Jesse Zhang - Building Decagon - [Invest Like the Best, EP.443]

Transcript from https://podmenti.com/t/158f2e8e227ffe7c

Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best.
This show is an open ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money.
If you enjoy these conversations and want to go deeper, check out Colossus Review, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus Review along with all of our podcasts at joincolossis.com.
Mm.
Patrick O'Shaughnessy is the CEO of Positive Sum.
All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Some.
This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.
Clients of positive sum may maintain positions in the securities discussed in this podcast.
To learn more, visit psum.vc.
Mm.
My guest today is Jesse Zhang.
Jesse is the co founder and CEO of Decagon.
Оно в фаст громай кастомер компанії.
Decagon provides a centralized AI engine to auto resolve issues at any time in every language and across every channel.
Jesse shares his systematic approach to finding product market fit by asking potential customers exactly how much they pay for solutions.
We explore why customer service and coding have emerged as two of the clearest AI use cases for enterprise.
And the key business and technical behind Decagon's momentum.
We also discussed the intense competitive dynamics of building an AI today.
Strategic decisions around building proprietary models and deploying AI agents at enterprise scale.
Please enjoy this great conversation with Jesse Zhang.
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.
Yeah, for sure. So
In our wall in the SF office, and we just did it in the New York office as well. We have this quote. It basically goes along the lines of
There's no challenge that can't be overcome and there's no enemy that can't be defeated.
And
We just like it.
I think it really fits our culture. 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 there's
All the stuff happening in an industry, like we have such a strong team, let's just go and win.
Motivated by my dad told me this, I don't know if this is actually validated or not at Huawei in in China. As 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.
And just big red letters across the back of the big hall.
So yeah, really like that. It's kinda interesting. Any time that someone walks in, it like stands out, the code stands out.
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.
Defeated, I've heard the word violence recently about a company culture aggression. Like 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, I think talent and senior people are like rallied around them, like they want to be in a culture that is defeating enemies or violent or aggressive.
Maybe just rip for a while on
What it's like to be building and competing.
in one of the big areas in your case, conversational AI, et cetera.
at this moment in time'cause it's just so different than it was a couple of years ago.
Our whole view is that any space that's worth going after, like any large hot market, it's gonna be competitive.
It's not really specific to AI, right? If you think about
Data breaks versus Snowflake or Ramp for Sprex and any time there's these
Big massive growth opportunities.
People rationally want to go after it.
So I I think in this generation
It's just
I think it almost subtracts like a
specific demographic of founder and people that want to build because
It is exciting, but of course.
Everyone knows it is competitive.
'Cause if there is market then everyone's trying to build it.
It's quite easy to start a company these days. You can raise funding really easily and so
When you go out there, you have to
at a certain point like have a pretty deep understanding of like what your competitive advantages are.
And one of those could be the culture.
And if you build a good culture, that's
pretty hard to replicate. It also lasts quite a long time. And to your point, if you have a culture where everyone is really geared towards succeeding and working hard and having some level of intensity.
It can go a long way. So I think to your point of a lot of companies adopting similar
Mindset.
From my observation, this generation of
company building has attracted like a
Almost like my demographic of person where
It's just like people that grew up in fairly competitive environments.
Academics or s whatever. And
A lot of these finders have done well.
Did a lot of math contests and coding contests growing up.
A lot of the people around my age are getting startups now and people are doing quite well. And I think there is some element of
If you really embrace this hardcore
lifestyle or environment uh growing up.
Then
Yeah, I think it lends itself pretty well to the current situation'cause there's a lot of parallels.
I was with Scott Wu from Cognition recently, who is well known to be like one of these math champions, you as well.
Talk about that environment.
What was that competitive market like?
What was it kinda let people into that world because obviously it shaped you a lot.
It's kind of like a
Interesting experience, I would say. Like I think
When you look back now that we're all grown up
It is kind of like a pretty
Intense.
Way to grow up?
But
I look back on my childhood with very fond memories.
you get a really nice community, a lot of people that are doing the same thing.
I grew up in Colorado, in Boulder.
Boulder's a pretty academic town, but there's not very many people that are like super gunning for like these contests.
lot of my friends had just kinda 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 doing the same things as you and you meet a lot of them, and then now that we're all grown up.
Those relationships have lasted for a long time.
But yeah, the environment is one where it's like similar to company building. You're like how well you're doing is like fairly objective.
There's a lot that has to go into it. There's a lot of preparation.
A lot of
having the feeling of
It is like a long term thing, but because your results are fairly objective, there's constant motivation to improve.
I think that's quite nice. One of my big th is that
This audience of
people or this sort of community of people.
there's a lot of untapped potential there into actually like turning them into people that
wanna do business or companies and things like that. A lot of them have historically gone into
Trading or
Academia.
I mean those are perfectly
Awesome jobs.
A lot of the folks here.
this background is correlated to one where it's a little bit more like risk averse and kind of just get your good grades and like follow a track.
And if you can kind of divert some of that talent into like company building, I think there's just a lot that can be done.
What are the parallels like what is it about the people or the training or the specific aptitude?
in the competitions that make you good at company building or and others good at 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.
If you could somehow index that group of people, you'd have like fantastic performance right now.
What is it? Like what are the parallels that make that true?
One is the competitive nature that we just talked about. The other is just like the problem solving.
So
I think one thing that I believe very strongly is that
One of the best things you can do when you're

have pretty good introspection on where your strengths are.
And at least for this group it's more on the problem solving side, just like
And the problem could be very vague. 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 from first principles of a lot of these markets.
Of course, that's not the only way to build a company.
Other people I think some people just have like very good intuition, especially on these like PLG type things.
But
In general, I think just the problem solving capability, like
What these
contests really teach you is like you're just solving problems.
And those problems, of course, are not real problems in real life, but the sort of thinking is the same.
Does it feel
More emotional now company building than it did in the contests.
Emotional.
I think I'm a bit older now, so it's not really that emotional. I started a company before this that was I would say way tougher than the current one.
I think just like mentally
Makes you feel a lot more
Calm and appreciative of when things are going well.
And s
Timing matters a lot, I think, when I first started the I graduate year early to try to start it and
I would say we had a very fortunate outcome, but the whole journey was like very
Bumpy.
What was it? What did it do?
So the company was called Low Key, we basically built
high performance video capture software for video games. So when people are playing games, you can really easily capture video clips and edit them and share them and
The whole goal of the
Products just like you just get as many users as possible.
And again, I think we exited at like a very fortunate time. It was twenty twenty one. But throughout the journey, like at the beginning I didn't really know what we were building and then trying a bunch of things and we're like new grad, you don't really have good intuition on like what idea is good or not. So we just worked really hard for you know three months and then you realize that
Obviously had no market.
So that's tough.
Then about a year and a half in, I had two of my good friends from school were my co founders. They both got burnt out and then decided to leave. So it was just me after that.
Trying to figure out what to do.
And so
That's I think when you
I think that that's way tougher than anything we're doing right now.
It's like a
The way you put it, like emotional thing, it's
You don't really know if there is any future or not.
There's a lot of pressure'cause you don't want like the first thing you work on to be
A failure, essentially.
So I think that was much tougher from like a psychological point of view.
Nowadays I think it's tough just just like a sheer amount of stuff to do.
We're not getting much sleep, but
In the grand scheme of things, you gotta be really grateful, even be in this position to have enough work to do and have enough interesting
problems to have
A team that you're really excited to work with every day.
How much do you think you're sleeping?
I mean, it really varies. This week in New York it's probably like four to five hours. I'd say and it's not good'cause like I'm not someone whose brain functions that well on
Less than eight hours.
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. 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 probably true of starting companies.
Think the first stage is like by far the hardest.
'Cause you're kind of just like finding direction.
And finding direction is very difficult.
Because
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.
Your goal could be, yeah, finding a direction, but it's more exploratory.
So I think what we actually
are quite good at now.
And Ashwin, my co founder, who's amazing, he also
has similar background, he started a company before that got acquired. I think
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
rational about things we're good 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 a f whatever you work on, it has to be something that
People.
We'll really invest in.
And
How do you
Tell if that's the case. You can just go really deep asking them and
I think people are usually a little bit
Almost embarrassed or
Not comfortable going super deep in these questions.
When you talk to a potential customer, they actually don't mind answering questions such as, Okay, if we built this for you, like exactly how much would you pay for it?
Like would your boss need to approve it or your boss's boss? Like who needs to approve it?
How would entire organization think about ROI? How would you present ROI to leadership to 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
sales qualification questions, but in founder form. And because you're a founder, it's like it just feels a lot less salesy for you to go deeper.
That process is what gives you a lot more signal.
When we first started, we 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. 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're looking at
things ranging from like like data analysis to like security to you know pre sales to
Ar stuff.
That process was very helpful.
It just shows you that there's a lot more signal to gain than just talking to customers, which is the general advice.
Or building something that customers want.
Like, yes, that is true, but it's it's very hard to just
Know that. Yeah, like they they'll just tell you what they want, but then it turns out that that's not useful.
What was the literal process? 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 what that person owns. If that person's very senior, you can ask him about multiple.
So if you talk to like a COO, for example, you can talk to a bunch of different use cases. That gives you some signal too. And then if you're talking to more of like a
V P of a certain area, you're probably focusing on one use case.
So maybe go
in detail through one of these conversations so that others maybe could benefit from what you've learned.
So what is the order of the questions? Like how would you structure those conversations to get the most information possible?
So yeah, let's say we
Get into the call.
And you start by just doing very high level discovery, like hey, what are the sorts of projects that are ongoing right now? How do you spend your time?
What is kind of like stressful for you right now, et cetera. And then you can kind of get a sense for the types of use cases.
And then very quickly you can just form hypotheses like literally on the fly of like okay, what would a product be that
Make sense here.
And so then you're kind of explaining like, Okay, yeah, so
What if something like an AI agent could do XYZ? Would that be helpful?
And most likely they will say yes,'cause
There's this thing that happens where if someone's on a call with you
They almost feel like they owe you like
A positive. You can take away.
So they're like, Oh yeah, yeah, that'd be great.
Now you've kind of solidified at least the potential product ideas.
They might adjust it a little bit. They'll be like, Yeah, no, actually no, it should work like this, and so on.
I remember we talked to a lot of ops leaders like Matt McGinnis from
Rippling is like a great friend of Decagon and telling us about all the different things that have it on his team'cause there's like so many.
And similar with other ops leaders, we would talk to it was kind of a range of companies as well.
People like ordering and so on.
And s you get down to it and you're like, Okay, great. So now we have these use cases.
How much would you pay for it?
And that kinda forces them to
Think
'Cause most people are not thinking about that as they're talking about ideas. There's like, Oh yeah, this would be cool.
As soon as you force them to think about how much you pay for something.
It kind of is a forcing function for finding some order of magnitude, some level of scale.
And so then they're like, Okay, well, yeah, you have five people doing this full time. If the AI agent could do this well.
Maybe we'd be able to get rid of one of them, assign one another one, and then, you know, as a result, I'd pay you like, you know, twenty K a year or something like that.
And then your head you're like okay cool. So now at least you have a general order of magnitude.
twenty K of course isn't amazing, but if it's like
I could quickly turn out a ton of these, like maybe that's interesting.
But most of the time
That's not the case,'cause there's not that many good ideas out there, honestly.
Most of the time at the end of this exercise, you're like, Okay, great, I'm 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 are paying you like a hundred dollar subscription for months. It's like a big company.
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. Essentially what happened with our company is like we were talking about all these use cases and they're like, Okay, great.
Yeah, if you did this, you know five people over here. But
By the way, we have a five hundred person support organization.
And there's a lot of opportunity there.
And we've Okay, great, tell us more, right? And then you kinda dig into it.
And
I think this just goes to show that
As a founder, you kinda have to
build your own conviction and kind of do this process yourself because at the time
Essentially what everyone told us.
Including like very smart
older founders that we knew and so on was that this use case like super obvious.
There's
probably just gonna be incumbents that just hack it onto the product.
And because it's so obvious.
There's probably a reason why like no one's super big right now or there's gonna be someone that's ahead.
But no one really knows.
And even for me right now and
Other founders talk to me about other spaces. It's like, yeah, like I have my own opinions, but 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 in hindsight, it turns out that
In any sort of wave at any time.
I would say like a very small number of good ideas.
And your job is to ideally find one of those at the right time. And so
Yeah, by the definition it's gonna 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
Low to mid, six figures.
as you near 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
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 kinda tallied up.
Even just the amounts that people said and added them together per idea.
This was probably like an order of matitude more than anything else.
So very specifically what DEFN does is AI customer service agent. So that's the simplest way to think about it.
And you're building a conversational AI that can just
be almost like a front end for a brand. Anytime someone wants to talk to the brand or any time the brand wants to talk to them, you can kind of initiate these conversations.
And of course, long term this is not specific to customer service, but I think
Again, going back to this exercise it
customer service is where we felt like the most
urgent need and in hindsight I can dissect why we think that is, but that's basically what we felt.
And what gave us conviction is that hey, we had
All these folks that are lined up, they were like very willing to invest
Six figures.
in a random two person team. They didn't even know that well.
Because it was actually like a very
Top mind.
Initiative for them.
Everything else it was just like a struggle to it's like how much you pay for that? I don't know, like
This is like really exciting, but uh you know, our budgets are tight right now and
Also, like it'd be hard to measure how well this is doing and so on. So
Yeah, th that gave us enough conviction. And then you just kinda take it step by step from there.
Say more about the comment you made about in hindsight, it's clear why this was the key problem.
Yeah, so
I would say
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, just really staying customer centric.
One of the properties is that the ROI is really easy to justify internally.
You have these numbers already tracked.
It's like hey, we have so much
Conversation volume.
Right now we have a simple chat bot or a simple IV R phone tree. It's
resolving, so to speak, like fifteen to twenty percent of that.
If you're able to take that to fifty, sixty, seventy, eighty, like
That's huge ROI and it's very easy to quantify. It's like, okay, well, I'm gonna take the total cost, I'm gonna chop off.
Sixty percent of it and that's what I'm saving.
The other property, which I think is a little underrated.
Is that
It's
Very easy to go live.
I think a lot of Gen AI use cases are struggling with that right now, especially at the enterprise level because
There's risk involved. People
Don't want to feel like
something could go wrong for them when they release your product. Leadership's gonna get mad at them. It's like, you know, why'd you do this?
So
That is a big deal with Gen AI. I think that is one of the reasons why there's been difficult for a lot of use cases to really take off.
Because
At the end of the day, there is always gonna be risk'cause the models are non deterministic and so you 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 any reason it needs to exit.
It'll just escalate to human
And that infrastructure's already set up. You already have your call center, you already have your telephony stack or whatever. So you just connect to it.
I think that property alone just makes things way easier. Cause these big enterprises that we work with, they're like, okay, great, we test it internally.
And then to go live, we're just gonna choose this one surface area and release it to five percent of the user base.
And even if that five percent, if anything goes wrong, it just escalates. And so
That gives them.
People enough comfort to go for it.
Those two things I think are one of the big reasons why.
It's probably the
How are you the use case with the most traction at the enterprise?
Coding is another one. Coding is a little bit different, it's a lot more bottoms up.
Can you compare it to coding?
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. Just look at the revenue curves.
Yours, Sierra's, obviously like cursor, cognition, et cetera.
Compare and contrast coding and customer service.
Yeah, they're very different.
Maybe one framework to think about this is that
At the end of the day, what
AI agents are there for
is to
Essentially replaced human labor.
That's why it's exciting. That's why everyone's
So focused on it.
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 service is generally outsourced already, especially for the tier one, tier two type inquiries that AI are is now handling.
It's generally not folks that are super highly paid.
And then on the other end it's
Engineers.
The one way to think about it is that like yeah, use case will start eating
The spectrum for both ends.
And the reason why is that
because engineers are the highest paid, they have the sophistication to like really leverage it well and like
Yeah, I just gives them so much leverage.
I mean, there's other factors as well. Like it just happens that coding is
Tokenizable and I'm also really good at it.
That's one way to think about it.
I don't know any company that's like, Hey, I would like to let go of a bunch of my engineers because now I have coding agents.
There's infinite engineering work to do, so you're just augmenting them.
On the other end, it is more of the replacement sense.
You have a large BPO.
And
that's costing you a ton of money and it's also like a really high operational
'Cause you have to.
hire people all the time. There's a ton of churn, you need to train them, you need to QA them.
You need sure that nothing goes wrong. So AI is really valuable there as well.
Because
The work is
Easier for the AI to do.
Yeah, can
Fully replaced.
I think that goes into the conversation of
think it's a little bit overblown of like, oh AI is replacing jobs and so on. Even the BPOs we talk to
Not really that concerned.
Because what typically happens
Anyways.
Is that
There's already very high turnover in these

People are just hopping around doing all sorts of different things.
Kind of just naturally let it decrease.
And then they just go on to do sort of the next level of task that they I can't do yet, right? And so maybe that's like data labeling or something.
That's generally what we're seeing from from the BPOs. But yeah, anyways, I think this spectrum is
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
the most replaceable end of the spectrum. That's a really interesting conclusion.
I want to talk about what you've begun to learn about how to do that second thing well.
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 low hanging 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.
Yeah, I would say the biggest learning we had is that
Oftentimes a long pole in the tent. And again, if by never if you can solve this well, it just makes things go a lot easier.
Is aligning on what does good look like.
And
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 nuanced than that.
What good looks like could be in sense of like Tony Brand guidelines or
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
what it's gonna look like. So we have in our product essentially like a testing or simulation suite of like, hey, we're gonna
Build out.
Ten thousand tests.
And
Each one's gonna be constantly running like five times and then you can
Get a sense of how well things are performing.
And that's actually pretty
Difficult and I do think that is
Broadly true for
Anyone that's trying to build in this style of company.
If you're gonna be replacing human labor, you need to know what good human labor is.
So
What we found is like okay, well, can someone just tell us?
What are the answers to all these questions and most people don't actually know?
'Cause these are large complex organizations. 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 very easy to extract these answers from
All the people that do know. So maybe it's all the CX leaders or people lead different areas of the product. And so you have to get them all together and
Get them to a line on like, Okay, here's what the eval is, essentially.
If you can do that well, then it makes everything a lot easier. Cause now you're just building, building, building. You have this like quantifiable score that's like, hey, here's how well the I is performing. And then once you're done building and the score is high, then you can go live.
Is there right a way to think about this? You just created like a captive reinforcement learning process within an organization. Is that like the simplified
Yeah. Yeah, yeah. That's a interesting way to think about it.
And it doesn't have the reinforcement learning in the pure sense of training a model.
It can just be reinforcement learning and
Make an agent improve.
And that could be
Compiling.
More evals that could be compiling.
Just like
More car rails guidelines around what it can't get to.
How fast does the spread happen?
If I'm a customer and I've got the five hundred person customer service call center or whatever.
I'm actually curious, I don't know what the volumes are, like how much
call volume or interaction volume of a center like that.
handles for a given.
But if I give you five percent 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 ten to fifteen to twenty percent?
Very fast.
I would say even for the large enterprises
within weeks.
Everyone wants to just go live to everything.
But 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'cause you have these metrics.
So even within a week.
You have
Five hundred person or I'd probably estimate
mid to high six figures of conversations a year.
Maybe slightly higher.
What you're doing there is
Making sure that things are going well. So within a week you can see like okay, what is the resolution rate? Is that what we expect? Okay, great.
What is the customer satisfaction? People have those scores as well. 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? So hey, we're both generating a ton of operational efficiency.
And our customers are happier.
So yeah, let's just
Set it up to everything.
What goes most wrong?
When something bad happens.
I'm sure this is happening less and less as the product's gotten better.
But even in the early days, what sort of thing would go wrong in one of the customer to AI interactions?
It arranges all sorts of different things.
Sometimes it's obviously like both sides, like us and the customer, we take everything very seriously.
Well there's just a lot of things you wouldn't expect.
In the early days.
We have a customer that is essentially like a large ticketing platform.
And one of the things that would happen was
Someone came in, they couldn't find their ticket.
I looked into their account, it's like hey, there's no tickets here.
And then they were just like, Okay, well what I'm gonna do is I'm gonna show up to the events and I'm gonna find eight homeless people from the city and bring them with me.
And the agent was like
Oh my God, that's so awesome that you're thinking about doing something nice for the community.
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. And generally it's in the spirit of that where you're trying to find the right level of guardrail and flexibility.
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 why probably not one reason we've been successful so far.
Is that what Gen AI really unlocks is
Super flexible, super personalized. One way you can think about it is like in the old days to map out a conversation, you just build like gigantic tree.
Decisions and
That's very hard because no one likes that experience and 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 it abstracts a lot of that tree into the neurons of the language model.
So that's really powerful.
On one end of the spectrum you're just looking for
Flexibility and
power and being able to sound really human like.
But with
The enterprise, there are a lot of things we don't necessarily want that as much. And you want full like rigor, right? If it's a regulated use case, like you cannot afford for it to ever deviate.
These three steps always have to be fallen in this order, and you can't go to step three until something has happened already. So you need to design a system that can be anywhere along that spectrum.
And back to your question of what could go wrong, well
The worst thing that's 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 just asking basic questions on their account, we don't want it to be like that. We don't have to be super freeform 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 other end of the spectrum? What have been the things that have gone way more right than you expected? Where has the potential of agents
outperformed your expectation in terms of what they can handle or what they can do.
It's really just elevating the experience.
One sort of
Metric, which is
Usually a secondary metric that folks 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 give me to representative. Like, I want to talk to you.
I've done that. You probably have as well. 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.
I think what surprised us was that if you just make it really clear off the bat.
That this is a different experience.
People are willing to give it a chance and then
the outcomes are just way different.
One of our customers
or a ring like the werewolf ring. We did a case study with them where
Before
having any sort of Gen AI system.
One in three customer that came in.
Would just s not bother seeing anything and just s keep jamming agent until they got to one.
Now it's one in twenty.
Because we just like spent a lot of time making the beginning of the process just feel very different.
And folks are willing to give it a chance. So I think that's been exciting.
Where do you think that can go? 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 now is voice voice models. And so 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 is a lot to be done there.
The bar is very high.
So if you just think about how humans communicate for literally the entirety of humanity
I don't know, a hundred fifty thousand years or something.
The UI for every human is language.
Spoken language. Listen, you speak.
And that's how our brains have evolved. That's the most natural way for us to communicate.
Only in the last what, like sixty years.
Of that entire time did we have keyboards and like phones and communicating through typing?
I would say fundamentally
And any sort of agent that communicates with humans, voice has to be a critical factor'cause that's just how we communicate.
Because our brains are so evolved for this.
It's very easy to tell when something feels not quite right.
And so the bar is very high. Uncanny values is quite large.
That's why there's a lot of effort going into making the voice.
Experience is good.
Yeah, see.
Chaty BD voice, for example, or the Sesame or like these voice to voice 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 that element of it.
But then you know for enterprise use cases like us.
There's still a ton of hurdles to
cross because those models even though they're good.
The hallucination rate's really high.
So you can't really use them necessarily.
as is in the current systems.
And so a lot of people what they do now is they
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 accurate.
And so
Lot of cool ideas there to explore on how do you make it both human like but accurate and how do you tie everything together.
So 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 the that way?
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.
The voice the voice is powerful because it takes into the entire audio of what you said, so it knows
Cadence and maybe how upset you are and
The tone and everything.
Latency is a lot less as well, because you're going straight from voice to voice.
And latency matters so much when we're talking.
When we're talking right now, our brains are constantly going of like okay, when's he done talking? When should I start talking?
If someone interrupts someone else, it's like in a polite way it's
People adjust very naturally.
So
That is the biggest
proponent of voice to voice. And ultimately
I think the prevailing view is that for s whatever the final experiences, if you really want to make it indistinguishable from a human, you have to
Do voice to voice, or you have to at least take into account the voice.
The issue with voice to voice though is that also fundamentally, because voice has a lot more dimensions to it.
The amount of tokens you generate per sentence is just a lot higher.
Then when you generate text.
The more tokens you have, 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?
Give us a sense of
How far we are from these being really good.
Think probably like eight X higher or something like that. Wow.
It is quite a bit higher.
Of course, you want to leverage that technology, so
Now maybe there's creative ways to make a hybrid of the two.
Maybe you can have a text model.
generate the content, but you take into account the audio from before as well and like
That makes something very realistic.
But at the same time, latency is still the hard problem because at the enterprise what's happening is
You are doing a lot before you can start responding.
you have to figure out like what are they asking about, like
What materials do I need to collect? Do I need to hit any APIs and get that data back?
And so we have to do it in a way that feels very natural.
And
Sometimes if you think about how human does it.
You might have to say something like Give me a sec to look that up because
It actually genuinely takes ten seconds for the API to come back. These are all interesting problems to think through.
So give us a sense today if you add up all the interactions.
some idea of how long they are, what type they are, voice versus text versus some other modality.
What is the entire corpus of interactions between a decagon agent?
And a customer look like today.
I would say
Pretty balanced at this point between chat and voice.
On a raw customer basis, there's more people in chat.
At least for us.
But if just think about the large enterprises like the Fortune One Hundred
They've just been around for so long.
And everyone just calls them.
So voice is just like disproportionately higher there. A lot of them are
Ninety, ninety five percent voice and five percent chat.
So
That's what we're seeing and then in terms of the types of conversations
It's generally ones that are fairly
You start with the sort of easier one, of course. And so
These are things where
They're question answer based.
So that's the tier one is like hey, you you answered the question based on
Well you statically know.
So that could be questions about how your loyalty system works or
Questions about if I bought something.
Would I still be able to refund it?
Then the next level is
It's still question answer based, but you're leveraging a lot of real time data.
So that could be, you know, I got two X points on this transaction, but I should have gotten five. Why is that? And then it'll 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 book through a travel agency if you have booked
directly with the airline you would have gone five X, but thing doesn't apply to travel agency.
Or whatever.
And then the third tier is you're actually taking action.
So
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 actually wouldn't expect it to 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'll do that.
I might 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 LMs.
In the spirit of that question of 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 one to one basis, like they're just learning more about a person.
And on the 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. 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
It's a very unstructured data. So what tip people typically would do is like okay, well every month we have a million conversations.
We'll have a full time team of twenty 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.
And that won't get you so far. But now what you can do is you can literally have a language model that reads every conversation and extract whatever info 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.
'Cause these organizations are big. So the people in leadership positions
They can only have such granular insight into what's happening.
But it'll literally just flag like hey, there's just two percent 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 how the human agents are handling it or based on the
Other than
procedures that we have. And here's a suggestion for how you should adjust the agent.
And that allows the agent to improve automatically over time.
And that's really critical.
So when you think about
Motes in the agency world.
A lot of it is around.
If you've been working with a client for a year.
Has your agent just continuously gotten better by learning from the data? And that's a different concept than just training on the data.
But
Has it continuously gone better?
to the point where it's just very difficult for another agent to come in and perform at the same level.
There's this funny
situation today where I think especially CEOs
really want AI in their businesses.
They want it now.
But they don't know what they want.
They don't have a good framework for thinking about, okay, I understand my business. I don't know where to go.
It seems like I would be missing a major boat.
If I don't deploy this in my business.
I don't know what to do. I don't know where to go first. I don't know who to call.
Have you developed any framework for those
company leaders that desperately want to use this technology in their business, but they simply don't know.
apart from automating customer service or something, 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.
Oh interesting.
I mean one framework is similar to the sort of framework you talked about before of two ends of the spectrum. And I would say most leaders we talk to are focused on the more bottoms up.
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
And 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 C suite's very, very invested in like, okay, where do we deploy AI?
That almost means that if you want to get something going at a larger organization, you have to have buy in from the top level.
'Cause it's gonna get up there anyways and they had to make the decision at the end of the day. So that's one.
Two the way they think about
The use cases, to your point, is
That's ROI. It's like where can we either save much money or
Make a lot of
New revenue.
And if you
Can I
In basic half a sentence explain that.
Then it's just not gonna work right now.
They are under a lot of pressure, right? They need to show like quick wins. If this is not gonna be a quick win.
They can point to like I save ten million dollars.
Then it's not gonna be something that's prioritized.
Do you think coding answers that well? Do you think the ROI is clear in coding?
It is.
And I know the coding agents quite well, and the way they do it generally is
One, it's very easy to test to play out to the engineers.
And then you just do a poll on the engineers of like, Hey, how much more productive do you think you are?
And engineers are often the most valuable resource in these organizations and so
Those
Answers are treated with high.
Importance.
Yeah. And it tells you that you're they're like fifty percent more productive. It's like okay, great.
Are you confident that that's true?
That they can self report.
and be accurate. There was that meter study or whatever that came out that actually
I don't know if the study's good or not, but productivity was down or flat or something like this.
the self reported productivity was at odds with actual measure productivity or something like this?
Oh yeah.
don't know nearly enough about that, but I'm just saying that it doesn't really matter.
If your entire engineering team is like, Hey, we love this, this is making us fifty or cent more productive. Yeah.
It's like building your thing. Exactly. And then if you think about it from the CEO and they're like reporting this to the board or something, it's like hey.
My entire engineering org said that they're fifty percent more productive.
This is like worthy investment because
These people are all paid this much.
Now we can accelerate the product, we can accelerate everything.
Yeah. Do you think that the future is I want to talk about brands and how a given company w might want its agent to feel and sound that an agent might be a way to express brand culture style.
Tone, whatever. Don't talk about that.
But do you think the end state here is that each company sort of has
almost like a named personified representative that you just come to expect to interact with. And it's not just customer service issues, but it's sales issues.
You ask it for advice on what shoe to buy or whatever it might be, and that that's all integrated.
Or that you'll have different agents for different parts of 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 the natural end state five years from now or something.
Yeah, I would say that in the natural end state it is more unified.
For
Exactly you listed, which is
People want a unified brand out there.
Rand's very important to them.
And for some businesses is more important than others, but
Eventually this becomes the front end for the business.
And so that means it's both how you gain new customers, but also how you support the existing ones and make them retain more and so on.
It's almost like
In the limit if you're working with a bank or airline or
Telecom company 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
Good on your website ever.
They just have this agent.
Where they're authenticated, it knows everything about them, and has all the context of your previous conversations.
It has memory.
And it can just solve your issue.
You can take actions for you. You need to book a flight, you need to upgrade a seat, you have questions about this or that.
So I think that's
The exciting vision that we're building towards. And in some ways, we often call this like a concierge. It's just a digital concierge that can do everything for you.
I mean, you also have to be pragmatic on both sides. You'll start with a clear use case.
But I think that is where folks are building towards.
And so in the near term, I do think that's
different teams'cause the reality is that
at these large companies different teams have
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 there right analogy here a company's website?
They'll think about their agent like they think about their website. 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
Is that a clean analogy? I think that's a good analogy. Just like a front end.
It's like UI.
But instead of visual UI, it's a conversational UI.
How do you feel brands?
pulling personality
Requests.
in their agent out of you.
What do they care about?
They want it to be nice, they want to be

How has that dimension evolved since you started?
People already almost always already have brand guidelines.
'Cause they need to
show brand guidelines to their human agents to train them.
The nice part is that they already have all this
training process for the humans and you should ideally be able to apply it in the same way as to the AI.
And so they'll have like hey, you need to do this, you need to always be confident.
Sometimes folks don't want the agent to apologize. Sometimes people really want it to be apologetic. So it'll much of different preferences.
And that needs to be taught to the AI.
In efficient way.
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 you're almost afraid to admit it because it feels so big version of what Decagon could be?
The reason why
We're excited and we feel like the market's so massive is that
Yeah, at the end of the day
What we
are building towards is this concept of becomes like a new UI for the product. Just think about
How much
these large companies invest in their mobile app or their
website and literally how everyone communicates with them.
So this could be a way where eventually
The way any user interacts with any
Brand.
is through an AI agent.
And it's for all sorts of different use cases.
benefits our customers for the AI to have all this context because it can kind of seamlessly flow between things.
A lot of them wanted
do sales type use cases at the end of a support flow, or vice versa.
That's exciting.
What internal
context or things do the best customers have that make this better. So you mentioned brand guidelines, like maybe there's a write up on what the brand guidelines are or whatever.
What are like the
Internal assets.
that companies have or don't have
But if they have them, it's made the decagon experience like way, way better.
Number one thing is just APIs for the AI to use.
So
APIs to take action, APIs to look up data, APIs to reformat things.
That is often like hey, if you have those, you already know that within the first month it's already gonna be a great experience.
If you don't yet, then we should try to build towards that as soon as possible so that the AI can actually achieve that.
Elevated experience.
That's the main thing. Most people already have documentation. It might not be up to date, but we can help with that.
Most people already have.
SOPs, I guess.
And then we are able to use that and generate our format.
We call them AOPs, agent operating procedures. They're just SOPs, but for AI. And then brand guidelines, like I said, people usually have those as well. So you just ingest them.
We were talking last time about in any business, but certainly in yours.
The three key stakeholders being
your team talent that you have to recruit. And I want to talk about that in detail.
Capital investors.
And customers.
Maybe suppliers too is a fourth category, but
Especially interested in the first three.
And the last time we were together I asked you like
Which one do you have trouble with? And we laughed because you said, Well, definitely not investors.
Talk a little bit about
the demand from investors to invest in companies like yours.
And how that feels. What are they doing to try to
Give you more money, get on the cap table.
How competitive does it feel? 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 is that?
F like.
It definitely feels like there's
Maybe a little bit too much excitement right now on the AI side.
It just seems way too easy to raise money.
So many
Companies out there.
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 also fortunate it was easy to raise, but it was for different reasons, like twenty twenty one.
Nowadays it's just
There aren't that many AI companies that have real, real traction on the revenue side.
Especially in the enterprise. And so
I think that's attractive to investors because
They want to deploy their capital into AI and that's like the biggest trend.
So I think we've had a great relationship with all our investors. I think we just really select it 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.
Yeah, that's kinda been an interesting 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 right.
He's thinking first principle is to make an investment.
the previous valuation should not be like a super big factor in that. It should be like how well is the business doing, how what do I believe the potential is?
So it feels like there's a little bit of mania, but
Yeah, we're definitely I would say more indexed on the other two things, like talents and customers.
What's the craest thing that an investor has done to try to invest in the business?
I don't think there's anything like super crazy
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.
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 and they're willing to do anything.
They're for sure not gonna be helpful afterwards.
I mean 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 how helpful someone will be.
And we obviously know a lot of investors very well. I think they like
No one has an issue with that. They know that they're
Going back to
Competition. They're also in a competitive sport.
They know that they need to earn basically the
ability to invest in the best companies.
And so I think from their perspective, they're happy to work for it. So if you just give them opportunity to, they will.
I'm gonna ask about this from both founder and investor 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 I'm sure helping you. Here's ten customers you can talk to. I'm I'm sure that's great.
But also on the underwriting side, them making sure they understand your business extremely well. I'm expecting it's a short period of time.
One of the very best.
Done.
In that window.
Number one, I think the best investors
Get this dynamic.
We've definitely talked to a lot of high profile investors where
They're just like not willing to help much until they're messed.
Totally they're right, right? It's like hey, we
A lot of our current investments, we don't wanna like use our social capital or whatever to
Help within it.
Like a new investor.
Or a new investment.
But
I think from the founder's perspective.
Okay, if that's the case, then it's hard to tell if you actually be useful or not.
There's no difference between you saying that and then someone who can't really help and just saying that.
I think 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 find.
we have a very strong network or we're very good at certain elements of go to market and we're just able to show that in the let's say
It doesn't have to be that long a period of time, like a couple of months before the round actually happens.
That's one thing.
I think the other thing is that's
If you just think about anyone you bring into the world, this could be employees or investors or advisors or anything.
What we really index a lot on is just
Cognitive ability.
Raw intellectual throughput.
And
You can almost feel that out in an investor in the same way you would feel
Feel it out in an employee.
By spending time with them and
Just like actually seeing if they're thinking about things from first principles of your
Company, for example, right? A lot of AI companies are growing.
much faster than traditional SaaS companies right now.
Because that's the case. A lot of
Things are different.
You generally don't want investors that are just like, so many reps, you gotta do things like XYZ Way.
You want people that are just intellectual curious and
Will think about things along with you and can't help problem solve.
What about on just like the pure underwriting side? So the investor comes in, they're really smart. Let's take that for granted.
They just want to understand your business, 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 deliberate they move or
Anything like that.
I think the
Best ones I would say
They only one they
Get a very deep understanding of our customers.
It's actually funny. Our customers have made s probably so much money on expert calls.
'Cause 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 quite a bit of research and
I'm like pretty
full view on your customers.
Unfortunately, I think what we've seen is that there is a lot of noise there'cause like a lot of people just lie on customer calls and
We have people who had said that they've used us and we've literally never heard of them.
But generally if you do enough research and you're good at it, then you can
underwrite the business that way'cause
That gives you the most signal.
And the other one is people that index on culture,'cause 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
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, starting with culture.
How would you describe it?
We talked about the quote on your wall is the first question.
So that's part of it, of course.
Extremely competitive, extreme bias to action.
Get things done.
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.
I would say there's definitely a level of intensity that's important and
Yeah, you definitely want to tell people that up front'cause 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 they're motivated by they view this as
maybe the prime of their career where, hey, I'm going to work hard, but we know that
Because of that I'm gonna build
lifelong relationships with like other amazing people.
I'm going to
Have good financial outcomes.
I'm gonna be able to leap prog steps in my career because the growth is just happening so quickly. So it's you're attracting people like that.
I think that
alone creates like a pretty strong foundation for the culture'cause you have people that are there to
Work.
We have a lot of people that live right next to the office. And part of that is because we've
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 have to be careful on top of that is we really want our office to be a place where people enjoy coming to work every day.
Because we spend a lot of time in there, it's like you want people to be
Happy to be there. They feel like
Everyone there supporting them.
Between the organizations you want
People that feel like they're pretty aligned and folks are working towards the same goal.
And that's an ongoing problem. Like I don't think like we've
Solved culture.
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. Yeah.
Obviously it's happening in the most extreme cases at the model layer.
between meta and anthropic and open AI, and there's great riveting stories to hear about 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?
what it's like to be in them. Obviously I'm sure you are too fighting for the best talent versus lots of other
Great companies that are being formed.
Yeah, tell us from the inside what this environment feels like.
Definitely feels like
Talent is a big team effort.
For anyone you want to hire, you need the whole team to swarm around them.
To win.
And when
highly sought after talent. And you have to go and do all the things right. There's some
Parallels to sales, of course.
You're trying to like
convince people that's hey this is the
The place they want to be.
So that involves oftentimes getting to know their families, getting to know their partners.
really figuring out what they want out of their own careers and
Making sure that you can design a role for them that is like that.
Unfortunately in the application layer, it is not as crazy as the meta's and the open AIs.
There's only so many top level researchers.
We have some very strong researchers on our team. It's a lot more of like a applied type research.
For us, it's the same thing that happens. We're going after, you know, a lot of people on our team, Carvard and MIT and Stanford, there's only so many of these folks that are
in the market any time who are like
in S F or going to the office and so
It is a competitive
Place to be?
I think we're kinda fortunate and now our talent brand has gotten a lot larger than when we were first starting. So
It definitely has gone easier to hire.
But at the same time our like the amount of people we need to hire has also gone up.
constantly just finding ideas of how to get new people. I mean we just open up our New York office, as you know and
Part of the reason for that is that hey, there's another town 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 L M.
versus the development of your own models on top of 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 thrive on good underlying data.
How do you think about that?
aspect of all of this where
Five years from now it's gonna be either your own model or your own model plus
something else or just your own data and context on top of the best model.
А до
I'm both curious about this in terms of how it impacts the product.
But also how it impacts your business mode, your
power in your business where you rely or don't rely on GPT, whatever.
When we first started
This was about two years ago.
People are still figuring out applications.
Almost no one was doing fine tuning.
In fact, anyone that was doing fine tuning, there's a lot of
writing at that time was like fine tuning quote unquote doesn't work'cause it doesn't really get you that many gains.
Another big reason to not do fine tuning at that time is that the models are changing so fast.
You're still kind of figuring out your use case. Why invest a bunch of time in the fine tuning? It's not reversible. You're gonna have to throw it out next time there's there's a new model release.
I think now the open source models, for example, have gotten to the point where
Definitely not it. I'm not smart enough to do everything, but there's
A lot of specific use cases where you just don't need that much intelligence.
Example would be
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
What path do I go down?
What data do I need?
Uh, you can make that fine tune model. That model doesn't have to be good at
math or coding or anything. It's just like you just need a smaller model that's just fine tuned on that.
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 tune models and that improves the entire system, both in terms of performance, but also latency and and so on.
So I think over time there's gonna be more and more of that.
I do think there's always there's still always gonna be a huge usage of the open AIs and troops of the world'cause
You just need intelligence. You need the best models.
So I think there's gonna be a balance, but
At least in the short term.
There's gonna be more and more of the fine tuning small models to happen.
What is your perception of the very biggest companies?
The entire market has been focused on rightly so the seven, eight, nine, ten biggest technology companies.
Which of them feel most important to you? And I don't mean open AI and atropic. I mean like Microsoft and Amazon and Apple and and these sorts of companies.
What is your relation and thinking about them today? They've been the driver of equity markets.
By a huge margin.
They're important companies.
How do you relate to them?
I mean work wise it's not super relevant, but of course we have our own opinions. I'm very bullish on Google, actually.
Why?
I just think that's with AI use cases, having individual like consumers is so important because that's where all the data comes from, anyways.
And Google is like much stronger there.
I mean you could say that.
Meta Facebook also has
That element and so
Yeah, maybe their new superintelligence lab will be able to
To make it work.
But something like
Anthropic, for example, where they haven't done
as well on the consumer side compared to like a chat G BT.
I think long term you do need that consumer buy in because
That's where all the new data is gonna come from.
Yeah, Google and I mean Google just has an amazing team and
We've had a couple of rocky starts, but yeah, hopefully.
It'll make it work out.
Of course, all the large mag seven basically are obviously super strong, so we don't really have strong views on them.
If I let you build a portfolio tomorrow where you got five slots, twenty percent each and five
private companies building in and around AI.
What portfolio would you build? Decagon excluded.
Yeah, I got excluded.
Let's see.
just kinda gravitate towards where most of the the talent is forming. I mean I mentioned close with
Cognition guys, cognition would be in there for sure.
Cursor also.
I'm actually kinda interested in like where those might run into each other in the future.
Let's see.
Three more slots.
Or me definitely would want to take a bet on the
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.
Still on the earlier side, but obviously very high potential.
Two more.
I like the portfolio so far.
Another friend of mine is is building a company called Pika building video models.
So
I just think very highly of that team as well, so we probably put them in there.
And the last one.
Probably would want some sort of bets on the underlying model side.
Even though
All the large language models are are out there, but
Another friend of mine I think very highly of they're building models, but not like the types of language models, but still foundation models for like healthcare, for example. So a friend of mine, Josh, is building Chi.
They're building a foundational model, so
Stuff like that, I think it's quite interesting.
Or even outputs.
Lockhease company, if it's called I think those are very exciting. It's just
Obviously it's early to see how those will turn out, but if I'm building a portfolio, definitely want one of those in there.
What do you think are I'm interested in both ends of the spectrum.
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
the world thinks where is it further behind?
It's a little bit more behind in being able to unlock a lot of the enterprise use cases, I would say.
Because of the non determinism.
So
Two things that need to happen.
One, you need to reframe the way that people think about agents.
There's the Waymo 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's a lot higher.
So it has to be near perfect.
So there's that dynamic that kinda needs to adjust in some folks' mind where instead of
evaluing AI in a way where you're just trying to find mistakes.
You're evaluating holistically looking at the sort of success rate.
And then if you can frame it that way, well, the success rate is gonna be way higher than humans because again, humans are not perfect. So I think that needs to happen and I don't think that's fulfilled yet in the enterprise. So
That trend needs to happen.
And then on the AI use case side, 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 as those models get better.
I think more enterprise use cases will be unlocked, but I do think the general public will just see a really cool demo and be like, Okay, oh wow, a voice is solved now. And
As a results, you know, the enterprise should be adopting it.
Left and right.
Yeah, C Suite will see that too, but then when they actually get into it, it's just harder to go live.
That's I would say the piece where it's
Not quite s
fully
there yet, and so it's a little bit slower than people think.
I think the
thing that's on the flip side of that where it's underestimated is just things are growing exponentially.
Improving exponentially.
And no one.
Is good at
Conceptualizing what exponential means.
And things like this. And so that could be from like a performance like cost perspective.
Right now.
I would argue that if you're building an application
Your
Margin shouldn't really matter that much.
People will often
critique the coding agents like they're hemorrhaging money. Yeah, but again, things are improving exponentially and like the costs will go down exponentially as well.
So zero margin doesn't really matter.
What really matters is you need to get market share and you need to get my share of users.
Perfectly fine not to have
good margins right now and it's just
Everything just improves way faster than people think.
It's slightly different at the enterprise. I mean, the same principle applies, but
generally don't want to be hemorrhaging cash with like an enterprise deal because
Those are just much longer term. And even though the costs will go down, their expectations might also change and so on. So you generally want to be fairly healthy there.
I think that's what's underestimated right now.
How do you know that cost will get
Way lower. Like I'm very curious about this margin question because
If you knew for sure.
Then we actually want to run super negative
gross margins.
If you knew that it was gonna get
Ninety eight percent cheaper or something like this.
cost of goods to serve a coding agent or something like that.
I think the argument would be get the install base. Just like have the best product and get the users and build the the affinity with that user base and don't care at all.
But that hinges.
A lot.
the confidence that you have that they'll cost will fall. So how do you know? How do you think about
that equation of win install based versus
Demonstrate good in economics now.
Well, I just think it's quite unlikely that
Where we're currently at is like the best that things will be.
There's so much effort getting 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 ways you can re architect your system so that it is more cost efficient.
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'cause you know that things will change in the future.
So
I think that's just one thing where people are like oh
It's kind of like a
a scheme where you take V V C dollars and then like the V C dollars go to the CIFs.
These companies are losing money.
If they wanted to, probably they could just like spend a month
Or even less.
And just massively improved their margins.
But it's not worth doing that work right now.
What you're really optimizing for right now is just quality and growth.
So if you can do that, then the optimization will always come later on.
How do you think about your margins? Like just
Putting it on Decagon, like do you care at all? Do you set them
What's your guardrails or parameters for
what's acceptable or what you're targeting.
Yeah, I would say the only thing that we have a principle for is not to have negative margins.
In general we have fairly healthy margins because
One way we you can think about it is if you just think about supply chain for any good.
Let's say you're buying like a croissant that's
the airport or something.
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 flour or like making the butter or whatever.
You're generally making 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 what you're building is actually solving the business.
And as a result, you can
Capture more of that because our customers the way they're thinking about it is
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 ROA that we're getting.
It's like we're
downsizing our operations by this much. We're actually generating more revenue now because the
AI can engage people and keep them retained.
So that is where we
probably see the most dynamic here. And I do think
This is generally not really a hot take.
in the early days we were like, Oh, there's just like chat GPT rappers and so on. And yeah, a lot of rappers, if it's too thin, it's like I'm not going to be valuable, but
If you have enough software built around the models.
then that's where you can actually almost capture the most 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 it's very easy for people to swap. It's not like moving from AWS to GCP is like very hard, but moving from
A
open AI model to anthropic model, you just change like one line of code.
Can you say a little bit more about this chat GPT wrapper concept?
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
AI work. It's traditional software work. It's
the ability to hook this system up to
Enterprise customers, it's good old fashioned product and infrastructure building that
Is very different from what OpenAI or Entropic are providing you.
And that's hard work. Like just like building any software system is hard work.
Everyone's very enamored of this idea that like in five 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 gonna mean lower software modes.
I don't think you think
So maybe describe your thinking or critique of this Chat GPT rapper concern that
People have.
Yeah, I think generally people
say rapper in a derogatory way. It's like hey, you're just a rapper.
It's not black and white. Yes, there are a lot of apps that are just wrappers that are not gonna become real businesses because
There's just not that much value.
I mean one argument.
don't know too much about this space, but at least from the outside it it has seemed like
Copywriting, for example, has been difficult because someone can just log into ChatGPT and just like, hey, write this for me and I'll just write it.
There are things where if there's not enough tooling of functionality on top of something for it to be really valuable and needed.
then maybe it is easier to just leverage the models.
But
Most of the time that's not the case, especially when you get into agents.
An agent is not just a model, right? You have to like design it, you have to be able to put in guard rails, you have to be able to teach how to do new things.
And that's where the software layer on top of the models come in.
And if that's valuable, then it's just
much harder for you to just like
B
Made obsolete by a model update.
The other side of it is like, okay, well now the labs are quite interested in building applications. So they're building a bunch of like coding applications and
Cloud code and so on. And so those will end up being competitors with
Cognition or
Cursor and
And maybe for that reason it is wise for the coding agents to start training stuff as well.
I think in our space
I would say for us right now, at least the sheer amount of
functionality of the bill because it is a very top down product.
It's quite large. That has nothing to do with AI. It's just stuff like okay, how do you
have like observability into like what the conversations are. How do you alert the team if something spikes and
How are you able to QA and have unit tests for the conversation so that 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 it's just like a lot of stuff that needs to be built.
How would you advise other founders thinking about the ideal customers to go after?
What are the
Most interesting qualities of your best customers. When you're qualifying them, are they gonna be you have limited time that you can only serve so many people. I know you're growing really fast, but
You can only serve so many customers at any given 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 intellectually just
at the leadership level, really curious and excited about technology.
You actually see a huge spectrum of that in the enterprise.
Yeah.
In my opinion, the best leaders and like probably the folks that we are most excited to work with.
They're just
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 gonna help cut through all the cruft and like bureaucracy.
To get something going.
And I think you can tell that pretty clearly in the first conversation.
You can tell if someone's if they're like legit about
This is something where
I'm gonna
both push aggressively, but also give you a ton of feedback and like the feedback's gonna be good.
Versus someone where they just
No, it's like a board mandate and
It's just like a AI is like a
on their to-do list.
Is there any leader that you've come across that most exemplifies
That
Posture.
Yeah, so I will say
In both the
sort of digital native segment. You have folks like Chime, for example, super impressed with the entire team top down.
Everyone that is in the org.
That's like a case study to study at some point. I think they've done a really good job on culture.
really making it so that people are very sophisticated about everything.
Everything that AI agent does, it's like
Super
data centric, they track everything, they're really thoughtful about how how to make updates and so on.
They've given us a ton of
Good product feedback.
As you're talking to your friends.
that are also building companies in this space. Where do you feel
that your world view is the most different from them.
Where's your view of things or your relative excitement about something?
The most divergent.
From
From others.
Yeah.
My obviously my friends are very high intellectual horsepower.
I think I generally lean a lot more
I don't know why this isn't like personality. I think I generally lean a lot more towards commercial
Elements of every idea.
And
Again, it's not the only way to build the company, but
In my opinion, if you were just trying to optimize for like the highest likelihood of success.
I think it should really index on the commercial side.
'Cause we've a lot of super smart intellectually curious people that are just
Building
Very cool projects.
And
You can make the argument for a lot of huge outcomes, you need to do that because you just need to do stuff for like
No one's gonna work on this because
Where are the commercial elements of this?
But
I think
I think Oshma was this way too, it just made a good fit. I think we're just like very locked in on okay, you just gotta be like super practical. How much would you pay for it? Exactly. Yeah, it's really interesting.
How do you mark milestones in the business?
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 deck. 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 do
Always is we always have like a flagpole 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 this the other day. I think another type of
rallying is around competition, right? I think when
People feel like they're in a battle and there's like
clear enemies then it makes sense
Again, you don't want it to get to the point where there's like acting animosity, but
Healthy level of competition. It it ties a team together'cause there's like something to focus on.
Same thing with milestones. Like if you give someone a veryone a clear milestone and this can be like
Pretty insignificant. Like last year we had
For our revenue milestone, we told everyone we get them like super nice jackets. We got like
Deck on the
Arteric jackets and everyone was like super excited about that.
Yeah, if you just think about like
The cost. Yeah, the cost of the jacket and just how much people get paid is like trivial.
But it just creates like this, hey, we're working towards these jackets.
It brings the teams together'cause now it just feels like
Everyone's working together towards this common goal.
That's gonna be part our culture. It's just finding what is the next milestone.
Anything that we haven't covered about either the business or
this exciting AI applications world that you feel especially passionate about. I feel we've done a good job of covering.
This has been a great conversation.
I think what is become almost a meme or hyped up a lot in
AI startups right now are like a couple of things.
One, obviously everyone's in person. It's like nine nine six or whatever.
don't actually think nine six is that healthy.
It happens in China and everyone's like
Super hardcore, but
One of the reasons is that no one has jobs over there. So it's very easy for
Employers that have leverage.
I think here you generally want to maintain a good balance'cause if you're working super high intensity
Need time to let relax a little bit.
But that is one element. The other element is the forward deployed engineers. So everyone's talking about forward deployed engineers.
And yeah, I just think it's kinda funny because my co founder came from Palantir, so they actually have forward point engineers.
What a four plane engineer a pounter means is
You're working on a
Ten, twenty five million dollar deal. And so you're actually just almost full time working with
either one or a small number of customers and like building very specifically for that.
And I think people are a little bit conflating that with
what startups do, which is startups are just very hands on and do things at the scale.
But
For you to actually have a FDE model, you need to have like massive clients. And most people do not have massive clients.
So I'm actually I'm kinda interested to see how that plays out because I do think there's like a over indexing on this for deployed engineering.
model right now.
Where yeah, I have a four point engineer and the deal sizes are like fifty K.
And that's something we think about a lot as well.
We are very hands on with our customers.
But
I think it's like you have to think about things holistically. You have to think about like okay, well, how do we scale quickly?
We don't have fifty K clients, but if for every fifty K client you have like someone that's like fully staffed them, that's like impossible to scale.
So you need to find that line in the middle.
And I think the
full four deploy model only works with the
How is your approach?
Presumably on this side you do care a lot about your margins.
margins you're much more open about if it's just like LLM cost or something like this.
But if it's fully baked.
People costs. Like that's gonna be a problem.
Yeah, it's not even a problem necessarily from the pure dollar margins, it's just prevents you from scaling. No one can hire
Good people.
that fast. It's just hard to hire good people.
So if your business is fully constrained on good people
And that's also not a good thing.
What do you think the minimum
customer size and revenue is to justify a forward deployed engineer model.
Oh probably a million.
Yeah. Yeah. Yeah. Fascinating.
Well, I think you know my traditional closing question for everybody, what is the kindest thing that anyone's ever done for you?

Or mutual friend Scott, give me a heads up here, so
I did put a lot of thought into it.
So when I was little
call ages five to thirteen, like elementary middle school.
pretty like lazy kid in general. I think most kids are. There's very few people that are just like intrinsically self motivated.
I wanna just like play games all the time or just like hang out with friends or play sports and
Yeah, my parents have like a very interesting way of
Raising us, me and my sister.
What they did was
Basically when we were really little.
It was like
An extreme level of discipline.
When I was little I played a lot of piano, essentially like
three, four hours a day and then four competitions, they just pull me out of school and just go hard at it.
And then I guess fortunately for me, my uh parents decided okay, math was probably a better way to go and
I was just like quite talented at math when I was little.
Even for that, it's just like full force, like you're just committing everything.
We
Did not have TV in the house, we didn't ha video games, we
Didn't really take vacation growing up.
You kind of
In this mindset of you're sacrificing
Most things to focus on one thing.
When you're a kid actually like you don't have no frame of reference, so
It doesn't feel hard necessarily.
'Cause your parents are kinda setting up the criteria for you and
And hindsight I had a very happy childhood.
But I think that level of
Discipline and
Also just competitiveness.
It's very hard to
Gain that.
after your childhood is over. Cause when you're in your childhood, your brain's still forming. So like that kind of forms your personality.
So one I'm very grateful for that.
And I think that's also why when I talk about my generation of
There were a lot of immigrant parents from my generation that came over grad school and they're all around my age and
I think that's like our crop of
Folks just are doing very well, probably because of that.
Probably some of the green.
And then what my parents did, 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.
I would say even though my parents were very
intense about things, they never had any like semblance of like overbearingness.
They wouldn't like
Prevent us from
Doing things we wanted to do.
Force us to like okay, you should like
Take this or whatever, and so on.
What happened was towards the end of middle school into high school, I think we had already kinda established these personalities.
Basically.
My parents just pounded a sort of lazy
Wanted to play around kid into someone that was just very, very driven.
Then to their credit, they just completely laid off.
They don't have any opinions on what we do for careers, what we should do. They're like very supportive.
So
I think that's very kind because
You cannot
replace you can't even pay for that.
You basically just need
Parents that are willing to spend a ton of time crafting this childhood.
For you to develop.
this and I think a lot of the things I have in life right now are from that.
So
That's probably the kindest thing and
My sister as well, like even growing up, she was
I think there was a lot of pressure on
Me and
In some way for like parents that immigrate who are also ambitious folks.
It's very hard for them to succeed themselves in a new environment, in a new country. So they put a lot of expectations on their children.
Yeah, my sister was also like she was just
Always try to sacrifice things for me and
When I was trying to achieve things and so
Yeah. Try to spend as much time with my parents as possible.
What was the climax of your math career?
Definitely peaks in high school. So in high school we did math contests.
Math Lympiad, so
There's like a major contest in the the USA Math Olympia in the US and
There's like essentially a camp for like the top folks. And so I went there a few years.
That's probably the peak.
Well, this has been so much fun. I'm so fascinated by the business that you built and are building. Thanks for explaining it to us and bringing us sort of right to that white hot center in so many different ways. Thanks for your time. Thanks for having me.

If you enjoyed this episode, visit joincolossis.com where you'll find every episode of this podcast complete with hand-edited transcripts. You can also subscribe to Colossus Review, our quarterly print, digital, and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most.
Learn more at join colossus dot com slash subscribe.