Dispatch from the Future: building an AI-native Company – Dan Shipper, Every, AI & I

Channel: aiDotEngineer

Published at: 2025-12-18

YouTube video id: MGzymaYBiss

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

[music]
I'm the last speaker of the day, so I'm
just between you and dinner or drinks.
So, I'm going to try to make this fun
and hopefully a little bit short.
So, first of all, I just want to say I'm
very glad to see everybody and I'm
actually kind of surprised to see so
many people here. Um, because I've been
I live here, but I've been traveling. I
was in Portugal uh last week and I was
on Twitter and someone said that
everyone was moving to San Francisco.
Uh, but it's great to have everybody
here instead because I [ __ ] love New
York.
>> [laughter]
>> Come on. Come on.
[applause]
Um, so I'm supposed to talk uh today
about uh how to build an a playbook for
how to build an AI native company. And
um I actually don't have one
unfortunately. Um
and that's because I think the playbook
is actually being invented right now. So
we're doing it at the company that I run
every but all of you are doing it here
today as well and and and so I don't
want to do this talk from the
perspective of I have all the answers
and I'm going to tell you the framework
and the playbook and all that kind of
stuff. Um but um I do think it is
helpful when we're in this beginning
stage of
uh learning how to use AI to do
engineering to build companies uh to
share like the the personal experiences
that we're having inside of our
companies um and uh and sort of
collaboratively figure out the playbook
together. So I think the best that I can
offer is really just sort of dispatches
from the future. Uh notes on what I've
figured out um and the work that we've
done inside of every um and I think the
the first big thing the first the first
big thing I really noticed is that there
is definitely a huge there's a 10x
difference between an org where 90% of
the engineers are using AI versus an org
where 100% of the engineers are using
AI. It's it's it's totally different.
Um, I think the I think the big thing is
if even 10% of your company is
uh is using a more traditional
engineering method, you you sort of have
to lean all the way back over into that
world. Um, and so it it prevents you
from doing some of the things that you
might do if everyone was uh not typing
into a code editor all the time. Um,
and I know this because this is what we
do at every um, which is the company
that I run. And it has totally
transformed what we are able to do as a
small company. Um, and so I think of us
as like a little bit of a lab for what's
possible that I I'm excited to share
with you. So for people who don't know,
I run every um, inside of every we have
six business units. We have four
software products. We run four software
products with just 15 people, which is
kind of crazy. Um, and these software
products are not toys. We've grown at
every we've grown MR by double digits
every month for the last 6 months. We
have over 7,000 paying subscribers and
over 100,000 free subscribers. Um, and
we've done this in a very capital-like
way. We've only raised about a million
dollars in total. Um and very
importantly for for this audience and
for this discussion um 99% of our code
is written by AI agents. Uh no one is
handwriting code. No one is writing code
at all. Um it's all done with cloud
code, codec, Droid, what have you. Um uh
coding agent of your of your choice.
Um, and also really importantly for the
size of team we are, each one of our
apps is built by a single developer,
which is crazy. And these are not like
uh little apps. Uh, here here's an
example. This is Kora, which is a um AI
email management app. Um, it's sort of
an it's it is it's an assistant for your
email. It on on the left over here, it
summarizes all of your all of your
emails that come in. So, you can kind of
read your email that way. This is what
my inbox looks like. on the right is a
um email assistant that you can ask
questions like I asked where's when's my
AI engineer talk um today and it gave me
just gave me the answer um and this is
built primarily by one engineer um that
he's got one or two contractors that
have helped in in certain ways but like
almost all of this is built by one guy
same thing for um
uh this app which is another one that we
we make called monologue which is a
speechtoext app It's sort of like Super
Whisper or Whisper Flow if you know of
those. Um, again, one guy, thousands of
users. Um, I I love it. It's a it's a
it's just a beautifully done app and
it's not it's not simple. It's
complicated. There's a lot of stuff to
it. Same thing for this app called
Spiral. You can see there's it's it's
big. Um, and again, one engineer.
So, obviously, this would not have been
possible um a few years ago. it would
not have been possible even a year ago.
And I think the big change that happened
that we're all starting to catch up to
is um it started with cloud code this
sort of like terminal UI that gets rid
of the code editor really push pushed us
into a place where um we are delegating
tasks to these agents. We are and and
that allows us to uh work in parallel
and do much more than we would have
ordinarily. Um,
so some of the things that some of the
things that I've noticed that we can do
that I I assume people in this room are
starting to see but
>> [snorts]
>> um I think is sort of important to put
put our finger on is uh the reason we
can go much faster is we can work on
multiple multiple features and bugs in
parallel. And I think that there's a um
there's like a little bit of a meme of
the vibe coder on Twitter that is oh
like they they have um they have four
panes open but they're not actually
doing any work. And I actually you can
do it that way. And I think there are
also definitely engineers and I know
that they are because they work at every
that are productively using four panes
of agents at the same time. Um, and
that's that's crazy and that that
contributes a lot to the um ability for
a single developer to build and run a
production application. Um, another like
really important thing about this, a
really big um, unlock is because code is
cheap, you can prototype risky ideas and
that allows you to do more experiments
than you would ordinarily. And that lets
you make way more progress because the
starting energy to try something is so
much lower because you just like say,
"Oh, go do this. go do some research on
this like big refactor I might want to
do and then you go off and do something
else. And that's a really big deal.
Um, and another really interesting thing
that I love about this stuff that I'
I've noticed in inside of inside of our
organization is we move we're moving a
bit more toward a demo culture where um
instead of you know previously if you
wanted to make something you'd have to
be like maybe write a memo or do a do a
deck or um or you know convince a bunch
of people that it was a good idea to
spend time on because you can vibe code
something uh in a couple hours that sort
of shows the thing that you're uh that
you want to make. It it allows you to
show everybody and uh I think that being
a being a sort of de democulture allows
you to do weirder things that you only
get if you can feel it. Um which is I
think really amazing
and beyond just like sort of the basic
productivity unlocks.
um
AI has and the way that we use it has
caused us to sort of invent an entirely
new set of engineering primitives and
processes which I am sure that everybody
in this room is starting to do already.
I think everyone is sort of approaching
the same things from different angles
and a lot of them definitely do echo
engineering processes from the past but
I think it's really helpful to try to
put our finger on okay what is the new
way of programming if we're moving up a
level of the stack and and we're moving
from you know Python and JavaScript and
scripting languages up into um up into
English and the uh the the name that
we've given to this process is
compounding engineering
Um, and the way that I talk about
compounding engineering is in
traditional engineering, each feature
makes the next feature harder to build.
In compounding engineering, your goal is
to make sure that each feature makes the
next feature easier to build. Um, and we
do that in this loop.
Um, the loop has four steps. The first
one is plan. And if you're you've been
here today, you've been paying
attention, you know how important it is
when you're working with agents to make
a really really detailed plan. So I
think everyone is doing that. Second
step is delegate. Just like go tell the
agent to do it. Everyone's doing that
too. Third step is assess. And we have
tons and tons of ways to um assess
whether the work that the agent did is
any good. There's tests, there's trying
it, there's having the agent uh figure
it out. There's there's code review,
there's agent code review, there's all
this types of stuff. And then the last
step which is I think the most
interesting one is codify. And this is
kind of like the the money step which is
where you compound
everything that you've learned from the
planning stage, the delegation stage,
the assessment stage back into prompts
that go into your, you know, your cloud
MD file or your um your sub aents or
your slash commands and you start to um
basically create this library. You take
all the tacet knowledge that you pick up
um that all your engineers are picking
up um as they find bugs, fix plans, um
delegate work, and you um you make it
into an explicit collection of prompts
that you can spread for your entire
organization.
And um when you do that really well,
there's a lot of like really interesting
um second order effects that are are not
I think that well understood or or that
commonly talked about that I think would
be interesting to to bring here because
my guess is that um some people are
already seeing this, but like maybe it
needs to be pushed on a little bit more
to like really be brought out and some
people uh it might be an interesting way
to get more of your organization to buy
into using these tools. tools 100% of
the time.
Um, so the first thing that you notice
if you sort of if you set up this
process and you and you're like 100%
bought in on something like compounding
engineering um is that tacet code
sharing becomes much easier. So uh we
have we have multiple products at every
a lot of a lot of products a lot of
times need to implement similar things
even if they use different technologies
or imple implementing similar things
like a team's feature or a certain type
of ooth or whatever. Um
previously in order to share code you'd
have to like abstract out whatever you
did into a library and then like allow
someone else to download it and it it'd
be hard to do or you'd have to talk
about it. with agents. Um you can just
point your cloud code instance at um the
repo from the developer sitting next to
you and learn the process that they went
through to build the feature that they
that you need to reimplement and
reimplement it yourself in your own tech
stack in your own framework and in your
own way. Um, and that's really really
cool to kind of have this the more
developers you have working on different
things inside of the org, the more you
can um share without any extra cost
because AI can just go read all the code
and and um and use it. Um, another
really cool thing that I've noticed is
that new hires are productive on their
first day because you've taken all of
the things that you've learned about
like, okay, how do I set up an
environment and what does a good commit
look like and all this kind of stuff and
on the first day they have all that set
up in their in their, you know, cloudmd
files or their cursor files or uh codec
files or whatever and um the agent just
sets up their local environment and
knows how to write a good PR. That's
really cool. It also helps if you um
want to hire like expert freelancers.
Like there's some there's one guy
there's one person who just is really
good at this one specific thing. You can
have them come in for a day and like do
that thing. It's I think of it a little
bit like um like a DJ or whatever can
like go in on like a couple bars of a
song. Like you can just sort of drop in
and that's really helpful. it's it would
ordinarily be like too hard to
collaborate because the the startup cost
is too high, but you can do that a lot
better now.
Um, another thing that I've noticed
which is really cool too is um
developers inside of every commit to um
other products. So, uh you know we have
four products that run internally.
Everybody uses all the products. If
someone uh runs into a bug or a paper
cutter, like a little minor quality of
life thing that they want, they will um
often just um they will often just uh
just submit a poll request for it to
other GM of the app um because it's very
easy for them to go download the repo
and figure out uh or have really have
Claude or Codeex figure out, okay, this
is how we fix the bug or this is how we
fix the paper cut. Um and that's really
really cool because you have this
much um much easier way of collaborating
across apps that I I think over the next
couple years I imagine that you will
also be able to let customers do this to
some extent like if you run into a bug
um this is you know speculative but if
you run into a bug you can have your
little agent fix it um and submit it as
a pull request it's a weird open source
thing but um yeah this is really really
cool and and definitely is happening a
lot inside of our company [snorts]
Um, another really cool thing is um, we
we have not this may get different as we
as we scale, but um, we have not yet had
to standardize onto a particular stack
or language. We instead let everyone
who's building different products like
pick the thing that they like best and
the reason is because it makes it AI
makes it much easier to translate
between them. Um, and it makes it much
easier to to jump into any language and
framework and environment and be
productive. And so it we don't uh it's
easier for us to let people just do the
thing that that they like and let AI
kind of like handle the translation in
between.
Um and the last thing which is my
favorite but like is also the horror I
think of of some developers and to some
degree maybe the horror of my team um is
that managers can commit code. um if
you're technical uh even the CEO and
um for for me like I have no business
committing code because we've got four
products we've got 15 people we're
growing really fast um I'm doing tons
and tons of other things but I can and I
I have like committed production code
over the last couple months and the
reason for that is AI allows um
engineers to work with fractured
attention so previously you might have
needed like a 3 or 4 hour block of focus
time in order to like get anything done.
Um, but with cloud code, you can kind of
like get out of meeting and say, "Hey,
like I want you to investigate this
bug." And then go do something else and
then come back and you have like a a
plan or like a um root cause fix and
then you can submit a PR. And it's not
easy. It's not magic, but it is actually
possible. And I think that's a that's
just a totally new way of thinking how
thinking of thinking about how managers
interact with the products that they
make.
So, um, just to just to summarize, um,
there's a I really think there's a 10x
difference in how things work when you
hit 100% AI adoption.
I think, um, from what we've seen, a
single engineer should be able to build
and maintain a complex production
product. what we call compounding
engineering, but I think what all of us
are are sort of pointing to um is I I
think really works to make each feature
easier to build and then creates all of
these sort of nonobvious second order
effects that makes it easier for the
entire organization to collaborate
together.
And very importantly, many people in San
Francisco don't know this yet. Um so
you're you're the first to hear it. Um
so that is my talk. So, if you're
interested in um in what we do, uh I run
every uh Every is the only subscription
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[music]