Invest Like the Best with Patrick O'Shaughnessy - Jesse Zhang - Building Decagon - [Invest Like the Best, EP.443]

Episode Date: October 6, 2025

My guest today is Jesse Zhang. Jesse is the co-founder and CEO of Decagon, one of the fastest-growing AI customer service companies. Decagon provides a centralized AI engine to auto-resolve issues at any time, in every language, and across every channel. Jesse shares his systematic approach to finding product-market fit by asking potential customers exactly how much they'd pay for solutions. We explore why customer service and coding have emerged as the two clearest AI use cases for enterprises, and the key business and technical factors behind Decagon's momentum. We discuss the intense competitive dynamics of building in AI today, strategic decisions around building proprietary models, and deploying AI agents at enterprise scale. Please enjoy my conversation with Jesse Zhang. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠ ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ramp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ramp’s mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ramp.com/invest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to sign up for free and get a $250 welcome bonus. – This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ridgeline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ridgelineapps.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about the platform. – This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AlphaSense⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Alpha-Sense.com/Invest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:05:49) Building a Company in a Competitive Market (00:07:26) Personal Background and Competitive Upbringing (00:10:32) Challenges and Lessons from Previous Ventures (00:12:21) Ideation and Customer Discovery Process (00:19:31) Developing and Refining AI Customer Service Agents (00:32:26) Voice AI and Future Prospects (00:38:20) Utilizing Customer Interaction Data (00:39:59) Frameworks for Implementing AI in Business (00:41:48) Evaluating the ROI of Coding Agents (00:42:53) The Future of Company Agents (00:45:15) Brand Personality in AI Agents (00:47:48) Investor Interest in AI Companies (00:54:32) The Competitive Landscape of AI Talent (00:57:21) Building Proprietary AI Models (01:10:36) Customer Qualification and Engagement (01:17:27) The Kindest Thing

Most software companies try to maximize your time on their app to juice engagement. Ramp does the exact opposite. Ramp understands that no one wants to spend hours chasing receipts, reviewing expense reports, and checking for policy violations. So they built their tools to give that time back, using AI to automate 85% of expense reviews with 99% accuracy. And since Ramp saves companies 5%, it's no wonder that Shopify runs on Ramp, Stripe runs on Ramp, and my business does too.

To see what happens when you eliminate the busy work, check out Ramp.com slash Inverc. Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest like the Best. This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus Review, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus review along with all of our podcasts at join colossus.com. Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions. expressed by Patrick and podcast guests are solely their own opinions and do not reflect the

opinion of positive sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of positive sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. My guest today is Jesse Zhang. Jesse is the co-founder and CEO of Decagon, one of the fastest growing AI customer service companies. Decagon provides a centralized AI engine to auto-resolve issues at any time in every language and across every channel. Jesse shares his systematic approach to finding product market fit by asking potential customers exactly how much they pay for solutions. We explore why customer service and coding have emerged as two of the clearest AI use cases for enterprise, and the key business and technical factors behind Decagon's momentum.

We also discuss the intense competitive dynamics of building an AI today, strategic decisions around building proprietary models and deploying AI agents at enterprise. prize scale. Please enjoy this great conversation with Jesse Zhang. Perhaps an interesting place to begin, I bet you don't expect this, is for you to tell us a little bit about the phrase that you've talked about that's written on your wall in the office. Yeah, for sure. So in our wall, in the SF office, and we just did it in the New York office as well. We have this quote. It basically goes along the lines of there's no challenge that can't be overcome and there's no enemy that can't be defeated. And we just like it. I think it really fits our culture.

People there are, we have a very competitive team. Everyone wants to win. We have a lot of energy when we're trying to go out and build because it just feels like there's all this stuff happening in an industry. We have such a strong team. Let's just go and win. Motivated by my dad told me this. I don't know if this is actually validated or not at Huawei and in China as obviously massive company, but they're known for just having like killer culture. And they have some version of this written in Chinese, of course, and just big red letters across the back of the big hall. So yeah, really like that. It's kind of interesting. Any time that someone walks in, it like stands out, the close stands out. I asked about it to begin because I'd love to spend some time on just what this environment is like building a company like yours against formidable competitors in probably the most exciting era of technology that any of us will ever live through. And words like this defeated, I've heard the word violence recently about a company culture, aggression. Like these are not words that were being used three years ago or four years ago. In fact, if you use them, it was a big problem for you.

And obviously that has completely shifted. And not only have founders started using these words, I think talent and senior people rallied around them, like they want to be in a culture that is defeating enemies or violent or aggressive, maybe just riff for a while on what it's like to be building and competing in one of the big areas in your case, conversational AI, et cetera, at this moment in time because it's just so different than it was a couple years ago. Our whole view is that any space that's worth going after, like any large hot market, it's going to be competitive.

This is not really specific to AI, right? If you think about Databricks or Snowflake or Ramfus Brex, and anytime there's these big massive growth opportunities, people are rationally want to go after it. So I think in this generation, it's just, I think it almost subtracts like a specific demographic of founder and people that want to build

because it is exciting. But of course, everyone knows it is competitive. Because if there is market, then everyone's trying to build it. It's quite easy to start a company these days. you can raise funding really easily. And so when you go out there, you have to at a certain point,

have a pretty deep understanding of what your competitive advantages are. And one of those could be the culture. And if you build a good culture, that's pretty hard to replicate. It also lasts quite a long time. And to your point, if you have a culture where everyone is really geared towards succeeding and working hard and having some level of intensity, it can go a long way. So I think to your point, a lot of companies adopting similar minds,

I do think, for my observation, this generation of company building has attracted like a almost like my demographic of person where it's just like people that grew up in fairly competitive environments, academics or whatever. And a lot of these founders have done well. Did a lot of math contests and coding contests growing up. A lot of the people around my age are doing startups now and people are doing quite well. And I think there is some element of, if you really embrace this hardcore lifestyle or environment growing up, then Yeah, I think it lends itself pretty well to the current situation because there's a lot of parallels. I was with Scott Wu from Cognition recently, who is well known to be one of these math champions,

you as well. Talk about that environment. What was that competitive market like? What was it? Kind of let people into that world because obviously it shaped you a lot. It's kind of like an interesting experience, I would say. Like I think when you look back, now that we're all grown up, it is kind of like a pretty intense way to grow up. But I look back on my childhood with very fond memories. You get a really nice community, a lot of people that are doing the same thing. I grew up in Colorado in Boulder. Boulder's a pretty academic town, but there's not very many people that are like super

gunning for like these contests. A lot of my friends had just kind of grew up in not the places you would think, right, California, Texas, New York. And so it gives you some level of community of like, okay, there's a lot of other people out there that are doing the same things as you and meet a lot of them. And then now that we're all grown up, those relationships have lasted for a long time. But yeah, the environment is one where it's like similar to company building. You're like how well you're doing is like fairly objective.

There's a lot that has to go into it. There's a lot of preparation. A lot of having the feeling of it is like a long term thing. But because your results are fairly objective, there's constant motivation to improve. I think that's quite nice. One of my big theseses is that this audience of people or this sort of community of people, there's a lot of untapped potential there and to actually like turning them into people that want to do business or companies and things like that. A lot of them have historically gone

into trading or academia. I mean, those are perfectly awesome jobs. A lot of the folks here, this background is correlated to one where it's a little bit more like risk averse and kind of just get your good grades and like follow a track. And if you can kind of divert some of that talent into like company building, I think there's just a lot that can be done. What are the parallels? Like what is it about the people or the training or the specific aptitude in the competitions that make you good? company building and others good at company building. This seems like an obvious true trend that there's enough sample size now with people that had this background that are doing extremely well

in this environment. Maybe it's the best. If you could somehow index that group of people, you'd have fantastic performance right now. What is it? What are the parallels that make that true? One is the competitive nature that we just talked about. The other is just like the problem solving. So I think one thing that I believe very strongly is that one of the best things you can do when you're building company or anything is just have pretty good introspection on where you're strengths are. And at least for this group, it's more on the problem solving side. Just like, and the problem could be very vague. Problem could be like, how do I build a successful company? And you kind of break down the problem. You can like think really critically and just from first

principles, a lot of these markets. Of course, that's not the only way to build the company. Other people, I think some people just have like very good intuition, especially on these like PLG type things. But in general, I think just the problem solving capability. Like what these contests really teach you is like you're just solving problems. And those problems, of course, are not real problems in real life, but the sort of thinking is the same. Does it feel more emotional now, company building than it did in the contests? Emotional? I think I'm a bit older now, so it's not really that emotional. I started a company before this that was, I would say, way tougher than the current one.

I think just like mentally makes you feel a lot more calm and appreciative of when things are going well. And timing matters a lot. I think when I first started, I graduated year early, to try to start it. And I would say we had a very fortunate outcome, but the whole journey was like very bumpy. What was it? What did it do? So the company was called Low Key. We basically built high performance video capture software

for video games. So when people are playing games, you can really easily capture video clips and edit them and share them. And the whole goal of the product is just like you just get as many users as possible. And again, I think we exited at like a very fortunate time was 2021.

But throughout the journey, like at the beginning, it didn't really know what we were building, And then trying a bunch of things, when you're like a new grad, you don't really have good intuition on like what idea is good or not. So you just worked really hard for, you know, three months. And then you realize that obviously had no market. So that's tough. And then about a year and a half in, I had two of my good friends from school where my co-founders. They both got burnt out and then decided to leave.

So it was just me after that, trying to figure out what to do. And so that's, I think, when you, I think that's way tougher than anything we're doing right now. It's like a, the way you put it like an emotional thing. It's you don't really know if there is any future or not. There's a lot of pressure because you don't want like the first thing you work on to be a failure, essentially. So I think that was a much tougher from like a psychological point of view. Nowadays, I think it's tough just like a sheer amount of stuff to do. We're not getting much sleep, but in the grand scheme of things, you've got to be really grateful even be in this position to have enough work to do,

have enough interesting problems to have a team that you're really excited to work with every day. How much do you think you're sleeping? I mean, it really varies. This week in New York, it's probably like four to five hours. And it's not good because I'm not someone whose brain functions that well on less than eight hours. So if you think about the difference between the first and the second company, like obviously now you have a company that's working extremely well, it's growing really really fast. What did you do at the beginning of Decagon that was informed by your prior experience to make this one go better? I think this is broadly true of starting companies. I think the first stage is like by far the hardest because you're kind of just like finding direction. And finding direction is very difficult because by definition it's not, something where you can just like, you have like a goal and you're just like grinding towards it and you can

get there. Your goal could be, yeah, like finding a direction, but it's more exploratory. So I think what we actually are quite good at now, and Ashton, my co-founder, who's amazing, he also has similar background. He started a company before that got acquired. I think when you start a company the first time, you often share the same experiences, which is like finding a direction is very difficult. So I think this time we were a lot more thoughtful about it. And again, it goes back to what your strengths are. I think we view our strengths as like, hey, we're very good at problem solving. We're very just rational about things. We're just good at like execution.

And so if that's the case, then I think we just try to systematize the ideation process. And it just comes down to, okay, you need to whatever you work on, it has to be something that people will really invest in. And how do you tell if that's the case? You can just go really deep asking them. And I think people are usually a little bit almost embarrassed or not comfortable of going super deep in these questions. When you talk to potential customer, they actually don't mind answering questions such as,

okay, if we built this for you, like, exactly how much would you pay for it? Like, would your boss need to approve it or your boss's boss? Like, who needs to approve it? How would the entire organization think about R.O.I. How would you present R.O.I. to leadership to protect yourselves and also, like, make you look good. And I think if you really go deep there,

it's almost like, you're basically asking like classic sales qualification questions, but in founder form. And because you're a founder, it's like, it just feels a lot less salesy for you to go deeper. That process is what gives you a lot more signal. When we first started, we fortunately were able to get in front of a lot of large companies, mostly digital native ones. And we just asked these questions and we kept digging in.

We had a bunch of different ideas, right? At the time, we're not tied to any idea. And it's just kind of open exploration. We were looking at things ranging from like data analysis to like security to, you know, pre-sales to ops stuff. And that process was very helpful. It just shows you that there's a lot more signal to gain than just talking to customers, which is the general advice or building something that customers want,

building something that people want. Like, yes, that is true. But it's very hard to just know that. Yeah, like they'll just tell you what they want, but it turns out that that's not useful. What was the literal process? Would you go to a single person and ask them about multiple of your ideas at once? Or would you target it more like one idea to one person?

You have to target it based on what that person owns. If that person is very senior, you can ask about multiple. So if you talk to like a COO, for example, you can talk about a bunch of different use cases. That gives you some signal too. And then if you're talking to more of like a VP of a certain area, you're probably focusing on one use case. So maybe go in detail through one of these conversations so that others maybe could benefit from

what you've learned. So what is the order of the questions? Like how would you structure those conversations to get the most information possible? So yeah, let's say we get into the call and you start by just doing very high level discovery. Like, hey, what are the sorts of projects that are ongoing right now? How do you spend your time? What is kind of like stressful for you right now, et cetera? And then you can kind of get a sense for the types of use cases.

And then very quickly, you can just form hypotheses, like literally on the fly of like, okay, what would a product be that makes sense here? And so then you're kind of explaining like, okay, yeah. So what if something like an AI agent could do XYZ? Would that be helpful? And most likely they will say yes, because there's this thing that happens where if someone's on a call with you, they almost feel like they owe you like a positive that you can take away. So they're like, oh, yeah, yeah, that'd be great.

Now you've kind of solidified at least the potential product ideas. They might adjust it a little bit. They'll be like, yeah, no, actually, though, it should work like this and so on. I remember we talked to a lot of ops leaders like Matt McGinnis from Rippling is like a great friend of Dekegon and telling us about all the different things that have it on his team because there's like so many. And similar with other ops leaders, we would talk to, it was kind of a range of companies as well, people like ordering and so on.

And you get down to it and you're like, okay, great, now we have these use cases. How much would you pay for it? And that kind of forces them to think, because, Most people are not thinking about that as they're talking about ideas. There's like, oh, yeah, this would be cool. As soon as you force them to think about how much you pay for something, it kind of is a forcing function for finding some order of magnitude, some level of scale. And so then they're like, okay, well, yeah, you have five people doing this full time.

If the AI agent could do this well, maybe we'd be able to get rid of one of them, assign one another one. And then, you know, as a result, I'd pay you like, you know, 20K or something like that. And then in your head, you're like, okay, cool. So now at least you have a general order of magnitude. 20K of course isn't amazing but if it's like I could quickly turn out a ton of these like maybe that's interesting

but most of the time that's not the case because there's not that many good ideas out there honestly most of the time at the end of this exercise you're like okay great glad I didn't pursue this further you know because that would have been

a waste of time and at the end it's like people are paying you like a $100 subscription for month and it's like a big company so you just do this exercise and the nice thing about this exercise as well is that it puts the customer in the same frame of mine as you

and so then they can tell you other things. Essentially, what happened with our company is like, we were talking about all these use cases and they were like, okay, great. Yeah, if you did this, you know, with five people over here, but by the way, we have a 500 person support organization and there's a lot of opportunity there. And we'd be like, okay, great, tell us more. Right. And then you kind of dig into it. And I think this just goes to show that as a founder, you kind of have to build your own conviction and kind of do this process yourself because at the time, essentially what everyone told us, including like very smart, older founders that we knew and so on was that excuse case is like super obvious.

There's probably just going to be in comments that are just tacking on to the product. And because it's so obvious, there's probably a reason why like no one's super big right now or there's going to be someone that's ahead. But no one really knows. And even for me right now and other founders talk to me about other spaces, it's like, yeah, I have my own opinions, but I don't really know the details of the space. And the only way you can really know is by talking to customers and getting that signal. And in hindsight, it turns out that.

in any sort of wave at any time, I would say like a very small number of good ideas. And your job is to ideally find one of those at the right time. And so yeah, by the definition, it's going to be like pretty non-obvious and pretty difficult. And so if you can do this process, well, it'll give you the most signal. Do you remember the highest number anyone said for how much they'd be willing to pay for something in one of these ideation sessions? Yeah. So the time was probably on the order of low to mid six figures. As you near the end of that process, and settled on what Dekegon does, which maybe probably is the right time

as you describe in detail what it is. What was like the final closing? Like, where did the conviction come from like, oh, this is clearly the thing after this discovery process? It was clearly the thing because if you just kind of tallied up even just the amounts that people said

and added them together per idea, this was probably like an order of magnitude more than anything else. So very specifically, what Dekegon does, it's an AI customer service agent. So that's the simplest way to think about it. And you're building a conversation AI that can just be almost like a front end for a brand. Anytime someone wants to talk to

the brand or anytime the brand wants to talk to them, you can kind of initiate these conversations. And of course, long term, this is not specific to customer service, but I think, again, going back to this exercise, customer service is where we felt like the most urgent need. In hindsight, I can dissect why we think that is, but that's basically what we felt. And what gave us conviction is that, hey, we had all these folks that were lined up. They were like very willing to invest six figures in a random two-person team. They didn't even know that well because it was actually like a very top of mind initiative for them.

Everything else, it was just like a struggle. It's like how much you pay for that? I don't know. This is like really exciting, but, you know, our budgets are tight right now. And also like it'd be hard to measure how well this is doing and so on. So yeah, that gave us enough conviction. And then you just kind of take a step by step from there. Say more about the comment you made about in hindsight.

It's clear why this was the key problem. Yeah, so I would say customer service has a bunch of nice properties that I think are very hard to reason through ahead of time, which is why I think I feel so strongly about this process of discovery, just really staying customer-centric. One of the properties is that the ROI is really easy to justify internally. You have these numbers already tracked. It's like, hey, we have so much conversation volume. Right now, we have a simple chat bot or a simple IVR phone tree. it's resolving, 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 ROI and it's very easy to quantify. It's like, okay, well, I'm going to take the total cost. I'm going to chop off 60% of it, and that's what I'm saving. The other property, which I think is a little underrated, is that it's very easy to go live. I think a lot of JaniI use cases are struggling with that right now, especially at the enterprise level, because there's risk involved.

People don't want to feel like something could go wrong for them when they release your product. Leadership's going to get mad at them. It's like, you know, why did you do this? So that is a big deal with GenAI. I think that is one of the reasons why there's been difficult for a lot of use cases to really take off. Because at the end of the day, there is always going to be risk

because the models are non-deterministic. And so something could always happen. But the nice thing with customer service is that you have a escalation path just naturally built in to the way the product works. The agent's having the conversation. If for any reason it needs to exit, it'll just escalate to, to a human. And that infrastructure is already set up. You already have your call center. You already have your telephony stack or whatever. So you just connect to it. I think that property alone

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

a lot more of bottoms up. Can you compare it to coding? It seems like these are the two areas where it's blindingly obvious that it's useful to customers and you can build great businesses around it. Just look at the revenue curves. Yours, Sierra is obviously like cursor, cognition, et cetera.

Compare and contrast coding in customer service? Yeah, they're very different. Maybe one framework to think about this is that at the end of the day, what AI agents are there for is to essentially replace human labor. That's why it's exciting. That's why everyone's so focused on it.

One thing you can do then is like you can just map out the spectrum of like how much that human labor currently costs. So with customer service, it's generally outsourced already, especially for the tier one, tier two type inquiries that AI is now handling. It's generally not folks that are super highly paid. And then on the other end, it's engineers, which is the most highly paid people. The one way to think about is that like AI use case will start eating the spectrum for both ends. And the reason why is that because engineers of the.

highest paid. They have the sophistication to like really leverage it well. And like AI just gives them so much leverage. I mean, there's other factors as well. It just happens that coding is tokenizable and the models are really good at it. That's one way to think about it. I don't know any company that's like, hey, I would like to let go of a bunch of my engineers because now I have coding agents. There's infinite engineering work to do. So you're just augmenting them. On the other end, it is more of the replacement sense. You have a large BPO and that's costing you a ton of money. and it's also like a really high operational thing to maintain because you have to hire people all the time.

There's a ton of turn. You know, train them, you know to QA them. You need to make sure that nothing goes wrong. So AI is really valuable there as well. Because the work is easier for the AI to do. It can fully replace. And I think that goes into the conversation of, I think it's a little bit overblown of like,

oh, AI is replacing jobs and so on. Even the BPO's we talk to, not really that concern because what typically happens anyways is that there's already very high turnover in these BPO's. people are just hopping around and doing all sorts of different things, kind of just naturally let it decrease. And then they just go on to do sort of the next level of task that they, I can't do yet, right?

And so maybe that's like data labeling or something. That's generally what we're seeing from the BPO's. But yeah, anyways, I think this spectrum is pretty real. And so then the question is like, what is the next thing that happens? So it's a really interesting conclusion, which is try to augment the very highest talent or replace the most replaceable end of the spectrum.

That's like a really interesting conclusion. I want to talk about what you've begun to learn about how to do that second thing well. So if others out there wanted to start a company or invest in a company that was doing sort of that end of the spectrum, heating its way in, as you described, what have you learned are the key things to like the setup process with a given company to increase the likelihood that you can replace a lot of the low-hanging fruit for types of customer service calls or types of what used to be human-to-human interaction and can now be handled by AI on one end of the spectrum. Yeah, I would say the biggest learning we had is that oftentimes a long pole in the

tent. And again, if you can solve this well, it just makes things go a lot easier, is aligning on what does good look like. And you would think that in our space, it's pretty easy because it's like, okay, maybe you have a bunch of questions and answers. And that's what good looks like. But unfortunately, it's a lot more nuanced than that. What good looks like could be in the sense of like Tony and Brand guidelines or how conversational you are. And even for the actual answers, like we work with a lot of enterprises where obviously their scope is broad. And so one of the things you need to set up beforehand is what's good look like. So we have in our product essentially like a testing or simulation suite of like, hey, we're going to build out 10,000 tests. And each one is going to

be constantly running like five times. And then you can get a sense of how well things are performing. And that's actually pretty difficult. And I do think that is broadly true for anyone that's trying to build in this style of company. If you're going to be replacing human labor, you need to know what good human labor is. So what we found is like, okay, well, can someone just tell us what are the answers to all these questions? And most people don't actually know because these are large complex organizations. No one is like the person where like, hey, I know how to answer all these questions.

And so you have to design a process where it's very easy to extract these answers from all the people that do know. So maybe it's all the CX leaders or people lead different areas of the product. And so you have to get them all together and get them to align on like, okay, here's what the Eval is essentially. If you can do that well, then it makes everything a lot easier because now you're building, building, building, you have this quantifiable score that's like, hey, here's how well AI is performing. And then once you're done building and the score is high, then you can go

live. Is there a way to think about this? You just created like a captive reinforcement learning process within an organization. Is that like the simplified version? Yeah. Yeah, yeah. That's a interesting way to think about it. And it doesn't have the reinforcement learning in the pure sense of training a model. It can just be reinforcement learning and making the agent improve. And that could be compiling more evals. That could be compiling just like more guardrails, guidelines around. on what it can I can't hit to. How fast does the spread happen? If I'm a customer and I've got the 500 person customer service call center or whatever,

I'm actually curious. I don't know what the volumes are, like how much call volume or interaction volume a center like that handles for a given company. But if I give you 5% of my workload and I'm satisfied with AI's performance, like it performs well and there's not lots of problems, how fast are people willing to go from 5 to 10 to 15 to 20%? Very fast. I would say even for large enterprises within weeks,

everyone wants to just go live to everything, but the reason why you stage it out is so you can make sure nothing's going wrong. And you can tell if something's going wrong, like almost immediately, because you have these metrics. So even within a week, you have 500 person or I would probably estimate

mid to high six figures of conversations a year, maybe slightly higher. What you're doing there is making sure that things are going well. So within a week, you can see like, okay, what is the resolution rates? Is that what we expect? Okay, great. What is the customer satisfaction?

people have those scores as well. And then they'll probably have some sort of accuracy metric based on a human review. If those all check out, there's really no reason why you shouldn't roll it out. And again, the business case is so obvious there. So, hey, we're both generating a ton of operational efficiency and our customers are happier. So, yeah, let's just send it out to everything. What goes most wrong? When something bad happens, I'm sure there's happening less and less as the product's gotten better.

But even in the early days, what sort of thing would go wrong in one of the customer-to-AI interaction? It ranges all sorts of different things. Sometimes it's obviously like both sides, like us and the customer, we take everything very seriously. But there's just a lot of things you wouldn't expect. In the early days, we have a customer that is essentially like a large ticketing platform. And one of the things that would happen was someone came in, they couldn't find their ticket. I looked into their account. It's like, hey, there's no tickets here. And then they were just like, okay, well, what I'm going to do is I'm going to show up to the event and I'm going to find eight homeless people from the city and bring them with me. And the agent was like,

like, oh my God, that's so awesome that you're thinking about doing something nice for the community. Things like that. It's like, okay, I will not expect that to happen. So those are just like tuning you have to do over time. And generally, it's in the spirit of that where you're trying to find the right level of guardrail and flexibility. I think that's the name of the game with RSpace at least. And that's sort of the way we've designed our product and why probably the one reason we've

been successful so far is that what Gen A.I. really unlocks is super flexible, super personalize. One way you can think about it is like in the old days to map out a conversation, you just build a gigantic tree at decisions. And that's very hard because no one likes that experience and you ask something that's not quite one of the branches and it just forces you down that branch and there's no way to go back. And what ALM does is it abstracts a lot of that tree into the neurons of the language model. So that's really powerful. On one end of the spectrum, you're just looking for flexibility and root power and being able to sound really human-like. But with the enterprise, there are a lot of things we don't necessarily want that as much.

And you want full, like, rigor, right? If it's a regulated use case, like you cannot afford for it to ever deviate. These three steps always have to be fallen in this order. And you can't go to step three until something has happened already. So you need a design system that can be anywhere along that spectrum. And back to your question of what could go wrong, well, the worst thing that's happened would be it just says something that's not supposed to say. And so you need to design an AI that's really robust to that.

And you can choose like, okay, for this use case, we really need it to be over here that's a lot more robust. But for other use cases, we're just asking a basic question on their account. We don't want it to be like that. We don't have to be super freeform and that's how we get the customer satisfaction up. I think most people are still focused on things that could go wrong because it's non-deterministic. What about the total other end of the spectrum? What have been the things that have gone way more right than you expected? Where has the potential of agents outperformed your expectation in terms of what they can handle or what they can do? It's really just elevating the experience. One sort of metric, which is usually a secondary metric that folks think about later,

but is quite important to us is just how often do people come in and just say like agent, agent, agent, like, give me too representative. I don't want to talk to you. I've done that. You probably have as well. You're just like calling into some sort of customer service and you're just like pressing to zero the whole time. And that's because people are used to bad experiences. So they've already lost the trust of these systems. I think what surprised us was that if you just make it really clear off the bat that this is a different experience. People are willing to give it a chance. And then the outcomes are just way different. One of our customers, or a ring, like the wearable ring, we did a case study with them where before having any sort of jane eye system, one in three customer that came in

would just not bother saying anything. Just keep jamming agent until they got to one. Now it's one in 20 because we just like spent a lot of time making the beginning of the process just feel very different. And folks are willing to give it a chance. So I think that's been exciting. Where do you think that can go? How good can the experience get in ways that it's not yet that good with subsequent evolution of your product, but also of the underlying capabilities of the model? The biggest frontier right now is voice, voice models. And so, you know, there's a lot of interesting startups as well working on voice. It's exciting because it's still, I would say, definitely not solved. There's a lot to be done there. The bar is very high. So if you just think about how

humans communicate for literally the entirety of humanity, I don't know, 150,000 years or something, the UI for every human is language, spoken language. Listen, you speak, and that's how our brains have evolved. That's the most natural way for us to communicate. Only in the last, what, like 60 years of that entire time did we have keyboards and like phones and communicating through typing. I would say fundamentally, and any sort of agent that communicates with humans, voice has to be a critical factor because that's just how we communicate. Because our brains are so evolved, for this, it's very easy to tell when something feels not quite right. And so the bar is very high. Uncanny Valley is quite large. That's why there's a lot of effort going into making the voice

experience is good. I would say Chachybdy Voice, for example, or the Sesame or like these voice-to-voice models, they're starting to feel very impressive. But you talk to it long enough, you can actually, it's like, you can definitely tell it's not a human. So there's that element of it. But then, even for enterprise use cases like us, there's still a ton of hurdles to cross because those models, even though they're good, the hallucination rate is really high. So, you can't really use them necessarily as is in the current systems. And so a lot of people, what they do now is they go from voice into text and then back to voice, and then you can run a lot more checks there to make sure that things are accurate.

And so a lot of cool ideas there to explore on how do you make it both human-like but accurate and how do you tie everything together? So that's where most of the work is going to these days. At the risk of getting too technical, why is voice-to-voice interesting and worth pursuing versus just always going back to text and being able to manage that way? So the fundamental difference is if you're just going to text, then no matter what, the final audio is just a narration of the text. The voice to voice is powerful because it takes into the entire audio of what you said.

So it knows cadence and maybe how upset you are and the tone and everything. Latency is a lot less as well because you're going straight from voice to voice. And latency matters so much when we're talking. When we're talking right now, our brains are constantly going. I'm like, okay, when's he done talking? when should I start talking. If someone interrupt someone else, it's like in a polite way.

People adjust very naturally. So that is the biggest proponent of voice to voice. And ultimately, I think the prevailing view is that first, whatever the final experience is, if you really want to make it indistinguishable from a human, you have to do voice to voice or you have to at least take into account the voice. The issue with voice to voice though is that also fundamentally, because voice has a lot more dimensions to it,

the amount of tokens you generate per sentence, is just a lot higher than when you generate text. The more tokens you have, the easier it is for something to go wrong. And so the hallucination rate has so far just been a lot higher. How much higher? Give us a sense of how far we are from these being really good. I think probably like 8x higher or something like that. Wow.

It is quite a bit higher. Of course, you want to leverage that technology. So now maybe there's creative ways to make a hybrid of the two. Maybe you can have a text model generate the content, but you take into accounts the audio from before as well. And like that makes something very realistic. But at the same time, latency is still the hard problem because at the enterprise, what's happening is you are doing a lot before you can start responding. You have to figure out what are they asking about?

Like, what materials do I need to collect? Do I need to hit any APIs and get that data back? And so you have to do it in a way that feels very natural. And sometimes if you think about how human does it, you might have to say something like, give me a sec to look that up because it actually genuinely takes 10 seconds for the API to come back. These are all interesting problems and think through. So give us a sense today, if you add up all the interactions, some idea of how long they are, what type they are, voice versus text versus some other modality. What does the entire corpus of interactions between a Decagon agent and a customer look like today? I would say pretty balanced at this point between chat and voice.

On a raw customer basis, there's more people in chat, at least for us. But if you're just thinking about the large enterprises, like the Fortune 100, they just been around for so long. and everyone just calls them. So voice is just disproportionately higher there. A lot of them are 90, 95% voice and 5% chat. So that's what we're seeing. And then in terms of the types of conversations, it's generally ones that are fairly,

you start with the sort of easier ones, of course. And so these are things where they're question-answer based. So that's the tier one is like, hey, you can answer their question based on what you statically know. So that could be questions about how your loyalty system works or questions about if I bought something, would I still be able to refund it? Then the next level is,

it's still question-answer-based, but you're leveraging a lot of real-time data. So that could be, you know, I got 2x points on this transaction, but I should have gotten five. Why is that? And then it would actually go and look into your account and reason through things,

okay, I see the counselor listed this type. Let me go find all the documentation on this type. And like, okay, actually, it's because you book through a travel agency. If you have booked directly with the airline, you would have gotten 5x, but it doesn't apply to a travel agency. Or whatever.

And then the third tier is you're actually taking action. So I lost my credit card. I needed a new one. And it's actually walking through a pretty large flow. And that's where AI agents have been really excellent because you actually wouldn't expect it to be able to do that. And so it's able to go in. It can be a pretty complicated system where it's like, okay, well, first, I need to

figure out what your address is and confirm if the address is correct. And then I need to look and see, hey, do you want me to lock the old card? Like, okay, great, I'll do that. I might need to check for fraud to make sure this. person is not just constantly asking for new cards. And it's just like all these things stitched together. That's really what makes it agentic. And that's why there's been such a step function improvement with LMs.

In the spirit of that question, what could go right? What could go right for the company based on the data they're gathering from these interactions that they've probably been doing nothing with historically? Like what new things can they do for their customer because of on a one-to-one basis, like they're just learning more about a person. And on the aggregate basis, they like, understand the behavior patterns of their customer base or something. Oh, yeah. That is a huge topic. I think that's a huge part of our product. We have a viewpoint that this data, of course, is super valuable because it's literally what your

customers are saying. But it's very underutilized because historically, it's a very unstructured data. So what people typically would do is like, okay, well, every month, we have a million conversations. We have a full-time team of 20 people, and they're just like sampling these conversations and trying to like check on a rubric and try to compile topics and things like that. And that will get you so far. But now what you can do is you literally have a language model that reads every conversation and extract whatever info you want from it. And so that allows you to do things like, okay, well, over time, there are these topics that people probably didn't even know about because these organizations are big. So the people in leadership positions, they can only

have such granular insight into what's happening. But it'll literally just flag like, hey, there's just two percent of conversations where things are not really going that well. And it's because we don't have context on this topic. And so let's flag that. Let's draft the suggestion for what could go better here based on with how the human agents are handling or based on the other procedures that we have. And here's a suggestion for how you should adjust the agent. That allows the agent to improve automatically over time. And that's really critical. So when you think about motes in the agentic world, a lot of it is around if you've been

working with a client for a year, has your agent just continuously gotten better by learning from the data? And that's a different concept than just training on the data. But has it continuously gotten better to the point where it's just very difficult for another agent to come in and perform at the same level. There's this funny situation today where I think especially CEOs really want AI in their businesses. They want it now, but they don't know what they want. They don't have a good framework for thinking about, okay, I understand my business. I don't know where to go. It seems like I would be missing a major boat if I don't deploy this in

my business. I don't know what to do. I don't know where to go first. I don't know who to call. Have you developed any framework for those company leaders that desperately want to use this technology business, but they simply don't know, apart from automating customer service or something, I don't mean specific use cases. I mean like a framework for thinking about what kinds of problems might be addressable by agents or by all alarms. Oh, interesting. I mean, one framework is similar to the sort of framework we talked about before of two ends of the spectrum. And I would say most leaders we talked to are focused on the more bottoms up end of the spectrum, which is like, where are the areas that we should just not have humans doing because it's so mundane and repeatable

and there's tons of cost efficiencies there. So I would say that's where folks are typically thinking of. So when we talk to leaders, and there's a couple observations. One, pretty much all AI initiatives are very top down at this point because it is such a board level mandate. So C-suite's very, very invested in like, okay, where do we deploy AI? It almost means that if you want to get something going at a larger organization, you have to have buy-in from the top level because it's going to get up there anyways and they have

to make decision at the end of the day. So that's one. Two, the way they think about the use cases, to your point, is back to ROI. It's like, where can we either save much money or make a lot of new revenue? And if you cannot in basically half a sentence, explain that, then it's just not going to work right now. They are under a lot of pressure, right? They need to show quick wins.

If this is not going to be a quick win, they can point to, like, I save $10 million, then it's not going to be something that's prioritized. Do you think coding answers that well? Do you think the ROI is clear in coding? It is. And I know the coding agents quite well. And the way to do it generally is, one, it's very easy to test to play out to the engineers. And then you just do a poll on the engineers of like, hey, how much more productive do you think you are?

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

us 50% more productive. Yeah. It's like super easy. And they're the one building your thing. Exactly. And then you think about from the CEO and they're like reporting this to the board or something, it's like, hey, my entire engineering org said that they're 50% more productive. This is like a worthy investment because these people are all paid this much. Now we can accelerate the product. We can accelerate everything. Yeah. Do you think that, the future is I want to talk about brands and how a given company might want its agent to feel and sound that an agent might be a way to express brand, culture, style, tone, whatever. I want to talk about that. But do you think the end state here is that each company sort of has almost like a named

personified representative that you just come to expect to interact with? And it's not just customer service issues, but it's sales issues. You ask it for advice on what shoe to buy or whatever it might be and that that's all integrated or that you'll have different agents for different parts of the company. Like, I guess what I'm trying to ask is what the future of a company's agent or agents looks like in the natural end state five years from now or something. Yeah, I would say that in the natural end state, it is more unified for the exact reason you listed, which is people want a unified brand out there. Brands very important to them. And for some business, it's more important than others. But eventually, this becomes the front end for the business.

And so that means it's both how you gain new customers, but also how you support the existing ones, make them retain more and so on. It's almost like in the limit, if you're working with a bank or airline or telecom company or whatever, the agent could be the only thing that most users interact with. They don't even have to touch your mobile app. They don't even have to go down your website ever. They just have this agent where they're authenticated and knows everything about them and has all the context of your previous conversations. It has memory. And it can just solve your issue. You can take actions for you. You need to book a flight and you need to upgrade a seat. You have questions about this or that. So I think that's the exciting vision that we're building

towards. And in some ways, we often call this like a concierge. It's just a digital concierge. I can do everything for you. I mean, you also have to be pragmatic on both sides. You know, start with a clear use case. But I think that is where folks are building towards. And so in the near term, I do think that's different teams, because the reality is that at these large companies, different teams have different budgets and they make different decisions. And so they might have different agents, but long term you either need to have a unified system that ties them together, like a unified framework, or they could just be literally the same agent. Is the right analogy here at company's website? They'll think about their agent, like they think

about their website. A lot of work will go into it. It'll look and feel a certain way, like it'll be kind of a unified interface with the world. Like, is that a clean analogy? I think that's a good analogy, just like a front end. It's like a UI, but instead of visual UI. It's a conversational UI. How do you feel brands pulling personality requests in their agent out of you? What do they care about? They want it to be nice. They want it to be concise. They want it to be funny. How has that dimension evolved since you started? People already almost always already have brand guidelines because they need to show brand guidelines to their human agents to train them. The next part is that they already have all this training process for the humans and you should

ideally be able to apply it in the same ways that AI. And so they'll have. like, hey, you need to do this. You need to always be confident. Sometimes folks don't want the agent to apologize. Sometimes people really want to be apologetic. People are just different preferences. And that needs to be taught to the AI in efficient way. That's also kind of a lower throughput way of communicating. I mean, the other way that you can show the agent is just give it a lot of examples. So here's examples of what great look like from our top agents and just learn from that. What is the very biggest, you're almost afraid to admit it because it feels so big version of what Decagon could be?

The reason why we're excited and we feel like the market is so massive is that, yeah, at the end of the day, what we are building towards is this concept of becomes like a new UI for the product. Just think about how much these large companies invest in their mobile app or their website. And this is literally how everyone communicates with them. So this could be a way where eventually the way any user interacts with any brand is through an AI agent and for all sorts of different use cases. The benefits our customers for the AI to have all this context because it can seamlessly flow between things. A lot of them want to do sales type use cases at the end of a support flow or vice versa. That's exciting. What internal context or things do the best customers have that make this better?

So you mentioned brand guidelines. Like maybe there's a write up on what the brand guidelines are or whatever. What are like the internal assets that companies have or don't have that if they have them, it's made the Decagon experience way way better? Number one thing is just APIs for the AI to use. So APIs to take action, APIs look up data, APIs to reformat things. That is often like, hey, if you have those, you already know that within the first month, it's already going to be a great experience.

If you don't yet, then we should try to build towards that as soon as possible so that the AI can actually achieve that elevated experience. That's the main thing. Most people already have documentation. It might not be up to date, but we can help with that. Most people already have SOPs, I guess. And then we are able to use that and generate our format. We call them AOPs, agent operating procedures that are just SOPs but for AI.

And then brand guidelines, like I said, people usually have those as well. So you just ingest them. We were talking last time about in any business, but certainly in yours, the three key stakeholders being your team, talent that you have to recruit. And I want to talk about that in detail, capital investors and customers, maybe suppliers too is a fourth category, but especially interested in the first three. And the last time we were together, I asked you like, which one do you have trouble with? And we laugh because you said, well, definitely not investors. Talk a little bit about the demand from investors to invest in companies like yours and how that feels. What are they doing to try to give you more money, get on the cap table, how competitive does it feel?

What does that feel like right now? Because it does seem like there's a handful of companies like yours that are in one of these white hot areas that have key traction, that have great teams. And basically every investor wants to be involved in those companies. What does that feel like? It definitely feels like there's maybe a little bit too much excitement right now on the AI side. It just seems way too easy to raise money. So many companies out there.

I mean, for us, we've been very fortunate, right? We don't take it for granted. I mean, I think in my first company, we're also fortunate. It was easy to raise, but it was for a different reason. It's like 2021. Nowadays, it's just there aren't that many AI companies that have real, real traction on the revenue side, especially in the enterprise. And so I think that's attractive to investors because

they want to deploy their capital into AI, and that's like the biggest trend. So I think we've had a great relationship with all our investors. I think we just really selected for folks that we get along with at a personal level, and we feel like we'll be very helpful for our go-to-market normally. Yeah, that's kind of an interesting process. So yeah, pretty much after every single time we've raised a round, we just like almost immediately gotten preempted. And that alone can't be right.

If you're just thinking of first principle is to make an investment, the previous valuation should not be like a super big factor in that. It should be like how well is the business doing? how what do I believe the potential is. So it feels like there's a little bit of mania, but yeah, we're definitely, I would say, more indexed on the other two things like talents and customers.

What's the craziest thing that an investor has done to try to invest in the business? I don't think there's anything like super crazy. The main thing that we do, and I would actually encourage more founders to do this, is that during the stage where people want to invest,

but they haven't yet, that's when they're most willing to be helpful. And so it's actually like a great way for you to use that to proxy how helpful they'll be afterwards. Because if they're not that helpful in that stage where they really, really want to invest, they're willing to do anything, they're for sure not going to be helpful afterwards.

I mean, they'll still be friendly and hopefully they'll not be detrimental. But that's like your opportunity to really test folks and see how helpful someone will be. And we obviously know a lot of investors very well. I think they like, no one has issue with that. They know that they're going back to competition. They're also in a competitive sport. they know that they need to earn basically the ability to invest in the best companies.

And so I think from their perspective, they're happy to work for it. So if you just give them an opportunity to, they won't. I'm going to ask about this from both founder and investor perspective. What advice would you give investors during that window? Like when there's a fundraising that's happening, you know, there's sort of an open process or a window or whatever. What have you seen the best of them do well? Not just, I'm sure, helping you.

Here's 10 customers you can talk to. I'm sure that's great. But also on the underwriting side, them making sure they understand your business. extremely well, I'm expecting it's a short period of time. What of the very best done in that window? Number one, I think the best investors get this dynamic. We've definitely talked to a lot of high-profile investors where they're just like not willing to help much until they're invested. Totally they're right, right? It's like, hey, we have a lot of our current investments. We don't want to like use our social capital or whatever to help put it in a new investor or

a new investment. But I think from the founder's perspective, okay, if that's the case, then it's hard to tell if you actually be useful or not. There's no difference between you saying that and then someone who can't really help and just saying that. I think the best ones just are able to give a lot of signal to the founder that like, hey, I'm really willing to help. I have the ability to help.

And we have a very strong network or we're very good at certain elements of go to market and we're just able to show that in that, let's say, it doesn't have to be that long period of time a couple months before the round actually happens. That's one thing. I think the other thing is that if you just think about anyone you bring into the org,

this could be employees or investors or advisors or anything. What we really index a lot on is just cognitive ability, raw intellectual throughput. And you can almost feel that out in an investor in the same way you would feel it out in an employee just by spending time with them and just like actually seeing if they're thinking about things from first principles of your company.

For example, right, a lot of AI companies are growing much faster than traditional SaaS companies right now. Because that's the case. A lot of things are different. You generally don't want investors that are just like so many reps. You got to do things like XYZ way. You want people that are just intellectual curious and we'll think about things along with you and can't help problem solve.

What about on just like the pure underwriting side? So an investor comes in. They're really smart. Let's take that for granted. They just want to understand your business, the good, the bad, the ugly as fast as possible. What have you seen the best do in that side of the investment process, including things like how fast they move or how deliberate they move or anything like that. I think the best ones, I would say they all mean one, they get a very deep understanding of our

customers. It's actually funny. Our customers have made probably so much money on expert calls because so many investors are hitting them up. And I think the best ones can, before they even talk to you, they probably already done quite a bit of research and have a pretty full view on your customers. Unfortunately, I think what we've seen is that there is a lot of noise there because like a lot of people just lie on customer calls. And we have people who have said, that they've used us and literally never heard of them. But generally if you do enough research and you're good at it, then you can underwrite the business that way because that gives you the most signal. And the other one is people that index on culture because I think culture is quite

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

to this culture. So everyone that has joined Decagon, I would say they want to work hard. They want to be around other people that are really smart and are like them. And they're motivated by the view this as maybe the prime of their career where, hey, I'm going to work hard, but we know that because of that, I'm going to build lifelong relationships with, like, other amazing people and going to have good financial outcomes. I'm going to be able to leaprog steps in my career because the growth is just happening so quickly. So you're attracting people like that. I think that alone creates like a pretty strong foundation for the culture, because you have people that are there to work. We have a lot of people that live right next to the

office. Part of that is because we've selected for people that like being in the office with other people, right? We're in the office a lot. Those are the foundations. And then I think what you have to be careful on top of that is we really want our office to be a place where people enjoy coming to work every day. Because we spend a lot of time in there, it's like you want people to be happy to be there. They feel like everyone there is supporting them. Between the organizations, you want people to feel like they're pretty aligned and folks are working towards the same goal. And that's an ongoing problem. Like, I don't think like we've solved culture. It's something we just put a lot of thought into, and we want to make sure that people feel like they will be very

fulfilled by staying here for a long time. One of the most interesting subplots of this entire business evolution and in and around AI is talent wars. Yeah. Obviously, it's happening in the most extreme cases at the model layer between meta and Anthropic and Open AI, and there's great riveting stories to hear about the lengths people will go to to secure a great, and the amount they'll pay to secure a great engineer or someone really key to the business. Can you give us your perspective on these talent wars, what it's like to be in them. Obviously, I'm sure you are too fighting for the best talent versus lots of other great companies that are being formed. Yeah, tell us from the inside what this environment feels like. Definitely feels like talent is a big team effort. For anyone

you want to hire, you need the whole team to swarm around them to win, to win. I always sought after talent. And you have to go and do all the things. There are some parallels to sales, of course. You're trying to convince people that, hey, this is the place they want to be. So that involves oftentimes getting into their families, getting to know their partners, really figuring out what they want out of their own careers and making sure that you can design a role for them that is like that. Unfortunately, in the application layer, it is not as crazy as the metas and the opening eyes. There's only so many top-level researchers. We have some very strong researchers on our team.

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

we just opened up our New York office, as you know. And part of the, Part of the reason for that is that, hey, there's another talent pool over here. We should leverage that. One of the questions that I think so many people are interested in for companies like yours is the use of whatever core underlying LLM versus the development of your own models on top of or replacing those underlying LLMs. You are gathering all this incredible data that's just yours. You don't have to share with anybody else. These models thrive on good underlying data. How do you think about that aspect of all of this, where, five years from now, it's going to be either your own model or your own model plus something else or just your own data and context on top of the best model. How do you think that will evolve? I'm both curious about this in terms of how it impacts the product, but also how it impacts

your business mode, your power in your business where you rely or don't rely on TPP, whatever. When we first started, this was about two years ago, people were still figuring out applications. Almost no one was doing fine-tuning. In fact, anyone that was doing fine-tuning, there was a lot of Writing at that time was like fine-tuning, quote, doesn't work because it doesn't really get you that many gains. Another big reason to not do fine-tuning at that time is that the models are changing so fast. You're still kind of figuring out your use case.

Why invests a much of time in the fine-tuning? It's not reversible. You're going to have to throw it out. Next time there's a new model release. I think now the open-source models, for example, have gotten to the point where it's definitely not smart enough to do everything, but there's a lot of specific use cases where you just don't need that much intelligence. Example would be even the agent, let's say the first thing the agent does,

is it just needs to think about like, okay, based on what the user said and everything before, like, what path do I go down? What data do I need? You can make that fine-tuned model. That model doesn't have to be good at math or coding or anything. It's just like, you just need a smaller model that's just fine-tuned on that. Nowadays, we're seeing much more of that because a lot of the applications have gotten more mature. So you know how your age and is structured. You know the places where you need models to run. And you can take, you know, smaller fine-tuned models. And that improves the entire system, both in terms of performance, but also latency and so on. So I think over time, there's going to be more and more of that. I do think there's always, there's still

always going to be a huge usage of open AI's anthropics of the world because you just need intelligence. You need the best models. So I think there's going to be a balance, but at least in the short term, there's going to be more and more of the fine-tuning small models that happen. What is your perception of the very biggest companies? The entire market has been focused on, rightly so, the 7, 8, 9, 10 biggest technology companies. Which of them feel most important to you? And I don't mean open AI an anthropic. I mean like Microsoft and Amazon and Apple and these sorts of companies. What is your relation and thinking about them today?

They've been the driver of equity markets by a huge margin. They're important companies. How do you relate to them? I mean, work-wise, it's not super relevant, but of course we have our own opinions. I'm very bullish on Google, actually. Why? I just think that with AI use cases, having individual consumers is so important because that's where all the data comes from anyways.

And Google is like much stronger there. I mean, you can say that meta, Facebook also has that element. And so, yeah, maybe their new superintelligence lab will be able to make it work. But something like Anthropic, for example, where they haven't done as well on the consumer side compared to like a chatch EBT, I think long term you do need that consumer buy-in because that's where all the new data is going to come from. Yeah, Google and I mean, Google just has an amazing team. And we've had a couple of rocky starts, but hopefully they'll make it work out. Of course, all the large Mag 7 basically are obviously super strong.

So we don't really have strong views on them. If I let you build a portfolio tomorrow where you got five slots, 20% each and five private companies building in and around AI, what portfolio would you build? Decagon excluded? Yeah, Decagon excluded. Let's see. Just kind of gravitate towards where most of the talent is forming. I mean, I mentioned I'm close with cognition guys. Cognition would be in there for sure.

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

And the last one, probably would want some sort of bets on the underlying model side, even though all the large language models are out there. But another friend of mine, I think, very highly out there. They're building models, but not like the types of language models, but still foundation models for like healthcare, for example. So a friend of mine, Josh is building Chai. They're building a foundational model. So stuff like that, I think is quite interesting.

Or even I would put Lockheys company physical. I think those are very exciting. It's just obviously as early to see how those. will turn out, but if I'm building a portfolio, I definitely want one of those in there. What do you think are, I'm interested in both ends of the spectrum, the people that aren't in your position who are both technical and commercially at the center of this wave, what do they overestimate and underestimate about the capabilities of AI today? Where is it further along than the world thinks? Where is it further behind?

It's a little bit more behind in being able to unlock a lot of the enterprise use cases, I would say, because of the nondeterminism. So two things I need to have. happen. One, you need to reframe the way that people think about agents. There's the Waymo effect that often happens where it will be objectively just way better than human drivers and human drivers make a lot of mistakes. But because we're investing in new technology, the bars a lot higher. So it has to be near perfect. So there's that dynamic that kind of needs to adjust in some folks' mind where instead of evaluating AI in a way where you're just trying to find mistakes, you're evaluating holistically looking at the sort of success rate. And then if you can frame it that way, well, the success rate is going

higher than humans because, again, humans are not perfect. So I think that needs to happen, and I don't think that's fully happened yet in the enterprise. So that trend needs to happen. And then on the AI use case side, I think the models don't need to get better in a lot of areas, right? We were just talking about voice-to-voice earlier. Hallucinations are too high there. As those models get better, I think more enterprise use case will be unlocked. But I do think the general public will just see a really cool demo and be like, okay, oh, wow, like voice is solved now. And as a result, you know, the enterprise should be adopting it left and right. And C-Swees will see that too. But then when they actually get into it.

to it, it's just harder to go live. That's, I would say, the piece where it's not quite fully there yet, and so it's a little bit slower than people think. I think the thing that's on the flip side of that, where it's underestimated, is just things are growing exponentially, improving exponentially, and no one is good at conceptualizing what exponential means and things like this. And so that could be from like performance, like cost perspective. Right now, I would argue that if you're building an application, your margin shouldn't really matter that much. People will often critique the coding agents like they're hemorrhaging money. Yeah, but again, things are improving exponentially and like the costs will go down exponentially as

well. So zero margin doesn't really matter. What really matters is you need to get market share and you need to get mine share of users. Perfectly fine not to have good margins right now. And it's just everything just improves way faster than people think. It's slightly different at the enterprise. I mean, the same principle applies, but generally don't want to be hemorrhaging cash with like enterprise deal because those are just much longer term. And even though the costs will go down, their expectations might also change and so on. So you generally want to be fairly healthy there. I think that's what's underestimated right now. How do you know that costs will get way lower? Like I'm very curious about this margin question because if you knew for sure, then we actually

want to run super negative gross margins. If you knew that it was going to get 98% cheaper or something like this, cost of goods to serve a coding agent or something like that, I think the argument would be get the install base, just like have the best product and get the users and build the affinity with that user base and don't care at all. But that hinges a lot on the confidence that you have that the cost will fall. So how do you know? How do you think about that equation of when install base versus demonstrate good good economic now? Well, I just think it's quite unlikely that where we're currently at is like the best that things will be. There's so much effort getting put into it. And like one of the main metrics is efficiency. The other piece is that even if,

things don't get better, there's a lot of ways you can re-architect your system so that it is more cost-efficient. It's just that it's not worth putting time into that right now where you can put that same amount of time into getting new customers because you know that things will change in the future. So I think that's just one thing where people are like, oh, it's kind of like a scheme where you take VC dollars and then like the VC dollars go to the chips. These companies are losing money. If they wanted to, probably they could just like spend a month or even less and just massively improve their margins. But it's just not worth doing that work. right now. Where you're really optimizing for right now is just quality and growth. So if you can do

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

you're generally making a margin on top of the costs of whatever your goods are. And that's why it's nice to be in the application layer is what you're building is actually solving the business. And as a result, you can capture more of that because our customers, the way they're thinking about it is great, we're investing in Decagon. We don't care that much about what Decagon is. cost are. In fact, we probably don't care about at all. What we care about is what is the business ROI that we're getting. It's like we're downsizing our operations by this much. We're actually generating more revenue now because the AI can engage people and keep them retained. So that is where we probably see the most dynamic here. And I do think this is generally not really a hot take

in the early days. We're just like chat cheap T rappers and so on. And yeah, a lot of rappers, if it's too thin, it's like, not going to be valuable. But if you have enough software built around the models, then that's where you can actually almost capture the most value. That's why I think the open AIs of the world will continue to move towards applications because it's quite hard for them to make money long term on like their API, for example, because there's like such high competition, all the labs are billing. It's very easy for people to swap. It's not like moving from AWS to GCP is like very hard, but moving from a open AI model to anthropic model, you just change like one line of code. Can you say a little bit more about this chat GPT wrapper concept? My sense,

is certainly for what you built, but probably for other companies that a lot of the work that your engineers are doing is not AI work. It's traditional software work. It's the ability to hook this system up to enterprise customers. It's good old fashioned product and infrastructure building that is very different from what Open AI or Anthropic are providing you. And that's hard work. Just like building any software system is hard work. Everyone's very enamored of this idea that like in five years, I can just show you a piece of software and just tell a coding agent and just like replicate this piece of software, and that's going to mean lower software modes. I don't think you think of it that way.

So maybe describe your thinking or critique of this chat chitpt rapper concern that people have. Yeah, I think generally people say rapper in a derogatory way. It's like, hey, you're just a rapper. It's not black and white. Yes, there are a lot of apps that are just wrappers. They're not going to become real businesses because there's just not that much value. I mean, one argument, don't know too much about this space, but at least from the outside, it has seemed like copywriting, for example, has been difficult because someone can just log in the

chat, EBT and just like, hey, write this for me and I'll just write it. There are things where if there's not enough tooling of functionality on top of something for it to be really valuable and needed, then maybe it is easier to just leverage the models. But most of the time, that's not the case, especially when you get into agents, an agent is not just a model, right? You have to design it. You have to be able to put in guardrails. You have to be able to teach how to do new things. And that's where the software layer on top of the model is coming. And if, If that's valuable, then it's just much harder for you to just be made obsolete by a model update. The other side of it is like, okay, well, now the labs are quite interested in building applications.

So they're building a bunch of coding applications and cloud code and so on. And so those will end up being competitors with cognition or cursor. And maybe for that reason, it is wise for the coding agents to start training stuff as well. I think in our space, I would say for us right now, at least the sheer amount of functionality you have the bill, because it is a very top-down product, is quite large that has nothing to do with AI. It's just stuff like, okay, how do you have like observability into like what the conversations are?

How do you alert the team if something spikes? And how are you able to QA and have unit tests for the conversations so that before you push it out to end users, you feel like, there's like, there's just all this like functionality that's there that doesn't really have anything to do with. AI, really. And so it's just like a lot of stuff that needs to be built. How would you advise other founders thinking about the ideal customers to go after? What are the most interesting qualities of your best customers? When you're qualifying them, are they going to be, you have limited time that you can only serve so many people. I know you're growing really fast, but you can only serve so many

customers at any given time. How do you qualify who you want to work with and don't? Like, what are the attributes that you've seen matter the most? Yeah, we want people that are intellectually just at the leadership level, really curious and excited about technology. You actually see a huge spectrum of that in the enterprise. And some of, in my opinion, the best leaders and like probably the folks that we are most excited to work with, they're just genuinely like, hey, we want to move on AI as fast as possible. We're very interested and just curious about how all your systems work. And as a result, I'm going to help cut through all the cruft and like bureaucracy to get something

going. And I think you can tell that pretty clearly in the first conversation. You can tell if they're like legit about this is something where I'm going to both push aggressively, but also give you a ton of feedback and like the feedback's going to be good versus someone where they just know it's like a board mandate and it's just like a AI is like a thing on their to do list. Is there any leader that you've come across that most exemplifies that posture? Yeah. So I will say in both the sort of digital native segment, you have folks like China. for example, super impressed with the entire team top down. Everyone that is in the org, that's like a case study to study at some point. I think they've done a really good job on culture, really making it so that people are very sophisticated about everything. Everything the AI

agent does, it's like super data-centric. They track everything. They're really thoughtful about how to make updates and so on. And they've given us a ton of good product feedback. As you're talking to your friends that are also building companies in this space, where do you feel that your worldview is the most different from them? Where's your view of things or your relative excitement about something the most divergent from others? Yeah, I mean, I would say my friends are very high intellectual horsepower. I think I generally lean a lot more, I don't know why this is, I mean it's like personality. I think I generally lean a lot more towards commercial elements of every idea.

And again, it's not the only way to build the company, but in my opinion, if you were just trying to optimize for like the highest likelihood of success, I think you should really index on the commercial side because a lot of super smart, intellectually curious people that are just building very cool projects. And you can make the argument for a lot of huge outcomes. You need to do that because you just need to do stuff where like no one's going to work on this because where are the commercial elements of this. But I think I think Oshman's this way too.

It's made a good fit. I think we're just like very locked in on, okay, you just got to be like super. practical loss. How much would you pay for it? Exactly. Yeah, it's really interesting. How do you mark milestones in the business? How do you motivate the team? What have you learned about how to rally around a given thing? I know you're super aggressive when you have a customer that you want to get that it's not just like an old school sales process. It's like an all hands on deck. Send engineers to whatever it takes. What have you learned about motivating milestones, rallying the team, organizing around common goals? Yeah, for us, I mean, one thing we do always is we all. We,

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

Like if you give someone, if everyone a clear milestone, and this can be like pretty insignificant. Like last year we had for our revenue milestone, we told everyone we'd get them like super nice jackets. And we got like Decagon, architect's jackets. And everyone was like super excited about that.

And if you just think about like the cost. Yeah, the cost of the jacket and just how much people get paid, it's like trivial. But it just creates like this, hey, we're working towards these jackets. It brings teams together because now it just feels like everyone's working together it's towards this common goal. That's been a big of our culture is just finding what is the next milestone. Anything that we haven't covered about either the business or this exciting AI

applications world that you feel especially passionate about, I feel we've done a good job of covering. It's been great conversation. I think what has become almost a meme or hyped up a lot in AI startups right now are like a couple things. One, obviously everyone's in person. It's like 996 or whatever. Don't actually think 996 is that healthy. It happens in China and everyone's like super hardcore, but one of the reasons is that no one has jobs over there. So it's very easy for employers to have leverage. I think here you generally want to maintain a good balance

because if you're working super high intensity, need time to relax a little bit. But that is one element. The other element is the forward-deploying engineers. So everyone's talking about forward-deployed engineers. And yeah, I think it's kind of funny because my co-finderer came from Palantir, so they actually have forward-de-point engineers. What a forward-plane engineer at Palantir means is you're working on a 10,

$25 million deal. And so you're actually just almost full-time working with either one or a small number of customers and building very specifically for that. And I think people are a little bit conflating that with what startups do, which is startups are just very hands-on and do things on a little scale. But for you to actually have an FDE model, you need to have massive clients. And most people do not have massive clients. So I'm actually, I'm kind of interested to see how that plays out because I do think there's

like an over-indexing on this forward-deployed engineering model. right now where, yeah, I have a forward point engineer and the deal sizes are like 50K. And that's something we think about a lot as well. We are very hands on with our customers. But I think it's like you have to think about things holistically. You have to think about like, okay, well, how do we scale quickly? We don't have 50K clients. But for every 50K client, you have like someone that's like fully staffed to them that's like impossible to scale. So you need to find that line in the middle. And I think the full forward deploy model only works with the balance your approach. Presumably on this side,

you do care a lot about your margins. Margins you're much more open about if it's just like LLM cost or something like this. But if it's fully baked, people costs, like that's going to be a problem. Yeah, it's not even a problem necessarily from the pure dollar margins. It's just prevents you from scaling. No one can hire good people that fast. It's just hard to hire good people. So if your business is fully constrained on good people, then that's also not a good thing. What do you think the minimum customer size in revenue is to justify a forward-deployed engineer your model. Oh, like probably a million. Yeah. Yeah. Fascinating. Well, I think you know my traditional closing question for everybody, what is the kindest thing that anyone's ever done for you?

As I mentioned, our mutual friend Scott gave me a heads up here. So I did put a lot of thought into it. So when I was little, call it ages five to 13, like elementary middle school, pretty like lazy kid in general. And most kids are. There's very few people that are just like intrinsically self-motivated. I wanted to just like play games all the time or just like hang out with friends. or play sports. And yeah, my parents had like a very interesting way of raising us, me and my sister. What they did was basically when we were really little, it was like an extreme level of discipline. When I was little, it played a lot of piano, essentially like three, four hours a day.

And then four competitions, they just pull me out of school and just go hard at it. And then I guess fortunately for me, my parents decided, okay, math was probably a better way to go. And I was just like quite talented at math when I was little. even for that, it's just like full force. Like you're just committing everything. We did not have TV in the house. We didn't have video games. We didn't really take vacation growing up.

You're kind of in this mindset of you're sacrificing most things to focus on one thing. When you're a kid actually, like, you don't have no frame of reference. So it doesn't feel hard necessarily. It's because your parents are kind of setting up the criteria for you. And in hindsight, I had a very happy childhood. But I think that level of discipline and, also just competitiveness, it's very hard to gain that after your childhood is over. Because when you're in your childhood, your brain's still forming. So that kind of forms your

personality. So one, I'm very grateful for that. And I think that's also why when I talk about my generation of, there were a lot of immigrant parents from my generation that came over for grad school and they're all around my age. And I think that's like our crop of folks just are doing very well, probably because of that, probably because of the upbringing. And then when my parents did, I think, which is the more unique side is that a lot of times what happens, especially it's like the stereotypical Asian parent, is that that just like continues. And you just have like overbearing parents. I would say even though my parents were very intense about things, they never had any like semblance of like overbearingness. They wouldn't like prevent us from doing things we

wanted to do, force us to like, hey, you should like pick this or whatever and so on. What happened was towards the end of middle school and the high school, I think we had already kind of established these personalities. Basically, my parents just pounded a sort of lazy, wanted to play around kid into someone that was just very, very driven. Then to their credit, they just completely laid off. They don't have any opinions on what we do for careers, what we should do. They're like very supportive. So I think that's very kind because you cannot replace, you can even pay for that. You basically just need parents that are willing to spend a ton of time crafting this childhood for you to develop this.

And I think a lot of the things I have in life right now are from that. So that's probably the kindest thing. And my sister as well, like even growing up, she was, I think there was a lot of pressure on me. And in some way for like parents that immigrate who are also ambitious folks, is very hard for them to succeed themselves in a new environment and new country. So they put a lot of expectations on their children. Yeah, my sister was also like,

she was just always tried to sacrifice things for me. and when I was trying to achieve things. And so, yeah, try to spend as much time with their parents as possible. What was the climax of your math career? Definitely peaked in high school. So in high school, we did math contests, math Olympiad. So there's like a major contest in the USA Math Olympiad in the U.S. And there's like essentially a camp for like the top folks.

And so I went there a few years. That's probably the peak. Well, this has been so much fun. I'm so fascinated by the business that you built in our building. Thanks for explaining it to us. and bringing us sort of right to that white-hot center in so many different ways. Thanks for your time. Thanks for having. If you enjoyed this episode, visit join colossus.com, where you'll find every episode of this podcast

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