How AI Agents Are Transforming Customer Support, with Decagon’s Jesse Zhang

Today on No Priors, co-founder and CEO of Decagon, Jesse Zhang, joins Elad to discuss the future of agentic customer support. Decagon provides AI-powered customer interactions for companies like Rippling, Notion, Duolingo, Classpass, Substack, Vanta, Eventbrite, and more. Jesse shares the thesis behind starting Decagon, why he sees customer support as the ideal entry point for agentic technology, and what areas of AI excite him most. They also discuss voice-based interfaces, issues with latency in current capabilities, and the connection between young math olympiad communities and today’s AI startups. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @TheJesseZhang Show Notes: 0:00 Introduction 0:30 Starting Decagon 3:15 Business impact of adopting agents for customer support and customer ops 8:00 AI infrastructure and models for customer success agents 12:05 Voice-based capabilities and text-to-speech engines 15:00 Combatting latency 16:25 Crossover of math and AI communities 21:12 Exciting areas of AI 25:29 Strengths and weaknesses of agents

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Speaker 00

  Hello and welcome to No Priors. Today I'm talking to Jesse Zhang, co-founder of Decagon. Decagon is an early-stage company building enterprise-grade generative AI for customer support. Founded in August of 2023, their platform is already being used by large enterprises and fast-growing startups like Rippling, Notion, Duolingo, ClassPass, Eventbrite, Vanta and more. Jesse, welcome to No Priors.

• 0:28Speaker 01

  Of course, thanks for having me, Yul.

• 0:30Speaker 00

  Absolutely. Maybe we can start a little bit with sort of your background and what Decagon does. You know, you're a serial founder, you started another company before this, Niantic bought. And you know, now you and Ashwin have started Decagon and you've been working on it for a while and have seen some really interesting adoption from companies like Rippling, Notion, Eventbrite, Vanta, Substack and many others, right? So you've really started to carve out a real space for the company. Can you tell us a little bit more about what Decagon does, how it works, what the focus is of the company?

• 1:02Speaker 01

  Of course, yeah. So a quick background on me, grew up in Boulder, did a lot of math contests, stuff like that. Growing up, studied CS at Harvard. As you mentioned, started a company right out of school. That company was eventually bought by Niantic and then I left to start this company. Ashwin and I, we met through mutual friends, officially met at this VC offsite. And when we got together, we were like, okay. Biggest learning from first company is that can't really overthink things too much. We started by just kind of obviously being interested in AI agents. It's very exciting technology, arguably like the coolest thing from this generation.

• 1:45Speaker 01

  And we just, you know, talked to a bunch of customers like the ones you listed. We, I think, over the years have gotten a lot better at figuring out, you know, how to talk to folks and, you know, what questions to ask. And through that process, we kind of arrived at, you know, our current use case as maybe what we think is like the golden use case for these AI agents, which is customer interactions, customer service.

• 2:09Speaker 01

  The use case is very tailor-made for what LLMs are good at. And so we started building from there, right? And we still weren't thinking too much about, you know, the vision or anything yet. It was just like, all right, we had a lot of customers in front of us. How can we make it so that they're happy and they really like what we're building? And then that led to kind of where we're at now. I would say right now as a company, Decagon, we ship these AI agents for folks to use on the customer service, customer experience side. The thing that's made us special so far is we have a huge sort of focus on transparency, I guess. So when people use us, especially these larger companies, it's very important for them that the AI agent is not a black box, that they feel like, okay, even though LLMs

• 2:53Speaker 01

  are cool and like, you know, there's a lot of things you can do with them, that they can see how decisions are being made, like what data is being used, how do you come up with answers? And if I want to get feedback, I can, that sort of thing. So currently, we're in production with a bunch of these, these large folks that have large support teams. Pretty much any company that has a large sizable support operation is a good fit for us.

• 3:15Speaker 00

  That makes a lot of sense. It's interesting because I feel like one of the things that that's been really striking over, say, the last year in the AI world is the CEO of Klarna

• 3:26Speaker 00

  posted on X or tweeted about the impact that AI has had on their customer support or service team. And Klarna is sort of like a buy now, pay later service out of Europe. And, you know, his tweet basically said in the first four weeks, they handled 2.3 million customer service chats. The customer satisfaction was on par with humans. It's 25% reduction in repeat inquiries relative to people. It resolved customer errands or issues in two minutes versus 11 minutes for a human agent. And instantly, they were live 24/7 in 23 markets in 35 languages because AI supports so many things. And so, you know, had a huge impact on that company. And I think they sort of shifted 700 full-time agents to do other

• 4:07Speaker 00

  work, right? In terms of the impact of Klarna itself as an organization. What sort of impact have you been seeing with your customers as they adopt this sort of technology? And how do you think through the lens of what you're really bringing to these customers and the sort of satisfaction that their own end users have?

• 4:24Speaker 01

  It's an interesting way to think about it, which is, you know, all these people are shipping this use case, right? It's like, you know, there's a lot of evangelists out there, which is nice. The Clarn article is awesome. It gave, there's a lot of tailwinds for the industry. And I think one interesting thing we've seen is that the benefits that people get are all roughly in the same vein, but different people prioritize different themes. And so at this point, it's not really even that much of a hot take to say that like in a couple of years, these agents are going to be super pervasive. People can use them for all these customer interactions. They're going to be everywhere. And so to your point, like what is the benefit? So for our customers, it's always the same.

• 5:05Speaker 01

  It's what fraction of total work, in this case, like conversations, can the AI agent do? So how much work is this saving us? And then two, how much happier are our customers, right? Like, what's the customer satisfaction score, MPS score? Those two are often just the leaders by far. As I said before, like different people maybe value each one slightly differently. And then there's kind of other things like, okay, well, we want to make sure that there's accuracy, right? Like if we're in a regulated industry, you know, this has to be very accurate for us. So that, those are kind of where the benefits lie. It's like, we're saving a bunch of money, we're saving kind of time and resources. But also on the other side, we're kind of making the customers happier.

• 5:47Speaker 01

  And so that can lead to higher retention, more conversions, and it's kind of a lot more upside there. It's like you're giving every customer a personal concierge, basically, in their pocket, that they can chat with anytime, any language, 24/7. And, you know, that's, that can be pretty transformational for a lot of businesses.

• 6:05Speaker 00

  Is there any example customer that you can talk about as a case study in terms of the impact this has had, how it's looked at their metrics, the success they've seen using Decagon?

• 6:15Speaker 01

  Of course, yeah. So we just did a big case study with a company called Built Rewards. Great use case for us. They have a very large user base, growing very quickly. You're actually using it to either make points or make payments. A lot of my friends use the product. And then as a result, because you have a large customer base, like people have questions, people will have things that they need help on. And so like the, the number of support inquiries basically grows linearly with the number of, number of users. And because of that, because they're growing so fast, basically exponentially, that means the number of support queries is also growing exponentially. So when they first started using us, that was like the main goal. It's like, holy crap, we're getting overwhelmed by all this volume. Can AI help here?

• 7:00Speaker 01

  And so the thing that ended up happening there was, yeah, within a, basically a month of starting to use us, they were able to stop scaling their, their team and the AI would take over a lot of the automation, just makes everything very smooth. And then now, basically, we're almost, almost a year in at this point, they've been able to really restructure their customer support team. And again, we, we published a case study on this where they were able to quantify like, okay, what are the savings, right? And so, so far, it's around sixty five agents of just like headcount. Safe, so very tangible difference. And then for us, it's also great because we're able to provide them the value there. It's like a very easy ROI.

• 7:45Speaker 01

  But the customer experience is also a lot snappier and, you know, they get a lot of social media posts about like, holy crap, like I just tried, you know, build reward support thing and it doesn't feel like any sort of AI or chatbot system we've ever used before. So that makes us happy.

• 8:00Speaker 00

  Could you tell me a little bit more about you built from a technology and infrastructure perspective? So I guess there's the core models that anybody can access, right? The, the GPT four O's of the world or GPT four, the Claude Sonnets, et cetera. And then there's all the stuff you've built on top of it to actually make this work well for your specific use case and for customer support agents. Could you, could you tell us a bit more about what you all have had to build over time?

• 8:22Speaker 01

  Of course. Like you said, everyone has the same access to the same models. We see ourselves very much as a software company. And we're obviously doing a lot of work around AI and like using AI models a lot. But I would argue that most, you know, applications nowadays are, they're real software companies and AI models are kind of tools that everyone can use. And so most of the sort of alpha or most of the specials stuff that you build is on top of models. It's either orchestration layer or the software around it. For us, there's been a big focus on both. The orchestration layer is kind of how you can use all these different models together. You probably have eval set up that measure how good each model is at certain things.

• 9:06Speaker 01

  You put them together and the whole goal of putting them together is to mold it around the business logic of the customer. That's part one. The other thing you build is just very classic software, right? You have this AI agent there. It's all the things I was saying before, like, you know, transparency is a big piece. You really don't want this to feel like a black box that's just there answering questions. And so, how can you build all the tooling to see like, okay, what's the data that the agent's using? What steps is it taking? Can I analyze all these conversations that are coming in? If you have a million conversations, it's like, okay, no one's reading all those. So, how can you make it so that the AI, the LLM can read every single conversation, tell you how stuff's going, find gaps in their knowledge, give you a breakdown of like, okay, here are the big categories you should

• 9:51Speaker 01

  care about. There's like been a trend here that's been interesting. So that's all the software around it that we're building. And that's typically how it's structured. And in the orchestration layer, I think it's going to be different for every agent, right? Like, our agent versus like a coding agent, that orchestration's going to look pretty different. But at the end of the day, you're kind of just building a sort of a structure on top of the LMs.

• 10:17Speaker 00

  Yeah. It seems like we're very early in the days of true agentic stuff. And that includes the ability to sequence chains of events that includes certain forms of reasoning. Obviously, there's things like O1 and other things that have been coming out to start to try and address this, but we seem quite early in the scaling curves. What do you think are the main pieces of technology that are missing to really take you or your sort of vision to the next level in terms of how these agentic systems should work?

• 10:46Speaker 01

  Yeah. So one thing we were talking about the other day is, there's actually different types of intelligence with the AI models. And a lot of the recent developments with O1 or Sonnet and stuff like that has been around, I guess, like quantitative reasoning intelligence. So they've gotten better at coding, they've gotten better at math. And for us, actually, those things help, but they're actually not the biggest difference maker. So in our use case, the type of intelligence that matters the most, we would probably describe it as instruction following. So you just have a bunch of instructions, like, can you follow it to a T V? And I'm sure there's other kind of types as well, but for us, we're excited to see developments on the other areas too.

• 11:30Speaker 01

  And people, everyone's saying like, oh, there's plateau happening with like, you know, the core models and the intelligence. I think when most people say intelligence like that, they're probably talking about the reasoning capabilities. For us and like the agentic flows that we use, instruction following is a huge piece because you have to like, you know, just think about like a customer service, you know, SOP or like a plate, like a workflow or something like that. Like you just have to be very accurate about it.

• 12:00Speaker 01

  And so I know there's research going on about this in the, in the major labs, and I think that's one thing we're looking forward to next year.

• 12:06Speaker 00

  One, one other area that it seems like really touches on customer success and customer support and sort of user experience is also voice-based support. And I think one of the things that's a little bit under discussed in the AI world, because we keep talking about large language models and understanding of text and obviously that stuff is crucial to everything else, but I feel like we almost under discuss text to speech engines and the ability to understand spoken word and then respond with audio, right? And so there's companies like Cartesian, Eleven Labs, OpenAI, Google, et cetera, who are starting to provide some of these services and APIs. How much of an impact does that happen, what you're doing, or is that a separate type of product? Or, you know, how do you think about the voice component of these things?

• 12:51Speaker 01

  Great question. Huge impact. So we have customers now trying our voice agents. And if you just think about our space, right, like you have, the, the overall problem is the same, which is you have a bunch of customers, they have, you know, questions or issues or things you need to talk about. And the channel really doesn't matter for them. It's like, some people prefer voice, some people prefer chat, some people prefer email, some people prefer SMS or something like that. And so our job is to handle all of those.

• 13:21Speaker 01

  And obviously you, you start with text because that's the most, it's like the easier one. It's like easy to evaluate for the customer as well. I think just now you're getting to the point where you have big companies that are very interested in voice and like actually they, they've seen the results of a text-based agent and they're like, okay, well, yeah, you should, we should be able to generate voices and do the same thing for phone calls. None of this would be possible without the the models that you just listed, right? And the those companies, so like 11 Labs, OpenAI is doing some cool stuff, Cartesia. And I think there's also been huge strides this year with those models around like how realistic the voices sound, also like latency matters a lot in our use case, because if

• 14:05Speaker 01

  you're making a phone call, like you expect things to feel very snappy. So yeah, big, big, uh, big topic for us. And as these companies get better, I mean, we're working with them pretty closely right now on how, how you can actually like build these things well at scale. But as they get better, that's also going to be huge for us to keep delivering these voice agents.

• 14:28Speaker 00

  Makes sense. Yeah, my sense is one of the issues is latency in terms of it takes enough time to take a audio stream or somebody's talking, translate it into text, feed that into a language model, and then output it as a voice again, that it feels, there's a lot of pauses or people have to kind of wait, and there's different things that people have been trying to do in the background, like streaming the potential solutions back out and then, you know, being able to try and shorten that latency timeline. Do you feel, do you feel latency is still an issue? Or is it just solved by integrating voice directly into the models in a deeper way for for some of these services? Or when do you think latency becomes a solved problem for these sorts of application areas?

• 15:12Speaker 01

  Latency is a big, big deal here, of course, with voice models. So nowadays, you have the the voice to voice models that we're playing around with. OpenAI is doing a lot of work here. I think there's obviously a lot of trade offs there, voice to voice, latency is great. Sometimes, though, with these production use cases, you do need the extra computation cycles, you know, fetch data, do multiple model calls. Or there's, there might be other reasons that you can't do voice to voice. And so, okay, that's, that's one option that you would consider. The other one is the one you described, where you're kind of going through, you're transcribing or, yeah, doing speech to text and then doing all the computation within text and then generating the voice at the end. That always causes a little bit of extra latency, of course.

• 15:57Speaker 01

  And so, as you mentioned, a lot of folks have figured out fairly clever ways to to get around that. You can, you know, start generating stuff first. In our use case, you can always do something like, hey, give me a sec, I'm looking up your data. So these are all things we're playing around with. I think for each customer that we work with, there's different trade offs. And so we're really trying to base what we build on the things that we're hearing from them and the sort of priorities that they have.

• 16:24Speaker 00

  That's cool. One thing that I think is kind of interesting is the number of companies in the AI world today that have been founded by people with Math Olympiad or IOI or other sort of backgrounds, right? And I think you were sort of involved with Math Olympiad stuff in high school. I think Decagon has actually hosted some Math Olympiad events for the team,

• 16:48Speaker 00

  which isn't like your typical happy hour. But there's other teams and companies. I mean, before that, there was Ramp and things like that. But I think the Brain Trust team and the Pika team and cognition, which launched Evan, and then you all kind of have that common thread. Where do you think that comes from? Like, why do you think this community is now so active in AI?

• 17:05Speaker 01

  It's a good question. I mean, we're actually all around the same age as well. So we've known each other since like, you know, middle school, high school. Well, one, it's a great community. For us, yeah, we have a lot of people on the team with math contests, coding contests backgrounds.

• 17:20Speaker 01

  I think it's more so that this community was always there. Like, Math Contest has been around for a while and a lot of super smart kids that go through that. It's also a great way for folks to kind of, you know, get to know each other and get connected and, you know, build friendships. And I think the main thing is that now in the last few years, maybe the last like, you know, five, six years, like, there's just because startups have been a lot more mainstream, a lot of folks in this demographic have gravitated towards startups as opposed to, you know, traditionally it'd be either academia or quant trading and things like that. So they're just the big influx of these super smart, super talented people that come into the startup

• 18:03Speaker 01

  world. And because there's this community aspect, you know, folks can see what other people are doing and like, you know, what what sort of works and types of companies that people are building that.

• 18:14Speaker 01

  No, which doesn't say they're all the same, but I think a lot of folks with these backgrounds are now kind of working on startups. And that's why there's a lot of, you know, I guess, progress in the companies that folks have been building.

• 18:28Speaker 00

  And are there, are there ways that you all have been sort of supporting each other through the startup journey? Cause I feel like every generation, there's sort of a clique of people who built some of the more interesting companies who all kind of interact. They provide advice, maybe they angel invest in each other, like there's kind of a thriving community. And every five to seven years, it kind of shifts who it is. And I feel like, you know, the IOI sort of Math Olympiad community or coding competition communities are kind of very engaged right now. Is there any formal version of that, or are you all just kind of informally helping each other?

• 18:59Speaker 01

  Yeah, I angel invested in a lot of the companies you just listed. A lot of their founders are angel investors in our company. It's, it's very informal, obviously. It's just like, you know, casual friends helping each other. I think the main thing is that with company building, there's just a lot of, lot of surface area, right? As you know, there's just like, how do you hire people? How do you like do sales? Like, how do you build this thing? Like, how do you structure like comp for like, I don't know, there's infinite things. So yeah, having the other data points is obviously super helpful. So hang out with them quite often, play games, play card games. It's a Chinese version of bridge that I play with a lot of these folks quite often.

• 19:38Speaker 01

  And it's just, it's fun where you just kind of hang out. Everyone's kind of in this relative same stage of life. And so, yeah, like you said, there is definitely a lot of camaraderie and help that goes around.

• 19:50Speaker 00

  Is coming from this background, from the sort of Math Olympiad community, impacted at all how you think about hiring? Or your hiring practices at Decagon?

• 19:58Speaker 01

  A little. I mean, if, if someone else has the same background and, you know, has gone through the same either contests or programs. Obviously that is pretty good signal since I have a good idea of like what those, those people have done. My co-founder Ashwin, also a similar background. He didn't grow up in the US, but in India, he did a lot of these contests as well. And so.

• 20:20Speaker 01

  Yeah, I think there's some correlation with people who just as kids just did a lot of this stuff and then now we're all adults and there's some sort of signal there when you're talking about hiring. But for the most part, there's so many talented people here, whether or not you did math contests or not, NSF, at Decagon, at other companies, that I think our hiring process has been more or less the same. It is like a nice sort of trigger for events, I guess. So when you host these events, people come out, you can get a nice community of folks that are interested in the same things. And we're going to be hosting probably more, and not all of them are going to be like contest based, obviously, like puzzles and things like

• 21:02Speaker 01

  that, where you just get a lot of fun engineers and people bringing their friends, and that's pretty important to us.

• 21:10Speaker 00

  And then I guess for AI writ large, what are you most excited about in the coming years? Or if you were to extrapolate out 12 to 24 months, what are you anticipating most candidly, or what are you waiting for?

• 21:22Speaker 01

  Obviously, the model's getting better is awesome. The model's getting better across different modalities, also awesome. We talked about voice. There's also other parts, other modalities that are also tangentially interesting to us, right? So you talk about a lot of our companies are, a lot of our customers have software products. And so it'd be awesome if you're asking questions to AI agents and it has like context of your entire screen and like all the interactions you've done and stuff like that, like that would be great. And you can even go a step further and have it actually like help you navigate stuff. So there's just so much you can do there, where you talk about the other modalities, or even just more advanced model capabilities. Like we've seen the computer use demo from Anthropic. Probably, in my opinion, not production ready yet, but as that gets better, there's a lot

• 22:06Speaker 01

  of cool things you can do there. So on the model side, that's one thing we're excited about. On the not like core model side, I think one thesis we have is As you know, the years go by, again, AI agents, I think at this point undeniable that there's going to be a reasonable explosion of them where there's used on a bunch of different use cases. I think some use cases will take longer than others, but the value that they're providing is is pretty undeniable. So there's definitely going to be a lot of AI agents out in the world, you know, in our use case, customer service and other use cases. But one thesis we have is that the nature of the work of, you know, human agents and you know, people like us is also going to change pretty drastically. And one of the

• 22:51Speaker 01

  things that are going to change is that there's going to be a lot more people that are supervising and editing agents. And so that's something we think about. We're excited for a lot of the sort of innovations there because right now, a big part, like I said before, we care about letting, you know, the human agents for our customers and their leadership team to go in and make changes and like monitor the agents, just have a lot of like visibility and control. And what does that look like, right? Like if you compare it to human, if you're monitoring human, you kind of, you can give them feedback in real time and be like, oh, no, no, don't, don't, don't do this. Like you did this thing wrong. Like, please do this next time. When you're doing that with the AI agent, there's like a lot of different possibilities because, you know, they have some things that are different

• 23:35Speaker 01

  than humans, right? They're infinitely scalable, they're, they're like, you can, you can like hard code things sometimes. So that's the other area probably going to next year that's where we're looking forward to.

• 23:47Speaker 00

  That's really cool. And do you view that as a main area of differentiation for you relative to some of the other folks in the market providing customer success and support to like?

• 23:57Speaker 01

  Yeah, right now, that's, that's probably the biggest thing. When we, the interesting thing about our space is that, and I think this will probably be true for a lot of AI agent spaces, is that the results are very quantifiable. Like you're, you're basically taking the agent and you can, you're benchmarking against, okay, how good would a human be? And like, how much money is this saving me? Like, how much better quality is the customer experience? And so because of that, when people evaluate us in our space, it's, it's pretty like quantitative evaluation. You're like, okay, cool. This kind of works. Like, let me just put you out into production for, you know, 1% of the volume and build up from there and maybe do that with another option or. You know, a lot of the old school companies like Salesforce are gonna be, this is a very exciting space for them too.

• 24:40Speaker 01

  So they're gonna have alternatives and then you just benchmark everyone, right? Like, how good are the stats? How good are the metrics? Like, how good of a job is everyone doing? And I think so far, we've been performing very well. And the main reason for that is this sort of transparency piece, you know, giving people observability, explainability, control over the AI. And there's still a long way to go in that field, right? Like, there's still so much more you could do. And that's, that's been our specialty so far.

• 25:07Speaker 00

  That's great. And I've had some conversations with your customers over time. As people have been trying to meet these agents, they've called me to ask questions about different companies in the space and everything else. And the three things they tend to point out is you, you all ship really fast. You're very responsive as a team and company. And the third and most importantly, just the product tends to outperform. And so I think that's really been, um, you know, uh, great to watch over time. How do you think about the areas where AI agents are gonna be successful versus not successful in the short run?

• 25:33Speaker 01

  So basically, one of the things that we have been thinking through, and this is something that was pretty big for us when we were first starting out, is that a lot of, there's gonna be a huge variance between like the different types of AI agents and how successful they'll be and like how quickly they'll take the rollout. Because when we were first starting the company, right? Like, uh, we, we were pretty open to what to build and we knew that AI agents was exciting. At that point, we didn't even know that if there would be any like real use cases that would emerge even in the next 12 or 24 months. But we're kind of exploring.

• 26:13Speaker 01

  I think our view is that for the vast majority of use cases right now, it is still like, like, there's not gonna be real commercial adoption with the state of the current models because of a bunch of things. So. One, one big thing is that if in a lot of spaces, you can't, there's really no like structure there to like incrementally build up. Like it has to be good, like almost perfect off the bat. So if you think about like a space like, you know, security or, or something like that where, okay, okay, you have all these like sims out there and it's like, it makes sense. There's like tons of logs, like that's, that's perfect for AI models. Uh, but the goal of that job is like, you need to catch like any small thing that happens. And

• 26:58Speaker 01

  so because the models are inherently non deterministic, it's very hard for buyers to like really trust a gen AI solution there. And so especially a jet tech solution. So like, I think that option there is going to be really, really, really slow, a lot slower than people think. Even though like people have cool demos and, you know, see things seem to work, like just getting really into our eyes, the option is going to be very slow. Um, so that's, that's like one interesting thing we've been thinking about. And the other side of that is that, uh, there are also a lot of spaces where, you know, on the surface, it seems like, oh, like AI agents will be perfect here. But then, uh, the sort of follow-up is that it's actually not that easy to quantify the ROI that's happening.

• 27:43Speaker 01

  I would, you know, one example I would give this is like, uh, you know, there's, there were a lot of like text to SQL companies, like stuff like that, where you could kind of see it working. But basically immediately everyone's reaction is, oh, this is cool, but you know, we're still going to have to have someone like monitoring it and, you know, editing it. And so it becomes kind of a copilot. Okay, cool. So then like, how do we measure like how much we should pay for one of these agents? Uh, it's very difficult because like, most teams don't have that many data scientists anyways. And so if you're, if you're claiming that you have an AI agent data scientist, it's like, okay, let's benchmark you against a real one. You're probably not going to be able to replace a real one. So I think that's the sort of thing where it's like, it's very hard to

• 28:27Speaker 01

  quantify the ROI. Like you're saving some people time, but Because of that, like it's, you know, like if you're, if I'm a large company, it's hard for me to justify, okay, I'm going to give you a large contract for this like AI agent data scientist. So I think those are the things that we're thinking through. We're not thinking through like in, in the moment, we're obviously just, you know, asking customers like what, what their willingness to, you know, to invest in certain things is. But in hindsight, I think we're looking back on the last year, that's been a big thing that's been true, which is the, the use cases that emerged, like you have to have those two qualities. It has to be able to be something that can be rolled out slowly and doesn't have to be perfect off the bat, but it's already providing value, right? Like

• 29:08Speaker 01

  I think coding agents is like a good example of this where you're just like sectioning off some tasks for them and like they'll do it. And the other piece is the ROI, like you have to really be able to easily quantify the ROI. In our case, luckily you have these support agent teams and, you know, people track metrics very closely. So that's something we've been thinking about. I think most, I think the takeaway from that is like, yeah, probably more bearish on a lot of these AI agent use cases in the near term. But I think as those models get better, there's going to be, they'll unlock a lot of new cases.

• 29:42Speaker 00

  Super interesting. Jesse, thank you so much for joining us today.

• 29:46Speaker 01

  Thanks, Elon. Thanks for hosting. It's great seeing you.

• 29:51Sarah Guo?

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