Stanford AI Club: Alfred Lin on Building in the AI Era

Uh good evening everyone. Welcome to the first event of Stanford AI club's uh 2026 2027 speaker series. It's a

privilege to welcome Alfred Lynn, a partner at Sequoia Capital. For over 15 years uh Sequoia Alfred has partnered with some of the most consequential companies of the past uh decade

including Airbnb, Door Dash, Citadel Securities, Khi, Zipline and etc. Uh before Sequoia, Alfred served as a chairman, COO, and CFO of Zappos,

helping scale the company through its $ 1.2 billion acquisition uh to Amazon.

He's also both a Harvard and Stanford alum, and we'll be having a audience Q&A if you want to ask him which school is better later. Uh and he has a background

better later. Uh and he has a background in mathematics and statistics. So,

everyone, please welcome me in joining Alfred. [applause]

Alfred. [applause] Thank you for having me. Um, I promise to be here for as long as you guys have good interesting questions, but we're going to cut it off as as soon as you stop having interesting questions.

The onus is on you guys.

No pressure. Um, I think first question, a lot of the people in this room know you for your amazing work at Zapos and then all the stuff you're doing at Sequoia, but I'd imagine that not a lot of people know about what came before

that. Um, so you were uh you were you

that. Um, so you were uh you were you grew up in Taiwan and you moved to New York as a kid and so we were wondering, you know, could you tell us a little bit about your upbringing and what that was like and how that shaped you today?

Yeah, sure. Um, yeah. Well, I don't usually talk about this, but I grew up with two tiger parents. They were both commercial bankers. They valued

commercial bankers. They valued education a lot. They had a hierarchy of the things that were acceptable for me to do. The highest was to be become a

to do. The highest was to be become a professor and contribute to the world's knowledge. If you couldn't do that, then

knowledge. If you couldn't do that, then be a doctor and cure disease. If you

couldn't do that, be a lawyer and fight for justice. And all the way at the

for justice. And all the way at the bottom, because they lend to businesses, was to become a business person. And if

you were in business, you were supposed to work with large companies, not small companies. And I basically chose to do

companies. And I basically chose to do the exact opposite of my parents' advice. Um because partly I saw them

advice. Um because partly I saw them basically built a life and a career in Taiwan and they were fairly successful and they decided to just risk it all.

Come to the US to give their kids a better life and it all worked out even though they took all these risks. And my

encouragement for generally people in this audience and for people in college is you're in this great situation.

you're in a privileged situation to take risk and especially in an environment where we have scaling laws and the biggest mega trend that we've seen uh

since the internet, since cloud, since broadband, you should take risks and if it doesn't work out, you can always fall back on the fact that you have a freaking

Stanford degree. Um, I was expecting to

Stanford degree. Um, I was expecting to get some laughs off, but wasn't so funny, huh? Um, when I was

growing up, I always sort of felt that there there always like things that are just a little bit off about the world.

And you should notice these things. If

there are things that you notice that's a little bit off, it's usually a good thing that it's a start of a idea to solve a problem in the world. Every

problem has a solution. Every solution

creates other problems and it just keeps going on and on and on. And I encourage you to find the problems that you want to keep solving and the next set of problems that you want to keep solving.

Whatever that area is, figure that out because then you can dedicate your life to being the best in the world at solving that set of problems and then being the the person that sort of

becomes world class at it. Be passionate

about it and then figure out the economic engine that will drive the solution for that um for that um those problems. And every single company I

work with, whether you know when I dropped out of my PhD program at Stanford to join this company, Link Exchange during the start of the internet, they solved these problems that other people thought were a little

silly, a little crazy, and it became a big problem. Link Exchange was the first

big problem. Link Exchange was the first company I joined. That company was sold to Microsoft for 265 million. Uh at that time, it was the third largest

acquisition at uh by Microsoft. today

maybe 265 million if you're joining a Neo lab they would raise that you know their first round of financing um but at that time it was it was a they solved this network problem they're

trying to link all these websites together and nobody ever thought that that was a big problem to solve it turned out to be a big problem next company I joined was tell me networks

they were automating 800 numbers uh and that company was sold to Microsoft for 800 million and then it was also the third largest acquisition ition um and they were building server

voice recognition servers uh by themselves because there wasn't no a concept of a cloud and basically they needed these 800 numbers to be processed in the cloud. There's no concept of a

cloud. So they built their own cloud.

cloud. So they built their own cloud.

They figured out a solution for that.

And then Zappa was selling shoes where people thought you can't sell shoes on the internet because you have to try them on. We solve that problem by

them on. We solve that problem by sending the shoes to your home and then you can try it at home. And so every problem has a solution. Every solution

creates new problems. And that's the thing that sort of keeps the economic journey uh forward and and um a fun fun

journey along the way. You have to enjoy you have to enjoy that process. I want

to press down on one of the important decisions I think you made. Right. So

you said you were went to Harvard, dropped out of Stanford PhD. At that

point like the world is kind of your oyster, right? Um and a lot of people

oyster, right? Um and a lot of people here in obviously very similar situation. You chose to join an early

situation. You chose to join an early stage company, a startup. Um but you've also now worked with many generational founders who founded straight out of college or people who probably went on

to have very successful careers but maybe started their careers in big tech or to the frontier labs. uh what is like the right thing for an ambitious ambitious student here to do? How do

they know what's uh the right thing to optimize for the right place to start their career?

Yeah, I think the the real answer is you just don't know. Um and there's multiple paths to success. Uh I think you should optimize for maximum learning, especially when you're young, and you

should maximize for working with really good people. And that's never served me

good people. And that's never served me wrong uh at the beginning of my career.

And also today like when we invest in founders, we want them to be people that we want to back for the for the long journey because every single company that is a successful company is never an

overnight success. It takes 10 years to

overnight success. It takes 10 years to build a really legendary company or longer. Look at how long Jensen Hang has

longer. Look at how long Jensen Hang has been at building GPUs. The first 20 years does not look like the last 10 years. And we just love him because of

years. And we just love him because of the last 10 years. He's a founder of the largest market cap company in the world.

It's about to approach $6 trillion. You

know, for the first 20 years, it was zero to hundred billion dollars. It

sounds like such a small number. It was

a huge number, but he kept going. He

kept going for 30 years. Find something

that you're passionate about for 30 years. Um, and just as an example of

years. Um, and just as an example of like there's multiple paths to success, I literally told my parents I did not want to go to college and uh I told them

they had a hard life coming to the to the United States. They saved up a bunch of money to send me and my brother to college and I told them please just give me the money that you're going to give

to this like university um Harvard and let me start a company because I had been uh in high school building computers with my dad. One of

the things that we did together was just build clone PCs back then. You know, you had the IBM PC that cost $45,000.

If you bought all the parts, you can build you could put it together and you could with the parts probably cost $1,000. You could sell it for $2,000.

$1,000. You could sell it for $2,000.

You can make a $1,000 by putting these things together. You know what that

things together. You know what that company is today? Like that's Dell Computers. So if my parents had just

Computers. So if my parents had just allowed me to start that company, [laughter] you know, I I might be as, you know, rich and powerful as Michael Dell, but

no, I work at Sequoia Capital.

My parents were kind of funny because they were like, "You have to go to college." I'm like, "But we're only

college." I'm like, "But we're only going to pay for four colleges." So, I literally only applied to four colleges.

Now, back then, you didn't have the common app and you can't apply to multiple colleges. I went to a high

multiple colleges. I went to a high school that limited you to apply to seven colleges. And I only applied to

seven colleges. And I only applied to the four colleges that my parents would pay for expecting not to get in and they they would have to give me the money.

That plan didn't work. I had to go to college and then I had to come to grad school because I got an NSF scholarship and like you got a scholarship to go get

a PhD you have to go. Uh and part of the reason I came was because I thought this side of the planet uh was where all the technology was

happening and because I did you know sort of invest in Microsoft and Intel because I was putting these computers together. I knew those were like

together. I knew those were like interesting companies and I wanted to come out here to learn what was going on and I just fell in love with this place.

Um, and so the path to success is just very very like interesting. Life is path dependent. But I could have ended up

dependent. But I could have ended up coming to Stanford, finishing my PhD. My

original plan was to join a hedge fund.

Um, and uh, I could have done that.

instead if you ask my parents which one of us I have younger brother um it's about 3 years apart we're very competitive which one of us would work in the entrepreneurial world and which

one would work for a hedge fund they would absolutely think that I would work in a hedge fund and my brother would work as an entrepreneur and it completely flipped my brother works at Citadel SEC Citadel and he runs global

fixed income for for Ken uh and I work with small companies and hope we like to back founders from idea to IPO and beyond and we work with the smallest

companies possible from from the idea with two or three people. My parents

think that was insane. She he my parents literally thought I was stupid. Um and

to the point where I when I joined my friends at Link Exchange uh when it was sold to Microsoft, they said, "Well, that's great. You can now go finish your

that's great. You can now go finish your PhD."

PhD." Um, and I did have some friends who at finished their PhDs and joined Google and had great lives and and you know,

you can do things in multiple ways of success. Don't feel like you're tied to

success. Don't feel like you're tied to whatever choices you make uh while you're young.

Yeah, definitely. And kind of zoning in on what you were saying at the start, the most important thing to maximize for in your career, especially early on, is learning. I think it goes without saying

learning. I think it goes without saying that we're in a very unique period of time with AI accelerating and rapidly saturating skills like math and coding.

So we're curious in your view, how do you view the importance of mathematics and computer science and being technical in today's era?

I I I have this concept of and and um we we think about things of holding two opposite ideas in tension at Sequoia. Uh

and this happens with many concepts. One

of which is fire and ice which I wrote about recently. One of which is like

about recently. One of which is like operating at the highest level of uh sort of strategic level and also at the lowest level of detail which is what Door Dash does and Tony Shu embodies

that. It has to do with the fact that

that. It has to do with the fact that things are getting commoditized at the same time. It could still be valuable.

same time. It could still be valuable.

And so yes, math and coding is being commoditized, but the understanding of how it works has not has never been more valuable. And you can see that by the

valuable. And you can see that by the amount of money that people are willing to pay for people who really really understand how the model works, who can

really really make magic out of these models. And the people who understand

models. And the people who understand that really well are no longer coding.

In the past, they were coding. But

today, do you really really understand the models to make the best use out of it? And if you do, you will do extremely

it? And if you do, you will do extremely well. And the and and the thing that I

well. And the and and the thing that I would encourage people who are very very technical is to also understand humans.

[clears throat] Humanities is actually very important. You know why? If you

very important. You know why? If you

want to be a successful founder, we build for 8 billion people on the planet. And maybe it'll get to 10, maybe

planet. And maybe it'll get to 10, maybe it'll get to six, whatever the number is. There's a lot of people on this

is. There's a lot of people on this planet and the people are the ones that have money. Um, it all flows through

have money. Um, it all flows through people. Whether it's an institution that

people. Whether it's an institution that invests your money, it's still people.

And so don't forget that you're building for human beings and you're building for the human race.

Maybe going deeper on this point of founding and you've worked with so many exceptional founders. You said that

exceptional founders. You said that great founders obviously here they're obsessed with this problem um and they qu question basically every assumption that people take for granted. Uh for a student who knows they want to like

found down the line um but they haven't found that meaningful problem yet. What

would you tell them to do? What advice

would you give?

I think it's a some of this is a journey. You may not be a founder

journey. You may not be a founder straight out of college. You don't need to be. There are plenty of people who

to be. There are plenty of people who have found companies in their 20s or 30s or 40s or 50s. Um I think the one thing I would continue to ask to think about is like what are interesting problems

for you and why do you want to solve those problems and how valuable are those problems? One of the sort of

those problems? One of the sort of concepts that we sort of think about is how you going to create value? How then

do you deliver value? How then then do you like uh capture value? Those are the three essential stages of company building. And you can learn that by

building. And you can learn that by joining a friend and building a company together. In the first three companies I

together. In the first three companies I worked at, I basically found people that I really enjoy working with and learning from. And uh you're here, you're in this

from. And uh you're here, you're in this privileged position uh to find people like that here and beyond. And some

maybe you don't start a company from here today, but you will find a set of people here that you can connect with, you can build with on the side, you can build with at another company, and

eventually maybe start a company together. I I think the sort of the

together. I I think the sort of the essence of something that started here uh is Door Dash. There are four co-founders that came together. They

were they two from the business school, two from the engineering school, two graduate students, two undergrads, they came together, they just iterated. If

you actually heard them like go through, it wasn't the actual Door Dash idea that they first worked on. They worked on a few things together and then eventually they came to the problem that they were

all passionate about and they wanted to go solve. And I think that's really

go solve. And I think that's really really powerful. And you know, I could,

really powerful. And you know, I could, you know, say that Stanford is above and above this like idea of allowing it to build while you're you're a student

because at Harvard, a similar company was supposed to start and the administration or Harvard student agency shut it down because you have to go through Harvard Student Agency. you have

this privilege here of being able to build because the administration here gives you a lot of latitude and you should appreciate that.

Talking more on on building, there's probably been the most amount of AI demos that we've ever seen coming out every single day. Um, and for both

people looking to start their companies here and also people coming out of college who want to join a startup, um, what kind of signals do you look for in

startups or ideas or demos that give you a hunch into distinguishing whether this is just going to be like another feature which gets, you know, killed by the next

soda model release or something that could actually endure into a into a prolonged and successful business. I

think it actually starts with the founders and their ability to tell you why this is not this is not going to be a feature. I think the if you sort of

a feature. I think the if you sort of bring it all back down to two people and an idea, there's nothing defensible about that. If a large company wants to

about that. If a large company wants to kill you, they will kill you. But you

know what? They're distracted. They got

bigger problems. They're trying to spend as much of their free cash flow to build data centers right now. They're not

actually building the feature that you're building. So don't worry about

you're building. So don't worry about the fact that it has no defensibility.

But then okay, you build this feature, you get some people interested. How

what's the next feature? How do you make that into a product suite? I think one of the things that we find with technical people is they're very very good at telling the technical story, but

they can't tell you the vision. And

that's a bit of a problem because you need to be able to inspire. If you're

going to be a founder, you need to inspire other people to join you. You

need to inspire them to join you for the long run. And you have people like Brian

long run. And you have people like Brian Chesy that can sell ice to Eskimos, but he just has a vision of what travel should look like that is different from

the world. And I also just, you know,

the world. And I also just, you know, sort of think about like how fragile that Airbnb sort of idea is. You need to stay with strangers. It's a listing service. They didn't complete the

service. They didn't complete the transactions. Then you need to to email

transactions. Then you need to to email someone, check them out. and then 48 hours later they respond to you. Whether

they'll rent you a room in their house, you we forget how fragile these things start out.

It's not the empire that it it looks [clears throat] like today. They're all

very, very fragile. And there are going to be lots of people who say no to that idea, including someone like my father

who when he was helping um his bank expand internationally, he would travel uh and stay at the same

hotel chain everywhere he went in the world and order the same exact room, the room with the same hairdryer down to the

where it's placed in which drawer or he knew exactly where the haird dryer was that he never used.

For him, that was the feature because travel can be hectic and he wanted no variance when he went to sleep. He

wanted zero variance in that room. For

Brian Chesy, that's a bug because that's not living locally. That's not belonging anywhere. That is a flaw in the system.

anywhere. That is a flaw in the system.

And back to like you have to observe these things. someone's feature is

these things. someone's feature is another person's bug and vice versa. And

so when he started, it's like, yeah, it's a stranger. They're a local that can show you around. They're a local that can tell you all the great things that are happening in this city. The

feature is not uh the conference room downstairs. The

feature is the kitchen, the living room where you can hang out as a family. This

is a different feature than what my father was looking for. These are human things that you want to observe, especially in this world where everything quote unquote is getting

commoditized with AI. The thing that doesn't go away is human connection. So,

you've lived through the internet era, the do boom, the mobile era, cloud, and now AI. Uh, you've called AI the biggest

now AI. Uh, you've called AI the biggest mega trend, per se, uh, of your career.

And what about this transition, I guess, feels fundamentally different that makes this the biggest mega trend compared to everything else? And is it analogous to

everything else? And is it analogous to like these previous bubbles bubbles that we've had or do you even think that this is a bubble?

That's a compounded question. Sorry.

Do I have to answer all of them or do you whatever you think is best? There's

a lot to unpack.

Every mega trend has been the me biggest mega trend of the time. So I lived my I didn't live through the semiconductor

start but started with PCs I talked about I was building you know sort of clone computers that was the biggest mega trend at that time the internet was the biggest mega trend at that time

cloud broadband was the biggest mega trend of that time cloud was the biggest mega trend of that time mobile was the biggest mega trend of that time AI is the biggest mega trend so that is just

in the world of accelerating change and unfortunately ly you guys are young and you think oh my gosh this is so overwhelming every generation had to

face that uh as well and it's the price of progress every generation has new sets of tools that they can play with and you're expected to do more than the

previous generation so you have that burden um and this is the largest one partly because we we got we got machines to be able to

reason and think uh previous generations of technology revolutions were the industrial revolution. We sort of we

industrial revolution. We sort of we created things that uh were were basically going to like take the muscle power away from humans and animals and

put them in machines. And then the other in sort of uh mega trends that we talked about were communications megatrons. As

our partner Constantine talks about this, you know, most of this mega trends in the pa in the past few mega trends, whether it's the internet or mobile were

communications mega trends. So, we've

connected everybody in the world. Now,

this is a compute mega trend. This a

computational mega trend. And the reason why it's so big is this computational mega trend is connected to 8 billion people across the world. It has the capacity to be connected to 8 billion

people around the world. And that's why it's moving. so much faster. In the

it's moving. so much faster. In the

past, everybody wasn't connected. Um,

and so how do I think about what you need to do? One of the thing biggest things that is this misconception about technology is that just because it's a mega trend, it's going to go very, very

fast.

It's going faster than it was before, but it still takes a long time. One of

the reasons I dropped out of my PhD program was the internet was happening.

And some of you may have heard this, but I made this prediction that Amazon was going to kill Walmart. Well, 30 years later,

Walmart is 30 times larger than it was in 1997.

So, Amazon did not kill Walmart. Amazon

last year was the first year that they had more revenue than than Walmart.

took a long time to for the internet everybody could everybody back then could see the internet was going to happen um but it took a long time for it to play out part of that is the def the gap

between what is possible with the technology and the diffusion to regular human beings you know what's

the slowest thing to move in technology the humans that work with it because you know we haven't really changed not that

much. Evolution moves very very slowly.

much. Evolution moves very very slowly.

And so one of the things that you can help solve is the gap between what the models are capable of doing and what people use it

for. That gap is going to always be a

for. That gap is going to always be a large gap. And if you think about it,

large gap. And if you think about it, technology has always made higher and higher abstractions away from the machine itself.

When the main frames come out, people had to program in machine code. My

father had to program in machine code.

I've never programmed machine code except for a class for in assembly. And

that's not even really machine code. And

none of you probably really want to work at machine code level either. We just

keep building more and more abstractions because that's what it takes to diffuse technology throughout society. And one

of the most underestimated things I think is what you need to do as a technologist to make it diffuse through the population.

Yeah, really like your point about like the inertia with which the economy moves and how slowly it takes for technology just throughout history to fully

integrate into society. So I guess like looking at current trends within AI systems, they're saturating benchmarks every single day and they're showing you

know super intelligence level capabilities in certain fields. So in

your experience working with founders, working with companies and seeing their struggles where in your mind are the biggest gaps between what AI models um can do and what people are actually

doing which models can't solve right now. I think there's a number of ways

now. I think there's a number of ways you can take this. The the models are clearly more powerful than we are using it for. And so there are many places

it for. And so there are many places where you can apply the model and get economic results. Um whether it's in

economic results. Um whether it's in bio, in law, my partner pack loves to say there is value in the application layer. All this whole like notion that

layer. All this whole like notion that software is dead is kind of BS because whether you call it software, you call it application, you call it the diffusion layer, that is the gap between

what the models are capable of and human beings using it. And so there's a ton of value there. uh and we're investors in

value there. uh and we're investors in uh in Harvey in open evidence in profound in in all these places where you're taking something that the model's

capable of but the people on the other side whether it's a lawyer or a doctor or marketing person or a finance person like in rogo or ropace uh examples they

are not going to go all the way down and understand how the model works to go program it uh there are specifically taking the models down to areas is where

you want to own your own intelligence.

How do you sort of own your own intelligence for a pharmaceutical company? You're going to take open

company? You're going to take open weight models and program it on your own data. And how do you do that? And how do

data. And how do you do that? And how do you go about doing that? These are going to be very important things that we're not uh just going to let two or three

models win. That's not how economies

models win. That's not how economies work. We don't like that. We don't like

work. We don't like that. We don't like that concentration of power. We like the fact that they keep pushing the the frontier, but then we don't want them to gain too much power and we don't want to put all our data there. So then we're

going to take some of the data out and we're going to we're going to we're going to train it ourselves. And so how you how you going to build tools for developers to to do that such as you

know so how do you build a a model for drug discovery like Chai Discovery is doing or isomeorphic is doing. How do

you build tools for developers like Langchain is doing? How do you build open weight models like reflection's doing? There there's so many things that

doing? There there's so many things that you can do because the diffusion gap is only getting bigger and bigger and bigger. And if you want if you fill that

bigger. And if you want if you fill that diffusion gap, there's just a lot of work to do. Again, every problem has a solution. Every solution has creates

solution. Every solution has creates other problems. We didn't have an alignment problem until we told the models to go figure out some cyber security like risks. Well, if you figure

out the cyber security risk, I guess it's going to figure out how to get out of the sandbox. Now, you have to go control the fact that it can get out of a sandbox. It's so fun because we're

a sandbox. It's so fun because we're always going to have more work to do, which is why not a doomster doom because

there's just so much work to do and we can keep improving things over and over again.

[clears throat] I think an interesting point you noted there was like value accuring and the application layer but I wonder do you think that there will be like someone that goes maybe one step deeper or maybe

this is like a logical next step for these application companies where you want to sort of uh take over the services layer where it's like maybe a trivial example is like a Harvey sells like a service right now to a lawyer but

what if Harvey can become the lawyer using their current tools.

I think all of those things are possible. Um remember Harvey started

possible. Um remember Harvey started building out on uh on frontier models and they're training their own models because they wanted to own their own

intelligence. They they work primarily

intelligence. They they work primarily with um large uh law firms, the top 10 or 100 law firms. Are they going to work just with those law firms or are they

going to work with corporates? They're

starting to work corporates. These

things are all possible. And because of the power of these models, I'd encourage everybody to just think much more ambitiously than than before. Uh when I

was growing up and I had to learn a whole programming language to go build a game like see the delight of kids being able to just vibe code a game and how quick they just enjoy playing that game.

Well, what are they going to do next?

They're going to create more games and they're going to do more things with it.

And I think there's going to just be a ton of things that is capable of being advanced in human potential because of it.

In a recent Twitter post, uh I think it's pinned on your Twitter, uh last time I checked, you wrote about the importance of the duality of fire and ice in a company, and you mentioned this

earlier, where fire represents the passion and the vision and the burning desire to solve a critical problem, but ice represents the composure and the

clear-minded strategy and the execution.

Um, I was wondering why this idea of fire and ice stood out to you in particular. Um, and maybe we were

particular. Um, and maybe we were wondering like could you give some examples in practice of where you've seen this be the difference maker in what makes a company great?

I think when when you start a company there's a lot of passion. It starts with a vision. It starts with creativity. It

a vision. It starts with creativity. It

starts with something that nobody's ever seen before. And then you quickly

seen before. And then you quickly understand that you need to take that idea and go execute it. You need to build a product. You need to sort of figure out how to get the product into

the hands of people. You need to figure out how to get it bought and people paying for it and all of that. Sort of

that journey is the entrepreneurial journey. If you only build a as I said

journey. If you only build a as I said like if you only create value and it never gets into the hands of other people, you've not delivered.

And so many companies start out with a lot of fire. Uh and over time they need the dis the cold hard discipline of management and discipline and that

discipline allows you to create to have more fire cuz then you can take on more ideas and then again you you need the discipline of it. My lived experience

with that was that Zapus. We started

with shoes, then we expanded to to handbags and we expanded to clothes and our journey was relatively short as opposed to [snorts]

uh Amazon. When we got acquired by

uh Amazon. When we got acquired by Amazon, we saw a very similar sort of approach where they started with books.

[clears throat] Then got the discipline of books being profitable. Then they

went to music. They got the discipline of music being profitable. that allowed

them to fuel the next category and the next category. Eventually, they became

next category. Eventually, they became the everything store. These ideas are the are just like this tension between the two. Sometimes you can find it in

the two. Sometimes you can find it in one person and and I've like talked about this where Tony Shu is like that one person where he has a lot of creativity yet he works at the lowest

level of detail and then there are certain situations where uh there's one person who has a lot of fire and another person who is who has a lot of ice on

the founding team which was what I I was the ice and Tony Shay uh different Tony at Zapos was the fire um And you you

should know yourself very clearly on which side you land and what you need to complement yourself with whether it's on a management team or uh or being a founding team.

I think one interesting thing that I saw quite a few questions about um of people who signed up was there's this notion right now that money and AI is moving very circularly, right? Um, and it's

like, oh, and Nvidia invests in this company. They have monies from Nvidia

company. They have monies from Nvidia and they go buy Nvidia GPUs with that money, right? Obviously, you move

money, right? Obviously, you move billions of dollars in AI. So, [snorts]

we're kind of wondering what is your take?

We invest small amounts of money, but I feel like you you'd know a lot about this and what what's your kind of take on this? Is this actually like something to be concerned about or is this like this is normal or are people

sort of misconstring or misunderstanding this? I think it's a good kind of

this? I think it's a good kind of concern to address. Um I I think if if money is only circular, it would be a

problem. But don't forget money has

problem. But don't forget money has always been circular. And so maybe you know one of the things that I would encourage people to sort of study is just take an economics class. They can

understand how the banking system works.

Uh you have $100, you deposit in the bank. What does the bank actually do?

bank. What does the bank actually do?

The bank takes they have a reserve requirement of 20%. They lend the $80 to other people. Those people take that money invested someone it goes into

someone else's bank account that goes in the bank whatever they have a 20% reserve. So there is velocity in money.

reserve. So there is velocity in money.

And so I don't think of this as like something that you should be 100% concerned about unless there's no reserve requirement, meaning all the

money that comes in goes exactly out and then I would be a little bit concerned, but that's not the case. And of course, Nvidia wants to invest because they want

you to use Nvidia GPUs as opposed to some other GPUs. And other companies are doing the same thing. But that but Jensen's reason for doing that is

because he wants to empower the ecosystem and that's a good thing. I

think it's good to point that out. I

think it's good to be cautious. I think

the bigger concern actually is just the amount of debt companies are taking on.

We have companies that used to be nothing but cash flow positive and now they're cash flow negative and they're taking on debt to build more data

centers. And as all of you know if you

centers. And as all of you know if you take economics leverage uh is very powerful. It can make you or

break you and uh and so I think the amount of leverage in the system that is actually the issue not necessarily this phenomenon. All right, Alfred, thank you

phenomenon. All right, Alfred, thank you so much. As promised, we have 20 minutes

so much. As promised, we have 20 minutes for audience Q&A. Nowadays, we see founders raising, you know, tens of millions of dollars before even scratching the surface of product market

fit. Um, therefore, what unconventional

fit. Um, therefore, what unconventional or underrated characteristics do you invested in in founders where you're taking a large risk?

Well, I think the one one thing I would say that's uncharacteristics is just generally the just understanding the founders's journey and what got them

here. Uh we we use the term we like to

here. Uh we we use the term we like to back people who are outliers and just they don't fit any pattern. Uh in some sense they're an anti-attern. Uh and

they got to where they are because they took risks throughout the life.

If you're a if you're going to be a founder, you need to be able to take risk and you need to be able to take it with a lot of like conviction. And

that's what we look for with founders.

Like how much conviction do you have on your idea and conviction to us is not passion. It's not just because you can

passion. It's not just because you can speak about it, but you actually have investigated the problem and really understand it. That's why you would come

understand it. That's why you would come up with a novel and compelling idea. And

I think one example I would always use is Tony Shu. So Tony Shu wanted to start at Door Dash because he knew the pain of

merchants. Uh because he he lived it. He

merchants. Uh because he he lived it. He

lived it with his his uh mom standing next to her washing dishes in restaurants. And the issue that he

restaurants. And the issue that he noticed was that the restaurant capacity, the kitchen capacity was always greater than the

front of uh restaurant capacity. That

insight didn't come from someone who just theoretically top down wanted [clears throat] to sort of create a food delivery service. It was real insight

delivery service. It was real insight from uh understanding the customer.

Um I work in investment banking this summer and I know Sequoia has a lot of investors from very different backgrounds. Some came from like finance

backgrounds. Some came from like finance or private equity or some had like founding or operating rules. So in your opinion, how do these different backgrounds shape how someone evaluates a company or works as a founder? And

with each of these different paths, what are some like strengths or blind spots that investors may have?

One of my partners would say there's many paths to nirvana. Um, and so I would say the if you had to break up early stage investing and growth

investing, the difference is early stage investing is about founder quality and growth stage investing is about business quality. And with the tools that you get

quality. And with the tools that you get from being in investment banking or private equity, you learn a lot about business quality. And what's missing may

business quality. And what's missing may be founder quality, founder empathy. And

if you work at a company and you work with found know you know that because you that's a lived experience. And so I think advice is free for a reason. It

only kind of works for the person that's giving it. So even here I kind of worked

giving it. So even here I kind of worked first in companies. And so I know a set of experiences that is helpful to founders because I live the problems

that they lived and I can provide that founder empathy is who's the best mentor that you had and what is the best advice that they gave to you?

I would encourage you to not have the one best mentor and have everybody be a mentor in your life. Um, my best mentor if you wanted to sort of narrow that down was my father who told me there's

something that you can learn from everyone. That was the best advice he

everyone. That was the best advice he ever gave me. And uh that's kind of how I view the world. Like if I have a math problem, I'll go find this the right

math person for that. Um if I have a business problem, I'll go find the right business person in the right business.

And I think it's really really important to just view the world as your oyster and being able to sort of learn from anyone. And

anyone. And you mentioned that great companies are generation centric and that they follow the trends of their time and they're aligned with them. I wonder what you think our generation's priors that we're

inheriting are. What are the priors that

inheriting are. What are the priors that are flawed that we have to recalibrate that may hinder great founders that I have not thought about very carefully? But I think the set of priors

carefully? But I think the set of priors that you're likely to inherit because you are taught by people from the previous generation is they built

companies that required a lot of people to write software.

And so the way they organize the companies are probably organized in a way that required a lot of people to write software to control it etc. And it

might be very very different in the future.

Um, I also just think that every generation always in the previous generation you required more

people to do certain jobs than the next generation, but it's not like the companies got smaller. the the jobs shifted

smaller. the the jobs shifted and uh like I think if you were building a company in [clears throat] automotive there's a

lot of command and control you listen to Jensen and there's very little command and control management systems need to change for the new technologies

and I don't know what the answer is for AI because certainly it's going to be much more decentralized than it was before this notion that one person can

build and code with an idea can build a product from beginning to end and launch it and generate revenue we've never seen before and so if you want to build a big

company with a big surface area with lots of people who are doing that what does that organization look like would be the the question I think also some frameworks are just wrong because they

were never right as lean startup was developed here every Every year the amount of capital that seed companies

raise since I've been at Sequoia has only gone up, not down.

And so I don't even understand whether lean startup ever work.

So you should be you should be wary of frameworks that gain popularity because it tells a good story. U one of the sort

of frameworks I have is beware of simple narratives and comps people hate this right because comps people will tell you have a simple narrative why because simple narratives are powerful we love

simple narratives you love simple answers you want and it's it works it works to a point but again the reason

why we're in whatever troubles we are is because we don't understand the nuances of that simple narrative and what breaks. But to your point, I would

breaks. But to your point, I would encourage all of you to think about your frameworks because all of us have frameworks. We

don't always think from first principles and know the edges of where that breaks.

One example for me over the 16 years I've been at Sequoia was like quite refreshing in one sense once I understood it. This whole idea every

understood it. This whole idea every every year some somehow the check sizes keep going up. It's like how can this be? It must be returns must be getting

be? It must be returns must be getting worse because we invest more dollars at higher valuations. So return well

higher valuations. So return well returns have not gotten worse because the outcomes have gotten bigger.

So don't cap your like thinking that the outcomes are like static. And the

example I would give is when I joined Sequoia the largest market cap company was $400 billion.

Today we have Nvidia that is few days ago is 5 trillion something today might be 6 trillion but it has 15x in 15

years. Um and so

years. Um and so one framework is you need more capital in this world to compete. The other

framework is you also if you're going to found a company and you want to build a legendary company today the distance you have to travel is 15x.

Now, unfortunately, our timelines have not increased. Our lifespans have not

not increased. Our lifespans have not increased. You just have to do a lot

increased. You just have to do a lot more work and accomplish a lot more in the same time frame because yes, your life expectancy will be slightly higher

than mine, but not unless we figure out how to join the singularity and merge with machines. I think you know your

with machines. I think you know your lifetimes will probably still be limited by 120 years or 150 years.

Quad might solve cancer. Who knows? They

might.

Hi, Alfred. Um, thank you so much for being incredible inspirations for Taiwanese Americans like us. Um, and I'm wondering as Jensen's doing a better job.

Sorry. [laughter]

No, no, I'm kidding. I'm kidding. But

lots of cool people. as a founder or an investor, you're always going to have to get some kind of buy in from other people, either more seuite than you or

whatever. Um, how do you do that?

whatever. Um, how do you do that?

Getting buy in.

Yes. Ne like how do you persuade other people?

I mean, I think the the sort of simple answer is like have such a compelling vision that everybody wants to join you.

That makes it simple. Um, and the other way I say it is if they don't want to join, they're not the right people. And

so find your community. Find the right people that want to join you. And so,

you know, you obviously want to create a vision that everybody wants to join, but if you can't do if you're you're probably not going to do that at the beginning, but when you sort of know what your vision is, then find the

people who want to join that that vision. If you were to start a AI native

vision. If you were to start a AI native hedge fund today, what do you think would be the most um durable edge that

AI can do?

Uh I think the one thing I would sort of think through is like what areas that regular like the sort of the there's a bunch of hedge funds that are using machine learning to

price things. And so your edge has to be

price things. And so your edge has to be that they are pricing something not as well as your AI model and that is a hard

task. So I would start pricing things

task. So I would start pricing things that they're not focused on and they may not be focused on cashods or they might be focused on crypto or they're not

focused on perpetuals or some other thing and that's probably the edge that you want to start with. And so this is more about the market [clears throat]

and focusing on the market where like I think sometimes you're like the market is more valuable than the technology and just focus on where like they're not focused. I'm a doctor by training and

focused. I'm a doctor by training and I'd love to get your idea of when you're thinking about investing and building in regulated markets. What frameworks and

regulated markets. What frameworks and thoughts do you have? And I'm thinking in the back of my mind about things like open evidence where without the support of Jammer, New England Journal of Medicine, these types of tools which are

normally behind a pay wall, that kind of value proposition isn't quite as strong.

And I'm sure there are plenty of other examples of other regated markets where the existing data brokers or whichever institutions have a lot of power. How

you think about breaking it and and working that space?

Yeah, I I think as a early stage for startup, you shouldn't try to break igopies that have existed for a long period of time. You should figure out

how to work around them. Um, and so open evidence didn't break anything. They

just figured out how to build a product that doctors love. Doctors don't want to use chat GPT. Even if they gave the same answers, what do they care about? They

care about evidence-based uh conversation and so instead of just asking chatpt they wanted something that is evidence-based and it was trained on a

corpus a body of work that is published and so whether it's regulated or not is not the

reason um you wouldn't want to break a oligopoly the reason not to break an oligopoly is their powerful and the way you sort of go around them is like you

sometimes partner with them in this the sense of like open evidence partner with getting all the corpus of articles that ever been published and and or sometimes

you work around them or with them in a different way. Uh Door Dash didn't work

different way. Uh Door Dash didn't work with large enterprise companies for a long period of time. They worked with the small and mediumsiz restaurants, not

with the the large McDonald's or Starbucks until they had some heft. So

the essence of the idea is to to work in areas that large companies do not care about, which is

basically that's kind of the reason why I have a job, right?

If big companies can do everything, I'd be out of a job, but they can't. They

work in areas where they just don't care.

Um, if you were if you were a student founder in our shoes, how would you spend the first 90 days working on a project or startup idea? One of my takeaways from your talk on this diffusion gap between technology and

adoption is that arguably more important than building is learning about your customers behavior and human habits. So,

how would you do that? Uh it's specific for the person. I would start with like finding a group of people that you want to work with that you want to go to battle with. And you know the example I

battle with. And you know the example I gave was Tony Shu Tony Shu and Evan Moore and Stanley Tang and Andy like

came together and just started working together. I I would find the set of

together. I I would find the set of people you think you want to go to battle with for the next 10 years and you work together to find out whether

you're compatible. You have like

you're compatible. You have like different skill sets. They happen to be a business person, a marketer, a product person and an engineer. Like it's like [clears throat] a good team. It's a

really really good solid team. They came

together. Brian Chesy,

uh, Nate and Joe, they came together. It

was Joe's idea, Brian's leadership, and Nate actually built the first like system. These people work together and

system. These people work together and for a period of time to find out that they can work together and then the problem kind of just falls out of that.

What are each person passionate about?

Why do they care? Um,

is this poem worth working on, etc. I think sometimes people overfocus on the problem and not focus on whether you have the right group of people coming

together and you have lots of people you can work with here. one one one of the biggest pieces of my my father gave me when I was leaving college we're graduating I you know he's meeting my friends we're having dinner he's like

what do you think of my friends and he's like your friends are great they're really smart I was like that's not sounding very positive what's what's wrong it's like they have too many

options and I think all of you have a lot of options and your ability to sort of think about all the different options and optimize

izing them will prevent you from focusing on the one or two things that can help you build a company because at the end of the day a company has one core idea,

one core vision and uh that's very very hard to say I'm going to dedicate the next 10 years for

that one thing and it's scary but you know what if it doesn't work you still have your Stanford degree to fall back on.

All right, I got it slightly more last the second time around.

Curious. I feel like I and the general public love stories about the founders. Great pops and what makes them unique. You mentioned Brian,

you know, Deis, the visionary, Cheryl Sandberg, The Business. I feel like we hear lots of those stories and I'm curious for you, what makes you exceptional at what you do and what is

your unique edge? How do you think about honing your craft?

[clears throat] Uh, I'm not that good.

I mean, if you look at the number of companies we invest in and how many times we're wrong, we're not that good.

Um, and so I think the one thing I would say that I'm a little unlike impervious to pain,

I uh don't feel risk the same way as most people feel risk. I think the challenge with hiring people from Stanford at Sequoia has been that people don't know how to take risk and deal

with failure.

Um, my parents told me I was a failure.

I'm never going to amount to anything. I

I'm not a good student. Um,

and so I never cared what other people think. The best thing the best thing

think. The best thing the best thing that can happen to you is figure out who you are and be your own person. And I figured that out

person. And I figured that out relatively early in life. The funny

thing is like I got good grades because I know how to test well. But I was not a good student and I never I never fit in anywhere.

And if you want to go on an entrepreneurial journey, you have to be okay with being an outsider for a long period of time.

That probably allows me to be a better operator investor than most because I will take all the signals from the

world and process it and make up my own mind. And I will always sort of try to

mind. And I will always sort of try to learn from my failures and not see it as a failure.

Um, one way to think about it is there's no such thing as failure. there there's

a there's mistakes that you learn from and there's mistakes you don't learn from. Don't don't do the latter. If you

from. Don't don't do the latter. If you

make mistakes, learn from them. It was

there was an expensive uh tuition, but learn from it.

What is your first job?

Like every single kid, I had a paper out as the first job.

Paper route. [clears throat]

What was the closest neardeath experience you faced for the company?

I I mean I had a real near-death experience. Isn't that more interesting?

experience. Isn't that more interesting?

Closest near death.

I I was uh I [clears throat] was driving to tell me networks and um I was driving

pretty fast. Uh it was during traffic

pretty fast. Uh it was during traffic and it was the first day of rain on 280 and someone cut me off and I was going

to hit them and rear end them. So

instead of that, I turned to the right and then my car spun 180°. I was in the opposite direction on 280.

What about your dad for a company?

I think [laughter] so many of us died.

Okay.

What's the what's the most interesting race or story for working with a founder and finally like signing a term for

them? I mean, he signed a term sheet.

them? I mean, he signed a term sheet.

He signed a term sheet at a bar.

How many drinks did you have? [laughter]

The founder had three drinks. I had

none.

[laughter] I'm not going to tell you this story.

Thank you. I had a good We had a good We had a good signing.

Thank you everyone. [applause]

Thank you everyone. [applause]