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We realized we were really inherently
excited about the future of coding. And
I think we took a step back and realized
that if we were being really consistent
with our beliefs, there was going to be
an opportunity for all of coding to
change in the next 5 years and for all
of software development to flow through
models. It felt like no one working on
the space at the time was really taking
that seriously. It felt like they had
great products and they were making them
a bit better, but they weren't really
aiming for a world where, you know, all
of coding as we know it today gets
automated and building software ends up
looking very, very different. Then with
that in mind, we set out to to work on
that.
Let's start this talk with sort of the
origin story of your journey as a
founder. You kind of have to go way back
to middle school when you were reading
the essays from PJ, right?
So early on I think uh you know had been
interested in in starting a company for
a long time. Had been interested in a
bunch of a bunch of other things too. I
think actually originally got into
programming being interested in in
starting something kind of commercial
where uh the first time that I ever saw
code. It was over some winter break and
my brother and I we wanted to create a
hit mobile game. We didn't really know
how to do that. We looked on Google how
do you create a game? We heard that you
need to download this application called
Xcode. Uh we did that and uh we were hit
with these weird colorful esoteric
symbols which were Objective C which you
know uh is still around but maybe a
little bit less popular than it was then
for good reasons and um stared at uh
this kind of impenetrable wall of
Objective C and my brother promptly
ejected. Didn't move on with
programming. He now is on a very
different career path. He's kind of
trying to paint or something like that.
Um but um I yeah kept going and uh
bought a book on Objective C and then
eventually uh started working on on
mobile games. That was the genesis of me
getting into programming and then along
the way also yes was a big fan of uh
PG's essays and uh Sam's essays too also
and a bunch of the folks in YC and that
was definitely a big inspiration even
from the very early stages of of high
school. I think the wildest thing about
cursor is that right now you're just 24
and build this monster of a company in a
really short amount of time. To a lot of
people it could seem that it's a bit of
a out of out of nowhere, but this was
really in the making for more than a
decade. You've been working and shipping
a lot of different projects, right? And
you were working in AI even when you
were in high school, right? Tell us a
bit about the projects and how you got
started with that.
Was lucky enough to find programming
early on. was also lucky enough to be
interested in AI early on uh and have
some great collaborators to work on AI
projects with soon after kind of the fay
into mobile games which also turned into
uh I wasn't very good at mobile games so
one of the things that I built and
actually one of the things that got most
popular which was kind of the
technically easiest thing to build which
was maybe a lesson in startups of you
know the code isn't everything uh was uh
this mobile game or this mobile app
where you could spoof high scores in
things like piano tiles and flappy
and then send them to your friends. And
that was kind of the thing that went
viral. It wasn't the, you know,
painstakingly handcrafting the game
engine yourself thing. But uh yeah, no,
soon after that uh got interested with a
friend in the idea of building a robotic
dog where we thought it would be really
great to have a robot that you could
teach to do things without programming
it. Um instead you could give it
positive and negative feedback like you
give a dog. So you could give it a treat
if it does some, you know, quote treat
if it does something good. Uh you could
say bad if it does something bad. and
then maybe it would you know you could
teach it to play fetch and things like
that. Uh that idea really animated us.
But we had no idea how to build it. And
so again you know started the place
where one would start which is Google
and kind of went down a lot of rabbit
holes and you know took us into a place
of uh learning about genetic algorithms
and maybe that was going to be helpful
for building this robot dog that we
wanted to build. And then we eventually
learned about you know this neural
network stuff because some people were
playing with taking genetic algorithms
and using them to evolve neural networks
at the time you know with work like
neat. And then uh eventually it took us
to RL reinforcement learning um which
was you know even back in 2015 uh people
had been working on it for a long time.
In the end my friend and I we did
eventually build a couple of robots. We
did we didn't do any sort of substantial
work that really lasted but we did work
that was interesting at the time in
taking reinforcement learning algorithms
and making them more data efficient. you
know, making them better at learning
from very, very few data points, you
know, order of tens of data points and
also from noisy data, you know, data
that a human's giving. It wasn't exactly
a dog, but we built a couple of robots
where one of them was this many axis
robot arm that could kind of swing a
paddle and play ping pong and if you put
the right sensor on it and then you gave
it the right sort of positive and
negative feedback, you could teach it to
swing when it sees a ball. And then uh
we had this Kiwi drive robot that we
would teach to follow a line. to do
that. It was actually kind of this great
education in ML. Um, partially because
of our dumb naive where we didn't really
know that there were things like Torch
and TensorFlow and kind of other you
know lots of building blocks we could
use from maybe we weren't good enough at
Googling.
So you like implemented your own neural
network from scratch.
Yeah. So
when you were like I don't know 16 17
the constraints of the problem were we
were dealing with uh robots and so we
were dealing with microcontrollers
um and so microcontrollers have very
little memory and they couldn't really
fit any of the normal standard ML
libraries. So as part of our bike
shedding trying to build a robot dog, uh
we implemented our own tiny neural
network library and I have memories of
us not really understanding any of the
internals of how these things worked or
not really understanding calculus but
kind of fumbling our way through
re-implementing um some important ideas
from neural networks. I you know I think
it it taught us a lot. I think there
were a lot of gaps of the fundamentals
that it took many years to fill in
later.
Then fast forward to the founding of
anyphere. It's a interesting name
because cursor is not what it is. When
you guys started, you had just um
graduated MIT, right? That was back in
2022.
What were the first idea that all four
of you started working on back in 2022?
Yeah. So, the the genesis of cursor was
in 2021. Uh my co-founders and I, we had
been interested in AI for a long time.
Each of us kind of had our own little
robot dog moment where one of my
co-founders he worked on uh trying to
build a competitor at Google actually uh
using LMS in in 2021 and and training
his own um and training his own
contrastive models. Uh one of my
co-founders uh worked on computer vision
in academia and you know some of us also
worked on recommendation systems at at
companies like Google. But uh we were
really interested in AI. In 2021 we were
trying to figure out what we do with
that interest. Do we go and work on AI
in academia or you know do we go join
you know a big existing AI effort or do
we start our own thing and there were
two moments that really got us excited.
One was seeing the first AI product
start to come out uh you know GitHub
copilot was really the canonical example
for us. The other was seeing work about
how it looked like AI was going to
predictably get better in the future as
you scale up these models. At the very
beginning of 2022, uh me and my
co-founders, we went on a like a
month-long hackathon basically and we
started hacking on ideas related to kind
of picking an area of knowledge work and
building what it looks like as AI gets
uh more and more mature. Um
you guys have collected a lot of data
for that first idea, right?
Yeah. So the first real idea that we
worked on for a long time was in me
mechanical engineering. was trying to
build a a co-pilot for mechanical
engineers and trying to train models to
kind of predict what you would do in a
CAD system like Solid Works or Fusion
360 um which is where uh Mecky's model
out parts in 3D on a computer. We picked
it because we thought it would be boring
and sleepy and uncompetitive and uh we
were kind of doing an armchair MBA thing
even though it was a horrible choice
from the get-go because none of us were
really mechanical engineers and also the
science wasn't really ready for that
area.
But you guys kept working at it for a
number of months, right? And you crawled
and got all these CAD files and actually
got something working with autocomp
completion, right? That was like the
first version of it working.
Yes, we a bunch of the work was in data
scraping. Honestly, it was trying to get
all the CAD CAD models in the internet.
There also all these different file
formats and trying to convert them all
into something that's canonical because
CAD is this weird software market where
there are all these different systems
that are pretty popular. It's very
fragmented. Uh there are also cloud CAD
systems that don't have easily
exportable files and they don't want you
to scrape their stuff and so there was a
bunch of work there. Also the training
infrastructure for doing any kind of
modeling work back then was pretty
rudimentary and so there was a lot of
work on the on the infra side there and
just a lot of experimenting with models
and a lot of experimenting with how you
even jerryrig an extension into these
CAD systems because they're the you know
we were building an extension these
applications aren't really extensible at
all. There were actually also other
projects that we were working on at the
time. So um two of my co-founders they
were working on an endtoend encrypted
messaging system um because one of them
has a background in security research
and the idea there was apps like Signal
and WhatsApp um they encrypt the body of
the messages but they don't hide who's
talking to who at what time which is
actually really crucial information if
you don't want to trust the messaging
app provider. So, you know, if a
journalist is talking to, you know,
talking to some informant in the
government, just knowing that they're
communicating at all is a is actually,
you know, a really big piece of
information.
So, that was in the middle of uh 2022.
So, you guys were working for about good
6 months on this idea.
Yes.
And how many users were you getting at
did you get at that point? So, you
shipped the product.
All all of these projects were elevated
and it had basically no users.
At what point did you realize that the
idea was not working? It's like, oh no,
we we're all working on this. We we're
trying to do the startup. It's not
working. And
what was that moment like?
I think it was a bit different for each
of the projects. I think for the
messaging system that uh two of my
co-founders worked on. It was really
technically impressive, but it had these
bad trade-offs where it wasn't very
scalable. And I think they tried to give
it to people and it didn't really work.
And then they tried to sell it B2B and
then it didn't really work. And I think
it was after a couple of months of
trying to get traction for the CAD
ideas. It was yeah many months of trying
to get the models to really be useful
um for end users and then also reckoning
around are we really interested in these
areas or is there something else that
we're inherently much more excited
about.
So there was a moment that you decided
okay these ideas are not working we have
to pivot again.
Yes. you you you turned through a three
ideas, three, four, five ideas before
landing into into uh code completion.
Yeah, I think that um so we had been
inspired by um tools like copilot really
early on um and we had avoided working
on um AI and coding because we thought
it was too competitive competitive then
still is competitive now. So
2022 GitHub copilot was making already
about 100 million revenue
I think or more
potentially more. Yeah.
And you guys are like, "Oh, we could
still do a better job than GitHub
Copilot because people thought the game
was done." It's like, "Hey, GitHub."
Well, I mean, we we didn't think we
could at at the start. And then I think,
you know, it was the desperation of
having worked on ideas for a while and
not really being excited about them
after a while and them not really
working out. And that kind of shapes, I
think, what you care about and what
you're aiming for. And we realized we
were really inherently excited about the
future of coding. I think also we got to
see how some of the other people in the
space were, you know, working on their
products. We got to see how the tech was
developing. And I think we took a step
back and realized that if we were being
really consistent with our beliefs, you
know, there was going to be an
opportunity for all of coding to change
in the next 5 years and for all of
software development to flow through
models. And it felt like no one working
on the space at the time was really
taking that seriously. like it felt like
they had great products and they were
making them a bit better but they
weren't really aiming for a world where
you know all of coding as we know it
today gets automated and um building
software ends up looking very very
different. Then with that in mind we set
out to to work on that.
That was a bold move because you said
okay we're going to stop working on all
these other ideas that we didn't have as
much of a background and you were
excited about programming even though
you had this big Goliath in the room
with GitHub Copilot. you decided to go
and let's just solve this problem.
It didn't really feel bold or like a
move at the time because it's like you
know bunch of people sitting around in
their living room like on laptops. It's
not like you know like pivoting some
giant company but uh yeah no we did and
uh you know initially we kind of waited
into it where we were thinking well you
know maybe we do this kind of very a
niche tool for basically security
reviews you know trying to detect future
CVES in your code or maybe we build
something that's just for this one niche
area of software. um you know we we
thought about building for quants and
actually um kind of prototypes and
things just for quantitative
researchers. But yeah, in in doing that
we were just brimming with ideas for
what cursor could be if it were just
about trying to be the best way to code
with AI uh in general. And then I think
that we just we had a ton of conviction
about that and we had a ton of
excitement about that and so at some
point we just decided to to go for it.
Yeah.
And that was end of uh 2022, right? when
you decided to make that move and how
quickly did you ship the first product
and what did the first product look like
and that was around you shipped it a
couple weeks later and what was what was
that look like?
Um it did take us a little bit of time
to ship something publicly.
Mhm.
It took us roughly I think three months
from first line of code to open it up
and G it. Originally what we did is we
built our own editor quote unquote from
scratch.
Oh my god.
Still it was still using a bunch of open
source building blocks. There are a lot
of great primitives like code mirror and
you know the language servers and
there's a lot of open source tech that
can that can help you build an editor
but uh yeah no it was called together
from scratch and there was our own
version of remote SSH or own copilot
integration at the time because we
didn't have anything like autocomplete
you know you have to build you know your
own peen system you have to build all
your own language server integrations
there's just a lot that ends up going
into um something as developed as you
know the code editor market you know
making something that can actually be
competitive there and service someone's
daily driver but it was I think it was
four weeks until we built something that
we could use as our daily driver. It was
maybe four weeks later where we gave it
to the first beta testers and then there
was another four weeks and then we g it
and it was still very very crude at the
time. It didn't feel like a big thing to
just open it up to the public.
What did you learn in that first
version? Because you you built a code
editor from scratch. You guys haven't
done the whole forking yet.
Yeah. And we had the fear of God in us.
I mean we had
people hadn't hadn't really liked some
of the things we built for a while. So I
think that you know we were kind of all
in on it and very focused. But what did
we learn from that? Um, I think that we
learned kind of the first initial set of
AI features where, you know, when we
started, I think that there was just one
key command and it pulled up this like
universal remote in the editor and then
you asked it to do something and then
entirely the AI would just figure out,
oh, do you what what what exactly do you
want it to do? um you know, do you want
something back that's like a chat
response or do you want um like a code
suggestion that you can then take or do
you want it to go search around your
codebase and answer a question or do you
want it to go spin for a really long
time or a short time and there wasn't a
lot of control and I think that we
learned you know given the tech of the
time um at the end of 2022 that you
actually it has to the form factor has
to look a bit different and so we
learned kind of the first early AI
features that then became part of the
core of cursor from iterating both for
ourselves and also giving it to people.
I think another thing we learned was,
you know, we were very rapidly building
a feature version of what we want in a
normal code editor plus then some AI
stuff that we thought was great. But
then, you know, a feature complete code
editor for the world, um, is going to be
a way, way, way longer road. We thought
that, you know, FS code had been
developed over the course of 12 years,
was one of the earliest TypeScript
projects, um, had lots of people on it.
Thought, oh yeah, of course, you can
kind of spin something up that's just
equivalent for the world in in a few
months. And I think that we learned very
rapidly that that wasn't the reality and
our time was going to be best spent just
focused on the AI stuff. And so similar
to how browsers often based themselves
off of Chromium's rendering engine, we
then switched to being based off of VS
Code.
The other thing is you guys had also
implemented your own models too. Like
back then you got a lot of inspiration
from uh codecs, right?
Mhm. Yes. So when we were setting out to
work on uh you know our our first idea
that we really spent a bunch of time on
which was trying to help mechanical
engineers be more productive using AI uh
one of the things when we raised our
first round of funding because we we
actually kind of needed money from the
get-go to do a little bit of model
training because you couldn't bootstrap
it with the models that existed off the
shelves. They weren't good enough at
that task. One of the papers that we
would tout around is actually the
original Codex paper u because by our
calculations codeex which was the first
this was the first autocomplete model
behind GitHub copilot it didn't really
cost that much money to train even
though even back then at kind of the
beginning and middle of 2022 people were
talking about how expensive AI models
were to train I think it cost uh my math
might be wrong but I think it was about
100k in training costs and then you know
during this fay into mechanical
engineering we had done our our own
training and then uh when we set off on
cursor. I think we were a little bit
burned by that. And so we wanted to be
as pragmatic as possible, not to
reinvent the wheel. And so we started by
doing none of that. And then over the
course of 2023, you know, in dialing in
the product, that ended up being a
really important product lever,
especially as we got to scale and we got
a bunch of people using the product. And
then that also gives you the ability to
use product data to make the product
better. And so that actually has been a
really important uh muscle to build in
the company. YC's next batch is now
taking applications. Got a startup in
you? Apply at y combinator.com/apply.
It's never too early and filling out the
app will level up your idea. Okay, back
to the video.
What happened then in 2023 was when you
were still not sure about whether cursor
was going to be a thing, right? You were
still debating with your co-founders
whether you should still pivot. It's
like, oh, is this idea still going to
work? And you were still trying to grow
it, right? Because it took a long time
to to get to revenue, right?
Yeah. I think that over 2023 it uh it
was growing the numbers were kind of
small and I think that also we were
working on something where there wasn't
always a clear next step. Um I think
that there are probably some markets
where you're really well served by
going immediately talking to a bunch of
people listing down their problems
really rigorously or you know really
kind of systematically and exhaustively
thinking through each problem what would
kind of be the direct solution and then
prioritizing them and then going from
there. Um, but I think that we were and
are in a space that's that's a bit bit
different than that. You know, I we're
this end user application that doesn't
have much of a complexity budget. We are
trying to uh build the best way to code
with AI. And so a lot of that is
figuring out, you know, given the tools
that you have today, what can you
actually do? There's a lot of things
that you could write down that that
would be useful if you could build them,
but then, you know, figuring out how to
build them and all the details. It's not
entirely clear how to move forward on
that. And so yeah, there were a lot of
times over the course of 2023 and then
you know actually also to add to this of
our early user base if you just kind of
followed the gradient of exactly what
they wanted you would get pulled in
slightly different directions than we
ended up in. You know we had a really
loud segment of users that didn't know
how to code at all and we talked about
you know should we focus on those folks.
We had a really loud segment of users
that wanted us to do things that were
very techstack specific. Um, you know,
just building for one technology and
making it much less of a horizontal tool
and we resisted doing that too. There
was a lot of early prototyping and uh
kind of wandering the desert in 2023 and
then you know figuring out things around
you know where does it make sense to not
just build the software but also build
our own models to improve the API models
or or to replace them in places like you
know for instance with our our tab you
know our next edit prediction and then
how exactly to do that.
You went from zero to 1 million around
2023 right and it was uh it took it took
a lot to get there right? Um, yeah, it
was a a bit more than that, but sort of
roughly that that
And then 2024 was a crazy year. You guys
went from one to 100 million
in one year. Tell us about this uh loss
of uh compounding power because you kept
that growing 10% week over week. How did
that happen?
So, the numbers felt small early on,
then the compounding kind of kept going.
I think that there were a couple of
things that really drove our growth. Um,
we're in this market where if you make
the product better, you kind of see it
in the numbers immediately where, you
know, things start to grow more. And so
we felt it around, you know, when we
first started to make cursor codebase
aware, when we first started to, you
know, be able to predict your next
action, when we made that then more
accurate, then when we made that faster,
then when we made that more ambitious,
um, you know, it could predict sequences
of changes, and then when we let the AI
model start to take more action within
your codebase and then do that really
fast, you know, speeding that up. And so
all along the way, um, you know, I we
kind of just focused on making the
product better. the compounding
continued. Um, and I don't think that
this is true of all markets, but I think
we're we're in a market where end user
preferences matter a lot. And if you
make the best thing, people hear about
it and talk about it. And that kept
going for, you know, a long time. I
think one of the funny things that a lot
of that that's happened around that time
we did see a big shift in the YC
companies as they were going through the
batch because we would ask what what
kind of tech stack you use to build your
applications and it was night and day
from one batch to the other. I remember
in 2023 I think it was maybe single
digit percentage of the batch would use
cursor then 2024 it was like 80%. It
just like spread like wild wildfires
like the best builders were using you
got onto their Twitter feed. Yeah.
The Twitter feed. Was that where a lot
of adoption? How how did all the growth
came from? So the the very early stages
when we were first launching the editor
we uh tried to kind of evangelize it on
on social networks and actually my uh
one of my co-founders when kind of the
the dopamine hit keeping him going in
2022 when we were working on some of
these ill- fated ideas started posting
on the internet and kind of explicitly
set out to try to gain a lot of
followers not by doing kind of normal
social media things but by talking about
AI actually it was kind of surprising
you know, the degree to which someone
could actually just read kind of all the
papers, think kind of deeply about what
was going on at the time, talk about
that publicly and then get recognized by
influential people in the space. And so
there was like this particular um open-
source model, Flynn T5 at the time that
multiple
uh AI efforts that ended up using that
model. They found out about, you know,
kind of the benefits of that model
directly from my co-founder just because
he was posting on Twitter and doing that
kind of consistently. But he became like
sort of niche, very niche, like sort of
niche niche niche of SF micro celebrity.
He would actually kind of evangelize the
product early on. And so we had this
kind of very movie magic demo. Um, you
know, when we when we first launched and
when we first did a wait list to just
get our initial batch of users. I think
that that was helpful getting the, you
know, us kick started. But then after
that, we kind of stepped away from that
and we kind of lived like monks 2023 and
just focused on the product. And it it
really just spread from word of mouth. I
remember there were a couple of times
during that year where there were
members of the team that would say
things like, "Guys, the product's
already good enough. Like, let's put it
aside. Let's just focus on growth
engineering." And um then there would be
like a two-month sprint on, you know,
doing some version of that. uh and it
just never it kind of washed away
compared to the to the other stuff that
we worked on that year.
And by that time um in 2024, how big was
Kurser? How many how big was the company
at that point?
Uh it was pretty small in 2023 where my
co-founders are are fantastic engineers
and so and there were four of us and so
we could go uh pretty far without hiring
anyone. We also had our own, you know,
set of missteps and figuring out the
first set of people to hire and how
exactly to do that. And um so we're both
very patient early on and also, you
know, focused on hiring a lot less than
we probably should have early on. Um I
think we ended
2023
at only single digits people. Like we
were less than 10 still.
Yeah.
Amazing. So, so
no, I guess one cu curious uh now
shifting gears a little bit about what
are your thoughts in terms of how the
future's going to look with uh coding.
We were kind of this this maybe middle
road bet from the start where when we
set out to work on the company and we
were kind of hiring our our first
people, we would get these weird looks
around, you know, why are you I mean at
the end of 2022 it wasn't really like
this, right? because kind of catchy
happened and then the whole world woke
up to things, you know, beginning of
2023. But especially during 2022, uh
when we were working on the CAD stuff
and then the early code stuff, um people
thought working on AI uh was it was kind
of weird to do. People were not entirely
convinced that it was a good use of time
and that there were going to be lots of
great applications to fall out of AI.
And then even the people who were
interested in AI,
there was I in our space, you know, a
bunch of people that were just focused
on optimizing kind of the form factor
that existed already um and just making
those products a little bit better. And
then at the same time, you know, in our
social circles and professional circles,
there's a bunch of people that, you
know, we're thinking, oh, why would you
work on anything other than AGI? And,
you know, all of the work that you're
doing right now in one or two years, you
know, circa 2022 is going to go away.
And yeah, I think that we've always had
this view that there's going to be lots
and lots of um incredibly valuable
things to build over the next couple
decades. AI is going to be this
transformative technology. Uh maybe more
so than you know any revolution in
recent technological revolution in
recent centuries, but it's going to take
a couple of decades. And it's going to
be this industrywide effort where there
are all of these independent
capabilities that each need to fall to
really get to you know a place where you
can entirely get to the end state of uh
transforming building software on
computers or kind of the other areas of
knowledge work that might be transformed
by AI. And yeah, I think concretely kind
of in the near term
um we think that for professional
engineers, which is the end user we
serve, uh the market that we serve, um
you know, code is still really important
and uh there will be this long messy
middle where um you will be working with
the AI more and more it will become like
a colleague more and more. It may also
become like a you know a very advanced
compiler that can start to hide some of
the code for you. You're going to have
to read the logic and um yeah, and
review it and and edit it. So, and
what do you think are the skills that
are still going to matter? What should
everyone still be studying or stop
studying?
I mean, I think that programming like
math is kind of just a good general
education.
Um, and I don't think that that goes
away. And I think that there's also lots
of practical skills that comes from
studying computer science right now. I
mean, often when people are kind of
entering dynamic industries, the like
specific stuff that they they study in
school uh isn't super crucial. It's more
the kind of learning that they get along
the way. I don't think that's changed
with AI. What advice do you have for the
audience? If you have like a young
Michael Trello, maybe not just three
years ago, they want to be like you
three years ago before they start
cursor. What should they be doing right
now?
I think just working on things that
you're interested in and
uh doing it with people both that you
enjoy being around but that you respect
a ton. Uh and taking that really
seriously. Yeah, I think that for a lot
of people that are in school, it there's
so many things that pulls you toward um
more checking boxes and less, you know,
uh focusing on building something up
over time uh and really focusing on on
something that you're that you're
interested in.
All right, let's give it a round of
applause to Michael.
Thank you so much.
Yeah, of course. Thank you for having
me.
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