# Gabe Stengel - Building Investing Superintelligence - [Invest Like the Best, EP.492] Invest Like the Best · 2026-09-22 · 64 min · https://traffic.megaphone.fm/CLS7922860814.mp3 ## Transcript Patrick O'Shaughnessy You and I have talked many times about this basic question over the last couple of years. You are effectively trying to build investing superintelligence tools to help investors do their jobs much faster, better, cheaper, easier, and with higher quality. But it's starting to feel like we're really eating a lot of the core functions that even a very smart analyst or portfolio manager was doing a couple of 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? Gabe Stengel 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. 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. 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, and how to integrate it into their systems. I think figuring out how to apply AI to the investment life cycle is the biggest challenge over the next 5 years for every great investor. ### The Model Era Advantage Patrick O'Shaughnessy There have been many companies on this trajectory where the product was terrible. Cognition is very famous for this; their ads now literally say, “Remember Devin? It's good now.” Lots of clearly great companies with great products had a stage of their AI business where the product stunk, and now it's excellent. If you think about the couple of 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? You can pick how many eras there are. I don't know how you frame it, but what could it do at each level, up to and including today? Gabe Stengel I actually tried starting Rogo 2 times before we got started. In high school, I had a friend whose dad was an investment banker who wanted an app for tracking the equity exchange ratio of 2 public companies as they were merging. We tried using really old AI techniques to do that. It was terrible. Then, in college, before GPT-3 came out, we published a paper on AI assistance for econometrics and financial econometrics. We tried commercializing it at the time, and nothing worked at all. When we actually started the business, it was when GPT-3 came out, pre-ChatGPT. In the early days of Rogo, it was clear how magical it was. You could demo things that were cool, but nothing worked at all. I would say that since things actually started to work, the eras have been very tied to the model eras. It was o1 Pro, and then probably Opus 4.5. o1 Pro was the first time you got enough reliability for it to be 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 that it wasn't so annoying you would just do it yourself. Then, with Opus 4.5, at the end of last year—the end of 2025, 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 them the right instructions and context. 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 wanted to build a product for it, but the models weren't quite there. People would try it and go, “This is terrible. This is garbage.” You had a disadvantage. We saw that, too. But now what we've seen is that if you were right about the end state and where the models were going, and you were building toward that, when they get there, it's magical. For me, a great product is reflected in all the feedback we get every day. People are saying all day, “This is transforming the way I work. I'm saving hundreds of hours a month. I'm doing things I never could have done before, so I'm smarter as a result and able to make better decisions. It's delightful, and I like using it. It brings me joy in my day-to-day because the UX, attention to detail, and craftsmanship are so obviously built for me and who I am.” That's been the best part of building the product. Patrick O'Shaughnessy Would you attribute that to taking seriously all the compliance, regulatory workflow, and last-mile hookup requirements, and then the models becoming good enough, so all of a sudden that was super valuable? Gabe Stengel There are also small details involved in understanding how someone within one of these firms works and building for that. I'll give you an example. We make it 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 they send it to an analyst. They can send it to our AI analyst over email, and we return that markup in 20 minutes instead of 2 days. At the same time, we alert the junior analyst on the deal about what's happening and show them the full auditability of all the little markups that were made in case they want to weigh in. That whole UX, that whole flow, makes it much easier for this financial professional—who hasn't 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. There are these small details in 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. Patrick O'Shaughnessy What's the bleeding edge of what it can do that impresses you the most? What kinds of jobs? ### Agents Expand The Investment Team Gabe Stengel The coolest thing that we're working on is taking innovations like Malt Book. Imagine if every PM at a hedge fund had 10,000 agents that were fraternizing, talking about ideas, reading through the notes, pontificating, and then, at the end of 24 hours of debate, gave you 1 idea. The reason you're able to do that is because investors are happy to pay $50,000 for 1 really good idea, whereas there are very few other domains where you can expend that many tokens just for 1 simple insight. What we're seeing right now, though, is that the models are smarter than anyone I know, anyone I spend time with. It's plumbing: connect it to your context, inform it about your thesis, tell it how you work, and try to integrate it into what you do. Building out all the plumbing to actually collect that data and context is what's cutting-edge to me. Patrick O'Shaughnessy It's always interesting to me that, for a product like this, 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 see how people want to use it. If I adopted a god's-eye view of Monday morning here in New York City, lots of Rogo 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. ### Dealmakers Become The Core Market Gabe Stengel Even though, in so many ways, I think public equities are the best application of AI because all the data is available and it's just about being as smart as possible, that's a very brute-force framing. Our early users in ICP and kinda core market is actually what I would describe as deal makers, or people who are transacting—buying companies, selling companies, and helping coordinate transactions. A lot of what we do is both make people smarter and 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 a deal? If you took a bird's-eye view of all the folks using Rogo, they're people who are either on the sell side or the buy side of a transaction. They're using it to 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 about customer concentration—or it's all the agents on the other side tearing through the data and mapping it to the firm's investment philosophy to say, “Is it lower than the concentration risk profile that we would want for this fund?” People access the system through all the classic channels you would use to access an AI tool: email, chatbots, proactive alerts, and those kinds of things. But then Rogo is 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. You need to update your CRM, update your portfolio monitoring systems, and update the way that you distribute information to your LPs after the fact. Half of our surface area is actually the underneath of the iceberg: interacting with these different systems of record based on what the humans are doing over the course of the deal. Patrick O'Shaughnessy 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 different. You're right that it's interesting: my first intuition would be that public markets are the best place to do this because there's so much data available. When do you think that transition happens? Gabe Stengel 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 who are great at it. We need to both hire 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 that the underlying systems, 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, create that kind of end-state product, and deliver that last mile 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 that there's been so much depth and TAM in this dealmakers vertical. 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 for dealmakers. Whereas for public equities, all that infrastructure—all those exchanges—already exists. I'd like to serve them because I want to serve the most sophisticated, smartest users who have inordinate amounts of knowledge about 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. Patrick O'Shaughnessy Interesting. One takeaway from that would be that 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. Gabe Stengel Yes. Patrick O'Shaughnessy And then applying your technology on top of that. Gabe Stengel Exactly. 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 in public equities, a lot of it has been automated. ### The Human Edge In Investing Patrick O'Shaughnessy Based on what you know, what skills do you think investment professionals, broadly speaking—public and private—should think about becoming more valuable as time progresses? And which skills become less valuable? It's kind of obvious which skills become less valuable. What the hell are we going to do? I think AI can do the soup-to-nuts diligence, the IC memo, objection handling, and all this kind of stuff. 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? Gabe Stengel I want to preface it all with this: I worked in finance for 2 years, so I'm 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. The jury is still out on whether or not that is something AI can eventually replace. When Move Thirty-Seven happened and Lisa Doll saw something I could never see, if that starts to happen in public equities, it's going to really change what matters. If that really changes how all of this works, I think the core skill set is people 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, speak to experts, and develop a relationship graph of people who can inform your model, maybe you're not the one who 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. ### Building The Financial AI Stack Patrick O'Shaughnessy Maybe it's a good time to talk about—I think about the assembly line. If Rogo puts out some really useful output, I want to learn about each component of the system that leads to that output. I'm especially interested in the data that you yourself have, use, buy, build, and whatever; how you do that; how you think about data; and then how you think about models. These are both questions that I'm curious about for your specific business, but I also think there will be some business like Rogo built in basically every vertical. I'm curious what can be abstracted to other professional services or other interesting verticals as well, and what will follow from AI progress. Talk us through, specifically, the data-and-model piece and how that's evolved over time. Gabe Stengel The early days of Rogo were kind of this Rube Goldberg contraption where you had 60 different model calls. A question comes in, and 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 data set? Now it comes back, and I need to call a different model to pull it all together.” As the models get smarter, you want to be less prescriptive, less of a Rube Goldberg machine, and just think about what the simplest, best, highest-quality tools are. In the same way, if you have the world's smartest human being starting here tomorrow and trying to be a great banker and investor, what are the tools that they would need that aren't just intrinsic to being smart? What are the data tools? What are the ways of going out and gathering information? What are the ways of auditing their own work? And what are the ways of presenting it and pushing it back into the systems that it needs? For us, we spend a lot of time thinking through 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 the compliance and regulatory requirements to make sure that, if someday a Delaware court judge makes AI inputs and research discoverable, you've done it all the right way, such that you're not intermingling information. 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. Then we spend a lot of time benchmarking these models and creating different evals and data sets so we can always decide which is the most performant, which is the cheapest from a token perspective, and which has the lowest latency, and route tasks to the appropriate types of models. Patrick O'Shaughnessy I'm sure your favorite question is: How do you ultimately compete with Anthropic and OpenAI, which view finance as one of the few big categories that you see them talking about and thinking about? What can you do in the long run that's counter-positioned against what they can or will do, do you think? ### Competing Beyond Foundation Models Gabe Stengel You want to build things that are perpendicular to what they want to build. 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, which we need to build for finance or for any other vertical. When you think about financial services and capital markets, how many businesses are there that generate more than 5 or 10 billion dollars by just going deep into those workflows, into the data sets, and into 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 data sets, different definitions of good, and different regulatory requirements. We can get to 5 billion dollars in revenue by going deep across those things and creating the systems—the systems of record—that help manage them. For Anthropic, that 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 1 trillion in revenue. There's so much depth to these systems that actually needs to be built beyond just intelligence. Patrick O'Shaughnessy Is there a favorite example of some pain-in-the-ass thing that you've had to wire up, some last-mile thing? Gabe Stengel Yeah. Think about if you are ingesting MNPI because you are working on transactions or deals that have an effect on the market. The compliance requirements you have for auditability, what you can flag, and how it feeds into the internal systems of an investment firm or a bank mean that, if you ever get audited or a regulator ever wants to see what you did, you need to have all that plumbing in place and 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's compliant, safe, and secure that coordinates the process. 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. 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 only 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. Patrick O'Shaughnessy It's interesting. I asked about data and models, but actually it sounds like the harness or infrastructure is probably the most important of the 3. Gabe Stengel 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. 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. It shows that the way that you harness these models is so important. I think people underappreciate that the reasons humans are high-agency and can do a lot is not just because we have high raw recall, IQ, and knowledge, but because there are 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: they have good spidey sense for when they see this sort of thing in the market. It actually triggers the recall from this event, then informs a creative decision. All those small microservices are things that need to be built out. Patrick O'Shaughnessy If I force you to become an investor and your only goal is to invest in Rogo-like businesses—one of these businesses that are, let's say, vertical applications 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? Gabe Stengel A few things. One is that 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, and 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. 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. 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. I don't have unique domain expertise, but I had enough to get started, and then I was so curious about finance, money, and capital markets. I grew up in New York, and I was surrounded by people whose only thoughts and conversations were about high finance. I was fascinated by it, and I wanted to learn about it. Then we assembled a team that was uniquely passionate about it too. I have over 100 people who have spent time within investment banks or investment firms across the world's best institutions, so we can constantly take the models as they're released and harness them for finance. Our job is really to catch the changes in the models and figure out how to apply them to 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. Anyone whose delivery method for their product is not something that they can fully cannibalize quickly—like a terminal or 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. Patrick O'Shaughnessy What's an example of that that you've done, like a tear-it-down-and-rebuild? Gabe Stengel The anecdote I'm most inspired by is Max Levchin, who talks about how at Affirm they rebuild the fundamental ledger technology every year. They rebuild it for a few reasons. One is that it's the most interesting engineering problem, so all the engineers want to work on it. It's a good way to retain talent and teach engineers about the core, fundamental business of Affirm. Number 2, it's a good way to make sure the system doesn't ossify and is constantly improving. We do that same exact thing for our harness and the core agentic system. We are constantly looking at it and realizing we're not even at a local minimum. It would be impossible if we were at a local minimum because the models are changing so quickly, and we need to redo the whole thing. Patrick O'Shaughnessy What's something that the models currently cannot do that, if they could, would really change the nature of the product? Gabe Stengel Compaction. 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? How does it have enough coherence and context on who you are and what you care about to make it feel like it's a true person you're speaking to that learns more and more about you? That's a hard problem. It compounds exponentially when you think about agents not just as one-to-one. Right now, almost all agents are one-to-one. You use ChatGPT individually, you use Copilot individually, and you use Gemini individually. As soon as these are actually things that converse with a lot of colleagues, or operate in a Slack channel with 100 people, or have to work across an entire company, the compaction problem just scales exponentially because the agent is having conversations with 100 different people and needs to be able to coordinate across those things. Being able to take all of that memory and all those interactions and actually lodge them 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. Patrick O'Shaughnessy What do you think the major kinds of AI software businesses are? We've got companies like Cognition or Cursor or something that can grow unbelievably quickly, and I'm especially curious for you to compare this to 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? Talk us through how people want to buy this stuff and what the emerging models are for AI software businesses. ### AI Pricing Moves Toward Outcomes Gabe Stengel 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, FactSet, Capital IQ, and PitchBook, so we have to build a very human business. Every time you sign a deal, it requires an AE, a solutions architect, and a sales engineer going in and shaking a lot of hands, explaining how it works, and explaining how to integrate it. You can't sign one deal where the usage just rises 100-fold. I look at how easy it was for Anthropic to sell to us, 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. 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. I do think there's a category that's just typical enterprise sales, but using AI models as a tailwind to build 100-times-better products. Then there are the token brokers—the token-consumption businesses—where you're selling into the parts of enterprise where they're used to buying usage-based tools: Cursor, Factory, Cloud Code, and others. You can go much further commercially with fewer people. Patrick O'Shaughnessy What's your prediction for how or if that will change in finance? Gabe Stengel I think that every business needs to go through 2 different pricing revolutions. You need to move to some sort of usage-based pricing, and then you need to move to some sort of outcome-based pricing. For me, if I can figure out a way to skip the token-based pricing, skip the usage-based pricing, 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 you earned. Or you know what the value to you is if you can produce a CIM if you're a bank, because you know what you charge these firms to actually sell the business. Patrick O'Shaughnessy Presumably, you can't change your seat price on the fly dynamically, at least with the same customer. So how do you deal with the problem of 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? Gabe Stengel The reality is, we are 1% of the way into our product roadmap. Ninety-nine percent 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. Patrick O'Shaughnessy It's so interesting to think about the shape of this in the future. You said you're 1% 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. ### Agents Rebuild Capital Markets Gabe Stengel Think about the percentage of all capital markets workflows, investment workflows, and investment banking jobs that are still completely human-rate-limited and done by human intermediaries. It's similar to other parts of financial services, where 15 or 20 years ago, every mortgage that someone got involved going in and speaking 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 in how companies transact, how companies raise capital, and 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, 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 whether it can sell a portfolio company to another sponsor, not 5 months. I think you're going to be able to price assets in an order of magnitude less time, and as a result, markets are going to be more transparent, more liquid, and more efficient, and there’s going to be a whole bunch more activity. Patrick O'Shaughnessy 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 everything or something. What do you need to build that you don't already have to enable that sort of capital market future? Gabe Stengel It's actually a very similar strategy to Bloomberg's strategy. Bloomberg's strategy was—and I'm going to omit a lot of details here—to 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 communication platform where you can actually transact in a bunch of asset classes that before were pretty opaque: Bloomberg Messenger. For us, it's to use a little bit of AI to get in the door, build out the full workflows, go from copilot chatbot to full autopilot tool, so I can make sure that I'm 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 autonomously do the work, I can actually transact for you. The difference between what I need to build and Bloomberg Messenger is that I don't need to build the communication channel for humans to transact. I need to build the communication channel for agents to transact across these businesses, across these investment firms. If you think about what the actual infrastructure needs to get built, think about 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 QoE, 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. Patrick O'Shaughnessy Tell us a little bit about the customer base today. How much of it is giant banks versus investment firms? Gabe Stengel It's mostly large banks, and that's very simply because that was my background. I worked as an investment banker for just a handful of years doing buy-side M&A coverage, which was super interesting. So we started targeting the banks pretty early on for a few simple reasons. One is that investment banks are the distribution channel for the rest of finance. A lot of the people 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. Two, 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 more people than if you land the 10 best single-portfolio-manager public-equities investors who each only have 10 investors. Patrick O'Shaughnessy If I think about a Bank of America or something, everyone's wondering how deep into the adoption curve we are 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. Gabe Stengel I would say the majority of firms are seeing a huge amount of individual productivity, and they're trying to figure out how to parlay that individual productivity into firm productivity that they can measure. Speak to any individual banker at a bank where we're deployed— Patrick O'Shaughnessy They're like, “Life's great.” Gabe Stengel They're like, “I'm 100 times more efficient than I used to be.” You'll speak to an MD who will say, “Gabe, I sent 5 pages to a client that before I would have had to go back and forth with an analyst on over 3 days to create, and I made it in 10 minutes myself.” 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? This is where it becomes not just an individual productivity tool problem, but a firm strategy problem. 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 JPMorgan. JPMorgan just announced that they're going to try to 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 who can be a deal team of 1, maybe you can enter parts of the market that before just made no sense. Patrick O'Shaughnessy So in some sense, the bottleneck at some point will be the creativity of the customer. You can provision unlimited capability, and you're soon going to be relying on them to figure out the answer to the question. Gabe Stengel It's them figuring out what they want to do. 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 the structure. Do they all work on different things? How much money do I give each of them? What do we attack? Patrick O'Shaughnessy 2 years ago, the most obvious question in this would have been about accuracy, and people used to use the word “hallucinations,” which seems to have dropped out of the conversation. 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 down to the decimal? What's the nature of that these days? Gabe Stengel 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 if I give you an answer, you know how to use it. If it's not accurate, 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 and 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 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. In the same way that there are going to be individual investors who 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 who 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 what data went in, that's not going to fly. Patrick O'Shaughnessy 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? You're rate-limited by the speed of humans to some degree in enterprise sales. How do you hire enough people fast enough? How do you think about being able to grow at the right rate when you don't have the API-usage growth of Anthropic or Cognition? It's so easy for them to grow 10x. It's much harder for you. How do you solve that? Gabe Stengel The core problem we need to solve is: how fast can you make a human being productive— Patrick O'Shaughnessy As a salesperson? Gabe Stengel A 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, push customers forward, and help our end users? 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. Patrick O'Shaughnessy Do you use AI to build tools for that? Gabe Stengel 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—” Patrick O'Shaughnessy That's how we do it. Gabe Stengel “—you're always being recorded. It's always being filtered into the company brain.” This isn't a kind of Big Brother situation. It's just that everyone you're going to speak to is going to have Granola or some meeting transcription running, because they need to use it, they need to have excellent recall, and they need to compound the knowledge that they have. As a result, we have this huge reservoir of information, and then we have all of these tools that people can use on top of it 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?” You can pull in the conversation that a peer of yours had 3 weeks ago, and you've 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. Patrick O'Shaughnessy Maybe describe the internal brain. How would one— Gabe Stengel 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's 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? 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, how I should think about the value proposition 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 that 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 all of the types of ROI metrics that firms we've worked with in the past would want to hear.” Patrick O'Shaughnessy What's the sales pitch to talent? Let's say there's somebody who, if you landed them tomorrow, would be transformative because they're so high-quality or well-known or whatever. What's the pitch to them to come work at Rogo versus go somewhere else that's exciting right now? Gabe Stengel It always depends on the person's motivation, 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 it's going to be most interesting is applied AI, because that's where AI intersects with humanity. 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, and every economy. If you can make that more efficient, you can supercharge the world. We're the category-leading player who has the best shot on goal to not just be the $100 billion business that does it, but the $500 billion business that completely transforms capital markets. There's such depth and complexity, and so many interesting problems, that it's incredibly exciting. We have a killer group of people who are super-smart, hungry, curious, and low-ego, and they're going to do it. Patrick O'Shaughnessy Good fucking pitch. Gabe Stengel Go on. Patrick O'Shaughnessy But I invested. 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 or 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? Gabe Stengel 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. When you think about a business like JPMorgan, some of its origins are J. Pierpont Morgan helping connect European investors with entrepreneurs in an emerging market—the United States—to finance railroads and the infrastructure build-out, and everything that allowed the US to be a juggernaut economy. That happened because there were intermediaries who helped connect folks who were risk-takers and capital allocators with folks who were entrepreneurial and wanted to innovate, and that had a profoundly great effect on the world. 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 or raise debt, and the 300,000 American businesses that couldn't even tell you what Goldman Sachs does. JPMorgan 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, company founders, and individuals can tap into capital markets, you can accelerate all innovation. Patrick O'Shaughnessy What are you learning from your peers who are building companies shaped like yours, but in other categories? ### Building A Black Hole Company Gabe Stengel I am learning how much aggression it takes to grow this quickly. I am learning how much chewing glass there 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. 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 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. Patrick O'Shaughnessy What is the glass like, and what is the aggression like? What does that mean? Gabe Stengel I worked during COVID, so I didn't even get to see what an office looked like. John, my cofounder, and I used to joke that it was a very expensive business-school education because we were just doing everything wrong. We 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, and didn't know how to set direction. A lot of the people problems that arise with scaling quickly feel like eating glass to me, anyway. People quit when you have retention issues. When you spend 6 months recruiting a candidate and 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 it makes you feel like an idiot for spending all that time and capacity on something that was the wrong call, that's chewing glass. When you get rejected by 40 investors in a row before you're able to raise capital, that's chewing glass. My experience of startup building is that it's like a roller coaster where you have to feel the extreme highs and the extreme lows, and I'm a super emotional guy. 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 or the high I was dealing with and think, “I could do that in my sleep now.” I think it's just about modulating those things and channeling the emotion to push the business forward, but not letting it distract you. Patrick O'Shaughnessy What about aggression? That seems like a trope. Gabe Stengel 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, and product goals. One of the reasons I love Pat is because he boils everything down into 2 bullet points, and it's logically infallible. He's just like, “Premise 1, premise 2, this is the result.” He goes, “Gabe, if everyone in finance is going to make a buying decision on AI in the next 18 months, and they are 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 not?” The answer was no. The reason it wasn't aggressive enough is because I was being soft. I think the reality is you need to be so, so, so aggressive, underwrite all that risk, and know the game that you're playing. My goal as a venture-backed business is to increase the tails of the distribution. It's fine if it increases the likelihood that I fail by 30% 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. Patrick O'Shaughnessy What's the most emotional low that you've faced? Gabe Stengel 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 BoxGroup, who was an early pre-seed and seed investor and introduced me to a bunch of all-time greats for the Series A. 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 2.5 years. David introduced us to 40 investors, and I met with everybody. I met with Sequoia, Kleiner, Benchmark, everybody. Forty people passed. It wasn't just that you got the email with the deck and it wasn't 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 investment committee. Oh, Gabe, let's go to dinner. Oh, Gabe, come in for the weekend afterwards. You know what? We're going to pass.” It's so personal because at that stage it has nothing to do with anything but you, right? It's like being broken up with by 40 girlfriends who, every time you fell in love, said, “Eh, not for you.” 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. Luckily, Keith Rabois came basically 1 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?” Patrick O'Shaughnessy Why do you think they didn't believe? Gabe Stengel For a lot of reasons. I think people underappreciated the TAM in finance, which was silly, but it's partly because I think San Francisco 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. There actually haven't been great venture-backed businesses in this market, so there's no intuition for the contours of the market and how large it is. Second, the product was terrible, and every investor said, “Oh, I work in finance. Let me use it. Is it going to transform what I do?” They tried it, and it was wrong half the time, and they said, “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.” The final reason was that 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 be. Patrick O'Shaughnessy What do you think changed after that? Because now it's a who's who of the cap table. Gabe Stengel Because I met all these guys very early on, and I met them at every single round. Patrick O'Shaughnessy And so— Gabe Stengel 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. By the way, a lot of the time it would manifest in a different way. You would actually lose the key employee you needed, or that excellent customer you thought you were going to land would totally dissipate, but we kept figuring out what to do and kept navigating the market. I think when you're investing in a market like today, especially in applied AI, where it's so turbulent and so ambiguous, you need to underwrite the founders being able to be extremely dynamic. Patrick O'Shaughnessy Why do you think there aren't more credible financial-services competitors? Gabe Stengel Distribution is really hard to crack. I think it's far more a people problem—building trust, 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 inter-network who worked at Goldman, Citi, Jefferies, Apollo, Ares, or Blackstone. 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 willing to eat glass, too. Patrick O'Shaughnessy I'm also really curious how you've dealt with technical talent, what matters in a technical person on your team, and how that has evolved. One intuition might be that 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 at least less so than in the past. So what does 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. Gabe Stengel I would say there are 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 are super, super important. There are also parts of our product surface area where 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. To the point you made, there have been a lot of companies trying to tackle financial AI. There have 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. Those former founders who can be galaxy-brained about the future of the product and navigate a course for what the UX should be, but also have engineering chops so they can constantly make the right decisions, are super important. Patrick O'Shaughnessy You said earlier to me, before we were recording, that this is the first time that we have a major innovator's dilemma in this category. Can you describe what you mean by that? Gabe Stengel I think for a lot of investment firms and a lot of high-finance financial services, the last 10 or 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, another fund, another fund, it's hard to mess up the business after that. I'm sure there are 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 or a shock to the market where every investment firm and 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, and AI-native investment banks to attack my business model, and I need to figure out what I'm going to do now.” There has not been an innovator's dilemma for private-equity firms, hedge funds, or investment banks in a long time. Patrick O'Shaughnessy AI-native—what does that term mean to you? What is the definition of that? Gabe Stengel In some ways, I think it just means being 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. You are just charting a trajectory toward integrating this alien, fundamental technology into everything that you do. It's showing up in every part of the business, and there's no part of the business that you hold sacred or that is immune to being revolutionized. Patrick O'Shaughnessy Gabe Stengel Yeah. Patrick O'Shaughnessy Do you want to do everything you guys said in Rogo, and yet I'm sure there are areas where you're unhappy with how much AI is used to do X, Y, or Z. How do you do it as a leader? How do you make sure your company keeps doing this as a practice and habit versus a one-time personal— Gabe Stengel I'll give you a very simple example. Every month, I have a report sent to me that shows me, for everyone in the company, how much they're using the various AI tools we have—the ones we've procured, the ones we've built internally, and all of these different things. I have a stack ranking within every division of the top 5 power users and the bottom 5 users. 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. It's hilarious, but people know it's coming. As we get bigger and fewer people know me or know that it's a joke and that I'm funny, they're actually terrified. I hope they're not terrified, and I hope that changes, but it's a strong incentive function to make sure you're using these things. Patrick O'Shaughnessy Let's say there's someone sitting down who runs one of these firms that's been a great business—hard-fought, but a great business. These businesses tend to be quite simple from an organizational and technical standpoint. They're mostly humans, right? There's not a lot of overhead in many of these businesses. Maybe they buy a lot of data or something. What questions would you encourage them to ask of themselves and of their business to stand the best chance of navigating this transition effectively? Gabe Stengel If you knew for sure that right now 90% of your enterprise value is in your people—your best investors and your best bankers are the people who bring in deals and bring in revenue, and that's what accrues enterprise value—and in 10 years the world's best investment firms and best banks will have 90% of their enterprise value not in people but in software, data, and systems, what would you start doing? You would start trying to figure out how to take all of what lives in the latent minds of your best people and put it into a system that you own and operate autonomously. The second thing I would think about is unpacking every part of the deal life cycle. In each one, try to chart where you think you're invaluable, where you have data, or where you have domain expertise that no one else has. Then be diligent about saying, “Okay, do I actually have something that no one else has in this market? 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 that case, AI is going to obviate that. Patrick O'Shaughnessy Have you seen anyone who is the most cutting-edge exemplar of this attitude in a big firm? Is there a favorite example of a person who's just— Gabe Stengel Oh, yeah. There's a few— Patrick O'Shaughnessy …piercing of this? Gabe Stengel The folks who today look the most prescient sounded crazy as batshit 2 years ago. They were the guys who came in and said, “We need to record every conversation. You're going to be able to pipe this conversation directly into my company brain, and there's going to be a digital clone of me. Then it's going to spit out the game theory on exactly what the investment should look like.” Two years ago, everyone listened to those kinds of guys and was like, “What the heck is Patrick talking about?” One example is there's a co-founder of firm Molus, John Momtazi, who was just prescient about where it was going. 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. He wanted to build a system such that anyone at the junior levels of that investment bank could immediately leverage his expertise, context, and relationships. That context and data wouldn't just power his ability to generate revenue; it would empower every junior in that bank to generate revenue. There are a number of folks who had their kind of Move 37 moment when they realized, “Wow, this is going to be so much more profound than anyone's expecting.” Patrick O'Shaughnessy What feels the most uncertain to you about the future of your business? Gabe Stengel I would say how quickly private markets actually transform. If you think about why different types of asset classes have increased in transaction volume and liquidity, often it has to do with standardization, because it gets easier 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 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 that it's still unclear to me how much alpha will be left in human relationships. 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, you can trust them, that can be fully automated. But for a lot of sponsor-owned businesses, or things like secondaries, private credit, or GP/LP secondaries, I do think it can be fully automated. How long it takes for the long tail of all these small and medium-sized businesses that have to deal with generational turnover to get comfortable clicking a button to sell their business, as opposed to shaking the hand of someone, is a little up in the air to me. Patrick O'Shaughnessy If you had several young founders here with us who were curious about how to navigate and interface with private markets investors, what would you teach them? What would you tell them to do and not do? Gabe Stengel My style for fundraising might not be everyone's style. I'm super direct and super transparent about what I'm worried about and where I want to go, but then you have to be very headstrong on that end state. I would say it's about reps and relationships. The folks who come out of nowhere and lead the Series C or the Series D are the folks I met at the seed, then the A, then the B, and then the C. They passed every time for all sorts of reasons, but they gathered a lot more data on me and the business. Patrick O'Shaughnessy Is there anything that you would encourage people not to do? Gabe Stengel I think there's a lot of “fake it till you make it,” and you need to have the bravado and confidence in what you're doing, even if you're not fully confident. 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 when it's the ebb and flow. I think people can sometimes misinterpret that as meaning they need to pretend they're something they're not. I don't think that's true. You need to believe that the 5% likelihood that you can be in 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 a 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. Patrick O'Shaughnessy Why are you scared and insecure? Gabe Stengel 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. 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? I'm scared the momentum will stop, or you'll hit a roadblock, go off course, and need to recatalyze momentum. It's so clear to me how hard it is to actually build a machine that gathers momentum. 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. Patrick O'Shaughnessy What have we missed? What have you learned about building a company like this in this era, where everything feels like a jump ball? 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. It's not just going to be Anthropic as one company to rule them all, or OpenAI. What else have we missed that you think is really important to the experience so far of building the business? Gabe Stengel I think 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, capital, and brand. They're going to be able to siphon in the resources to actually execute. AI is an amazing tailwind, but the execution bar to compete is higher than it's ever been, too. Everyone is getting pulled into the big leagues and is having their welcome-to-the-NFL moment. You have to move faster, be stronger, and be more resilient and agile than you ever expected, and having the right team is more important than ever. The right team these days is extremely, extremely expensive. If you can't figure out 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. Patrick O'Shaughnessy When I do these, I always ask my favorite question last. What is the kindest thing that anyone's ever done for you? Gabe Stengel I benefited so much from having parents who were enormously kind, generous, and selfless, but it showed up in such different ways for my mother versus my father. 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. Patrick O'Shaughnessy Forty nos in a row. Gabe Stengel You couldn't do anything right. Forty nos in a row, an 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. If I came home and I had done something wrong, he was seething, could barely look at me, and couldn't understand it. He was someone who was so disciplined, so good, had such high standards, and so his version of kindness was figuring out how he could 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 understand why I had the opportunities I had and couldn't take advantage of them. He took the time to make me better while staying true to his principles, his standards of excellence, and his definitions of good, too. Patrick O'Shaughnessy You're building a fascinating business. It's been so cool to watch it get better. Thanks for doing this with me. Gabe Stengel Thank you.