10 People + AI = Billion Dollar Company?
Summary
TLDRIn this episode of 'Light Cone,' hosts Gary, Jared Harge, and Diana debate the future of programming in the age of AI advancements. They discuss the impact of AI on job automation, the necessity for learning computer science, and the potential for AI to transform the way we build software. The conversation touches on the state of AI programmers, the importance of human creativity in coding, and the possibility of smaller teams creating billion-dollar companies. The hosts agree that despite AI's growing capabilities, the need for human ingenuity in programming remains crucial.
Takeaways
- 🧑💻 The script discusses the evolving role of AI in programming and its implications for the tech industry, suggesting that AI may not fully replace the need for human programmers.
- 🚀 It highlights the potential for AI to automate certain aspects of coding, particularly for junior developers, but acknowledges the limitations in creating complex systems.
- 🌟 The conversation references the impact of AI advancements like GitHub Copilot and the sbench dataset, which have spurred interest in AI programming capabilities.
- 🔑 The importance of learning computer science and coding is debated, with the argument that even if AI becomes advanced, understanding programming can enhance logical thinking and problem-solving skills.
- 🏆 The script suggests that the future might see a rise in smaller teams or even solo founders capable of creating successful startups due to the democratizing effect of technology and AI.
- 🤖 The discussion touches on the idea that AI could change the nature of work, possibly reducing the need for large teams and allowing for more efficient, smaller companies.
- 💡 It is suggested that AI and automation may lead to a shift in the types of jobs available, with a potential decrease in junior programming roles and an increase in demand for more senior or specialized skills.
- 🛠️ The script emphasizes that despite AI advancements, there is still a need for human creativity and craftsmanship in building products, particularly in the early stages of a startup.
- 🌱 The conversation speculates on the potential for AI to enable more people to pursue entrepreneurial ventures by reducing the barriers to entry in terms of technical skill and resource requirements.
- 🔮 The script concludes with a general agreement that learning to code is still valuable, as it provides a foundational understanding necessary for innovation and effective communication with AI tools.
- 🏁 The participants express a collective hope for a future where AI contributes to a flourishing of entrepreneurship, allowing more individuals to realize their ideas and contribute to societal progress.
Q & A
What is the main topic discussed in the transcript?
-The main topic discussed is the impact of AI on programming and the future of software companies, particularly in relation to whether learning to code is still necessary.
What controversial statement did Jensen make that sparked discussion?
-Jensen stated that it is vital that children learn computer science and programming is almost exactly the opposite of what is necessary because computing technology should be advanced enough so that nobody has to program, making everyone in the world a programmer.
What are some companies mentioned that are working on AI programming tools?
-Companies mentioned include Sweep and Fume, which are developing coding assistance tools for developers.
What significant advancement in AI programming occurred about eight months ago?
-The significant advancement was the release of the benchmarking dataset called sbench by the Princeton NLP group, which includes real programming problems and has spurred interest and development in AI programming.
What historical dataset is compared to sbench in terms of its impact on AI development?
-sbench is compared to the ImageNet dataset, which was a groundbreaking dataset in deep learning and computer vision from Stanford's lab led by Fei-Fei Li.
What is one reason programming tasks are challenging for AI, according to the transcript?
-Programming tasks are challenging for AI because real-world problems are messy and complex, requiring not just programming skills but also the ability to understand and model the intricacies of real-world systems.
What argument does Jared make against Jensen's statement?
-Jared argues that even if AI can eventually build great apps from English descriptions, learning to code is still valuable because it makes people smarter and improves their logical thinking skills.
What is the Jevons Paradox and how does it relate to the discussion?
-The Jevons Paradox states that increasing the efficiency of a service leads to an increase in demand for that service. It is used to explain why the demand for programmers has not decreased despite advancements in programming tools.
What prediction is made about the future of unicorn companies?
-The prediction is that AI will lead to an increase in the number of unicorn companies started each year because it will be easier to get ideas off the ground and into prototype and initial user stages.
What is a key takeaway about the importance of learning to code?
-The key takeaway is that learning to code remains essential because it provides foundational knowledge and good taste needed to build great products, even in a future where AI might handle many programming tasks.
Outlines
🤔 The Future of AI in Programming
The discussion opens with the state of AI in programming, questioning its reliability and potential impact on software companies. Gary, Jared, and Diana, who have funded companies worth billions, address a controversial statement by Jensen Huang about the future of programming. Jensen argues that computing technology should evolve so that no one needs to program, and everyone becomes a programmer. The conversation delves into whether learning computer science is still valuable in light of AI advancements that can code, debating the implications for the next generation of founders.
📈 AI Programming and Benchmarking
The discussion shifts to the reliability of AI programmers, highlighting the launch of tools like Devon that assist developers with coding tasks. Diana and Jared compare the progress in AI programming to the breakthrough in deep learning brought by the ImageNet dataset. They explain that the recent release of the sbench dataset, which benchmarks AI performance on real-world programming tasks, has spurred significant advancements in AI programming. The conversation explores how this benchmark is driving rapid improvements and the limitations of AI in building complex systems from scratch.
🔍 Challenges of AI in Real-World Programming
Jared and Diana discuss the limitations of AI in programming, emphasizing the gap between AI's current capabilities and the complexities of real-world programming. They draw an analogy between AI programming and image recognition, noting that while AI can handle small bugs, it struggles with building new systems. The conversation touches on the role of human ingenuity in programming and the importance of understanding underlying principles, despite advancements in AI tools. They explore the potential of AI to assist in certain tasks while highlighting the unique challenges of real-world engineering.
🛠️ Evolution of Programming Languages and AI
The team discusses the evolution of programming languages and how higher-level abstractions have made programming more accessible over time. They question whether natural language programming will ever fully replace traditional coding, highlighting the complexity of data modeling and the importance of human insight. The conversation explores the role of AI in translating business requirements into data models and the potential of AI to handle some programming tasks. They also debate the future of small teams in tech companies, considering the impact of AI on team size and company structure.
🏢 The Dynamics of Tech Companies
The panel reflects on the dynamics of tech companies, comparing startups to families and sports teams. They discuss the challenges of scaling a company while maintaining a cohesive culture and the pitfalls of viewing a company as a family. The conversation covers the experiences of managing large teams and the desire among experienced founders to have fewer employees. They explore the idea that smaller, more efficient teams may become the norm in the future, driven by advancements in AI and automation.
🤖 The Human Aspect of Programming
Jared makes a controversial argument that learning to code is essential because it makes people smarter, supported by evidence from AI studies. The panel discusses the importance of understanding programming to leverage AI tools effectively. They debate whether AI will significantly reduce the need for programmers or if it will simply shift the nature of programming work. The conversation highlights the role of human creativity and problem-solving in programming, even as AI automates routine tasks.
🔍 Economic Implications of AI
The discussion examines the economic implications of AI, referencing the Jevons paradox to explain why increased efficiency in programming does not lead to fewer programmers. Instead, demand for programming skills has increased. The panelists consider how AI might create more opportunities for startups and smaller companies, potentially leading to a landscape with many smaller, highly valuable companies rather than a few large ones. They also discuss the need for human capital and the role of education in preparing future entrepreneurs.
🚀 The Future of Startups in the AI Era
The conversation concludes with reflections on the future of startups in the AI era. The panelists agree that learning to code remains essential, as foundational knowledge in computer science and engineering is crucial for building innovative products. They predict that AI will make it easier for more people to start companies, leading to an increase in the number of startups and potentially more billion-dollar companies. The discussion underscores the importance of craftsmanship and good taste in building successful tech companies and the ongoing need for skilled programmers.
Mindmap
Keywords
💡AI programmers
💡LLMs (Large Language Models)
💡GitHub Co-pilot
💡Computer Science Education
💡Benchmarking Datasets
💡Deep Learning
💡Junior Developers
💡Product Managers
💡Implementation
💡Engineering Systems
💡Human Capital
Highlights
The rise of AI and its potential to transform the workforce, with companies potentially having fewer employees due to automation.
Controversy around the necessity of learning computer science, with a debate on whether AI will negate the need for widespread programming skills.
The impact of AI on the next generation of founders and the question of whether computer science remains a valuable field of study.
The role of AI in coding assistance and its current limitations in handling complex system development.
The significance of the sbench dataset in advancing AI programming capabilities, akin to the impact of ImageNet on computer vision.
Historical parallels between the development of AI programming and the breakthroughs in deep learning sparked by AlexNet.
The current state of AI in solving programming tasks and the gap between AI performance and human performance on benchmarks like sbench.
The distinction between AI's ability to handle idealized tasks versus the messiness of real-world engineering problems.
The importance of human creativity and craftsmanship in programming, which AI has yet to fully replicate.
The potential for AI to change the dynamics of company size and structure, possibly leading to smaller teams or even single-founder companies.
The debate on whether programming is an art or a science, and the implications for AI's role in the creative process.
The idea that learning to code makes individuals smarter, and the potential cognitive benefits beyond just vocational skills.
The evolution of programming languages and the abstraction of coding tasks, leading to the current discussion on natural language programming.
The challenges of data modeling and the intricacies of translating real-world complexities into AI-understandable formats.
The potential for AI to take over certain programming tasks, freeing up human developers to focus on more complex and creative work.
The philosophical and practical implications of treating all aspects of business and management as engineering problems.
The importance of technical founders having a deep understanding of computer science to effectively 'whisper' to AI and leverage its capabilities.
The future of work and the possibility of a post-abundance era where AI enables more people to pursue creative and impactful endeavors.
Transcripts
what is the state of this these AI
programmers like is it reliable yet and
where are we at well we just see
software companies have way less
employees and Converge on a point where
you could have unicorns billion dollar
companies that have like 10 people on
them if we imagine a world where there
could be companies less than 10
employees maybe you could still be a
family but is that still a good idea I
have a controversial argument against
what Jensen said this one will probably
piss some people off
[Music]
nice welcome to another episode of the
light cone I'm Gary this is Jared Harge
and Diana and collectively we funded
companies worth hundreds of billions of
dollars and
today we're talking about this one very
controversial clip that lit up the
internet from Jensen hang I going to say
something and it it's it's going to
sound completely
opposite um of what people feel you
probably recall uh over the course of
the last 10 years 15 years um almost
everybody who sits on a stage like this
would tell you it is vital that your
children learn computer
science um everybody should learn how to
program and in fact it's almost exactly
the opposite it is our job to create
Computing technology such that nobody
has to
program and that the programming
language is
human everybody in the world is now a
programmer so what do you guys think is
this true we're at the dawning of llms
we infused the rocks with electricity
and recently they learned how to talk
and now they can code what does it mean
I guess the question is are the are the
next generation of Founders or young or
anyone who's young looking to figure out
what they want to do with the career
should they still study computer science
is that still a good bet on the long run
and a lot of us spent a long time
telling people over all of these
Generations yeah you should learn to
code if you're a non-technical Founder
you should learn to code it's like the
most important thing to do during
college like definitely no matter what
else you do learn how to code right so
the question is that whether llms and AI
is just going to automate all of these
jobs and I think we have different views
on it right we funded a couple a number
of companies that are actually doing
building coding assistance that are
taking task of
developers and what does the future look
like for that I mean I guess the analogy
that you could say I don't really agree
with this but uh you could say that
given um photography you didn't have to
learn how to uh you know use a
paintbrush in order to create
representations of real life and uh
today you can prompt using an you know
using a diffusion model you can actually
you know just write out what you want
and an image will be developed for you
will this transition to code and some of
the question that Diana has done a
little bit of research on and I think
Jared you too is uh what is the state of
this these AI programmers like is it
reliable yet and where are we at related
to to Jensen's clip is the launch of
Devon which also like took the internet
by storm and has inspired many Founders
to go into this area including a lot of
the companies that we've we funded in
the in the past two batches it could be
interesting to talk about that history
and what the state-of-the-art is with AI
programmers yeah so right now these the
companies that I funded with companies
like sweep we also work with fume um a
lot of them are solving a lot of tasks
for more Junior developers that have to
do with like fixing the HTML tag here or
a bug here and there that's fairly small
but it's a a bit more difficult when you
want it to actually build more complex
systems like build me the distributor
system of the back end that was scale
that we cannot do today I think it's
important to like to put context around
Jensen street that like three months ago
basically AI could not program usefully
at all it was hitting like almost a zero
and what really changed um I actually
think think it goes back to before Devon
I actually think the real unlock for the
current surge of interest in AI
programmers goes back eight months ago
to when the Princeton NLP group released
this benchmarking data set called sbench
and sbench is a data set of GitHub
issues taken from real programming
problems and so it's a it's a good
representative data set of real world
programming task the kind of things that
programmers actually do and um this data
set finally made it possible for people
to really tackle this problem Alum of
building an AI programmer and to like
try an algorithm and Benchmark it and
see how good it is and to compete with
other people on the internet Diane and I
were actually just talking about how if
you look back in the history of of
machine learning a lot of the big
unlocks came from somebody publishing a
a benchmarking data set going back to
the very beginning of deep learning do
you want to talk about how deep learning
actually got started really yeah so this
uh Benchmark withu bench is very
reminiscent of image net which was a
groundbreaking data set from the lab at
Stanford from f f Lee and it was a very
challenging Dat Ass Say and one of the
biggest one that had a lot of images and
lots of classes where the task for uh
algorithm was to classify and see what
the image was because at the time like
the biggest unsolved problem in machine
learning this is like hard to believe
was like to look at to to get a computer
to look at a picture of a cat and be
able to tell you this is the picture of
a cat that was like
totally intractable in 2006 for because
a cat can have lots of variations it's
actually a very hard problem because you
have cats that are yellow they're black
they could be in different positions
they could be like sleeping they could
be like laying down and they all look
very different but how do you encode
that when you have limited sets on that
so before 2006 the traditional methods
in machine learning were more
statistical you would do things where
more discriminant you would have things
like support Vector machines you would
use things with feature exraction that
were with hand-coded uh signal
processing feature
extractors and with putting things in
like the frequency domain or all these
sorts of things that people try or
wavelets whatever and people tried it
and that data set was really hard the
error rate was like really really high
like over 30% 40% and for a bit of
context human perception on this data
set is about 5% accuracy more or less
and error rate error error rate correct
yes 5% error rate and then all these
standard methods were like 50% or more
or 30 above so which is really bad it's
like way way bads so then came about
Alex NE right Jared yep a group from the
University of Toronto and they had
trained a deep Learning Net using gpus
and it was one of the first cases of
people training deep learning networks
using gpus and Alex net blew the
performance of everybody else out of the
water was way better than all the other
techniques and I remember the day that
that news article dropped it like took
the like programming internet by storm I
would argue that the AI race that we're
in right now was is we're literally
still riding the wave that was kicked
off by Alex net in 2012 like it it it
just kicked off this incredible race
yeah it was the first time that at that
point it was getting to that human level
perception then people found this this
this phenomenon of stacking neural Nets
with lots of L layers people didn't
exactly knew what was happening in the
middle people treated like this black
box was actually starting to work so the
interesting learning from this lesson is
that sweet bench is that moment in time
where we can measure something and then
we can get better at it because before
with image net there wasn't big enough
of a data set to do that so we will make
progress in terms of programming but now
the question is are we going to get to
the point that we're going to get AI
algorithms that are just as good as
programing with the humans is coding
like a image recognition task one of the
reasons this wouldn't happen because so
far like if you zoom out you have uh
programming is one of the most promising
early use cases for llms since they've
like launched essentially right you have
like the co-pilot term which really was
the GitHub co-pilot specifically like a
co-pilot for programmers data compute
everything is scaling the models keep
getting better um we now have like you
said like a benchmark and like human
attention focus on trying to make this
better like what are the reasons we
won't just this isn't just a straight
scaling law oh I I think we will we're
now at like 14% on sweet bench that's
like the state-ofthe-art performance and
it's still well below Human Performance
I'm not sure what human performance
would be but certainly a skilled
programmer could probably solve most of
s bench given enough time so like I
think the swe bench mark is going to go
like is I think we're going to see rapid
improvements for for the reasons that
Tiana mentioned but sweet benches it's a
collection of small
bugs in existing repositories which is
quite different from like building a new
thing from scratch and so even when we
get to a thing that can solve you know
half of sweet bench that's still pretty
far from something where you could just
give it instructions for an app to build
and you could just go build the whole
app yep I me the way I think about it um
those was kind of what my question is
really sweet B
the kind of tasks that are in sweep
bench analogous to image recognition but
I think programming Falls in a different
kind of category of problems that it can
solve it is a bigger set because sweet
bench is like a subset it's still like
in this idealized world and maybe to put
a bit of context I think in terms of
engineering there's two categories of
problems and how we model the world
there's sort of the design world that is
all like perfect where you have all the
perfect engineering tolerances all the
simulation data and all the laws of
physics work perfect in that simulated
world and then you have the reality
which is messy I think the world of AI
llms and all that I think do a good job
with this design world but when you
encountering real world a lot of stuff
breaks and you end up with when I was
working and building all these
engineering system hot fixes that come
in and it's like random magic numbers to
make the system work or like you could
imagine all the self driving car I'm
pretty sure there's a lot of magic
numbers because it's just the placement
of sensors that like M kind of like
physics physics you have all these
coefficients of uh friction and they're
not pretty like the laws of physics like
Newton they're like beautiful equations
in this Ideal World but in the real
world when you need to get systems to
work like engineering and systems and
for startups they solve real problems
you encounter friction and there's all
sorts of coefficients of friction that
depending on all the materials and that
world is infinite so my argument is that
I don't think LMS are going to be able
to really Encompass and really manage
the whole real world the real world is
like infinite are you like going to the
Jensen original video
I you basically saying hey like
basically the dream situation is you
type in I want um an app that helps me
share blah blah blah photos yeah and the
software just magically figures out how
to build it yeah and I guess one way
like to build on that analogy like if I
I I think the world that and Jenson was
envisioning was a world in which
programmers are like product managers
today if you think about a product
manager a product manager basically
build an application by writing English
right they write a SPC and then
programmers go and they translate that
into like working code and so maybe in
the future that's how apps will be built
is you'll just like write English and
the like the the AI will take care of
the translation which I think gets into
like the heart of a this debate that has
always happened amongst engineers and
non-engineers in Silicon Valley which is
how much of programming is an
implementation thing it's just hey like
you have the idea and the implementation
are separate versus actually like you
only get the ideas in the process of
implementing and like Paul Graham is a
huge proponent of the latter right like
in multiple ways like in programming
it's like the whole reason he's such a
proponent of list from the early days is
you want a very flexible language
because you only get the good ideas once
you start building and his philosophy
actually uh translates over to writing
where writing is literally thinking yeah
your the process of actually writing is
thinking and I remember um when I was
learning how to do YC interviews
watching him and being in the room with
him and asking him like well how you
know what are you exactly looking for
and um one thing that he disabused me of
was sometimes people would come in and
I'd look at you know what they did in
the past and you know I generally felt
like well this looks like someone who's
smart and with it and they did some
impressive things in the past surely
they thought through this and they just
didn't say it in the meeting and uh one
of the things Paul would always say is
like oh no no no if they don't say it
then they themselves do not know like
the writing is actually thinking and um
I guess to sort of torture this analogy
but I kind of like it that um we have
we're sort of in this moment where uh if
we take the analogy of like the the
camera like made it so that you don't
have to paint anymore the subtlety there
is that like Aesthetics in the world
still exist and I think the Artistry of
creating software or technology products
is actually um in that interface between
the human and the technology itself so
my argument would be if you're doing
backend software and you're writing apis
and models um that might get a lot of
help from these types of you know uh AI
programmers right like you can actually
strongly type this stuff and then you
you can actually use language to
translate that into uh saying what the
product should actually do but there is
still an Artistry in that interface of
what should actually even do and how I
think that's a very good point Gary I
think maybe the other thing way to think
about this Advent with lmsm programming
if you think about the history of uh
computer science and programming
languages as we progress we became more
and more in higher language abstractions
so we started with in the early days it
was just very very much like coding an
assembly yes and it would took like so
many lines of code to just do addition
right then you went up and did a bit of
things like with Fortran and then C++
where you had to like really know about
the metal still and manage your own
memory then you went into things that
with more uh dynamically typed languages
you didn't have to think about the type
like JavaScript and pyth right or duct
typing right and now this is like a new
thing with programming with English but
you still need the Artistry
craftsmanship to come up with the design
and the architecture and interestingly
the best programmers today even if they
are programming in Python they've
learned C and they actually like know a
lot about how the computers like how the
steps below the stack work even if
they're using the the higher abstraction
I was curious to ask um everyone here
like another potential counter for
example is the natural language to seel
idea that has been around for years and
years and has never really taken off and
I always wondered how much of that is
because it's hard to build and Implement
and how much of it is it because it's
actually like it's not as simple as just
I need someone to like translate my
thoughts into a squl query it's knowing
like the right questions to ask about
the data and like having some
representation of how the pieces fit
together you have to have some sense of
like the relational database in your
head at least the concepts to ask the
right questions if it's true that that's
there is some step before of like
thinking involved then you can't just
extrapolate from like hey it's it's just
like we we started with like you know
binary code and we just like abstracted
all the way eventually to natural
language there's going to be some like
gap between like the highest level of
abstraction you can get in actual
natural language I think so I mean we we
kind of looked into a lot of these kinds
of ideas and fund this some companies
doing this kind of this kind of idea um
I think AI will get to the point that
you could actually do the translation
from English to SQL but I think the
hardest part is not that the problem
with all these data modeling why data
engineering Orcs are so big because when
I had to kind of manage these teams
they're very messy the reasons because
the hardest part is the data modeling
because that's trying to encapsulate the
real world and the real world is messy
we have all these like annoying
coefficients and frictions that we have
to model it's like okay this person
talks to who and this workflow Works to
who and it's all very very messy that a
perfect model and AI can't really
encapsulate and you kind of need the
human to kind of think through it yeah
and that layer is like how do you put an
llm to kind of par through that and
translate to the business requirements
of the data model because if the data
model is wrong then it just causes all
sorts of issues and that's where things
get hard what do you think Jared I have
a controversial argument against what
Jensen said this one will probably piss
some people off nice
my argument is that even if everything
that Jensen predicts comes true and in
the future you will be able to build a
great app just by writing English you
should still learn how to code because
learning how to code will literally make
you smarter we have an interesting piece
of evidence for this which is there's a
lot of studies now that show that the
way llms learn to think logically is by
reading all the code in GitHub and
basically learning how to code and I
think programmers have long suspect at
this that learning how to code made them
smarter but it was kind of hard to prove
with humans and now we have some actual
evidence that this is really true
there's definitely some evidence that um
for some certain class of uh problems
with llms you're way better off having
the uh llm write code to solve the
problem then to try to solve the problem
itself exactly yeah so tool use is
actually uh a very weird emergent
behavior and property of these systems
summing up it's like okay let's say that
one thing is probably uncontroversial is
there is ABS going to be some Sunset of
programming work that will just be
subsumed by llms maybe it's going to be
jior engineering work like gluc code a
whole bunch of certain type of
programming work we can all admit does
not involve High creativity High human
reasoning I should worry more about all
the Death shs where all this stuff is
gets like outsourced that type of stuff
that gets outsourced to Dev shops or
even like Frank like Fang companies that
have like armies of Junior employees and
so one potential consequence of that is
if we're not that far away from the
junior AI software engineer is will we
just see software companies have way
less employees and Converge on a point
where you could have unicorns billion
dollar companies that have like 10
people on them Sam mman had a recent
comment about this that also when kind
of viral on the internet the idea that
in the future unicorns could have 10
employees or few or fewer which is only
H well it's never quite happened I think
WhatsApp and Instagram are probably the
closest to that ever happening yeah it
feels like we've always had this has
been a a thought for the last decade
Plus at silic Valley and we've always
had flashes of oh like Instagram gets
bought for a billion dollars with like
20 employees WhatsApp gets bought for
$13 billion with 15 employees or
whatever the numbers are but we've never
seen like a sustained Trend that we can
point to it's always like these flashes
but maybe now we're at the point where
we will just see a Trend it's
interesting I feel like people who were
new to Silicon Valley and new to being
Founders they want to have more
employees because employees are like
correlated with status essentially yeah
and we know the like more experienced
Founders who've been doing this for a
while and they are obsessed with this
idea of having fewer employees having as
few as possible because after once you
like manage a large company with lots of
employees you realize how much it sucks
and that's why everyone that that's why
this meme has has been around in Silicon
Valley for a long time yeah it feels
like there's often two types of people
who really push for and are motivated
for this smaller employee idea or
smaller teams idea it's that profile and
then it's also just Engineers who are
naturally more inclined towards like
computers versus people don't are not
excited about the idea of like managing
lots of people which toally the Paul gr
thing like he was into this in 2005 long
before it was like a trend in in Silicon
Valley yep and it had to be a
combination of foresight and personal
preference right like just not wanting
to be like in an office with hundreds of
people I met up with um mark pinkis from
Zinga here at YC recently and the most
interesting thing he told me was I think
at some point a company gets to about a
thousand people and even the most
forceful the most sort of with it CEO uh
you sort of lose the capability to H
really impose your will on the company
right around when ,000 people and if I
reflect on some of the founders that we
interact with sort of regularly who have
thousands of employees like that's
actually uh sort of what their daily
lived experience is like there these
things that you know you know are sort
of extremely true the company must go in
this direction and then even then you're
like a little bit boxed in and you're
like unable to enforce that I have to
say I feel like of Founders I work with
especially s the younger hardcore
technical Engineers I think they
actually grow into the leading bigger
teams and just viewing people as a
resource that should be used well
example I can have like Patrick hon of
stripe I worked with him on our first
startup together when he was like 19 and
he was definitely the sort of archetype
of incredibly intense engineer who
wanted to be working on hard engineering
problems all the time and view to too
many people around as like a distraction
from like the core work to not want to
be hiring people and don't want to be do
any of this stuff at some point I think
once he started stripe like something
changed where he realized that the way
to achieve like his Ambitions was to
just take an engineering mind like view
the company as like another product that
needs to be like engineered and built
and people are a core component of that
and I think he just embraced the I need
to be a very effective leader hire a
manager of people and so I'm not saying
in this new AI world rpe wouldn't have
less employees if it would started today
but I don't think he would have this
internal motivation to be like I need to
just not hire anyone so much anymore it
just be like more of like a expected
value calculation of what is it better
for me to Ultimate or is it better for
me to like rally people and use them as
a resource what do you all think I mean
these are hard things for a young
founder to sort of approach and actually
these are sort of some of the reasons
why my startup didn't go as far as I
wanted it to uh I think the maybe most
toxic or you know difficult thing that I
struggled with was this idea that like
somehow your startup is your family and
you know there's actually a clip online
of um I think Brian chesky of Airbnb in
a prior era actually like you know
saying that relatively emphatically and
then today if you ask him he would say
oh no no no this is definitely not a
family uh a family has all these old
weird traumas like imagine you know uh
bringing home
uh you know a boyfriend or girlfriend
and they're like sitting with your
family and you know they go back and
they're like well what happened there
like what you know why is that like that
and it's like oh you don't want to ask
you know like let's let's not ask about
that right like you don't want to like
that having a family be your model of a
company is actually kind of a bad thing
uh and the much more functional version
of it is actually a sports team like
here's actually what we're trying to do
and you know basically we need to win
I think wanting to win uh is sort of the
ideal analogy whereas you know for
family there's these weird things like
oh we just want love and I was like oh
no no that's not what a company is for
that's not what a startup is for we're
here to solve problems and win I guess I
really wish that I uh had someone tell
me that when I was uh you know sort of
27 going through my first uh stint at YC
I think that's a hard transition I
personally went through that because we
were we went from very small engineering
team to a very large one once we went
through nian was Pokémon go and all of
that hyper success with Pokémon go is
very jaring when you go from that small
intimate team and go into like a
engineering orc of like 500 people it it
really that that that concept of going
from this is your tribe and people and
family where where you really know each
other and everyone to getting the best
the best performance out of everyone is
very different and that's hard and what
could be interesting with this era where
if we imagine a world where there could
be companies less at 10 employees maybe
you could still be a family but is that
still a good idea I don't actually
believe this true was about talking
about is Jared to your point of like
programming just sort of makes you
smarter um there's certainly some kind
of learning Founders go through when
they hire people build teams deal with
conflict fire people learn how to get
the most out of them um that probably
just makes them more effective overall
like maybe smart is not the word but
like certainly makes you more effective
figuring out how to work well with
people and get the best out of them yes
you you learn a lot about people in the
process of having to build a company and
a team yeah and I I was thinking about
what you said Harge about Patrick
hollison and how he went from being a
programmer to like learning how to run a
company and I was realized like that's
that's not just Patrick Hollis that's
actually like all of our best Founders
are like exactly like that and sometimes
people wonder how we can fund like you
know 18-year-olds with no prior
management experience and expect them to
build a big company someday and it's
exactly that it's because they treat it
like an engineering problem yeah
actually and that's where you C you get
back to the sort of program is the small
set basically it's like can you actually
just treat everything as a programming
problem it all just starts with video
games and then learning to code so
that's sort of the path this is
something I take away from I read the
Larry Ellison Oracle biography and like
a bunch of nuggets from there but like
one really interesting one is there's a
period in time where he completely
ignored just like the finance function
at the company because he thought it was
the most boring thing in the world and
then Oracle went through a near-death
experience where they weren't on top of
their budgets and expenses and just
almost ran out of money and he like
forced himself to have to get on top of
it so they would not die from running
out of money again and like the only way
he could do it was to be like okay this
is just like I'm going to treat this
like a programming problem like it's
just numbers it's process like I'm just
going to optimize this as though I would
like coding and he got really into it
and just actually started really
enjoying the whole process of process
optimization which then fed back into
Oracle in a weird way because oracle's
business was a lot of like going to
companies figuring out which of their
processes were messy and trying to sell
them software to like solve it he
experienced the problem himself and then
he built the solution that he wanted and
then he was able to sell that solution
to everybody else cuz everyone else had
the same problem y basically but again
it all came from like an engineer who
wanted to avoid a messy people process
problem just taking it on and treating
it like a programming problem and
actually becoming more effective at it
than like the team that was built to
work on it I see this a lot with our
technical program with our technical
Founders who are doing B2B companies
where they treat their sales org this
way they definitely treat sales like a
programming optimization problem yep
it's like stereotypical actually so what
do we think the net effect of this is
going to be overall if
AI you know makes us all more productive
if AI can start taking away some of the
junior programming work do we see a lot
more unicorns does it make it possible
for one company to become worth like a
trillion dollars or do we see like a
long tail of lots of like unicorns
started by much smaller teams and do we
think the teams will even shrink cuz um
if we go back to predictions in the
early 2000s there were a lot of people
who were predicting that at as
programming got more efficient companies
would be smaller because in the in the
90s to build an internet startup you had
to build everything yourself you had to
build you to have people who knew how to
Rack servers you had to hire people who
knew to optimize databases you had to
hire like people to run payroll and then
all of that stuff got like turned into
like SAS services or infrastructure open
source and so like you could focus on
just your core competency and there were
a lot of people who were predicting that
this meant that companies would have
fewer employees because they wouldn't
need all those people that you needed in
the past I remember racking servers but
I bet a lot of people watching this have
never even stepped foot in don't even
know what that phrase means what is a
you know what's a rack like how does
that even work you just go and you know
click a button on a website and like
boom I have a server right like that's
how it works right yeah and before rest
we're looking at some data earlier and
what we discover is is I it didn't
happen actually like companies didn't
get smaller and Harge discovered the
reason why there's this concept in
economics called the jeans parad stocks
which is essentially once you make any
um service more efficient like you make
it cheaper to deliver you increase
demand for it and so you actually just
get more consumption and like examples
would be Excel spreadsheets making it
easier to do financial analysis did not
decrease the number of financial
analysts it actually just like increase
them I think typewriters being replaced
by word processor is kind of another
example of where yes the strict role of
being a typist and a typewriter away but
the demand for people with word
processing skills went way up so
software became cheaper to make but at
the same programmers became more
efficient but it did not reduce the
demand for programmers it actually
increased the demand for programmers
which I think we actually see it in the
number of uh companies apply to YC there
was this essay from PG just 15 years ago
that he he couldn't imagine the world
where we'd have more than 10,000
applications per year and at this point
we're getting over 50,000 applications
per year more than that it is becoming
easier to start companies more than ever
because there's so much INF
infrastructure built but at the same
time the requirements to be good at it
and be a good founder are higher I think
it requires having even better taste and
more craftsmanship to become the best
founder now right yeah sometimes we joke
that if we went through YC now in our
younger self would we have gotten it
it's actually very competitive now
because the Baseline is just so much
higher yep so there's this things that
at the end you still need a computer
science degree and engineering degree to
really build that taste and
craftsmanship to really have know what
to build and build it well you need to
whisper to the AI and llm but how do you
even whisper to it you don't know how
all this stuff works there's this
amazing Rick and Morty uh meme where
there's a little robot on the table
passing butter and he goes up to uh Rick
the master he's like what is my purpose
and it says you pass butter and then he
goes oh my God and the funniest thing
about that is like you know there's so
many people in the world who basically
have that job and they're not like
robots they're human beings you know
like their nine to-5 is something that
is incredibly rote and not that
invigorating or exciting to them uh and
yet that's like sort of their entire
lives and how could we not celebrate the
fact that now we have more software more
tooling potentially robotics coming
around the way like that might free that
person from having to pass butter and
they can go off and do something else
something more creative like ideally
maybe they learned a code maybe they
learn to actually create things way off
on the side in areas that uh open AI or
you know sort of Microsoft or like
whoever the tech Giants are like those
companies can't do everything they
probably shouldn't do everything not
only that it's not clear to me that Lina
con will allow that so you know given
that actually maybe that's the opport
Unity like rather than just a few
companies worth a trillion dollars my
you know my genuine hope and I think
that we're trying to Manifest this world
is actually thousands of companies worth
a billion dollars or more and you know
some of those might have a thousand
employees some of them might only have
10 some of them might even be just one
founder sitting there doing that thing
but at the end of the day ultimately
making it better for a real customer a
real problem a real thing in society
that free someone from being a butter
passing robot that's a human I think
this such a good point Gary and I 100%
agree with that I think part of it is
we're in this world of post abundance of
sorts where it's easier to it's easier
to build things it's easier to get the
infrastructure up and running if you get
the right opportunity and there's a lot
of capital too if you know where to tap
but the bottleneck is can you enable
this equation of human capital to flor
and match that opportunity and get the
smart people that can do it and have a
lot of the ambition in front of this
capital and this is why right now our
job is one of the coolest we get to do
that and enable this flourishment of a
lot of people that maybe got have been
passed in different situations and give
them a chance to build these companies
that will go against the trillion dollar
ones right just a thousand billion
dollar companies we have all definitely
lived through and hugely benefited from
this trend of
the more powerful technology becomes the
easier it is to get a company off the
ground clearly like just open source
software I mean I just think back to
even when Jared and I first moved here
like Rays was first taking off and that
was a huge Innovation yeah oh that made
me feel so powerful because before I had
to use Java and it was so disempowering
right you had rails and you had Heroku
kind of like come in and just make it
easy to like deploy and do like you know
you could be your own CIS admin
essentially and so I just think that we
all that clearly made it easier for
anybody to get their company off the
ground it didn't necessarily mean these
companies got much smaller we didn't get
like lots of 10 person unicorns but we
certainly got a more um a w cast a wider
net of people who could prove out that
they had an idea that people wanted with
early signs attraction which then is
what the kind of you need to attract
like the human capital and the actual
Capital to go out and scale these things
so I think even if we end up in a world
where like AI is not going to be able to
to like build like your perfect complex
distributed system and scale to like 100
million active users even if it means
slightly more people can take their idea
and turn it into something and get it
off the ground and get their first
thousand users or their first bit of
Revenue the human capital will come the
actual Financial Capital will come and
we'll just get more of these things
which is great for everyone I love that
hard and I think that will that's that's
one prediction I think we can definitely
agree is going to come true and how cool
that is because there there must be so
many great ideas that just never get off
the ground because the person who has
the idea just kind of can't go zero to
one to to to getting that flywheel going
Orting in front of the right people I
felt very lucky that I I grew up in jail
in the middle of this desert there's
like nobody really worked on computers
and they were just in the internet and
going through YC was one of those
moments that changed my life and the
trajectory of it and really uplift it
and I hope that happens for a lot of
more people that we can work with
well so it sounds like the verdict is in
learn to
code yes you should learn to code sorry
Jensen is brilliant but he is not right
every single time I think one thing that
is uncontroversial is that over the last
10 years there have been more unicorns
started each year right like and that's
been because technology has made it more
possible for people to get their ideas
off the ground I think I AI only
accelerates that Trend right I think we
should just expect to see more unicorns
started per year than ever because it is
easier to go from getting your idea to
like a prototype to your first uses than
it ever has been and at the same time it
still table Stakes to be able to program
and code because so much of the
foundation knowledge you have to have
good taste to build something great and
you only get the good taste by going and
studying engineering and computer
science the most important thing to me
that I really want to manifest in the
world that I think we get to do all the
time at y see is that there are people
here who are crafts people or who could
be Crafts People and those are the
people who are going to go on to build
the future so with that we'll see you
next time
[Music]
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