No Priors 第132期｜对话 Decagon CEO兼联合创始人 Jesse Zhang

Today, we're lucky to have with us Jesse Zhang. Jesse is the co-founder and CEO of Decagon, which provides customer service and related AI for all sorts of enterprises, including banks, telecom providers, airlines, and many of the biggest and most important tech companies. Jesse Prior started Loki, which was acquired by Niantic, and we're very excited to have him join us today. Jesse, thanks for joining us today.

Jesse Zhang

Thanks for having me.

Can you tell us a little about Decagon and why you started the company? How did you get going?

Jesse Zhang

Yeah, of course. Decagon, for those who are not really familiar with us, is an AI customer service agent. You can think of us as working with a large bank or airline, or just people that have large contact volumes. The AI's job is to have a very engaging and personalized conversation with the user, resolve it, save the company a bunch of money, and ideally drive more revenue in the future because folks are more engaged.

As we've grown, it's becoming more and more of a conversational UI for the brand. It's how every user can interact with the brand. We often use the term “concierge” to describe this, but that's what we do.

You're working right now with some big banks—some of the world's biggest banks. You're working with airlines and telcos. You've actually gotten to very big customers very quickly. How did you go about doing that, or how did it happen?

Jesse Zhang

Yeah. We started out mostly with digital-native companies. A lot of startups do that, and digital natives are much more willing to try startups because they can move faster.

Like late-stage tech companies and things like that.

Jesse Zhang

Yeah, like Ripling and Notion. Folks like them were great partners, and they also helped us iterate on the product a lot. That's where we started. As we've gone on, I think we were naturally pulled upmarket just because of the demand. As you might imagine, that's where most of the large contact volumes are, so it happened a lot faster than we thought. I would say a lot of these enterprises also moved a lot faster than we would have expected. That's why we ended up there.

I think one of the underappreciated things about AI traction is that 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. All these markets are kind of open now that weren't before and would have been much harder to enter.

Jesse Zhang

Yeah. Another specific dynamic is that, at the enterprise, it's becoming much more of a top-down motion. In the past, many of these technologies could have been just one team trying to vet them or decide whether to adopt them. Now it's an AI transformation, and the C-suite and the board are all very focused on how to adopt AI. Customer service is often one of the biggest areas, or probably the lowest-hanging fruit. That's how these conversations have progressed.

How much of an impact are you having in terms of some of these teams? I know that you're giving a lot of leverage to these customer service organizations. Are you making people 2 times more productive? I'm curious whether there's a way to measure the outcome here.

Jesse Zhang

Yeah. For most of the large enterprises, the first thing they'll measure is the efficiency you're getting them. Whatever they're spending on their contact center or operations, how much are you cutting that down by? We've done case studies now where folks have been able to cut that down by 60–70%.

Oh, wow.

Jesse Zhang

That's a great success case, right? It's a very clear business case. You can show it to everyone.

Mm-hmm.

Jesse Zhang

The secondary thing, oftentimes, that folks will even put at the same level, if not higher, is customer satisfaction. You need to measure that and make sure that your customers are having a good time and are more engaged—if not more engaged, then at least happier than previously.

So you're basically providing these customer service AI agents and workflows that function 24/7 in multiple different languages out of the box. Do you basically do a lot of integrations into what they're already providing, or how do you tend to work with folks?

Jesse Zhang

Yeah. The way you should think about agents here is that they're more of a substitute for mundane human labor. Whatever systems they're already using, generally an AI agent—at least when you first deploy it—is not going to disrupt the tooling you currently have. Whatever CRM they're using and whatever telephony stack they have, we'll just integrate with that. Then it's doing all the tasks you would expect a human to do, and over time that just continues scaling.

One of the benefits of AI agents is that they're always on. They're awake 24/7, you don't have to train them really, there's no churn, and you can just scale them out.

You co-founded this with Ashwin, and you're both second-time founders. What made you decide to work on this problem in particular? I feel like, with many people's first company, they really focus just on the product and the technology. With your second company, you're often more likely to also focus on the customer side and the commerciality. Was that your story, or were you always more commercially focused in terms of how you thought about problems in the world to solve?

Jesse Zhang

Yeah. One of my theses is that there's a lot of untapped potential in really strong technical folks and in making them a bit more commercial. The types of problems on the go-to-market side are generally a little bit hairier, so a lot of folks don't like the messiness. Especially, a lot of technical folks enjoy the engineering and product problems more.

But they're still very interesting problems and very rewarding. If you can do that well, that's how you get your company to grow a lot faster, because you just do more sales. At the end of the day, it's still problem-solving. Ashwin and I were both from technical backgrounds. We just got along very well, we're at similar stages in life, and we had both started a company before, as you said.

The first time, you kind of lack a little bit of the commercial sense, and you're just generally trying to figure things out. It's very hard to build intuition about what is a good idea and what isn't. It's definitely easier the second time around.

How do you think about how you hired, or what sort of people you looked for, for the team the first time around versus this time? What are you optimizing for in the people that you bring on board in your second company?

Jesse Zhang

Yeah, we're a little fortunate now. We've built a bit of a brand around our talent, and we have a fairly interesting culture now. We're generally just selecting for very smart people. We care more about that than direct experience and so on. Early on, experience is still quite important. I don't think we hired straight out of college for our first pretty large number of hires, but of course now we are. You want a little bit of that blend, but the first thing we select for is how smart you are.

That has worked out well for us. We apply that philosophy basically across the organization. Obviously, engineering is generally easy to test for, but even in sales and marketing, that's been a core part of our philosophy.

And the other piece is that you're an office-based company. There's a lot of news now about how companies work really hard.

Jesse Zhang

Yeah, sure.

It's the 996 culture and so on.

Jesse Zhang

I don't think we over-rotate on stuff like that. We're just looking for people where, when you meet them, you can tell that they really see this as 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 something out of it. They get to accelerate their careers and work on very interesting problems.

Are you in the office every day, 5 days a week, in terms of when people are supposed to be in?

Jesse Zhang

Yeah, we're 5 days a week, and a lot of folks come in on the weekends, but it's not a requirement.

Yeah, makes sense. It definitely feels like you have a hardworking culture. People want to put in the time because it's interesting. If you look at professional athletes in training, they're always saying, “I train 6 or 7 days a week. I work hard at my craft.” There was almost this period in Silicon Valley where people didn't want to say that. I feel like, with this wave of AI, suddenly it's come back—that it's good to do that, that that's how you build a winning company and a winning culture. It seems like you've adopted that as how you approach things as well.

Jesse Zhang

Yeah. I think pretty much all the AI companies that are doing well have pretty heavy in-office cultures.

Jesse Zhang

It’s just—you get way more done, especially in the early stage. I think after a certain point of scale, you could definitely make the argument that it matters less, but as of right now it matters a lot.

Elad Gil

Yeah. It also seems like there are certain roles that have always been remote throughout history, in terms of certain sales roles or—

Jesse Zhang

Or the like. Well, then really you’re supposed to be at the customer side of your office, right? If you’re doing some form of field sales or the like.

Elad Gil

So, it seems like a lot of people have gone back to the pre-COVID era for the startups that seem to be working best, which I think is really interesting. Obviously, things are working really well for you all. How are you thinking about the main types of roles that you want to build out in the company now, or things that you’re hiring for or looking for?

Jesse Zhang

Right now, we’re mostly building for scale. What that means is, of course, we need to hire a lot more ICs. We’re bringing in more leaders and adding a bit more structure.

The interesting thing we’re thinking about now is a people function. We never really needed that, but we’re approaching 200 people, so you definitely need folks to be thinking about that full-time.

Elad Gil

Mm-hmm.

Jesse Zhang

It’s more around org design and the right way to structure our operating cadence between teams. We have an office now in New York, and we’re spinning one up in Europe. There are a lot more of those problems now, and that’s definitely something we’re thinking about.

Elad Gil

I think if you were to give founders advice around 1 thing they should do that goes against their instinct the first time they’ve scaled a company, what is that thing? Or how would you think about a big takeaway you’ve had as you’ve gone from, “Okay, we have this nimble team that’s grinding out a new product,” to, “Okay, we’re scaling, things are working really well, we have product-market fit, and we have to move as fast as possible”? Is there a big mental transition that happens, or is there a specific tactic you’d suggest?

Jesse Zhang

I would say for us, we hit our stride fairly early in this company, so it didn’t feel like there was a before and after.

When we were building, we stayed really close to the customer, which is always helpful. I think over time, the adjustment we’re learning to make is thinking more medium- to long-term versus short-term, because at the beginning, 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 you also have an obligation to, because if you don’t, eventually you get to a point where things really start breaking and you feel like, “Oh, man, I should have scaled this better,” and so on.

We’re definitely in that journey right now, and we’re trying to be as mindful of it as possible. Maybe 1 related thing is that we spend a good amount of time studying later-stage teams that have done this well. There are obviously organizations that we admire where we—

Elad Gil

Who are some people you think have done it well?

Jesse Zhang

Ramp comes to mind for sure. Databricks, if you’re thinking about it a bit more like this, is just a company that has always executed well. I think Ali data bicks is 1 of the most impressive CEOs.

Elad Gil

Yeah. He’s actually—I would probably go so far as to say he’s my favorite CEO, and he’s been very kind to us with his time.

Jesse Zhang

That’s another good example, honestly. It’s very strong technical folks who have also done very well applying that to commercial problems and execution. That’s definitely the DNA we want to build at Decagon.

Elad Gil

Do you screen for commerciality in the people who join? If so, how can you do that? Say you have an engineer: do you try to find people who are more commercially minded, or do you think that self-selects for the culture?

Jesse Zhang

I don’t think it’s super important for every engineer in the company to be commercially minded, for example. I think it’s definitely very important for the founders and then maybe the folks immediately around the founders.

That’s why, generally, when I talk to engineers who want to join startups—for example, let’s say they eventually want to start their own company, which is a very common profile—in my opinion, it’s much more useful to join somewhere where they’ve already got the commercial side figured out, and you can actually see it in action and build that intuition, than to join something pre-PMF.

I think that’s a very common misconception, because it’s like, “Oh, well, the smaller the team, the closer I am to learning how to be a founder.” But if you join a pre-PMF team and never actually get to see the commercial side in action, you’re not really learning much. You’re essentially learning what not to do.

Unfortunately, the reality is that most companies don’t hit that point. Our discussion with engineers these days is, “Hey, it’s very important for you to join, if you want to start your own company eventually. Decagon is the golden age to do that because we have a lot of the basics figured out, but there’s still so much that isn’t figured out, and a lot of it is very close to the commercial side.”

Elad Gil

Yeah, that makes sense. I think a lot of the golden periods for many companies are between 50 and 100 people, up to 1,000, maybe 2,000, if the thing keeps going in terms of growth, because that’s the era where I think you see the most change. Going from 2,000 to 15,000 at Google, which is roughly when I was there, was also a magical period of change.

Yeah.

Elad Gil

And so I guess it depends on the size of the market and the way the teams run and everything else.

Yeah.

Elad Gil

I guess it also seems like you can learn a lot more from success than from failure, and it sounds like, in the context of Decagon, it’s a really great moment to join because things are working and people can learn in different areas. Are there particular areas where you’d really like to attract people? Is it international? Is it somewhere else?

Jesse Zhang

The way I would think about that is, if you’re a founder, you’re training your own neural network, right? You need positive examples and negative examples. For my first company, I started right after college. Basically, the first 2 years were just negative examples. You’re failing.

That’s helpful in some sense because you can brute-force it and try to learn. But if you get some positive examples sprinkled into your learning, your learning rate is just way faster. I think that’s the misconception.

As we expand internationally, that’s important too. I think an interesting thing with each new office is that you also have to rethink things. We worked really hard to build our current culture in the SF office.

When you spin up New York, you’re obviously sending some folks out, but you have to be mindful of that culture as well, because once it’s set, it becomes its own living thing. Europe is a whole different thing because the culture over there is naturally a little bit different, so you have to be a little bit mindful. It’s also naturally more isolated.

You have to serve wine at lunch and that kind of stuff.

Elad Gil

When you talk about having to shift the way that you think about things more toward medium- and long-term planning, is that org design? Is that internationalization? Is that product roadmaps? Is it capitalization? What are the main components that you’ve had to start thinking about longer term?

I’d probably say it’s more org design and product roadmaps. Org design, in terms of how you allocate resources, is important because there are a lot of types of work that don’t yield immediate returns. It’s not going to close a customer for you, but if you don’t do it, in 6 months you’ll really regret it, and you’ll be in a spot where it’s much harder to do that work.

Elad Gil

What’s an example of that?

Core product work. 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, and we’ll probably still be fine for now.

But you can definitely foresee that if you don’t invest in this, closing each incremental customer in the future will require the same level of work, if not more, because then you just have more overhead. You want that to go down over time.

That’s the classic type of thing where you have to shift your mindset a bit. I think in the early days, it’s really good to have a greedy mindset. It’s just, “I really need to optimize for this 1 thing and get it over the line,” instead of planning too long term, because if you do that, you could end up burning a quarter and not getting anywhere. Over time, you have to make that switch.

Did you set off to do customer service when you started Decagon, or is that something you all discovered early on as you were iterating on ideas?

Oh no, definitely not. I didn't come in with any preconceived notion. I had a lot of empathy for the problem from my first company. It was a consumer company, so we had a lot of users, but our general approach, going back to the commercial side, was that I think we're just a lot better at being commercial about this in the early days. And so we talked to a lot of customers and had a very disciplined process of evaluating ideas. 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 six-figure contracts, which, at the time, when you're at zero ARR, it's like, “Oh wow, that's huge.” And a lot of folks were willing to do the same thing.

It was the only idea we really explored that had that property, where people were like, “Hey, yeah, if you did this, I would literally pay you money because I can justify it.” The flip side of that at the time was more just, “Oh, well, this is such an obvious idea. Why do this? Because people would have thought of this before.”

But that's a whole other thing. Once you start doing anything, once you get into it, you understand there's way more nuance than the overall narratives. The sheer fact that people were willing to talk to us—two people—and willing to pay us money was signal enough that it was worth doing.

Elad Gil

I guess when I look at the history of technology, any time there's a big platform shift, the providers of the platform start to forward-integrate into the biggest applications on the platform. An example of that would be after Microsoft launched its OS, they forward-integrated into what became Office, right? Those were 4 separate companies doing PowerPoint and Excel and all this stuff, and then eventually Microsoft just subsumed the functionality of those things and cross-sold them as a bundle.

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 OpenAI or Anthropic, Anthropic is already providing Claude Code. They're already kind of forward-integrating in different verticals. They mentioned financials as another area that they're moving into. OpenAI 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. I think OpenAI, for example, most of their revenue and most of their margin, for sure, is coming from ChatGPT in the application layer because you actually own the customer. You're kind of indexing more on the problem you're solving rather than the costs of your model.

The API business, for example, I don't know. They're probably not expecting to make that much money from that long term, and they probably see it more as a wedge.

Elad Gil

Some of those work out well, right? One could argue AWS and the cloud providers are good examples of what was perceived as a lower-margin business that has enormous scale and can throw off a ton of cash. These API-driven businesses strike me as something similar.

I'm just more curious: How do you think about defensibility relative to these things?

So I guess the point I'm trying to make is I think it makes a lot of sense for them to push into the application layer. In terms of what applications, generally they'll probably start with applications where it's more consumer-primary because it's just more self-contained and easier to build the software on top.

Long term, they may move into more enterprise things. I don't think it's super useful for applications like ours to spend a ton of time thinking about what the AI labs will do. I do think the more enterprise you are, the thicker the layer of software is. It's not even just stuff related to the models. It's, okay, how do you have observability and monitoring on all the conversations? How do you learn from the conversations? How do you really dissect the insights? How do you build a testing and simulation suite for QA of the conversations? There's just so much to build.

That's what we're focused on right now. I think that might make sense. And, yeah, who knows? Maybe one day we'll collaborate with the labs. We already have great relationships with the larger ones. But I think before they tackle our space, there will probably be other spaces they have to tackle first. Coding is probably one of them.

I guess, on a related note, how do you think about differentiation? What do you do uniquely, or how do you think you'll build that out over time?

Jesse Zhang

When we first started the company, this idea was very easy to grok, right? There are a lot of big platforms out there too. You have Salesforce with Agentforce and Google, as well as some of the more AI-native players. What's worked for us so far is a couple of things. I think, one, we just have a relatively young, intense team, and that has lent itself to a couple of things. The biggest one is speed.

We're just able to move really fast, and that shows itself in building the product and executing on the go-to-market side. Specifically in the product, I would say we've differentiated ourselves by taking this approach: This should be a very productized space. You should have an AI agent that's really easy for nontechnical people to work with, and for them to build the agent, iterate on it, and analyze it.

That's in pretty stark contrast to how the industry has always worked. If you think about the Salesforces of the world, just the classic SaaS, it is much more of 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 have to get engineers to go through everything. At the enterprise, there's so much complexity and nuance that you have to resolve.

I think our view so far has been different in that one of the things that LLMs unlock is that you can really empower the nontechnical business users, and that has, I would say, been pretty well received. Different teams have different strategies, of course, but for the folks that we're working with, and especially as you go more upmarket, I think people really like that strategy.

There are definitely some teams out there that are more engineering-driven. If the engineering team owns the entire customer service deployment, then maybe our current approach doesn't make as much sense. But I would say what we found is that even when the engineering teams are very much involved, they don't necessarily want to be on the hook for every little change.

In that case, we can work very well with them. You have them still owning how the AI agent interacts with the systems and connects APIs and so on, while we allow them to offload the logic-building to the business users. So that's probably what's made us different so far. Again, obviously, we respect the Salesforces 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, we really want to differentiate on execution.

Elad Gil

If you look at the big shift that's happening right now in AI, because of the capability set, we're basically moving from software as a service to some form of labor or cognition as a service, right? You see that sometimes in the pricing models, where people, instead of charging per seat, will maybe have some baseline platform fee, but then they'll charge based on utilization for 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.

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 coming on the horizon?

Jesse Zhang

Yeah, I think those pricing models are pretty use-case-specific. If you're using a coding agent, for example, charging based on almost the GPU usage or something like the number of cores you use could be interesting.

For us, it's actually quite different because you have a very tangible output that you can measure the agent by, which is the conversation it's having. 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.” When you deploy an AI agent, it makes sense to use the same pricing model instead of pricing at a flat per-seat rate because there's not really a seat concept here.

You also don't want to price per minute of the call. That's just kind of weird, and it also incentivizes the agent to have really long calls. So you price based on the number of conversations that it can have. It can be any conversation, or it can be a conversation that doesn't require a human.

So maybe that makes it apples to apples. Our customers generally come in and buy an allotment of conversations for the term, and then they burn down. We'll probably start seeing that more and more in the AI agent space, where you generally price per the output that it's doing. I think that works. I think that's very clearly the right pricing model for our space, makes sense to buyers, and makes 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, the market equivalent is sort of the people working in that sector, right? It's not actually the seats for the company. You're talking about their salaries versus seats, 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 services TAM, or services revenue, is part of the market because you're shifting that into software. That's why, when we think about ourselves as well, even us plus all of our competitors plus everyone working on AI agents generally is probably still a grain of sand in the overall market right now. That's exciting because there's a lot to do.

How do you think about this relative to the overall customer journey? 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 funnel. 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. It's kind of interesting, actually, when we first started the company because, of course, we're engineers and we haven't 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 a system that can have any conversation."

It turns out that, at most customers, all the different types of conversations are just owned by completely different teams and completely different budgets. The reservations team at a hotel is probably going to be different from the customer service team.

Overall, though, eventually you want this to be a unified concierge experience. That's what a lot of leaders are excited by: can you have just something intelligent that's there for the end user? It becomes the go-to way that they interact. Eventually, if it's good enough, most consumers will just interact with the agent instead of logging into the mobile app or the website and so on.

How do you define success for your company in the long run? It's 5 years from now, 10 years from now, and you're looking back. What would make you feel like you've accomplished what you set out to do?

Jesse Zhang

Well, on one hand, there is 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 the winner in this exciting market.

So how's that defined? In 5 years, we want to, of course, be working with the largest companies and powering the conversations for all the major brands out there, and essentially just reinventing the way that most consumers interact with products and have conversations. The other metric is that we'd like to get there through having a very sharp product and go-to-market execution. In the same way that I'm currently talking about the Databricks and the Ramps of the world, we want to build a business like that where we're doing everything super sharply and very thoughtfully.

I remember reading once that somebody asked Larry Page, in the early days of Google, what he was hoping to accomplish, and he said, "I want to have a billion-dollar company." The person replied, "Oh, you mean a billion-dollar market cap?" And he said, "No, a billion dollars of revenue." At the time, that was an insane goal, and everyone was mind-blown by how ambitious he was. Then you look in hindsight, and I don't know if that's the revenue they do in a day or what they do—some crazy overshoot on the outcome. So I think that's a very tough question, but I'm sort of curious how you thought about it.

It's tough at this point. I mean, we have what, the Databricks are like single-digit billions of revenue, and they'll probably say that they're still very early on, right?

Mm-hmm.

So, yeah, we don't think about things that far ahead. I just don't think that's useful. Obviously, we're extremely ambitious, and we want to build a company of that scale or more, but it's also one step at a time.

As we talk about thinking ahead on longer time frames—5 years, 10 years, whatever it may be—one could imagine that eventually customer support and customer service really becomes very agentic. At the same time, people probably have 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, or how should we think about that future world or potential future world?

Jesse Zhang

Oh, I think that world is basically here. You have all these consumer agents that are going out there and ordering DoorDash for you and so on. At some point, they'll maybe call an airline to reschedule your flight or something, and then maybe they'll talk to our agent. You'll have agents talking to each other.

I think in the near term they'll still communicate in natural language, just because each agent also needs to be compatible with humans, right? 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. I think it'll be interesting to have 2 agents interacting, just spinning tokens at each other, and getting something done.

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. Each agent still has to be compatible with natural language. 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.

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 broader in terms of being able to make purchasing decisions, upsell folks, and be proactive and reach out when you detect an issue.

These types of conversations make a lot more sense for having these personal agents in there, like someone doing your shopping for you and just going and buying it. They can talk to their agent to actually get it done. The personal agent knows their personal preferences. They know what to give in on if something's out of stock and maybe they should go for a different choice.

Yeah, it's kind of weird to think about. All these interactions are happening outside of humans, and stuff is still 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, 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."

If you go back to Roman times, you had these open Roman baths, but if you were very wealthy, you'd have a bath in your own home. Obviously, we all have baths at home, right? We almost forget that that's a technology innovation and evolution.

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, go and do things for him, and book flights for him. 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. I think that is an interesting framework. It makes you think: what are the other things that folks are doing? At least in our context, we definitely expect more of these AI assistants to be part of the ecosystem.

Mm-hmm.

Amazing. Yeah. Well, thanks so much for joining me today.

Jesse Zhang

Thanks for having me.

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