Invest Like the Best with Patrick O'Shaughnessy
· Jesse Zhang
[00:00:00] S1: —take away. So they're 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 yeah, no, actually, no, it should work this, and so on. I remember we talked to a lot of ops leaders, Matt MacInnis from Rippling is a great friend of Decagon and telling us about all the different things that happen on his team because there's so many. And similar with other ops leaders we would talk to. It was kind of a range of companies as well, people like Oura Ring 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. They're 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 other one, and then as a result I'd pay you 20k a year 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 churn out a ton of these, 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 because that would have been a waste of time. And at the end, people are paying you like $100 subscription per 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 mind 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, we have 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.
· Jesse Zhang, Patrick O'Shaughnessy
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 very smart older founders that we knew and so on, was that excuse case super obvious. There's probably just going to be incumbents that are just tacking onto the product. And because it's so obvious, there's probably a reason why 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 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 pretty non obvious and pretty difficult. And so if you can do this process well, it'll give you the most signal. [00:00:00] S0: 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? [00:00:00] S1: Yeah. So the time was probably on the order of low to mid six figures.
· Jesse Zhang, Patrick O'Shaughnessy
[00:00:00] S1: Yeah. So the time was probably on the order of low to mid six figures. [00:00:00] S0: As you neared the end of that process and settled on what Decagon does, which maybe probably is the right time to ask you to 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.
· Jesse Zhang, Patrick O'Shaughnessy
[00:00:00] S1: 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 Decagon does, it's an AI customer service agent. So that's the simplest way to think about it. And you're building a conversational 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. And 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 to. It's like how much we pay for that, I don't know. This is really exciting, but our budgets are tight right now and also 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 it step by step from there. [00:00:00] S0: Say more about the comment you made about. In hindsight, it's clear why this was the key problem.
· Jesse Zhang
[00:00:00] S1: Yeah. So I would say customer service has a bunch of nice properties that I think are very hard to reason through ahead of time. Which is why I think I feel so strongly about this process of discovery. Just really staying customer centric. One of the properties is that the ROI is really easy to justify. Internally you have these numbers already tracked. It's like, hey, we have so much conversation volume right now. We have a simple chatbot or a simple IVR phone tree. It's resolving, so to speak, like 15 to 20% of that. 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 Gen AI use cases are struggling with that right now, especially at the enterprise level, because there's risk involved. People don't want to feel like something could go wrong for them. When they release your product, leadership's going to get mad at them. It's like, you know, why'd you do this? So that is a big deal with Gen AI. I think that is one of the reasons why there's been difficult for a lot of use cases to really take off because at the end of the day there is always going to be risk because the models are non-deterministic and so something could always happen. But the nice thing with customer service is that you have a escalation path just naturally built into the way the product works. The agent's having the conversation. If for any reason it needs to exit, it'll just escalate to 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 traction at the enterprise. And coding is another one.
· Jesse Zhang, Patrick O'Shaughnessy
Coding is a little different. It's a lot more bottoms up. [00:00:00] S0: 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 looking at the revenue curves, your peers are obviously like Cursor, Cognition, et cetera. Compare and contrast. Coding and customer service.