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

· Jesse Zhang

[00:00:00] S1: 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 a croissant at the airport or something. That last step where you're actually solving someone's problem, that's where you generally can capture the most margin. Every step along the way—whoever's enriching the flour or 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 problem. And as a result, you can capture more of that. Because our customers, the way they're thinking about it is, "Great, we're investing in Decagon. We don't care that much about what Decagon's costs are." In fact, we probably don't care about at all. What we care about is what is the business ROI that we're getting. It's 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, people were, "Oh, there's ChatGPT wrappers," and so on, and yeah, a lot of wrappers. If it's too thin, it's 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 OpenAIs of the world will continue to move towards applications because it's quite hard for them to make money long-term on their API, for example, because there's such high competition. All the labs are building, it's very easy for people to swap. It's not moving from AWS to GCP is very hard. But moving from an OpenAI model to Anthropic model, you just change one line of code.

· Patrick O'Shaughnessy

[00:00:00] S0: Can you say a little bit more about this ChatGPT wrapper concept? My sense is certainly for what you built, but probably for other companies, that a lot of the work that your engineers are doing is not AI work, it's traditional software work. It's the ability to hook this system up to enterprise customers. It's good old-fashioned product and infrastructure building that is very different from what OpenAI or Anthropic are providing you. And that's hard work. Just like building any software system is hard work. Everyone's very enamored of this idea that in five years I can just show you a piece of software and just tell a coding agent, just replicate this piece of software and that's going to mean lower software moats. I don't think you think of it that way. So maybe describe your thinking or critique of this ChatGPT wrapper concern that people have?

· Jesse Zhang

[00:00:00] S1: Yeah, I think generally people say wrapper in a derogatory way. It's like, hey, you're just a wrapper. It's not black and white. Yes, there are a lot of apps that are just wrappers that are not going to become real businesses because there's just not that much value. I mean, one argument: I don't know too much about this space, but at least from the outside it has seemed like copywriting, for example, has been difficult because someone can just log into ChatGPT and just say, hey, write this for me and I'll just write it. There are things where there's not enough tooling and functionality on top of something for it to be really valuable and needed, then maybe it is easier to just leverage the models. But most of the time that's not the case. And especially when you get into agents, an agent is not just a model, right? You have to design it. You have to be able to put in guardrails, you have to be able to teach it how to do new things. And that's where the software layer on top of the models come in. And if that's valuable, then it's just much harder for you to just be made obsolete by a model update. The other side of it is okay, well now the labs are quite interested in building applications. So they're building a bunch of coding applications and Claude Code and so on. And so those will end up being competitors with Cognition or Cursor. And maybe for that reason it is wise for the coding agents to start training stuff as well. I think in our space, I would say for us right now at least, the sheer amount of functionality you have to build because it is a very top-down product, is quite large. That has nothing to do with AI. It's just stuff like, okay, how do you have observability into 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 confident? There's just all this functionality that's there that doesn't really have anything to do with AI really. And so it's just a lot of stuff that needs to be built.

· Patrick O'Shaughnessy, Jesse Zhang

[00:00:00] S0: 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, 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? What are the attributes that you've seen matter the most? [00:00:00] S1: 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 probably the folks that we are most excited to work with, they're just genuinely, 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 bureaucracy to get something going. And I think you can tell that pretty clearly in the first conversation. You can tell if someone, if they're legit about this is something where I'm going to both push aggressively but also give you a ton of feedback and the feedback's going to be good versus someone where they just know it's a board mandate and it's just AI is a thing on their to-do list. [00:00:00] S0: Is there any leader that you've come across that most exemplifies that posture?

· Patrick O'Shaughnessy, Jesse Zhang

[00:00:00] S0: Is there any leader that you've come across that most exemplifies that posture? [00:00:00] S1: Yeah. So I will say in both the sort of digital native segment, you have folks at Chime for example, super impressed with the entire team. Top down, everyone that is in the org. That's 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 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. [00:00:00] S0: 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?

· Patrick O'Shaughnessy, Jesse Zhang

[00:00:00] S0: 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? [00:00:00] S1: 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 personality. I think I generally lean a lot more towards commercial elements of every idea. And again, it's not the only way to build a company. But in my opinion, if you were just trying to optimize for 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 no one's going to work on this because where are the commercial elements of this? But I think Austin is this way too. It's made a good fit. I think we're just very locked in on okay, you just got to be super practical. [00:00:00] S0: How much would you pay for it? [00:00:00] S1: Exactly.