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0:00 What we're seeing right now though is the models are smarter than anyone I know, anyone I spend time with. 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, you know, 24 hours of endless, you know, debate just gave you one idea. In 10 years, the world's best investment firms, best banks will be have 90% of their enterprise value, not in people, but in software and data and systems.
0:26 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. There's no part of the business that you hold sacred that is immune to being revolutionized. You and I have talked many times about this basic question that I'll start with over the last couple of years. You're effectively trying to build investing super intelligence 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 two years? I think two years is actually easier to reason about than 10 years or 20 years because in two 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 you know, Daario has the 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. You know, what would Goldman do?
1:54 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 advant advantage of that, how to integrate it into their system. I think figuring out how to apply AI into the investment life cycle is the biggest challenge over the next 5 years for every great investor. So there's been many companies on this trajectory where the product was cognition is very famous for like literally their ads now say remember Devon like it's good now.
2:23 >> 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 sort of like the eras of Rogo and its product? Like you pick how many eras it is. I don't I don't know how how you frame it up, but like what it could do at each level up up to and including today.
2:50 >> Yeah. I actually tried starting Rogo two 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 two public companies as they were merging. And so we tried using really old AI techniques to do that. Terrible. And then in college before GB3 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 GBD3 came out, pre- chat GBT. 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 nothing worked at all. I mean, I would say since things actually started to work, the eras are very tied to the model eraser, right? It was 01 Pro and then probably Opus 45. 01 Pro was the first time you got enough reliability where it was a good search tool at the very least, right? like you could say help me calculate this sort of you know 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 45 and the kind of end of last year the end of 2025 into 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 movers 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.
4:22 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 trans 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 the best part of the product building.
5:06 and and you would attribute that to you took seriously all the compliance regulatory workflow like 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 it. 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 is 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.
6:12 >> What is like the bleeding edge of what it can do that impresses you the most? Like what kinds of jobs? Oh, I mean I think the the the coolest things that we're working on is taking these kind of these innovations like malt book. 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, you know, 24 hours of endless, you know, 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 expand that many tokens just for, you know, 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 trying to 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.
7:08 It's always interesting to me for a product like this that of course you 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 revealed how people want to use it. If I adopted a god's eye view of, you know, it's it's Monday morning here in New York City. Lots of Roger 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 like how it's being used right now as we record.
7:38 >> 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 you know 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, you know, helping coordinate transactions. And so a lot of what we do is both make people smarter but actually do the dealmaking.
8:07 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, you know, putting things into a data room, this is the company's model, you know, this is the, you know, PowerPoint that describes their customers. This is 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 is it lower than the kind of concentration risk profile that we would want for this fund, too?"
8:56 And then the kind of components of the system are people access it via all the kind of classic channels you would access an AI tool, email, you know, chat bots, proactive alerts, and those kind 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, but you need to update your CRM, you need to update your portfolio and monitoring systems, you need to, you know, update the way that you distribute information to your LPs after the fact.
9:29 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 do doing over the course of the deal. So, 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 that that is effectively there there's a process to their workflow as well, but it's very very different.
9:56 it would Yeah, you're 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. In fact, I 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 endstate product and that last mile delivery delivery for public equities investors. You know, we actually 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 in TAM in this deal makers vertical. And then for my endstate vision of actually being the full kind of infrastructure for private markets where people can transact very effectively, that is far more important to the dealmakers.
11:12 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 they they follow. And I'd like to make them even smarter because that sounds super cool. But I can build a huge huge businesses 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.
11:46 >> Yes. >> And then applying your tool 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 I mean 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 I mean kind of obvious the skills that become less valuable. But yeah, another way of asking like what the hell are we going to do if the thing 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 of a job for at least a junior person in the investing world. So what skills do you think people will still matter a lot in a couple years?
12:36 >> I mean I want to preface it all with you know 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 you know 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 jury is still out on on whether or not that is something that AI can eventually replace, right? I think when you know move 37 happened and lease it all 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. RAMP is the only platform built to make your finance team leaner, faster, and better, saving businesses 5% annually on average, so you can stay focused on growth. RAMP just opened for business in the United Kingdom. So, if you're running a business in the UK, you can now use RAMP's AI powered finance platform to manage cards, expenses, bills, approvals, and accounting all in one place. I run my business on RAM, and so should you. Learn more at ramp.com/invest.
14:03 RO is the AI platform purpose-built for financial institutions, serving tens of thousands of bankers and investors at hundreds of leading firms worldwide. Rogo's AI agents autonomously execute large chunk of your firm's workflows. They can screen deals, draft sims, run buyer outreach, and due diligence on data rooms, all with full security and compliance handled. Every action feeds Rogo Intelligence, the firm's context layer, turning years of deal experience into a secure, governed institutional memory that makes each new transaction stronger than the last. To learn more, visit rogo.com/invest.
14:38 OpenAI, Cursor, Anthropic, Perplexity, and Verscell all have something in common. They all use WorkOS. And here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, skim, arbback, and audit logs. That's where work OS comes in. Instead of spending months building these missionritical capabilities yourself, you can just use work OS APIs to gain all of them on day zero. That's why so many of the top AI teams you hear about already run on work OS. Work OS is the fastest way to become enterprise ready and stay focused on what matters most, your product. Visit works.com to get started. Maybe it's a good time to talk about literally the I think of like the assembly line like if if Rogo puts out some really useful output. Tell me about the entire system.
15:26 I want to learn about each component part of the 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 I and 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 you know what can be abstracted to other professional services or other verticals that are 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 >> yeah 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, you know, what companies is Patrick asking about? Now, what are their tickers? Now, how do I feed those tickers into an API call to, you know, Bloomberg or Faxit or some internal data set? Now, it comes back and I need to call a different model to to pull it all together. And as the models get smarter, you kind of want to, you know, 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 banker and 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 you know 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 you know banker outputs is that you're going to be able to see the full lineage of how those things are created.
17:20 and then we spend a lot of time benchmarking these models and creating different kind of evals and data sets 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 task to the appropriate type of models >> I'm sure your favorite question is how do you ultimately compete with anthropic and open AAI who view finance as a big it's one of the few categories that like you see them talking about and thinking about what can you do in the long run that they that's counterpositioned against what they can or will do you I mean, you want to build things that are perpendicular to what 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 data sets, 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 data sets, 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 forth anthropic would kind of be like, you know, stopping on the side of the road to pick up a penny because they're on the pathway of trying to go from a hundred billion in in revenue to a trillion in revenue.
18:44 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 thing? Yeah, I mean think about, you know, if you're 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 kind of 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 wants to see what you did, do you have all that plumbing in place that is pixel perfect? That's one example.
19:21 Another example is you know if you actually want to transact and you want to you know 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 you know open airropic will ever want to build a data room business and 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.
19:58 >> So, it's interesting. I asked about data and models, but actually it sounds like harness or infrastructure is probably the most important of the three. >> Think about the fundamental difference between cloud code when it came out and cloud co-work versus openai and chatbt. 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 the models to exercise more of their longunning capabilities. And that's why they had a runup 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, you know, the reasons that humans are high agency and can do a lot is not just because we have high, you know, kind of just raw recall and IQ and knowledge, but there's all these different microervices in your brain.
20:48 How do you, you know, put knowledge away? How do you retrieve it? How do you trigger things? Emotions are a way to trigger all these different microservices. And you know that's why a great investor might have great judgment is because they have you know a 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 kind of small microservices are things that need to be built out. If you if I force you to become an investor and your only goal is to invest in rogo like businesses like one of these businesses that's let's say a vertical application that's wiring up I think about it as just like 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?
21:30 >> 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, you know, walk into and sell the basic version of chatbt or co-work 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, you know, 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.
22:19 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 a hundred 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.
22:43 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 you know their kind of 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 in the >> what's an example of that that you've done like a tear it down and rebuild. I mean I the the story the anecdote I'm most inspired by is Max Lebchin who talks about how at a firm they rebuild the kind of 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 a firm but then number two it's a good way to make sure that system doesn't oify and it's constantly improving and so we do that same exact thing for our harness and the kind of 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.
24:05 >> Compaction. So it's you know if you are 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.
24:28 that's a hard problem and it compounds exponentially when you think about agents not just as a onetoone right now almost all agents are one to one you use chatbt individually you use copilot individually you use Gemini individually as soon as these are actually things that you know can sort with a lot of colleagues or in a slack channel with a hundred people have to work across an entire company now the compaction problem just scaled exponentially because it's having you know conversations with a hundred different people and needs to be able to coordinate across those things and so being able to take all that memory, all those interactions and actually lodge it into, you know, the kind of 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.
25:11 >> 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 like the old classification system for software companies. I'm curious how people buy rogo like what kind of category you would put it in. Is it usage based? Is it seatbased? Is it something else? Like talk us through the how people want to buy this stuff and sort of what the emerging models are for AI software businesses.
25:37 >> 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, you know, in a similar category to Bloomberg to Faxet to Capital IQ to Pitchbook. and so we have to build a very human business. Every time you sign a deal, it requires, you know, an AE and a solutions architect and a sales engineer going in shaking a lot of hands, explaining how it works, explaining how to integrate it. You can't sign one deal where the usage just, you know, rises 100fold. I look at, you know, 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 just the, you know, riding the the coattails of token consumption isn't going to work for enterprise sales and we're one of those.
26:25 and so it's actually pretty interesting because we have to build a go to market machine five 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, you know, AI models as a tailwind to build a hundred times better products. And then there are the the 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, cloud code and and others. And so you can go much further commercially with fewer people.
26:56 >> What's your prediction for how or if that will change in finance? >> I think that every business needs to go through two different pricing revolutions. You need to move to some sort of usage based and then you need to move to some sort of outcomebased. For me, if I can figure out a way to skip the skip the token base, skip the usage base, 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 sell to 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, you know, random way of trying to assign dollars per token. That is, you know, 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, you know, these firms to actually sell the business. presumably you can't change your seat price like 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.
28:12 >> 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 of this in the future and you said you're 1% penetrated into the road map. So give us a sense of where you think this is all going from your product perspective. I mean not not industrywide but if you're if you have that much of the road map ahead of you still just describe that to us. I mean, think about 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.
28:56 And it's kind of similar to other parts of of financial services where 15 years ago, 20 years ago, every mortgage that someone got, you went in, you spoke to a, you know, 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 Mort mortgage. I think there's going to be a huge amount of innovation in how companies transact, how companies raise capital, how companies raise debt.
29:29 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 at a company to go online and, you know, click a button and try to raise capital the way that someone can go on to Robin Hood and click a button and buy an equity. I think it'll be take five 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 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.
30:02 >> So, I'm I'm going to focus on the specific future of automated risk pricing. I don't know, for lack of a better simple term. >> you know, we're we're three years from now and everything you just said is true, where I can raise single-digit millions of dollars of debtor equity, kind of like filling out an online form and the thing can just price the risk for me and and give me an offer. it's like it's like open door 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?
30:31 >> It's actually a very similar strategy to Bloomberg strategy. Bloomberg 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 co-pilot chatbot to a full autopilot tool so I can make sure that I'm 100% accurate on your IC memo or on the the DDQs 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 in 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, you know, 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 QV, you know, the legal adviserss and so on, and then actually run an auction process where you have a bunch of sponsors, you know, providing bits. There's a huge amount of software to be built out. and I think you know sometimes we talk about that as looking like an exchange for a lot of these asset classes that are not standardized.
31:56 >> Tell us a little bit about the customer base today. Like how much of it is giant banks versus investing firms versus you know public versus >> it's mo it's 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. You know, a lot of the folks who then end up as great investors started, you know, in their first two years as an analyst at Goldman in a TMT program or something of that nature.
32:30 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 far more people than if you land, you know, the the 10 best, you know, single portfolio manager, you know, 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. May maybe you have a biased sample because you know your customers are using Rogo and they're using it a lot, but G give us a sense of where you think we are. Seems really hard to pin down a good answer to this.
33:05 I would say that that 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. I you speak to any individual banker at a bank we're deployed with. >> They're like life's great. >> Oh, they're like I'm I'm a hundred times more efficient than I used to be. Right? You'll speak to an MD who will say, "Gabe, I sent five pages to a client that, you know, 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." And these are bankers who have who haven't done any sort of analysis in 25 years, right?
33:41 Like 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, you know, 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 do you want to use this thing to enter parts of the market that before, you know, 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 in 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, you know, just made no sense.
34:28 >> So, in some sense, the bottleneck at some point will be the creativity of the customer, not the 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 a hundred great investors start here tomorrow working for you, h how would you channel that productivity? It would take you a while to figure out like what's the structure?
34:52 Do they all work on different things? How much money do I give each of them? What do we attack? >> So interesting. Two years ago, the the most obvious question in this would have been about accuracy and people to use the word hallucinations, which like seems to have dropped out of the conversation. I can't remember the last time someone said hallucinations to me. What can you teach us about that problem? Ensuring accuracy where accuracy matters a lot to the decimal.
35:15 Like 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 two 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 co-pilot 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 a decision, you would be able to understand why cuz 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, you know, what was going on, you're going to need to do the same thing with agents.
36:19 And then especially in parts of capital markets that you know 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 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 like the fastest growing enterprise SAS companies of the last era. How do you solve that problem?
36:45 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 you know like like it's so easy for them to grow 10x it's much harder for you how do 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 saleserson >> a salesperson but 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 ward 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.
37:32 >> So do you like use AI to build tools to do that? >> Oh the internal tools we have are kind of magical. I mean so first off every internal conversation that happens at Rogo is recorded 100%. when anyone starts, we say, "Hey, just FYI, Patrick, that's how we do. It's always being recorded. It's always being, you know, 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 your had three weeks ago and you'd never even met that person because they're stationed in Apac. And 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.
38:29 >> So, so maybe describe the internal brain. >> It's called Shrek for some reason cuz my engineers thought it would be hilarious to call it Shrek. You can actually 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, right? Like it's like what are our values? What are the things we want to deliver to clients? What are our north northstar metrics? And so it can shape every answer and deliverable that way.
38:57 And it's both proactive, proactive and reactive. someone can go in and say, "Hey, you know, 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.
39:22 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? Like, if you're, let's say there's somebody that if you landed them tomorrow would be transformative because they're so high quality or wellknown or whatever, what is the pitch to them to come work at RO versus go somewhere else that's exciting right now?
39:46 >> 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 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 gold to not just be the hundred billion dollar 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.
40:47 >> Good pitch. Glad I joined. Glad I invested. >> Say more about this capital markets piece. Like I I I do think that that is historically as markets get more efficient and liquid. their their positive impact in my opinion grows a lot. and you can see this you you can chart this through market history which isn't that long 300 3 400 years of like proper proper markets. >> What do you think is possible? Like 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. Look, you're always at risk of, you know, sounding like the the billionaire private equity guy, you know, 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 of high finance. And when you think about a business like JP Morgan, you know, some of the origins are JPont Morgan helping connect European investors with the entrepreneurs and 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, you know, folks who were risktakers 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, right? 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. Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up-to-date proof of trust without all the manual work so you can close deals faster and with less friction. Customers report a 526% return on investment and more than 16,000 companies use Vanta, including Ramp, Harvey, and Snowflake. Get started with $1,000 off at vanta.com/invest.
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44:12 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 am 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 hundred flesh wounds that are inflicted on him at any given time and just think about the kind of endstate goal of where he's going to be 3 years from now. And the only two things that matter to get there is pretty amazing.
44:58 >> What is the glass like and what is the the aggression like? Like what does what does that mean? >> I worked during co so I didn't even get to see what an office looked like. It's been a very, you know, we used, 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.
45:26 and so when people quit, when you have retention issues, when you, you know, spend six 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 you know makes you feel like an idiot for expending 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 there's you have to feel the extreme highs and feel the extreme lows.
46:01 And I'm a super emotional guy and I try not to let the team feel it. But like 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 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, what a joke. Like that, you know, I I could do that in my sleep now.
46:23 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 too much. >> What about aggression? Like what like that seems kind of like a trope. >> So, you've interviewed Patrady. Patrady was at our board meeting on on Wednesday and we presented what was 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 he boils everything down into like two bullet points and it's logically infallible and you know he's just like premise one premise two this is the result and he goes Gabe well if everyone in finance is going to make a buying decision on AI for the next 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 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 benture back business is to increase the tales of the distribution. It's fine if it gets 30% more likelihood that I fail if the odds that I become a hundred billion dollar company also increase by 20%. but you actually have to be be okay with raising both of those tales at the same time.
47:49 >> What's the most emotional low that you faced? >> When we were raising our series A, we we didn't have any kind of star investors in our cap table yet. but there was a great investor, David Tish, at Box Group, who was an early preed seed investor who introduced me to a bunch of all-time greats for the for the series A. And I thought, "Wow, this is, you know, I I've seen what it does to get a blue chip in your cap table, what it does for recruiting talent for, you know, being a becoming more of a black hole for brand and customers and so on.
48:24 This is finally the opportunity we're going to have to do that." We've been building for two and a half years and David introduced us to 40 40 investors and I met with everybody. I met with, you know, Sequoia, Kleiner, Benchmark, everybody. And 40 people passed. And it wasn't just like, oh, you know, this is I you get the email with the deck and you 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, you know, for the weekend afterwards. You know what?
48:54 We're going to pass. And it's so personal cuz 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, " not for you." And that was actually Thrive too at the series A. Thrive spent so much time with me. 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 Ra boy was the was you know came basically a month after everyone else had rejected us. And Keith was like Gabe this is kind of this isn't a contrarian bet. It's basically just Harvey for finance. Why would I do it?
49:30 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 of a less intuition for finance because they didn't fund ion group or Bloomberg or S&P or Faxbook, Paxet or Pitchbook, Morning SAR and so on. And so there actually haven't been great venturebacked businesses in this market. And so there's no intuition for the kind of contours of the market and how large it is. Two is the product was terrible. And so every investor said, "Oh, I work in finance. let me use it.
50:07 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, right? Like it's like it's like 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 be.
50:29 >> What do you think changed after that? Cuz now you now now it's a now it's a who's who of of for Capable. Well, cuz I met all these guys very early on and I met them at every single round and 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 know, 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 that's when when you're investing in, you know, 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.
51:09 >> Why do you think there aren't more like pretty credible financial services competitors? >> I think 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. And so the the you know the standard to execute to really crack this market I think is 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 inner network who worked at Goldminer City or Jeff or Apollo or Aries 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 e-class, too. I'm also really curious how how you've dealt with technical talent and like what matters in a technical person on your team and how that has evolved.
52:09 One one intuition might be the value of domain expertise from the technical person has gone up as the ease of execution has has changed a lot. You know, 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 like the engineering or technical team look like over time? And I'm asking this again because I'm curious about thinking about other other companies that want to tackle this in whatever their industry might be.
52:32 >> Yeah. 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, right? They're folks who started businesses, persevered, ate glass, had a lot of product intuition, figured out how to channel it, and then, you know, the business may be petered out. And to to the point you made, you know, 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 six different kind of fledgling financial AI startups. And so those former founders who can be kind of galaxyrained 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.
53:34 >> You said earlier to me before we were recording that this is the first time that we have like a major innovators dilemma in in this category. Can you describe what you mean by that? >> Yeah. Well, I 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 know, you can make a lot of money being a capital allocator, being an investor.
53:54 it's a very hard industry to enter and then especially if you're in private markets, you know, your businesses have some natural momentum because when you raise a fund and another fund and another fund, it's hard to, you know, mess up the business after that. And I'm sure there's a hundred people who have started funds who know it's a hundred 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.
54:38 >> 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 pill that you don't worry about what's possible or what you know 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 as like so you're a leader in a company that's very AI pill 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 like 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 on a every month I have a report that gets sent to me that shows me at everyone at the company how much are 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 five power users and the bottom five users and we post that everywhere and the bottom user in each division gets a print out with a dun 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 I'm like you 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 is, but it's a strong incentive function to actually make sure you're using these things. If you were to list questions that like let's say there's someone sitting down that runs one of these firms that's been a great business, hard fought, but a great business and fairly these businesses tend to be quite simple from a organizational and technical standpoint. It's mostly humans, right?
56:36 There's not a lot of not a lot of overhead in lots of these businesses. maybe they buy a lot of data or something. What questions would you encourage themselves to ask of themselves, of their business to stand the best chance of navigating this transition effectively? >> I would say if you knew for sure that right now 90% of your enterprise value is in your people, right? Your best investors, your best bankers are the people that bring in deals, bring in revenue and actually that's what acrru enterprise value. And in 10 years the world's best investment firms, best banks will be 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 life cycle and in each one try and chart where do you think that like 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 in the market in which case AI is going to obiate that.
57:57 >> Have you seen anyone be like 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 of this. >> There's a It's funny the the the folks that today look the most preient sounded crazy batchet two years ago, right? They they were the guys that came in and said, "We need to record every conversation. you know, 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, you know, game theory on exactly what the investment should look like. And two years ago, everyone listened to those kind of guys and were like, "What the heck is Patrick talking about?" One example is there there's a co-founder of a of firm, Mullis, John Mumazi, who is was just preient 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.
58:50 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 there are a number of folks who, you know, had their kind of move 37 moment where they realized, wow, this is going to be so much more profound than anyone's expecting.
59:15 >> What what to you feels the most uncertain about the future of of your business? >> I would say the how quickly private markets actually do transform, right? If you if you think about why different types of asset classes have increased in transaction volume and you know 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 market force that forces some additional standardization that really accelerates, you know, the 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 is it's still unclear to me how much alpha will be left in the kind of human relationships, right? I talk about that micro cap 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, you know, the long tale 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 to someone. That's 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 have you what would you teach them? What would you tell them to to do to not do? My style for fundraising might not be everyone's style. I'm super direct. I'm super transparent about, you know, what I'm worried about, where I want to go.
61:06 but then you have to be very headstrong on that end state. And then I would say it's 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 they passed every time for all sorts of reasons, but they gather a lot more data on me and the business.
61:25 >> 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 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 and say that you are confident about where you're going. Even in the moments where, you know, it's the eb and flow.
61:50 It's the eb and flow. and I think, you know, people can misinterpret that sometimes as like they need to pretend there's something they're not. I think that's not true. You need to believe that that 5% likelihood that you can be in a hundred billion dollar business is likely. And you don't need to pretend it's 100% likely, but you should be able to to delineate with very clear road map and strategy how it is possible that you can become an hundred billion dollar business and then have confidence that if those things play out, you will be.
62:22 >> Why are you scared and insecure? I'm so paranoid about everything that can go wrong. And every day it feels like you're on the knife's edge of 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 know you hit a roadblock and then you go off course and then you need to reatalize 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 kind of halts that velocity if I wasn't constantly petrified of those moments, I wouldn't be doing, you know, everything under the sun to prepare for them and make sure we can have >> What have we missed? Like what what 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 wiring last mile wiring is hard and it's not just going to be anthropic. It's one company to rule them all or open AAI.
63:24 And yeah, 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 too 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 and agile than you ever expected. And having the right team is more important than ever.
64:02 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 at 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? You know, I would say that I 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.
64:46 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 and couldn't do anything right 40 nos in a row, idiot failing out, flunking out, whatever it is, no matter what, she she acted like I was maybe the smartest person on earth. And it's like that was irrational, but you need some of that irrational confidence that comes from just undying love. My dad was very different. You know, my dad if I came home and I had done something wrong would 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, you know, how does he understand who I am and why I am failing at this thing and then help me. And so, you know, he would sit down and 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.
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