The Future of AI Agents | Jesse Zhang Interview

What we are building towards is kind of this concept of a new UI for the product, right? Just think about how

product, right? Just think about how much these large companies invest in their mobile app or their website. And

this literally how everyone communicates with them. So this could be a way where

with them. So this could be a way where eventually the way any user interacts with any brand is through an AI agent.

Perhaps an interesting place to begin, I bet you don't expect this, is for you to tell us a little bit about the phrase that you've talked about that's written on your wall in the office.

There's no challenge that can't be overcome and there's no enemy that can't be defeated.

We just like it. I think it really fits our culture. Um, people there are We

our culture. Um, people there are We have a very competitive team. Everyone

wants to win. we have a lot of energy when we're trying to go out and build because it just feels like hey it's you know there's all this stuff happening in the industry like we have you know such

a strong team let's just go and win uh motivated by so my my dad told me this I don't know if this is uh actually validated or not but at Huawei in in

China there's uh it's obviously massive company but they're known for just having like killer culture and they have some version of this written in Chinese of course like in just big red letters

across the back of the the big uh big hall. Um so yeah, really like that and

hall. Um so yeah, really like that and uh it's kind of interesting any time that someone walks in, they it like stands out. The code stands out.

stands out. The code stands out.

I I I asked about it to begin because I'd love to spend some time on just what this environment is like building a company like yours against formidable competitors in probably the most exciting era of technology that any of

us will ever live through. And words

like this uh defeated I've heard the word violence recently about a company culture aggression like these these are not words that were being used three years ago or four years ago in fact if you use them it was a big problem for

you and obviously that has completely shifted and not only have founders started using these words but like I think talent and and senior people are like rallied around them like they they want to be in a culture that is

defeating enemies or violent or aggressive. So m maybe just riff for a

aggressive. So m maybe just riff for a while on what it's like to be building and competing in one of the you know one of the big areas in your case um conversational AI etc at this moment in

time because it's just so different than it was a couple years ago. Our whole

view is that any space that's worth going after like any large hot market, it's going to be competitive. And this

is not really specific to AI, right? If

you think about data bricks versus snowflake or ramp versus bricks and like anytime there's these big massive growth opportunities like you know people are rational want to go after it. Yeah, it

is it is exciting but of course people everyone knows it is competitive right because if there is market then everyone's trying to build it. There's

it's quite easy to start a company these days. you can raise funding really

days. you can raise funding really easily. And so when you go out there,

easily. And so when you go out there, you have to have a pretty deep understanding of like what your competitive advantages are. And you

know, one of those could be the culture.

And if you build a good culture that's pretty hard to replicate, it also kind of lasts quite quite a long time. And um

yeah, to your point, if you you have a culture where everyone is really geared towards just like succeeding and working hard and having some level of intensity, it can go a long way. So yeah and then I

I think to your point of sort of a lot of companies adopting similar mindsets I do think from my observation this generation of company building has

attracted like a almost like my demographic of person where we uh it's just like people that grew up in fairly like competitive environments academics

or or whatever and um a lot of these founders have done well right like uh did a lot of math contests and coding contests growing up and a lot of the people around my age are doing startups now and people are doing quite well and

I think there is some element of like hey if you if you really embrace this sort of like hardcore uh lifestyle or environment growing up then yeah I think it lends itself pretty

well to the current situation because there's a lot of parallels.

I was with Scott uh Woo from Cognition recently who is is well known to be like one of these math champions you as well.

Talk about that environment like what was that competitive market like? What

was it kind of let people into that world? Because obviously it shaped you a

world? Because obviously it shaped you a lot when you look back just like now that you know we're all grown up. It is kind of like a pretty

intense way to grow up. But I think I I mean I I look back on my childhood with very fond memories. It's like a you get a really nice community, a lot of people that are kind of doing the same thing. I

grew up in Colorado in Boulder and so Boulder is a pretty academic town, but there's not very many people that are like super gunning for like, you know, these contests or like performing at a

and a lot of a lot of my friends just kind of grew up in not the places you would think, right? Like California,

Texas, New York. And so it gives you some level of community of like, okay, there's yeah, there's a lot of other people out there that are kind of doing the same things as you and you kind of meet a lot of them. And then now that we're all grown up, you know, those relationships have last lasted for for a

long time. But yeah, the environment is

long time. But yeah, the environment is one where it's like, you know, it's like similar to company building. You're like

how well you're doing is like fairly objective. Um there's a lot that has to

objective. Um there's a lot that has to go into it. There's a lot of preparation. Um a lot of just like

preparation. Um a lot of just like having the feeling of it's it is like a long-term thing, but because you're your results are fairly objective, there's

just constant motivation to improve. Um

and so I think that's that's quite nice.

One of my big thesis is that there's a lot of untapped potential there into actually like turning them into people that, you know, want to do business or companies and things like that. A lot of them have historically gone into, you

know, trading or uh academia. And I

mean, those are perfectly like awesome jobs, but it's it's more of like a lot of the folks here um what the sort of this background is correlated to one where it's a little bit more like risk averse and kind of just like, you know,

get your good grades and like follow a track. And if you can kind of divert

track. And if you can kind of divert some of that talent into like, you know, company building, I think there's just a lot that can be done.

What are the parallels? Like what what was it what is it about the people or the training or the specific aptitude in the competitions that make you good at company building and others good at

company building? Like this seems like

company building? Like this seems like an obvious true trend that there's enough sample size now of people that had this background that are doing extremely well in this environment.

Maybe it's the best. You know, if you could somehow index that group of people, you'd have like fantastic performance right now. what is it like what what is what are the parallels that make that true?

So I mean one one is the competitive nature that we just talked about. The

other is just like the problem solving, right? So I think one thing that I

right? So I think one thing that I believe very strongly is that um one of the best things you can do when you're building a company or anything is just like have pretty good introspection on

where your strengths are. And at least for the for this sort of group it's it's more around the problem solving side.

Just like really figuring out, you know, and and the problem could be very vague, right? The problem could be like how do

right? The problem could be like how do I build a successful company and you kind of break down the problem you can like think really critically and just first principles of a lot of these markets and of course that's not the

only way to build a build a company like other people I think some people just have like very good intuition especially on these like PLG type like things in general I think just the problem solving capability like you know what what these

contests really teach you is like you're just solving problems right and those problems of course are not like real problems in real life but the sort of thinking is the Does it feel uh more emotional now

company building than it did in the contests?

I'm a bit older now, so it's not really that emotional. I started a company

that emotional. I started a company before this that was uh I would say way tougher than the current one. I think

just like mentally makes you feel a lot more calm and honestly like appreciative of when things are going well. And

timing matters a lot, right? I graduated

a year early to try to start it and I would say we had a very fortunate outcome but the the whole journey was like very bumpy like what was it what did it do?

Uh so the company was called Loki. We

basically built um sort of high performance video capture software for video games. So when people are playing

video games. So when people are playing games you can you know really easily capture video clips and edit them and share them and um and the whole goal of the product is just like you just get as many users as possible. We exited at

like a very fortunate time. was 2021 but like throughout the journey like we were at the beginning didn't really know what we were building and then just like trying a bunch of things and you're like new grad you don't really have good intuition on like what idea is good or

not so you just like work really hard for you know 3 months and so then then you realize that what you're building basically had just like it was just obviously had no market and uh so that's tough uh and then about a year and a

half in I had uh two of my good friends from school my were my co-founders they both got burnt out and then decided to leave so it was just me after that and just like trying to figure out what to do. I think that that's way tougher than

do. I think that that's way tougher than anything we're doing right now because it's like a it's like a the way you put it like an emotional thing. It's you don't really know if

thing. It's you don't really know if there's any future or not. Uh there's a lot of pressure because you don't want like the first thing you work on to be like you know a failure essentially. Um

so I think that was much tougher from like a psych psychological point of view. Nowadays I think it's tough just

view. Nowadays I think it's tough just just like a sheer amount of stuff to do.

We're not getting much sleep. But again,

like in the grand scheme of things, you got to be really grateful for to be even even be in this position, to have enough work to do, to have enough interesting um problems, to have a team that you're really excited to work with every day.

How much do you think you're sleeping?

Uh I mean, it really varies. Like this

week in New York, it's probably like four to five hours a day. And it's not good because like I'm not someone whose brain functions that well on less than eight hours, but it's been Yeah. So if you think about the

Yeah. So if you think about the difference between the first and the second company, like obviously now you have a company that's working extremely well. It's growing really, really fast.

well. It's growing really, really fast.

What What did you do at the beginning of Decagon that was informed by your prior experience to make this one go better?

I think this is broadly true of starting companies. I think the first stage is

companies. I think the first stage is like by far the hardest cuz you you're kind of just like finding direction, right? And finding direction is very

right? And finding direction is very difficult because like by definition it's not something where you can just like you have like a goal and you're just like grinding towards it and you can get

there. I mean your goal could be yeah

there. I mean your goal could be yeah finding a direction but it's it's more exploratory. I think what we actually

exploratory. I think what we actually have are quite good at now and and Ashman my co-founder who's who's amazing he also has a similar background he started a company before that got acquired when you start a company the first time you often share the same

experiences which is like finding a direction is very difficult so I think this time we were a lot more thoughtful about it and again it goes back to what your strengths are. I think we view our

strengths as like, hey, we're very good at problem solving. We're very just like rational about things. We're just good at like execution. And so if that's the

case, then I think we just try to systematize the ideation process. And it

just comes down to okay, you need to whatever you work on, it has to be something that like people will really invest in. And how do you tell if that's

invest in. And how do you tell if that's the case? you can just like go really

the case? you can just like go really deep asking them. And I think people are usually a little bit like almost embarrassed or not comfortable going super deep in these questions. But when

you talk to potential customers, they actually don't mind answering questions such as like okay if we built this for you like exactly how much would you pay for it? Like would your boss need to

for it? Like would your boss need to approve it or your boss's boss who needs to approve it? Um how how would the entire organization think about ROI?

like how would how would you present ROI to leadership to you know you know protect yourselves and also like make you look good and I think if you really go deep there it's almost like you're

basically asking like classic like sales qualification questions but in founder form and if because you're a founder it's like it just feels a lot less salesy for you to go deeper and that process is what gives you a lot more

signal. So when we first started we, you

signal. So when we first started we, you know, fortunately were able to get in front of a lot of large companies, mostly digital native ones and we just asked these questions and we kept digging in and uh we had a bunch of

different ideas, right? At the time we're not tied to any idea and it's just kind of open exploration. We were

looking at things ranging from like data analysis to like security to, you know, pre-sales to, you know, ops stuff. And

that process was was very helpful because it just shows you that there's a lot more signal to gain than just like you know talking to customers which is the just the general advice or building something that customers want building something that people want like yes that

is true but it's very hard to just like know what they're like they they'll tell you what they want but it turns out that that's like so what was this what was the literal process? So you would go would you go to

process? So you would go would you go to a single person and ask them about multiple of your ideas at once or would you target it more like one idea to one person? You have to target it based on

person? You have to target it based on what that person owns. But if that person is very senior, you can ask him about multiple.

Yeah.

So if you talk to like a COO, for example, you can talk about a bunch of different use cases and that gives you some signal, too. And then if you're talking to more of like a a VP of a certain area, you're probably focusing on one use case.

So So maybe go uh in detail through one of these conversations so that others maybe could benefit from what you've learned. So what what is the order like

learned. So what what is the order like what is the order of the questions? Like

how would you structure those conversations to get the most information possible? We get into the

information possible? We get into the call and you start by kind of just doing very high level discovery like hey what what are the sorts of projects that are ongoing right now? How do you spend your time? What is kind of like stressful for

time? What is kind of like stressful for you right now? Etc. like that. And then

then you can kind of get a sense for the the types of use cases. And then very quickly you can just like form hypothesis like literally on the fly of like okay what would a product be that makes sense here? And so then you're

kind of explaining like okay yeah so you know what if something like an AI agent could do XYZ like would that be helpful?

And most likely they will say yes cuz I think there's there's this thing that happens where if if someone's on a call with you, they almost feel like they owe you like something good that you can take away.

So they're like, "Oh yeah, yeah, that'd be great." Now you've kind of solidified

be great." Now you've kind of solidified at least the potential product ideas, right? And they might adjust it a little

right? And they might adjust it a little bit. They'll be like, "Yeah, no,

bit. They'll be like, "Yeah, no, actually no, like it should it should work like this, right?" And and so on. I

remember we talked to a lot of ops leaders like um you know Matt McInness from uh Ripling is like a great friend of of Decagon and he was kind of telling us about all the different things that happen on his team because there's like

so many and similar with other ops leaders we talked to and it was kind of a range of companies as well um people like Aura Ring and and so on and you get down to it and you're like okay great

now we have these use cases how much would you pay for it and that kind of forces them to think because most people are not thinking about that as they're talking about ideas. There's like, "Oh, yeah, this would be cool." So, as soon

as you force them to think about how much you'd pay for something, it it kind of is a forcing function for finding some order of magnitude, some level of scale. And so then they're like, "Okay,

scale. And so then they're like, "Okay, well, yeah, you know, we have five people doing this full-time. If the AI agent could do this, well, maybe we'd be able to, you know, get rid of one of

them, assign one another one, and then, you know, as a result, I'd pay you like, you know, 20k a year or something like that." Um, and and then in your head

that." Um, and and then in your head you're like, "Okay, cool. So now at least you have a general order of magnitude." 20K of course isn't amazing.

magnitude." 20K of course isn't amazing.

But if it's like, you know, I could quickly turn out a ton of these. Like

maybe that's that's interesting. Uh, but

most of the time that's not the case cuz it's like there's not that many good ideas out there honestly. And so most of the time at the end of this exercise you're like, "Okay, great. Glad glad I didn't pursue this further, you know, because like that would have been a waste of

time." And at the end it's like people

time." And at the end it's like people are paying you like a $100 subscription per month and it's like a big company, you know. So you just do this exercise

you know. So you just do this exercise and the nice thing about this exercise as well is that it puts the customer in the same frame of mind as you and so then they can tell you other things. So

like one essentially what happened with our company is like we were talking about all these use cases and they were like okay great yeah if you did this you know we have five people over here but by the way we have a 500 person support

organization and there's a lot of opportunity there and we'd be like okay great tell us more right and then you kind of dig into it as a founder you kind of have to build your own conviction and kind of do this process

yourself because at the time essentially what everyone told us including like you know very smart older founders that we knew and so on was that uh yeah this this use case is like super obvious.

There's probably just going to be incumbents that just tacking on to the product and um there's because it's so obvious like there's probably a reason why like nothing's no one's super big right now or there's going to be someone

that's ahead, right? Like they don't like no one really knows. It's just like and even for me right now when like other founders talk to me about other spaces it's like yeah like I have my own opinions but like I don't really know the details of the space and the only

way you can really know is by talking to customers and getting that signal.

And yeah I think in hindsight it turns out that in any sort of wave at any time there's I would say like a very small number of like good ideas and your job

is to ideally find one of those in at the right time. And so, yeah, by the definition, it's going to be like pretty non-obvious and pretty difficult. And

so, if you can do this process, well, it'll give you the most signal.

Do you remember the highest number anyone said for how much they'd be willing to pay for something in one of these ideation sessions?

Yeah. So, at the time, it was probably on the order of like low to mid six figures. as you neared the end of that

figures. as you neared the end of that process and settled on what Decagon does, which maybe probably is the right time to ask you to describe in detail what it is. What was like the final closing like um convict where did the

conviction come from? Like, oh, this is clearly the thing after this discovery process.

It was clearly the thing because if you just kind of tallied up even just the thing the the amounts that people said and added them together per idea, this was probably like an order of magnitude

more than anything else. And uh yeah, so very specifically what Dekon does, it's um it's AI customer service agent. So

that's the simplest way to think about it. And you're building a conversational

it. And you're building a conversational AI that can just you know be almost like a a front end for a brand. Anytime

someone wants to talk to the brand or anytime the brand wants to talk to them, you can kind of initiate these conversations. And of course longterm

conversations. And of course longterm this is like not specific to customer service. But um I think again going back

service. But um I think again going back to this exercise, I customer service is where we felt like the most urgent need.

And and in hindsight, I can dissect like why that why we think that is, but um that that's basically what we felt. And

what gave us conviction is that hey, we had all these folks that were lined up that were they were like very willing to invest, you know, six figures uh in a random two person team that they didn't

even know that well because it was actually like a very top of- mind initiative for them. And everything

else, it was just like a struggle to, you know, it's like how much you pay for that? I don't know. like this is like

that? I don't know. like this is like really exciting but you know our budgets are tight right now and also like it'd be hard to measure you know how how well this is doing and and so on. So yeah

that that gave us enough conviction and then you just kind of take a step by step from there.

Say more about this the comment you made about in hindsight it's clear why you know this was the key problem. The

customer service has a bunch of nice properties that I think are very hard to reason through ahead of time, which is why I think I feel so strongly about this process of

discovery and like just really staying customer centric. One of the properties

customer centric. One of the properties is that the ROI is really easy to justify internally. You have these

justify internally. You have these numbers already tracked, right? It's

like, hey, we have so much conversation volume right now. you know, we have a simple

right now. you know, we have a simple chatbot or a simple IVR phone tree.

It's, you know, kind of resolving, so to speak, like 15 to 20% of that. If you're

able to take that to 50, 60, 70, 80, like that's huge ROI. And it's very easy to quantify that. It's like, okay, well, I'm going to take the total cost. I'm

going to chop off like, you know, 60% of it, and that's that's what I'm saving, right? Uh, the other property, which I

right? Uh, the other property, which I think is a little underrated, is that it's very easy to go live. And I think a lot of genai use cases are struggling

with that right now, especially at the enterprise level because there's risk involved, right? Like people don't want

involved, right? Like people don't want to feel like like something could go wrong for them when they release your product and like you know their leadership is going to get mad at them. It's like you know why

why' you do this? So that is a big deal with geni. I think that is one of the

with geni. I think that is one of the reasons why there's kind of it's been kind of difficult for a lot of use cases to really take off because at the end of the day there is always going to be risk

because the models are non-deterministic and so you can always something could always happen but the nice thing with customer service is that you have a escalation path just naturally built into the way the product works. the

agent's having the conversation. If for

for any reason it needs to exit, it'll just escalate to a human and that infrastructure is already set up, right?

You already have your call center, you already have your telephone stack or whatever. So, you just connect connect

whatever. So, you just connect connect to it. I think that property alone just

to it. I think that property alone just makes things way easier because these big enterprises that we work with, they're like, "Okay, great. Like, we

test it internally." Uh, and then to go live, we're just going to choose this one surface area and release it to 5% of the user base. And even for that 5%, if anything goes wrong, it just escalates.

And so that gives people enough comfort to go for it. And those two things I think are one of the big reasons why it's probably the use case with the most traction at the enterprise. And coding

is another one. Coding is a little bit different. It's a lot more bottoms up.

different. It's a lot more bottoms up.

Can you compare it to coding? Like it

seems like these are the two areas where it's blindingly obvious that it's useful to customers and you can build great businesses around it because just basic looking at the revenue curves. Yeah.

Yours, Sierra's obviously like cursor, cognition etc. Compare and contrast coding and customer service. Yeah, they're very different.

service. Yeah, they're very different.

And maybe one framework to think about this is that at the end of the day, what AI agents are there for is to yeah, essentially replace human labor

and that's why it's exciting. That's why

everyone's, you know, so focused on it.

So, one one thing you can do then is like you can just map out the spectrum of like how much that human labor currently costs. So, with customer

currently costs. So, with customer service, it's generally outsourced already. It's um especially for the tier

already. It's um especially for the tier one, tier two type inquiries that AI is now handling, it's generally not folks that um are super highly paid and then

on the other end it's engineers which are the most highly paid people and so I actually think like maybe one thing the one way to think about it is that like AI use case will start eating the spectrum for both ends and the

reason why is that uh because engineers are the highest paid like they have the sophistication to like really leverage it well and like it just AI just gives them so much leverage.

And I mean there's other factors as well. Like it just happens that coding

well. Like it just happens that coding is like tokenizable and like it's really like the models are really good at it.

Um so that's one way to think about it.

And so I I don't know any company that's like hey I would like to just let go of a bunch of my engineers because now I have coding agents. It's just like there's infinite engineering work to do.

So you're just augmenting them. On the

other end uh it is more of like like the replacement sense. And so you have a

replacement sense. And so you have a large BO and you know that's costing you a ton of money and it's also like a really high operational thing to maintain because you have to hire people

all the time. There's a ton of churn you need to train them you need to QA them uh you need to make sure that you know nothing goes wrong. So AI is really valuable there as well because you're able to like because the the work is you

know um easier for the AI to do it can you know fully replace you know I think it's a little bit overblown of like oh AI is replacing jobs and so on. I think

most of even the BPOS we talked to uh they're not really that concerned because what typically happens anyways is that there's already very high turnover in these BPOS because like people are just hopping around doing all

sort of different things and so what most enterprises will do is like it's not like hey I'm going to massively it's like you just like kind of just naturally let it decrease and then they just go on to do sort of the next level

of task that the AI can't do yet right and so maybe that's like data labeling or something so that that's generally what we're seeing from from the BPOS's Yeah. Anyways, I think the spectrum is

Yeah. Anyways, I think the spectrum is like pretty real and so then the question is like what is the next thing that happens?

So it's a really interesting conclusion which is try to augment the very highest talent or replace you know the most replaceable end of the spectrum. That's

like a really interesting conclusion. Um

I want to talk about how kind of what you've begun to learn about how to do that second thing well. Um, so if others out there wanted to start a company or invest in a company that was doing sort of that end of the spectrum eating its

way in as you described, what have you learned are the key things to like the setup process with a given company to increase the likelihood that you can replace a lot of the, you know, the

lowhanging fruit for types of customer service calls or types of what used to be human to human interaction and can now be handled by AI on one end of the spectrum. I would say the the biggest

spectrum. I would say the the biggest learning we had is that um oftentimes a long pole in the tent and again if by definition if you can solve this well it just makes things go a lot easier is

sort of aligning on what does good look like. You would think that in our space

like. You would think that in our space it's pretty easy because it's like okay maybe you just have a bunch of questions and answers and that's what good looks like. But unfortunately it's a lot more

like. But unfortunately it's a lot more nuanced than that like you know what good looks like could be in the tone of in in the sense of like tone and brand guidelines or um how conversational you are and even for the actual answers like

we work with a lot of enterprises where obviously their scope is broad and so one of the things you need to set up beforehand is you know what what this could look like. So like we have in our product essentially like a testing or

simulation suite of like hey we're going to build out 10,000 tests and each one is going to be constantly running like you know five times and then you can get a sense of how how well things are

performing and that's actually pretty difficult and I I do think that is broadly true for anyone that's trying to build in this style of company. If

you're going to be replacing human labor you need to you know know what good human labor is. What we found is like okay well can someone just tell us like what are all the what are the answers to all these questions and most people don't actually know like no one in their

head is like because these are large complex organizations right no one is like the person where like hey I know how to answer all these questions and so you have to design a process where it's it's very easy to extract these answers from all the people that do know so

maybe it's all the you know CX leaders or and people lead different areas of the product and so you have to get them all together and get them to align on like okay here here's what the eval is essentially And if you can do that well, then it makes everything a lot easier

because now you're just building, building, building. You have this like

building, building. You have this like quantifiable score that's like, hey, here's how well how well the AI is performing. And then once you're done

performing. And then once you're done building and the score is high, then you can go live.

Is the right way to think about this that you just created like a captive reinforcement learning process within an organization. Is that like the

organization. Is that like the simplified version?

Yeah. Yeah. Yeah. That's a interesting way to think about it. Um, and it doesn't have to be reinforcement learning in the sense in the pure sense of training a model. It's like

it can just be reinforcement learning and you know making the agent improve and that could be you know compiling more evals that could be compiling uh

just like more guard rails guidelines around what it can and can't do. How

fast does the spread happen? If I'm a customer and I've got the 500 person, you know, customer service call center or whatever and there I'm actually curious. I don't know what the volumes

curious. I don't know what the volumes are like how much call volume or or interaction volume a center like that handles for a given company, but if I give you 5% of my workload and I'm

satisfied with AI's performance, like it performs well and there's not lots of problems, how fast are people willing to go from five to 10 to 15 to 20%.

Uh, very fast. Yeah, I would say even for the large enterprises like within weeks because the the only the everyone wants to just go live to everything, right?

But the the reason why you stage it out is so you can make sure nothing's going wrong and you can tell if something's going wrong like almost immediately because you have these metrics, right?

So if even within a week uh you have so yeah a 500 person org I would probably estimate like mid to high six figures of conversations a year um you know maybe

slightly higher and what you're doing there is like you're you're kind of um making sure that you things are going well. So, you know, within a week you

well. So, you know, within a week you can see like, okay, what is the resolution rate? Is that what we expect?

resolution rate? Is that what we expect?

Okay, great. Uh, what is the customer satisfaction? People have those scores

satisfaction? People have those scores as well. And and then they'll probably

as well. And and then they'll probably have some sort of accuracy metric based on like human review. If those all check out, there's really no reason why you shouldn't roll it out. And again, the business case is so obvious there,

right? It's like, hey, we're both

right? It's like, hey, we're both generating a ton of operational efficiency and our customers are happier. So, yeah, let's just send it

happier. So, yeah, let's just send it out to everything.

What goes most wrong? like when when something bad happens and I'm sure this is happening less and less as the product's gotten better, but even in the early days like what sort of thing would go wrong in one of the you know customer

to AI interactions?

Yeah. So it's it ranges all sorts of different things and it's kind of funny when I mean sometimes it's uh obviously like you we both sides like us and the customer we take everything very

seriously but there's just a lot of things you wouldn't expect right in the early days we have this uh we have a customer that is essentially like a large ticketing platform someone came in they couldn't find their ticket the AI

looked into their account it's like hey uh there's no tickets here and then they were just like okay well what I'm going to do is I'm going to show up to the event and I'm going to find eight homeless people from the city and bring

them with me. It was just and the agent was like, "Oh my god, that's so awesome that you're thinking about doing something nice for the community." And

uh it's just like things like that where it's like, "Okay, I would not expect that to happen." So those are just like tuning you have to do over time.

Yeah.

And generally it's in the in the spirit of that where you're kind of you're trying to find the right level of guard rail and flexibility, right? I think

that's the name of the game with our space at least and that's sort of the way we've designed our product and and why probably the number one reason we've been successful so far is that what

Genai really unlocks is just this like super flexible super personalized and like one way you can think about it is like in the old days to map out a conversation you just build like gigantic tree right of decisions and

that's very hard because no one likes that experience and like you you ask something that's not quite one of the branches and it just forces you down that branch and there's no way to go back. And what a LM does is kind of it

back. And what a LM does is kind of it abstracts a lot of that tree into the the neurons of a of a language model.

And so that's really powerful. U so you you kind of on one end of the spectrum, you're just looking for uh you know flexibility and rig just like power and just like being able to sound really

humanlike. But with the enterprise,

humanlike. But with the enterprise, there are a lot of things we don't necessarily want that as much and you want just like full like rigor, right?

If it's a regul regulated use case like you cannot afford for it to ever deviate and so you want like these three steps always have to be followed in this order and you can't go to step three until you know something has happened already and

uh so you need to design a system that can be anywhere along that spectrum and you know back to your question of like what could go wrong well yeah the the worst thing that to happen would be it just says something that's not supposed

to say and so you need to design an AI that's really robust to that and you can choose like okay for this use case we really need it to be over here that's a lot more robust But for other use cases where if you're just asking basic questions on their account, we we don't want it to be like

that. We want it to be super free form

that. We want it to be super free form and that's how we get the customer satisfaction up.

I think most people are still focused on things that could go wrong because it's non-deterministic. What about the total

non-deterministic. What about the total other end of the spectrum? Like what

have been the things that have gone way more right than you expected? Like where

have the where has the the potential of agents like outperformed your expectation in terms of what they can handle or what they can do? Yeah, it's

it's really just um elevating the experience, right? So one one sort of

experience, right? So one one sort of metric which is usually a secondary metric that folks kind of think about later but is is quite important to us is just how often do people come in and

just say like agent agent agent like get me to representative like I want to talk to you and uh I've done that you probably have as well where you're just like calling into some sort of customer service and you're just like pressing

zero the whole time and that's because people are used to bad experiences so they've already lost the trust of these systems and I think What surprised us was that if you just make it really

clear off the bat that this is a different experience, uh people are willing to give it a chance and then the outcomes are just way different, right?

So we have um one of our customers uh yeah or a ring like the wearable ring.

Um we did a case study with them where um before having any sort of ji system like one in three customer that came in would just not bother saying anything

and just keep jamming agent until they got to one. Uh now it's one in 20. It's

because you we just like spent a lot of time making the beginning of the process just feel very different and uh folks are willing to give it a chance and so I think that's that's been exciting.

Where do you think that can go? like how

how good can the experience get in ways that it's not yet that good with subsequent evolution of your product but also of the underlying capabilities of the model. The biggest frontier right

the model. The biggest frontier right now is voice voice models and so I know there's a lot of interesting startups as well working on voice. It's exciting

because it's still I would say definitely not solved. there's a lot to be done there and the the bar is very high, right? So, if you just think about

high, right? So, if you just think about like how humans communicate, right? For

literally the entirety of humanity, right? Like I don't know 150,000 years

right? Like I don't know 150,000 years or something, the UI for every human is language and it's just like spoken language. So, you like you listen, you

language. So, you like you listen, you speak, and that's how our brains have evolved. That's like the most natural

evolved. That's like the most natural way for us to communicate. only in the last what like 60 years of that entire time did we have keyboards and like phones and you're kind of just

communicating through typing and so I I would say fundamentally and any sort of agent that communicates with humans like voice has to be a critical factor because that's just how we communicate because our brains are so sort of I

don't know evolved for this it's very easy to tell when something feels not quite right and so the bar is is very high that sort of uncanny valley is is quite large and um that's why there's a

lot of effort going into making the voice experiences good. Um, even you know chatbt I would say chatbt voice for example or the sesame or like these voicetooice models they're starting to feel very impressive but if you talk to

it long enough you can actually it's like you can definitely tell it's not a human.

So there's there's that element of it but then you know for enterprise use cases like us like there's still a ton of hurdles to cross because those models even though they're good the hallucination rate is really high. So

you can't really use them necessarily as is in in kind of the current systems. And so a lot of people what they do now is they you know go from voice into text and then back to voice and then you can

run a lot more checks there to make sure that things are um things are accurate.

A lot of cool ideas there to explore on how do you make it both humanlike but accurate and how do you tie everything together. So that's that's where most of

together. So that's that's where most of the work is going to these days. At the

risk of getting too technical, why is voice to voice interesting and worth pursuing versus just always going back to text and being able to manage it that way?

Yeah. So, the fundamental difference is if you're just going to text, then no matter what, the final audio is just a narration of the text. Um, the voice to voice is is powerful because it it takes

into the entire entire audio of what you said. So, it knows, you know, your

said. So, it knows, you know, your cadence and, you know, how maybe how upset you are and just like the tone and everything.

Uh the latency is a lot less as well because you get to you're going straight from from again voice to voice and latency matters so much when we're talking right like when we're talking right now our brains are constantly going like okay when's he done talking

when should I start talking if someone interrupts someone else it's like in a polite way is is just people adjust very naturally that is the biggest proponent of voice to voice and ultimately the

prevailing view is that for whatever the final experience is if you really want to make it indistinguishable from a human you have to do voice to voice um or you have to at least take into account the voice. The issue with voice

to voice though is that also fundamentally because voice is has a lot more dimensions to it like the the amount of tokens you generate like per sentence is just a lot higher than when

you generate text and the more tokens you have the higher like the easier it is for something to go wrong and so the hallucination rate has so far just been a lot higher.

How much higher? Like give us a sense of how far we are from these being really good.

I think probably like 8x higher or something like that.

Wow.

So it is quite a bit higher. Yeah. I

mean of course you you want to kind of leverage that technology. So now now maybe there's creative ways to make a hybrid of the two, right? Maybe you can have a text model generate the content but you take into account the audio from

before as well and like you know that makes something uh something very realistic. But at the same time latency

realistic. But at the same time latency is still the hard problem because at the enterprise what what's happening is you are doing a lot before you can start responding right you have to figure out like what are they asking about like

which what materials do I need to collect do I need to hit any APIs and get that data back and so you have to do it in a way that feels very natural and you know sometimes if you think about how human does it you might have to say

something like you know give me a sec to look that up because you know it actually genuinely takes like 10 seconds for the API to come back. Um so these are all kind of interesting problems to think through.

So give us a sense today in a in a given you know if you add up all the interactions um some some idea of how long they are what type they are voice versus text versus some other modality

like what what is just the entire corpus of interactions between a decagon agent and a customer look like today?

Pretty balanced at this point between chat and voice. We there's on a raw customer basis there's more people in chat at least for us. Uh but the if you just think about the large enterprises

like the Fortune 100, they just been around for so long and everyone's just calls them. So voice is just like disproportionately higher

there. Like a lot of them are 90 95%

there. Like a lot of them are 90 95% voice and you know 5% chat. In terms of the types of conversations, it's generally ones that are fairly

um you start with the sort of easier ones of course. And so these are things where you can just they're kind of question answer based, right? So that's

the tier one is like hey you just you can answer their question based on what you statically know. So that could be, you know, I have questions about how your loyalty system works or I have

questions about, you know, if I do um like if I bought something like would I still be able to refund it? Like things

like that. Then the next level is is still question answer based, but you're leveraging a lot of real-time data. So

that could be, you know, I got, you know, 2x points on this transaction, but I should have gotten five. Like why is that? And then it would actually go and

that? And then it would actually go and look into your account and reason through things like, okay, I see the council said this type. let me go find all the documentation on this type and like okay actually it's because you booked through a travel agency if you

have booked directly with the airline you would have gotten 5x but you know this thing doesn't apply to a travel agency or whatever right uh and then the sort of third tier is you're actually taking action so um you know I lost my

credit card I need a new one and it's actually walking through a pretty large flow and that's where AI agents have been really excellent because you you you actually wouldn't expect it to be

able to do that and so it's able to go in it can be a pretty complicated system where it's like, okay, well, first I need to figure out what your address is and confirm if the address is correct, and then I need to look and see, hey, do

you want me to lock the old card? Like,

okay, great. I I'll do that. Um, might I need to check for fraud to make sure this person is not just constantly asking for new cards? And it's just like all these things stitched together.

That's really what makes it agentic. And

that's why there's been such a step function improvement with Loom. In the

spirit of that question of like what could go right, what could go right for the company based on the data they're gathering from these interactions that they probably have been doing nothing with historically like what new things can they do for their customer because

of on a onetoone basis like they're just learning more about a person and on an aggregate basis they like understand the behavior patterns of their customer base or something.

Oh yeah, that is a huge topic. I think

that's a huge part of our product. Like

we we have a viewpoint that this data of course is super valuable because it's literally what your customers are saying but it's very underutilized because historically there's it's a very unstructured data. So what people

unstructured data. So what people typically would do is like okay well every month we have a million conversations. We'll have a full-time

conversations. We'll have a full-time team of you know 20 people and they're just like sampling these conversations and trying to like check on a rubric and try to compile topics and things like that right and that will only get you so far. But now what you can do is you can

far. But now what you can do is you can literally have a language model that reads every conversation and extracts whatever info you you want from it. And

so that allows you to do things like okay well over time there are these topics that people probably didn't even know about because these organizations are big right so the people in leadership positions they can only have

such granular insight into what's happening but it'll just flag like hey there's this 2% of conversations where things are not really going that well and it's because we don't have context

on this topic and so let's flag that.

Let's draft a suggestion for what could go better here based on what the how the human agents are handling or based on you know the other procedures that we have and here's a suggestion for how you

we should adjust the agent that allows the agent to improve automatically over time and that's really critical. So when

you think about like moes in the agentic world, a lot of it is around well if you've kind of been working with a a client for a year, has your agent just continuously gotten better by learning

from the data and and that's is like that's a different concept than just like training on the data, but has it continuously gotten better to the point where it's just very difficult for another agent to come in and perform at

the same level? M there's this there's this funny situation today where I think especially CEOs really want AI in their businesses. They want it now, but they

businesses. They want it now, but they don't know what they want. Like they

they they don't have a good framework for thinking about okay, I understand my business. I don't know where I don't

business. I don't know where I don't know where to go. Like I it seems like I would be missing a major boat if I don't deploy this in my business, but I don't have I don't know what to do. Like I

don't know I don't know where to go first. I don't know who to call. Do you

first. I don't know who to call. Do you

have you developed any framework for those company leaders that like desperately want to use this technology in their business but they simply don't know of of course apart from automating

you know customer service or something not like I don't mean specific use cases I mean like a framework for thinking about what kinds of problems might be addressable by agents or by LLMs

I mean one framework is similar to the sort of framework we've talked about before of two two ends of the spectrum and I would say most leaders we talk to are focused on the more bottoms up uh end of the spectrum which is like where

are the areas that we should just not have humans doing because it's so mundane and repeatable and there's tons of cost efficiencies there so I would say that's where folks are typically thinking of so when we talk to leaders I

think there's a couple observations one pretty much all AI initiatives are very top down at this point because it is such a board level mandate so the seauite's very very invested in like

okay where do we deploy AI that almost means that if you want to get thing going at a larger organization, you have to have buyin from the top level because it's gonna get up get up there anyways

and they have to make the decision at the end of the day. So that's one. Two,

the way they think about like the use cases to your point is uh back to ROI, right? It's like where can we either

right? It's like where can we either save a bunch of money or make a lot of new revenue. And if you cannot like in

new revenue. And if you cannot like in basically half a sentence explain that, then it's like it's just not going to work right now because they're they are under a lot of pressure, right? they

needed to show like quick quick wins and if this is not going to be a quick win and they can point to like I saved $10 million then it's not going to be something that's prioritized.

Do you think coding answers that well?

Like do you think the ROI is clear in coding?

Yeah, it is. Um and I know the coding agents quite well and the way they do it generally is uh one it's very easy to test. So it's

like let's just deploy out to the uh engineers and then you just kind of like do a poll on the engineers of like hey how much more productive do you think you are? And engineers are often the

you are? And engineers are often the most valuable resource in these organizations. And so they're like those

organizations. And so they're like those answers are treated very uh with with high I guess like importance.

Yeah.

And uh yeah engineer tells you that you're they're like 50 50% more productive. It's like okay great. You

productive. It's like okay great. You

know are you confident that that's true that that they can self-report and be accurate? there was that uh meter study

accurate? there was that uh meter study or whatever that came out that actually like I I don't know if the stud is good or not but that actually productivity was like down or or flat or something like this. So like the as like the

like this. So like the as like the reported the the self-reported productivity was at odds with like actual measured productivity or something like this.

Oh yeah. I mean I I actually don't have opinion. I don't know nearly enough

opinion. I don't know nearly enough about that. But I'm just saying that

about that. But I'm just saying that doesn't doesn't really matter, right?

Like if people if their entire engineering team is like hey we love this this is making us 50% more productive. It's like building your

productive. It's like building your thing.

Exactly. And then you think about from the like the CEO and they're like reporting this to the board or something. It's like hey like my entire

something. It's like hey like my entire engineering or said that they're 50x 50% more productive. This is like a worthy

more productive. This is like a worthy investment because these people are all paid this much and like now we can accelerate the product. We can

accelerate everything. Do you think that the future is that every when I talk about brands and how they uh how a given company might want its agent to feel and sound that like an agent might be a way

to express brand culture, style, tone, whatever? You want to talk about that?

whatever? You want to talk about that?

But do you think the end state here is that each company sort of has like a almost like a named uh personified representative that you just come to expect to interact with and not it's not just customer service issues, but it's

sales issues. you know, you ask it for

sales issues. you know, you ask it for advice on what shoe to buy or whatever it might be and that that's all integrated or that that that you'll have different agents for different parts of the company. Like I guess what I'm

the company. Like I guess what I'm trying to ask is what the future of a company's agent or agents looks like in in the natural end state, you know, 5 years from now or something. Yeah, I

would say that in the natural end state it is more unified for the exact reason you listed which is people want a unified brand out there and the brand's very important to them and for some

businesses is more important than others but eventually this is like this becomes the front end for the business right and so that means it's both how you gain new customers but also how you support the existing ones and make them get make

them retain more and so on it's almost like you know in the limit right like if you're working with a bank or airline or telecom company or or whatever. The

agent could be the only thing that most users interact with. They don't even have to touch your mobile app. They

don't even have to, you know, go to on your website ever. They just have this agent where they're authenticated. It

knows everything about them. It has all the context of your previous conversations. It has memory and it can

conversations. It has memory and it can just solve your issue. It can take actions for you. You need to book a flight. You need to upgrade a seat. You

flight. You need to upgrade a seat. You

have you have questions about this or that. I think that's the exciting vision

that. I think that's the exciting vision that we're building towards. And in some ways we often call this like a concierge where it's like a it's just a digital concierge that can do everything for you. But I mean you also have to be

you. But I mean you also have to be pragmatic on both sides like you have to you know start with a clear use case.

But I think that is where folks are building towards. And so in the near

building towards. And so in the near term, I do think that different teams because the reality is that at these large companies, different teams have, you know, different budgets and they make different decisions and so they

might have different agents. But long

term, you either need to have a unified system that ties them together, like a unified framework, or they could just be literally the same agent.

Is the right analogy here? Like a

company's website, like they'll think about their agent, like they think about their website, like a lot of work will go into it. it'll look and feel a certain way, like it'll be kind of a unified interface with the world. Like,

is that a Yeah.

Is that a clean analogy?

I think that's a good good analogy. It's

it's just like a front end, right? It's

like a UI. Um, but instead of visual UI, it's a conversational UI.

How do you feel brands pulling, you know, personality requests in their agent out of you? Like, do do they care about what do they care about? Like, do

they want it to be nice? They want it to be, you know, concise. Do they want it to be funny? Do they like h how has that dimension evolved since you started?

people already almost always already have brand guidelines u because they need to show brand guidelines to their human agents right to train them and so the nice part is that they already have all this training process for the humans

and you should ideally be able to apply it to the same in the same ways to the AI and so they'll have like hey you need to do this you need to always be confident you know sometimes folks don't want the agent to apologize sometimes people really want to be apologetic so

it's just a bunch of different preferences and that needs to be taught to the AI in a in an efficient Okay. Um,

but yeah, that's also kind of a lower throughput way of communicating. I mean,

the other way that you can show the agent is just give it a lot of examples.

So, here's examples of what great look like from our top agents and just learn from that.

What is the very biggest like you're almost afraid to admit it because it feels so big version of what Decagon could be.

Yeah. At the end of the day, what we are building towards is kind of this concept of um you know, it becomes like a a new UI for the the product, right? Just

think about how much these large companies invest in their mobile app or their website. And this is just

their website. And this is just literally how everyone communicates with them. So, this could be a way where

them. So, this could be a way where eventually the way any user interacts with any brand is through an AI agent and for all sorts of different use cases, right? And

it benefits the AI uh benefits our customers for the AI to have all this context because it can seamlessly flow between things. It's like a lot of them

between things. It's like a lot of them want to do sales type use cases you know at the end of a support flow or vice versa and um that's exciting.

What internal context or things do the best customers have that make this better? So you mentioned brand

better? So you mentioned brand guidelines like maybe there's a write up on what the brand guidelines are or whatever. what what are like the

whatever. what what are like the internal assets that companies have or don't have that if they have them it's made the decagon experience like way way better. I mean the number one thing is

better. I mean the number one thing is just APIs for the AI to use, right? So

APIs to take action, APIs to look up data, APIs to um you know reformat things. That is often like hey if you

things. That is often like hey if you have those you already know that within the first month it's already going to be a great experience. If you don't yet then you know we should try to build towards that as soon as possible so that

the AI can actually achieve that elevated experience. So that's that's

elevated experience. So that's that's the main thing. Most people already have documentation. It might not be up to

documentation. It might not be up to date but you know we can help with that.

Most people already have sort of like SOPs, I guess. Um, and then we we are able to use that and kind of generate our format. We call them AOPs, agent

our format. We call them AOPs, agent operating procedures. They're just SOPs,

operating procedures. They're just SOPs, but for AI and uh and then brand guidelines, like I said, people usually have those as well. So, you just ingest them.

We were talking last time about, you know, in any business, but certainly in yours, the three key stakeholders being uh your team, talent that you have to recruit, and I want to talk about that in detail. Um, capital, investors, and

in detail. Um, capital, investors, and customers. um maybe suppliers too is is

customers. um maybe suppliers too is is a a fourth category, but uh especially interested in the first three. And last

time we were together, I asked you like, you know, which one do you have trouble with? And we laugh because you said,

with? And we laugh because you said, well, definitely not investors. Um talk

a little bit about the demand from investors to invest in companies like yours and how that feels like. What are

they doing to try to give you more money, get on the cap table? How

competitive does it feel? What does that what does that feel like right now?

because it does seem like there's a handful of companies like yours that are in one of these white hot areas that have key traction that have great teams and basically every investor wants to be involved in those companies. What what

does that felt like?

Yeah, it it definitely feels like there's maybe a little bit too much excitement right now in the on the AI side. Uh it's just like it's it just

side. Uh it's just like it's it just seems way too easy to raise money. um so

many companies out there and I mean for us we've been very fortunate right we don't take it for granted I mean I think in my first company we're also fortunate to it was easy to raise but it was for different

reasons like 2021 nowadays it's just I think there aren't that many AI companies that have like real real traction uh on the revenue side uh especially in the enterprise and so I

think that's attractive to investors because you know they want to deploy their capital into AI and that's like the biggest trend. So, I think we've been uh we've had a great relationship with all all our investors. I think we

just really selected for folks that we get along with at a personal level and we feel like will be very helpful for our go to market normally. And uh yeah, that that's kind of been an interesting process. So, yeah, pretty much after

process. So, yeah, pretty much after every single time we've raised a round, we just like almost immediately gotten preempted. And that alone can't be

preempted. And that alone can't be right, you know? It's like if you're if you're just thinking on first princip uh first principles to make an investment like the previous valuation should not be like a super big factor in that. It

should be like how well is the business doing? How how like what do I believe

doing? How how like what do I believe the potential is? So it feels like there's a little bit of mania but um yeah we're definitely I would say more indexed on the other two things like

talent and customers.

What's the craziest thing that an investor has done to try to invest in the business? The main thing that we do,

the business? The main thing that we do, and I would actually encourage more founders to do this, is that during the stage where people want to invest, but they haven't yet, uh, that's when they're most willing to be helpful. And

so, it's actually like a great way for you to use that to proxy how helpful they'll be afterwards. Because if

they're not that helpful in that stage where they really really want to invest, they're willing to do anything, they're for sure not going to be helpful afterwards, right? they'll still be

afterwards, right? they'll still be friendly and like hopefully they'll not be detrimental, but that's like your opportunity to really test folks and see like how helpful someone will be. And we

obviously know a lot of investors very well and I think they like no one has issue with that, you know, like they know that they're going back to, you know, competition. They're all they're

know, competition. They're all they're also in a competitive sport and they know that they need to earn basically the uh ability to invest in the best companies and so I think their perspective they're happy to work for

it. So if you just give them opportunity

it. So if you just give them opportunity to, they will. I'm going to ask about this from both founder and investor perspective. What advice would you give

perspective. What advice would you give investors during that window? Like when

there's a fundraising that's happening, you know, there's sort of an open process or a window or whatever. What

have you seen the best of them do well?

Not just in like I'm sure helping you.

Here's 10 customers you can talk to. I'm

sure that's great. Um, but also on the underwriting side, like them making sure they understand your business extremely well in a short per usually what I'm expecting is a short period of time.

What what have the very best done in that window? So, number one, I think the

that window? So, number one, I think the best investors um are they kind of like get this dynamic, right? Like we we've definitely talked to a lot of high-profile investors where they're

just like not willing to help much until they're invested. And it's like totally

they're invested. And it's like totally they're right, right? It's like, hey, we have a lot of our current investments.

We don't want to like use our social capital or whatever to help with an like a new investor or a new investment. But

I think from the founders's perspective, like okay, if that's the case, then it's hard to tell if you'll actually be useful or not, right? It's like there's not there's no difference between you saying that and then someone who's like who can't really help and just saying

that. So I think the the best ones just

that. So I think the the best ones just are able to give a lot of signal to the founder that like, hey, I'm really willing to help. I have the ability to help. And let's say it doesn't have to

help. And let's say it doesn't have to be that long a period of time, like a couple months before the round actually happens, right? So that that's one

happens, right? So that that's one thing. I think the other thing is that

thing. I think the other thing is that um I think if you just think about any anyone you bring into the world, this could be uh you know employees or

investors or advisers or anything. What

we really index a lot on is just cognitive ability, just like raw intellectual throughput.

And you can almost like feel that out in an investor in the same way you would feel it out in an employee just by, you know, spending time with them and just like actually seeing if they're thinking about things from first first principles

of your um of your company. Like for

example, right, like a lot of AI companies are growing much faster than traditional SAS companies right now. And

so because that's the case, like a lot of things are different and you generally don't want investors that are just like, hey, I've seen this so many reps like you got to

do things like XYZ way. You want people that are just intellectually curious and will think about things along with you and can help problem solve.

What about on just like the pure underwriting side? So an investor comes

underwriting side? So an investor comes in, they're really smart, let's take that for granted, and they just want to understand your business, like the good, the bad, the ugly as fast as possible.

What have you seen the best do in that side of the investment process including things like how fast they move or how how deliberate they move or anything like that?

The best ones I would say they I mean one they get a very deep understanding of of our customers. Um, and so it's actually funny. Our customers have made

actually funny. Our customers have made probably so much money on expert calls because so many investors are hitting them up and I think the best ones can before they even talk to you, they've probably already done like quite a bit

of research and have like a pretty full view on your customers. Unfortunately, I

think what we've seen is that there is a lot of noise there because like a lot of people just lie on customer calls and uh like we have people who had said that they've used us and we like literally never heard of them. But generally if

you do enough research and you're good at it then you can kind of underwrite the business that way because that gives you the most signal. And the other one is uh people that index on culture because I think culture is quite important and a lot of good investors

know how important that is and so if they feel like you are becoming a a place where good talent is congregating then folks will index on that more.

Let's talk about that. Let's talk about recruiting and culture. Uh starting with culture how would you describe it? Uh we

talked about the you know the quote on your wall as the first question. So

that's part of it of course like extremely competitive, extreme extreme bias to action, you know, get things done. What what are the other key

done. What what are the other key components of your culture? Like when

you're sitting with a new recruit, what do you tell them about what kind of place it is?

There's definitely a level of intensity that's important and you Yeah, you definitely want to tell people that upfront because you want them to self- select into this culture. So everyone

that has joined Decagon, I would say they want to work hard. They want to be around other people that are really smart and are like them. And uh they're motivated by, you know, they view this as kind of like, you know, maybe the

prime of their career where, hey, I'm going to work hard, but we know that because of that, I'm going to build sort of lifelong relationships with like other amazing people. I'm going to, you know, have good financial outcomes. I'm

going to be able to leap frog steps in my career because the growth is just happening so quickly. So, it's it's kind of you're you're attracting people like that. Yeah. I I think that alone creates

that. Yeah. I I think that alone creates like a pretty uh strong foundation for the culture because you have people that are are there to, you know, work. Uh we

have a lot of people that live like right next to the office and part of that is because like we've, you know, just selected for people that like being in the office with other people, right?

We're in the office a lot. Those are the foundations. And then I think what you

foundations. And then I think what you have to be careful on top of that is like okay well you still need the we really want our office to be a place where people enjoy coming to work every day because because we spend a lot of

time in there. It's like you want people to be um you know happy to to be there.

They feel like everyone there is supporting them. Uh even between the

supporting them. Uh even between the organizations you want people to feel like they're pretty aligned and um you know folks are kind of working towards the same goal. And that's an ongoing problem. Like I don't think like we've

problem. Like I don't think like we've solved the culture. Um, but it's it's something we just put a lot of thought into and we want to make sure that people feel like they will be very fulfilled by staying here for a long time.

One of the most interesting subplots of this entire business evolution and in and around AI is talent wars. Um

obviously it's happening in the most extreme cases at the model layer you know meta between meta and anthropic and open AI and there's you know great riveting stories to hear about uh what people the lengths people will go to to

secure a great and the amount they'll pay to secure a great engineer or someone really key to the business. Can

you give us your perspective on these talent wars? Uh what it's like to be in

talent wars? Uh what it's like to be in them? Um obviously I'm sure you are too

them? Um obviously I'm sure you are too fighting for the best talent versus lots of other great companies that are being formed. Um yeah, tell tell us from the

formed. Um yeah, tell tell us from the inside what this what this environment feels like.

Yeah, I mean it definitely feels like talent is a big team effort. Like you

you for anyone you want to hire, you need the whole team to swarm around them for, you know, to win highly sought after talent. Um and you have to go and

after talent. Um and you have to go and you know do do all the things right there. There are some parallels to sales

there. There are some parallels to sales of course like you you're trying to like uh convince people that hey this is the the place they want to be. Um, so that involves, you know, oftentimes getting

to know their families, getting to know their partners, uh, really figuring out like what they want out of their own careers and and making sure that you can design a role for them that that is like that. Unfortunately, in the application

that. Unfortunately, in the application layer, it is not as crazy as you know the the metas and the open eyes uh, who are just like there's only there's only

so many top level researchers. Um, and

we have we have some very strong researchers on our team. Uh, it's a lot more of like applied for us. It's the

it's the same thing that happens, right?

Like we're going after, you know, a lot of people on our team from like Harvard and MIT and Stanford. There's only so many of these these folks that are in the market any time who are like in SF

or going to the office and so it is a competitive place to be. Um, I think we're kind of fortunate and now we our talent brand has gotten a lot larger than when we were first starting and so it definitely

has gotten easier to hire, but at the same time our like the amount of people we need to hire has also gone up and so it's just constantly just like finding ideas of how to get new people. I mean,

we just opened up our New York office as you know and that's part of the reason for that is that hey, there's another talent pool over here. We should

leverage that. One of the questions that I think so many people are interested in for companies like yours is the use of whatever core underlying LLM versus the

development of your own models on top of uh or or replacing those underlying LLMs. You are gathering all this incredible data that's just yours. You

don't have to share with anybody else.

These models, you know, thrive on on good underlying data. How do you think about that that aspect of all of this where you know five years from now it's going to be either your own model or your own model plus uh something else or

just your own data and context on top of the best model. How do you think that will evolve? Um I'm both curious about

will evolve? Um I'm both curious about this in terms of how it impacts the product but also how it impacts your uh your business mode your uh power in your business where you rely or don't rely on

you know GPT whatever. When we first started, this was like about two years ago, people were still figuring out applications, almost no one was doing fine-tuning.

In fact, anyone that was doing fine-tuning, there's a lot of like writing at that time was like finetuning quoteunquote doesn't work because it doesn't really get get you that many gains. And another big reason to not do

gains. And another big reason to not do finetuning at that time is that the models are changing so fast and like your applic.

So like why invest a bunch of time into fine tuning? It's not reversible. You're

fine tuning? It's not reversible. You're

going to have to throw it out next time there's a there's a new model release. I

think now the open- source models for example have gotten to the point where um they're definitely not not smart enough to do everything but there's a lot of specific use cases where you just

don't need that much intelligence. Um

example would be like even the agent let's say the first thing the agent does is it just needs to think about like okay based on what the user said and everything before like what path do I go down right or what data do I need you

can make that a fine tuned model that model doesn't have to be good at like math or coding or anything it's just like you just need a smaller model that's just fine-tuned on that and nowadays we're seeing much more of that

because a lot of the applications have gotten more mature so you know how your agent is structured you know the places where you need models to run and you can take you know smaller fine-tuned models and that improves the entire system both

in terms of performance but also latency and so on. So I think over time there's going to be more and more of that. Um I

do think there's always there's still always going to be sort of like a huge usage of open AI anthropics the world because yeah if you just need intelligence like you need the best models. Um so I think there's going to

models. Um so I think there's going to be a balance but at least in the short term there's going to be more and more of the the fine-tuning small models that happen. What is your perception of the

happen. What is your perception of the very biggest companies like the entire market has been focused on rightly so the seven eight nine 10 biggest technology companies which of them feel

uh most important to you and I don't mean open eye and anthropic I mean like you know Microsoft and Amazon and Apple and these sorts of companies what is your relation and thinking about them

today they've been the driver of you know equity markets by a huge margin um they're important companies how do you relate to them I work-wise you don't it's not super

relevant but of course we have our own opinions I'm very bullish on Google actually um why I just think that with AI use cases having the having individual like

consumers is like so important because that's where all the data comes from any anyways and Google is like much stronger there um I mean you could say that you know meta Facebook also has that element

and so yeah maybe their new super intelligence lab will will be able to to make it work um But but something like you know Anthropic for example where they haven't done as much as well on the

consumer side you know compared to like a chatbt I think longterm you do need that consumer buy in because that's where all the new data is going to come from. So yeah Google and I mean Google

from. So yeah Google and I mean Google just has an amazing team and um they've had a couple of like rocky starts but you hopefully they'll make it work out.

Um I mean of course all the large uh like all the mag seven basically are obviously super strong. So we don't really have strong views on on on them.

If I let you build a portfolio tomorrow where you got five slots 20% each in in five private companies you know building in and around AI which five would you what portfolio would you build?

Decagon excluded.

Yeah Deagon excluded. Um let's see I just kind of gravitate towards where most of the uh the talent is forming. I

mean, uh, I mentioned I'm close with, uh, the cognition guys, so cognition would be in there for sure. Cursor also,

uh, I'm actually kind of interested in like where those might run into each other in the future. For me, definitely would want to take a bet on the sort of hardware layer, even though it's like a much higher variance. So, last time we

were talking about etched, like companies like that. I think you probably put one of those in there. Uh,

still on the earlier side, but yeah, obviously very high potential. Yeah,

another friend of mine is is building a company called Pika, like building video models. Um, so I just think very highly

models. Um, so I just think very highly of of that team as well. So we probably put them in there. Probably would want some sort of bet on the just like

underlying model side even though I mean all the large language models are are out there but another friend of mine who I think very highly of they're building we're building models but not not like the types of language models but still foundation models for like you know

healthcare for example. So uh friend of mine Josh is building Chai and they're building a foundational model. So stuff

like that I think is quite interesting.

Um or even I would put uh you know Locky's company physical like those I think those are very exciting. It's just

I mean it obviously is early to see you know how how those would turn out but if I'm building a portfolio definitely want one of those in there.

What what do you think are I'm I'm interested in both sides of the spectrum. uh the the people that aren't

spectrum. uh the the people that aren't in your position who are both technical and commercially at the center of this wave, what do they overestimate and underestimate about the capabilities of

AI today? Where is it further along than

AI today? Where is it further along than the world thinks? Where is it further behind?

It's a little bit more behind in uh being able to unlock a lot of the enterprise use cases, I would say, because of the non-determinism. So, you

there's like two things that need to happen. One, you need to reframe the way

happen. One, you need to reframe the way that people think about agents because like there's the Whimo effect that often happens where it'll be objectively just way better than human drivers and human drivers make a lot of mistakes, but

because we're investing in new technology, the bar is a lot higher. So,

it has to be near perfect. So, there's

there's that dynamic that kind of needs to adjust in some folks mind where instead of evaluating AI in a way where you're just trying to find mistakes, you're evaluating holistically and looking at the sort of success rate. And

then if you can kind of frame it that way, well the success rate is going to be way higher than humans because again humans are not perfect, right? So I

think that needs to happen and I don't think that's fully happened yet in the enterprise. So that that trend needs to

enterprise. So that that trend needs to happen. And then on the AI and on the

happen. And then on the AI and on the sort of use case side, yeah, I think the models still need to get better in a lot of areas, right? We were just talking about voice to voice earlier.

Hallucinations are too high there. And

so as those models get better, um I think more enterprise use cases will be unlocked. But I do think the the general

unlocked. But I do think the the general public will just see a really cool demo and be like okay oh wow like voice is solved now and as a result like you know enterprise should be adopting it left and right and sees will see that too but

then when they actually get into it it's just uh it's hard harder to go live right so that that's I would say the piece where it's not quite you know fully there yet and

so it's a little bit slower than people think on the flip side of that where it's like a little hard it's like underestimated is just things are growing exponentially uh improving moving exponentially and no one is good

at kind of conceptualizing what exponential means in in things like this and so that could be from like a perform performance like cost perspective right like right now I would argue that if

you're building an application your margin shouldn't really matter that much like people will often sort of critique the coding agents like they're hemorrhaging money yeah but you know the again things are improving exponentially

and like the cost will go down exponentially as well So your margin doesn't really matter. Like

what really matters is you you need to get market share and you need to kind of get mind share of of users. So it's like perfectly fine not to have good margins right now. And it's just I think people

right now. And it's just I think people the model's just everything just improves way faster than people think.

Um it's slightly different at the enterprise. I mean the same principle

enterprise. I mean the same principle applies but you generally don't want to be hemorrhaging cash with like an enterprise um sort of deal because

like those are just much longer term and even though the cost will go down they'll their their expectations might also change and so on. So you generally want to be fairly healthy there. Um but

that that I think that's what's underestimated right now.

How do you know that cost will get way lower? Like I'm I'm very curious about

lower? Like I'm I'm very curious about this margin question because if you knew for sure then the argument would be actually you want to run super negative gross margins like if you knew that it

was going to get you know 98% cheaper or something like this on cost of goods to serve coding agent or something like that whatever I think the argument would be get the install base just like have

the best product and get get the users and and build the you know build the um the affinity with the with that user base and don't care at all. Um, but that hinges a lot on the confidence that you

have that those co those costs will fall. So, how do you know like how do

fall. So, how do you know like how do you think about that equation of like win install base versus like demonstrate good unit economics now?

Well, I just think it's quite unlikely that the where we're currently at is like the best that things will be, right? Like there's just so much effort

right? Like there's just so much effort getting uh put into it and like one of the main metrics is efficiency. The

other piece is that even if things don't get better, there's a lot of like uh ways you can rearchitect your system so that it is more costefficient. It's just

that it's not worth putting time into that right now where you can put that same amount of time into getting new customers because you know that like things will change in the future. So I

think that's just one thing where people are like oh you know like it's kind of like a a scheme where you know you take VC dollars and then like the VC dollars go to the chips and like you know these

companies are losing money. like if they wanted to probably they could just like spend a month or even less and just like massively improve their margins, but no one is it's just not not worth doing

that work right now cuz what you're really optimizing for right now is just quality and growth. So if you can do that then the optimization can always come later on.

How do you think about your margins?

Like just putting it on Decagon like do you do you care at all? Do you set them?

Do you like what's what what's your guardrails or parameters for what's acceptable or what you're targeting?

The only thing that we have a principle for is not to have negative margins.

Um, and in general, we have fairly healthy margins because I mean one way you can think about it is if you just think about, you know, the supply chain for any good, right? Like let's say you're buying like a croissant at, you

know, the airport or something. Um, that

last step where you're actually solving someone's problem, that's where you generally can capture the most margin.

every step along the way like whoever's enriching the flower or like you know making the butter or whatever like you're generally uh making like a margin on top of the costs of whatever your

goods are and that's why it's nice to be in the application layer is because you're what you're building is actually solving the business need and as a result you can um you know capture more of that because our customers the way

they're thinking about it is like great we're investing in decagon we don't care that much about what decagon's costs are in fact we probably don't care about at all what we care about is what is the business ROI that we're getting, right?

It's like we're downsizing our operations by this much. We're actually

generating more revenue now because the AI agent can can engage people and keep them retained.

So, that is where we probably see the most um dynamic here. And I do think this is generally not really a hot take like um in the early days, oh, there's

like chach rappers and so on. And yeah,

a lot of rappers if it's too thin, it's like not going to be valuable. But if

you have enough software built around the the models, then that's where you can actually almost capture the most value. That's why I think the open AIs

value. That's why I think the open AIs of the world will continue to move towards applications because it's quite hard for them to make money long term on like their API for example

because there's like such high competition all the labs are building and it's like it's very easy for people to swap. It's not like a like moving

to swap. It's not like a like moving from AWS to GCP is like very hard but moving from like a open AI model to anthropic model. You just like change

anthropic model. You just like change like one line of code you know. C

can you say a little bit more about this chat GBT wrapper concept? Like my sense is certainly for what you built but probably for other companies that a lot of the work that your engineers are

doing is not like AI work. It's

traditional software work. It's the

ability to hook this system up to enterprise customers. It's, you know,

enterprise customers. It's, you know, good oldfashioned like product and and infrastructure building that is diff very different from what OpenAI or Anthropic are providing you. And that's

hard work. Like just just like building any software system is hard work. And

and everyone's very enamored of this idea that like in 5 years I can just show you a piece of software and just tell a coding agent just like replicate this piece of software and that's going to mean lower software modes. I don't

think you think of it that way. So maybe

describe like your thinking or critique of this like chaty rapper concern that that people have.

People say rapper in a derogatory way. It's

like hey you're just a rapper and yeah I mean again it's not like it's not black and white like yes there are a lot of apps that are just rappers that are not going to become real businesses because like it's there's just not that much

value. Um I mean one argument and again

value. Um I mean one argument and again I don't know too much about this space but at least from the outside it has seemed like um copywriting for example has been difficult because you know someone can just log into chatbt and

just like hey write this for me and I'll just write it. So there there are things where if there's not enough like tooling and functionality on top of something for it to be really valuable and like needed then maybe it is easier to just

leverage the models. But most of the time it's that's not the case. And

especially when you get into agents um an agent is not just a model, right? You

have to like design it. You have to be able to put in guardrails. You have to be able to teach how to do new things.

And that's where the sort of software layer on top of the models come in. And

if if that's valuable, then it's just much harder for for them to be made obsolete by a model update.

Um I mean the thing the the other side of it is like okay well now the labs are quite interested in building applications, right? So they're building

applications, right? So they're building a bunch of cloud coding applications and uh like cloud code and so on and so those will end up being competitors with

cognition or uh cursor and and maybe for that reason it is wise for the coding agents to start training stuff as well.

I would say for us right now at least the the just like sheer amount of like functionality you have to build because it is a very top down like product is quite large that has nothing to do with

AI. It's just like stuff like okay, how

AI. It's just like stuff like okay, how do you, you know, have like observability into like what the conversations are? How do you alert uh

conversations are? How do you alert uh the team if like something spikes and um how do you like how are you able to QA and have unit tests for the the conversations so that like before you

push it out to end users, you feel it's like there's just all this like functionality that's there that doesn't really have anything to do with AI really. And so you just it's just like a

really. And so you just it's just like a lot of stuff that How would you uh advise other founders thinking about the ideal customers to go after? What are the what are the most

after? What are the what are the most interesting qualities of your best customers? Like when you're qualifying

customers? Like when you're qualifying them, are they going to be you know you have limited time like you can only serve so many people. I know you're growing really fast, but you can only serve so many c customers at any given time. How do you qualify who you want to

time. How do you qualify who you want to work with and don't? Like what are the attributes that you've seen matter the most? Yeah, we want people that are

most? Yeah, we want people that are intellectually just like at the leadership level just like really curious and excited about technology and

you actually see a huge spectrum of that in the enterprise and some of in my opinion the best leaders and like probably the folks that we are most excited to work with they're just like

genuinely like hey we want to move on AI as fast as possible we're very interested and just curious about how all your systems work and as a result I'm going to help just like cut through

all the just like croft and like bureaucracy to get something going.

And you can actually I think you can tell that pretty clearly in the first conversation. You can tell if someone's

conversation. You can tell if someone's if if they're like legit about like, you know, this is something where I'm going to both push aggressively but also give you a ton of feedback and like the

feedback is going to be good versus someone where they just they know it's like a board mandate and it's just like a AI is like a a thing on their to-do list basically.

How do you mark milestones in the business? Like how do you how do you

business? Like how do you how do you motivate the team? What have you learned about how to rally around a given thing?

I know you're super aggressive when you have a customer that you want to get that it's not just like a old school sales process. It's like an all hands on

sales process. It's like an all hands on deck, you know, send engineers, do whatever it takes. What have you learned about motivating milestones, rallying the team, organizing around common goals?

Yeah, for us, I mean, one thing we we do always is we always have like a sort of a flag pole that's within sight that can just kind of rally everyone around it.

Having things to rally around are quite helpful. I was kind of thinking about

helpful. I was kind of thinking about this the other day. I think another type of rallying is around just competition, right? I think when people feel like

right? I think when people feel like they're, you know, in a battle and there's like, you know, clear enemies, then it makes sense to and like again, you don't you don't want to get to the point where people are just actively

like there's like active animosity, but just kind of healthy level of competition. It kind of ties a team

competition. It kind of ties a team together because there's like, you know, something to focus on. Same thing with milestones. Like if you give someone a

milestones. Like if you give someone a if you give everyone a clear milestone and this can be like pretty insignificant like last last year we had for our revenue milestone we told everyone we get them like super nice

jackets. You got like, you know, Decagon

jackets. You got like, you know, Decagon Arcterics jackets and everyone's like super excited about that about that. And

if you just think about like the cost of the jacket, the cost of the jacket and just how much people get paid, it's like just like trivial, but it just creates like this, hey, we're we're working towards like

these jackets, right? And uh it brings the team together because now, you know, like it just feels like everyone's working together towards this common goal.

And uh that's been a big part part of our our culture is just finding what is the next milestone.

Anything that we haven't covered about either the business or this exciting you know AI applications world that you feel especially passionate about.

I think what is become almost a meme or hyped up a lot in AI startups right now are like a couple things. One obviously everyone's in

things. One obviously everyone's in person. It's like 996 or whatever. Um I

person. It's like 996 or whatever. Um I

actually I don't actually think 996 is that healthy. uh it happens in China and

that healthy. uh it happens in China and everyone's like super hardcore but one of the reasons is that like no one has jobs over there so it's it's very easy for uh employers to have leverage I

think here you generally want to maintain a good balance because if you're working super high intensity uh you need time to let like relax a little bit but that is that is one element the other element is the forward deployed

engineers so everyone's talking about forward deployed engineers I just think it's kind of funny because my my my co-founder came from Palanteer so they actually have forward deployed engineers what a forward play an engineer at Palunteer means is like you're working

on a you 10 $25 million deal and so you're you're actually just like you almost full-time working with either one or a small number of customers and like building very specifically for that. I

think people are a little bit conflating that with what startups do which is startups are just very hands-on and do things that don't scale but for you to actually have a FTE model you need to have like massive clients and most

people do not have massive clients.

So I'm actually I'm kind of interested to see how that plays out because I do think there's like a overindexing on this like forward deployed engineering model right now where it's like yeah

it's like I have a for engineer and the deal sizes are like 50k you know and that's something we think about a lot as well like we we are very hands-on with our customers. You have to think about

our customers. You have to think about like okay well you know how do we scale quickly right if you if for every you we don't have 50k clients but if you if for every 50k client you have like someone that's like fully staffed to them that's

like impossible to scale right so you need to find that line in the middle and I think the full forward deploy model only works with the the palunteer approach presumably on this side you do care a

lot about your margins like margins you're much more open about if it's just like LLM cost or something like this but if it's fully baked like people cost like that's going to be a problem.

Yeah. And it's not even a problem necessarily from the pure dollar margins. It's just like it just prevents

margins. It's just like it just prevents you from scaling. No one can hire good people like that fast, right? Like good

it's just hard to hire good people. So

if your business is fully constrained on good people, then that's also not a good thing.

What do you think the minimum uh customer size and revenue is to justify a forward deployed engineer model?

Oh, like probably a million.

Yeah. Yeah. Fascinating. Well, I think you know my traditional closing question for everybody. What is the kindest thing

for everybody. What is the kindest thing that anyone's ever done for you?

I did put a lot of thought into it. I

think um so when I was little, like uh call it ages, you know, 5 to 13, like elementary, middle school. Um it's

pretty like lazy kid in general. And I

think most most kids are. There's very

few people that are just like intrinsically self-motivated.

I Yeah. wanted to just like play games all the time or just like hang out with friends or play sports. And um yeah, my parents had like a very kind of

interesting way of of raising us, me and my sister. And so what they did was

my sister. And so what they did was basically when we were really little, uh it was like an extreme level of like uh discipline. Um like when I was little, I

discipline. Um like when I was little, I played a lot of piano. It's like

essentially like three, four hours a day. And then for competitions, like

day. And then for competitions, like they just pull me out of school and just like go hard at it.

And then I guess fortunately for me, my uh parents decided that like okay, math was probably a better way to go and they like um I I was like quite talented at math when I was little. And so and then

even for that it's just like full force like you're just committing everything.

Um we did not have TV in the house. We

didn't have video games. We uh don't didn't really take vacation growing up.

And it's just kind of like uh you're you're kind of in this mindset of like you're sacrificing like most things to kind of focus on one thing.

And when you're a kid actually like it doesn't you don't have no frame of reference. So you don't know um it

reference. So you don't know um it doesn't feel hard necessarily. It's cuz

like because your parents are kind of setting up the criteria for you. And I

would say in in hindsight I had a very very happy childhood. But I think that level of just discipline and um just

yeah also just competitiveness is like it's very hard to uh gain that like after your childhood is over because when you're in your childhood your brain's still forming so it kind of like

that kind of forms your personality. I'm

very grateful for that. And uh and I think that's also why when I talk about sort of like my generation of like we there were a lot of like immigrant parents from my generation that came over for grad school and then like you

know they're all around my age and I think this like our crop of folks just are doing very well partly because of that like partly because of the upbringing. And then what my parents did

upbringing. And then what my parents did um I think which is the more unique side is that a lot of times what happens especially it's like the stereotypical Asian parent is that that just like

continues and you just have like overbearing parents. Um and I I would

overbearing parents. Um and I I would say even though my parents were very sort of intense about things, uh they they never had any like sort of semblance of like overbearingness like

they wouldn't they wouldn't like you know prevent us for from like doing things we wanted to do or like force us to like hey you should like pick this or whatever and so on. And what happened was like towards the end of middle

school into high school, I think we had already kind of established these personalities and like we like basically my parents like just pounded a uh sort

of lazy like wanted to play around kid into uh someone that was just like very very driven and um and then then then to their credit they just like completely

like laid off like um and they don't like they don't have any opinions on like what we do for careers and like what we should do. They're like very supportive. You basically just need

supportive. You basically just need parents that are willing to spend a ton of time like crafting this childhood for you to kind of develop this. And I think a lot of the things I have in life right

now are like from that.

So yeah, that's probably the the kindest thing. And then my like my sister as

thing. And then my like my sister as well, like she was just always try to sacrifice things for for me and like for when I was trying to achieve things. And

so, um, yeah, and now we've, uh, I'm try to spend as many as much time with our parents as possible.

Well, this has been so much fun. I'm so

fascinated by the business that you've built and are building. Thanks for

explaining it to us and bringing us sort of right right to that white hot center in so many different ways. Thanks for

your time.

Well, thanks for having me.

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