GitHub CEO Thomas Domke -- The One-Person, Billion-Dollar Startup

Channel: Alex Kantrowitz

Published at: 2024-07-24

YouTube video id: oU2Qj_CENVE

Source: https://www.youtube.com/watch?v=oU2Qj_CENVE

so the example of the $1 billion value
company created by one person do you see
that as something that's
feasible I think so I think you know the
the you know I've seen a bunch of
examples where small companies like
Instagram comes to mind you know that
that started really small and and by the
time they got acquired they were still
very still very small WhatsApp um is a
similar example um and so I think you
know uh that that can exist um the
question is a single person company how
they also manage in uh support and
accounting and all the other things that
that are outside of creativity and maybe
a Copart also helps them with that um
you know answering support questions but
I think there's you know it's more fun
if you have a smaller team uh of of
people
available yes
um so let me let me read you this
example that I saw on Reddit of a coder
that was talking a little bit about how
they've worked with generative AI to
build um so they say it's mind-blowing
how quick I can move now they're using
Sonet 3.5 which is an anthropic model um
I'm pretty sure I could Implement copies
of the technical parts of the most
popular apps in the app St App Store 10
times as fast as I could before large
language models uh I still need to make
architectural and infrastructure
decisions but stuff like programming the
functionality is literally 10 times
faster right now and this is the process
that they use the first thing they do is
they think hard about the feature and
probably discuss it with Claude the
second thing they do is write a basic
spec for the feature uh it's just a few
sentences and bullet points and also
iterate with Claud on the spec and then
they are sure to provide Claude with all
the relevant context and ask for the
implementation the code so basically
what they're doing here is brainstorming
an app with Claude specking out an app
with Claude and then having Claude code
it I mean that is that is remarkable is
this something that we're going to see
be more
common I think we're going to see it at
a smaller scale um for small projects
you can probably um you know get there
even without aot lot of computer science
Computer Engineering knowledge for
larger projects I think the step missing
is the architect you know the software
engineering expert that that knows which
database to pick you know which cloud
provider uh how to make the app you know
scale from from 10 users to to 10
million users and um the model can kind
of help you with that by giving you
options right we have all seen that you
ask jpd a question an open-ended
question it gives you options and it
explains to you kind of like how to get
there but to navigate then this you know
tree of information you still have to
have you know subject matter expertise I
don't think that goes away but I think
you know if you have a well defend task
um that you can describe you know to
certain level um to to model or to to a
whole system like co-pilot you will um
you will have that agent if you want to
call it like that um do the job job for
you um um to to 90% you know of of what
you expect you know the flip side of
that is if you think about um when you
work uh with with other people whether
it's on software on other projects it's
like how long can you have a person go
by themselves when you give them a task
until they're going so far off track of
what you actually wanted to achieve
whether is what you describe to them
right like more often than not we need
the feedback loop as as humans we we can
work an isolation for too long until
we're either completely off track or we
come back you know uh with a work result
that that isn't isn't really uh what
what the what the person that or manager
or you know or customer expected us to
do and and and and I think this is where
you know we have the boundaries of these
models of the human can do that because
ultimately you know the customer can't
describe it um or the manager can
describe it to the level of degree that
you can actually fulfill all the all the
the requirements then can do that by
themselves as well that's why we believe
the human needs to be in the center the
human needs to be involved at the step
of the rate to make sure that we're not
getting into the wrong direction but
this is exactly what the person is
describing that they're not only asking
Claude to write the code but they're
dialoguing with the the AI bot about the
different spec and the decisions and how
to you know set up the components and
things like this and then it builds only
at the last
step you know I called this uh I don't
know what it was I called the second
brain it's it's kind of like we have an
extra uh you know outside of our brain
memory uh chip that that gives us all
the information that we can store
ourselves and even if you know we have a
lot of things that we learn uh uh you
know in in University in high school and
and and even before that that we can't
really store um and and we often forget
these things and um um and so the AI is
helpful um to to retrieve this
information again um you just need to
know how to ask the right question and
and then work with you that's that's why
we ultimately called it co-pilot it it
it helps you you know to um have fun
with the things that you want to work on
and it takes you know uh over the boiler
plate as we call encoding you know the
stuff that that surrounds all the cre
creative part of the process right now
with Gmail so Gmail allows me to write
emails within uh within the Gmail
application and will suggest some text
for me as I write sometimes accepted
sometimes I don't uh but also like I
could just go to Claude and ask Claud to
write the email so I'm thinking from
your in your circumstances like is there
a is there a reason why people should be
using like the co-pilot within GitHub as
opposed to like having this conversation
with let's say an anthropic bot and just
having that write the code and then
dumping it into the um code editor by
the way you can also use the AI to
summarize the email so um it's basically
the
the with the AI and the other side
summarizes the email with the AI at
which point you can ask the question why
not just send the prompt to the other
person uh and and and save the time on
all the uh you know friendliness and and
the salutation and and what not that we
put into emails because it's or just
become proficient at writing concisely
but I think as a journalist I you know I
know that that part of of the world is
not going to um is is more difficult to
prti than others sorry go ahead yeah you
know and I think to some degree that
will happen and so some to some degree
we have so much information around us
now that um the summary is is good
enough um if if you only want to read
the headline and the summary and not
dive into 10,000 uh bir article because
it ultimately means you have more time
uh for other things we all you know
dealing with limited attention um
limited lifetime
ultimately and so if that can short some
of these things um that means I have
more time for other things coming back
to your question now why not use the
generic chatbot um the the power of
Copart is that it lives you know in the
work environment of the developer so
yeah you can copy and paste everything
that you see in front of you into a
generic chatboard and um and have it
give you an answer but it's much much
more powerful to have the chatboard sit
within your uh environment um where it
knows you know what files are open it
knows what you wrote you know before
that um it can look at adjacent tabs it
can even look you know uh in developer
um in the developer world uh at the
output as a debug output is what we call
that and the console and error messages
and those kind of things and so it has
much more context available that helps
it to answer the question you know
within the specific context of the
project you're working on you know a
very simple example is that it knows you
know whether you like your um like your
variable names uh with camel case or
capitalized and um or whether you write
in in German or English we're just
looking at at the context of your file
right so one question for you so
basically you can write the prompt of
what you want and the code will be
developed on the back end um or will
help you devot to develop the code on
the back end is there going to come a
point where we're not going to need code
at all to build because like if you can
just prompt then why do we really need
to be in the code
you might say we we're already at that
point to some degree where you know when
you ask and go CH GP ask and CH go and
ask chat GPT a question uh you get an
answer and you must when you ask a
question to plot the chart for example
or do a math mathematical com um
calculation it um it actually generates
a python script that then you know plots
that data into a chart and it shows you
the chart and it still shows you that
step where you see between uh the python
script but uh you they could as well
hide that um and you just see the see
the chart output right like in in many
ways cat gbt is giving you an answer
without you ever having to worry how
that was generated and so yes we're
going to see computer systems where
large language models are just one
building block in addition to code or
maybe it's multiple language models and
image models and you know time series
models and whatnot Plus Code combined um
to to generate um all the output um that
the developer or the user expects um do
we still need engineers then to code
yeah because I well first of all you
know there's billions of lines of code
out there that still have to be
maintained you know one of the examples
I'd like to get is that most banks are
still running Cobalt code that's a
programming language from uh invented in
the late 50s when Eisenhower was a
president that runs on mainam I'm
talking about like to build new things
like is there going to come a time where
we're just going to have prompters
instead of
well most most developers work on an
existing code basis so I wouldn't I
would push back a little bit on
maintenance um we're building on top of
an existing World um I think there's
developers have always moved up the
attraction thata you know we used to
build it all ourselves and it came the
internet and we used started sharing
software components soal open source um
uh you know nowadays most applications
are sitting on stack of a thousand uh uh
components um already and you're
building the 10% lay on on top of that
now that 10% layer you know might you
know at some point be written uh an 80%
by AI or replaced by AI but that means
you have more time for the remaining 20%
on top of that the pile is getting
always bigger and the the developers are
are still going to have enough work you
know CFT out for them in fact I'd say
you know AI has created more work for s
for developers because now somebody has
to build all these AI systems and we're
not at all at a point where can just you
know have an AI engineer quote unquote
do the job of of a real human and like
that doesn't exist and um even if it
exists it's it works well not demo um
but it doesn't doesn't actually do any
real work wait so help me Square the
fact that AI has created more work for
developers yet developers are more
efficient well you mentioned you know
all the companies with an AI strategy so
the all these companies that create an
AI strategy including ourselves you know
now are not only building GitHub we also
building co-pilot and so now we have you
know two products to maintain and
copilot as an AI system is still a lot
of code that we're wearing their in they
out um in addition to maintaining and
extending uh the platform that GitHub is
with with over 100 million developers
and if you look at you know all the
companies building AI today they uh they
car that out either as a new team as a
new Focus area in addition to all the
things that they were working on before
and um you know there's thousands of new
startups uh around the world that have
made AI um that their investment goal
you know the thing that differentiates
themselves from from the existing uh
companies and as such you know more work
has been created for developers um uh
working on AI systems okay so you're in
a very interesting spot because you're
work you're running GitHub and GitHub is
part of Microsoft and GitHub with
co-pilot might be the perfect company to
implement generative AI because it's one
of those things where like there's
usually a right answer to the question
uh there's libraries and libraries of
code uh stored on your platform
effectively that makes it not easy but
more straightforward to train on and
when people are writing the large
language model can rely on that history
to predict or to suggest what the next
bit of cod should be so it's almost like
the perfectly suit the most perfectly
suited discipline to use large language
models for is code and in fact if you
looked at the discussion of gender and
ve
recently there's been a lot of
discussion of how it hasn't really
proved its economic value outside of
coding now that's the bull that's the
bare case but anyway I'm throwing it out
there for a point of discussion the
question that I have and lots of people
have is is this now something that what
you're seeing in your field where it
improves the employees Effectiveness by
55% makes them happy or allows them to
do more and build more is that
generalizable to other fields and if so
if you think that the case then why
because that's the bet that Microsoft is
making right it's not just coding it's
everything you know I think we have um
forgotten how many things AI already
does for for us or you know we are not
realizing it um you know the image
recognition in my car the street but
that is we're talking about generative
AI in particular so we can go on for
days about how AI has been you know for
feed ranking and computer vision fine
but the big moment right now is all
about generative and generative has been
something that GitHub has written with
co-pilot at this amazing moment but
that's the question is that type of
technology in particular transferable
elsewhere I think one one scenario that
comes to mind um uh that we are already
using at GitHub is um support um and so
um you know if you look at our support
system uh uh today you actually find the
G up support cow pilot um that tries to
help you before you you know submit your
ticket to human and we actually see um
and it generally aners so it's generally
uses the same L langage models to to
stay within the scope of your question
and we see that the number of um tickets
that get solved that way uh is above 50%
so we know 50% of those questions that
go through the support copilot get
solved by support copilot and do not get
submitted into to human and so such it
makes you know supporting uh our our
developers um um our customers um more
efficient for us as a company so i' say
you know that's that's definitely
another scenario where we see the um um
efficiency gains for us as a company
other similarly you know we have an
internal tool called octobot you know
like octo cat or or or logo um I he my
t-shirt and it it helps you know our
folks internally um to to solve it
problems and our it team is getting in
no more than three hours um per it
supporter back through that internal
tool by just by you know helping um uh
employees you know to solve their own IT
issues instead of instead of having to
talk to a human first and it's all you
know along the same ways which uh along
the same lines and which is like
generating text you know that helps you
to solve the task um uh that you're that
you're that you have a problem with
whether it's in support an it all that's
in coding um to to focus on the things
that um that you're really that you're
really getting value out right okay I
want to talk about what the next set of
botels might bring uh but let's take a
break before we do that so uh we'll be
back right after this to talk about the
next set of models and a bunch of other
stuff so stay tuned we'll be back right
after this and we're back here with
GitHub CEO Thomas donke we're talking
about everything that AI can do and sort
of how the advances uh might help Propel
not just coding but everything else
forward all right so here's what I'm
hearing about the next set of models
that are coming and I'm talking about
like the GPT fs and the cloud 4S and
whatever it might be um that there the
one thing that I'm hearing is that
they're going to be much much better uh
for coding and I'm curious to hear your
perspective on how much further there is
to go for these models to be able to
handle code and what you think even you
know these models getting even better at
coding might pretend for um you know in
terms of it might pretend for what
you're seeing with the um developer
community
today you believe you know one of the
things these new models will be able to
help with is um we call agentic um
abilities um solving multi-step tasks um
one classic example in uh in software
today um in small and big companies is
that you don't have to go for too long
until you have Tech debt uh until you
have code that is old and needs to be
maintained that needs to be updated that
needs to be scanned uh for security
vulnerabilities and if you look into the
backlog of most Engineers today on the
one side they have all the Innovation
you know all the cool stuff they want to
work on and on the other side they have
all the M maintenance tasks um you know
and one of them is burning down security
vulnerabilities um that have you know
stacked up over time and so we think you
know the next generation of models will
be able uh will be helping with burning
down these security vulnerabilities um
in fact you know we already have um a
feature and Market that we call autofix
um that uh helps with known issues um uh
uh and burns those down um but it only
only works right now in a single file
and as you have more powerful models you
can do that in across multiple files
basically solving the issue not just in
one place but in multiple places and how
do you teach like a model to be able to
do that like I you know obviously
reasoning agentic stuff has all been
talked about is breaking stuff down to
its component parts and then learning
how to work on it one by one I mean it
seems sort of antithetical to the way
that LMS work today they not
antithetical but very different which is
that they just kind of like take a
prompt and then just spit back a bunch
of
information you know you described it
yourself earlier which is like this
multistep is that in the first step you
get an answer from the model that you
know describes the um the solution and
then what you typically do when you
reason with the model is you ask it you
know about more about the first step and
then about the second step and so you're
drilling yourself down into this tree of
of different steps and um I think as we
you know move forward agents will be
able to to do that uh to a certain
degree themselves and um and the tricky
part is then to figure out when do I
have to come back to the human and ask
the question uh that I need the pilot to
make the decision and and not um have
the co-pilot basically go down the wrong
path okay this is sort of a
controversial question in the AI world
but I'm going to ask it to you do you
think we're going to get to the point
where we're talking right now about
effectively AI taking the Reign and
starting to to build and coming back to
the human do you think we're going to
get to a point where AI is going to just
improve
itself it seems like a bit of a trick
question I'd say you know obviously we
have seen the alpha go and and Alpha
fold that already happening B AI has
improved itself right and has learned to
play goal goal and and then got as good
as the best go players and in fact got
better and what we also saw then
afterwards is that the best go players
figured out how to beat the model um and
then so even though there was like a
period of time when everybody was kind
of like depressed that now that the game
is wounded uh the best players figured
out they can still beat um after for go
and so I think you know there's
definitely going to be um problems that
AI will be able to solve for us as I
mentioned you know burning down security
bilities as an example and I think most
folks will be very happy about that
because then gives them more time to
work on stuff that they actually want to
work on instead of doing the same
security vulnerability fix over and over
again in in multiple files um you know
is the AI you know going to get to the
singularity I I don't know um and I
think you know if I if I know the answer
and how to get there I probably you know
I build that company myself you build it
okay but um you know I'm joking a little
bit but like I think you know we will
see if um we'll see over the next few
years um if AI can not only do what it
is instructed by humans but can actually
get to a place where it can create
itself in in the sense of you know not
getting an instruction first and and
kind of like you know produce ideas um
today you know while it may appear that
a CLA or chbt is gener generating stuff
at the end of the day it's just
predicting the next word right the next
word after that it it has no
consciousness because it cannot say no
to you it can only predict an answer
that says I I don't want to prct the
answer but you know it still gives you
an output it can be silent if a you Bel
right and look it wasn't a trick
question like the question is not can
the model uh sort of learn to get better
as it goes right which is still
following the model like the question is
like the basic design of these AI
programs can AI learn to make them even
better like can AI be able to take a GPT
4 and turn it into GPT 5 right that's
the real question you think that's going
to happen it's I think it's like
predicting the future and I I don't know
if the if I could if I can I don't know
um I haven't seen uh you know I haven't
seen any indication that that's possible
today um but you know maybe I'm on your
podcast again three years you tell me
see isie maybe the AI is going to do
podcast between the two of us no but
like look you know
all if you look at the technology today
it's it's super powerful it helps uh
developers and and support agents and it
employees and to to achieve their job
faster which ultimately means you know
they have more time for other things in
life I think that's remarkable and I
would I'm not too worried about you know
AI taking over these jobs and and
replacing them with a fully automated um
employee now another thing that I find
interesting is this sort of constraint
on Computing and I was looking at your
Twitter and I saw that you recently
praised the fundraising of a company
called etched which has built chips that
are purpose built for inference which is
effectively running these AI models
which are extremely expensive to run now
but it's much cheaper to run on an edge
chips how uh important do you think
Hardware Innovation is for these
Technologies to be able to be cost
effective and grow to the point that
sort of the industry is betting on and
and then on that note what do you think
about
Ed it's incredibly exciting that we have
silicon companies in Silicon valy again
I think that's number one there's
Innovation and silicon um and um there's
not only Edge there's you know a bunch
of companies that are going in the same
direction and um I think it's just
fascinating to see after you know we
believe that um Mo's law is over and and
there's no more innovation in chips and
we're back you know to a world where
there's Innovation across the whole
stack um we talked a lot about models
and we talked about copilot and we
talked about agents which you know is
going up the stack but there's also
innovation going down the stack you know
from from the model to the data center
um all the way down to the Chip And um I
think you know we are going to see uh
much more on that uh in the in the
coming years um the cost to run
influence will come down with the
specialized chips the models itself
become more efficient um you know GPT 40
mini uh was you know announced last week
um uh which is but much faster and much
more efficient and I think you know we
are going to have innovation on the top
end where bigger models come out and do
more stuff and we going to have
Innovation on the efficiency side where
the functionality that you know a few
years ago required you know more gpus
and more time is now is now done you
know much faster and and I think if you
the it's incredibly important to have
that because the faster you get the
response the faster you're able to
iterate whether you do that manually you
know why they asking questions or
whether they doing that automatically in
a in a copile where you need to do
multiple steps to to um uh generate code
or um you know in copile you're always
generating um 10 responses you can
actually see them in your editor if you
if you open the side panel so because we
then want to pick the best one uh for
the context you're working on you can
cycle through those right so if you can
get those 10 faster you actually
probably get the higher acceptance rate
on the developer because they saw the
suggestion uh before they kept typing um
whatever they were typing yeah and you
just mentioned that opening eye has
reduced the cost to use uh GPT 40 I got
a question uh I asked like what should I
ask you and Alex wilham from TechCrunch
he he asked me he's like um why is
GitHub co-pilot so cheap when the
perceived value is so high why not add a
zero what do you
think we really happy about the price
point I think there's a balance and with
every price point and every new product
to find between um Mass adoption and and
the value getting out of the out of the
product um um you know $10 for
individuals and and $19 uh for employees
in a company uh per month um is a great
price to uh for all these productivity
gains uh it has allowed us to um uh go
to 1.8 million paid seeds and um we're
really happy about about um you know the
competitiveness of of that price point
are we going to get to a point where
most of the code that's being generated
is generated by Ai and the developers
are basically Auditors of that
code I believe so yeah I said actually
two years ago uh at a conference that uh
my Poli make then 80% of code is going
to be written by AI um in 5 years so I
guess I have three years to go um to to
for that to become true um last year we
already said that on average 46% of code
is written by copilot in those files
that's enabled and for some languages
over 60% again I don't think that's a
bad thing I think it's a great thing
because it means uh developers have more
time to write the thing that actually
matters um the thing that is creative
that's the thing that's new the thing is
differentiated um and they don't have to
write all the boil up dat anymore okay
okay and then all right last question
for you you said that you have 100
million users on GitHub today you think
that you're going to get to a billion
with this so I'm curious like why you
think AI is going to drive so many
people to start coding and then what
does that mean for a broader
economy I believe that today the biggest
adoption blocker is the complexity of
the technology the complexity of a
language that is not the language that
we learn and use every single day when
we communicate and programming languages
are great because they're deterministic
um you know the same thing does the same
uh has the same output every time you
write it um but it's hard to learn uh
it's hard to learn uh when you're a kid
um uh it's much harder to learn than
playing an instrument um or drawing an
image um because you have to learn the
thing first before you can produce
anything and then you still have to
develop your craft and and do it over
and over again uh to actually get good
at it and I think AI is going to
accelerate that massively and you know
one 1 billion developers um by 2030 or
so is a little bit under 10% of the
population depending on where Ro
population is going um that's actually a
low number if you think about it because
um we all use computers every single day
yet we are not able to create the thing
most people are not able to create the
thing that runs on those computers um
and I think you know most people are
able to go to Home Depot and and buy a
screwdriver and and put a school in a
wall and I think that's it's it's just a
going to be a fundamental skill of
humans um to to to be able to control
the computer and um create something on
them rather then they use that and
become a professional software developer
that makes money by doing so that's a
very different question in the same way
that not everybody that has you know
some uh uh skills at home on Home
Improvement uh is becoming a
professional contractor professional um
uh musician a professional artist right
like those things are decoupled and I
think for economy it means that we have
a much higher um literacy in uh
in computer engineering in computer
science in software ultimately and that
means we will be able to solve more
problems because ultimately we TR
strongly believe at GitHub that most
human progress is going to be achieved
with the help of software and um without
that software without software developer
we're not going to you know climb the
the evolution ladder so people used to
say Lear to code to people who lost
their jobs first as a helpful suggestion
and then as kind of an insult and then
they started to wonder
maybe they shouldn't be learning the
code because that's going to be taken
over by AI but your your stance on this
is no we're still going to need the
coders and we still have code look look
the AI and the um you know co-pilot is
not going to replace the code the code
is just lower in the abstraction level
in the same way that you know your your
chip in your computer still has an
instruction set you know used to do
Punch Cards and then we had Assembly
Language now they go into very technical
stuff but you know the the chip at the
end of the day is still um you know lots
of little switches um that switch
between zeros and ones that doesn't go
away it just moves into into a layer
where it doesn't bother us as much and
it doesn't you know keep us from
building the things we we want to build
and I think that's the the true power of
generi very cool well I think you should
release this uh app this flight tracker
app that you uh worked on I would
definitely like to look uh you know it
it looks horrible um it it solves one
purpose I know every time you know two
years today I I know whether I've been
been on that specific plane you know it
has every plane has the tail tail
numbers like a license plate and so it
can kind of track oh I've been on this
flight on that exact plane before um but
it looks horrible it's kind like asking
me uh to uh to go on stage uh uh with
Taylor Swift and sing and do it with her
I wouldn't I wouldn't do that either
even though I sing in the shower right
and that's I think that can I well
describes like the intention here is
like one thing is the freedom of being
creative and the other is being so good
that you can can become a professional
right well toas look you're right the
the gates of nerditude have swung wide
open and I'm totally into it thanks so
much for joining great to see you thank
you so much for super fun awesome all
right everybody thank you so much for
listening we'll be back on Friday
breaking down the news with Ronan Roy
and we'll see you next time on big
technology podcast