Speaker A: An AI agent should just be the front door of your business. And every interaction, whether it's like reactive or proactive with a customer, should be handled by AI.

Speaker B: This narrative dominated the first half of 2026, which is that Anthropic, OpenAI. They're the last startups. They're going to take over everything.

Speaker C: Even once you have AGI, agents are going to need somewhere to store work and pull information from and reason about things. I don't think software as a whole in any meaningful way is going away. Unfortunately, the Frontier Labs, they do have small models, but you can't really control them in the way that you want. So, today 90% of our workflow is on open source

Speaker D: on the specific task we want them to do. They actually outperform the large smart state-of-the-art model.

Speaker E: The thing that we built was not an agent that does customer support well, but rather an agent that follows business process. Well,

Speaker F: instead of us having to write these SOPs, it just does all of that. Let's say like we hit AGI and the models can do all sorts of things we can't even imagine today. What's Decagon's moat and like why does Decagon like 10 years from now still have a right to exist?

Host: Hey guys, welcome back to the studio.

Guest: Thanks for having us.

Jesse: Yeah, good to see you.

Host: Thank you for being here. Before we get into customer support, um I actually wanted to widen out a bit. Um, and uh, Jesse, I'm going to actually um, mention a piece that you wrote recently that went pretty viral because it's right in the middle of the zeitgeist of conversation right now on open source versus closed source models. then thinking machines um Llama 3 you know some of some very interesting open source models came out sort of right after um and there's this really interesting debate going on around what does it mean to own your destiny when it comes to AI especially in the enterprise um and what does that evolution look like by use case um so actually since that's a pretty live topic right now why don't we start there

Jesse: sounds good um so I'm gonna talk about our journey first uh just to make it very concrete for people so when we started the any uh the goal was just to get something working, right? So if when you get something when you're the goal is to get something working, of course you're just going to use the frontier models because you want to get something out there and have actually deliver value. And so we were using opening anthropic at that time they were kind of like one uping each other in terms of how the how the models performed. And then at some point as we got to larger scale and we started working with larger and larger companies and they had you know millions of customers and then we we also have uh we also launched our voice agent right so a big factor became latency so it wasn't just like can you deliver good responses you have to deliver them really fast and uh the only way to get latency down um but also kind of you make our agent operate the way we want it to is to use smaller models and when you want to go to smaller models unfortunately the the Frontier labs, they do have small models, but you you can't really control them in the way the way that you want and most small models out of the box are not going to be good enough at the task that we want them to do. So, you have to fine-tune them, you have to change them. And so, that's when we started looking at open source. So, this was about year plus ago. And um it worked really well because if you think about it in the agent, right? So, in our agent, our agent's job is to have conversations. So, it needs to do a lot of things at once, right? And like one one the first step it might do is like hm what topic is this person talking about or and something else it might do is oh is this person a bad actor that's coming in and trying to mess things up. There's all these like tasks it has to do. Each individual task doesn't need all of the intelligence of a big model. So you know all the frontier models are obviously very smart but they can do a bunch of different things. Like they can do math they can do coding. Like you just need them to be good at that one task. And so that's why you can use a smaller model and if you fine-tune it to be really good at that task and can be just as good or better than the big models, right? So that was that was step one for us. You know, about a year ago, we were like, okay, let's start using these open source models. Uh we we we took the small ones and then um you know, that's why we have now a uh a research team and it's a very expensive team, but it's we we have it because, you know, we need people that are really good at taking these open source models and and tuning them and and so on. So today 90% of our workflow is on open source and um you know again the main reason was for latency to really optimize our voice agents and um I think we've just over the last year we've seen tremendous improvement in like how how it sounds how it feels and but still also like keeping the accuracy high um and then the remaining 10% of course we're still using the uh the closed source models and the frontier models for a lot of you know new new projects or new products and uh I think that's just where the industry is moving to. So if you kind of were to generalize this every model you can kind of evaluate along three dimensions. It's you know cost intelligence and latency and depending on what you need you want to kind of be at the limit of those three and sometimes you can trade off right so in our case we knew that we actually pull back on intelligence because we all had to do was that one task but now we get these latency advantages.

Host: I want to push a tiny bit on that point actually because um often times you know when you see these debates being had on Twitter the the trade-off tends to be oh do we want uh you know the smartest model that is very expensive or can we like dumb it down a little bit and get it cheaper. I actually think that is a false trade-off, right?

Jesse: Because what we've seen in practice is even if you have a quote dumber model, you can get it, and we've seen this in practice, you can get it to higher performance on that specific task. So when we fine-tune smaller, dumber models, it's that they're just not as general purpose, but on the specific task we want them to do, they actually outperform the large, smart, state-of-the-art models, right? So we end up getting all three things. It is better at the toss. It is cheaper and it is faster.

Host: And so do you feel like today like you need the most Frontier models for really anything at Decagon because your performance is very good already. So

Jesse: we we do and we often need them we often end up needing them for auxiliary tasks, right? Where when you have uh when you sort of have auxiliary models to our sort of primary conversational flow, right? you have an agent and it's helping a customer with their rebooking or it's helping them with a process in healthcare then these are like well-defined pots. So we have smart bos models to do that but we've for instance recently launched do autopilot right which is our agent that improves the core conversational agent. Now, for something like autopilot, it is doing a very complicated job, right? It's saying, I'm going to go and review a million conversations that just happened. I'm going to try and find trends. I'm going to create variants of the primary model and see which of those variants does better. So, now this is a much more broad open-ended exploratory task. So, we think for jobs like that, frontier models that are very smart, that can try out a lot of things make a lot of sense.

Host: H do you think that um I mean you guys obviously and you referenced it um Jesse that you have a research team right you guys launched Decagon Labs but even before the formal launch it's always been a part of your culture um do you think that enterprises will get there as well on post training open source models

Guest: oh okay interesting what's the timeline

Jesse: yeah I think I think they'll get there but it'll probably take longer than people think because you know even with our team fine-tuning these models is non-trivial

Speaker A: it's not just like oh you And it's like all right, we made the decision to use open source. Like let's just use open source. Like you have to get the data. And then more importantly, you have to like have good eval. Um and if you think about our evals, right, our evals are very specific to us. You can't just like use some public eval set and like that that just does the job. It's like we're testing it on our task and so we have to generate our own benchmarks and evals. Uh but I think the point is that um at a certain point it's strictly better to use open source models because when your use case is solidified and you're in production at scale and you're pretty sure this is the the sort of shape of the agent then there's no reason not to use open source because you you get these latency benefits and at the same time you get the cost benefits. I mean again we didn't do these for cost benefits but that's like a nice side effect right

Speaker B: and once you're there it's like why why use Frontier for that? But for everything that's new and sort of experimental or you're as Asha was saying what or like kind of these products where you really need the intelligence you're still going to use frontier models and it's just so much easier to use frontier models you know they're worrying about the infra you just like like they just um you just use the APIs and I think that's why in enterprises right now even though there's there's a lot of hype for open source the sort of share of of open source inference is actually going down right now because people are spinning up all these new use cases and if you're spinning up new use caseas is of course you're going to use the frontier models until until they're working.

Speaker A: Yeah.

Speaker B: But of those use cases, you know, some might die off, but like some might like the enterprises are like, "Okay, great. We want to keep shipping this and like roll it out."

Speaker A: Once it's at that point, they're heavily incentivized to use open source. It's way cheaper and faster.

Speaker B: At that point, they'll maybe they can do it in house or maybe they'll need help uh from people to to help them fine-tune it. But that that will eventually happen. I just think it'll be kind of slow. Even right now in our experience

Speaker B: like enterprises have a lot of desire to move but they can only do so many use cases at once. You know they there is inertia there and they have to you have to go through all the you know model risk governance and all the security things and so um I think it'll take time but it will get there.

Speaker A: Yeah. the the the other reason I think it makes a lot of sense for uh enterprises to kind of build their you know cool bundle of labs is that the shape of these models is changing constantly right we don't just build our set of open source models and then you know it's done we can move on to our next thing and maybe we'll revisit this in two years

Speaker B: you often need to train new models all the time

Speaker A: because

Speaker B: as the frontier changes as the capability of the models changes you come up with new use cases for them. You find new places where you're like, "Oh, this task seems to be getting repeated a lot because now I have this totally new like, you know, frontier model or open source model that now has this capability that they didn't have before, right? So we find ourselves constantly training net new models and deprecating old ones that are no longer relevant because you know maybe the frontier has advanced a lot. The open source frontier has advanced a lot and you know the model out of the box can do a lot of things that it couldn't do before. So we

Speaker A: because the model landscape is changing so quickly.

Speaker A: Uh Dagon Labs is in a way a model factory of sorts, right? We really built it to um like uh compress the time between new model coming out and you know useful fine-tuned to our task model kind of popping out the other end.

Speaker B: Yeah.

Speaker A: Just because happens all the time.

Interviewer: How do you guys think about what to inhouse from a talent perspective versus there's also a pretty broad ecosystem right now that is you know could be RL as a service eval etc. like how do you guys what's your framework for hey this is mission critical and we need to do this best versus yes it'd be great to you know outsource this.

Speaker A: Um you know in practice we've seen that so many things uh relevant to model training are so tightly coupled to the use case that we have

Speaker A: right that we find that we end up needing to build a lot of tooling internally when we have um open-source uh open source models that we want to fine-tune we find that if we can clearly tailor our eval to customer outcomes it's way better than just looking at like loss curves over time right we're not just saying oh can I do this one specific task. We're measuring the entire system end to end. We're not just saying is this model good at this task. We're saying is this model working in concert with all these other models delivering the end customer outcome that we care about. And because that is so unique to our setup, we've found that in practice, we've needed to build a lot of uh a lot of the infrastructure that we need to train these models and evaluate them. Now, for other things like getting labeled data and measuring the diversity of our data sets, we're like, "Yep, these are tasks that are common across fossil companies." In which case, we want to buy things from other vendors because that'll just help us get those models to production faster. Ultimately, the only thing that we care about is how can we get the best model to production as quickly as we can.

Interviewer: And so, it sounds like, you know, there's a lot of talk right now about like tokconomics and how expensive it is to actually run a lot of these models. Based on this conversation, it doesn't sound like you guys spend that much time actually thinking about the cost of these models. Is that correct? It's really about the performance for you.

Speaker B: Performance uh latency and accuracy is definitely the driver factor for most of this, right? Cost is a nice benefit in that, you know, uh surprisingly this is one of the few like tasks where you kind of get all the things for free, right? like we we don't actually have to trade off cost and latency and performance and so just by optimizing for the driver is actually latency and performance and we just get cost as a nice side benefit

Interviewer: and do you think that's like something that's unique to the way Decagon is run or do you think there's something about like this conversation about tokconomics that for some reason just doesn't affect you guys in the same way?

Speaker B: No, I think if you're a growth stage company, then you're honestly like obviously we have to be responsible with our costs, but that's not the highest priority, right? The highest priority is just growing. And when you're talking to a customer, they don't really care what your costs are. They just care about how well your agent performs.

Speaker A: So that's um that that's the main thing. So actually, if you look at like our unit of output of our agent, which is in our case a conversation, um that's really what our customers care about. They don't really care about how much that conversation costs to us or how many tokens are in it. And actually over time, the number of tokens we're using per conversation has gone up because we're actually doing more model calls to make the quality better, to do more checks, to um paralyze more things. And that's just the stage we're in right now. Like eventually, you know, if we've won the market, then like that's when it's like, okay, yeah, you know, now it's time to look at our cost and see if we can optimize further. But it's just not the top priority.

Speaker B: Yeah. I also think we as a company are at a different stage than a lot of people that are talking about tokconomics uh on on on X right because the tokconomic debate really is around uh hey I'm on a frontier model today should I like how do I analyze my cost and should I move to open source right and there I actually think it makes complete sense if we ran our entire business exclusively on frontier models I would care a lot about cost and I would think a lot about that but once you're already once you've already

Speaker A: made the jump to saying okay now now we know how to think about open source models and we know how to decompose a problem we know how to uh build train and deploy these models very very quickly all of a sudden that the cost aspect becomes a lot less pressing um but if you're still in the world where you're using frontier models for everything then I do thinking about toolics makes makes a lot of sense

Speaker B: I just want to make this meta observation that a lot of the conversation even just now has in about things like training models, right? Uh we're talking about reinforcement learning and um it really does blow away what I think is, you know, a lingering misperception about what an AI application is. Um and just to get into that debate a little bit because I think it's sort of this narrative that dominated the first half of 2026, which is that anthropic open AI, they're the last startups. They're going to take over everything. applications, their thin UIs with FTEEs, you know, with implementation attached to it. Um, you know, we talked a little bit about Deck Gun Labs, but can you guys just share what is your like how do you think about this potentially false dichotomy of app versus infrastructure company? Um, clearly you guys are so much more than the UI or the implementation. Um and uh you know does agent lab you know popular uh description um floating around the last couple of weeks like does that describe it like how do you guys think of decagon?

Speaker A: Um so I'll give a quick perspective just like from the from the POV of like an enterprise and then maybe we can talk about like broader the industry. So let's say I'm like a Fortune 100 company, right? and I'm looking out there on all my use cases and I have a choice of partnering with an application company or uh sort of using the labs and building from scratch. Um I think there is a lot of merit to partnering with the labs in certain cases. I think if you look at our case right like we we just talked about all this fine-tuning stuff. I think a common misconception that people have is you know fine-tuning is is a way to like customize it for that customer. In fact, most of the fine tetuning we do is like customizing it for our use case, like the customer service use case.

Speaker C: Yeah.

Speaker A: And it's worth it for us to do it because that's all we do, right? We do these agents across all of these different customers. And so it is worth it for us to put in a ton of time and research into like how do you tune this one model to be good at selecting customer service topics. But if you're the enterprise, is it really worth your valuable research resources to like tune a model for these like customer service behaviors? Probably not, right? So that that's like that's one reason why people um partner with applications. Another reason is

Speaker A: let's say I do put in the engineering effort to build like a some agent myself using the frontier models. Um and you know to my earlier point you know I'm not fine-tuning for behavior but I'm I'm sort of teaching the AI my own procedures. And again that doesn't happen through finetuning that that happens like in context because if you were to fine-tune on that you would have to reverse it every single time you you change your procedures which doesn't make sense. And so you kind of build out your logic and you're building your business logic in well then you launch the agent and then the second day you look at your conversations and you're like oh well actually I need to change these three things

Speaker A: and now it's more engineering effort to do that and it's like constantly engineering effort. I think people will partner with applications when the use case calls for a broader platform where there's a lot of value in using you know the stuff that we've fine-tuned and using the the software stack we've built on top of the models to capture business logic and that stuff has nothing to do with the models right like how how the business logic gets captured by this AI like how do you handle someone calling in because you know their flight was canceled and they need to rebook three people at once it's like that is business logic that the AI needs to and you're encoding that, but that has nothing to do with the models themselves. And so that that has to exist in the application layer. I think that's where applications will still shine because you still need the application there and it's not so much the models. And the labs themselves will have more application capabilities, but those will be fairly general. Like they're you can maybe build general agents that can do this thing or that thing. But

Speaker A: for a lot of these like core verticals like ours, our our our thesis is that, you know, you're going to need something that's like very deep and has all the integrations, has all the ability to capture business logic, has the ability to run, you know, tests and experiments and then review the conversations and run QA and like, you know, have tooling for your compliance team to monitor like what's happening. So that that's our thesis on it. And so it's kind of it's it's not black and white like there will be some use cases where it does make sense to use the the frontier models but there will be these like core verticals where going super deep makes sense.

Speaker C: Yeah. I also think you know everyone is kind of bleeding into everybody else's space a little bit right like

Speaker A: that's a convergence

Speaker C: all the all the labs are building applications on top of it because rightly so they're saying this is how the enterprises do us more right and this is how the enterprises see ROI from using our products uh us on the application layer we're realizing that hey we can squeeze out a lot more performance and latency and cost for the use cases that we care about by building our own models, right? Which and and I think this uh this split this kind of bleed over makes sense and I think it'll continue. I'm not as bought into the the labs are less startup view of the world though and I think this is true both for SAS companies and for the new AI startups because in a way we human beings are kind of AGI right and human beings have needed to use software for lots of things you know you need databases to put stuff in you need CRM to track things and I think even once you have AGI all our AGI agents are going to need somewhere to store work and pull information from and reason about things. So I think you know a certain class of SAS companies that were solely built for people to do work might face a bit of heat but I I don't think software as a whole in any meaningful way is going away and I still think you know on the application layer

Speaker C: there's so many different kinds of work that that need to be done that can be done faster more efficiently more cheaply uh so I think there will always be a space for application layer companies maybe in the long term application layer companies just become labs for specific verticals, you know, because your primary product ends up being the models that are just really good at doing those specific tasks. But I think the application layer is probably here to stay.

Speaker B: Uh I also want to kind of pick on another thing that you kind of said in tossing.

Speaker C: Yeah, please.

Speaker B: And maybe this is like a a spicier take of oh are application layer companies just for deployed companies that are kind of doing the last mile of work.

Speaker C: Yes. Right. Well, well, I was saying that's a misperception, but I think it is a common one. Yeah,

Speaker B: I think it's a really hot thing. Also, again, not to pick on tech Twitter to be like, oh, like, you know, we need to bring back the four deployed engineer and you know, every company is hiring tons of four deployed engineers. Uh, I think this is a trap

Speaker B: actually.

Speaker C: Oh, same more. Okay. My my view on this is that for deployed engineers are necessary or newly necessary for early stage AI companies because the workflows are new, right? If you're building a SAS company 5 years ago, most SAS products are pretty well explored, right? like you roughly know what the user is trying to do and your job is maybe come with a slightly cleaner workflows but broadly you know what the user is trying to do with a design app or a CRM or

Speaker A: Uh but long term, I think they should just be building product, right? Like once you know what the workflow is, you should not be relying on four deployed engineers anymore because once you know what the workflow is, if you can productize it, you should productize it and then become, you know, typical company with these scaling properties of a tech company.

Speaker B: Yeah. And if you can't do that, then you're just building a glorified consulting truck.

Speaker C: Well, I'd love to dig into this more because I know when we started working together, probably almost exactly three years ago, and you guys landed on this idea, there were two things that were relatively contrarian that now feel kind of standard. The first was when you started an AI customer service, a lot of people were like, that's a G GPT rapper. And we talked a lot about like why that's not the case. And then the second thing you did was say like, hey, we actually want to do the work ourselves, like we don't want to just be a software platform. And now we have the term for that. That's like AI agents and everything. and you've popularized agent PMs and forward deployed as a new type of role or more common type of role um in Silicon Valley, but your like agent PM/forward deployed team has actually evolved a lot since you first got started to now and so would love to talk about that like in the early days what did it actually look like and then as you started to learn about these workflows and were able to productize them better um how has that function actually evolved for you guys? Yeah, if you look at a lot of our four deployed teams, they are building product in one way or another, right? So all of our for deployed engineers for instance build core product, right? Their job is to go in and understand as we're working with an enterprise, what are the things that they need that the product does not do today, but the output of that is not a one-off thing that is just built for that customer. It is something that is contributed to core product in a way that the next 10 customers that ask about the same thing get it for free.

Speaker A: Right? Uh similarly our agent PMs are working with our customers to understand you know how is the product broken today? How can this actually be deployable within an enterprise? What are the new things that we need to build to our core product to make it deployable within an enterprise? Uh so at the end of the day all of this boils out boils down into product improvements either through actual product improvements or through honestly process improvements right like we work with very very large enterprises and we help them through the journey to go from okay this is what your or looks like today here is how we can take you through changing your processes through implementing new technology into this world where AI agents are doing a lot of work for you. So a lot of their product work is also processizing a lot of you know how we make that how we help a company through that transition.

Speaker C: And also you used to be a deployment strategist at Palunteer. So you're very familiar with the forward deployed model that Palunteer popularized. How different is that at what you did at Palanteer versus the way you conceptualize this role at Deagon. So I do think people use the term FD very loosely at Silicon Valley.

Speaker B: Yeah. And I think it's it's dangerous to mix the two of free consulting work versus actually doing product. Uh you know Sham um who's the CTF founder today had had a phrase um internal I think now it's been written about a ton. He would say uh forward deployed engineers eat pain and excrete product. No, that was kind of

Speaker A: Yeah, I think it's a it's like a massive misconception because people are like, "Oh, Palunteer is such a hot company and like they're doing so well and like this is like a cool take on how to implement stuff." But first of all, very few companies, if any, can do what Palanteer does, which is like close massive deals off the bat and like it's kind of worth it to spend all that effort. So I think a lot of the uh you know people that are doing this like super forward deployed strategy and like hey we'll do any AI use case for you um they will eventually have to reckon with like okay can we find a product that's scalable otherwise you are just building like a a modern like accent or something which yeah could be could be good if if that's what you want to do but I think people kind of conflate the two it's like hey I'm building a hot like software company and you know we also have FTEES and like that's our big strategy So that's something that we've generally been very mindful about from the beginning is that we really view ourselves uh in our core it's a it's a productled company right um in in the sense of you know we're building a a core product that everyone can use and we're not just here to even though we have a large team now just like build whatever use case that pops up because you know ultimately everything has to go into the core product otherwise you can't scale essentially

Speaker B: um at the same time of course you know I should say we also kind of salesled in sense of like the product is informed by sales. So we're not sitting there and just you know coming up with product to build and so you kind of have this system where as a was saying we have all these people that are you know for deployed in the sense of they work very closely with our customers but their job isn't just to spin up like random new use cases here and there and like just doing whatever the customer wants because in the short term that it could actually lead to like larger contracts and you can kind of hunt for wherever the the pain is but then long term it's just very difficult to scale. So their job is to kind of compile all of those learnings from sales into a core product. And the goal at the end of the day is like we should have the best product out there and like be able to iterate on that faster than anyone else. And that's in our space at least our our vision of like hey the the winner in this space is going to be very product driven.

Speaker C: I actually want to pull on this thread of you know that you talked about where your FDs are actually approving the product. You know Decon is a very product driven company. Um, so just to kick off with a very small seemingly unrelated anecdote, but um I was just trying to convince uh someone on the broader team to not leave a 16Z for a Frontier Lab. And one of my arguments was that there was a good long-term career here. And the response uh that this person gave me was we'll have AGI, we don't need careers in the long term. Um, and it really hit me because I thought I was AGI pill, but I had really not thought about from that perspective. Um, and I'm curious, you know, we talk about the product improving, right? Can you make that concrete for us? Like what are some of the oh moments as you improve your product for your customer either from your end or your customer's end if you can share on like, oh my god, I didn't realize AI could do that. And then of course I'm going to ask you your thoughts on AGI and what that timeline looks like because you're in the nitty-gritty trenches of the enterprises using AI. So you may have a different perspective than you know we do.

Speaker A: The first thing I want to say is uh I'm like certain there will be careers after agi. The reason for that is

Speaker B: like most of our jobs

Speaker A: for sure are for jobs are kind of like made up to begin. Most jobs are made selfless. A very real job

Speaker B: unless you're like building infrastructure or like growing food or something. It's like like most jobs are kind of like layers of abstraction built on top of like other stuff, right?

Jesse: And that that isn't to say like the jobs aren't valuable. It's just they're kind of made up. So when AGI is here, like it'll change people's jobs, but every people sell jobs because you're still going to do things for other humans and and whatever. So I I don't really believe that careers will be gone after AGI. I don't think people are just going to be sitting around.

I'll say one observation which is like for me it was definitely um duet so early days when we're building the product right like the the core problem we're solving again being back to salesled is we talked to a bunch of customers and they're like yeah the value we want to get out of what you're building for us is that we can put it in front of customers they can have conversations and it's giving them much better experience and also it's like you know way easier for us operationally right it's like you're saving cost and you're making customers happier so that's that's like the first agent that we built and that was like the core agent we we worked on for the first like year year to two years and the agent on our end when we were building it consisted of a ton of stuff like we would have to um you know write these procedures and we kind of create our own format of procedures we call them agent operating procedures that teach the AI how to do things we had to write these tools that the procedures can use to access systems and pull APIs and whatever and then after that we need to like create all these tests to make sure that this thing is working well and that you can kind of simulate all these different sit situations and afterwards once they're in production like we would manually be reading conversations, right? There's like a ton of work that goes into it even though the core product we're building is is itself an agent.

And so what duet is is it's kind of a separate agent. It's like a second agent that's much bigger, much slower, but its job is to do all the tasks I just described. So now instead of us having to write these AOPs and write these integrations and tools into their systems and write these tests and monitor the conversations, do just does all of that, right? So it's like a it's a second agent that is smart enough to do all these things and um it's it just feels very magical because you can just literally tell it like hey I've nothing built yet so far, but here's a bunch of transcripts I have and here's some documentation. like you go figure out the best way to like do these all these procedures I want

Jesse: and it'll go do it and then of its own accord it'll also write the tests and simulations that go along with those and once you're done with that and you actually put in front of customers it'll be the one that's monitoring all the conversations and it'll flag things where things are going well or poorly and it'll say like yeah I read you know these thousand conversations and actually there's this one topic that we do really poorly on and I've noticed that and I've also drafted these improvements for you so it's like wow It's like one agent that can do all of that. And so that that's very magical because well first of all it was not possible when we first started the company. It only became possible when all the reasoning models got better and of course anthropic open are making these reasoning models mostly for like the cloud codes of the world but they are also really good for like duet for example and that was kind of like a moment where it's like oh wow like first of all you can just see the improvement over time of the models.

Host: Yeah.

Jesse: And two, it can do all these tasks where it just would not be able we would not have expected AI to be able to do all these tasks at once, but it can do them very well. And um I think that's that was like a very visceral moment of like, okay, wow, the models are getting a lot better and they're becoming like very generalized, you know, like they can do all these things. Like clearly the models were not trained on like our specific task, which is you writing these procedures and and writing these tests, but they're still good at it.

Speaker B: And also um to your other question of how you know how did we come up with all of this and how how did the product improve every single thing that Jesse just talked about was the result of four deployed people doing things and us figuring out how to productize it right so for instance um we realized that hey when we go into a new customer we need to spend all this time writing up the AOPs manually and we're like wow this is quite a lot of time how do we productize this and we built that into duet and then the Second part which we called duet autopilot was uh once we built duet you know people were using duet to write things up and then we're like oh wow there's still a lot of time that goes into iterating upon the agent right once it goes live like reviewing conversations figuring out how to improve it and we're like great let's productize that as det autopilot in fact AOPs themselves were a result of this exact scenario because before AOPS you would have to write all these procedures in code and then we found that oh it's taking a lot of for deployed engineering work to write all these things in code. Wow, wouldn't it be so much easier and efficient if we could productize it by writing it in plain text, right? So the way we think about like product improvements for deployed engineering investment, everything is built around what can we productize from forward deployed work so that engineers and any kind of customerf facing resources on our team don't need to be kind of as heavily involved.

Host: H can I ask you as like kind of a blunt question on this line of thinking? So I know we've already talked about like why open Aan anthropic won't be the last startups and you've talked about like why there's room to have like much more specific use cases and specific companies but you know you you you've also got had like oh moments where you're like these labs are getting so much better and the models are getting so much better. Um and so long term let's say like we we hit AGI and the LA the models can do all sorts of things we can't even imagine today. What's decagon's moat at the end of the day after all of that and like why does decagon like 10 years from now still have a right to exist?

Speaker B: I think in the short term actually it is the ability to work with enterprise resources and what I mean by this is that the capability of models today is far greater than they are being used for within the enterprise right by which you can't just take a model and say I'm just going to give this model access to everything within the enterprise and I'll it'll just figure everything out right like that's practically not how these things So to make models like this deployable within the enterprise, right? Like let's assume that every model is like just perfect and makes no mistakes. But to make something like this deployable within the enterprise, you need to say, okay, I need a way to be able to tell the model what it can and cannot do and make sure that it cannot do anything like catastrophically wrong. Then I need a way to make sure that, you know, hundreds of people within the enterprise can collaborate to make sure that the agent is behaving as expected in the use cases in which they are experts. Then I need a way to be able to test this model and make sure that it doesn't cross any like regulatory lines that I have and test it, make sure it works well. Then I need a way to, you know, look over the, you know, millions of conversations that happen for me to extract insights for the rest of my teams, right? Right. So there's a lot of just infrastructure and software that you need to build around these models to make them deployable within an enterprise to make them be uh work with all the legacy systems that these companies have. And so I think for the next few years that's probably going to be a you know the primary thing that these models need to be able to work. uh now once that gets commoditized because the agents can build that on the fly that I don't know and we'll figure out in three years from now by the way I think just

Interviewer: listening to you guys it's very clear that you guys deeply understand how to sell AI to the enterprise and I don't just mean you know mid-market newly IPOed companies I'm talking about some of the largest companies in the world um and I think this is particularly interesting because you you both are very very technical but also go to market animals Um, so I want to talk a little bit about that. Um, but you know, it just feels like you've turned what maybe started as feeling more like a David and Goliath with multiple Goliaths. Um, uh, market dynamic to really a two-horse race between you and Sierra. Um, so share a little bit more about why some of the largest enterprises in the world are buying from you guys. And I think what's really remarkable to us as people have, you know, studied application software for, you know, over a decade, the sales cycles are crazy fast. I mean, I'm sure you guys with urgency want them to be faster, but like usually you don't sell contracts that big to enterprises that big, right? That takes two years sometimes. So, um, maybe just share a little bit more on this is a long question, but like how did you grow that commercial? did you come with that into founding the business and what's resonating with these large companies that enables you to get in and move so quickly?

Decagon Founder: Yeah, I mean we we have a lot of respect for for Sierra and also just you know other folks in the space generally the big platforms like they all I think the platforms themselves move a bit slower but I think that we've we met a lot of those teams they're very competent teams they're optimizing for a lot of different things at once. uh Decon versus Sierra. I mean our our most recent customer actually uh turned off of Sierra to come to Dekagon and sort of the reasoning if when we asked them was um it was this kind of goes back to this like deployment model. It's like you know when when they worked with Sierra it was mostly FDES and it just felt like a black box where you know the the FDs were good but they had to go through the FDS for everything. So to build new journeys or to uh even get like a deeper understanding of what was happening in the conversations and then over time that that kind of just created a lot of drag on how quickly they could move right so in the initial deployment that was good but then over time maybe the FDES were staffed other things and it just took them a while uh to get insight into what was happening and then build out new journeys right so over over the course of the year they maybe built out uh I think they said three

Decagon Founder: um and so the reason they came to us is because they they had that frustration and they wanted to kind of have a different model where it was a lot more productized, right? Back to us being like very product driven in our vision. It's they should have a a core product that even if we're there helping them, even if we are forward deployed, it is like a in in service of helping them build a product that they can use themselves and iterate really fast and and kind of have everything in their control, right? So, we like to call this like a glass box approach instead of a black box. And uh and yeah, so within a basically a month they spun up like seven new journeys on Ducky.

Interviewer: Wow. Yeah. Mostly

Decagon Founder: and it had taken three a year to get three.

Interviewer: Yeah.

Decagon Founder: Okay. Interesting. So it's just kind of the speed of iteration like some teams will really like this like hey we have control of it. We our teams especially our non-technical people can come in and do things and they understand what's happening in the in the conversations. And maybe there's other teams out there that actually do like the like, hey, you guys do everything for us and uh that approach. But that that's kind of the difference in the approaches and that's why we've been I would say having a lot of success there. And then zooming out just selling to the enterprise. Yeah. I mean this is something that uh you know Asha and I have never sold to the enterprise before and I think it just so happens that both of us um find sales exciting

Decagon Founder: and the enterprise it was kind of like a quick learning curve for us. So, I don't think I think a lot of it honestly came kind of naturally just cuz our space is so hot and like generally in these conversations we're not really having to convince people to like invest in this space. It's like more of hey, we're the right approach for you. So, like you partner with us and uh yeah, with the enterprises it's it's really just about like navigating the orgs and really having empathy for what they value and what they're afraid of.

Decagon Founder: Um, so yeah, I mean we we just back to being salesled, right? Like we we always from the beginning were like, hey, we're going to be like extremely strong on the go to market side and that's going to inform the product even though we have this product driven philosophy like we don't want to just be dreaming up random products to build. Um, so we always had that DNA and then in the early days we were just yeah pushing really hard and I think I also want to say I think we got kind of blessed with a a really strong early sales team

Decagon Founder: and we have a lot of really talented people in that group and that helped us really get leverage as we were talking to you know the big enterprises

Interviewer: some of whom cold applied to you guys in the early days I remember.

Decagon Founder: Yes. Yeah. Yeah.

Interviewer: Yeah. Cold cold applied. Um

Decagon Founder: cuz I think they were working in the in the space already and they they were seeing from afar like what Decagon was starting to do.

Interviewer: Yeah.

Decagon Founder: Yeah. So some of them had like non traditional sales backgrounds, you know. So they had nontraditional sales backgrounds. They're coming into sales. The other profile we had a lot of in the early days were just like like Ivy League athletes, I guess. And those profiles were kind of a good foundation for the group. And you know, we've had to scale that team really fast, which is never easy. So there are things that we're still trying to catch up on in terms of enablement and and or structure, but um because we've always had that intensity on the sales side and the whole company knows that like you know everything starts with sales and it kind of propagates back um you know we've always had that focus.

Decagon Founder: the, you know, the other thing I think that helped us a lot, um, you know, to your earlier point about how did you get some of these large deals closed so quickly was, uh, I think we were very curious about how we could productize parts of it within the enterprise, right? By which I mean, we aren't a company that just says, "Hey, here's a product. We'll throw it over the wall and, you know, you get it a year later." Because within a lot of these enterprises, the question they have internally in addition to will this product work for me is can I actually get this live? Right? And within a lot of these enterprises, it's actually complicated, especially if you're in financial services and you're regulated for instance. So we actually spent a lot of time sort of uh mapping out that part of the journey very well. So that when we walk in to one of these enterprises, we can walk them through in very granular detail, how we go from this first meeting today to going live at 100%.

Decagon Founder: So yeah, you know, at this stage for a company like you, uh this is what your model risk process is likely to be. This is how uh your testing process should look like. This is how we should do the initial roll out. This is how we should catch any issues that come up and how we're going to fix them and how we'll ensure that they don't happen again. So the product and technology part of what we sell is important, but for these large companies, equally important is us uh helping them think through the process to actually get this deployed and at scale because I think that is something that often gets overlooked by by tech companies selling into enterprise.

Interviewer: Yeah, for sure. And um by the way, I can also personally attest to the strength of your early go to market team, having met a bunch of them. Um, that being said, I don't want to underplay how many times a decision maker has told me that one of the

Speaker A: ...reasons of many, right, that they're going with Decagon is they want to make a bet on you guys. They're they'll literally say, "We think the founding team of Decagon is going to move the fastest. This is a very fast-paced market. it's changing on a weekly if not daily basis and we think that you guys are going to look at the chess pieces and make the right moves because it's not sort of like a oh everything's going to be the same in you know a year or even six months type of market. Um so I'm curious I mean don't give up too much alpha here but how much time do you guys spend on sales as founders and how has that evolved over time?"

Speaker B: >> Uh I probably spend most of my time like 80% maybe.

Speaker C: >> Yeah. Okay. Um, and yeah, I think a lot of it is just pushing speed. So, some of it is like, hey, like as one of the founders, you just have to be in calls and like, you know, people want to meet the founders, but also it's just how can I be the sort of the main force that's pushing our team to go faster, but also just like the the partnership to move faster. One of the things with that is like you know now we work with you know several of the largest you know banks in the world and airlines and and telos and no matter how fast you go like there's all there it's those are still like massive organizations and it will take time and so one of the things that we really try to do is you know kind of take the project and piece meal it so we're not just deploying across every surface area every use case at once that's really just pick you know one or two of the top use cases and just get a win.

Speaker B: And

I would say that's that's been helpful, right? If you navigate like a big bank like it's not going to be like a quick like, you know, you just close a big deal,

right? At least for us it's not maybe some people can do it but um it's yeah it's still a lot of effort and it's it takes time and so you have to figure out ways to you know design things from a process perspective and an oracle perspective and in a product perspective as well that like just keep shortening that and like finding clever tactics and uh that kind of takes founder involvement. um you you wouldn't really expect a like a a sales team to just like constantly be coming up with new configurations there because you know they're not responsible for the product. They're not responsible for you know the end to end you know process that the company runs.

Speaker B: >> So having the founder involved there is very important I would say and then the sales people their job is to kind of execute on the sales side like build champions and navigate the org and so on. The other reason um to also spend a ton of time with both sales prospects and existing customers is also because the market is changing so quickly being able to really quickly understand

what are the things that we are not doing today that we should be

doing very quickly right because model capabilities are changing all the time as models roll out people are seeing other things that happen and they're like oh wow this is really cool and like I'm seeing this here and you guys aren't doing that as well. Um so being able to like stay really really close to that feedback loop because um that's something we never want to never want to let go of.

Speaker A: >> Yeah. Do you want to talk about how that like informs product roadmap? Meaning

probably like two two years ago or so uh most of Decagon's customers were focused purely on customer support and nowadays I would say that's that's actually not true. You guys have broadened your vision to mostly like to be like an AI concierge for your customers. Do you want to talk about what the distinction between those two actually is and in practice like what that means for both your sales teams and your product teams?

Speaker B: >> So you know all the when we launched the original set of use cases that we sold were in customer support. Um and the reason was because one that was one of the biggest challenges that a lot of our early customers were facing and two that was where the capabilities of the models ended at the time right like that is all that is the pretty much the limit of what they were capable of doing. Now, however, as models have gotten better and our customers have realized, well, why would I have one set of models that just learns about my customers when they have another when they have a problem and something else when they come to me to buy something, right? And so, we had a customer that we originally went live with them for customer support. Uh, and then they realized they were like, well, you know a lot about our product now. You know about the capabilities that it has because you need to do that for customer support. you know how we like talking to our customers and our brand. Um, can you help us with inbound sales, right? When someone comes in, answer questions about us, do some discovery and then, you know, assign it to the right uh uh enterprise rep if it's, you know, a deal of large enough value. We had another customer that um started using us for a lot of operational workflows, right? So uh we are able to now proactively reach out to them once we start seeing um any kind of issues on on that on that customer's account. Um because ultimately at the end of the day the thing that we built and we kind of built this intentionally from the start was not an agent that does customer support well but rather an agent that follows business process well right and executing on operational workflows doing sales lead qualifications on certain customer support questions at the end of the day is just an agent following a business process. And we kind of built it flexibly enough to kind of do all these things because we realized that hey at a certain points the models are going to get better and they have.

Speaker A: >> And what do they specifically get better at that allows you to do that?

Speaker B: >> It is specifically the ability to follow instructions. Well, right. So when you had um models, you know, let's say a few years ago, you'd have to give it very very tight guidance, very specific instructions that you didn't want it to deviate from. And as the models got smarter, you could kind of give it broader and broader guidance, bigger and bigger instructions, and just trust that the models have good enough sense to interpreted like a human would and kind of fill in any missing gaps, right? Because for again for customer support, you can have a very tight path that the model should follow. And that's all you really need. Whereas for sales qualifications, you kind of want to ask open-ended discovery questions. The conversation is going to kind of bob and weave. And so you need the model to kind of fill in with reasonable things. So that's specifically kind of what the what the models got better at over the years.

Speaker C: >> Yeah. Um I think if if you think about you know what is the like 12 month product road map because things are moving so fast like the answer the real answer to that is like we kind of like see how it evolves and we obviously know what we're working on now but I think realistically in in today's AI world it's very difficult to have like a a 12-month road map to a tea. you maybe know have like some themes of what you want to build but ideally if you have those things you just build it like right now because it's so so fast to build things now so um I would say that that is that is one element but then the sort of long-term vision is still very clear to us now right which is we use the term concierge but really it just means like hey an AI agent should just be the front door of your business or your brand and every interaction whether it's like reactive or proactive with a customer uh should be handled by by AI and we've already seen that AI is very good at that right customer service is like a huge pillar of that where it's all these inbound interactions but why not have also be able to do all these other things. So over time again we're we're not trying to figure out on our own what these things are. We kind of have now have a lot of customers that will give us signal on the things that they care about and those will be the things that we built.

Interviewer: We've talked a lot about how the models are getting better and obviously your capabilities are moving along even ahead of that progress. What are some of the bottlenecks right now that you're seeing? Whether that's on the capability side. I don't know, it could also be around persistent memory. I don't know if you guys feel like that's kind of up to snuff on where you would like it, or could be other bottlenecks, but curious like what would you like to see and what's kind of holding you back?

Speaker A: Hiring.

Interviewer: Got it. So, it's less on the AI side. It's actually like...

Speaker A: It's actually like we are voracious consumers of tokens but we would always love more great people. I think there's so much to build these days that it's you know...

Interviewer: And why can't you hire AI agents to do the things that you're doing?

Speaker A: Yeah, we are voracious consumers of tokens, really like our token bills are very very large. But you know there are still things I don't quite yet think we're at the point where we can have the AI agents make decisions on what to build and kind of have that...

Speaker B: ...the taste of is this done yet.

Speaker A: Right, so we can outsource a lot of specific execution steps but I don't yet think they're at the point where they can make the call on what to build, what to exclude, things like that.

Interviewer: So the models are improving, have they since like you found it three years ago, has it changed your hiring needs at all? Because a lot of people are like, "Oh, you can build like one-person unicorns, you know, like..."

Speaker A: Yeah, you know, I think this argument does get tossed around a lot, but I think an easy counter example to this is all the AI coding startups are hiring like crazy. You know, they're like the most sophisticated users presumably of these models and they are hiring like crazy. I think the reason is just because everybody has access to these tools and so if our competitors are going to use them and build more things, we need to build more things, right? If somebody else said, "Oh, here's our road map and now we can get through it in a third of the time." And then they just stop hiring. We would just take that to mean, "Wow, we can get through it in a third of the time. Great, let's build three times as much stuff." And turns out everybody, you know, does the same calculus. So, everybody both needs to keep hiring more and ships more. So, it is great for consumers of these models. But I don't think it has materially changed our hiring plan.

Interviewer: It's so funny. I thought you were going to say something like, I don't know, latency of voice models or something like that, but it seems like on the technological bottleneck side, it's...

Speaker A: Yeah, there's still stuff that we're waiting for, right? Like voice-to-voice models is an interesting frontier that there's still research happening on. You know, getting smaller models to be smarter out of the box, right. So there are going to be still developments that we are watching closely that we care about, but from a business perspective it's less on the model side and more on the just like can you build the company fast.

Interviewer: Yeah, actually maybe since we're on the topic of hiring, and you know I feel like 'grind slop' has become this theme on X. And it's so funny because, you know, as a VC, like I'll admit, you guys are famously in the office six, seven days a week. I hope this doesn't get marked as grind slop now, but you know, constantly hustling for your teams and your customers. And it's so funny to see that kind of turned on its head. And so I'm just curious, what are your thoughts on the narrative out there and like, grind slop is such a general term, but like you guys grind, but there's also a lot of camaraderie and excitement in the office. So just say more about that and like how you're building the culture.

Speaker A: Yeah, I mean grind is a funny term. We have never posted grind slop because we kind of view that like working hard is just like I don't think people are working hard to grind. It's just like oh there's a lot of stuff to do. People spend time there mostly because like, hey, this is like a fun time of our life. Like we want to take advantage of our talent and sort of potential so that it's not wasted. I think that's the main reason, and it's a very sort of like many orders from the main goal which is like can you build a good product and can you win in a space, and it's like one of the effects of that is that people work harder.

And even from the beginning, like yes we have an office culture, but like we're never mandating people to come in on the weekends. We don't really care how long they are in the office. It's just people are in the office just so that like we can maximize communication and that's how we view it. Like I don't view ourselves as abnormally grindy or abnormally ambitious. I think we just like I have a lot of ambitious people around me like us and all these people so it kind of just becomes normal and like everyone works hard so it's just kind of like normal. So I don't think it's something that we view as you know something like super special.

Speaker B: Yeah. And we kind of also view a lot of what we do very much as a team sport, right? Where we very rarely have lines between our orgs in that you will very commonly see engineers on early stage sales calls. You will see salespeople like debugging parts of the product. You will see our APM team in absolutely both ends of the spectrum. And so I think being able to have teams that are so kind of disparate from a function perspective all working together kind of needs people in the office because everybody's kind of jamming on ideas together, but two it also makes it fun in a way because you know everyone is like working together towards some very specific outcome right? It's either building this thing for this customer in time for the deal to close or launching this new thing. And because it's so many different teams working together, it's kind of a we're all in this together to get this across the line.

Interviewer: Yeah. Well, and how does that scale as you know, you've launched a lot of new offices recently. You have a new Australia office, London office, your New York office is growing like crazy. How do you maintain that culture when you guys are both in San Francisco?

Speaker A: I don't know. I mean, yeah, I don't think it's a solved problem for us. Like we're constantly working on it and like every single time the company gets to the next phase there's like new things we have to institute to just make sure that everyone understands the culture and there's like very high accountability and um you know there is pressure because there should be and so that's something that we're constantly adding on because in the first 100 people it doesn't really matter because everyone kind of knows each other, but like as you grow people might not have no one's communicated the vision to them or communicated the culture to them.

And um yeah, I know like Ben was talking to us about the A16Z culture, right? It's like how it's very like action-oriented and you can't just like put fluffy stuff on there. And so yeah, it's all these things that we're trying to do a better job of as we grow. Uh the other thing is also we bring everybody that we hire out to San Francisco for a couple weeks when they start so they're kind of immersed in the kind of the original kind of soup of culture. Uh, and then for every new office, right, at this point, New York and London, all these offices are big enough that they have the original Decagon culture there anyway, but for brand new offices, we actually have people from one of these hubs go out and spend a few months there really...

Speaker A: until the office becomes big enough and has its own kind of culture so that it doesn't kind of become too different from what kind of the original decagon culture was. Actually on the topic of international we're so we brought on Ragu Ragnaram last year obviously former CEO of VMware and in addition to investing he's helping us build out a ton of our international capabilities and I think one of the things that we've seen is that our AI companies are just getting pulled internationally way earlier like for you to have an Australia office it seems premature except for the fact that you have customer poll your scale is quite large for you know how long ago you were founded. Um but maybe say more about that like does that translate like does your product translate well to these other geos are there more enterprise concerns or fewer or is it kind of just comparable to the US market?

Speaker B: Yeah, I think there's two trends. one is that AI is just such a phenomenon that every buyer out there has you know tried chat GBT or whatever and so there is a lot of just top down pressure from boards and sees to just get moving on something and if you think about a lot of these businesses they're like hm how do we adopt AI well it's you know let's do some coding agents and like let's do customer service because those are like the obvious ones and so there is a lot of pull to to your points

Speaker C: and then the other trend is that language is a lot easier with AI. So

Speaker B: yeah,

Speaker C: in the past maybe a blocker would be, oh, my language just doesn't work in or my app just doesn't work in German or

Speaker B: right,

Speaker C: pick your language, right? But now it's a lot easier is to adapt your app. So

Speaker B: I think for those reasons, international has been a lot faster. Like at the same time, right, like we also want to make sure that we're not getting spread too thin. And so it's kind of this balance where it's like very unclear what the perfect answer is, but you know, we've kind of made a determination of like which markets we're okay with really investing in and if we're going to invest, we're going to, you know, really invest. And generally those markets are ones where we've already just like naturally picked up some customers out of the US office or something.

Speaker C: Yeah.

Speaker B: And then now it's like time to deploy people. Um because yeah even though these there are these trends that make it easier to go internationally like you know there's also other things that you don't really think about right like you have to have data residency there's all these things where and there's like local competitors which are just know the market a lot better and so you have to navigate that well.

Speaker A: Yeah absolutely. Um do you think there is a like a space for like local competitors to the large categories that we know about? I mean there is space for them. I think the question is like long-term is there consolidation and it's it's you you could say that for both geographies but also verticals and then also market segments, right? So like there are going to be people that find their niche in different places. Like our view the reason why we've kind of built so horizontally is that we believe that in our space the winners are going to be horizontal. there's just not that much that is like super verticalized that where like you could see a a pure vertical solution surviving and historically that's just been true in our space right like Salesforce very horizontal Zenness very horizontal like all these solutions are very horizontal because you just gain more from having that scale and having a very robust and deep product than you do from like having very vertical specific features and over time we're also going to build you know vertical features into it but our view is that like from a vertical and sort of market point of view there will be consolidation.

Speaker A: Just sort of on related to consolidation but maybe not just geographic consolidation. I'm curious if AI concierge becomes really the interface between the business and its customers. Do you see what like how does CRM and all of these you know sort of traditional data very important systems of record right for the end customer um how do those evolve in this new world and do you see any consolidation there in terms of your own role

Speaker B: yeah I don't um I don't think it's necessarily an either or right

Speaker B: because ultimately what we're what we're doing here is trying to democratize uh access or availability of these concierges, right? Which is if you were going to a business where you were spending $100,000 a year, you would get the most personalized treatment. They would know exactly who you are, what your preferences were. They want to help you, you know, shop, for instance. They'll shut the whole store down for you.

Speaker A: Actually, I don't know if they do that for,

Speaker B: but you get the general, right?

Speaker A: You've never you've never tried that before.

Speaker B: I've never tried those. Um, however, if you're at a business where you're spending $10, they can't do this for you because they can't make it economical, right? It is not a lack of desire to do this for their customers. It is just that the unit economics don't support that.

Speaker B: And so, effectively, all we're doing here is saying, okay, if you could give them that experience for 10 cents, all of a sudden it is economical and they'd want to do that. Now to the point of does that mean CRM go away? I mean my answer is no because if you had u you know if you have a company today where the con your concierge is a human being they still write your info in a CRM so they can track it for later. So, and I think when we have these AI agents, they will need somewhere to put that information.

Speaker B: Um, so I don't necessarily think those kind of auxiliary pieces of software go away because um for us as we're building these concierges, our goal really is how do we create that great experience? We will still need places to put that data somewhere, right?

Speaker C: Yeah.

Speaker C: Um, I actually think CRM could do quite well. um they'll they'll be slightly different in the sense of like CRM are kind of databases in a way and you know the frustration people have with them sometimes is that the interfaces are really difficult to use etc but in the future maybe the agents are just using the interfaces and you don't you don't even have graphical interfaces or whatever and so the CRM are still very valuable because they they hold the source of truth and you know then the agents they're just kind of getting pinged a lot more because the agents are are using them so that is one possible world that's that's kind of bullish on CRM and

Speaker C: for us personally like so far with Decagon there's like we have zero desire to build a CRM because we think there's so much to do in the agentic layer and that's where we want to focus

Speaker A: yeah okay so SAS is not dead

Speaker A: cool maybe um last thing is we were chatting right before we we started recording that you guys have done some little AI experimentation on your own as you just figure out how to you know make your own life more productive and effective and also it sounds like you've done something kind of interesting.

Speaker B: Yeah. Um, you know, I think the models have gotten very smart uh over the last several years and you know the two of us will often use it to brainstorm ideas, right? Because they are genuinely very smart at at coming up with great ideas. However, the bottleneck I realize at least for a lot of the work that I do is business context, right? there's a lot of context for every idea around okay the constraints that we have the goals that we're going for and things like that that is difficult to reexlain to the agents every time and so I actually spent a while building agents for myself to capture all the business context well so it just kind of looks over my shoulder all the time and is constantly compiling context on oh here are the people that we've hired here are the people that we need to hire here are the deals that we're working on here's here are the problems. Here are the current challenges that we have. So that later on I can just go to it and say, "Hey, there's this new person that we're

Speaker A: "thinking of hiring. What do you think?" And now it's able to automatically reasonable, well, we had two candidates that were very similar and you know, these candidates had these kind of uh drawbacks or skills that they didn't have. And so if we hire this person, it's going to be another person that has those, you know, kind of exact same things. So we need someone complimentary. So this is probably not the best person to have or >> you know in this deal we're barreling down a very similar path because you know we didn't validate these things early enough so this time we should validate them uh uh slightly earlier. So it's really all about how do you capture context well because then you know my job is making decisions with lots of context. So if I can outsource that more and more to a model maybe I can put myself out of job quicker.

Speaker B: >> Sounds like you created Jesse. There we go.

Speaker A: >> I do think um like one of the big struggles of being a solo founder is you don't have anyone to bounce ideas off of. So you just arrive at conclusions a lot slower.

Speaker B: >> You guys were both solo founders in the past, right?

Speaker A: >> Yeah. So like us working together, I think the reason it was so much easier is that we could, you know, iterate on things much faster. You kind of just talk something out >> and uh yeah, now you've talked to Fable or whatever. It's like it's pretty good, honestly. seem like very original >> because in the past it's just like they just kind of are kind of sick fence, right? Where they just agree with you. Oh, that's a good idea. But now they're like, "No, that's a bad idea. >> Don't do that."

Speaker A: I actually uh for a while Mark at one point had uh uh posted the prompt that he used for Claude where it's like, you know, disagree with me, be very direct, things like that. So, um I actually used that for one. It was great. And the the funny story on this was I really enjoyed it because it would agree with me very aggressively. It would disagree with me very aggressively. And um I took it to my wife and I was like, "Ah, this is great. You should use it." She put it on and then, you know, a day later she was like, "Wow, Claude was being so mean to me all day. I had to turn it off." Like he just kept telling me, "Yeah, it was just disagreeing with me so aggressively."

Speaker B: >> Amazing. Um well, since we're on this topic of f founders using AI to do things, um I have to bring up the whole uh AI slop um fiasco maybe is a strong word that Brian Chesy just went through. Um and I want to bring it up because um you both have grown your profile a lot over these last few years. And you know, you know, I sort of mentioned Jess, you had these pieces that were just hitting the zeitgeist of the discussion that everyone wanted to have and came in with a very differentiated take. Um, and uh, you know, that's the kind of stuff that we see exactly hit on. You know, you're hitting the conversation right at the, you know, right message, right time. Uh, unique message, right time. Um, do you use AI for writing? Um, and separately, um, this is a more of a meta question, but how important is X and like the sentiment on X to you?

Speaker A: >> Uh, so yeah, for most of our early days, it was mostly LinkedIn with the reasoning of like, hey, our customers are on LinkedIn. You know, we're not going to get someone seeing us on Twitter and then coming as a customer probably. But I think the the sort of learning we had with X is or at least I was kind of reflecting on it. X is kind of like like the timeline that people talk about. >> And most of that timeline is not really about your company. Like if you're if you're very like >> just like promoting yourself on X like it's going to get no traction whatsoever.

Speaker C: >> Totally.

Speaker A: >> But it is sort of like a single timeline that everyone reads. So it kind of like it kind of like mind controls everyone to be thinking about the same thing.

Speaker C: >> Yes. And that's where it's valuable to have some say in it. And uh who was this guy listening to? There's like there's this guy like Jeremy GeFon or something who's on Patrick Ashanti's podcast who I like and he was like doing he like made some claim about how like you know it used to be that >> like the the the big status symbols in the world is like everyone wants to be a billionaire cuz like he calls it like the priest class or whatever. So it's like billionaires are the priest class but nowadays actually it's when people become billionaires now they want to become ex influencers because like those people hold the real power because they can like influence what the whole world's thinking about. >> Um so that's like one reason to have some presence on X I suppose >> and when we now when I post on X I don't really post about like Decon specifically it's more more about you know our thoughts on what is happening. So, I think that's that's like a good good way to do it. And but it is very different.

Speaker B: >> You write it all yourself, right?

Speaker C: >> Yeah. Um I mean AI is good for um sort of helping you brainstorm like what topics to write about. I think that's that's pretty good. >> But I think that was that's kind of like a learning that like LinkedIn and X work very differently. You can't just like come up with something cool >> and >> like post the same thing on both >> because like very few things like do well on both. >> Yeah. So like LinkedIn's really good for you know classic stuff. You're making announcements and um talking about your product and you know fund raise or whatever. I guess you can do fundraising on X as well but X is a lot more about kind of like placing yourself on top of that single timeline that everyone's on.

Speaker B: >> And is that so is it too much of a simple you know simplification to say X for hiring especially you know AI research talent etc. um maybe ecosystem as well and then LinkedIn more for enterprise customers or are you actually seeing enterprise CIOS pay attention to X? Hard attribution is hard obviously.

Speaker C: >> Yeah, it's hard because you know I think you could say like oh well you know I I I made a post on X and then you know the people on all in podcast were talking about it and like CIOS definitely that I saw that so it's like a >> like indirectly I'm sure we got like some eyeballs from CIOS like is the CIO themselves like scrolling X all day maybe not >> but if you are part of that major timeline then you have there's always these like secondary effects and then and then like reporters will reach out to like from like mainstream media and then if they write about you then like Ashan was on the New York Times recently you know it's like if they write about you then those for sure get eyeballs so >> you were

Speaker B: >> Mhm. But it was just open source stuff.

Speaker A: >> Oh, Kimberly's only on X. So, >> you didn't put the next article. I didn't see it.

Speaker B: >> Um, I guess maybe a last uh question would be just since we touched on like hot button X topics. um one and this is kind of a serious one actually but it sort of re-entered the narrative I think with um you know the anthropic video and it's just sort of maybe never left the narrative but it's on this concept as progress gets better around jobs um and the messaging around that um it's a sensitive topic obviously but it's interesting because I really think customer support was maybe the first endto-end use case where you could really take an entire job um or sorry do an entire job I should say take is the wrong word um versus coding was always you know pair programming to start with um how has that you know we we sort of joked before but I you know we weren't really joking that um AI is actually creating jobs I'm curious how do you turn that narrative on its head that folks are just losing their jobs right like do you see the upleveling of folks folks with AI where you know maybe they were doing this job and now they're doing something else.

Speaker C: >> Yeah. I mean we we we see this all the time. Uh because uh if you recall earlier on when we were talking about okay what are we truly doing? Um, we found that in a for a lot of our customers, there's actually just more demand for things like customer support than there's supply, >> right? >> Where companies realize that they're like, okay,

Jesse: If our cost of doing customer support drops by 30%. Most of them are not just immediately saying, okay, now what I will do is, you know, let go of 60% of my team. They're saying okay now that this thing which is clearly valuable for my customers is much cheaper let me do more of it so that my customers retain for longer so that you know they don't turn off as much so that they activate sooner things like that

Jesse: You know we had a customer in the early days um they and this was you know probably two and a half years ago at this point um where they said you know our ticket volume you know the amount of customer support inquiries that we get uh per month was I I think I think it was like 50,000 a month or something based on uh you know the existing surfaces that they had. Once they started using us they said wow turns out our customers have a lot of problems they said let us make support more easily accessible right so instead of it just being in one part like buried within a support panel. They're like let's put support on every page and let's make it more prominent in places where people are more likely to get stuck. Let's allow immediate support for even free users rather than only paying users. Right. So because of this kind of

Host: there's more kind of latent demand for support. Yeah.

Jesse: Uh than there is supply. So automating things doesn't necessarily result in just kind of people laying off their entire teams.

Host: That may be the best example of Jevons paradox in real life that I've heard. So it's exciting.

Jesse: Yeah. I think it's like uh AI will uh kill jobs but not careers in a way because like those jobs that are being done currently should not be done by humans. Like they're very mundane and menial. It's like it's like a super high volume use case and people are just like kind of picking up the phone. I was like, "Okay, let me click here, click here, and like, okay, here's the answer, right?" And that should be done by AI. But there is actually like a near infinite amount of things that people could be doing to make their customers happier and, you know, take care of them more. And so people will end up doing those things and more and more of the mundane repeatable things will get eaten up by AI. So that that's that's what we think will happen.

Host: Jesse, I think on a podcast, maybe Patrick O'Shaughnessy's podcast a couple months ago, you had mentioned that even your customers when they've been using BPOs for customer support instead, you haven't actually seen like layoffs at the BPO. It just turns out that those employees have gone and done other things instead. Is that still true?

Jesse: Oh, uh, I mean, it's it's it really depends on the situation. So, there are definitely scenarios where people use their BPOs a lot less or don't need the BPO anymore. There are other situations where they are not in cost cutting mode whatsoever and their goal is to either their business is growing so quickly that they don't want to scale their operations along with their growth and so with Decagon they can kind of like keep it flat or whatnot or it's yeah actually we still need people but now there's all these other things they could be doing and it could be more revenue generating things. um you know as that's like a big area for for us even is like as the AI matures you first start with these cost cutting use cases because those are easy but then like revenue generating use cases should also be able to be done through this conversational interface so yeah there's it really depends on the customer but um you know we definitely have customers that have made massive changes

Host: actually I'd like to end it on that uplifting note um and just to repeat what Jesse said it may kill jobs but not careers I love that um thank you so for joining us, guys. A pleasure to have you.

Guests: Thanks for having us.