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

Interactive Transcript

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spk_0 Hello and welcome everyone, I'm Patrick O'Shanasi and this is Invest Like The Best.

spk_0 This show is an open-ended exploration of markets, ideas, stories, and strategies that will help

spk_0 you better invest both your time and your money. If you enjoy these conversations and want to go

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spk_0 join Colossus.com. Patrick O'Shanasi is the CEO of Positive Sum. All opinions expressed by Patrick

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spk_0 discussed in this podcast. To learn more, visit psum.vc.

spk_0 My guest today is Jesse Zhang. Jesse is the co-founder and CEO of Decogon, one of the fastest-growing

spk_0 AI customer service companies. Decogon provides a centralized AI engine to all the

spk_0 resolved issues at any time in every language and across every channel. Jesse shares his systematic

spk_0 approach to finding product market fit by asking potential customers exactly how much they pay for

spk_0 solutions. We explore why customer service and coding have emerged as to the clearest AI use

spk_0 cases for enterprise and the key business and technical factors behind Decogon's momentum.

spk_0 We also discuss the intense competitive dynamics of building an AI today, strategic decisions

spk_0 around building proprietary models and deploying AI agents at enterprise scale. Please enjoy this

spk_0 great conversation with Jesse Zhang. Perhaps an interesting place to begin, I bet you don't expect this,

spk_0 is for you to tell us a little bit about the phrase that you've talked about that's written on

spk_0 your wall in the office. Yeah, for sure. So in our wall, in that stuff office, and we just

spk_0 did in the New York office as well, we have this quote. It basically goes along the lines of,

spk_0 there's no challenge that can't be overcome, and there's no enemy that can't be defeated.

spk_0 And we just like it. I think it really fits our culture. People there are, we have a very

spk_0 competitive team. Everyone wants to win. We have a lot of energy when we're trying to go out and build

spk_0 because it just feels like, and there's all this stuff happening in the industry, we have such a

spk_0 strong team. Let's just go and win. Motivated by my dad told me this, I don't know if this is

spk_0 actually validator or not at Huawei and in China. It's obviously a massive company, but they're

spk_0 known for just having like killer culture, and they have some version of this, written in Chinese,

spk_0 of course, and just being read letters across the back of the big hall. So yeah, really like that.

spk_0 It's kind of interesting. Any time the song walks in, it like stands out, the coast stands out.

spk_0 I asked about it to begin because I'd love to spend some time on just what this environment is

spk_0 like building a company like yours against formidable competitors in probably the most exciting

spk_0 era of technology that any of us will ever live through. And words like this, defeated. I've

spk_0 heard the word violence recently about a company culture, aggression. These are not words that were

spk_0 being used three years ago or four years ago. In fact, if you use them, it was a big problem for you.

spk_0 And obviously that has completely shifted. And not only have founders started using these words,

spk_0 I think talent and senior people rallied around them. They want to be in a culture that is

spk_0 defeating enemies or violent or aggressive. Maybe just for a while on what it's like to be building

spk_0 and competing in one of the big areas in your case, conversational AI, etc. at this moment in time.

spk_0 Because it's just so different than it was a couple years ago. Our whole view is that any space

spk_0 that's worth going after, like any large hot market, it's going to be competitive. This is not

spk_0 really specific to AI, right? If you think about data bricks or snowflake or ramp for spreex. And

spk_0 anytime there's these big massive growth opportunities, people are rationally want to go after it.

spk_0 So I think in this generation, it's just, I think in all my sick tracks, like a specific demographic

spk_0 of founder and people that want to build because it is exciting. But of course,

spk_0 everyone knows it is competitive. Because if there is market, then everyone's trying to build it.

spk_0 It's quite easy to start a company these days. You can race funding really easily. And so when you go

spk_0 out there, you have to at a certain point have a pretty deep understanding of what your competitive

spk_0 advantages are. And one of those could be the culture. And if you build a good culture, that's

spk_0 pretty hard to replicate. It also lasts quite a long time. And to your point, if you have a culture

spk_0 where everyone is really geared towards succeeding and working hard and having some level of intensity,

spk_0 it can go a long way. So I think to your point of a lot of companies adopting similar

spk_0 mindsets, I do think for my observation, this generation of company building has attracted like

spk_0 almost like my demographic of person, where it's just like people that grew up in fairly

spk_0 competitive environments, academics or whatever. And a lot of these founders have done well,

spk_0 do a lot of math contest and coding contest growing up. A lot of the people around my age are

spk_0 doing startups now and people are doing quite well. And I think there is some element of, if you

spk_0 embrace this hardcore lifestyle or environment growing up, then yeah, I think it lends itself

spk_0 pretty well to the current situation because there's a lot of parallels. I was with Scott Wu

spk_0 from cognition recently who is as well known to be like one of these math champions you as well.

spk_0 Talk about that environment. What was that competitive market like? What was it? Can

spk_0 it let people into that world because obviously it shipped you a lot? It's kind of like an interesting

spk_0 experience I would say. Like I think when you look back now that we're all grown up, it is kind of

spk_0 like a pretty intense way to grow up. But I look back on my childhood with very fun memories.

spk_0 You get a really nice community. A lot of people that are doing the same thing. I grew up in Colorado

spk_0 in Boulder. Boulder is a pretty academic town, but there's not very many people that are like

spk_0 super gunning for like these contests. A lot of my friends had just kind of grew up in not the

spk_0 places you would think, California, Texas, and York. And so it gives you some level of community of

spk_0 like, okay, there's a lot of other people out there that are doing the same things as you and

spk_0 meet a lot of them. And then now that world grown up, those relationships have lasted for a long time.

spk_0 But yeah, then the environment is one where it's similar to company building. You're like, how

spk_0 well you're doing? Like fairly objective. There's a lot that has to go into it. There's a lot of

spk_0 preparation. A lot of having the feeling of it is like a long term thing, but because your results

spk_0 are fairly objective, there's constant motivation to improve. I think that's quite nice. One of my

spk_0 big these uses is that this audience of people or this sort of community of people, there's a lot of

spk_0 untapped potential there and to actually like, turning them into people that want to do business

spk_0 or companies and things like that. A lot of them have historically gone into trading or academia.

spk_0 Now, I mean, those are perfectly awesome jobs. A lot of the folks here, this background is

spk_0 correlated to one where it's a little bit more like risk averse and kind of just get your good grades

spk_0 and follow track. And if you can kind of diverse some of that talent into like company building,

spk_0 I think there's just a lot that can be done. What are the parallels? What is it about the people

spk_0 or the training or the specific aptitude in the competitions that make you good at company building

spk_0 or and others good at company building? Like this seems like an obvious true trend that there's

spk_0 enough sample size now of people that had this background that are doing extremely well in this

spk_0 environment. Maybe it's the best if you could somehow index that group of people you'd have

spk_0 like fantastic performance right now. What is it? Like what are the parallels that make that true?

spk_0 One is the competitive nature that we just talked about. The other is just like the problem solving.

spk_0 So I think one thing that I believe very strongly is that one of the best things you can do when

spk_0 you're building company or anything is just have pretty good introspection on where your strengths are.

spk_0 And at least for this group, it's more on the problem solving side just like and the problem

spk_0 can be very vague. Problem could be like how do I build a successful company? And you kind of break

spk_0 down the problem. You can like think really critically and just from first visible is a lot of

spk_0 these markets. Of course, that's not the only way to build a company. Other people, I think some people

spk_0 just have like very good intuition, especially on these like PLG type things. But in general, I think

spk_0 just the problem solving capability. Like what these contests really teach you is that you're just

spk_0 solving problems. And those problems of course are not real problems in real life, but the sort of

spk_0 thinking is the same. Does it feel more emotional now company building than it did in the contests?

spk_0 Emotional. I think I'm a bit older now. So it's not really that emotional. I started a company

spk_0 before this that was I would say a wait tougher than the current one. I think just like mentally,

spk_0 makes you feel a lot more calm and appreciative of when things are going well. And timing matters a lot.

spk_0 I think when I first started I graduated year early to try to start it. And I would say we had a

spk_0 very fortunate outcome. But the whole journey was like very bumpy. What was it? What did they do?

spk_0 So the company was called Loki. We basically built high performance video capture software for video

spk_0 games. So when people are playing games, you can really easily capture video clips and edit them and

spk_0 share them. And the whole goal of the product is just like you just get as many users as possible.

spk_0 And again, I think we exited it like a very fortunate time was 2021. But throughout the journey,

spk_0 like at the beginning, didn't really know what we were building. And then trying much of things,

spk_0 when you're like a new grad, you don't really have good intuition on what ideas could or not.

spk_0 So you just worked really hard for three months. And so then you realize that obviously you had no

spk_0 market. So that's tough. And then about a year and a half in and I had two of my good friends from

spk_0 school, where my co-founders, they both got burnt out and then decided to leave. So it was just me

spk_0 after that trying to figure out what to do. And so that's I think when you, I think that's a way

spk_0 tougher than anything we're doing right now. It's like a way you put it like emotional thing. It's

spk_0 you don't really know if there is any future or not. There's a lot of pressure because you don't

spk_0 want like the first thing you work on to be a failure essentially. So I think that was a much

spk_0 tougher from like a psychological point of view. Nowadays, I think it's tough just just like a

spk_0 sheer amount of stuff to do. We're not getting much sleep, but again, the grand scheme and things,

spk_0 you got to be really grateful even being in this position to have enough work to do, to have

spk_0 enough interesting problems to have a team that you're really excited to work with every day.

spk_0 How much do you think you're sleeping? I mean, it really varies. This week in New York, it's

spk_0 probably like four to five hours. And it's not good because like I'm not someone who's brain

spk_0 functions that well on less than eight hours. So if you think about the difference between the first

spk_0 and the second company, like obviously now you have a company that's working extremely well,

spk_0 it's growing really really fast. What did you do at the beginning of Decagon that was informed

spk_0 by your prior experience to make this one go better? I think this is broadly true starting companies.

spk_0 I think the first stage is like by far the hardest because you're kind of just like finding

spk_0 direction and finding directions is very difficult because by definition, it's not something where you

spk_0 can just like you have like a goal and you're just like grinding towards it and you can get there.

spk_0 Your goal could be like, by finding a direction, but it's more exploratory.

spk_0 So I think what we actually are quite good at now. And Asha and my co-founder, who's amazing,

spk_0 he also has similar background. He started company before that got acquired. I think when you

spk_0 started company the first time, you often show the same experiences, which is like finding directions

spk_0 very difficult. So I think this time we were a lot more thoughtful about it. And again, it goes

spk_0 back to what your strengths are. I think we view our strengths as like, hey, we're very good at

spk_0 problem solving. We're very just rational about things. We're just good at execution. And so

spk_0 if that's the case, then I think we just try to systematize the ideation process. And it just

spk_0 comes down to okay, you need to, whatever you work on, it has to be something that people will

spk_0 really invest in. And how do you tell if that's the case? You can just go really deep asking them.

spk_0 I think people are usually a little bit almost embarrassed or not comfortable going super deep

spk_0 in these questions. When you talk to a potential customer, they actually don't mind answering questions

spk_0 such as, okay, if we built this for you like exactly how much we'd pay for it. Like, would your boss

spk_0 need to approve it or your boss's boss? Like who needs to approve it? How would entire organization

spk_0 think about ROI? How would you present ROI to leadership to protect yourself and also like make you

spk_0 look good? And I think if you really go deep there, it's almost like you're basically asking like

spk_0 classic sales qualification questions, but in powder form and because you're a founder, it's like,

spk_0 it just feels a lot less salesy for you to go deeper. That process is what gives you a lot more

spk_0 signal. When we first started, we fortunately were able to get in front of a lot of large companies,

spk_0 mostly digital native ones. And we just asked these questions and we kept digging in. We had a bunch

spk_0 of different ideas right at the time. We're not tied to any idea and it's just kind of open

spk_0 exploration. We're looking at things from like data analysis to like security to presales,

spk_0 to ops stuff. And that process was very helpful. It just shows you that there's a lot more

spk_0 signal to gain than just talking to customers, which is the general advice or building something

spk_0 that customers want, building something that people want. Like, yes, that is true, but it's very

spk_0 hard to just know that. Like they build to tell you what they want, but then it turns out that

spk_0 that's not useful. What was the literal process? Would you go to a single person and ask them about

spk_0 multiple of your ideas at once? Or would you target it more like one idea to one person?

spk_0 You have the target it based on what that person owns. If that person is very senior,

spk_0 you can ask him about multiple. So if you talk to like a COO, for example, you can talk about

spk_0 a bunch of different use cases. That gives you some signal too. And then if you're talking to more

spk_0 of like a VP of a certain area, you're probably focusing on one use case.

spk_0 So maybe go in detail through one of these conversations so that others maybe could benefit

spk_0 from what you've learned. So what is the order of the questions? Like how would you structure

spk_0 those conversations to get the most information possible? So yeah, let's say we get into the call

spk_0 and you start by just doing very high level discovery, like, hey, what are the sorts of projects

spk_0 that are ongoing right now? How do you spend your time? What is kind of like stressful for you

spk_0 right now? Etc. And then you can kind of get a sense for the types of use cases. And then very

spk_0 quickly, you can just form hypothesis, like literally on the fly of like, okay, what would a product

spk_0 be that makes sense here? And so then you're kind of explaining like, okay, yeah. So what if something

spk_0 like an AI agent could do XYZ? Would that be helpful? And most likely they will say yes,

spk_0 because there's this thing that happens where if someone's on a call with you, they almost feel

spk_0 like they owe you like a positive. Yeah, you can take away. So they're like, oh yeah, that'd be great.

spk_0 Now you've kind of solidified at least the potential product ideas. They might adjust a little bit.

spk_0 They'll be like, yeah, no, actually, though, it should work like this and so on. I remember we

spk_0 talked a lot of ops leaders like Matt McGinnis from a Ripley is like a great friend of ducking on

spk_0 and you're telling us about all the different things that have an honest team because there's

spk_0 like so many. And similar with other ops leaders, we talked to it was kind of a range of companies as

spk_0 well, people like ordering and so on. And you get down to it and you're like, okay, great. Now we

spk_0 have these use cases. How much would you pay for it? And that kind of forces them to think because

spk_0 most people are not thinking about that as they're talking about ideas. They're like, oh, yeah,

spk_0 this would be cool. As soon as you force them to think about how much you pay for something,

spk_0 it kind of is a forcing function for finding some order of magnitude, some level of scale.

spk_0 And so then they're like, okay, well, yeah, you have five people doing this full time. If the AI

spk_0 agent could do this well, maybe we'd be able to get rid of one of them, assign one other one,

spk_0 and then as a result, I'd pay you like 20k a year or something like that. And then you're

spk_0 head, you're like, okay, cool. So now at least you have a general order of magnitude. 20k, of course,

spk_0 isn't amazing. But if it's like, I could quickly turn out a ton of these, like, maybe that's interesting.

spk_0 But most of the time, that's not because there's not that many good ideas out there, honestly.

spk_0 Most of the time I ended up this exercise, you're like, okay, great. Glad I didn't pursue this

spk_0 further, you know, because that would have been a waste of time. And at the end, it's like,

spk_0 if you were paying you like a hundred dollars subscription from one thing, it's like a big company.

spk_0 So you just do this exercise. And the nice thing about this exercise as well is that it puts

spk_0 the customer in the same frame of mind as you. And so then they can tell you other things.

spk_0 Essentially what happened with our company is like, we were talking about all these use cases.

spk_0 And they're like, okay, great. Yeah, if you did this, you know, with five people over here. But,

spk_0 by the way, we have a 500 person support organization. And there's a lot of opportunity there.

spk_0 And we'd be like, okay, great. Tell us more, right? And then you kind of dig into it. And

spk_0 I think this just goes to show that as a founder, you kind of have to build your own conviction and kind

spk_0 of do this process yourself because at the time, essentially what everyone told us, including like

spk_0 very smart older founders that we knew and so on was that excuse case, like super obvious.

spk_0 There's probably just going to be incumbents that just tacking onto the product. And because it's

spk_0 so obvious, there's probably a reason why like no one's super big right now or there's going to be

spk_0 someone that's ahead. But no one really knows. And even for me right now and other founders

spk_0 talking about other spaces, it's like, yeah, I have my own opinions, but I don't really know the

spk_0 details of the space. And the only way you can really know is by talking to customers and getting

spk_0 that signal. And in hindsight, it turns out that in any sort of wave, anytime, I would say like a

spk_0 very small number of good ideas. And your job is to ideally find one of those at the right time. And

spk_0 so yeah, by the definition, it's going to be like pretty non-obvious and pretty difficult. And so

spk_0 if you can do this process, well, it'll give you the most signal. Do you remember the highest number

spk_0 anyone said for how much they'd be willing to pay for something in one of these ideation sessions?

spk_0 Yeah. So it's time it was probably on the order of low to mid six figures.

spk_0 As you'd near the end of that process and settled on what Decagon does, which maybe probably is

spk_0 the right time, as you describe in detail, what are these? What was like the final closing like,

spk_0 where did the conviction come from? Like, oh, this is clearly the thing after this discovery process.

spk_0 It was clearly the thing because if you just kind of tallied up, even just the amounts that people

spk_0 said and added them together per idea, this was probably like an order of magnitude more than

spk_0 anything else. So very specifically what Decagon does is say I customer service agent. So that's

spk_0 the simplest way to think about it. And you're building a conversation lay eye that can just be

spk_0 almost like a front end for a brand. Anytime someone wants to talk to the brand or anytime the

spk_0 brand wants to talk to them, you can kind of initiate these conversations. And of course, long

spk_0 term, this is not specific to customer service, but I think again, going back to this exercise,

spk_0 customer service is where we felt like the most urgent need. And in hindsight, I can dissect why

spk_0 we think that is, but that's basically what we felt. And what gave us conviction is that, hey, we

spk_0 had all these folks that were lined up. They were like very willing to invest six figures in a random

spk_0 two person team. They didn't even know that well because it was actually like a very top mind

spk_0 initiative for them. Everything else, it was just like a struggle to, it's like how much you pay

spk_0 for that. I don't know. Like, this is like really exciting, but you know, our budgets are tight

spk_0 right now. And also like it'd be hard to measure how well this is doing and so on. So yeah,

spk_0 that gave us enough conviction. And then you just kind of take a step by step from there.

spk_0 Say more about the comment you made about in hindsight, it's clear why this was the key problem.

spk_0 Yeah. So I would say customer service has a bunch of nice properties that I think are very hard

spk_0 to reason through ahead of time, which is why I think I feel so strongly about this process of

spk_0 discovery, just really staying customer centric. One of the properties is that the ROI is really

spk_0 easy to justify internally. You have these numbers already tracked. It's like, hey, we have so much

spk_0 conversation volume. Right now we have a simple chatbot or a simple IVR phone tree. It's resolving,

spk_0 so to speak, like 15 to 20% of that, if you're able to take that to 50, 60, 70, 80, like that's huge

spk_0 ROI and it's very easy to quantify. It's like, okay, well, I'm going to take the total cost. I'm

spk_0 chop off 60% of it and that's what I'm saving. The other property, which I think is a little underrated,

spk_0 is that it's very easy to go live. I think a lot of Gen AI use cases are struggling with that right

spk_0 now, especially at the enterprise level because there's risk involved. People don't want to feel like

spk_0 something could go wrong for them when they release your product. Leadership's going to get mad at

spk_0 them. It's like, you know, why'd you do this? So that is a big deal with Gen AI. I think that is

spk_0 one of the reasons why there's something difficult for a lot of use cases to really take off.

spk_0 Because at the end of the day, there is always going to be risk because the models are non-deterministic,

spk_0 and so something could always happen. But the nice thing with customer service is that you have

spk_0 a escalation path to naturally built in to the way the product works. The agent's having the

spk_0 conversation. If for any reason it needs to exit, it'll just escalate to a human. And that infrastructure

spk_0 is already set up. You already have your call center. You already have your telephony stack or

spk_0 whatever. So you just connect to it. I think that property alone just makes things way easier

spk_0 because these big enterprises that we work with, they're like, okay, great. We test it internally,

spk_0 and then to go live, we're just going to choose this one surface area and release it to 5% of the

spk_0 user base. And even for that 5%, if anything goes wrong, it just escalates. And so that gives people

spk_0 enough comfort to go for it. Those two things I think are one of the big reasons why it's probably

spk_0 the, I would argue, the use case with the most traction at the enterprise. Coening is another one.

spk_0 Coening is a little bit different. It's a lot more bottoms up. Can you compare it to coding?

spk_0 It seems like these are the two areas where it's blindingly obvious that it's usually customers

spk_0 and you can build great businesses around it. Just look at the revenue curves. Your Syras,

spk_0 obviously like cursor cognition, et cetera. Compare and contrast coding and customer service.

spk_0 Yeah, they're very different. Maybe one framework to think about this is that at the end of the day,

spk_0 what AI agents are there for is to essentially replace human labor. That's why it's exciting. That's

spk_0 why everyone's so focused on it. One thing you can do then is like you can just mac out the

spk_0 spectrum of like how much that human labor currently costs. So what customer service is generally

spk_0 outsource already, especially for the tier one tier two type inquiries that AI is now handling.

spk_0 It's generally not folks that are super highly paid. And then on the other end, it's engineers

spk_0 who are the most highly paid people. The one we think about is that like AI use case will start

spk_0 eating the spectrum for both ends. And the reason why is that because engineers of the highest paid,

spk_0 they have the sophistication to like really leverage it well. And like,

spk_0 AI just gives them so much leverage. I mean, there's other factors as well. It just happens that

spk_0 coding is tokenizable and the models are really good at it. That's one way to think about it.

spk_0 I don't know any company that's like, hey, I would like to let go of a bunch of my engineers

spk_0 because now I have coding agents. There's infinite engineering work to do. So you're just augmenting

spk_0 them. On the other end, it is more of the replacement sense. You have a large BPO and that's

spk_0 costing you a ton of money. And it's also like a really high operational thing to maintain. Because you

spk_0 have to hire people all the time. There's a ton of churn. You need to train them. You need to

spk_0 QA them. You need to make sure that nothing goes wrong. So AI is really valuable there as well.

spk_0 Because the work is easier for the AI to do. It can fully replace. And I think that goes into

spk_0 the conversation of I think it's a little bit overblown of like, oh, AI is replacing jobs and so on.

spk_0 Even the BPO as we talk to, and then I'm really that concerned because what typically happens

spk_0 anyways is that there's already very high turnover in these BPO's. People are just hopping

spk_0 around doing all sorts of different things. Kind of just naturally let it decrease. And then they

spk_0 just go on to do sort of the next level of task that they I can't do yet. And so maybe that's

spk_0 like data labeling or something. That's generally what we're seeing from the BPO's. But yeah,

spk_0 anyways, I think this spectrum is pretty real. And so then the question is like, what is the next

spk_0 thing that happens? So it's a really interesting conclusion, which is try to augment the very highest

spk_0 talent or replace the most replaceable end of the spectrum. That's a really interesting conclusion.

spk_0 I want to talk about what you've begun to learn about how to do that second thing well.

spk_0 So if others out there wanted to start a company or invest in a company that was doing sort of

spk_0 that end of the spectrum, heating its way in as you described, what if you learned or the key

spk_0 things to like the setup process with a given company to increase the likelihood that you can

spk_0 replace a lot of the low-hang fruit for types of customer service calls or types of what used to

spk_0 be human to human interaction and can now be handled by AI on one end of the spectrum.

spk_0 Yeah, I would say the biggest learning we had is that oftentimes a long pull in the tent.

spk_0 And again, if you can solve this well, it just makes things go a lot easier, is aligning on what

spk_0 does good look like. And you would think that in our space, it's pretty easy because it's like,

spk_0 okay, maybe you just have a bunch of questions and answers. And that's what good looks like. But

spk_0 unfortunately, it's a lot more nuanced than that. What good looks like could be in sense of like

spk_0 tone and brand guidelines or how conversational you are. And even for the actual answers, like we

spk_0 work with a lot of enterprises where obviously their scope is broad. And so one of the things you

spk_0 need to set up beforehand is what's good look like. So we have in our product essentially like a

spk_0 testing or simulation suite of like, hey, we're going to build out 10,000 tests. And each one is

spk_0 going to be constantly running like five times and then you can get a sense of how well things are

spk_0 performing. And that's actually pretty difficult. And I do think that is broadly true for anyone

spk_0 that's trying to build in this style of company. If you're going to be replacing human labor,

spk_0 you need to know what good human labor is. So what we found is like, okay, well, can someone just tell

spk_0 us what are the answers to all these questions? And most people don't actually know because these are

spk_0 large complex organizations. No one is like the person where like, hey, I know how to answer all

spk_0 these questions. And so you have to design a process where it's very easy to extract these answers

spk_0 from all the people that do know. So maybe it's all the CX leaders or people lead different areas

spk_0 of the product. And so you have to get them all together and get them to align on like, okay,

spk_0 here's what the Evales essentially. If you can do that well, then it makes everything a lot easier.

spk_0 Because now you're just building building building, you have this quantifiable score that's like,

spk_0 hey, here's how well they are performing. And then once you're done building and the score is high,

spk_0 then you can go live. Is it right? I would have to think about this. You just created like a captive

spk_0 reinforcement learning process within an organization. Is that like the simplified version? Yeah,

spk_0 yeah, yeah. That's the interesting way to think about it. And it doesn't have the reinforcement

spk_0 learning in the pure sense of training a model. It can just be reinforcement learning and

spk_0 make an agent improve. And that could be compiling more Evales that could be compiling just like

spk_0 more car rails, guidelines around what it can't get to. How fast does this spread happen?

spk_0 If I'm a customer and I've got the 500 person customer service call center or whatever,

spk_0 I'm actually curious. I don't know what the volumes are like, how much call volume or interaction

spk_0 volume, a center like that handles for a giving company. But if I give you 5% of my workload

spk_0 and I'm satisfied with AI's performance, like it performs well. And there's not lots of problems.

spk_0 How fast are people willing to go from five to 10 to 15 to 20 percent? Very fast.

spk_0 I would say even for a large enterprises within weeks, everyone wants to just go live

spk_0 everything. But the reason why you stage it out is so you can make sure nothing's going wrong.

spk_0 And you can tell us something's going wrong, like almost immediately because you have these metrics.

spk_0 So even within a week, you have 500 person or probably estimate mid to high six figures of

spk_0 conversations a year, maybe slightly higher. What you're doing there is making sure that things

spk_0 are going well. So within a week, you can see like, okay, what is the resolution rate? Is that

spk_0 what we expect? Okay, great. What is the customer satisfaction? People have those scores as well.

spk_0 And then they'll probably have some sort of accuracy metric based on like human review.

spk_0 If those all check out, there's really no reason why you shouldn't roll it out. And again,

spk_0 the business case is so obvious there, right? So hey, we're both generating a ton of operational

spk_0 efficiency. And our customers are happier. So yeah, let's just send it up to everything.

spk_0 What goes most wrong when something bad happens, I'm sure they're just happening less and less

spk_0 as the product's gotten better. But even in the early days, what sort of thing would go wrong in one

spk_0 of the customer to AI interactions? It ranges all sorts of different things. Sometimes it's

spk_0 obviously like both sides like us and the customer would take everything very seriously. But there's

spk_0 just a lot of things you wouldn't expect in the early days. We have a customer that is essentially

spk_0 like a large-tickening platform. And one of the things that would happen was someone came in,

spk_0 they couldn't find their ticket. I looked into their account and it's like, hey, there's no tickets

spk_0 here. And then they were just like, okay, well, what I'm going to do is I'm going to show up to the

spk_0 events. And I'm going to find a homeless people from the city and bring them with me. And the agent

spk_0 was like, oh my god, that's so awesome that you're thinking about doing something nice for the community.

spk_0 Things like that were just like, okay, I would not expect that to happen. So those are just like

spk_0 tuning you have to do over time. And generally, it's in the spirit of that where you're trying to find

spk_0 the right level of guard rail and flexibility. I think that's the name of the game with our space

spk_0 at least. And that's sort of the way we design our product. And why probably there weren't

spk_0 we've been successful so far is that what Jenny, I really unlocks is super flexible, super personalized.

spk_0 One way you can think about it is like in the old days to map out a conversation, you just build

spk_0 like gigantic tree decisions. And that's very hard because no one likes that experience. And you

spk_0 ask something that's not quite one of the branches and just forces you down that branch. And there's

spk_0 extracts a lot of that tree into the narrowlands of a language model. So that's really powerful.

spk_0 On one end of the spectrum, you're just looking for flexibility and rigged power and being

spk_0 with the sound really human like, but with the enterprise, there are a lot of things we don't

spk_0 necessarily want that as much. And you want full rigger, right? If it's a regulated use case, like you

spk_0 cannot afford for it to ever deviate. These three steps always have to fall in this order. And you

spk_0 can't go to step three until something has happened already. So you need a design system that can be

spk_0 anywhere along that spectrum. And back to your question of what could go wrong. Well, the worst

spk_0 thing that's happened would be it just says something that's not supposed to say. And so you need

spk_0 to design an AI that's really robust to that. And you can choose like, okay, for this use case,

spk_0 we really need it to be over here. That's a lot more robust. But for other use cases, we're just

spk_0 asking you to base a question on their account. We don't want it to be like that. We don't have to

spk_0 be super freeform. And that's how we get the customer satisfaction up. I think most people are

spk_0 still focused on things that could go wrong because it's non-deterministic. What about the total

spk_0 other end of the spectrum? What have been the things that have gone way more right than you

spk_0 expected? Where has the potential of agents outperformed your expectation in terms of what they

spk_0 can handle or what they can do? It's really just elevating the experience. One sort of metric,

spk_0 which is usually a secondary metric that folks think about later, but is quite important to us is

spk_0 just how often do people come in and just say like agent agent agent like you mean to

spk_0 representative? I want to talk to you. I've done that. You probably have as well. You're just like

spk_0 calling into some sort of customer service and just like pressing the zero the whole time.

spk_0 And that's because people are used to bad experiences. So they've already lost the trust of these

spk_0 systems. I think what surprised us was that if you just make it really clear off the bat that

spk_0 this is a different experience, people are willing to give it a chance. And then the outcomes are

spk_0 just way different. One of our customers or a ring like the wearable ring. We did a case study

spk_0 with them where before having any sort of Jenny i system one in three customer that came in

spk_0 would just not bother saying anything. Just keep jamming agent until they got to one. Now it's one

spk_0 in 20 because we just like spent a lot of time making the beginning of the process just feel very

spk_0 different. And folks are willing to give it a chance. So I think that's been exciting.

spk_0 Where do you think that can go? How good can the experience get in ways that it's not yet that good

spk_0 with subsequent evolution of your product but also the underlying capabilities of the model?

spk_0 The biggest frontier right now is voice voice models. And so know there's a lot of interesting

spk_0 startups as well working on voice. It's exciting because it's still I would say definitely not solved.

spk_0 There is a lot to be done there. The bar is very high. So if you just think about how humans

spk_0 communicate for literally the entirety of humanity, I don't know 150,000 years or something,

spk_0 the UI for every human is language spoken language. Listen, you speak, and that's how our brains

spk_0 have evolved. That's the most natural way for us to communicate. Only in the last what like 60

spk_0 years of that entire time did we have keyboards and like phones and communicating through typing.

spk_0 I would say fundamentally and any sort of agent that communicates with humans voice has to be

spk_0 a critical factor because let's just tell we communicate because our brains are so evolved for

spk_0 this. It's very easy to tell when something feels not quite right. And so the bar is very high

spk_0 uncanny value is quite large. That's why there's a lot of effort going into making the voice

spk_0 experiences good. I would say a chatty beauty voice, for example, or the sesame or like these voice

spk_0 to voice models, they're starting to feel very impressive. But you talk to along enough, you can

spk_0 actually it's like you can definitely tell it's not a human. So there's that element of it. But then

spk_0 even for enterprise use cases like us, there's still a ton of hurdles to cross because those

spk_0 models even though they're good, the hallucination rates really high. So you can't really use them

spk_0 necessarily as is in the current systems. And so a lot of people what they do now is they

spk_0 go from voice into text and then back to voice. And then you can run a lot more checks there to

spk_0 make sure that things are accurate. And so a lot of cool ideas there to explore on how do you make it

spk_0 both human-like but accurate and how do you tie everything together. So that's where most of the

spk_0 work is going to these days. At the risk of getting too technical, why is voice to voice interesting

spk_0 and worth pursuing versus just always going back to text and being able to manage that that way?

spk_0 So the fundamental difference is if you're just going to text, then no matter what,

spk_0 the final audio is just a narration of the text. The voice to voice is powerful because it takes

spk_0 into the entire audio of what you said. So it knows cadence and maybe how upset you are, the tone

spk_0 and everything. The latency is a lot less as well because you're going straight from voice to voice.

spk_0 And latency matters so much when we're talking when we're talking right now or brains are constantly

spk_0 going like, okay, when's he done talking? When should I start talking? If someone interrupts

spk_0 someone else, it's like in a plightway, people adjust very naturally. So that is the biggest

spk_0 proponent of voice to voice. And ultimately, I think the prevailing view is that first,

spk_0 whatever the final experience is, if you really want to make it indistinguishable from a human,

spk_0 you have to do a voice to voice or you have to at least take into account the voice. The issue

spk_0 with the voice to voice though is that also fundamentally because voice has a lot more dimensions to it.

spk_0 The amount of tokens you generate per sentence is just a lot higher than when you generate text.

spk_0 The more tokens you have, the easier it is for something to go wrong. And so the host nature is

spk_0 so far, I've just spent a lot higher. How much higher give us a sense of how far we are from these

spk_0 being really good? Think probably like 8x higher or something like that. Well, it is quite a bit

spk_0 higher. Of course, you want to leverage that technology. So now maybe there's creative ways to

spk_0 make it hybrid of two. Maybe you can have a text model generate the content, but you take into

spk_0 account the audio from before as well. And that makes something very realistic. But at the same

spk_0 time, latency is still the hard problem because at the enterprise, what's happening is you are doing a

spk_0 lot before you start responding. You have to figure out where they're asking about, when materials

spk_0 do I need to collect, do I need to hit any APIs and get that data back? And so we have to do it in a

spk_0 way that feels very natural. And sometimes if you think about how a human does it, you might have to

spk_0 say something like, give me a sec to look that up because it actually genuinely takes 10 seconds

spk_0 for the API to come back. These are all interesting problems to think through. So give us a sense

spk_0 today. If you add up all the interactions, some idea of how long they are, what type they are,

spk_0 voice versus text versus some other modality, what is the entire corpus of interactions between a

spk_0 Decagon agent and a customer look like today? I would say pretty balanced at this point between chat

spk_0 and voice on a raw customer basis. There's more people in chat at least for us. But if you're

spk_0 thinking about the large enterprises, like the Fortune 100, they just been around for so long,

spk_0 and everyone's just calls them. So voice is just disproportionately higher there. A lot of them are

spk_0 90, 95% voice in 5% chat. So that's what we're seeing. And then in terms of the types of conversations,

spk_0 it's generally ones that are fairly, you start with the sort of easier one, of course. And so

spk_0 these are things where the question answer based. So that's the tier one is like, hey, you can answer

spk_0 their question based on what you statically know. So that could be questions about how your loyalty

spk_0 system works or questions about if I bought something, but I still be able to refund it. Then the next

spk_0 level is it's still question answer based, but you're leveraging a lot of real time data. So that

spk_0 could be, you know, I got two x points on this transaction, but I've gotten five. Why is that? And then

spk_0 it'll actually go in looking to your account and reason through things like, okay, I see the

spk_0 accounts listed at this type. Let me go find all the documentation on this type and like, okay,

spk_0 actually it's because you book through a travel agency. If you have booked directly with the

spk_0 airline, you would've gotten five x, but you think it doesn't apply to a travel agency or whatever.

spk_0 And then the third tier is you're actually taking action. So I lost my credit card. I need a new one.

spk_0 And it's actually walking through a pretty large flow. And that's where AIS and so I've been

spk_0 really excellent because you actually wouldn't expect it to be able to do that. And so it's able to

spk_0 go in. It can be a pretty complicated system where it's like, okay, well, first I need to figure

spk_0 out what your address is and confirm if the address is correct. And then I need to look and see,

spk_0 hey, do you want me to lock the old card? Like, okay, great. I'll do that. I might need to check for

spk_0 fraud to make sure this person is not just constantly asking for new cards. And it's just like all

spk_0 these things stitched together. That's really what makes it agentic. And that's why there's been such

spk_0 a step function improvement with LMS. In the spirit of that question, what could go right?

spk_0 What could go right for the company based on the data they're gathering from these interactions

spk_0 that they probably have been doing nothing with historically? Like what new things can they do for

spk_0 their customer because of on a one-to-one basis, like they're just learning more about a person.

spk_0 And on the aggregate basis, they like understand the behavior patterns of their customer base or

spk_0 something. Oh, yeah. That is a huge topic. I think that's a huge part of our product. We have a

spk_0 viewpoint that this data, of course, is super valuable because it's literally what your customers

spk_0 are saying. But it's very underutilized because historically, it's a very unstructured data. So what

spk_0 people typically would do is like, okay, well, every month we have a million conversations. Well,

spk_0 full-time team of 20 people and they're just like sampling these conversations and trying to

spk_0 like check on a rubric and try to compile topics and things like that. And that won't get you so far.

spk_0 But now what you can do is you can literally have a language model that reads every conversation

spk_0 and extract whatever info you want from it. And so that allows you to do things like, okay, well,

spk_0 over time, there are these topics that people probably didn't even know about because these

spk_0 organizations are big. So the people in leadership positions, they can only have such granular insight

spk_0 into what's happening. But it'll just flag like, hey, there's just 2% of conversations where

spk_0 things are not really going that well. And it's because we don't have contacts on this topic.

spk_0 And so let's flag that. Let's draft a suggestion for what could go better here based on how the

spk_0 human agents are handling or based on the other procedures that we have. And here's a suggestion

spk_0 for how you should adjust the agent. And that allows the agent to improve automatically over time.

spk_0 And that's really critical. So when you think about modes in the agentic world, a lot of it is

spk_0 around if you've been working with a client for a year, has your agent just continuously gotten

spk_0 better by learning from the data? And that's a different cost of then just training on the data.

spk_0 But has it continuously gotten better to the point where it's just very difficult for another

spk_0 agent to come in and perform at the same level. There's this funny situation today where I think

spk_0 especially CEOs really want AI in their businesses. They want it now, but they don't know what they

spk_0 want. They don't have a good framework for thinking about, okay, I understand my business.

spk_0 I don't know where to go. It seems like I would be missing a major boat if I don't deploy this in

spk_0 my business. I don't know what to do. I don't know where to go first. I don't know who to call.

spk_0 Have you developed any framework for those company leaders that desperately want to use this

spk_0 technology in their business, but they simply don't know apart from automating customer service

spk_0 or something. I don't mean specific use cases. I mean like a framework for thinking about what kinds

spk_0 of problems might be addressable by agents or by all ons. Oh, interesting. I mean, one framework

spk_0 is similar to the sort of framework you talked about before with two ends of the spectrum.

spk_0 And I would say most leaders we talked to are focused on the more bottoms up end of the spectrum,

spk_0 which is like where are the areas that we should just not have humans doing because it's so mundane

spk_0 and repeatable. And there's tons of cost efficiencies there. So I would say that's where folks are

spk_0 typically thinking of. So when we talk to leaders, I think there's a couple observations. One,

spk_0 pretty much all AI initiatives are very top down at this point because it is such a

spk_0 board level mandate. So the C-suite is very, very invested in like, okay, where do we deploy AI?

spk_0 That almost means that if you want to get something going at a larger organization,

spk_0 you have to have buy-in from the top level because it's going to get up there anyways.

spk_0 And they have the mates that sit at the end of the day. So that's one.

spk_0 Two, the way they think about the use cases to your point is that ROI. It's like, where can we

spk_0 either save much money or make a lot of new revenue? And if you cannot in based

spk_0 half a sentence explain that, then it's just not going to work right now. They are under a lot of

spk_0 pressure. They need to show like quick wins. If this is not going to be a quick win, they can point

spk_0 to like, I say $10 million, then this is not going to be something that's prioritized.

spk_0 Do you think coding answers that well? Do you think the ROI is clear and coding?

spk_0 It is. And I know the coding ages quite well. And the way to do it generally is,

spk_0 one, it's very easy to test to play out to the engineers. And then you just do a poll and

spk_0 engineers will like, Hey, how much more productive do you think you are? And engineers are often

spk_0 the most valuable resource in these organizations. And so those answers are treated with high

spk_0 importance. Yeah. And she tells you that you're like 50% more productive. It's like, okay,

spk_0 great. Are you confident that that's true? That they can self-report and be accurate? There was

spk_0 that meteor study or whatever that came out that actually, I don't know if the study's good or

spk_0 not, but productivity was down or flat or something like this. The self-reported productivity was

spk_0 at odds with actual measure productivity or something like this? Oh, yeah. Don't know

spk_0 nearly enough about that. But I'm just saying that it doesn't really matter. If your entire

spk_0 engineering team is like, Hey, we love this. This is making us 50% more productive. Yeah.

spk_0 It's like super easy. You're building your thing. Exactly. And then you think about from the CEO

spk_0 and they're like, reporting this to the board or something. It's like, Hey, my entire engineering

spk_0 work said that they're 50% more productive. This is like a worthy investment because

spk_0 these people are all paid this much. Now we can exalt the product. We can accelerate everything.

spk_0 Yeah. Do you think that the future is going to talk about brands and how a giving company

spk_0 might want its agent to feel and sound that an agent might be a way to express brand culture style,

spk_0 tone, whatever. Don't talk about that. But do you think the end state here is that each company

spk_0 sort of has almost like a named, personified representative that you just come to expect to interact

spk_0 with? And it's not just customer service issues, but it's sales issues. You ask it for advice

spk_0 on what should buy or whatever it might be and that that's all integrated or that you'll have

spk_0 different agents for different parts of the company. Like, I guess what I'm trying to ask is,

spk_0 what the future of a company's agent or agents looks like in the natural end state five years from

spk_0 now or something? Yeah, I would say that in the natural end state, it is more unified for the

spk_0 exact reason, new listed, which is people want a unified brand out there. Brands very important to

spk_0 them. And for some business, it's more important than others, but eventually this becomes the front

spk_0 and for the business. And so that means it's both how you gain new customers, but also how you

spk_0 support the existing ones, make them retain more and so on. It's almost like in the limit,

spk_0 if you're working with a bank or airline or telecom company or whatever, the agent could be the

spk_0 only thing that most users interact with. They don't even have to touch your mobile app. They

spk_0 don't even have to go down your website ever. They just have this agent where they're authenticated

spk_0 and knows everything about them and has all the context of your previous conversations. It has

spk_0 memory and it can just solve your issue. You can take actions for you. You need a book of flight,

spk_0 you have an upgrade of C, you have questions about this or that. So I think that's the exciting

spk_0 vision that we're building towards. And in some ways, we often call this like a concierge. It's just

spk_0 the digital concierge that can do everything for you. I mean, you also have to be pragmatic on both

spk_0 sides. You know, start with a clear use case. But I think that is where folks are building towards.

spk_0 And so in the near term, I do think that's different teams because the reality is that at these

spk_0 large companies, different teams have different budgets and they make different decisions. And so

spk_0 you either need to have a unified system that ties them together like a unified framework or they

spk_0 could just be literally the same agent. Is the right analogy here? A company's website. They'll

spk_0 think about their agent like they think about their website. A lot of work will go into it. It'll

spk_0 look and feel a certain way like it'll be kind of a unified interface with the world. Like, is that

spk_0 a is that a clean analogy? I think that's a good analogy. Just like a front end. It's like a UI.

spk_0 But instead of visual UI, it's a conversational UI. How do you feel brands pulling personality

spk_0 requests in their agent out of you? What do they care about? They want it to be nice. They want to be

spk_0 concise. They want it to be funny. How is that dimension evolved since you started?

spk_0 People already almost always already have brand guidelines because they need to show brand

spk_0 guidelines to their human agents to train them. The next part is that they already have all this

spk_0 training process for the humans. And you should ideally be able to apply it in the same ways that

spk_0 they are. And so they'll have like, hey, you need to do this. You need to always be confident.

spk_0 Sometimes folks don't want the agent to apologize. Sometimes people really want to be apologetics.

spk_0 So people have a lot of different preferences. And that needs to be taught to the AI in a

spk_0 efficient way. That's also kind of a lower throughput way of communicating. I mean, the other way

spk_0 that you can show the agent is just to give you a lot of examples. So here's the examples of

spk_0 what great look like from our top agents. And just learn from that. What is the very biggest

spk_0 you're almost afraid to admit it? Because it feels so big version of what Decagon could be.

spk_0 The reason why we're excited and we feel like the market is so massive is that, yeah,

spk_0 at the end of the day, what we are building towards is this concept of becomes like a new UI for

spk_0 the product. Just think about how much these large companies invest in their mobile app or their

spk_0 website. And it's literally how everyone communicates with them. So this could be a way where eventually

spk_0 the way any user interacts with any brand is through an AI agent. And it's for all sorts of

spk_0 different use cases. It benefits our customers for the AI to have all this context because it can

spk_0 seamlessly flow between things. A lot of them want to do sales type use cases at the end of a

spk_0 support flow or vice versa. That's exciting. What internal context or things do the best customers

spk_0 have that make this better? So you mentioned brand guidelines like maybe there's a write-up on what

spk_0 the brand guidelines are or whatever. What are like the internal assets that companies have or don't

spk_0 have that if they have them, it's made the Decagon experience way way better. Number one thing is

spk_0 just APIs for the AI to use. So APIs to take action, APIs to look up data, APIs to reformat things

spk_0 that is often like, hey, if you have those, you already know that within the first month,

spk_0 it's already going to be a great experience. If you don't yet, then we should try to build towards

spk_0 that as soon as possible so that the AI can actually achieve that elevated experience. That's

spk_0 the main thing. Most people already have documentation. It might not be up to date, but we can help with

spk_0 that. Most people already have SOPs, I guess, and then we are able to use that and generate

spk_0 our format. We call them AOPs, HN Operating Procedures, they're just SOPs but for AI. Then

spk_0 we're brand guidelines, like I said, people usually have those as well, so you just ingest them.

spk_0 We were talking last time about in any business, but certainly in yours, the three key stakeholders

spk_0 being your team, talented, you have to recruit. I want to talk about that in detail. Capital

spk_0 investors and customers. Maybe suppliers too is a fourth category, but especially interested

spk_0 in the first three. Last time we were together, I asked you which one you have trouble with,

spk_0 and we laughed because you said, well, definitely not investors. Talk a little bit about the demand

spk_0 from investors to invest in companies like yours and how that feels. What are they doing to try to

spk_0 give you more money, get on the cap table, how competitive does it feel? What does that feel

spk_0 like right now? Because it does seem like there's a handful of companies like yours that are in one

spk_0 of these white-hot areas that have key traction that have great teams. Basically, every investor wants

spk_0 to be involved in those companies. What does that feel like? It definitely feels like there's

spk_0 maybe a little bit too much excitement right now on the AI side. It just seems way too easy to

spk_0 raise money. So many companies out there. I mean, for us, we've been very fortunate, right? We

spk_0 don't take it for granted. I mean, I think in my first company, we're also fortunate. It was

spk_0 easy to raise, but it was for different reasons, like 2021. Nowadays, there aren't that many AI

spk_0 companies that have real little traction on the revenue side, especially in the enterprise.

spk_0 So I think that's attractive to investors because they wanted to play their capital into AI,

spk_0 and that's the biggest trend. So I think we've had a great relationship with all our investors.

spk_0 I think we just really selected for folks that we get along with at a personal level. We feel

spk_0 like we'll be very helpful for our go-to-market normally. That's kind of an interesting process.

spk_0 So we have pretty much after every single time we've raised around, we just almost immediately

spk_0 gotten preempted. That alone can't be right. Here's just thinking about first principles to

spk_0 make an investment. The previous valuation should not be a super big factor in that. It should

spk_0 be how well it's business doing. What do I believe the potential is? So it feels like there's a

spk_0 little bit of mania, but yeah, we're definitely, I would say, more indexed on the other two things,

spk_0 like talents and customers. What's the craziest thing that an investor has done to try to invest in

spk_0 the business? I don't think there's anything like super crazy. The main thing that we do,

spk_0 and I would actually encourage more founders to do this, is that during the stage where people

spk_0 want to invest, but they haven't yet, that's when they're most willing to be helpful. It's actually

spk_0 like a great way for you to use that to proxy how helpful there'll be afterwards. Because if they're

spk_0 not that helpful in that stage where they really, really want to invest and they're willing to do

spk_0 anything, they're for sure not going to be helpful afterwards. They'll still be friendly and

spk_0 hopefully they'll not be detrimental, but that's your opportunity to really test folks and see

spk_0 how helpful someone will be. We obviously know a lot of investors very well, and I think they

spk_0 know what has issue with that. They know that they're going back to competition. They're also

spk_0 in a competitive sport. They know that they need to earn basically the ability to invest in the best

spk_0 companies. I think from their perspective, they're happy to work for it. If you just give them

spk_0 an opportunity too, they won't. I'm going to ask about this from both founder and investor perspective.

spk_0 What advice would you give investors during that window? When there's a fundraising that's happening,

spk_0 this is an open process or a window or whatever, what have you seen the best of them do well?

spk_0 Not just I'm sure helping you. Here's 10 customers you can talk to. I'm sure that's great.

spk_0 But also in the underwriting side, then making sure they understand your business extremely well,

spk_0 I'm expecting a short period of time. What have the very best done in that window?

spk_0 Number one, I think the best investors get this dynamic. We've definitely talked a lot of

spk_0 high profile investors where they're just not willing to help much until they're invested.

spk_0 Totally they're right. It's like, hey, we have a lot of our current investments. We don't want to

spk_0 use our social capital or whatever to help within it like a new investor or a new investment.

spk_0 But I think from the founder's perspective, if that's the case, then it's hard to tell if you

spk_0 actually be useful or not. There's no difference between you saying that and then someone who

spk_0 can't really help and just saying that. I think the best ones just are able to give a lot of

spk_0 signal to the founder that like, hey, I'm really willing to help. I have the ability to help.

spk_0 We have a very strong network or we're very good at certain elements of go to market and we're

spk_0 able to show that in the, let's say, isn't that going to be that long a period of time,

spk_0 like a couple of months before the round actually happens. That's one thing. I think the other

spk_0 thing is that if you just think about anyone you bring into the work, this could be employees

spk_0 or investors or advisors or anything. What we really index a lot on is just

spk_0 cognitive ability, broad, intellectual throughput. You can almost feel that out in an investor

spk_0 in the same way you would feel it out in an employee just by spending time with them and just

spk_0 actually seeing if they're thinking about things for the first principles of your company. For example,

spk_0 a lot of AI companies are growing much faster than traditional SaaS companies right now because that's

spk_0 the case. A lot of things are different. You generally don't want investors that are just so many

spk_0 reps. You got to do things like X, Y, Z, Y. You want people that are just intellectual curious and

spk_0 we'll think about things along with you and can't help problem solve. What about just like the pure

spk_0 underwriting side so that investor comes in, they're really smart. Let's take that for granted.

spk_0 They just want to understand your business. The good, the bad, the ugly as fast as possible.

spk_0 What have you seen the best do in that side of the investment process, including things like how

spk_0 fast they move or how deliberate they move or anything like that? I think the best ones, I would say

spk_0 they only won. They get a very deep understanding of our customers. It's actually funny. Our customers

spk_0 have made probably so much money on expert calls because so many investors are thinking about and

spk_0 I think the best ones can, before they even talk to you, they've probably already done quite a

spk_0 bit of research and have a pretty full view on your customers. Unfortunately, I think what we've

spk_0 seen is that there is a lot of noise there because a lot of people just lie on customer calls and

spk_0 we have people who have said that they've used us and they've literally never heard of them.

spk_0 Generally, if you do enough research and you're good at it, then you can underwrite the business

spk_0 that way because that gives you the most signal. The other one is people that index on culture because

spk_0 I think culture is quite important and a lot of good investors know how important that is and so

spk_0 if they feel like you are becoming a place where good talent is congregating, then folks will

spk_0 index on that more. Let's talk about that. Let's talk about recruiting and culture, starting with

spk_0 culture. How would you describe it? We talked about the quote on your wall as the first question.

spk_0 So that's part of it, of course. Extremely competitive, extreme bias to action,

spk_0 good things done. What are the other key components of your culture like when you're sitting

spk_0 with a new recruit? What do you tell them about what kind of place it is? I would say there's

spk_0 definitely a level of intensity that's important and yeah, you definitely want to tell people

spk_0 that up front because you want them to self-select into this culture. So everyone that has joined

spk_0 Decigon, I would say they want to work hard. They want to be around other people that are really

spk_0 smart and are like them and they're motivated by, they view this as maybe the prime of their career

spk_0 where hey, I'm going to work hard but we know that because of that, I'm going to build lifelong

spk_0 relationships with other amazing people and going to have good financial outcomes. I'm going to

spk_0 be able to leap frog steps in my career because the growth is just happening so quickly. So it's

spk_0 you're attracting people like that. I think that alone creates a pretty strong foundation for the

spk_0 culture because you have people that are there to work. We have a lot of people that live right

spk_0 next to the office and part of that is because we've selected for people that like being in the office

spk_0 with other people right, we're in the office a lot. Those are the foundations and then I think

spk_0 what you have to be careful on top of that is we really want our office to a place where people

spk_0 enjoy coming to work every day because we spend a lot of time in there. It's like you want people

spk_0 to be happy to be there. They feel like everyone there is supporting them. Between the organizations,

spk_0 you want people to feel like they're pretty aligned and folks are working towards the same goal.

spk_0 And that's an ongoing problem. I don't think we've solved the culture. It's something we just

spk_0 put a lot of thought into and we want to make sure that people feel like they will be very fulfilled

spk_0 by staying here for a long time. One of the most interesting subplots of this entire business

spk_0 evolution in and around AI is talent wars. Obviously it's happening in the most extreme cases at

spk_0 the model layer. Between meta and entropic and open AI and there's great riveting stories to

spk_0 hear about. The lengths people will go to to secure a grade and the amount they'll pay to secure a

spk_0 grade engineer or someone really key to the business. Can you give us your perspective on these

spk_0 talent wars? What it's like to be in them? Obviously I'm sure you are too fighting for the best talent

spk_0 versus lots of other great companies that are being formed. Tell us from the inside what this

spk_0 environment feels like. Definitely feels like talent is a big team effort. For anyone you want to

spk_0 hire, you need the whole team to swarm around them to win. To win highly sought after talent.

spk_0 And you have to go and do all the things right. There are some parallels to sales, of course.

spk_0 You're trying to convince people that hey, this is the place they want to be. So that involves

spk_0 oftentimes getting to other families. Getting to know their partners. Really figuring out what they

spk_0 want out of their own careers and making sure that you can design a role for them that is like that.

spk_0 Unfortunately in the application layer, it is not as crazy as the meta and the opening

spk_0 eyes. There's only so many top level researchers. We have some very strong researchers on our team.

spk_0 It's a lot more of like a applied type research. For us, it's the same thing that happens. We're going

spk_0 after a lot of people on our team, Harvard and MIT Stanford. There's only so many of these folks that are

spk_0 in the market any time who are like in SF or going to the office. And so it is a competitive

spk_0 place to be. I think we're kind of fortunate. And now our talent brand has gotten a lot larger than

spk_0 when we were first starting. And so it definitely has gotten easier to hire. But at the same time,

spk_0 our like the amount of people we need to hire has also gone up constantly just finding ideas

spk_0 of how to get new people. I mean, we just hope up our New York office as you know. And part of

spk_0 the reason for that is that hey, there's another talent pool over here. We should leverage that.

spk_0 One of the questions that I think so many people are interested in for companies like yours is

spk_0 the use of whatever core underlying LLM versus the development of your own models on top of

spk_0 or replacing those underlying LLMs. You are gathering all this incredible data that's just yours.

spk_0 You don't have to share with anybody else. These models thrive on good underlying data.

spk_0 How do you think about that aspect of all of this where five years from now, it's going to be either

spk_0 your own model or your own model plus something else or just your own data and context on top of

spk_0 the best model. How do you think that will evolve? I'm both curious about this in terms of how it

spk_0 impacts the product, but also how it impacts your business mode, your power in your business where

spk_0 you rely or don't rely on TPPT, whatever. When we first started, this was about two years ago,

spk_0 people are still figuring out applications. Almost no one was doing fine tuning. In fact,

spk_0 anyone that was doing fine tuning, there's a lot of writing at that time was like fine tuning.

spk_0 Conoco doesn't work because it doesn't really get you that many gains. Another big reason to not

spk_0 do fine tuning at that time is that the models are changing so fast. You're still figuring out

spk_0 your use case. Why that's the much time in fine tuning. It's not reversible. You're going to have

spk_0 to throw it out next time there's a new model release. I think now the open source models, for

spk_0 example, have gotten to the point where there's definitely not enough to do everything, but there's

spk_0 a lot of specific use cases where you just don't need that much intelligence. Example would be even

spk_0 the agent. Let's say the first thing the agent does is it just needs to think about like, okay,

spk_0 based on what the user said and everything before what path do I go down, what data do I need?

spk_0 You can make that fine tune model. That model doesn't have to be good at math or coding or

spk_0 anything. You just need a smaller model that's just fine tune on that. Nowadays, we're seeing much more

spk_0 of that because a lot of the applications have gotten more matured. So you know how your agent is

spk_0 structured. You know the places where you need models to run and you can take smaller fine

spk_0 tune models and that improves the entire system, both in terms of performance, but also latency

spk_0 and so on. So I think over time there's going to be more and more of that. I do think there's always,

spk_0 there's always going to be like a huge usage of the open eyes and for opposite the world because

spk_0 you just need intelligence. You need the best models. So I think there's going to be a balance,

spk_0 but at least in the short term, there's going to be more and more of the fine tuning small models

spk_0 that happen. What is your perception of the very biggest companies? The entire market has been

spk_0 focused on rightly so. The 7, 8, 9, 10 biggest technology companies, which of them feel most

spk_0 important to you and I don't mean open AI and a throttback. I mean like Microsoft and Amazon and Apple

spk_0 and these sorts of companies. What is your relation and thinking about then today? They've been the

spk_0 driver of equity markets by huge margin. They're important companies. How do you relate to them?

spk_0 I mean work wise, it's not super relevant, but of course we have our own opinions. I'm very bullish

spk_0 on Google actually. Why? I just think that with AI use cases, having individual consumers is so

spk_0 important because that's where all the data comes from anyways. Google is much stronger there.

spk_0 I mean, you could say that Meta Facebook also has that element. So maybe their new super intelligence

spk_0 lab will be able to make it work. But something like Anthropic for example, where they haven't

spk_0 done as well on the consumer side compared to a Chatchee BT. I think long term you do need that

spk_0 consumer buy-in because that's where all the new data is going to come from. Yeah, Google and

spk_0 I mean Google just has an amazing team and we've had a couple of rocky starts, but hopefully

spk_0 they'll make it work out. Of course, all the large mag 7 basically are obviously super strong. So

spk_0 we don't really have strong views on them. If I let you build a portfolio tomorrow where you got

spk_0 five slots, 20% each in five private companies building in and around AI, what portfolio would you

spk_0 build? Dacking on excluded. Yeah, Dacking on excluded. Let's see, you just kind of gravitate

spk_0 towards where most of the talent is forming. I mean, I mentioned close with cognition guys,

spk_0 cognition would be in there for sure. Cursor also. I'm actually kind of interested in where

spk_0 those might run into each other in the future. Let's see. Three more slots. Or me definitely would

spk_0 want to take a bet on the hardware layer even though it's like a much higher variance. So

spk_0 the last time we were talking about etched companies like that, I think you probably put one of those

spk_0 still on the earlier side, but obviously very high potential. Two more. I like the portfolio so far.

spk_0 Another friend of mine is just building a company called Pika building video models. So I just

spk_0 think very highly of that team as well. So we probably put them in there and the last one probably

spk_0 would want some sort of bets on the underlying model side, even though all the large language

spk_0 models are out there. But another friend of mine, I think very highly of their building models,

spk_0 but not like the types of language models, but still foundation models for like healthcare,

spk_0 for example. So friend of mine, Josh is building Chi. They're building a foundation model. So

spk_0 stuff like that, I think it's quite interesting. Or even I would put Lockhees Company,

spk_0 physical, and I think those are very exciting. It's just obviously as early to see how those would

spk_0 turn out. But if I'm building a portfolio, definitely one of those in there. What do you think

spk_0 are? I'm interested in both ends of the spectrum. The people that aren't in your position who are

spk_0 both technical and commercially at the center of this wave. What do they overestimate and underestimate

spk_0 about the capabilities of AI today? Where's it further along than the world things? Where is it

spk_0 for the behind? It's a little bit more behind in being able to unlock a lot of enterprise use

spk_0 cases, I would say, because of the non-determinism. So two things that need to happen. One, you need to

spk_0 reframe the way that people think about agents. There's the way MoEffect often happens where

spk_0 it will be objectively just way better than human drivers and human drivers make a lot of mistakes.

spk_0 But because we're investing in new technology, the bars law higher. So it has to be near perfect.

spk_0 So there's that dynamic that kind of needs to adjust in some folks' mind where instead of

spk_0 evaluating AI in a way where you're just trying to find mistakes, you're evaluating holistically

spk_0 looking at the sort of success rate. And then if you can frame it that way, well, the success rate

spk_0 is going to be way higher than humans because again, humans are not perfect. So I think that needs

spk_0 to happen. And I don't think that's fully happened yet in the enterprise. So that trend needs to

spk_0 happen. And then on the AI use case side, I think the model still need to get better in a lot of areas.

spk_0 Right. We just talking about voice to voice earlier.

spk_0 Pollucinations are too high there as those models get better. I think more enterprise use cases will

spk_0 be unlocked. But I do think the general public will just see a really cool demo and be like, okay,

spk_0 oh, wow, like voices solve now. And as a result, you know, the enterprise should be adopting it

spk_0 left and right. As these weeks, we'll see that too. But then when they actually get into it,

spk_0 it's just harder to go live. That's, I would say the piece where it's not quite fully there yet.

spk_0 And so it's a little bit slower than people think. I think the thing that's on the flip side of

spk_0 that where it's underestimated is just things are growing exponentially, improving exponentially.

spk_0 And no one is good at conceptualizing what exponential means and things like this. And so that

spk_0 could be from like performance like cost perspective. Right now, I would argue that if you're building

spk_0 an application, your margin shouldn't really matter that much. People will often critique the

spk_0 coding agents like they're hemorrhaging money. Yeah, but again, things are improving exponentially

spk_0 and like the costs will go down exponentially as well. So the margin doesn't really matter.

spk_0 What really matters is you need to get market share and you need to get my share of users.

spk_0 Perfectly fine not to have good margins right now. And it's just everything just improves

spk_0 away faster than people think. It's slightly different at the enterprise. I mean, the same principle

spk_0 applies, but generally don't want to be hemorrhaging cash with like an enterprise deal because

spk_0 those are just much longer term. And even though the cost will go down, their expectations might

spk_0 also change and so on. So you generally want to be fairly healthy there. I think that's what's

spk_0 underestimated right now. How do you know that cost will get way lower. Like I'm very curious

spk_0 about this margin question because if you knew for sure, then we actually want to run super negative

spk_0 gross margins. If you knew that it was going to get 98% cheaper or something like this,

spk_0 cost of goods to serve coding agent or something like that, I think they're going to be get the

spk_0 install base just like have the best product and get the users and build the affinity with that

spk_0 user base and don't care at all. But that hinges a lot on the confidence that you have that

spk_0 they'll cost will fall. So how do you know how do you think about that equation of win install

spk_0 base versus demonstrate good unit economics now? Well, I just think it's quite unlikely that

spk_0 where we're currently at is like the best that things will be. There's so much effort getting

spk_0 put into it and like one of the main metrics is efficiency. The other piece is that even if things

spk_0 don't get better, there's a lot of ways you can re architect your system so that it is more cost

spk_0 efficient. It's just that it's not worth putting time into that right now where you can put that

spk_0 same amount of time into getting new customers because you know that things will change in the future.

spk_0 So I think that's just one thing where people are like, oh, it's kind of like a scheme where you

spk_0 take the VC dollars and then like the VC dollars go to the chips. These companies are losing money.

spk_0 If they wanted to, probably they could just like spend a month or even less and just

spk_0 massively improve their margins. But it's just not worth doing that work right now where you're

spk_0 really optimizing for right now is just quality and growth. So if you can do that, then the optimization

spk_0 will always come later on. How do you think about your margins? Like just putting it on decking on,

spk_0 like do care at all. Do you set them? What's your guardrails or parameters for what's acceptable or

spk_0 what you're targeting? Yeah, I would say the only thing that we have a principle for is not to have

spk_0 negative margins. In general, we have fairly healthy margins because one way you can think about it is

spk_0 if you just think about supply chain for any good, let's say you're buying like a croissant at the

spk_0 airport or something. That last step where you're actually solving someone's problem, that's where

spk_0 you generally can capture the most margin. Every step along the way, like whoever's enriching the flower

spk_0 or like making the butter or whatever, you're generally making a margin on top of the costs

spk_0 of whatever your goods are. And that's why it's nice to be in the application layers. What your

spk_0 building is actually solving the business. And as a result, you can capture more of that because

spk_0 our customer is the way they're thinking about it is great. We're investing in decking on. We don't

spk_0 care that much about what decking on costs are. In fact, we probably don't care about at all. What we

spk_0 care about is what is the business our way that we're getting. It's like we're downsizing our operations

spk_0 by this much. We're actually generating more revenue now because the AI can engage people and

spk_0 keep them retained. So that is where we probably see the most dynamic here. And I do think this is

spk_0 generally not really a hot take in the early days where there's like chatchy pt wrappers and so on.

spk_0 And yeah, a lot of wrappers, if it's too thin, it's like not going to be valuable. But if you have

spk_0 enough software built around the models, then that's where you can actually almost capture the most

spk_0 value. That's why I think the opening AI is the world will continue to move towards applications

spk_0 because it's quite hard for them to make money long term on like their API, for example,

spk_0 because there's like such high competition. All the labs are building. It's very easy for people to

spk_0 swap. It's not like moving from AWS to CCP is like very hard. But moving from a open-air model to

spk_0 an anthropic model, you just change like one line of code. Can you say a little bit more about this

spk_0 chatchy pt wrapper concept? My sense is certainly for what you built, but probably for other companies

spk_0 that a lot of the work that your engineers are doing is not AI work. It's traditional software work.

spk_0 It's the ability to hook this system up to enterprise customers. It's good old-fashioned

spk_0 product and infrastructure building that is very different from what open AI or

spk_0 anthropic are providing you. And that's hard work, like just like building any software system

spk_0 is hard work. Everyone's very enamored of this idea that like in five years, I can just show

spk_0 you a piece of software and just tell a coding agent just like replicate this piece of software. And

spk_0 that's going to mean lower software modes. I don't think you think of it that way. So maybe

spk_0 describe your thinking or critique of this chatchy pt wrapper concern that people have.

spk_0 Yeah, I think generally people say wrapper in a derogatory way. It's like, hey, he's just a

spk_0 wrapper. It's not a black and white. Yes, there are a lot of apps that are just wrappers that

spk_0 are not going to become real businesses because there's just not that much value. I mean, one argument

spk_0 don't know too much about this space, but at least from the outside, it has seemed like

spk_0 copywriting, for example, has been difficult because someone can just log in the chatchy pt and

spk_0 just like, hey, write this for me. And I'll just write it. There are things where if there's not

spk_0 enough tooling or functionality on top of something for it to be really valuable and needed,

spk_0 then maybe it is easier to just leverage the models. But most of the time, that's not the case.

spk_0 And especially when you get into agents, an agent is not just a model, right? You have to like design

spk_0 it. You have to be able to put in guard rails. You have to be able to teach how to do new things.

spk_0 And that's where the software layer on top of the model is coming. And if that's valuable,

spk_0 then it's just much harder for you to just like be made obsolete by a model update.

spk_0 The other side of it is like, okay, well, now the labs are quite interested in building applications.

spk_0 So they're building a bunch of coding applications and cloud code and so on. And so those will end

spk_0 up being competitors with cognition or cursor. And maybe for that reason, it is wise for the

spk_0 coding agents to start training stuff as well. I think in our space, I would say for us right now,

spk_0 at least the sheer amount of functionality of the build because it is a very top-down product,

spk_0 is quite large. That has nothing to do with AI. It's just stuff like, okay, how do you have

spk_0 like observability into like what the conversations are? How do you alert the team if something spikes?

spk_0 And how are you able to QA and have unit tests for the conversation so that before you push it out

spk_0 to end users, you feel like there's just all this like functionality that's there that doesn't

spk_0 really have anything to do with AI really. And so it's just like a lot of stuff that needs to be

spk_0 how do you advise other founders thinking about the ideal customers to go after? What are the

spk_0 most interesting qualities of your best customers when you're qualifying them? Are they going to be

spk_0 you have limited time that you can only serve so many people? I know you're growing really fast,

spk_0 but you can only serve so many customers at any given time. How do you qualify who you want to work

spk_0 with and don't like what are the attributes that you've seen matter the most? Yeah, we want people

spk_0 that are intellectually just at the leadership level really curious and excited about technology.

spk_0 You actually see a huge spectrum of that in the enterprise and some of my opinion the best

spk_0 leaders and like probably the folks that we are most excited to work with, they're just genuinely

spk_0 like, hey, we want to move on AI as fast as possible. We're very interested and just curious about

spk_0 how all your systems work. And as a result, I'm going to help cut through all the crufts and

spk_0 bureaucracy to get something going. And I think you can tell that pretty clearly in the first

spk_0 conversation. You can tell if someone if they're like legit about this is something where I'm going

spk_0 to both push aggressively, but also give you a ton of feedback and like the feedback is going to be

spk_0 good versus someone where they just know it's like a board mandate and it's just like an AI is

spk_0 like a thing other to do list. Is there any leader that you've come across that most exemplifies

spk_0 that posture? Yeah, so I'll say in both the sort of digital native segment, you have folks like

spk_0 chime, for example, have super impressed with the entire team top down. Everyone that is in the org,

spk_0 that's like a case that you just study at some point. I think they've done a really good job on

spk_0 culture, really making it so that people are very sophisticated about everything. Everything

spk_0 that AI agent does, it's like super data-centric, they track everything, they're really thoughtful about

spk_0 how to make updates and so on. And they've given us a ton of good product feedback.

spk_0 As you're talking to your friends that are also building companies in this space, where do you feel

spk_0 that your worldview is the most different from them? Where's your view of things or your

spk_0 relative excitement about something the most divergent from from others? Yeah, I mean,

spk_0 I would say my friends are very high intellectual horsepower. I think I generally lean a lot more,

spk_0 I don't know why this isn't any personality. I think I generally lean a lot more towards commercial

spk_0 elements of every idea. And again, it's not the only way to build the company, but in my opinion,

spk_0 if you were just trying to optimize for the highest likelihood of success, I think you should really

spk_0 index on the commercial side because a lot of super smart intellectuals curious people that are just

spk_0 building very cool projects. And you can make the argument for a lot of huge outcomes. You need

spk_0 to do that because you just need to do stuff where the one's going to work on this because

spk_0 where are the commercial elements of this. But I think I think the awesome is this way too,

spk_0 which made a good fit. I think we're just very locked in on, okay, you just got to be super

spk_0 miraculous. How much would you pay for it? Exactly. Yeah, it's really interesting. How do you

spk_0 mark milestones in the business? How do you motivate the team? What have you learned about

spk_0 how to rally around a given thing? I know you're super aggressive when you have a customer that you

spk_0 want to get that it's not just like a old school sales process. It's like in all hands-on-back

spk_0 a standard engineer is to whatever it takes. What have you learned about motivating milestones,

spk_0 rallying the team, organizing around common goals? Yeah, for us, I mean, one thing we do always

spk_0 is we always have like a flagpole that's within sight that can just kind of rally everyone around it.

spk_0 Having things to rally around are quite helpful. I was kind of thinking about this other day. I think

spk_0 another type of rallying is around competition, right? I think when people feel like they're in a battle

spk_0 and there's like clear enemies, then it makes sense. Again, you don't want to get to the point

spk_0 where there's like actually animosity, but just like my healthy level of competition, it ties the

spk_0 team together because there's like something to focus on. Same thing with milestones. Like if you

spk_0 give someone a very want to clear milestone and this can be like pretty significant. Like last year

spk_0 we had for our revenue milestone, we told everyone we'd get them super nice jackets. We got like

spk_0 decking on our tariff jackets and everyone was super excited about that. And I just think about like

spk_0 the cost of the jacket and just how much people get paid is like trivial. But it just creates like this

spk_0 hey we're working towards these jackets. It brings the teams together because now

spk_0 it's feel like everyone's working together towards this common goal. That's been to be our culture

spk_0 is just finding what is the next milestone. Anything that we haven't covered about either the business

spk_0 or this exciting AI applications world that you feel especially passionate about. I feel like we've

spk_0 done a good job of covering. This is a great conversation. I think what has become almost the meme

spk_0 or hyped up a lot in AI startups right now are like a couple things. One obviously everyone's in

spk_0 person. It's like 996 or whatever. It's the one actually think 996 is that healthy. It happens in

spk_0 China and everyone's like super hardcore. But one of the reasons is that no one has jobs over there.

spk_0 So it's very easy for employers that leverage. I think here you generally want to maintain a good

spk_0 balance because if you're working super high intensity need time to relax a little bit. But that is

spk_0 one element. The other element is the forward point engineers. So everyone's talking about forward

spk_0 point engineers. And yeah, I just think it's kind of funny because my co-founder came from

spk_0 Palantir. So they actually have forward point engineers. What forward point engineer at Palantir

spk_0 means is you're working on a 10, 25 million dollar deal. And so you're actually just almost full

spk_0 time working with either one or a small number of customers and like building very specifically for

spk_0 that. And I think people are a little bit confiding that with what startups do which is startup

spk_0 search is very hands on and do things a little scale. But for you to actually have a FDE model,

spk_0 you need to have a massive clients. And most people do not have massive clients. So I'm actually

spk_0 I'm kind of interested to see how that plays out because I do think there's like a over indexing

spk_0 on this forward employed engineering model right now where yeah, I have a forward point engineer

spk_0 and the deal sizes are like 50k. And that's something we think about a lot as well. We are very

spk_0 hands on with our customers. But I think it's like you have to think about things holistically. You

spk_0 have to think about like, okay, well, how do we scale quickly? We don't have 50k clients. But

spk_0 for every 50k client, you have like someone that's like fully staffed them. That's like impossible

spk_0 to scale. So you need to find that line in the middle. And I think the full forward point model

spk_0 only works with the Palantir approach. Presumably on this side, you do care a lot about your margins.

spk_0 Margians, you're much more open about if it's just like LLM cost or something like this. But if

spk_0 it's fully big, people costs like that's going to be a problem. Yeah, it's not even a problem

spk_0 necessarily from the pure dollar margins. It's just prevents you from scaling. No one can hire

spk_0 good people that fast. It's just hard to hire good people. So if your business is fully constrained

spk_0 on good people, then that's also not a good thing. What do you think the minimum customer size

spk_0 in revenue is to justify a forward deployed engineer model? Probably a million. Yeah, yeah,

spk_0 fast. Well, I think you know my traditional closing question for everybody. What is the kindest

spk_0 thing that anyone's ever done for you? As I mentioned, our mutual fence Scott, give me a heads-up

spk_0 here. So I did put a lot of thought into it. So when I was little, call it ages 5 to 13, like

spk_0 elementary middle school, pretty like lazy kid. In general, most kids are, there's very few people

spk_0 that are intrinsically self-motivated. I wanted to just play games all the time or just hang out

spk_0 with friends or play sports. And yeah, my parents had a very interesting way of raising us,

spk_0 me and my sister. What they did was basically when we were really little, it was like an extreme

spk_0 level of discipline. When I was little, it played a lot of piano, essentially like three, four

spk_0 hours a day. And then four competitions. They just pulled me out of school and just go hard at it.

spk_0 And then I guess fortunately for me, my parents decided, okay, math was probably a better way to go.

spk_0 And I was just like quite talented at math, almost little. Even for that, it's just like full force.

spk_0 I used to committing everything. We did not have TV in the house. We didn't have video games. We

spk_0 didn't really take vacation growing up. You're kind of in this mindset of your sacrificing most

spk_0 things to focus on one thing. When your kid actually, like, you don't have no frame of reference,

spk_0 so it doesn't feel hard necessarily. It's because your parents are kind of setting up the

spk_0 criteria for you. And I had a very happy childhood. But I think that level of discipline and

spk_0 also competitiveness is very hard to gain that after your childhood is over. Because when you're

spk_0 in your childhood, your brain is still forming. So that kind of forms your personality. So one,

spk_0 I'm very grateful for that. And I think that's also why I want to talk about my generation of,

spk_0 there were a lot of immigrant parents from my generation that came over to grad school and

spk_0 they're all around my age. And I think this like our crop of folks just are doing very well,

spk_0 partly because of that, partly because of the green. And then while my parents did, I think,

spk_0 which is the more unique side is that a lot of times what happens, especially it's like a stereotypical

spk_0 Asian parent is that that just like continues. And you just have like overbearing parents.

spk_0 I would say even though my parents were very intense about things, they never had any like

spk_0 semblance of like overbearingness. They wouldn't like prevent us from doing things we wanted to do,

spk_0 force us to like, hey, you should like pick this or whatever and so on. What happened was

spk_0 towards the end of middle school into high school, I think we had already kind of established these

spk_0 personalities. Basically, my parents just pounded a sort of lazy, wanted to play around kid into

spk_0 someone that was just very, very driven. Then to their credit, they just completely laid off.

spk_0 They don't have any opinions on what we do for careers, what we should do. They're like very supportive.

spk_0 So I think that's very kind because you cannot replace the UK and the pay for that. You basically

spk_0 just need parents that are willing to spend a ton of time crafting this childhood for you to develop

spk_0 this. And I think a lot of things I have in life right now are from that. So that's probably the

spk_0 kind of thing in my sister as well. Like you can growing up, she was, I think there was a lot of

spk_0 pressure on me and in some way for like parents that immigrate, who are also ambitious folks,

spk_0 is very hard for them to succeed themselves in a new environment in new country. So they put a lot

spk_0 of expectations on their children. Yeah, my sister was also like, she was just always tried to sacrifice

spk_0 things for me. And when I was trying to achieve things. And so yeah, try to spend as much time

spk_0 with their parents as possible. What was the climax of your math career? Definitely peaked in high school.

spk_0 So in high school, you did math contests, math, limpy ads. So there's like a major contests in the

spk_0 the USA math limpy ad in the US. And there's like essentially a camp for like the top folks. And so

spk_0 I went there a few years. That's probably the peak. Well, this has been so much fun, so fascinated by

spk_0 the business that you've built in our building. Thanks for explaining it to us and bringing us

spk_0 sort of right to that white hot center in so many different ways. Thanks for your time. Thanks, Pavy.

spk_0 If you enjoyed this episode, visit join Colossus.com where you'll find every episode of this

spk_0 podcast complete with hand-edited transcripts. You can also subscribe to Colossus Review,

spk_0 our quarterly print digital and private audio publication featuring in-depth profiles of the

spk_0 founders, investors and companies that we admire most. Learn more at joinclossus.com slash subscribe.

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

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