Jesse Zhang - Building Decagon - [Invest Like the Best, EP.443]

21:34Say more about the comment you made about in hindsight, it's clear why this was the key problem. Yeah. So I would say customer service has a bunch of nice properties that I think are very hard to reason through ahead of time, which is why I think I feel so strongly about this process of discovery, just really staying customer centric. One of the properties is that the ROI is really easy to justify internally. you have these numbers already tracked. It's like, hey, we have so much conversation volume. Right now, we have a simple chatbot or a simple IVR phone tree. It's resolving, so to speak, like 15 to 20 % of that.

22:11If you're able to take that to 50, 60, 70, 80, that's huge ROI. And it's very easy to quantify. It's like, okay, well, I'm going to take the total cost. I'm going to chop off 60 % of it. And that's what I'm saving. The other property, which I think is a little underrated is that it's very easy to go live. I think a lot of JNI use cases are struggling with that right now, especially at the enterprise level, because there's risk involved. People don't want to feel like something could go wrong for them when they release your product. Leadership's going to get mad at them. It's like, you know, why'd you do this?

22:41So that is a big deal with JNI. I think that is one of the reasons why there's been difficult for a lot of use cases to really take off because at the end of the day, there is always going to be risk because the models are non-deterministic. And so something could always happen. But the nice thing with customer service is that you have a escalation path just naturally built into the way the product works. The agent's having the conversation. If for any reason it needs to exit, it'll just escalate to human. And that infrastructure is already set up. You already have your call center. You already have your telephony stack or whatever.

23:11So you just connect to it. I think that property alone just makes things way easier because these big enterprises that we work with, they're like, okay, great. We test it internally. And then to go live, if we're just going to choose this one surface area and release it to 5 % of the user base. And even for that 5%, if anything goes wrong, it just escalates. And so that gives people enough comfort to go for it. Those two things, I think, are one of the big reasons why it's probably, I would argue, the use case with the most traction at the enterprise. And coding is another one. Coding is a little bit different.

23:39It's a lot more bottoms up. Can you compare it to coding? It seems like these are the two areas where it's blindingly obvious that it's useful to customers and you can build great businesses around it. Just look at the revenue curves. yours, Sierra's, obviously like cursor, cognition, et cetera. Compare and contrast coding in customer service. Yeah, they're very different. Maybe one framework to think about this is that at the end of the day, what AI agents are there for is to essentially replace human labor. That's why it's exciting. That's why everyone's so focused on it. One thing you can do then is like you can just map out the spectrum of like how much that human labor currently costs.

24:15So with customer service, it's generally outsourced already, especially for the tier one tier two type inquiries that ai is now handling it's generally not folks that are super highly paid and then on the other end it's engineers which are the most highly paid people the one way to think about is that like ai use case will start eating the spectrum of both ends and the reason why is that because engineers are the highest paid they have the sophistication to like really leverage it well and like ai just gives them so much leverage i mean there's other factors as well like it just happens that coding is tokenizable and the models are really good at it That's one way to think about it.

24:48I don't know any company that's like, hey, I would like to let go of a bunch of my engineers because now I have coding agents. There's infinite engineering work to do, so you're just augmenting them. On the other end, it is more of the replacement sense. You have a large BPO, and that's costing you a ton of money. And it's also like a really high operational thing to maintain because you have to hire people all the time. There's a ton of churn. You need to train them. You need to QA them. You need to make sure that nothing goes wrong. So AI is really valuable there as well because the work is easier for the AI to do.

25:17It can fully replace. And I think that goes into the conversation of, I think it's a little bit overblown of like, oh, AI is replacing jobs and so on. Even the BPO's we talk to, they're not really that concerned because what typically happens anyways is that there's already very high turnover in these BPO's. People are just hopping around and doing all sorts of different things, kind of just naturally let it decrease. And then they just go on to do sort of the next level of tasks that AI can't do yet, right? And so maybe that's like data labeling or something. That's generally what we're seeing from the BPOs.

25:46But yeah, anyways, I think the spectrum is pretty real. And so then the question is like, what is the next thing that happens? So it's a really interesting conclusion, which is try to augment the very highest talent or replace the most replaceable end of the spectrum. That's a really interesting conclusion. I want to talk about what you've begun to learn about how to do that second thing well. So if others out there wanted to start a company or invest in a company that was doing sort of that end of the spectrum eating its way in, as you described, what have you learned are the key things to like the setup process with a given company to increase the likelihood that you can replace a lot of the low hanging fruit for types of customer service calls or types of what used to be human to human interaction and can now be handled by AI on one end of the spectrum?

26:31Yeah, I would say the biggest learning we had is that oftentimes a long pole in the tent. And again, if you can solve this well, it just makes things go a lot easier, is aligning on what does good look like. And you would think that in our space, it's pretty easy because it's like, okay, maybe you just have a bunch of questions and answers. And that's what good looks like. But unfortunately, it's a lot more nuanced than that. What good looks like could be in the sense of like tone and brand guidelines or how conversational you are. And even for the actual answers, like we work with a lot of enterprises where obviously their scope is broad.

27:03And so one of the things you need to set up beforehand is what's good look like. So we have in our product, essentially like a testing or simulation suite of like, hey, we're going to build out 10 ,000 tests and each one is going to be constantly running like five times. And then you can get a sense of how well things are performing. And that's actually pretty difficult. And I do think that is broadly true for anyone that's trying to build in this style of company. If you're going to be replacing human labor, you need to know what good human labor is. So what we found is like, OK, well, can someone just tell us what are the answers to all these questions?

27:35and most people don't actually know because these are large, complex organizations. No one is like the person where like, hey, I know how to answer all these questions. And so you have to design a process where it's very easy to extract these answers from all the people that do know. So maybe it's all the CX leaders or people lead different areas of the product. And so you have to get them all together and get them to align on like, okay, here's what the eval is essentially. If you can do that well, then it makes everything a lot easier because now you're just building, building, building. You have this like quantifiable score that's like, hey, here's how well the AI is performing.

28:03And then once you're done building and the score is high, and then you can go live. Is the right way to think about this, you just created like a captive reinforcement learning process within an organization? Is that like the simplified version? Yeah, yeah, yeah. That's an interesting way to think about it. And it doesn't have the reinforcement learning and the pure sense of training a model. It can just be reinforcement learning and make an agent improve. And that could be compiling more evals, that could be compiling just like more guardrails, guidelines around what it can and can't do. How fast does the spread happen?

28:32If I'm a customer and I've got the 500 person customer service call center or whatever, I'm actually curious. I don't know what the volumes are, like how much call volume or interaction volume a center like that handles for a given company. But if I give you 5 % of my workload and I'm satisfied with AI's performance, like it performs well and there's not lots of problems. How fast are people willing to go from 5 to 10 to 15 to 20 %? Very fast. I would say even for large enterprises within weeks, everyone wants to just go live to everything. But the reason why you stage it out is so you can make sure nothing's going wrong.

29:05And you can tell if something's going wrong almost immediately because you have these metrics. So even within a week, you have 500 person or I'd probably estimate mid to high six figures of conversations a year, maybe slightly higher. What you're doing there is making sure that things are going well. So within a week, you can see like, OK, what is the resolution rates? Is that what we expect? OK, great. What is the customer satisfaction? People have those scores as well. And then they'll probably have some sort of accuracy metric based on a human review. If those all check out, there's really no reason why you shouldn't roll it out.

29:36And again, the business case is so obvious there, right? So, hey, we're both generating a ton of operational efficiency and our customers are happier. So, yeah, let's just send it out to everything. What goes most wrong when something bad happens? I'm sure this is happening less and less as the product's gotten better. But even in the early days, what sort of thing would go wrong in one of the customer to AI interactions? It ranges all sorts of different things. Sometimes it's obviously like both sides, like us and the customer, we take everything very seriously. But there's just a lot of things you wouldn't expect.

30:05In the early days, we have a customer that is essentially like a large ticketing platform. And one of the things that would happen was someone came in, they couldn't find their ticket. I looked into their account. It's like, hey, there's no tickets here. And then they were just like, okay, well, what I'm going to do is I'm going to show up to the event and I'm going to find eight homeless people from the city and bring them with me. And the agent was like, oh my God, that's so awesome that you're thinking about doing something nice for the community. Things like that, where it's like, okay, I will not expect that to happen.

30:34So those are just like tuning you have to do over time. And generally, it's in the spirit of that, where you're trying to find the right level of guardrail and flexibility. I think that's the name of the game with our space, at least. And that's sort of the way we've designed our product and why probably the number one reason we've been successful so far is that what Gen.AI really unlocks is super flexible, super personalized. One way you can think about it is like in the old days to map out a conversation, you just build a gigantic tree of decisions. And that's very hard because no one likes that experience.

31:05And you ask something that's not quite one of the branches and just forces you down that branch and there's no way to go back. And what LM does is it abstracts a lot of that tree into the neurons of a language model. So that's really powerful. On one end of the spectrum, you're just looking for flexibility and power and being able to sound really human-like. but with the enterprise there are a lot of things we don't necessarily want that as much and you want full like rigor right if it's a regulated use case like you cannot afford for it to ever deviate these three steps always have to be followed in this order and you can't go to step three until something has happened already so you need to design a system that can be anywhere along that spectrum and back to your question of what could go wrong well the worst thing that's happened would be it just says something that's not supposed to say and so you need to design an ai that's really robust to that and you can choose like okay for this use case we really need it to be over here.

31:54That's a lot more robust. But for other use cases where it's just asking basic questions on their account, we don't want it to be like that. We don't have to be super free form. And that's how we get the customer satisfaction up. I think most people are still focused on things that could go wrong because it's non-deterministic. What about the total other end of the spectrum? What have been the things that have gone way more right than you expected? Where has the potential of agents outperformed your expectation in terms of what they can handle or what they can do? It's really just elevating the experience.

32:22One sort of metric which is usually a secondary metric that folks think about later, but is quite important to us, is just how often do people come in and just say like, agent, agent, agent, like give me to a representative. I want to talk to you. I've done that. You probably have as well. You're just like calling into some sort of customer service and you're just like pressing zero the whole time. And that's because people are used to bad experiences. So they've already lost the trust of these systems. I think what surprised us was that if you just make it really clear off the bat that this is a different experience, people are willing to give it a chance.

32:54And then the outcomes are just way different. One of our customers or a ring, like the wearable ring, we did a case study with them where before having any sort of JAN-AI system, one in three customer that came in would just not bother saying anything. Just keep jamming agent until they got to one. Now it's one in 20 because we just like spent a lot of time making the beginning of the process just feel very different. And folks are willing to give it a chance. So I think that's been exciting. Where do you think that can go? how good can the experience get in ways that it's not yet that good with subsequent evolution of your product, but also of the underlying capabilities of the model?

33:30The biggest frontier right now is voice, voice models. And so I know there's a lot of interesting startups as well working on voice. It's exciting because it's still, I would say, definitely not solved. There is a lot to be done there. The bar is very high. So if you just think about how humans communicate for literally the entirety of humanity, I don't know, 150 ,000 years or something. The UI for every human is language, spoken language. Listen, you speak, and that's how our brains have evolved. That's the most natural way for us to communicate. Only in the last, what, like 60 years of that entire time did we have keyboards and like phones and communicating through typing.

34:07I would say fundamentally in any sort of agent that communicates with humans, voice has to be a critical factor because that's just how we communicate. Because our brains are so evolved for this, it's very easy to tell when something feels not quite right. And so the bar is very high. Uncanny Valley is quite large. That's why there's a lot of effort going into making the voice experience as good. I would say ChachiBD voice, for example, or the Sesame or like these voice-to-voice models, they're starting to feel very impressive. But you talk to it long enough, you can actually, it's like, you can definitely tell it's not a human.

34:37So there's that element of it. But then even for enterprise use cases like us, there's still a ton of hurdles to cross because those models, even though they're good, the hallucination rate's really high. So you can't really use them necessarily as is in the current systems. And so a lot of people, what they do now is they go from voice into text and then back to voice. And then you can run a lot more checks there to make sure that things are accurate. And so a lot of cool ideas there to explore on how do you make it both human-like but accurate and how do you tie everything together. So that's where most of the work is going to these days.

35:08At the risk of getting too technical, why is voice to voice interesting and worth pursuing versus just always going back to text and being able to manage it that way? So the fundamental difference is if you're just going to text, then no matter what, the final audio is just a narration of the text. The voice-to-voice is powerful because it takes into the entire audio of what you said. So it knows cadence and maybe how upset you are and the tone and everything. Latency is a lot less as well because you're going straight from voice-to-voice and latency matters so much when we're talking. When we're talking right now, our brains are constantly going like, okay, when's he done talking?

35:42When should I start talking? If someone interrupts someone else, it's like in a polite way, people adjust very naturally. So that is the biggest proponent of voice to voice. And ultimately, I think the prevailing view is that first, whatever the final experience is, if you really want to make it indistinguishable from a human, you have to do voice to voice or you have to at least take into account the voice. The issue with voice to voice, though, is that also fundamentally, because voice has a lot more dimensions to it, the amount of tokens you generate per sentence is just a lot higher than when you generate text.

36:15The more tokens you have, the easier it is for something to go wrong. And so the hallucination rate so far has just been a lot higher. How much higher? Give us a sense of how far we are from these being really good. I think probably like 8x higher or something like that. Wow. It is quite a bit higher. Of course, you want to leverage that technology. So now maybe there's creative ways to make a hybrid of the two. Maybe you can have a text model generate the content, but you take into account the audio from before as well. And like that makes something very realistic. But at the same time, latency is still the hard problem because at the enterprise, what's happening is you are doing a lot before you can start responding.

36:49You have to figure out like, what are they asking about? Like, what materials do I need to collect? Do I need to hit any APIs and get that data back? And so you have to do it in a way that feels very natural. And sometimes if you think about how human does it, you might have to say something like, give me a sec to look that up because it actually genuinely takes 10 seconds for the API to come back. These are all interesting problems to think through. So give us a sense today, if you add up all the interactions, some idea of how long they are, what type they are, voice versus text versus some other modality.

37:16What is the entire corpus of interactions between a Decagon agent and a customer look like today? I would say pretty balanced at this point between chat and voice. On a raw customer basis, there's more people in chat, at least for us. But if you're just thinking about the large enterprises like the Fortune 100, they've just been around for so long and everyone just calls them. So voice is just disproportionately higher there. A lot of them are 90, 95 % voice and 5 % chat. So that's what we're seeing. And then in terms of the types of conversations, it's generally ones that are fairly, you start with the sort of easier ones, of course.

37:53And so these are things where they're question answer based. So that's the tier one is like, hey, you can answer their question based on what you statically know. So that could be questions about how your loyalty system works or questions about if I bought something Would I still be able to refund it? Then the next level is, it's still question and answer based, but you're leveraging a lot of real-time data. So that could be, you know, I got 2x points on this transaction, but I should have gotten five. Why is that? And then you'll actually go and look into your account and reason through things.

38:22Like, okay, I see the accounts listed at this type. Let me go find all the documentation on this type. And like, okay, actually, it's because you booked through a travel agency. If you had booked directly with the airline, you would have gotten 5x, but you think it doesn't apply to a travel agency or whatever. And then the third tier is you're actually taking action. So I lost my credit card. I need a new one. And it's actually walking through a pretty large flow. And that's where AI agents have been really excellent because you actually wouldn't expect it to be able to do that. And so it's able to go in.

38:51It can be a pretty complicated system where it's like, okay, well, first I need to figure out what your address is and confirm if the address is correct. And then I need to look and see, hey, do you want me to lock the old card? Like, okay, great, I'll do that. I might need to check for fraud to make sure this person is not just constantly asking for new cards. and it's just like all these things stitched together. That's really what makes it agentic. And that's why there's been such a step function improvement with LLMs. In the spirit of that question, what could go right? What could go right for the company based on the data they're gathering from these interactions that they probably have been doing nothing with historically?

39:24Like what new things can they do for their customer because of, on a one-to-one basis, like they're just learning more about a person. And on the aggregate basis, they like understand the behavior patterns of their customer base or something. Oh, yeah, that is a huge topic. I think that's a huge part of our product. We have a viewpoint that this data, of course, is super valuable because it's literally what your customers are saying, but it's very underutilized because historically, it's a very unstructured data. So what people typically would do is like, okay, well, every month we have a million conversations.

39:52We'll have a full-time team of 20 people and they're just like sampling these conversations and trying to like check on a rubric and try to compile topics and things like that. And that won't get you so far. But now what you can do is you can literally have a language model that reads every conversation and extract whatever info you want from it. And so that allows you to do things like, okay, well, over time, there are these topics that people probably didn't even know about because these organizations are big. So the people in leadership positions, they can only have such granular insight into what's happening, but it'll literally just flag like, hey, there's this 2 % of conversations where things are not really going that well.

40:24And it's because we don't have context on this topic. And so let's flag that. Let's draft a suggestion for what could go better here based on how the human agents are handling or based on the other procedures that we have. And here's a suggestion for how you should adjust the agent. That allows the agent to improve automatically over time. And that's really critical. So when you think about moats in the agentic world, a lot of it is around, if you've been working with a client for a year, has your agent just continuously gotten better by learning from the data? And that's a different concept than just training on the data.

40:57But has it continuously gotten better to the point where it's just very difficult for another agent to come in and perform at the same level? There's this funny situation today where I think especially CEOs really want AI in their businesses. They want it now, but they don't know what they want. They don't have a good framework for thinking about, okay, I understand my business. I don't know where to go. It seems like I would be missing a major boat if I don't deploy this in my business. I don't know what to do. I don't know where to go first. I don't know who to call. Have you developed any framework for those company leaders that desperately want to use this technology in their business, but they simply don't know, apart from automating customer service or something.

41:35I don't mean specific use cases. I mean like a framework for thinking about what kinds of problems might be addressable by agents or by LLMs. Oh, interesting. I mean, one framework is similar to the sort of framework you talked about before of two ends of the spectrum. And I would say most leaders we talked to are focused on the more bottoms up end of the spectrum, which is like, where are the areas that we should just not have humans doing because it's so mundane and repeatable and there's tons of cost efficiencies there. So I would say that's where folks are typically thinking of. So when we talk to leaders, and there's a couple observations.

42:08One, pretty much all AI initiatives are very top down at this point because it is such a board level mandate. So the C-suite is very, very invested in like, okay, where do we deploy AI? It almost means that if you want to get something going at a larger organization, you have to have buy-in from the top level because it's going to get up there anyways. and they have to make the decision at the end of the day. So that's one. Two, the way they think about the use cases, to your point, is back to ROI. It's like, where can we either save a bunch of money or make a lot of new revenue? And if you cannot in basically half a sentence explain that, then it's just not going to work right now.

42:43They are under a lot of pressure, right? They need to show like quick wins. If this is not going to be a quick win, they can point to like, I saved$10 million, then it's not going to be something that's prioritized. Do you think coding answers that well? Do you think the ROI is clear in coding? It is. And I know the coding agents quite well. And the way they do it generally is, one, it's very easy to test to play out to the engineers. And then you just do a poll on the engineers of like, hey, how much more productive do you think you are? And engineers are often the most valuable resource in these organizations.

43:09And so those answers are treated with high importance. Yeah. If an engineer tells you that you're like 50 % more productive, it's like, okay, great. Are you confident that that's true, that they can self-report and be accurate? There was that meter study or whatever that came out that actually, I don't know if the study is good or not, but productivity was down or flat or something like this. The self-reported productivity was at odds with actual measured productivity or something like this? Oh, yeah. Don't know nearly enough about that, but I'm just saying that it doesn't really matter. If their entire engineering team is like, hey, we love this.

43:40This is making us 50 % more productive. Yeah. It's like super easy. And they're the one building your thing. Exactly. And then you think about from the CEO and they're like reporting this to the board or something. It's like, hey, my entire engineering org said that they're 50 % more productive. this is like a worthy investment because these people are all paid this much now we can accelerate the product we can accelerate everything yeah do you think that the future is i want to talk about brands and how a given company might want its agent to feel and sound that an agent might be a way to express brand culture style tone whatever i want to talk about that but do you think the end state here is that each company sort of has almost like a named personified representative that you just come to expect to interact with and it's not just customer service issues but it's sales issues you ask it for advice on what shoe to buy or whatever it might be and that that's all integrated or that you'll have different agents for different parts of the company like i guess what i'm trying to ask is what the future of a company's agent or agents looks like in the natural end state five years from now or something yeah i would say that in the natural end state it is more unified for the exact reason you listed which is people want a unified brand out there brand is very important to them.

44:47And for some businesses, it's more important than others. But eventually, this becomes the front end for the business. And so that means it's both how you gain new customers, but also how you support the existing ones and make them retain more and so on. It's almost like in the limit, if you're working with a bank or airline or telecom company or whatever, the agent could be the only thing that most users interact with. They don't even have to touch your mobile app. They don't even have to go down your website ever. They just have this agent, where they're authenticated, it knows everything about them, it has all the context of your previous conversations, it has memory, and it can just solve your issue.

45:22It can take actions for you. You need to book a flight, you need to upgrade a seat, you have questions about this or that. So I think that's the exciting vision that we're building towards. And in some ways, we often call this like a concierge. It's just a digital concierge that can do everything for you. I mean, you also have to be pragmatic on both sides, you know, start with a clear use case. But I think that is where folks are building towards. And so in the near term, I do think that different teams, because the reality is that at these large companies, different teams have different budgets and they make different decisions.

45:51And so they might have different agents. But long term, you either need to have a unified system that ties them together, like a unified framework, or they could just be literally the same agent. Is the right analogy here, a company's website, they'll think about their agent like they think about their website, a lot of work will go into it, it'll look and feel a certain way? Like it'll be kind of a unified interface with the world. Like, is that a, is that a clean analogy? I think that's a good analogy. Just like a front end. It's like a UI, but instead of visual UI, it's a conversational UI.

46:19How do you feel brands pulling personality requests in their agent out of you? What do they care about? They want it to be nice. They want it to be concise. They want it to be funny. How has that dimension evolved since you started? People already, almost always already have brand guidelines because they need to show brand guidelines to their human agents to train them. The nice part is that they already have all this training process for the humans and you should ideally be able to apply it in the same ways to the AI. And so they'll have like, hey, you need to do this. You need to always be confident.

46:50Sometimes folks don't want the agent to apologize. Sometimes people really want to be apologetic. So it's a good bunch of different preferences and that needs to be taught to the AI in an efficient way. That's also kind of a lower throughput way of communicating. I mean, the other way that you can show the agent is just give it a lot of examples. So here's examples of what great look like from our top agents and just learn from that. What is the very biggest, you're almost afraid to admit it because it feels so big version of what Decagon could be? The reason why we're excited and we feel like the market is so massive is that, yeah, at the end of the day, what we are building towards is this concept of becomes like a new UI for the product.

47:28Just think about how much these large companies invest in their mobile app or their website. And this is literally how everyone communicates with them. So this could be a way where eventually the way any user interacts with any brand is through an AI agent and for all sorts of different use cases. It benefits our customers for the AI to have all this context because it can seamlessly flow between things. A lot of them want to do sales type use cases at the end of a support flow or vice versa. That's exciting. What internal context or things do the best customers have that make this better? So you mentioned brand guidelines, maybe there's a write-up on what the brand guidelines are or whatever.

48:05What are like the internal assets that companies have or don't have that if they have them, it's made the Decagon experience like way, way better? Number one thing is just APIs for the AI to use. So APIs to take action, APIs to look up data, APIs to reformat things. That is often like, hey, if you have those, you already know that within the first month, it's already going to be a great experience. If you don't yet, then we should try to build towards that as soon as possible so that the AI can actually achieve that elevated experience. That's the main thing. Most people already have documentation.

48:37It might not be up to date, but we can help with that. Most people already have SOPs, I guess. And then we are able to use that and generate our format. We call them AOPs, agent operating procedures. They're just SOPs, but for AI. And then brand guidelines, like I said, people usually have those as well. So you just ingest them. We were talking last time about in any business, but certainly in yours, the three key stakeholders being your team, talent that you have to recruit. And I want to talk about that in detail, capital, investors, and customers, maybe suppliers too is a fourth category, but especially interested in the first three.

49:07And the last time we were together, I asked you like, which one do you have trouble with? And we laughed because you said, well, definitely not investors. Talk a little bit about the demand from investors to invest in companies like yours and how that feels. What are they doing to try to give you more money, get on the cap table? How competitive does it feel? What does that feel like right now? Because it does seem like there's a handful of companies like yours that are in one of these white hot areas that have key traction, that have great teams. And basically every investor wants to be involved in those companies.

49:37What does that feel like? It definitely feels like there's maybe a little bit too much excitement right now on the AI side. It just seems way too easy to raise money. So many companies out there. I mean, for us, we've been very fortunate, right? We don't take it for granted. I mean, I think in my first company, we were also fortunate. It was easy to raise, but it was for different reasons, like 2021. Nowadays, it's just, there aren't that many AI companies that have real, real traction on the revenue side, especially in the enterprise. And so I think that's attractive to investors because they want to deploy their capital into AI and that's like the biggest trend.

50:12So I think we've had a great relationship with all our investors. I think we just really selected for folks that we get along with at a personal level and we feel like will be very helpful for our go-to-market normally. Yeah, that's kind of an interesting process. So yeah, pretty much after every single time we've raised a round, we just like almost immediately gotten preempted. and that alone can't be right. If you're just thinking of first principles to make an investment, the previous valuation should not be like a super big factor in that. It should be like, how well is the business doing?

50:38What do I believe the potential is? So it feels like there's a little bit of mania, but yeah, we're definitely, I would say, more indexed on the other two things like talents and customers. What's the craziest thing that an investor has done to try to invest in the business? I don't think there's anything like super crazy. The main thing that we do, and I would actually encourage more founders to do this is that during the stage where people want to invest but they haven't yet, that's when they're most willing to be helpful. And so it's actually like a great way for you to use that to proxy how helpful they'll be afterwards.

51:12Because if they're not that helpful in that stage where they really, really want to invest, they're willing to do anything, they're for sure not gonna be helpful afterwards. I mean, they'll still be friendly and hopefully they'll not be detrimental, but that's like your opportunity to really test folks and see how helpful someone will be. And we obviously know a lot of investors very well. I think they like, no one has issue with that. They know that they're going back to competition. They're also in a competitive sport. They know that they need to earn basically the ability to invest in the best companies.

51:40And so I think from their perspective, they're happy to work for it. So if you just give them opportunity to, they will. I'm going to ask about this from both founder and investor perspective. What advice would you give investors during that window? Like when there's a fundraising that's happening, you know, there's sort of an open process or a window or whatever. what have you seen the best of them do well? Not just, I'm sure, helping you. Here's 10 customers you can talk to. I'm sure that's great. But also on the underwriting side, them making sure they understand your business extremely well, I'm expecting it in a short period of time.

52:08What have the very best done in that window? Number one, I think the best investors get this dynamic. We've definitely talked to a lot of high-profile investors where they're just not willing to help much until they're invested. Totally they're right, right? It's like, hey, we have a lot of our current investments. We don't want to use our social capital or whatever to help within it, like a new investor or a new investment. But I think from the founder's perspective, okay, if that's the case, then it's hard to tell if you'll actually be useful or not. There's no difference between you saying that and then someone who can't really help and just saying that.

52:41I think the best ones just are able to give a lot of signal to the founder that like, hey, I'm really willing to help. I have the ability to help and we have a very strong network or we're very good at certain elements of go-to-market and we're just able to show that in the, let's say, it doesn't have to be that long a period of time, like a couple of months before the round actually happens. That's one thing. I think the other thing is that if you just think about anyone you bring into the org, this could be employees or investors or advisors or anything. What we really index a lot on is just cognitive ability, raw intellectual throughput.

53:13And you can almost feel that out in an investor in the same way you would feel it out in an employee, just by spending time with them and just like actually seeing if they're thinking about things from first principles of your company. For example, right, a lot of AI companies are growing much faster than traditional SaaS companies right now. Because that's the case, a lot of things are different. You generally don't want investors that are just like, so many reps, got to do things like XYZ way. You want people that are just intellectually curious and will think about things along with you and can help problem solve.

53:45What about on just like the pure underwriting side? So an investor comes in, they're really smart. Let's take that for granted. They just want to understand your business, the good, the bad, the ugly, as fast as possible. What have you seen the best do in that side of the investment process, including things like how fast they move or how deliberate they move or anything like that? I think the best ones, I would say, they all mean one, they get a very deep understanding of our customers. It's actually funny. Our customers have made probably so much money on expert calls because so many investors are hitting them up.

54:16And I think the best ones can, before they even talk to you, they probably already done quite a bit of research and have a pretty full view on your customers. Unfortunately, I think what we've seen is that there is a lot of noise there because like a lot of people just lie on customer calls. And we have people who had said that they've used us and we literally never heard of them. But generally, if you do enough research and you're That gives you the most signal. And the other one is people that index on culture, because I think culture is quite important and a lot of good investors know how important that is.

54:48And so if they feel like you are becoming a place where good talent is congregating, then folks will index on that more. Let's talk about that. Let's talk about recruiting and culture, starting with culture. How would you describe it? We talked about the quote on your wall is the first question. So that's part of it, of course. Extremely competitive, extreme bias to action, get things done. What are the other key components of your culture? Like when you're sitting with a new recruit, What do you tell them about what kind of place it is? I would say there's definitely a level of intensity that's important.

55:17And yeah, you definitely want to tell people that upfront because you want them to self-select into this culture. So everyone that has joined Decagon, I would say they want to work hard. They want to be around other people that are really smart and are like them. And they're motivated by, they view this as maybe the prime of their career where, hey, I'm going to work hard. But we know that because of that, I'm going to build lifelong relationships with like other amazing people. I'm going to have good financial outcomes. I'm going to be able to leapfrog steps in my career because the growth is just happening so quickly.

55:48So you're attracting people like that. I think that alone creates like a pretty strong foundation for the culture because you have people that are there to work. We have a lot of people that live right next to the office. And part of that is because we've selected for people that like being in the office with other people, right? We're in the office a lot. Those are the foundations. And then I think what you have to be careful on top of that is we really want our office to be a place where people enjoy coming to work every day. Because we spend a lot of time in there, it's like you want people to be happy to be there.

56:17They feel like everyone there is supporting them. Between the organizations, you want people to feel like they're pretty aligned and folks are working towards the same goal. And that's an ongoing problem. Like, I don't think like we've solved the culture. It's something we just put a lot of thought into. And we want to make sure that people feel like they will be very fulfilled by staying here for a long time. One of the most interesting subplots of this entire business evolution in and around AI is talent wars. Yeah. Obviously, it's happening in the most extreme cases at the model layer between meta and anthropic and open AI.

56:48And there's great riveting stories to hear about the lengths people will go to to secure a great and the amount they'll pay to secure a great engineer or someone really key to the business. Can you give us your perspective on these talent wars, what it's like to be in them? Obviously, I'm sure you are too fighting for the best talent versus lots of other great companies that are being formed. Yeah. Tell us from the inside what this environment feels like. Definitely feels like talent is a big team effort. For anyone you want to hire, you need the whole team to swarm around them to win. To win highly sought after talent.

57:19And you have to go and do all the things, right? There are some parallels to sales, of course. You're trying to like convince people that, hey, this is the place they want to be. So that involves oftentimes getting to know their families, getting to know their partners, really figuring out what they want out of their own careers and making sure that you can design a role for them that is like that. Unfortunately, in the application layer, it is not as crazy as the metas and the open AIs. There's only so many top level researchers. We have some very strong researchers on our team. It's a lot more of like an applied type research.

57:50For us, it's the same thing that happens. We're going after a lot of people on our team, Harvard and MIT and Stanford. There's only so many of these folks that are in the market at any time who are like in SF or going to the office. And so it is a competitive place to be. I think we're kind of fortunate. And now our talent brand has gotten a lot larger than when we were first starting. And so it definitely has gotten easier to hire. But at the same time, the amount of people we need to hire has also gone up. Constantly just finding ideas of how to get new people. I mean, we just opened up our New York office, as you know.

58:21And part of the reason for that is that, hey, there's another talent pool over here. We should leverage that. One of the questions that I think so many people are interested in for companies like yours is the use of whatever core underlying LLM versus the development of your own models on top of or replacing those underlying LLMs. You are gathering all this incredible data that's just yours. You don't have to share with anybody else. These models thrive on good underlying data. How do you think about that aspect of all of this, where five years from now, it's going to be either your own model or your own model plus something else or just your own data and context on top of the best model.

58:58How do you think that will evolve? I'm both curious about this in terms of how it impacts the product, but also how it impacts your business mode, your power in your business where you rely or don't rely on TPT, whatever. When we first started, this was about two years ago, people were still figuring out applications. Almost no one was doing fine tuning. In fact, anyone that was doing fine tuning, there's a lot of writing at that time was like fine tuning, quote unquote doesn't work because it doesn't really get you that many gains. Another big reason to not do fine tuning at that time is that the models are changing so fast.

59:29You're still kind of figuring out your use case. Why invest a bunch of time in the fine tuning? It's not reversible. You're going to have to throw it out next time there's a new model release. I think now the open source models, for example, have gotten to the point where it's definitely not smart enough to do everything, but there's a lot of specific use cases where you just don't need that much intelligence. Example would be even the agent, let's say the first thing the agent does is it just needs to think about like, okay, based on what the user said and everything before, what path do I go down?

59:55What data do I need? You can make that a fine-tuned model. That model doesn't have to be good at math or coding or anything. It's just like, you just need a smaller model that's just fine-tuned on that. Nowadays, we're seeing much more of that because a lot of the applications have gotten more mature. So you know how your agent is structured, you know the places where you need models to run, and you can take smaller fine-tuned models and that improves the entire system, both in terms of performance, but also latency and so on. So I think over time, there's going to be more and more of that. I do think there's always going to be a huge usage of the open AIs and anthropics of the world because you just need intelligence.

1:00:30You need the best models. So I think there's going to be a balance, but at least in the short term, there's going to be more and more of the fine-tuning small models that happen. What is your perception of the very biggest companies? The entire market has been focused on, rightly so, the 7, 8, 9, 10 biggest technology companies. Which of them feel most important to you? And I don't mean open AI and anthropic. I mean, like Microsoft and Amazon and Apple and these sorts of companies. What is your relation and thinking about them today? They've been the driver of equity markets by a huge margin. They're important companies.

1:01:01How do you relate to them? I mean, work-wise, it's not super relevant, but of course we have our own opinions. I'm very bullish on Google, actually. Why? I just think that with AI use cases, having individual consumers is so important because that's where all the data comes from anyways. and Google is like much stronger there. I mean, you can say that Meta, Facebook also has that element. And so, yeah, maybe their new super intelligence lab will be able to make it work. But something like Anthropic, for example, where they haven't done as well on the consumer side compared to like a chat GBT. I think long-term you do need that consumer buy-in because that's where all the new data is going to come from.

1:01:39Yeah, Google and I mean, Google just has an amazing team and we've had a couple of rocky starts, but hopefully they'll make it work out. Of course, all the large Mag7 basically are obviously super strong. So we don't really have strong views on them. If I let you build a portfolio tomorrow where you got five slots, 20 % each and five private companies building in and around AI, what portfolio would you build? Decagon excluded? Yeah, Decagon excluded. Let's see. Just kind of gravitate towards where most of the talent is forming. I mean, I mentioned I'm close with Cognition guys. Cognition would be in there for sure.

1:02:13Cursor also. So I'm actually kind of interested in like where those might run into each other in the future. Let's see. Three more slots. For me, definitely would want to take a bet on the hardware layer, even though it's like a much higher variant. So last time we were talking about etched, like companies like that. I think you probably put one of those in there. Still on the earlier side, but obviously very high potential. Two more. I like the portfolio so far. Another friend of mine is building a company called Pika, building video models. So I just think very highly of that team as well. So you probably put them in there.

1:02:46And the last one, probably would want some sort of bets on the underlying model side, even though all the large language models are out there. But another friend of mine, I think very highly of they're building models, but not like the types of language models, but still foundation models for like healthcare, for example. So a friend of mine, Josh is building CHI. They're building a foundational model. So stuff like that, I think it's quite interesting. or even I would put Lockheed's company, physical. I think those are very exciting. It's just obviously as early to see how those would turn out.

1:03:17But if I'm building a portfolio, definitely want one of those in there. What do you think are, I'm interested in both ends of the spectrum, the people that aren't in your position who are both technical and commercially at the center of this wave, what do they overestimate and underestimate about the capabilities of AI today? Where is it further along than the world thinks? Where is it further behind? It's a little bit more behind in being able to unlock a lot of the enterprise use cases, I would say, because of the non-determinism. So two things that need to happen. One, you need to reframe the way that people think about agents.

1:03:50There's the Waymo effect that often happens where it will be objectively just way better than human drivers, and human drivers make a lot of mistakes. But because we're investing in new technology, the bar is a lot higher. So it has to be near perfect. So there's that dynamic that kind of needs to adjust in some folks' mind, where instead of evaluating AI in a way where you're just trying to find mistakes, you're evaluating holistically looking at the sort of success rate. And then if you can frame it that way, well, the success rate is going to be way higher than humans, because again, humans are not perfect.

1:04:19So I think that needs to happen. And I don't think that's fully happened yet in the enterprise. So that trend needs to happen. And then on the AI use case side, I think the models need to get better in a lot of areas, right? We were just talking about voice to voice earlier. Hallucinations are too high there. As those models get better, I think more enterprise use case will be unlocked. But I do think the general public will just see a really cool demo and be like okay oh wow like voice is solved now and as a result you know the enterprise should be adopting it left and right and c-suite will see that too but then when they actually get into it it's just harder to go live that's i would say the piece where it's not quite fully there yet and so it's a little bit slower than people think i think the thing that's on the flip side of that where it's underestimated is just things are growing exponentially, improving exponentially, and no one is good at conceptualizing what exponential means in things like this.

1:05:08And so that could be from like performance, like cost perspective. Right now, I would argue that if you're building an application, your margin shouldn't really matter that much. People often critique the coding agents like they're hemorrhaging money. Yeah, but again, things are improving exponentially and like the cost will go down exponentially as well. So your margin doesn't really matter. What really matters is you need to get market share. I need to get my share of users. Perfectly fine not to have good margins right now. And it's just, everything just improves way faster than people think.

1:05:38It's slightly different at the enterprise. I mean, the same principle applies, but generally don't want to be hemorrhaging cash with like an enterprise deal because those are just much longer term. And even though the costs will go down, their expectations might also change and so on. So you generally want to be fairly healthy there. I think that's what's underestimated right now. how do you know that costs will get way lower like i'm very curious about this margin question because if you knew for sure then we actually want to run super negative gross margins if you knew that it was going to get 98 cheaper or something like this cost of goods to serve coding agent or something like that i think the argument would be get the install base just like have the best product and get the users and build the affinity with that user base and don't care at all but that hinges a lot on the confidence that you have that the cost will fall so how do you know?

1:06:25How do you think about that equation of win install base versus demonstrate good economics now? Well, I just think it's quite unlikely that where we're currently at is like the best that things will be. There's so much effort getting put into it. And like one of the main metrics is efficiency. The other piece is that even if things don't get better, there's a lot of ways you can re-architect your system so that it is more cost efficient. It's just that it's not worth putting time into that right now where you can put that same amount of time into getting new customers because you know that things will change in the future so i think that's just one thing where people are like oh it's kind of like a scheme where you take vc dollars and then like the vc dollars go to the chips these companies are losing money if they wanted to probably they could just like spend a month or even less and just massively improve their margins but it's just not worth doing that work right now what you're really optimizing for right now is just quality and growth so if you can do that then the optimization will always come later on how do you think about your margins?

1:07:23Like just putting it on Decagon, like do you care at all? Do you set them? What's your guardrails or parameters for what's acceptable or what you're targeting? Yeah, I would say the only thing that we have a principle for is not to have negative margins. In general, we have fairly healthy margins because one way you can think about it is if you just think about supply chain for any good, let's say you're buying like a croissant at the airport or something, that last step where you're actually solving someone's problem, that's where you generally can capture the most margin. Every step along the way, like whoever's enriching the flour or like making the butter or whatever, you're generally making a margin on top of the costs of whatever your goods are.

1:08:01And that's why it's nice to be in the application layer is what you're building is actually solving the business. And as a result, you can capture more of that because our customers, the way they're thinking about it is great. We're investing in Decagon. We don't care that much about what Decagon's costs are. In fact, we probably don't care about at all. What we care about is what is the business ROI that we're getting? It's like we're downsizing our operations by this much. We're actually generating more revenue now because the AI can engage people and keep them retained. So that is where we probably see the most dynamic here.

1:08:32And I do think this is generally not really a hot take. In the early days, we were like, oh, there's like chat GPT wrappers and so on. And yeah, a lot of wrappers, if it's too thin, it's like, I'm not going to be valuable. But if you have enough software built around the models, then that's where you can actually almost capture the most value. That's why I think the open AIs of the world will continue to move towards applications because it's quite hard for them to make money long-term on like their API, for example, because there's like such high competition. All the labs are building. It's very easy for people to swap.

1:09:02It's not like moving from AWS to GCP is like very hard, but moving from a open AI model to anthropic model, you just change like one line of code. Can you say a little bit more about this ChatGPT wrapper concept? my sense is certainly for what you built, but probably for other companies that a lot of the work that your engineers are doing is not AI work. It's traditional software work. It's the ability to hook this system up to enterprise customers. It's good old fashioned product and infrastructure building that is very different from what OpenAI or Anthropic are providing you. And that's hard work.

1:09:34Like just like building any software system is hard work. Everyone's very enamored of this idea that like in five years, I can just show you a piece of software and just tell a coding agent, just like replicate this piece of software. And that's going to mean lower software motes. I don't think you think of it that way. So maybe describe your thinking or critique of this chat GPT wrapper concern that people have. Yeah, I think generally people say wrapper in a derogatory way. It's like, hey, you're just a wrapper. It's not black and white. Yes, there are a lot of apps that are just wrappers and not going to become real businesses because there's just not that much value.

1:10:07I mean, one argument, don't know too much about this space, but at least from the outside, it has seemed like copywriting, for example, has been difficult because someone can just log into ChatGPT and just like, hey, write this for me and I'll just write it. There are things where there's not enough tooling and functionality on top of something for it to be really valuable and needed. Then maybe it is easier to just leverage the models. But most of the time, that's not the case. And especially when you get into agents, an agent is not just a model, right? You have to like design it. You have to be able to put in guardrails.

1:10:36You have to be able to teach how to do new things. And that's where the software layer on top of the models come in. And if that's valuable, then it's just much harder for you to just like be made obsolete by a model update. The other side of it is like, okay, well now the labs are quite interested in building applications. So they're building a bunch of coding applications and cloud code and so on. And so those will end up being competitors with cognition or cursor. And maybe for that reason, it is wise for the coding agents to start training stuff as well. I think in our space, I would say for us right now, at least the sheer amount of functionality you have to build because it is a very top-down product is quite large.

1:11:17That has nothing to do with AI. It's just stuff like, okay, how do you have like observability into like what the conversations are? How do you alert the team if something spikes? And how are you able to QA and have unit tests for the conversation so that before you push it out to end users, you feel It's like, there's just all this like functionality that's there that doesn't really have anything to do with AI really. And so it's just like a lot of stuff that needs to be built. How would you advise other founders thinking about the ideal customers to go after? What are the most interesting qualities of your best customers?

1:11:48When you're qualifying them, are they going to be, you have limited time. You can only serve so many people. I know you're growing really fast, but you can only serve so many customers at any given time. How do you qualify who you want to work with and don't? Like what are the attributes that you've seen matter the most? Yeah, we want people that are intellectually just at the leadership level, really curious and excited about technology. You actually see a huge spectrum of that in the enterprise. And some of, in my opinion, the best leaders and like probably the folks that we are most excited to work with, they're just genuinely like, hey, we want to move on AI as fast as possible.

1:12:20We're very interested and just curious about how all your systems work. And as a result, I'm going to help cut through all the crufts and like bureaucracy to get something going. And I think you can tell that pretty clearly in the first conversation. You can tell if someone, if they're like legit about this is something where I'm going to both push aggressively, but also give you a ton of feedback and like the feedback is going to be good versus someone where they just know it's like a board mandate. And it's just like AI is like a thing on their to-do list. Is there any leader that you've come across that most exemplifies that posture?

1:12:56Yeah, so I would say in both the sort of digital native segment, you have folks like Chime, for example, super impressed with the entire team top down. Everyone that is in the org, that's like a case study to study at some point. I think they've done a really good job on culture, really making it so that people are very sophisticated about everything. Everything the AI agent does, it's like super data centric. They track everything. They're really thoughtful about how to make updates and so on. and they've given us a ton of good product feedback. As you're talking to your friends that are also building companies in this space, where do you feel that your worldview is the most different from them?

1:13:36Where's your view of things or your relative excitement about something the most divergent from others? Yeah, I mean, I would say my friends are very high intellectual horsepower. I think I generally lean a lot more, I don't know why this is, I mean, it's like personality. I think I generally lean a lot more towards commercial elements of every idea. And again, it's not the only way to build a company. But in my opinion, if you were just trying to optimize for like the highest likelihood of success, I think you should really index on the commercial side because a lot of super smart, intellectually curious people that are just building very cool projects.

1:14:14And you can make the argument for a lot of huge outcomes. You need to do that because you just need to do stuff where like no one's going to work on this because where are the commercial elements of this? but I think I think Oshman's this way too it's made a good fit I think we're just like very locked in on okay you just got to be like super practical how much would you pay for it exactly yeah it's really interesting how do you mark milestones in the business how do you motivate the team what have you learned about how to rally around a given thing I know you're super aggressive when you have a customer that you want to get that it's not just like a old school sales process it's like an all hands on deck send engineers do whatever it takes what have you learned about motivating milestones, rallying the team, organizing around common goals?

1:14:56Yeah, for us, I mean, one thing we do always is we always have like a flagpole that's within sight that can just kind of rally everyone around it. Having things to rally around are quite helpful. I was kind of thinking about this the other day. I think another type of rallying is around competition, right? I think when people feel like they're in a battle and there's like clear enemies, then it makes sense. Again, you don't want to get to the point where there's like active animosity, but just kind of a healthy level of competition. It ties the team together because there's like something to focus on.

1:15:31Same thing with milestones. Like if you give someone, everyone a clear milestone, and this can be like pretty insignificant. Like last year we had, for our revenue milestone, we told everyone we'd get them like super nice jackets. We got like Decagon Arc'teryx jackets and everyone was like super excited about that. And if you just think about like - The cost. Yeah, the cost of the jacket and just how much people get paid is like trivial. But it just creates like this, hey, we're working towards these jackets. It brings the teams together because now it just feels like everyone's working together towards this common goal.

1:16:00That's been a big problem for our culture is just finding what is the next milestone. Anything that we haven't covered about either the business or this exciting AI applications world that you feel especially passionate about? I feel like we've done a good job of covering. It's been a great conversation. I think what has become almost a meme or hyped up a lot in AI startups right now are like a couple of things. One, obviously everyone's in person. It's like 996 or whatever. Don't actually think 996 is that healthy. It happens in China and everyone's like super hardcore. But one of the reasons is that no one has jobs over there.

1:16:35So it's very easy for employers to have leverage. I think here you generally want to maintain a good balance because if you're working super high intensity, you need time to relax a little bit. But that is one element. The other element is the forward-deployed engineers. So everyone's talking about forward-point engineers. And yeah, I just think it's kind of funny because my co-founder came from Palantir, so they actually have forward-point engineers. What a forward-point engineer at Palantir means is you're working on a$10,$25 million deal. And so you're actually just almost full-time working with either one or a small number of customers and like building very specifically for that.

1:17:08And I think people are a little bit conflating that with what startups do, which is startups are just very hands-on and do things on a little scale. but for you to actually have a FDE model, you need to have massive clients and most people do not have massive clients. So I'm actually, I'm kind of interested to see how that plays out because I do think there's like a over-indexing on this forward-employed engineering model right now where, yeah, I have a forward-employed engineer and the deal sizes are like 50K. And that's something we think about a lot as well. We are very hands-on with our customers but I think it's like, you have to think about things holistically.

1:17:41You have to think about like, okay, well, how do we scale quickly? We don't have 50k clients, but for every 50k client, you have someone that's fully staffed to them that's impossible to scale. So you need to find that line in the middle. And I think the full forward deploy model only works with the Palantir approach. Presumably on this side, you do care a lot about your margins. Margins you're much more open about if it's just like LLM cost or something like this. But if it's fully baked people cost, that's going to be a problem. Yeah. It's not even a problem necessarily from the pure dollar margins, it just prevents you from scaling.

1:18:13No one can hire good people that fast. It's just hard to hire good people. So if your business is fully constrained on good people, then that's also not a good thing. What do you think the minimum customer size and revenue is to justify a forward deployed engineer model? Oh, probably a million. Yeah. Yeah. Fascinating. Well, I think, you know, my traditional closing question for everybody, what is the kindest thing that anyone's ever done for you? As I mentioned, our mutual friend Scott gave me a heads up here. So I did put a lot of thought into it. So when I was little, call it ages five to 13, like elementary, middle school, pretty like lazy kid in general.

1:18:50Most kids are. There's very few people that are just like intrinsically self-motivated. I wanted to just like play games all the time or just like hang out with friends or play sports. And yeah, my parents had like a very interesting way of raising us, me and my sister, what they did was basically when we were really little, it was like an extreme level of discipline. When I was little, it played a lot of piano, essentially like three, four hours a day. And then for competitions, they just pull me out of school and just go hard at it. And then I guess, fortunately for me, my parents decided, okay, math was probably a better way to go.

1:19:25And I was just like quite talented at math when I was little. Even for that, it's just like full force, like you're just committing everything. We did not have TV in the house. We didn't have video games. We didn't really take vacation growing up. You're kind of in this mindset of you're sacrificing most things to focus on one thing. When you're a kid, actually, like you don't have no frame of reference. So it doesn't feel hard necessarily. It's because your parents are kind of setting up the criteria for you. And in hindsight, I had a very happy childhood. But I think that level of discipline and also just competitiveness is very hard to gain that after your childhood is over.

1:20:03Because when you're in your childhood, your brain's still forming. So that kind of forms your personality. So one, I'm very grateful for that. And I think that's also why when I talk about my generation of, there are a lot of immigrant parents from my generation that came over to grad school and they're all around my age. And I think that's like our crop of folks just are doing very well, partly because of that. probably because of the upbringing. And then what my parents did, I think, which is the more unique side is that a lot of times what happens, especially it's like a stereotypical Asian parent, is that that just like continues.

1:20:33And you just have like overbearing parents. I would say even though my parents were very intense about things, they never had any like semblance of like overbearingness. They wouldn't like prevent us from doing things we wanted to do, force us to like, hey, you should like pick this or whatever, and so on. What happened was towards the end of middle school into high school, I think we had already kind of established these personalities. Basically, my parents just pounded a sort of lazy, wanted to play around kid into someone that was just very, very driven. Then to their credit, they just completely laid off.

1:21:07They don't have any opinions on what we do for careers, what we should do. They're like very supportive. So I think that's very kind because you cannot replace it. You can't even pay for that. You basically just need parents that are willing to spend a ton of time crafting this childhood. for you to develop this. And I think a lot of the things I have in life right now are from that. So that's probably the kindest thing. And my sister as well, like even growing up, she was, I think there was a lot of pressure on me and in some way for like parents that immigrate, who are also ambitious folks, it's very hard for them to succeed themselves in a new environment, a new country.

1:21:45So they put a lot of expectations on their children. Yeah. My sister was also like, Like she was just always trying to sacrifice things for me when I was trying to achieve things. And so, yeah, try to spend as much time with my parents as possible. What was the climax of your math career? Definitely peaks in high school. So in high school, we did math contests, math Olympiads. So there's like a major contest in the USA Math Olympiad in the US. And there's like essentially a camp for like the top folks. And so I went there a few years. That's probably the peak. Well, this has been so much fun. I'm so fascinated by the business that you've built and are building.

1:22:19Thanks for explaining it to us and bringing us sort of right to that white hot center in so many different ways. Thanks for your time. Thanks for having me. If you enjoyed this episode, visit joincolossus.com where you'll find every episode of this podcast complete with hand edited transcripts. You can also subscribe to Colossus Review, our quarterly print, digital and private audio publication featuring in-depth profiles of the founders, investors and companies that we admire most. Learn more at joincolossus.com slash subscribe.