10 People + AI = Billion Dollar Company?

Summary

In 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

00:00

🤔 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.

05:02

📈 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.

10:04

🔍 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.

15:05

🛠️ 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.

20:06

🏢 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.

25:07

🤖 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.

30:07

🔍 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.

35:07

🚀 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.

Keywords

💡AI programmers

AI programmers refer to artificial intelligence systems that can perform tasks traditionally done by human programmers, such as coding and debugging. In the video, the concept is discussed in the context of the potential for AI to automate certain aspects of programming, thereby reducing the need for human programmers in some cases. The debate revolves around whether this automation is a positive development that will free up programmers to focus on more complex tasks or a threat to the programming profession.

💡LLMs (Large Language Models)

Large Language Models (LLMs) are a type of AI that can generate human-like text based on the input they receive. The video discusses the role of LLMs in the context of programming, suggesting that these models could eventually be able to write code or even create entire applications based on natural language descriptions provided by humans. The potential of LLMs to democratize programming and the challenges they pose to traditional programming education are key points in the discussion.

💡GitHub Co-pilot

GitHub Co-pilot is an AI tool designed to assist programmers by suggesting code as they write. It is an example of how AI is currently being integrated into the programming workflow to enhance productivity. In the video, it is mentioned as part of the broader conversation about the impact of AI on programming and whether it could lead to a future where programming is less of a specialized skill and more of a universal one.

💡Computer Science Education

The video script questions the long-standing advice that everyone should learn computer science and programming. It discusses the potential shift in the value of computer science education due to advancements in AI and LLMs, suggesting that the focus might need to change from learning to code to learning to effectively communicate with AI systems to generate code.

💡Benchmarking Datasets

Benchmarking datasets, such as sbench mentioned in the script, are used to evaluate the performance of AI models. In the context of AI programming, these datasets provide a standardized set of problems to test the capabilities of AI systems in solving real-world programming tasks. The script discusses the significance of sbench in advancing the field of AI programming by providing a way to measure and compare the performance of different AI models.

💡Deep Learning

Deep learning is a subset of machine learning that uses neural networks with many layers to learn and make decisions. The video script references the history of deep learning and its breakthrough moment with AlexNet, which significantly improved image recognition capabilities. The discussion suggests a parallel between the advancements in image recognition through deep learning and the potential advancements in AI programming.

💡Junior Developers

The script mentions junior developers in the context of AI assisting with tasks that are typically assigned to less experienced programmers, such as fixing bugs or minor issues in code. The implication is that AI could potentially take over some of the more routine and less complex aspects of programming, which could change the nature of work for junior developers and the skills they need to develop.

💡Product Managers

Product managers are professionals who guide the development of a product, from concept to market launch. In the video, there is a discussion about the potential future where product managers could describe their product requirements in English, and AI would translate that into functional code. This highlights a potential shift in the collaboration between product managers and programmers.

💡Implementation

Implementation refers to the process of turning an idea or design into a finished product or system. The video script discusses the debate over whether programming is primarily an implementation task or a creative process where ideas are formed and refined through the act of coding. This debate is central to the discussion about the role of AI in programming and whether it can automate the creative aspects of coding.

💡Engineering Systems

Engineering systems encompass the complex, real-world applications of scientific principles to design and build structures, machines, devices, and processes. The script uses the analogy of engineering systems to discuss the limitations of AI in programming, suggesting that the 'messiness' and unpredictability of real-world applications may be beyond the scope of AI's current capabilities.

💡Human Capital

Human capital refers to the people who bring their skills, knowledge, and expertise to an organization. In the context of the video, the discussion revolves around how AI advancements might impact the demand for human capital in programming and whether it could lead to a shift in the types of skills and expertise that are most valuable in the job market.

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

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