Paying Engineers like Salespeople – Arman Hezarkhani, Tenex

Channel: aiDotEngineer

Published at: 2025-12-19

YouTube video id: 4mRekpZpBZs

Source: https://www.youtube.com/watch?v=4mRekpZpBZs

[music]
How's everybody feeling? It's been uh 7
and 1/2 hours. We doing what? Are we
doing okay?
>> Oh, yeah.
>> Awesome. I'm Arman. uh like the voice of
God apparently. That's what they're
called. Voice of God apparently. Uh so
my name's Arman. I'm one of the
co-founders and managing partners at a
company called 10X. Uh my co-founder is
Alex who's been uh kindly announcing
everybody all day. We do a lot of cool
work. We uh we help companies with their
AI transformation. We have incredible
clients all over the world. But I'm not
going to talk about any of that today.
I'm going to talk about something much
more niche. I'm going to talk about how
we pay engineers.
And we pay engineers like salespeople.
Earlier, I was just in the green room
with a bunch of distinguished engineers
that I've grown to uh respect for my
entire career. And we were talking and I
was telling them that we pay engineers
based on the story points that they
complete. And we had a lot of people
roll their eyes and and laugh. And they
asked, "What do you mean?" And I said,
"Clients pay us for the number of story
points that we deliver." and we pay
engineers based on the number of story
points that they complete. And similar
to the looks that I'm getting from some
of you, there was skepticism.
And I know this sounds crazy, but it's
working. We've been able to hire
incredible engineers, many of whom have
started and exited uh companies before
this. We have been able to hire
worldclass machine learning and AI
researchers. We've hired rocket
scientists from NASA. We are shipping
code incredibly quickly and it's
maintainable and high quality code. Of
course, that is everyone's dream.
Everybody wants to hire great people.
Everyone wants to deliver really uh fast
code.
So, my goal here is not to convince you
all to adopt our model. My goal is to
show you what compensation looks like in
AI and hopefully provide a new
perspective on the fact that things
might change as we introduce this
technology. Before I jump in though, I
want to talk about uh how we got here.
So, I'm a software engineer by training.
I went to Carnegie Melon and then I
taught there in their school of computer
science. After that, I went to Google
and I helped them scale their AI, cloud,
and mobile practices internationally
before starting a few venture-back
startups. And in my last startup, I
would work out of a Weiwork. And I was
sitting in this uh 33 Irving Weiwork. If
any of you are from New York, you you
might have worked out of that Weiwork.
And they have these big tables and there
were 12 of 12 of us kind of sitting
around. No one's talking. Everyone has
their headphones in. And I look to my
left and I see I see somebody with
Visual Studio Code open, right? I'm
like, "Okay, I have a fellow engineer to
my left." And I see that he was typing,
but I didn't see a chat window. This
person was typing into the code editor.
They were typing fo
[snorts] like a caveman. this this poor
person was typing like with their little
chopstick fingers individual characters.
I I I couldn't believe it. On my
computer, I had 45 agents. Three were
ordering me lunch. Two were writing
code. One was doing research. Just
different worlds were happening on my
computer versus this person's computer.
And I felt bad. I thought maybe we
should do a GoFundMe or something. But I
I I tried to look deeply at what is
actually causing this difference. Why am
I using AI in the way that I am? And why
is this person not?
There are different ways that that
people try AI and there are different
reasons why people don't use it. We've
all heard people who have tried it and
have said it's not as good as me. We've
all heard people who have not tried it
because they don't want to. But
regardless, my belief is that this is an
incentive issue. For me, I was a founder
and I wanted to squeak out every bit of
incremental value and and efficiency
that I could. And so I would sit on
Twitter and LinkedIn and read blog posts
and try to understand what is the
cutting edge in software engineering and
what's going to give me the ability to
output more code, higher quality,
faster. And because of that, I was using
all these all these different agents.
But this person probably worked at a
startup, probably had a base salary with
an annual bonus and some equity. And
that was supposed to be the model that
incentivized people to be innovated, to
be innovative, and to work smarter and
faster and harder. But it wasn't
working. And so, in order to understand
how we got to where we are, I'm going to
do a brief uh history of compensation.
And this is by no means accurate. I'm
making a lot of things up here. It's all
illustrative. Okay, so back in the day,
we had some cavemen who were writing
code. We were we were uh probably
inscribing C in a in a tablet somewhere
and we were paying people hourly, right?
This makes sense. I look at somebody
sitting in a chair and I'm going to pay
them some amount of dollars for some
amount of time. That makes sense for me
and it makes sense for the for the
engineer. But why is that broken? I
actually I want to hear from people. Why
is hourly broken?
>> Slow.
It's slow output.
>> No upside.
>> There's no upside. There's no reason to
work faster, right? And in fact, there's
a disincentive to work faster. And so,
what if I I notice this as the buyer of
this technology and I say, "Okay, how
long is it going to take you? It's going
to take you five hours. Okay, so I'll
pay you 500 bucks, right? Hourly $100.
Multiply that by five." And then you as
the engineer, if you work faster, great.
You get to keep the $500. And if you
work slower, that's on you. As
engineers, we're really, really bad at
estimating how long things are going to
take. And so because of that, I'm not
going to say it's going to take five
hours. I'm going to say it's going to
take 15 hours, 20 hours, so that I have
no downside. And so again, as the buyer,
I don't want to pay you based on the
project.
So what if we hire people on salary and
give them a bonus, right? Well, we in
the startup community know what happens
in that when when this is the case.
People punch in at five, leave or nine,
leave at five. And so I'm Larry Paige. I
noticed this and I see why am I working
so hard at Google? Why am I putting my
blood, sweat, and tears into this? It's
because I have some of the upside. I own
the company, right? And so when we exit
for for many, many dollars, I'm going to
see that. So what if I can share that
with my employees? And that's when
equity comes in. And and this has
worked. This has worked for many many
years to incentivize employees. This is
this is the foundation of the startup
community that we all know and are a
part of. It's incredible.
But
the not every company is Google. In
fact, for every one Google, there are
many many failures. And software
engineers know this, right? For those
who want to take the risk, many will
just go to YC or or start their own
company. And for the ones who don't want
the risk, they're opting for cash over
equity. Many of us who've hired
engineers know that the cash is
non-negotiable equity. Yeah, sure. I'll
take some upside.
And so my contention is that this model
needs to be reinvented in the age of AI.
We need to directly incentivize people
to use these tools and to use them well
and to still maintain really high
quality standards of code. And so here's
how it works for us. So we basically
just to take a step back, we do two
types of work at 10X. One is road
mapping and one is execution. So
companies come to us and they say hey we
want AI. That's generally the request.
Sometimes it's more specific. It's like
hey I want my customer service team to
have 10% more uh output using AI, right?
But but generally they come to us with a
request. We do a bunch of studying and
learning and then we output a road map
and based on that road map they can take
it and work on it on their own or we can
do it. For a lot of things, we're taking
off-the-shelf tools, but a lot of what
we do is custom builds, and that's where
the story point model comes in. So, we
will build a roadmap for a lot of our
clients, but once they see that, then
they're putting in requests on their own
as well. And we have two roles in the
company that are client facing. One is
the strategist and the other is the AI
engineer. The strategists are all are
mostly technical. And so, we've have we
have former PMs, we have former
engineers. They are doing PM type work,
consulting type work. They're the ones
that are taking the product requirements
and distilling that down with the
client. Then they hand that over to the
engineer and the engineer puts together
an architecture design document. They
spend a lot of time doing that. In fact,
that is where most of our engineering
time goes. Then they write code and they
start implementing that that
architecture design document includes
tickets and each ticket is graded on
some number of story points. This is a
very traditional method of doing work,
right?
And when that ticket is accepted, the
engineer gets paid a a fee per story
point that they complete. Our engineers
have a flat base that they're paid and
then every quarter we round up based on
the story points that they've completed.
And again, this has led to us being able
to hire incredible people, but we've
also been able to do incredible work.
So, I'm going to walk through a couple
of projects that we've done. So, this is
one. This is a billboard company.
If you go to Times Square right now,
you'll see some billboards that they've
sold that inventory for.
They sell in two ways. One is you can
call them up traditional sales you can
buy that inventory but the other is they
have like an Uber for billboards type of
product where you can go online you can
upload a PNG you can choose where you
want this to run and for how long
similar to like a Facebook or Google ad
it's very similar to that experience and
they came to us and they said hey we
think that there's some opportunities
for AI in our product we did an analysis
and we found a few one of them is this
we found that when an image is uploaded
to their system it has to go through two
rounds
of moderation. One is internal to the
company and the other is with the
billboard owner internal to their
company. They're spending money on that
to actually hire the people to do that
and there's a lot of inaccuracy
and it takes a lot of time. So that
costs them money and it costs them
revenue because every moment that the
billboard is not running, they're not
making money. And so we found what if we
could build an AI model that can
actually do this moderation for them. We
scoped that out. We built the
architecture uh design doc. We broke it
down into tickets and we built this for
them. We did it in two weeks and we got
to 96% accuracy when compared to the
human moderator. We've done a lot of
other projects with this company as
well. This is another company. This is
they work with retailers all around the
world and currently they have devices in
these retailers and they're low power
devices. And so because of this they're
able to run one AI model on device and
what this model does is does heat
mapping. So imagine there's a camera in
this room looks down and it can
basically generate a heat map of where
the traffic is throughout the day. And
for retailers, of course, this is very,
very useful. But there's other things
you can do too, right? If we just sit
here for a few minutes, we can probably
come up with a lot of ideas of if you
have a camera with a chip, you can make
a lot of money from that. You can show
really useful information. And so that's
what we did. We we came up with what are
some of the things that we could do with
this? if you put a little bit little bit
more power in that ship, if you make the
models, if you quantize them so they can
run in parallel, what could you do? And
so we gave them this report and then we
built them five models that can run in
parallel. It does everything from heat
mapping to Q detection to theft
detection and more. And again, we start
with the product requirement stock. We
break this down into architecture. Then
we build it and then we pay engineers
based on the output.
This is the big question. What are the
risks? Right? Right? I just talked about
dandelions and rainbows, right? Uh so I
promised you that my goal is not to
convince you to do this. And part of
this is showing you what the potential
risks are. These are a few that come up.
One is what if an engineer inflates the
story points, right? What if an engineer
says, "Okay, you want me to add a
button? 45 story points." Right?
What if an engineer rushes and quality
drops? You're saying that it took two
weeks to do that. Well, was it good? Did
it work?
And what if engineers get sharp elbowed?
I started this by saying that we
compensate engineers like salespeople.
It's not a it's not a culture that we
necessarily want to emulate in software
engineering, right? So, how do we how do
we uh make sure that that's not
happening?
First of all, I mentioned that we have
two different roles and we compensate
like a counterbalance. So strategists
are compensated based on NR which really
is like customer happiness
and every single ticket has to be
approved internally with multiple rounds
of QA of which the strategist is
involved but also by the client and so
there's a counterbalance to every single
ticket that is delivered.
Uh I skipped to the second one. For the
first one inflating story points the
strategists are the ones who scope it.
And again we have to review all of that.
And for the third, how do you make sure
that all of this is correct? And how do
you make sure that there's no sharp
elbows? How do you make sure that
everybody is happy and the dandelions
and rainbows are continue throughout
this parade of joy? Well, you have to
hire the right people, and this is what
I tell everybody.
We make hiring incredibly difficult for
ourselves so that everything else is
easy. And that is a principle that we
all know and we all stand true to. And
this is incredibly important with AI. My
co-founder, Alex, always says, "AI makes
people look like one of those crazy
mirrors where any one of your
attributes, it makes it 10 times
larger." If you're a great engineer, AI
makes you great. If you're not, it makes
you sloppier. And this is the case with
all of these things. You have to start
with hiring.
Our belief is that AI gives people
superpowers and it makes all of us
smarter, faster, and better what we do.
But my belief is that the current way
that we compensate people is actually
holding them back. And I would invite
you to think about how can you
compensate people on your team
differently, whether it's software
engineering or anything else. If you
want to unlock your employees potential,
feel free to reach out at armon 10x.co.
Thank you.
Heat.
[music]