Series A. Thrive spent so much time with me. I went to dinner with Avery and Vince, fell in love with them and the firm because they were awesome. And then crushing blow. And luckily Keith Rabois came basically a month after everyone else had rejected us. And Keith was like, Gabe, this isn't a contrarian bet. It's basically just Harvey for finance. Why would I do it? And I said, Keith, if it's not contrarian, why did Every single one of your friends just say it was a bad idea and not believe in me.

Why do you think they didn't believe?

For a lot of reasons. I think people underappreciated the TAM in finance, which was silly, but it's partly because I think SF has less intuition for finance because they didn't fund Ion Group or Bloomberg or S&P or FactSet or PitchBook, Morningstar, and so on. And so there actually haven't been great venture-backed businesses in this market. And so there's no intuition for the kind of contours of the market and how large it is. 2 is the product was terrible. And so every investor said, oh, I work in finance, let me use it. Is it going to transform what I do? And they tried it and it was wrong half the time. They go, this is never going to work. And it's like, guys, you know, we're on the exponential. It's going to work in 6 months or 12 months or 18 months or whatever it is. I'm going to figure it out. And the final reason was people didn't think I could figure it out. People didn't have enough data points of watching me chew glass and watching me figure out what the next iteration would What do you think changed after that?

Because now it's a who's who of capital.

Well, because I met all these guys very early on and I met them at every single round. And so every time I said, hey, we're going to do this. And they said, yeah, there's no way. And then every time we would do it. And by the way, a lot of the times it would manifest in a different way. You would actually lose the key employee you needed, or that actually excellent customer that you thought you were going to land totally dissipated. But we kept figuring out what to do and kept navigating the market. And I think When you're investing in a market like today's, especially in applied AI, where it's so turbulent, so ambiguous, you need to underwrite the founders being able to be extremely dynamic.

Why do you think there aren't more credible financial services competitors?

Distribution is really hard to crack. I think it's far more a people problem on building trust and delivering value and working with these institutions than just an engineering and product problem. But then there's an enormously high engineering and product burden too. The standard to execute to really crack this market, I think, is pretty high. And I was just so lucky that a number of the early people I hired were ex-finance and just killers. And we just hired folks from in our network who worked at Goldman or Citi or Jefferies or Apollo or Ares or Blackstone. And so we had a team of people who were diehard, ambitious, curious, smart, And then just good humans, low ego, humble, young enough to be open-minded and to want to eat glass too.

I'm also really curious how you've dealt with technical talent and what matters in a technical person on your team and how that has evolved. One intuition might be the value of domain expertise from the technical person has gone up as the ease of execution has changed a lot. You don't need to be super technical to write good code or less so. than in the past. So what does the shape of the engineering or technical team look like over time? I'm asking this again because I'm curious about thinking about other companies that want to tackle this in whatever their industry might be.

So I would say there's definitely problems where domain expertise is increasing in value and product intuition and having more of a GM-like mindset versus an engineer-like mindset is super, super important. There are also parts of our product surface area where it's like you just need raw, gritty engineering talent because you're scaling things up so aggressively. But if I look at the folks on our team who have been so excellent, a lot of them are former founders. They're folks who started businesses, persevered, ate glass, had a lot of product intuition, figured out how to channel it, and then the business maybe petered out. And to the point you made, there's been a lot of companies trying to tackle financial AI. There's been a lot of really smart product-minded, engineering-minded domain experts who have tried to tackle finance AI, and we've acquired 6 different fledgling financial AI startups. And so those former founders who can be galaxy-brained about the future of the product and navigate a course on what the UX should be, but also have engineering chops, and so they can constantly make the right decisions, is super important.

You said earlier to me before we were recording that this is the first time that we have like a major innovator's dilemma in this category. Can you describe what you mean by that?

Yeah, well, I think for a lot of investment firms and a lot of high finance financial services, the last 10, 20 years have been pretty good. You can make a lot of money being a capital allocator, being an investor. It's a very hard industry to enter. And then especially if you're in private markets, your businesses have some natural momentum because when you raise a fund and another fund, another fund, it's hard to mess up the business after that. And I'm sure there's 100 people who have started funds who know it's 100 times harder than I just described. But to be fair, there hasn't been a moment and a shock to the market where every investment firm, every bank is saying, oh wow, I need to completely rethink what I'm doing. And now there's going to be an opportunity for hundreds of AI-native disruptors, AI-native investment firms, AI-native investment banks to attack my business model. And I need to figure out what I'm going to do now. And there has not been an innovator's dilemma for private equity firms, for hedge funds, for investment banks in a long time.

AI-native. What does that term mean to you? Like, what is the definition of that?

In some ways, I think it just means willing to constantly reinvent everything you're doing. And being so AI-pilled that you don't worry about what's possible or what might seem completely far-fetched, but you are just charting a trajectory towards integrating this alien fundamental technology into everything that you do. And it's showing up in every part of the business. And there's no part of the business that you hold sacred that is immune to being revolutionized.

How do you do that? Like, so you're a leader in a company that's very AI-pilled that want to make sure— do you want to do everything you just said in Rogo? And yet I'm sure there are areas that you're unhappy with the status of how much AI is used to do X, Y, or Z. How do you do it as a leader? Like, how do you make sure that your company keeps doing this as a practice and habit versus like a one-time or—

So I'll give you a very simple thing, which is every month I have a report that gets sent to me that shows me everyone at the company, how much are they using the various different AI tools we have, both the ones we've procured, the ones we've built internally. All of these different things. And I have a stack ranking within every division of the top 5 power users and the bottom 5 users. And we post that everywhere. And the bottom user in each division gets a printout with a dunce cap and we post it around the office. And it's hilarious, but people know it's coming. And by the way, as we get bigger and fewer people know me and know it's kind of a joke and I'm funny, they're actually terrified. And I hope they're not terrified. And I hope that changes. But it's a strong incentive function. To actually make sure you're using these things.

If you were to list questions, let's say there's someone sitting down that runs one of these firms. It's been a great business, hard fought, but a great business. And these businesses tend to be quite simple from an organizational and technical standpoint. It's mostly humans, right? There's not a lot of overhead in lots of these businesses. Maybe they buy a lot of data or something. What questions would you encourage them to ask of themselves, of their business? To stand the best chance of navigating this transition effectively.

If you knew for sure that right now 90% of your enterprise value is in your people, your best investors, your best bankers are the people that bring in deals, bring in revenue, and actually that's what accrues enterprise value. And in 10 years, the world's best investment firms, best banks will have 90% of their enterprise value, not in people, but in software and data and systems. What would you start doing? You would start trying to figure out how do you take all of what lives in the latent minds of your best people and putting it into a system that you own and operate autonomously. So that's a big one. The second thing I would think about is unpack every part of the deal lifecycle and in each one, try and chart where do you think you are invaluable or you have data or you have domain expertise that no one else has. And then be diligent about saying, okay, do I actually have something that no one else has in this market? And is it a relationship? Is it context that no one else has? Or do I just think I'm smarter and better read on the subject area and the market, in which case AI is going to obviate that?

Have you seen anyone be the most cutting-edge exemplar of this attitude in a big firm? Like, is there a favorite example of a person that's just—

Oh yeah, there's a few. There's a— it's the folks that today look the most prescient. Sounded crazy batshit 2 years ago. They were the guys that came in and said, we need to record every conversation. You're going to be able to pipe in this conversation directly into my company brain, and there's going to be a digital clone of me, and then it's going to spit out the game theory on exactly what the investment should look like. And 2 years ago, everyone listened to those kind of guys and were like, what the heck is Patrick talking about? One example is there's a co-founder of Firm Mollis, John Momtazi, who was just prescient about where it was going, and he wanted digital clones of all their best bankers. He wanted systems that could basically show up to calls, speak on his behalf, know how he thinks, and then ingest that context. And then he wanted to build a system such that anyone in junior levels of that investment bank could leverage his expertise and context and relationships immediately so that that context and that data wasn't just powering his ability to be revenue generating, but it was empowering the ability of every junior in that bank to be revenue generating. And so there are a number of folks who had their kind of Move 37 moment where they realized, wow, this is going to be so much more profound. than anyone's expecting.

What to you feels the most uncertain about the future of your business?

I would say how quickly private markets actually do transform. If you think about why different types of asset classes have increased in transaction volume and have increased in liquidity, often it has to do with standardization because it gets easier then to track those assets and trade them. Private markets have been immune to standardization because there's so much unstructured data, AI should fix that. That said, is there going to be a regulatory or a market force that forces some additional standardization that really accelerates the kind of transparency and liquidity you can have in private markets? That's a little bit out of my control. The other thing is it's still unclear to me how much alpha will be left in the human relationships, right? I talk about that microcap M&A. It's very hard to imagine that if you're a small business owner and you've been building a business for 20 years and you want to make sure that if you hand that business off to someone else that you trust them, that you can shake their hand that, that can be fully automated. But for a lot of sponsor-owned businesses or things like secondaries or private credit or GPLP secondaries, I do think it can be fully automated. But how long it takes for the long tail of all these small businesses, all these medium-sized businesses that have to deal with generational turnover, for them to get comfortable clicking a button to sell their business as opposed to shaking the hand of someone, that's a little up in the air to me.

If you had several new young founders here with us and they were curious about How to navigate and interface with private markets investors, what would you teach them? What would you tell them to do, to not do?

My style for fundraising might not be everyone's style. I'm super direct. I'm super transparent about what I'm worried about, where I want to go. But then you have to be very headstrong on that end state. And then I would say it's about reps and relationships and the folks who come out of nowhere and lead the Series C or the Series D are the folks that I met at the seed and then the A and then the B and the C, and passed every time for all sorts of reasons, but they gather a lot more data on me and the business.

Anything that you would encourage people to not do?

I think there's a lot of the fake it till you make it, and you need to have the bravado and the confidence in what you're doing, even if you're not fully confident. And I'm a deeply paranoid, deeply insecure, deeply scared person, but you need to put on the face and say that you are confident about where you're going, even in the moments where, you know, it's the ebb and flow, it's the ebb and flow. And I think people can misinterpret that sometimes as like they need to pretend they're something they're not. I think that's not true. You need to believe that that 5% likelihood that you can be a $100 billion business is likely, and you don't need to pretend it's 100% likely, but you should be able to delineate with very clear roadmap and strategy how it is possible that you can become a $100 billion business and then have confidence That if those things play out, you will be.

Why are you scared and insecure?

I'm so paranoid about everything that can go wrong every day. It feels like you're on the knife's edge of a thousand things collapsing. And I think the reality of this sort of business building is that it's a game of compounding momentum. How do you do every small thing to just race a little bit faster downhill? And I'm just scared the momentum will stop, or you hit a roadblock and then you go off course and then you need to recatalyze momentum. And it's so clear to me how hard it is to actually build a machine that gathers momentum. So if there's any stumbling block that halts that velocity, if I wasn't constantly petrified of those moments, I wouldn't be doing everything under the sun to prepare for them.

What have we missed?

Like what that you've learned about building a company like this in this era where everything feels like a jump ball, like it feels like there's going to be a Rogo or a Harvey or whatever for every place that there can be, especially where the last mile wiring is hard and it's not just going to be Anthropic is one company to rule them all. What else have we missed that you think is really important to the experience so far of building the business?

I think that there are going to be businesses that solve all these problems, but the businesses that do it are going to become black holes for talent and capital and brand, and they're going to be able to siphon in the resources to actually execute because AI is an amazing tailwind, but the execution bar is higher than it's ever been to, to compete And everyone is getting pulled into the big leagues and is having their welcome to the NFL moment. You have to move faster and be stronger and be more resilient, agile than you ever expected. And having the right team is more important than ever. And the right team these days is extremely, extremely expensive. And so if you can't figure out what is the strategy to become a black hole for talent and capital as quickly as possible, I do think you are far more at risk of being roadkill of a lab or a company that can.

When I do these, I always ask my favorite question last. What is the kindest thing that anyone's ever done for you?

I benefited so much by having parents who were enormously kind and generous and selfless, but it showed up in such different ways for my mother versus my father. And my mother's version of kindness was no matter what I did, I was amazing and smart and could do no wrong, even though growing up, That was absolutely not the case, but she instilled in me the confidence to believe in myself. Even when you were in those low trajectories where it felt like everything was going to go sideways, anything, right? 40 nos in a row, idiot, failing out, flunking out, whatever it is, no matter what, she acted like I was maybe the smartest person on earth. That was irrational, but you need some of that irrational confidence that comes from just undying love. My dad was very different. My dad, if I came home and I had done something wrong, was seething, could barely look at me, couldn't understand it, right? Like he was someone who was so disciplined, so good, had such high standards. And so his version of kindness was figuring out how does he understand who I am and why I am failing at this thing and then help me. He would sit down and go over every detail with me, even though he sometimes just couldn't even understand why I had the opportunities I had and I couldn't take advantage of them. And take the time to make me better while staying true to his principles and his standards of excellence and his definitions of good too.

You're building a fascinating business. It's been so cool to watch it get better. Thanks for doing this with me.

Thank you.

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Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc.

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