No Priors: Artificial Intelligence | Technology | Startups - AI Agents Talking to AI Agents: Reinventing Commerce with Decagon CEO Jesse Zhang

Episode Date: September 18, 2025

The traditional call center may soon be a thing of the past. Jessie Zhang is building AI agents designed to replace monotonous human labor and transform how consumers interact with brands. Elad Gil sits down with Jesse Zhang, co-founder and CEO of Decagon, an AI agent company at the forefront of AI customer service. Jesse talks about how Decagon secured large enterprise clients and the impact of its AI agents, his journey as a second-time founder, and Decagon’s company culture. Plus, they discuss what the future of agentic customer service may look like. Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @thejessezhang | @DecagonAI  Chapters: 00:00 – Jesse Zhang Introduction 00:30 – Decagon’s Services 01:11 – Decagon’s Customers and Growth 02:41 – Productivity Gains with Decagon 03:33 – How Decagon Integrates in Customer Workflows 04:25 – Jesse’s Second Time Founder Story 05:41 – Jesse’s Hiring Philosophy 09:13 – Counter-intuitive Advice for Founders 11:19 – How Decagon Thinks About Talent 14:12 – Areas for Longer Term Planning 15:37 – Decagon’s Path to Customer Service 16:57 – Thoughts on Pushing Into the Application Layer 19:40 – What Decagon Does Uniquely  22:05 – Pricing Services in the AI Age 24:46 – How Decagon Sees Customer Service 25:53 – Defining Long-Term Success for Decagon 27:41 – Jesse’s Views on an Agentic Future 31:22 – Conclusion

Today we're lucky to have with the Sunno Pryor's Jesse Zang. Jesse is the co-founder and CEO of Decagon, which provides customer service and other related AI for all sorts of different enterprises, including banks, telecom providers, airlines, and of course many of the biggest and most important tech companies. Jesse Pryor started Loki, which was acquired by Niantik, and we're very excited to have them join us today on Pryors. Jesse, thanks for joining us today on Pryors. Thanks for having me.

Can you tell us a little about Decagon and why you started the company, how you started it, how you all got going? Yeah, of course. So Decagon, for those who are not really familiar with us, we're an AI customer service agent. And so you can kind of think of us, you know, if we're working with a large bank or airline or just people that have large contact volume, the AI's job is to, you know, have a very engaging and personalized conversation with the user and resolve it and, you know, save the company a bunch of money and, you know, ideally drive more revenue in the future because folks are more engaged. And as we've grown, it's kind of becoming more and more of, you kind of think of like a conversational UI for the brand where it's, it's how every user can interact with it. And we often use the term like concierge to describe this, but that's what we do. And you're working right now with some big banks or some of the world's biggest banks. You're working with airlines, telcos. Like you've actually gotten to very big customers very quickly.

How did you go about doing that or how did it happen? Yeah. So, I mean, as you know, we started out mostly with the like digital native companies. A lot of startups do that. And digital natives, of course, are much more willing to try out startups. They can move faster. So that'd be like late stage tech companies and things like that. Yeah, like Ripley, Notion, folks like them, they were like great partners.

And they also just helped us iterate on the product a lot. So that's where we started. As we've gone on, I think just naturally we're kind of pulled up market just because of the demand. And as you might imagine, that's where most of the large contact volumes are. So it just happened a lot faster than we thought. And I would say a lot of these enterprises also move them. a lot faster than we would have expected. So that's why we ended up there.

And I think it's one of the underappreciated things about AI traction is a lot of companies are willing to try things in a way they weren't willing to before because it's such a big technology shift. And so all these markets are kind of open now that weren't before or that would be much harder to do. Yeah. I mean, another specific dynamic is that at the enterprise, it's becoming a lot more of a top down motion.

So, you know, in the past, any of these technologies could have been just like one team trying to vet it or decide it. But now it's like a, it's an eight. AI transformation, and the C-Suite, the board are all, like, very big on how do we adopt AI. And, you know, customer service is often one of the biggest areas for probably the most lowest-hanging fruit. So that's how these conversations have progressed? And how much of an impact are you having in terms of some of these teams? So I know that you're giving a lot of leverage to these customer service orgs.

Like, are you making people two times more productive? Or I'm just sort of curious is there a way to measure the outcome here? Yeah. And most of the large enterprises, the first thing they'll measure is just what? is the, I guess, like, efficiency that you're gaining them. So whatever they're spending on their contact center or their operation, how much can you cut that down by? And we've done case studies now where, you know, folks have been able to cut that down by, you know, 60, 70%. That's like a great success case, right? Because it's like a very clear business case. You can show

it's everyone. And then if the sort of secondary thing, oftentimes, you know, folks will even, you know, put this at the same level, if not higher, is just the customer satisfaction. So you need You need to measure that and make sure that your customers are having good time and more engaged, if not just like more also just happy than previously. So you're basically providing these customer service AI agents slash workflows that help, I guess, function 24-7 and multiple different languages out of the box. And do you basically do like a lot of integrations into what they are already providing or how do you tend to work with folks? Yeah, I think the way you should think about agents here are that it's more of a substitute for the more. Monday in human labor. So whatever systems they're already using, generally an AI agent, at least when you first deploy, is not going to disrupt the tooling you currently have. So whatever CRM

they're using, whatever, you know, telephony stack, we will just integrate with that. And then it's kind of doing all the tasks you would expect a human to do. And over time, that's just continue scaling. And so one of the benefits of AI agents is that they're always on, you know, they're awake 24-7. You don't have to train them really. There's no turn. You You can just, like, scale them out. And then you co-founded this with Ashwin, and you are both second-time founders. What made you decide to work on this problem in particular? Because I feel like many people's first company, they really focus just on the product and the

technology. And then on your second company, you're often more likely to also focus on the customer side, the commerciality. Was that your story or were you always kind of more commercially focused in terms of how you thought about problems in the world to solve? Yeah, I mean, one of my, I guess, theses is that there is a lot of untapped potential and just like really strong technical folks and making them a bit more commercial

because the types of problems on the go-to-market side they're, I would say generally a little bit more hairy and so a lot of folks don't like the messiness and especially a lot of technical folks enjoy the engineering product problems more. But they're still kind of very interesting problems, very rewarding. And if you can do that well,

that's how you get your company to grow a lot faster because you just do more sales. And at the end of the day, it's still problem solving. So, yeah, I mean, Asho and I were both technical backgrounds. We just got along very well. these similar stages in life. We both started a company before, as you said.

And the first time is when you kind of lack a little bit of the commercial sense. And you're just generally just trying to figure things out. It's very hard to build the intuition of what is a good idea and what isn't. And so it is definitely easier the second time around. How do you think about how you hired or what sort of people you look for for the team the first time around versus this time? Like what are you optimizing for in the people that you bring on board in your second company? Yeah, I mean, we're a little fortunate now. I think we've built a bit of a brand around our talents. And I think we have like a, you know, fairly interesting culture now. The way I would describe it is, yeah, we're generally just selecting for very smart people, first of all. I think we care more about that than like, you know, direct experience and so on. I think early on experience is still quite important. I think, I don't think we hired straight out of college, you know, for our first pretty large number of hires. But I mean, of course,

now we are. So you want a little bit of that blend, but the first thing we select for is just you know, how smart you are. And that's worked out well for us. We apply that philosophy based across the org. I think obviously engineering is very generally easy to test for, but even on sales and marketing. So that's been a core part of philosophy. Yeah, the other piece is just, you know, we're in office. There's a lot of, I guess, like, fun news now. How companies work really hard. Yeah, yeah, sure. It's like the 996 culture and so on. I mean, I don't think we, we, like, overrotate on stuff like that. I think we just, we're just looking for people where, um, you can tell when you meet them that they really see this as like, like,

ideally like a, like a, like a, they wanted to be like a highlight of their career. They want to put in the time and they want to be in a position where if they put in the time, they'll get to, you know, accelerate their career. They get to, uh, work on like very, very interesting problems. So are you in office every day, like five days a week in terms of when people are supposed to be in or? Yeah, we're, uh, five days. And then a lot of folks come in, on the weekends, but it's not like a requirement. Yeah, exactly. I mean, it definitely feels like you have sort of this hardworking culture.

People want to put in the time because, you know, it's interesting because if you look at professional athletes in training, they're always like, yeah, I train six, seven days a week. I work hard at my craft. And there was almost this period in Silicon Valley where people didn't want to say that. And I feel like with this wave of AI, suddenly it's come back that it's good to do that. You know, that's how you build a winning company in a winning culture. Yeah.

So it seems like you all have kind of adopted that as well. how you approach things as well. Yeah, I think pretty much all the AI companies are doing well have pretty heavy in office cultures. It's just you get way more done, especially in the early stage. I think after a certain point of scale, like, yeah, you could definitely make the argument that it matters less, but as of right now, it matters a lot. Yeah, it also seems like there's certain roles that always have been remote, like throughout all of history, you know, in terms of certain sales roles or the like, well, then really you're supposed to be at the customer side is your office, right, if you're doing some form of like field sales or the like. So it seems like a lot of

people have sort of gone back to the pre-COVID era for the startups that seem to be working best, which I think is really interesting. And, you know, obviously things are working really well for you all. Yeah, exactly. How are you thinking about the main types of roles that you want to build out in the company now or things you're hiring for or looking for? Right now, we're kind of mostly building for scale. So what that means is, of course, we need to hire a lot more ICs. We're bringing in more kind of like leaders and adding a bit more structure. The interesting thing we're thinking about now is like a people function. You never really needed that.

But, you know, we're approaching 200 people. It's you definitely need folks to be thinking about that full time. And it's more around like org design and what is the right way to structure our operating cadence between teams. We have an office now in New York. We're going to be spinning one up in Europe. There's a lot more of those problems now. And so that's definitely something where we're thinking about. I think that if you were to give founders advice,

around one thing that they should do that is against their instinct the first time they've scaled a company. What is that thing? Or how would you think about a big takeaway that you've had as you've gone from, okay, we have this nimble team that's grinding on a new product into, okay, we're scaling, things are working really well. We have product market fit. And we have to move as fast as possible. Like, what's that, is there a big mental transition that happens? Is there a specific tactic you'd suggest? So I would say for us, we kind of hit our stride fairly early in this company. So it didn't feel like there was a before and after. I would say when we were building, well, one, we stayed really close to the customer, which is always helpful. I think over time,

the adjustment we are learning to make is thinking more like medium to long term versus short term. Because I think at the beginning, you have to, you have to think short term. You're just optimizing for closing the deal or closing a couple of customers. But once you have your legs under you, you both can think more long-term and also you have an obligation to because if you don't, then eventually you get to a point where things really start breaking and you feel like, oh, man, I should have, you know, scaled this better and so on. So we're definitely in that journey right now. We're trying to be as mindful of it as possible. Yeah, maybe one related thing is that we do spend a good amount of time like studying sort of later stage teams that have done this well. And there's obviously org that we admire

where we've... Who are some people you think have done it well? I mean, Ramp comes to mind for sure. or Databricks if you're thinking about bit more like there's just like companies I've just always executed well. I think I think Alia Databricks is one of the most impressive CEOs. Yeah. Just in terms of like how he thinks about things and, you know, depth of reflection on different topics. It's like really impressive. Yeah, he's actually, I would probably go far as to he's my favorite CEO. And he's been very kind to us with his time. And that's another good example, honestly. It's like very strong technical folks that have, I think, also done very well applying that to, you know, commercial problems and execution.

And that's definitely the DNA we want to build at DeCagon. Do you screen for commerciality and the people who join? And if so, how can you do that? So say you have an engineer, do you try to find people who are more commercial-minded? Or do you think that self-selects into the culture? I don't think it's super important for every engineer in the company to be commercial-minded, for example. I think it's definitely very important for the founders and that may be the folks immediately around the founders.

That's why I think generally when I talk to engineers that want to join startups, for example. And let's say they eventually want to start their own company, which is a very common profile. It's, in my opinion, it's like way more useful to join somewhere where they've already kind of got the commercials figured out and you can actually see it in action and build that intuition than to join something pre-PMF. And I think that's like a very common misconception because it's like, oh, well, the smaller the team, like the closer I am to, you know, learning how to be a founder. But if you join a pre-PMF team and you never actually get to see the commercials in action, you're not really learning much. You're just kind of learning essentially

what not to do. And unfortunately, the reality is that most companies don't hit that point. Our sort of like discussion we have with engineers these days is, hey, it's like, yeah, it's very important for you to join. If you want to, you know, start your own company eventually, like, you know, DeCon is like the golden age to do that because we have a lot of the basics figured out where there's still so much to, that isn't figured out. And a lot of it is kind of very close to the commercials. Yeah, that makes sense. Yeah, I think a lot of the golden periods for many companies is between, say, 50 and 100 people up to, you know, a thousand, maybe 2,000 of the thing keeps going in terms of growth, because that's the era where I think you see the

most change, although, you know, also going from 2000 to 15,000 at Google, which was roughly when I was there was also sort of this magical period of like change. Yeah. And so I guess it depends on the size of the market and the way the teams run and everything else. So yeah. Yeah, I guess also, it seems like you can learn a lot more from success and from failure. And it sounds like in the context of Decagon, it's a really great moment to join because, you know, things are working and so people can learn different areas. Are there areas in particular that, you know, you'd really like to attract people? Like, is it international? Is it somewhere else? Yeah. I mean, the way I would think about that is like, you're kind of, if you're a family, you're like training your own, like, neural

network, right? And you need like positive examples and negative examples. Like from my first company, I mean, I started right after college. Like, basically the first two years was just like negative example. You're just like failing. And that's like helpful in some sense, because you can just kind of like brute force it and like try to like learn. But if you get some positive example sprinkled in, your learning rate is just like way faster. Yeah, I think that's the misconception. So, yeah, I mean, as we expand internationally, it's like, I mean, that's important, too. I think an interesting thing with each new office is that you also have to kind of rethink the, like, we worked really hard to build our current culture and an SF office. And we've been up New York.

We're going to obviously sent some folks out, but got to be mindful of that culture as well, because once it's set, it's kind of like becomes its own living thing. Europe is a whole different thing because the culture over there is naturally a little bit different. And so you have to be a little bit mindful. It's also just also naturally more isolated. You have to sort of wine at lunch and that kind of stuff. When you talk about having to shift the way that you think about things more towards medium and long-term planning, is that org design, is that internationalization, is that product roadmaps?

Is it like what is that capitalization? Like I'm sort of curious, like, what are the main components that you've had to start thinking longer term on? Yeah, it's probably to say it's more org-design and product roadmap. Or design in terms of how you allocate resources. because there are a lot of types of work that don't yield immediate returns like it's not going to close a customer for you

but if you don't do it you will in six months one really regret it and then two you'll just be in a spot where it's like much harder to do that work what's an example of that? Like just like core product work right like you know

there's a bunch of core product work that is important for closing customers in the future it's not going to close any customers now will probably still be fine for now but you can definitely foresee that okay well if you don't invest in this, then closing each incremental customer in the future will require the same level of work,

if not more, because then you just have more overhead. And you want that to have to go down over time. And so that's the classic type of thing where you have to shift your mindset of it. Because I think in the early days, it's like really good to have a greedy mindset. It's just like, I just really need to optimize for this one thing. Just get it over the line instead of just planning too long term. Because if you do that, you could just end up burning a quarter and like not getting anywhere. And so I think over time you have to make that switch. Did you set off to do customer service when you started at Kagan?

Or is that something that you all discovered early on as you were iterating on ideas or things like that? Oh, no, definitely did not come in with any pre-conceived notion. I had like a lot of empathy for the problem, just from my first company. It was a consumer company. So we had a lot of users. But our general approach kind of going back to the commercial side was I think we're just a lot better at being commercial about this. in the early days. And so we just talked to a lot of customers and had a very disciplined process of evaluating ideas. And yeah, it turns out that this has been one of the big use

cases. What made you realize that this was the thing to do? The real answer is we just saw a lot of folks that were willing to pay us like, you know, six-figure contracts, which at the time, when you're at zero error, it's like, oh, wow, that's huge. And a lot of folks that were willing to, you know, do the same thing. And it was the only idea we really explored that really had that property where people were like, hey, yeah, like, if you did this, I would literally pay you money because I can justify it. The sort of flip side of that at the time was more just, oh, well, this is such an obvious idea. Like, why I do this? Because, you know, people would have thought of this before. But that's a whole other thing. I think, like, once you start doing anything, once you get into

it, you know, you understand there's way more nuance than the overall narratives. The sheer fact that people are willing to talk to us, like, you know, two people and willing to pay us money was signal enough that it was worth doing. I guess when I look at sort of the history of technology, Anytime there's a big platform shift, the providers of the platform start to forward to integrate into the biggest applications on the platform. So an example of that would be after Microsoft launched its OS, it forward integrated in what became office, right? There were four separate companies doing PowerPoint and Excel and all this stuff. And then eventually they just subsume the functionality of those things in cross-sold that is a bundle. And then that happened later with Google, where they started adding vertical searches for the biggest categories of search.

if you think of that in the context of the foundation model providers like Open AI or Anthropic Anthropic is already providing cloud code they're already kind of forward integrating in different verticals they mentioned financials

as another area that they're moving into Open AI famously tried to buy windsurf and sort of enter coding more directly do you think about that at all in the context of what you're doing given just the size of the market

and the velocity at which you're getting adoption? Yeah I think it makes a lot of sense for the labs like I think Open AI, for example, most of their revenue and most of their margin for sure is coming from chat GPT in the application layer. Because you actually own the customer, you get, you're kind of indexing more on the problem you're solving rather than, you know, the costs of your model. And the API business, for example, is, they're probably not expecting even to make that much money from that long term and they probably see them more as a wedge.

Some of those work out well, right? Like, in other words, one could argue AWS and the cloud providers are good examples of what was perceived as a lower margin business that has enormous scale. scale and can throw off a ton of cash. Yeah. And so, you know, these API to run businesses strike me as something similar. I'm just more curious, like, how do you think about defensibility relative to these things? And, you know. Yeah.

So I guess the point I'm trying to make is I think it makes a lot of sense for them to push into application layer. And I think they will. In terms of what applications, I mean, generally, they'll probably start with applications where it's more consumer prosumer-e because there's, it's just more self-contained. It's like easier to build the software on top. Long-term, they may move into the more enterprise-y things. I don't think it's like super useful for applications like us to like, you know, spend a ton of eye thinking about like, you know, what the AI labs will do.

I do think the more enterprise you are, like the more, the thicker layer of software is, it's not even just like, it's not even stuff related to the models. It's like, okay, how do you have like observability and monitoring on all the conversations? How do you learn from the conversations? How do you learn from the conversations? How do you build like a testing like simulation suite for the, for QA of the conversations? And there's just so much to build. that's what we're focused on right now. I think, yeah, it might make sense. And yeah, who knows maybe one day will collaborate with the labs. We have great relationships with the larger ones. But I think before they tackle our space, there will probably be other spaces. They have the tackle first. Yeah. Coding is probably one of them up. I guess John really didn't know. How do you think about differentiation? Like, what do you do uniquely or how do you think that you're built them out over time? When we first started the company, it's this idea is like very easy to grok, right? There's a lot of big platforms out there too. You know, like Salesforce, the agent force and Google. some of the more AI native players. What's worked from us so far is a couple of things.

I think one, we kind of have a unique, we just have like a relatively young, intense team, and that has led itself to a couple of things. I think the biggest one is just speed. So we're just able to move really fast, and that shows itself in building the product and executing on the go-to-market side. And specifically in the products,

I would say we've kind of differentiated ourselves in taking this approach of, like, hey, this should be a very productized space. you should have an AI agent that's really easy for non-technical people to work with and for them to build the agent, iterate on it, analyze it. And that's in pretty stark contrast to how the industry has always worked. If you think about, you know, the sales forces of the world and just the classic SaaS, it is a very more like a technical endeavor.

You have to bring someone in to do the configuration. You have to have technical resources. As you scale, you can build something quite powerful, but it just becomes very slow and expensive to maintain because you use engineers to go through everything. And at the enterprise, there's so much complexity and nuance that you have to resolve that. So I think our view so far has been kind of different in that one of the things that LMs unlock is that you can really empower the non-technical business users.

And that has, I would say, been pretty well received. I mean, different teams of different strategies, of course, but for the folks that we're working with, and especially as you go more out market, I think people really like that strategy. There are definitely some teams out there that are more engineering driven, which, like, the engineering team, it owns the entire customer service deployments. Then, you know, maybe our current approach doesn't make as much sense. But I would say what we found is even when the engineering teams are very much involved, they don't necessarily want to be on the hook for every little change. And so in that case, we can work very well with them. And you have them still owning, how does the AI agent interact with the systems and connect API and so on.

where, and then we allow them to offload, so the logic building to the business users. So that's probably what's made us different so far. Again, obviously, we respect like the sales forces of the world, they build amazing businesses, but we just don't think that's the right approach for the AI era. And then on our end, yeah, I think we're just, we really want to differentiate on execution. If you look at the big shift that's happening right now in AI, because of the capabilities that we're basically moving from software as a service to basically some form of like labor or cognition as a service, right?

And so you see that sometimes in the pricing models where people, instead of charging per seat, well, maybe have some baseline platform fee, but then they'll charge on utilization or other things. Because fundamentally, it's almost like you're helping augment an agent versus just having a piece of software that they're living in or using. Yeah. And I think that's a very big shift. How do you think about the long-term version of that relative to your business or what do you see sort of coming on the horizon? Yeah, I think those pricing models are pretty use case specific. So if you're using a coding agent, for example, I think, you know, charging based on almost like the LGPU usage or something, like the number of cores you use could be interesting.

For us, it's actually quite different because you have like a very tangible output that you can measure the agent by, which is the conversation it's having. And when you talk to customers, that's generally how they think about it too. It's like, hey, we have a cost per contact or a cost per conversation. And so when you kind of deploy an AI agent, it makes sense to use the same pricing model. instead of pricing like a flat per seats because they're not really like a seats concept here you also don't want to price per like minute of the call either like that that's just kind of weird and also incentivizes the agent to just like have really long calls so you price basically the number of conversations that it can have it can be any conversation or it can be a conversation

that you know doesn't require a human so maybe that makes it apples to apples and then our customers generally come in and buy a sort of a lot of minute of conversations for the term and then they burned down. And we'll probably start seeing that more and more in the AI agent space where you generally price per like the output that it's doing. I think that that works. I think that's just very clearly the right pricing model for our space and makes sense to buyers and make sense to us as well. Yeah, it also really changes how you think about the total addressable markets for some of these things. Because if you're charging per seat, you're really limited by the number of people working at the company. If you're charging per conversation or per some aspect of code written or other

things. And really, the market equivalent is sort of the people working in that sector. Yeah. Right? It's not actually the seats for the company. So, you know, you're talking about their salaries versus seats. And so that's a pretty big shift in terms of how to think about Tam. Yeah. It's also just kind of like now the entire surfaces from Tam, our services revenue, is now part of the market because you're kind of shifting that in his software. And that's why when we can think about ourselves as well, like even us plus like all of our, competitors plus like everyone working on like generally eye agents is probably still like a grain of sand in the overall market right now and that's that's exciting because there's a lot to do

how do you think about this relative to the overall customer journey so particularly for certain types of consumer companies there's customer service but customer service almost starts when somebody just shows up to the website for the first time to purchase something right there's almost this whole like funnel yeah how does that impact what you build or how you work with your customers that's why we use the term concierge and that's how we think about it and it's kind of interesting actually, when we first started the company, because, you know, of course, we're engineers and we haven't, you know, worked in contact centers ourselves, we kind of assumed that that's how most customers would view it as well. It's like, hey, well, you're building the system

that can have any conversation. It turns out that in most customers, all the different types of conversations are just owned by completely different teams, completely different budgets. So, you know, if the reservations team at a hotel is probably going to be different than, you know, the customer service team. Overall, though, eventually you want this to be a unified, you know, concierge experience. And that's what a lot of leaders are excited by is like, can you have just something intelligent that is just there for the end user? It becomes like the go-to way that they interact. And eventually, if it's good enough, most consumers will just interact with the agent instead of even logging into the mobile app or the website. So how do you define

success for your company in the long run? So it's five years from now, 10 years, you're looking back. Yeah. What would make you feel like you've accomplished what you set out to do? Well, on one hand, there is like a specific goal for a company, right? We want to grow. We want to grow the, scale of the business and we want to be, you know, the winner in this, in this, like, exciting market. So how's that defined? I mean, in five years, we want to, of course, be working with the largest companies and have them just like all the, just powering sort of the conversations for all all the major brands out there and essentially just reinvent the way that most consumers interact with, you know, products and have conversations. And the other metric is, yeah, we'd like to

get there through just having a very sharp, you know, product and, you know, just to go to market execution in the same way that I'm currently talking about like the data bricks and the ramps of the world like we want to build you know a business like that where we're just like doing everything like super sharp and very thoughtfully I remember um reading once that somebody asked the page in the early days of Google what he was hoping to accomplish and he said I want to have a billion dollar company and the person replied with oh you mean a billion dollar market cap he said no a billion dollars of revenue at the time that was like this insane goal and it was like mind blown he's so ambitious and then you look in hindsight and

i don't know if that's like the revenue they do in a day or you know i don't know if they you know it's some crazy uh overshoot on outcome so i think that's a very tough question but i was sort of curious how you thought about it uh it's yeah it's tough on this at this point i mean we have what the databases are like single digits billions of revenue and they'll probably say that they're still very early on right so i yeah we don't think about things that far ahead I just don't think that's useful. Obviously, we're like extremely ambitious. And so we want to build the company of that scale or more. But it's also one step at a time. As we talk about thinking ahead much on longer time frames, five years, 10 years, whatever may be,

one can imagine that eventually customer support and customer service really becomes very agentic. And at the same time, people probably have the agents going and buying things for them or interacting on their behalf. How do you think about that future? When do you think that is? Are there any non-obvious things we should think about for that? Or, you know, how should we think about that future world or potential future world. Oh, I think that world is basically here. I mean, you have the, you know, all these consumer agents that are going out there and they go order DoorDash for you and so on.

And at some point, they'll, you know, maybe they'll call in to an airline to reschedule your flight or something. And then maybe they'll talk to our agent. And then you'll have agents talking to each other. I think in the near term, they'll still communicate natural language just because, like, each agent also needs to be compatible with humans, right? So if they talk to a human agent, a human support agent, or if we talk to a human customer, of course, that has to be compatible. But as they become more prevalent, you'll probably end up with slightly more efficient ways of communicating.

And I think that'll be interesting. We just have two agents interacting and they're just like spitting tokens at each other and you can just get something done. But I think ultimately it'll still be rooted in natural language because I don't think anytime soon we'll be in a world where 100% of interactions are done by that. So each agent still has to be compatible with natural language. Yeah, that's something we'll have to think about soon. It's not something we're seeing at scale now where you have agents writing in for you. I mean, part of the vision we talked about before, right, is that right now a lot of the conversations are more reactive support. It's like, hey, I have an issue. Can you fix it? But over time, it'll be more and more kind of broader, right, in terms of like being able to do purchasing decisions, be able to upsell folks, be able to be proactive and reach out when you detect an issue. And these types of conversations, I think, make a lot more sense. for having these personal agents in there, like someone doing your shopping for you and just goes and buys it and they can talk to their agent to actually get it done. And the personal agent knows their personal preferences. They know what's a given on if there's, you know, not this thing's out

of stock and maybe go for a different choice. Yeah, it's kind of, it's kind of weird to think about that. It's just like all these interactions happening outside of like humans and still stuff's getting done. But I think it'll be here sooner than later. It's really interesting. It's almost like every person has a personal assistant, a personal shopper, or whatever it may be. I remember one person I used to work with a lot. His view was that a lot of technology is basically looking at what the richest people in a society are doing and then saying that'll be available for everyone. And so if you go back to Roman times, you had these open sort of Roman baths, but if you were very wealthy, you'd have a bath in your own home. And obviously we all have baths, right? At home. And I think

we almost forget that that's like a technology innovation and evolution. And so it seems like a similar thing. If you look at Bill Gates or whoever, he probably has a staff of people who buy clothes for him and go and do things for him and book flights for him. And so therefore, everybody will have this at some point. It'll just be agents. It sounds like interacting with each other. Yeah, I doubt they're booking flights. But yeah, no, I agree. Yeah, I mean, I think that is, I mean, it's an interesting framework. It makes you think, like, what are the other things that folks are doing? But, yeah, at least in our context, yeah, we definitely expect more of these sort of AI assistance to be part of the ecosystem.

Amazing, yeah. Well, thanks so much for you joining me today. Thanks for having you. Find us on Twitter at No Pryors Pod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no dash priors.com.