How Enterprise AI Really Gets Deployed

24:53Here 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. And also, you used to be a deployment strategist at Palantir, so you're very familiar with the forward deployed model that Palantir popularized. How different is that at what you did at Palantir versus the way you conceptualized this role at Decagon? Because I do think people use the term FDE very loosely.

25:23Yeah, and I think it's dangerous to mix the two of free consulting work versus actually doing product. You know, Sham, who's the CTO of Palantir today, had a phrase internally. internal, I think now it's been written about a ton, he would say, forward deployed engineers eat pain and excrete product. Yeah, that was kind of the thing. Yeah, I think it's like a massive misconception because people are like, oh, Palantir is such a hot company and they're doing so well. This is like a cool take on how to implement stuff. But first of all, very few companies, if any, can do what Palantir does, which is like close massive deals off the bat and it's kind of worth it to spend all that effort.

26:05So I think a lot of the people that are doing this like super forward deployed strategy and like, hey, we'll do any AI use case for you. They will eventually have to reckon with like, OK, can we find a product that's scalable? Otherwise, you are just building like a modern like Accenture or something, which could be good 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 FDEs 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 in our core.

26:41It's a product-led company, right? In the sense of, you know, we're building 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. At the same time, of course, you know, I should say we also kind of sales-led in the sense of the product is informed by sales. So we're not sitting there and just coming up with product to build. And so you kind of have this system where, as Ashwin was saying, we have all these people that are forward deployed in the sense that they work very closely with their customers.

27:18Their job isn't just to spin up random new use cases here and there and just doing whatever the customer wants. Because in the short term, it could actually lead to larger contracts and you can kind of hunt for wherever the pain is. But then long term is just very difficult to scale. So their job is to kind of compile all 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 everyone else. And that's in our space, at least our vision of like, hey, the winner in this space is going to be very product driven.

27:51I actually want to pull on this thread of, you know, that you talked about where your FDs are actually approving the product. You know, Dexcon is a very product driven company. So just to kick off with a very small, seemingly unrelated anecdote, but I was just trying to convince someone on the broader team to not leave A16Z for a frontier lab. And one of my arguments was that there was a good long-term career here. And the response that this person gave me was, we'll have AGI, we don't need careers in the long term. It really hit me because I thought I was AGI-pilled, that I had really not thought about from that perspective.

28:31And 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 shit 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. The first thing I want to say is I'm like certain there will be careers after AGI.

29:06The reason for that is like most of our jobs, for sure are for jobs, are kind of like made up to be done. Most jobs are made up. I speak self-dressing. I have a very real job. Unless you're like building infrastructure or like growing food or something. It's like most jobs are kind of like layers of abstraction built on top of like other stuff, right? And 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 will change people's jobs, but people will stop jobs because you're still going to do things for other humans and whatever. So I don't really believe that careers will be gone after AGI.

29:43I don't think people are just going to be sitting around. I'll say one observation, which is like for me, it was definitely duet. So early days when we were building the product, right, like the core problem we're solving, again, being back to sales led, 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 costs and you're making customers half a year.

30:10So that's like the first agent that we built. And that was like the core agent we worked on for the first year, two years. and the agent on our end when we were building it consisted of a ton of stuff like we would have to 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 have 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 situations.

30:44And afterwards, once they're in production, we would manually be reading conversations. There's a ton of work that goes into it, even though the core product we're building is itself an agent. And so what Duet is, it's kind of a separate agent. It's like a second agent that's much bigger and 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 I just does all of that? So it's like a second agent that is smarter than us to do all of these things.

31:19And it just feels very magical because you can just literally tell it like, hey, I have nothing built yet so far, but here's a bunch of transcripts I have and here's some documentation. You go figure out the best way to do all these procedures I want. 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 it 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. It'll say, yeah, I read these 1 ,000 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.

31:57So it's like one agent that can do all of that. And so 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, Anthropoc open AI are making these reasoning models mostly for like the cloud codes of the world. But they are also really good for like do it, 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. And two, it can do all these tasks where it just would not be, you would not have expected AI to be able to do all these tasks at once, but it can.

32:36It can do them very well. And I think that's a very visceral moment of like, wow, the models are getting a lot better and they're becoming very generalized. They can do all these things. Clearly, the models were not trained on our specific task, which is writing these procedures and writing these tests, but they're still good at it. And also, to your other question of how did we come up with all this and 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, we realized that, hey, when we go into a new customer, we need to spend all this time writing up the AOPs manually.

33:19And 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 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 do 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.

33:50And then we found that, oh, it's taking a lot of forward-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. So the way we think about like product improvements, forward deployed engineering investment, everything is built around what can we productize from forward deployed work so that engineers and any kind of customer facing resources on our team don't need to be kind of as heavily involved. 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 OpenAI and 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.

34:34But, you know, you've also got had like, oh, shit moments where you're like, these labs are getting so much better and the models are getting so much better. And so long term, 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 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? 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.

35:12By 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 it'll just figure everything out. Right. Like that's practically not how these things work. 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, OK, 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're experts.

35:53Then I need a way to be able to test this model and make sure that it doesn't cross any regulatory lines that I have and test it, make sure it works well. Then I need a way to look over the millions of conversations that happen for me to extract insights for the rest of my teams. 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 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, you know, the primary thing that these models need to be able to work.

36:32Now, 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 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 IPO'd companies. I'm talking about some of the largest companies in the world. And I think this is particularly interesting because you both are very, very technical, but also go-to-market animals. So I want to talk a little bit about that. But, you know, it just feels like you've turned what maybe started as feeling more like a David and Goliath, with multiple Goliaths, market dynamic to really a two-horse race between you and Sierra.

37:18So 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 is people have studied application software for 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 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?

37:57Yeah, I mean, we have a lot of respect 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 met a lot of those teams. They're very competent teams. They're optimizing for a lot of different things at once. ZEC Conference of Sierra, I mean, And our most recent customer actually turned off to Sierra to come to Decagon. And sort of the reasoning when we asked them was, it kind of goes back to this deployment model. It's like, when they worked with Sierra, it was mostly FDEs.

38:28And it just felt like a black box where the FDEs were good, but they had to go through the FDEs for everything. So to build new journeys or to even get a deeper understanding of what was happening in the conversations. and then over time 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 to other things and it just took them a while to get insight into what was happening and then build out new journeys, right? So over the course of the year they maybe built out I think they said three.

39:00And so the reason they came to us is because 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 core product that even if we're there helping them, even if we are forward deployed, it is like an in-service of helping them build a product that they can use themselves and iterate really fast 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 yeah, so within basically a month, they spun up like seven new journeys on Decagall.

39:36Wow, interesting. Mostly. And it'd taken three, a year to get three. Yeah. Okay. Interesting. And 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 and 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 that approach. But that's kind of the difference in their approaches. And that's why we've been, I would say, having a lot of success there.

40:07And then zooming out, just selling to the enterprise. Yeah, I mean, this is something that's, you know, Ashwin and I have never sold to the enterprise before. And I think it just so happens that both of us find sales exciting. And the enterprise, it was kind of a quick learning curve for us. So I don't think, I think a lot of it honestly came kind of naturally just because 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 um so yeah i mean we just back to being sales led right like we always from the beginning we're like hey we're gonna be like extremely strong on the go-to-market side and that's gonna 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.

41:04So 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 really strong early sales team. 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. Some of whom cold applied to you guys in the early days, I remember. Yes. Yeah. Yeah. Cold applied. I think they were working in the space already and they were seeing from afar like what Decagon was starting to do yeah so some of them had like non-traditional sales backgrounds you know so they had non-traditional sales backgrounds they're coming into sales the other profile we had a lot of in the early days were just 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 in org structure.

42:01But 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. We've always had that focus. The other thing I think that helped us a lot, you know, to your earlier point about how did you get some of these large deals closed so quickly was, I think we were very curious about how we could productize parts of it within the enterprise. 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 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?

42:47And 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 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%. So, you know, at this stage for a company like you, this is what your model risk process is likely to be. This is how your testing process should look like. This is how we should do the initial rollout.

43:25This 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 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 tech companies selling into enterprise. Yeah, for sure. And by the way, I can also personally attest to the strength of your early go-to-market team having met a bunch of them. That being said, I don't want to underplay how many times a decision maker has told me that one of the reasons of many, right, that they're going with Decagon is they want to make a bet on you guys.

44:11They'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. 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? Probably most of my time, like 80 % maybe. Yeah, okay. And yeah, I think a lot of it is just pushing speed.

44:52So 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 partnership to move faster. One of the things with that is like, you know, now we work with, you know, several of the largest banks in the world and airlines and telcos. and no matter how fast you go, like 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 piecemeal it.

45:29So we're not just deploying across every surface area, every use case at once. Let's really just pick, you know, one or two of the top use cases and just get a win there. And I would say 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 So at least for us, it's not me. Maybe some people can do it, but it's, yeah, it's still a lot of effort and it takes time. And so you have to figure out ways to, you know, design things from a process perspective and an org perspective and a product perspective as well that like just keep shortening that and like finding clever tactics.

46:05And that kind of takes founder involvement. You wouldn't really expect a, like 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 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.

46:50Because model capabilities are changing all the time. As models roll out, people are seeing other things that happen. They're like, oh, wow, this was really cool. And like, I'm seeing this here and you guys aren't doing that as well. So being able to like stay really, really close to that feedback loop, because that's something we never want to let go of. Yeah. Do you want to talk about how that like informs product roadmap, meaning probably like two years ago or so, most of Decagon's customers were focused purely on customer support. And nowadays I would say that's actually not true. You guys have broadened your vision to mostly like to be like an AI concierge for your customers.

47:25Like, 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? So, you know, when we launched, the original set of use cases that we sold were in customer support. 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?

48:12And so we had a customer that we originally went live with them for customer support. 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. Can you help us with inbound sales, right? When someone comes in, answer questions about us, do some discovery, and then assign it to the right, enterprise rep if it's a deal of large enough value. We had another customer that started using us for a lot of operational workflows.

48:48So we are able to now proactively reach out to them once we start seeing any kind of issues on that customer's account. 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, answering customer support questions. At the end of the day, it's 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 point, the models are going to get better, and they have.

49:29And what do they specifically get better at that allows you to do that? It is specifically the ability to follow instructions well, right? So when you had 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 interpret it 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 pot that a model should fall and that's all you really need.

50:10Whereas 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 model got better out of over the years. Yeah, I think if you think about, you know, what is the 12-month product roadmap? Because things are moving so fast, the real answer to that is like, we'll kind of see how it evolves. And we obviously know what we're working on now. But I think realistically in today's AI world, it's very difficult to have a 12-month roadmap to a T.

50:45You maybe know how some themes of what you want to build. But ideally, if you have those things, you should just build it right now because it's so fast to build things now. So I would say 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, should be handled 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.

51:19But why not also be able to do all these other things? So over time, again, 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 build. Hmm. We've talked a lot about how the models are getting better and, you know, obviously your capabilities are moving along even, you know, 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, you know, around persistent memory.

51:52I 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? Hiring. Got it. So it's less on the AI side. It's actually like people. We are voracious consumers of tokens, but we've always loved more great people. I think there's so much to build these days. Yeah. And why can't you hire AI agents to do the things that you're doing? Yeah, we are voracious consumers of tokens. 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 the taste of is this done yet 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.

53:00So the models improving, have they since like you founded 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? Yeah, you know, I think, you know, 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 these models and they are hiring like crazy. And 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?

53:42If somebody else said, oh, here's our roadmap 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 shifts more. So it is great for consumers of these models, but I don't think it has maturely changed our hard-on plan. 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.

54:14But it seems like on the technological bottleneck side, it's pretty... Yeah, there's still stuff that we're waiting for, right? Like, you know, voice-to-voice models is an interesting frontier that there's still research happening on, you know, the getting smaller models to be smarter, 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? 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.

54:53And it's so funny because, you know, as a VC, like I'll admit, you know, when I, I mean, you guys are famously in the office six, seven days a week. I hope this doesn't get marked as grindslop now, but, you know, I think 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 is your what are your thoughts on the narrative out there? And like grindslop is such a general term, but like, you know, you guys grind, but there's also a lot of camaraderie and excitement in the office. So like just, you know, say more about that and like how you're building the culture.

55:32yeah I mean grindslop is a funny term so we have never posted grindslop because we kind of view that like working hard is just like I don't I don't think people are like working hard to grind it's just like oh there's a lot of stuff to do like we kind of people spend time there mostly because like hey it's you know 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, that's the main reason. And it's a very sort of like, it's like many orders from like the main goal, which is like, can you build a good product and can you win in a space?

56:12And, but it's like one of the effects of that is that people work harder. And even from the, from the beginning, like we, 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, 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 like, you know, abnormally grindy or abnormally ambitious. I think like we're, I think we just, like I have a lot of ambitious people around me like Ashwin and all these people so they kind of just become normal and like everyone works hard so it's just kind of like normal.

56:52So I don't think it's like something that we view as something like super special. 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 like a function perspective all working together, kind of, one, kind of needs people in the office because everybody's kind of jamming on ideas together.

57:40But 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 getting this, 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, it's kind of a, we're all in this together to cross the line. Yeah. 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?

58:14I don't know. I mean, I don't think it's a solved problem for us. Like we're constantly working on it. And every single time the company gets to the next days, 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, 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 and everyone's, but as you grow, people might not have, no one's communicated the vision to them or communicated the culture to them.

58:47And yeah, I know Ben was talking to us about the A16Z culture, right? It's like how it's very action-oriented and you can't just 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. the other thing is also we um 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 uh the original kind of soup yeah 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 deck and gone 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, 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 diagonal culture was.

59:48Actually, on the topic of international, we're, so we brought on Ragnarok 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 Australian office, it seems premature, except for the fact that you have customer pull. Your scale is quite large for, you know, how long ago you were founded. But maybe say more about that. Like, does that translate, like, does your product translate well to these other geos?

1:00:31Are there more enterprise concerns or fewer? Or is it kind of just comparable to the U.S. market? Yeah, I think there's two trends. One is that AI is just such a phenomenon that every buyer out there has tried ChatGPT or whatever. And so there is a lot of just top-down pressure from boards and C-suites to just get moving on something. And if you think about a lot of these businesses, they're like, hmm, how do we adopt AI? Well, let's do some coding agents and let's do customer service because those are the obvious ones. And so there is a lot of pull to your points. And then the other trend is that language is a lot easier with AI.

1:01:09So in the past, maybe a blocker would be, oh, my language just doesn't work, or my app just doesn't work in German, or pick your language right but now it's a lot easier to adapt to your app so i think for those reasons international has been a lot faster i think 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 were um okay with really investing in and if we're going to invest we're going to really invest and generally those markets are ones where we've already just like naturally picked up some customers out of the U.S.

1:01:49office or something. And then now it's like time to deploy people. Because, yeah, even though there are these trends that make it easier to go internationally, there's also other things that you don't really think about, right? You have to have data residency. There's all these things where there's local competitors which know the market a lot better and so you have to navigate that well. Yeah, absolutely. Do you think there is 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?

1:02:28And 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 pure vertical solution surviving. And historically that's just been true in our space, right? Like Salesforce, very horizontal, ZNS, 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.

1:03:11And 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. 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, like, how does CRM and all of these, you know, sort of traditional data, very important systems of record, right, for the end customer, how do those evolve in this new world? And do you see any consolidation there in terms of your own role?

1:03:54Yeah, I don't think it's necessarily an either or, right? Because ultimately, what we're doing here is trying to democratize access or availability of these concierge issues, 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 shop, for instance. They'll shut the whole store down for you. Actually, I don't know if they do that for$100 ,000. But you get the job. Yeah, you've never tried that before.

1:04:36However, if you're at a business where you're spending$10, dollars, 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. 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 CRMs go away? I mean, my answer is no, because if you had, you know, if you have a company today where your concierge is a human being, they still write your info on a CRM so that they can track it for later.

1:05:17So, and I think when we have these AI agents, they will need somewhere to put that information. So I don't necessarily think those kind of auxiliary pieces of software go away because 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? Yeah. I still think CRMs could do quite well. They'll be slightly different in the sense of CRMs are kind of databases in a way and 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 even have graphical interfaces or whatever.

1:06:06And so the CRMs are still very valuable because they hold the source of truth. And then the agents, they're just kind of getting pinged a lot more because the agents are using them. So that is one possible world that's kind of bullish on CRMs. And for us personally, so far with Decagon, 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. Okay, so SaaS is not dead.

1:06:33Cool. Maybe last thing is we were chatting right before 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 Ashima, it sounds like you've done something kind of interesting. Yeah. You know, I think the models have gotten very smart 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 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?

1:07:11There'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 re-explain 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 is 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 thinking of hiring what do you think and now it's able to automatically be a no-board well we had two candidates that were very similar and you know these candidates had these kind of 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 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.

1:08:28So if I can outsource that more and more to a model, maybe I can put myself on a job quicker. Sounds like you created Jesse. There we go. I do think like one of the big struggles being a solo founder is you don't have anyone to bounce ideas off of. So you just arrive at conclusions a lot slower. You guys were both solo founders in the past. 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 yeah, now you talk to Fable or whatever. It's like, it's pretty good, honestly. It seemed like very original.

1:09:06Because in the past, it was just like, they just kind of are kind of sycophants, right? Or 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. I actually, for a while, Mark at one point had posted the prof that he used for Claude, where it's like, you know, disagree with me, be very dirty, things like that. So I actually used that for Juan. It was great. 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 I took it to my wife and I was like, oh, this is great.

1:09:42You 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. He just kept telling me. Yeah, he was just disagreeing with me, so aggressively. Amazing. Well, since we're on this topic of founders using AI to do things, I have to bring up the whole AI slop fiasco, maybe is a strong word, that Brian Chesky just went through. And I want to bring it up because you both have grown your profile a lot over these last few years. And, 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.

1:10:28And, 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, unique message, right time. Um, do you use AI for writing? Um, and separately, um, this is more of a meta question, but how important is X and like the sentiment on X to you? 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 sort of learning we had with X is, or at least I was kind of reflecting on it, X is kind of like the timeline that people talk about.

1:11:15And most of that timeline is not really about your company. Like if you're very like just like promoting yourself on X, it's going to get no traction whatsoever. Totally. But it is sort of like a single timeline that everyone reads. So it kind of like mind controls everyone to be thinking about the same thing. Yes. and that's where it's valuable to have some say in it and uh who was i listening to there's like there's this guy like jeremy giffon or something who's on uh patrick ashansky's podcast i like and you like doing he like made some claim about how like you know it used to be that like the the big status symbols in the world is like everyone wants to be a billionaire because 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 X influencers.

1:12:07Because, like, those people hold the real power because they can, like, influence what the whole world's thinking about. 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, Declan specifically. It's more about, you know, our thoughts on what is happening. So I think that's, like, a good way to do it. But it is very different. You write it all yourself, right? Yeah. I mean, AI is good for sort of helping you brainstorm like what topics to write about. I think that's pretty good. But I think that's kind of like a learning that like LinkedIn and X work very differently.

1:12:44You 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 is really good for, you know, classic stuff. You're making announcements and talking about your product and, you know, fundraise 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. 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, et cetera, maybe ecosystem as well, and then LinkedIn more for enterprise customers?

1:13:25Or are you actually seeing enterprise CIOs pay attention to X? attribution's hard, obviously. Yeah, it's hard because, you know, I think you could say like, oh, well, you know, I made a post on X and then, you know, the people on the All In podcast were talking about it. Like CIOs definitely look at that. Yes, I saw that. It's like indirectly, I'm sure, we got like some eyeballs from CIOs. 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 like reporters will reach out to like from my mainstream media.

1:14:03And then if they write about you, then like Ashton was on the New York Times recently, you know, it's like if they write about you, then those for sure get eyeballs. You were? But it was just open source stuff. I know. Kimberly's only on X, so. You didn't post an X article, I didn't see it. I guess maybe a last question would be just since we touched on like hot button X topics. One, and this is kind of a serious one actually, but it sort of re-entered the narrative, I think with, 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 and the messaging around that.

1:14:46It's a sensitive topic, obviously, but it's interesting because I really think customer support was maybe the first end-to-end use case where you could really take an entire job. Sorry, do an entire job, I should say. Take is the wrong word. Versus coding was always, you know, pair programming to start with. How has that, you know, we sort of joked before, but, you know, we weren't really joking that 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 up-leveling of folks with AI where, you know, maybe they were doing this job and now they're doing something else?

1:15:30Yeah, I mean, we see this all the time because if you recall earlier on when we were talking about, okay, what are we truly doing? We found that, you know, 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 if our cost of doing customer support drops by 30 percent 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.

1:16:20You know, we had a customer in the early days, they, and this was, you know, probably two and a half years ago at this point, where they said, you know, our ticket volume, you know, the amount of customer support inquiries that we get per month was, I think, I think it was like 50 ,000 a month or something based on, you know, the existing services 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.

1:17:02Let's allow immediate support for even free users rather than only paying users, right? So So because of this kind of, there's more kind of lated demand for support than there is supply. So automating things doesn't necessarily result in just kind of people laying off their entire teams. That may be the best example of Jevin's paradox in real life that I've heard. So it's exciting. Yeah, I think it's like AI will 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 a super high volume use case and people are just like picking out the phone.

1:17:41It's like, okay, let me click here, click here. And I'm 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 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's what we think will happen. 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.

1:18:17It just turns out that those employees have gone and done other things instead. Is that still true? Oh, I mean, it really depends on the situation. So there are definitely scenarios where people use their BPO a lot less or don't need the BPO anymore. the 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 For us, even as the AI matures, you first start with these cost-cutting use cases because those are easy, but then revenue-generating use cases should also be able to be done through this conversational interface.

1:19:12So yeah, it really depends on the customer, but we definitely have customers that have made massive changes. Actually, I'd like to end it on that uplifting note. And just to repeat what Jesse said, it may kill jobs, but not careers. I love that. Thank you so much for joining us, guys. A pleasure to have you. Thanks for having us. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X and A16Z, and subscribe to our Substack at a16z.substack.com.

1:19:52Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com forward slash disclosures.

1:20:30Thank you.