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0:58

You may have heard me reference the idea of

1:00

maniacs on a mission and how much that idea

1:02

excites me. Well, David Senra is

1:04

my favorite maniac on one of my favorite

1:06

missions with his weekly crafting of the Founders

1:08

Podcast. Through studying the lives of

1:11

legends, he weaves together insights across history

1:13

to distill ideas that you can use

1:15

in your work. Founders

1:17

reveals tried and true tactics, battle-tested

1:19

by the world's icons and has

1:21

David's infectious energy to accompany them.

1:24

With well over 300 episodes, your heroes are

1:26

surely in the lineup and his

1:28

recent episode on Oprah is particularly great. Founders

1:31

is a movement that you don't want to miss.

1:33

It's part of the Colossus network and you can

1:35

find your way to David's great podcast in the

1:37

show notes. Hello

1:42

and welcome everyone. I'm Patrick O'Shaughnessy and this

1:45

is Invest Like the Best. This

1:47

show is an open-ended exploration of markets,

1:49

ideas, stories, and strategies that will help

1:51

you better invest both your time and

1:53

your money. Invest Like the Best

1:55

is part of the Colossus family of podcasts and you

1:57

can access all our podcasts including the free free podcast.

2:00

including edited transcripts, show notes, and

2:02

other resources to keep learning at

2:04

joincolossus.com. Patrick

2:07

O'Shaughnessy is the CEO of Positive Some.

2:10

All opinions expressed by Patrick and podcast

2:13

guests are solely their own opinions and

2:15

do not reflect the opinion of Positive

2:17

Some. This podcast is for

2:19

informational purposes only and should not be

2:21

relied upon as a basis for investment

2:23

decisions. Clients of Positive Some

2:26

may maintain positions in the securities discussed

2:28

in this podcast. To

2:30

learn more, visit psum.vc. My

2:36

guest today is Pat Grady, a longtime growth

2:38

investor at Sequoia and one of the firm's

2:40

senior leaders. Pat has been

2:43

a part of a long list of

2:45

legendary investments ranging from Snowflake, Zoom, ServiceNow,

2:47

Qualtrics, Okta, HubSpot, Notion, and OpenAI among

2:50

many others. There aren't many investors

2:52

who reference as well as Pat, both inside and

2:54

outside of his firm. We talk

2:56

about investing, building an investing firm and

2:58

building enduring companies. Please enjoy this great

3:01

conversation with Pat Grady. Pat,

3:04

I'm lucky in this conversation in that I

3:07

know a lot of your partners have interviewed

3:09

them, understand the business, and I'm fascinated by

3:11

Sequoia's history and the way that you invest,

3:13

but I've spent less time on the growth

3:15

side. And so I'm really excited to do

3:17

this with you today. I thought

3:19

a fun place to begin would be to talk

3:21

about the nature of

3:24

internal, healthy peer pressure at

3:27

Sequoia. How

3:29

it's created and

3:31

maintained in a way that is on that line

3:35

of healthy and overly intense. I

3:38

would love to hear as much detail as you

3:40

can manage about where this

3:42

internal pressure comes from and

3:44

how you tend to that flame. I'll

3:47

start with where it comes from. And

3:50

the simple answer is Don

3:52

Valentine. Don Valentine's the founder

3:54

of Sequoia Capital, one of the many brilliant

3:56

things that he did to build an organization

3:59

that would endure. build

10:00

values that have led to this high standard you

10:02

hold for yourself? Yeah, I

10:04

think this is on our website. But one of the

10:06

things I remember my dad always saying when we were

10:09

growing up was when your values are clear, decision making

10:11

is easy. And I think that was a

10:13

line he got from Walt Disney or something like that. But

10:17

I've always really believed in that. I

10:19

put a lot of thought into family

10:22

values and the team values and try to

10:24

make sure that those serve as the guidelines.

10:26

So my wife Sarah and I have, we

10:28

actually have family values and we have four of them.

10:31

And we had a fairly complicated form that

10:33

our kids didn't really understand. And we simplified

10:35

them. And they sound like motherhood

10:37

and apple pie. They're just work hard, be

10:39

kind, think for yourself, family first. That's it.

10:42

It's pretty straightforward. But with those four, you can

10:44

pretty much derive the right

10:47

answer to any given question and any given

10:49

situation. Or if the girls do something that

10:51

they shouldn't have done, we can usually explain

10:54

why they shouldn't have done it in the context of

10:56

those values. Or here at

10:58

Sequoia, the two things we care about most

11:00

as a partnership or performance and teamwork, but

11:03

then each team operates in

11:05

a slightly different context. And so our

11:07

team, the growth team has its own

11:09

values, which are value number

11:11

one, aggressive, but humble, value number two,

11:14

demanding and supportive, value number three, high

11:17

give is at zero volt, value

11:19

number four, strong under scrutiny. Those

11:22

four things also collectively we think can

11:25

get you to most of the behaviors that are

11:28

required to be effective over the long term at

11:30

Sequoia. Which of those do

11:32

you think is the hardest to put into practice on the Sequoia

11:34

growth side? Which of those values? I

11:36

think they're all hard. I mean, they wouldn't be good values

11:38

if they weren't hard. I mean, I'll

11:40

take demanding and supportive, which our partner, Rubby,

11:42

I think deserves credit for bringing that one

11:44

to us. There are

11:47

plenty of organizations that are 10 out of

11:49

10 demanding, but they may not be great

11:51

places to work. There are plenty of organizations

11:53

that are 10 out of 10 supportive, but

11:55

they may not be high performance. Most

11:58

people think there's an inherent trade off between. these

12:00

two things, we do not. We

12:02

think that being demanding of one another is being supportive,

12:05

and that the best thing we can do for each

12:07

other is to demand excellence, first of

12:09

ourselves and then of each other. And

12:11

so trying to be 10 out of 10 demanding and

12:13

10 out of 10 supportive, it's

12:15

hard because it's not necessarily human nature, but

12:18

we think it's part of the key to

12:20

our performance here. Do you

12:22

think that some of these things you're talking

12:24

about can be taught or are they just

12:26

things that are innate in a person? And

12:28

you were just hardwired this way, I read

12:30

that you had an inside sales job and

12:32

you wanted to win that competition every day.

12:34

And at a young age, you

12:36

just had this, what seems like from the

12:38

outside pre-wiring to be

12:40

ultra competitive and just

12:42

look at the world a certain way. Is

12:44

that your experience with founders and with people that

12:46

you work with that either have it or you

12:48

don't? And it's just unfortunate for the people

12:51

that don't have it. We believe that

12:53

our business is an apprenticeship business. I think in some

12:55

ways life is an apprenticeship business. I

12:58

won the cosmic lottery to

13:00

be born to wonderful, kind,

13:03

hardworking, loving parents. And

13:06

I think when you start there, you're off to

13:08

a pretty good start. And I think

13:11

the stuff that they taught me was sort

13:13

of reaffirmed throughout my life. First at Boston

13:15

College, which is a Jesuit

13:17

Catholic institution where the

13:19

Jesuit motto is men and women for others.

13:21

And then the BC motto is ever to

13:23

excel. And if you put those two

13:25

things together, you have this concept of whatever

13:28

you do, you should do to the very best of your

13:30

ability. But don't forget that we're all

13:32

in this world together. And so you shouldn't do it

13:34

just for yourself. You should do it for the community

13:36

and the world around you. When

13:38

I graduated from school and

13:40

started working at Summit Partners

13:42

in Boston, I was

13:44

lucky enough to work for a guy

13:47

named John Carroll, who I

13:49

didn't know much about before going to work for him.

13:52

And it turned out that he was both

13:56

probably the best investor at

13:58

Summit. and the

14:01

best human being you could possibly hope to work

14:03

for. And so in my

14:05

first few months at Summit, there was a situation

14:07

that came up where somebody

14:09

was trying to take

14:12

credit for a thing that I had done, and

14:14

I wasn't sure if I should fight for credit,

14:16

just let it go. And

14:19

so I went to John Carroll or JC, and

14:21

I said, hey, JC, I've got this situation, what

14:23

would you do? And

14:25

his answer was, in my

14:28

career, I've always taken the

14:30

highest possible moral road, and

14:33

I have never regretted it. And

14:36

that was very clarifying for me. And

14:38

so I let the situation go, and it turns out that

14:40

it was for the best. And it

14:43

reinforced what I heard at BC, reinforced what I heard

14:45

from my parents. And then I got

14:47

to Sequoia, and I remember

14:49

before joining Sequoia, people had told me, Doug

14:52

Leone is the greatest salesperson you will ever

14:54

meet. And so I was very

14:56

excited. I was like, okay, I can't wait to see

14:58

the magic of Doug Leone, the salesman. And

15:01

my basic job when I got here was

15:03

find companies that were interesting enough to bring

15:05

Doug to meetings. And

15:07

so Doug and I did hundreds of meetings in

15:09

my first couple of years here, maybe thousands. We

15:11

did a ton of meetings. And

15:14

after my first couple dozen meetings with Doug,

15:17

the thing that blew me away was when we went to

15:19

a meeting and anything related to

15:21

an investment came up, where maybe you're supposed

15:23

to be negotiating the investment. Doug

15:26

was so unbelievably transparent, it

15:28

blew me away. And

15:31

I remember afterwards, I said, well, Doug, how do

15:33

you, for example, a negotiation with

15:35

Doug might be, you're gonna wanna

15:37

pay X, we're gonna wanna pay one half X.

15:39

Why don't we just call it 0.75X and call

15:42

it a day? And

15:45

the whole thing would take 30 seconds. And

15:47

the other person would say, yeah, okay, that

15:49

seems right. He

15:52

wasn't making stuff up. He was actually telling them what

15:54

we wanted to pay. And they was actually guessing what

15:56

they wanted. And then it was just meeting in the

15:58

middle at a price that we didn't. the

18:00

market dynamics, that's all very doable. But

18:02

I think the difference between the

18:04

point of view that we're going to have on the investment and

18:07

the point of view that somebody else is going to have on

18:09

the investment is not going to be a function of

18:11

better understanding the cohorts or

18:14

listening just a little bit more carefully to

18:16

the customers. I think it's going

18:18

to be a function of actually understanding the people. And

18:22

when that model ends at the out year,

18:24

five years from now, has

18:26

that founder gotten 10 times better? And do

18:28

they still have gas in the tank? Or

18:30

is that founder exhausted and just

18:32

barely clinging to life? And

18:35

so the way that I like to do that is

18:37

to go for a long

18:39

walk with founders and try to understand

18:42

who they are and all the

18:44

ways that don't show up on LinkedIn. What was their

18:46

childhood like? What experience in their

18:48

childhood most contributed to who they are today?

18:50

What characteristic did they take from their mom?

18:52

What characteristic did they take from their dad?

18:55

If they have brothers and sisters, how do

18:57

they view themselves in relation to their brothers

18:59

and sisters? And what was the happiest

19:01

moment from their childhood? What was the biggest mistake that

19:03

they made in their childhood? All those

19:06

sort of questions that in and of themselves may not

19:08

tell you much, but when you go

19:10

deeper and deeper and deeper, you really start to

19:12

understand who they are and what they value. And

19:15

if you can understand that, you start to understand what

19:17

drives them. And then you can start to

19:19

map that onto the business they're trying to build and

19:22

figure out if they're actually likely to build

19:24

something for the long-term or

19:26

if this is more of a passing fancy and they

19:29

want to play the game of entrepreneur versus actually building

19:31

something that matters. When

19:33

you want to win an investment

19:35

and it's competitive, meaning there are other

19:37

very talented investors, very smart

19:39

investors who can do a lot for

19:41

the business. There's this plethora of amazing

19:43

people out there. When you're

19:45

up against that sort of situation, how do you

19:47

win? I'm assuming your win rate at

19:50

Sequoia in general and for growth specifically is very

19:52

high and that you're seeing

19:54

most of the opportunities out there because of

19:56

the size of your business and platform. So

19:58

how do you keep... that win rate really

20:00

high. What are the things you do to

20:03

win when it's competitive? I

20:06

think one of our core beliefs

20:08

is that anybody can beat us on any given day. So

20:11

back to your comment earlier about how do we

20:13

keep that pressure on ourselves, we truly believe that.

20:16

And we believe that in

20:19

part because our competitors are

20:21

no joke. They're very smart people who

20:23

know what they're doing, who work really

20:25

hard, who have killer instincts, who

20:28

have a nice way with founders at lots

20:30

of other firms. And any given one of them

20:32

could beat us on any given day. And so

20:35

when we're in a competitive situation, we

20:37

can't just waltz in and hope that the

20:39

Sequoia Business Card is going to give us

20:41

the advantage. It is hand-to-hand combat, and we

20:43

have to be on our absolute best if

20:45

we have any hopes of making that investment.

20:48

So what does it mean to be at your absolute best? I

20:51

think the biggest mistake people make is selling

20:53

by telling founders how awesome you are. Founders

20:56

don't care how awesome you are. They want

20:59

to know how awesome they have a chance to become. The

21:01

thing that we try to do is not

21:03

to sell the merits of Sequoia and

21:05

all the wonderful value added hands-on company

21:07

building stuff we can do for you.

21:10

Maybe we'll sprinkle in a little bit of that here and there.

21:13

But the thing we really want to do is understand who

21:15

are you, what do you want

21:17

to become, and what is it you want to

21:19

build. And if we can understand

21:21

those things, and we can feed that

21:23

back to you to show you that we understand those things,

21:25

and to show you that we are interested in those things,

21:27

and we want to be a part of those things, and

21:30

maybe some of the resources that we have here

21:32

could help you in achieving those things. But the

21:34

main thing is not our resources. The main thing

21:37

is your vision and your dream. And

21:39

if you believe that we

21:41

believe in you, and we are going

21:43

to be here to support you, and we

21:46

shouldn't be trying to win an investment if that's not

21:48

true, and so we should be able to articulate that

21:50

to you in a very authentic way, if you

21:53

believe that, it starts to become fairly

21:55

straightforward. And so we hope

21:57

to be in situations where the right people are going

21:59

to be able to the

26:01

vision of the future that this founder hoped to

26:03

build with the company and this founder hoped to

26:05

build for him or herself. And

26:07

that was the most potent elixir

26:09

you could possibly imagine. And maybe

26:12

not 100% of the time, but 95% of the time

26:14

after Doug unfrolled one of those, the

26:19

founder would be desperate to work with us. And

26:22

then all we had to do is decide whether or not we wanted to.

26:24

So that was a pretty magical experience. What's

26:26

your version of that? Now you're the Doug. What

26:29

do you do in those meetings? Obviously

26:31

everyone's style is different. How have you honed it and

26:33

shaped it in your own way? Well,

26:36

first off, I will never be the Doug. They're the only

26:38

one. It

26:40

is not possible for there to be another duck. One

26:42

other trick that I had when I was in

26:44

my 20s and early 30s and having witnessed

26:47

Doug do that so many times, when

26:50

I would go into these meetings and that was

26:52

2007 when I joined here was the early days

26:54

of the cloud transition. Most of

26:57

the founders that I was meeting with, they'd been

26:59

in the enterprise for 20 years. And

27:01

so almost every meeting I went

27:03

into the founder was meaningfully older than I

27:05

was. I looked at a kid who

27:08

happened to have a very nice business card, but

27:10

otherwise no experience that could be relevant to them.

27:12

And so I had to

27:14

figure out how to get credibility. And

27:18

the way that I would usually do that, mirroring

27:20

what I'd learned from Doug, was to start the

27:22

meeting by saying something like, hey, I

27:24

don't know much about your market, but

27:26

it seems like you have a chance to do blah, blah,

27:29

blah. And the

27:31

blah, blah, blah would be whatever thesis I

27:33

might have come up with based on looking

27:35

at their website or talking to some of

27:37

their customers or studying some of their competitors

27:39

or whatever the case might be. And

27:42

that was usually enough to

27:44

get the benefit of the doubt and to

27:46

get them thinking, okay, this guy kind of

27:48

understands who I am and what I'm trying

27:51

to build here. And I would say that

27:53

if you fast forward to today, that's still

27:55

the most powerful thing you can do is to make

27:57

a founder feel like they're seen. You

27:59

understand. them and try to validate their

28:01

ambition through the way that you

28:04

describe their business, I think that's still the most

28:06

powerful thing that we can do. And

28:08

then beyond that, that might

28:10

get you a nice in-person interaction with the

28:12

founder, but then ultimately we are in a

28:14

product-led growth business. Our product is

28:17

the service that we provide to our founders.

28:19

And when our founders tell other founders what

28:21

they think of us, that's ultimately

28:23

the thing that gets them over the line.

28:25

And so our objective function

28:27

is to maximize net multiple money returns for

28:30

our limited partners, not to maximize founder and

28:32

PS, but if we can

28:34

have our cake and eat it too, that's the best

28:36

of both worlds. And so founder and PS and net

28:38

multiple money returns are the two metrics that we probably

28:41

care most about. Thinking about

28:43

the emails that you would write reminds me that

28:45

a bunch of people have mentioned to me how

28:47

good you are at memo writing. And

28:49

I know memos are taken very seriously

28:51

at Sequoia, but I'd love to hear

28:54

for you specifically how you

28:56

define great business or investing writing,

28:59

what you are seeking to do in those

29:01

memos and what you respect most

29:03

when you see it in other similar

29:05

memos. This comes back to

29:07

that expression, listening happens at the year. I

29:10

think there are a lot of memos that

29:12

get written speeches that get given where the

29:14

objective is to impress upon other people

29:16

how smart you are. And I think when

29:19

you write a memo or you give a talk or

29:21

whatever, you should do so with the service mindset. My

29:23

objective is not to impress upon anybody how

29:26

smart I am. My objective is for them

29:28

to understand. And if I

29:30

actually want them to understand, I need to

29:32

make it as simple as humanly possible. And

29:34

if that means sacrificing a few details for

29:36

the sake of clarity, not in

29:38

a way that distorts the picture, but in a

29:40

way that clarifies the picture because people aren't going

29:43

down rabbit holes that don't represent first order issues,

29:45

that's probably worth it. And so

29:47

a great investment memo is three pages, not 12 pages.

29:51

And when you're done with those three pages, you should have

29:53

an accurate point of view on everything that

29:55

is good, everything that is bad,

29:58

and the so what will When we mash

30:00

that soup altogether, should we make this

30:02

investment or not and why? And

30:05

I think you can do that in three pages generally. Take

30:08

a bunch of the exhibits, the cohort charts and everything

30:10

else, throw those in the appendix. You

30:12

can express that in one sentence. You don't need to have everybody

30:14

dig through all the data on their own. Go ahead and throw

30:16

that in the appendix in case anybody wants to go back there

30:18

and play with it. But keep

30:21

the narrative upfront, keep it tight,

30:23

keep it crisp, and make sure

30:25

that the thing you're optimizing for

30:27

is clarity and understanding, not trying

30:29

to impress upon people how smart you are. Do

30:32

you remember reading a memo, not one

30:34

you wrote, but another one about a

30:36

company that got you the most physically

30:38

excited? A hundred percent, yes.

30:41

The first one that comes to mind is in

30:43

2009, a

30:46

few weeks before the market bottomed

30:48

out, we got into business with

30:50

Airbnb at the seed stage. And

30:53

that seemed like a crazy idea, but it was working

30:55

and we fell in love with Brian and Joe and

30:57

Nate. And anyway, so we were lucky enough to be

30:59

in business there and be at the seed stage. Fast

31:02

forward to 2012, the

31:05

company was clearly working, even

31:08

as existing shareholders and board members, we weren't

31:10

sure exactly what to make of it. And

31:13

there was a growth realm coming together. So

31:15

the first memo comes out and

31:18

it's a typical investment

31:21

memo. It talks about the market and it

31:23

talks about the numbers and it talks about

31:25

the team and it talks about the competitors

31:27

and all that good stuff. And

31:30

there was a model at the end of it that

31:32

did your typical simple linear

31:34

extrapolation with decelerating growth

31:36

rates and maybe some flat margins.

31:40

And it got you a two and a half X return. And

31:43

so you read that and you sort of yawn. Okay, two

31:45

and a half X, who cares? And

31:47

so we decided on that Monday not

31:50

to move forward, but we were interested. So we decided to do

31:52

some more work the following Friday.

31:54

A second memo comes out page

31:56

one of the memo. Then to

31:58

become a one. $100 billion

32:01

company and here is why. And

32:03

it laid out with perfect clarity, why

32:07

they had a chance, not just

32:09

to be a two and a half X multiple

32:11

of money, but to return the fund a couple

32:13

times over because of the

32:15

size of the market, because of the structural

32:18

superiority of the business model, because

32:20

of the creativity and passion

32:22

and clarity and mission orientation

32:25

of the founders. And

32:27

it takes courage to do that because most of

32:29

the time when you look at a business that

32:31

was probably grossly overvalued by

32:33

any traditional metric at even a $2

32:36

billion entry price and say

32:38

that it will someday be a $100 billion business, you

32:41

get laughed out of the room. But

32:43

it turns out those are the only investments that

32:46

actually matter. And if

32:48

you don't have the conviction that the company has

32:50

a chance to be something truly special, you

32:53

shouldn't be recommending that we invest. You

32:55

sent me these really fascinating criteria for making

32:57

an investment at the growth stage one

33:00

of which is, I can't remember the statistical term,

33:02

leptocritosis or something like that. Some

33:05

very fancy sounding, kritosis metric

33:08

that basically is like what

33:10

you just said. We're not making a growth investment to

33:12

earn a three X return. That may happen a lot.

33:14

We're only gonna make an investment if we

33:16

feel even at the growth stage that it

33:18

has this crazy asymmetric upside. Why that specific

33:20

thing is one of the small handful of

33:23

things that was on your list of criteria.

33:25

Well, first I wanna just highlight the choice

33:27

of words there. The choice of words is

33:30

important. We have leptocritic return profiles. That

33:32

comes from Rulof. So Rulof might be the

33:34

only licensed actuary who is active in the

33:36

venture capital business today. And so

33:38

his actuarial statistics background blessed us

33:41

with that word. But leptocritic

33:43

return profile to your point basically means.

33:45

Fat right tail. Better

33:47

than typical chance that the 10 X plus

33:50

return. Yeah. The reason we do that

33:52

is because our experience has been if

33:54

you quote unquote underwrite to a three X return,

33:56

you end up with a two X. If

33:59

you think something actually. has a chance to be

34:01

a 10x plus for return, maybe you

34:03

end up with a 3x or a 5x or a 7x or something shorter

34:06

there. But if you don't think

34:08

that that upside potential is there, it's

34:10

probably just not a good enough company, or

34:12

it's probably just not a big enough market.

34:15

And so we don't underwrite

34:17

to a 10x expecting every investment to produce

34:19

a 10x return, but if we can't see

34:22

a 10x return, it's probably just not good

34:24

enough. There's three really

34:26

fascinating business criteria, which again, you're evaluating

34:28

companies when you can sink your teeth

34:30

into them. There's customers, there's revenue, there's

34:33

sometimes profit, there's a team, there's lots

34:35

you can dig into. And

34:37

I'd love to just spend a minute on each

34:39

of these concepts, because a lot of the things

34:41

you've talked about, they're just very simple and elegant,

34:43

but I'm sure that there's just tremendous amounts of

34:45

nuance and depth underneath the hood. The first is

34:47

the term you use is that it's an emerging

34:49

market leader, which sounds like it could be a

34:51

double entendre, like meet a few different things. So

34:54

maybe describe that one. Well,

34:56

first, the process by which we came to this

34:58

fact, so it's many years of iteration, but there

35:00

was an offsite we did, I think it was

35:02

called the Inn at Pelican L down in SoCal,

35:05

where we locked ourselves in a room for

35:07

about two days and spent

35:09

the entire two days just debating

35:11

these words on a whiteboard to

35:13

come up with exactly the right criteria

35:16

to define our investments. So the emerging

35:18

market leader, it is a little bit of a

35:20

double entendre. The thing that people

35:22

misunderstand about this most frequently is, oh,

35:25

okay, so we need to invest

35:27

in a company that is the market leader today. No,

35:30

we need to invest in the company that we believe

35:33

will be the market leader tomorrow. They

35:35

could be two people with an idea today.

35:37

It can be objectively number seven today. Google

35:40

was not the first search engine. Flextronics

35:42

was not the first contract manufacturer. There

35:44

are lots of examples of companies that were not

35:47

the first and did not start in a market

35:49

leadership position, but because they had

35:51

a better architecture or a better business model

35:53

or a better team, they ended up being

35:55

the market leaders over time. And

35:57

one thing that is sort of objectively true particularly

36:00

in the world of technology is whoever ends up

36:02

number one in the market doesn't

36:04

just have their proportional share of the market

36:06

cap, they have a disproportionate share of the

36:08

market cap. And so investing in number

36:10

two or number three in a market maybe can make a

36:13

little bit of money, but you're not going to produce outsized

36:15

returns for your limited partners. So it's really important for us

36:17

to invest in the companies that we think are going to

36:19

be number one in the market. The

36:21

double entendre is that we think the

36:24

market is emerging and

36:26

we think that the company is emerging

36:29

to become the leader of that market.

36:31

So the market itself might not have much of

36:33

a TAM today, but we have a

36:36

reason to believe that it's going to have a TAM tomorrow.

36:38

And a good example of that, I remember

36:40

when Okta was going public in 2017 while they were on

36:42

the road show, Forrester

36:45

published a report that said the TAM for cloud

36:47

identity was 150 million. Well, at

36:50

that time had, I think a little more

36:52

than 150 million of revenue. And

36:54

so people look

36:56

at a static moment in time and

37:00

have a hard time extrapolating that to five or 10 years from now

37:02

when the company has matured and trying to have that point of view

37:04

about where the market is going

37:08

coupled with the point of view about what position the company

37:10

is going to occupy in that market.

37:13

That is what ultimately gets us to whatever that out your

37:15

revenue projection might be. It's not a function of the financial

37:17

model. It's a function of the market dynamics in the company's

37:21

position. The second respecting

37:23

the specificity of the word choices

37:26

here is maybe the most

37:28

interesting one, which is unique and compelling value

37:30

proposition, which on the surface sounds like, yeah,

37:32

sure, like sounds great. But I think behind

37:34

each of those words is something that is

37:36

incredibly important as you evaluate the businesses. So

37:38

maybe describe why those specific words. Yeah.

37:40

So the first one emerging market leader is

37:43

a comment on revenue scale. The

37:45

second one, unique and compelling value

37:47

proposition is a comment on margin

37:49

structure. And so if

37:51

you have a unique value

37:53

proposition, that should show up in

37:55

gross margin. It should show up in

37:57

gross margin because if your product is truly unique, you

38:00

should be a price-setter, not a price-taker. And

38:02

if you are a price-setter, you should be able

38:05

to set a price that's gonna provide you

38:07

with nice gross margins. And so

38:09

unique value prop gets you to a good

38:11

gross margin. Compelling value

38:13

prop is a comment

38:15

on operating margin. If

38:18

your product is truly so compelling, you

38:21

shouldn't have to bludgeon people to death with sales and marketing

38:23

to get them to try it and to get them to

38:25

pay you for it. So if

38:27

it is truly compelling, that should

38:29

show up in the efficiency of your go-to-market

38:31

organization. Or maybe there's a number like new

38:33

ARR divided by sales and marketing. Or

38:36

there's a number like LTV to CAC. Or

38:38

there's a number like payback period. There should

38:40

be some number, or maybe 99% of

38:43

your new customers come in organically. There

38:45

should be some number that basically demonstrates how

38:47

compelling the value prop is that

38:50

leads to low sales and marketing, which in turn

38:52

leads to a high operating margin. And so if

38:54

you have an emerging market leader, chances are you'll

38:56

have good revenue scale. If you

38:58

have a unique and compelling value proposition, chances

39:01

are you'll have a nice margin structure associated

39:03

with that revenue scale. And those

39:05

are the ingredients that should ultimately determine

39:07

the quote unquote out-year financial model as

39:09

opposed to the typical linear extrapolation that

39:12

you might otherwise see. I'm

39:14

realizing now that the third one, which is

39:16

listed as sustainable competitive advantage, is the perfect

39:18

third domino, which is, okay, you get to

39:20

revenue scale, you've got good margins, how do

39:22

you protect them? Exactly. And so

39:24

what are you thinking when you're trying to suss

39:27

out the end state potential future

39:29

moat or sustainable competitive advantage? How do

39:31

you do that? It seems really

39:34

hard to know ahead of time. This

39:36

rhymes with the conversation we're having on people. And

39:39

the reason, this is a hotly debated

39:41

term, sustainable competitive advantage versus saying moats,

39:44

because moats is probably the more common

39:46

vernacular. The reason it's sustainable competitive

39:48

advantage and not moats, a

39:50

moat implies something that has

39:52

been built and will protect you forever

39:54

after, whereas the sustainable

39:57

competitive advantage is a bit

39:59

more dynamic. It is an advantage

40:01

that you are building every single day. And

40:04

the number one sustainable competitive advantage that we

40:06

see out of companies, it's not

40:08

a network effect. It's not an ecosystem advantage.

40:10

It's not some piece of IP that's impossible

40:12

for other people to replicate. It

40:15

is the DNA of the team. And

40:17

the canonical example of this was in 1999, 2000, the

40:21

smart money would have bet on eBay, but

40:23

it turns out you should have bet on Amazon. People

40:26

thought the marketplace business model of eBay was

40:28

so elegant and defensible, and

40:30

selling books online was a commodity business. It turns

40:33

out that one of those companies had Jeff Bezos

40:35

and the other one didn't. And

40:37

it was the founder and the DNA and the

40:39

culture that was created that led to the compounding

40:41

advantages over time. I think a more

40:43

modern example of that would be DoorDash. There were

40:45

plenty of people who tried to get into this

40:47

delivery business. There was only one Tony Hsu. And

40:50

so our partner Alfred who sponsored

40:52

that investment, he met Tony

40:54

at the seed stage and

40:57

liked him, didn't quite have conviction, kept in touch

40:59

with him. And then I remember Alfred told a

41:01

story about he happened to go to dinner with

41:03

Tony. He was at a

41:05

dinner sitting next to Tony prior to the Series

41:08

A and spent the entire dinner

41:10

talking with him about how DoorDash worked and

41:13

the level of detail and nuance and

41:15

grasp of the business fundamentals that Tony

41:17

had blew Alfred away. And

41:20

it was coming out of that that Alfred came back and

41:22

said, we have to make this investment, not

41:24

because of the business model, not because of

41:26

the market, but just because of the founder.

41:29

This is the sort of person who's gonna

41:31

create compounding advantages forever. Yeah,

41:33

having interviewed Tony, his command of

41:35

that business, which is a very

41:38

complicated business is truly unbelievable. Just

41:40

a special human. I've

41:42

also loved the way you've

41:44

articulated how you do

41:46

interviews, reference checks, and just evaluate

41:49

a person. You mentioned some of your

41:51

favorite questions to like ask on the long walk,

41:53

ask about their family, ask about how they make

41:55

decisions, all these sorts of things. There was a

41:57

couple in there that I'd love to actually take

41:59

and turn on. you. And one of them, which

42:01

I liked a lot was if you had this

42:03

magic wand that you could change something about yourself,

42:05

what you would change. I'm curious what your answer

42:07

is to that specific question. I

42:10

was afraid you're going to do this and I really

42:12

should have prepared to answer my own questions because I

42:14

haven't. But I

42:17

have always admired, I

42:20

guess it would be overly simplistic to say

42:22

extroverts. People who are

42:24

charming can command a room, natural

42:27

networkers are part of Carl Eschenbach

42:29

is a good example. Carl

42:31

is now spending 99.9% of his time as CEO

42:34

of Workday, but he still helps us

42:36

out on stuff from time to time. And Carl is

42:39

here at Sequoia for about seven years. But when he

42:41

walks into a room, it's as

42:43

if there's this hushed voice that follows him

42:45

just whispering, and it just

42:48

the presence that he has is unbelievable. And

42:50

his wife's the same as what he has the

42:52

same way. Carl and Anna just they light

42:54

up a room. Actually, my wife

42:56

is like that she lights up a room as well. I don't

42:59

light up a room.

43:02

I'm probably hiding in the corner hoping that somebody I

43:04

already know comes over and talks to me so I'm

43:06

not forced to go network. And

43:09

that's something that I have worked on over

43:12

and over and over again and forced myself

43:14

into awkward situations over and over and over

43:16

again. And as much as I've done it,

43:19

it's still not comfortable. I'm still not good

43:21

at it. And so that's probably the magic

43:23

wand is to make myself a little more

43:25

extroverted, a little more charming, a little more

43:27

able to light up the room the way

43:29

that some other people can. Well,

43:31

we have to lead into our strengths. And you

43:33

said earlier that you had this brute force mentality.

43:35

And I love this line about keeping

43:38

going until nothing surprises you when you're investigating

43:40

a person doing reference checks, I guess maybe

43:42

even investigating a business, maybe say a little

43:44

bit more about what that actually takes

43:47

and means to keep surprises

43:49

you. I just think it's like a really nice heuristic. We

43:52

get the question a lot from founders. What

43:55

are you looking for? And

43:57

what they want is a simple answer. They want

43:59

to hear where we're

44:01

looking for your first 10 POCs to convert,

44:04

or we're looking for X million of revenue

44:06

growing Y percent year over year. That's the

44:08

sort of stuff that they want. The answer

44:10

that I give them is a very frustrating

44:13

answer, but it's the real answer, which is

44:15

the thing that we're looking for is not

44:17

perfection. The thing that we're looking for is

44:19

clarity. Whatever story

44:21

you tell needs to be internally

44:23

consistent. Whatever evidence is

44:25

available to support that story needs

44:28

to support the story. It can't be disconfirming

44:30

with important aspects of the story. I mean,

44:33

it would be snowflake and zoom. So

44:35

we were lucky enough to get into business with

44:37

Erica zoom when the company was close to a

44:39

hundred million of revenue. It was probably 85, 90

44:41

in that neighborhood. And at that

44:43

scale, zoom was already 80% plus gross margin.

44:45

And as much as they were trying to

44:47

hire more people and burn cash, they just

44:49

couldn't do it. The money from customers was

44:51

coming in too fast. And so

44:54

they were trying to burn cash, but they kept

44:56

generating cash every single quarter. And so

44:58

zoom had perfect margins across the

45:00

board, exponential growth, snowflake. We're

45:02

lucky to get into business with when they were

45:05

just shy of 50 million of ARR. So

45:07

not quite the same scale, but similar snowflake

45:10

would have been more of the workday snowflake

45:12

at that time had maybe 50% gross margins

45:15

and burning a whole ton of cash. And

45:17

when we went from our first investment in snowflake

45:19

to our second investment in snowflake, which

45:21

is only about six months later, what

45:23

triggered it was bad news. So

45:26

we do these semi annual portfolio reviews where our

45:28

portfolio companies send us a bunch of information. The

45:31

information comes in from snowflake. They're

45:33

behind the revenue plan. Gross margins are

45:36

worse than expected free cash flow is

45:38

worse than expected. And we

45:40

got that and said, Oh man, I'm in big trouble

45:42

here. Maybe we shouldn't make this investment. And

45:45

so I got some time with Brad Floring

45:47

who is the still the VP of FPNA

45:49

at snowflake and asked him to

45:51

walk me through what was going on in the numbers. And

45:54

it turns out it was all good news. It

45:56

turns out the reason it was all good

45:59

news, which is hard to appreciate. Why was

46:01

revenue behind? Well, revenue is behind because they're

46:03

landing much bigger deals than they were expecting

46:05

to land, and those much

46:07

bigger deals take more time to ramp up.

46:09

It's the ramping up of that stuff that determines the

46:11

revenue, not the landing of the deals. Why

46:14

is the gross margin behind? Well, the gross

46:16

margin is behind because they're getting pulled globally

46:18

faster than they expected, which means opening up

46:20

availability zones in regions that are going to

46:22

be underutilized around the world, which means that

46:25

COGS utilization is not as high as you

46:27

might expect it to be. The

46:29

gross margins are also down because they're starting

46:31

to get asked to do these full-scale teradata

46:33

replacements in the enterprise far earlier than they

46:36

had anticipated, which means snapping up on professional

46:38

services, which goes into COGS. And

46:40

then operating margins are down because it turns out

46:42

that the per rep productivity was much higher than

46:44

they were expecting, and so they were loading up

46:47

on the sales organization because the reps were just

46:49

producing way faster and at a way higher level

46:51

than they were expecting. And so all

46:53

of the reason that the numbers were

46:55

bad turned out to be good

46:57

reasons. In that situation, we ended

47:00

up having clarity on why the numbers were

47:02

the numbers and what was actually happening in

47:05

the business. And as a result, we went

47:07

from an initial $15 million investment to another

47:09

$200 million investment a couple

47:11

months later. Anyway, the moral

47:13

of the story is you just keep asking questions until

47:15

the picture that's in your head becomes clear, and it

47:18

doesn't have to be perfect. It does have to

47:20

be clear. If it's not clear, you're

47:22

not going to have a good understanding of what risks

47:24

you are taking and what return you can expect in

47:26

exchange. Do you have a specific

47:28

goal in your mind when

47:30

you're doing a reference check, that specific

47:33

unit of investigation? Yes.

47:35

If it's a reference check on a person, I

47:38

want to understand the vector that is that

47:40

human being. We're lucky

47:42

to be surrounded by people who are pretty

47:44

creative and good thinkers. And so this is

47:46

a framework that we learned from Elon Musk

47:49

about the output of an organization is the

47:51

vector sum of its individuals. And

47:53

the point is that a vector has both magnitude and direction.

47:55

So you want to hire people with high magnitude, but then

47:57

you have to make sure they're all pointing in the same

47:59

direction. And so the thing that I

48:02

want to get out of a reference check is the magnitude

48:04

and the direction. And the magnitude

48:06

is almost a top grading exercise. How

48:09

good has this person been at each step of their

48:11

life? Were they the best person in their high school?

48:13

Were they the best person in their college? Were they

48:15

the best person in job number one? Were they the

48:17

best person in job number two? And

48:19

that's a fairly blunt way to look at it. There

48:21

are plenty of dimensions beyond did you have the

48:24

highest GPA or did you have the best performance

48:26

reviews that indicate exceptional

48:28

performance. And so this is in part

48:30

where the direction component comes in. Maybe

48:32

they were not the best person in their high school

48:34

because the thing they cared most about in high school

48:37

was building businesses on the side. And maybe they were

48:39

incredibly successful in that endeavor. Or maybe they just fell

48:41

in love with coding and it turned out that they

48:43

were a phenom in the open source world while they

48:46

were failing in their history class. And

48:48

so understanding what they actually care about and

48:50

trying to figure out whether they've been exceptional

48:53

at the things they really care about, that

48:55

defines the direction of the vector and the magnitude of

48:57

the vector. And that's the thing I'm trying to suss

48:59

out. I know you studied physics

49:01

in undergrad. The vector thing reminded me. And

49:04

there's a lot of physics envy in investing. A

49:06

want for formulas and variables. We've even

49:09

done it a little bit today. Here's

49:11

our three things. Where

49:13

does the physics background help you and

49:15

where does physics in general should be

49:17

left behind or fall short in the

49:19

world of investing? My

49:21

Catholic guilt compels me to knowledge that I

49:24

was only a physics major for two years

49:26

of college. I love switching to economics and

49:28

finance with a concentration in math. And so

49:31

I just want to be clear, but I

49:33

appreciate it. One way that

49:35

I think about this, I'll use the analogy

49:38

of the two critical ingredients in

49:41

an investment are the people in the market.

49:44

The market determines how big the company can

49:46

get and the people determine how big the

49:48

company will get. And I

49:51

think similarly, when you approach things

49:54

with the view of physics, you're

49:56

sort of understanding the rules of the system.

50:00

the individual agents who are operating in

50:02

that system that will

50:04

ultimately determine the outcome. And

50:06

so I think the physics point of view

50:08

is very complimentary to a much

50:10

more human point of view. And

50:13

if you can get both and you understand

50:15

the system level dynamics, but then you also

50:17

understand the individual actors within that system, that's

50:19

where I think you end up with the

50:21

highest likelihood of making a good decision. Could

50:24

we apply all of that to the world

50:27

of AI today, both the market and the

50:29

sorts of people that you're beginning

50:31

to see thrive in a frontier,

50:33

in a Wild West feeling part

50:36

of the world and incredibly exciting,

50:38

but uncertain new technology. I would

50:40

love you to just frame up first how you think

50:42

about the market and your shorthand

50:44

for what the opportunity is here and how to

50:47

think about it. And then I'd love to talk

50:49

about some more specifics, but maybe just starting broad

50:51

strokes, what has you excited? What has

50:53

you pausing? What has you most interested in the

50:55

world of AI? As context

50:57

for this, my first 10 years or

51:00

so at Sequoia were basically

51:02

focused on the cloud transition and

51:04

it was 2007 to 2017,

51:06

which is pretty wonderful time to have that as a focus. Come

51:10

2017 ish, it

51:12

felt like the vast majority

51:15

of first-class market opportunities in the world

51:17

of cloud software had already been occupied.

51:20

If you went to the other major tectonic shift that

51:22

was happening at the time, which was mobile, the top

51:24

10 apps in the app store had

51:26

been pretty static for a number of years. And

51:29

so it felt like we were getting fairly late cycle

51:33

as far as these technology platform shifts go. A

51:35

bunch of us here started trying to think

51:37

about what the next major platform shift might

51:40

be. And at the time,

51:42

our shorthand was data. And

51:44

the reason our shorthand was data is

51:46

because we just observed that the best

51:49

application experiences we were seeing tended

51:51

to be fueled by a pretty healthy dose of machine

51:53

learning. And so it felt like

51:55

the companies that were making use of all the

51:57

data that was available were just creating better experiences.

52:00

and creating better businesses than companies that were

52:02

not. And so we had this loose

52:04

hypothesis that the next major platform shift was going to

52:06

be something related to data. That was

52:09

part of what informed the Snowflake investment. That was

52:11

certainly what informed the Confluent investment, the DBT investment,

52:13

some of the other things in the modern data

52:15

stack. But it also led us

52:17

toward natural language processing, natural language understanding,

52:20

which is almost a predecessor term for

52:22

what we now think of as LLMs.

52:25

It led us to Hug and Face, it led us

52:27

to OpenAI, it eventually led us to a bunch of

52:30

different application or developer companies around that whole theme. But

52:33

I mention that because the thinking that's gone

52:35

into the AI theme for us really

52:38

began in earnest many years ago when we

52:40

were seeing the maturation of the cloud and

52:42

mobile cycle and trying to figure out what

52:44

might be next. I'd

52:47

say the reason that we have conviction in the AI

52:49

theme and sort of what it is we've actually thought

52:51

about kind of has to do

52:53

with the precedent conditions coupled

52:55

with just what we're observing in the environment.

52:57

And when I talk about the precedent conditions,

53:01

the idea of a neural net has been around for

53:04

literally 70 or 80 years, but

53:06

it hasn't been possible given

53:09

computes, given bandwidth, given

53:11

data, given talent. It

53:14

hasn't been possible to put it into practice the way

53:16

it is today until very recently. And

53:19

the major accelerant, of course, was the

53:21

release of chat GPT, which

53:23

we think will end up being this generation's

53:25

Netscape moment. You know, if you go back

53:28

to 1996 when the browser first came out

53:30

of Netscape, that opened the eyes of

53:32

everybody to the power of the Internet. I

53:34

think similarly when chat GPT came out in the fall of

53:36

2022, it opened

53:38

everybody's eyes to what was going on with LLMs

53:41

or AI more broadly and gave

53:43

people a visceral sense for what could be done. And so

53:46

that was sort of a step function increase

53:49

in the activity in this area. Earlier

53:51

that summer, we had stable diffusion. If you

53:53

remember, summer of 2022, stable diffusion came out.

53:56

All of a sudden, people are creating these fantastical images

53:58

and sending them around on Twitter. That

54:00

took the AI market from researchers

54:02

to researchers plus machine learning engineers.

54:05

When chat GPT came out that fall, it

54:07

was another step function increase and the people

54:09

were paying attention and it went from ML

54:11

engineers to all engineers, product managers, founders, consumers,

54:14

boardrooms of Fortune 500 companies. And

54:18

all that energy that's been focused on this

54:20

has started to lead to some pretty interesting

54:22

applications. How big do you think

54:24

this would be if I froze

54:26

the current frontier model

54:29

capabilities? If I said we're never

54:31

going to get anything better than GPT 4.0 or

54:33

Claude 3.0 or 4.0, the

54:37

best ones that are out there today, do you

54:39

think that it's still exciting

54:41

or is most of the

54:44

excitement dependent on continued and

54:46

successful scaling of the

54:48

quality and the capability of these things? I

54:51

think that's a fantastic question and we think

54:53

about that a lot. And the short

54:55

answer is I think if

54:58

you froze capabilities today and the

55:02

only thing that you invested in was

55:04

optimization, making it cheaper, making it faster,

55:06

making it easier. If

55:08

you did that, you would revolutionize almost

55:11

every industry on earth. I

55:13

think the capabilities that exist today

55:16

are so unbelievably powerful and have

55:18

only just begun to be harnessed.

55:21

There is an interesting question. If

55:23

you were Sam Altman, what would you do? I

55:26

think what he's doing currently is probably

55:29

the right move, which is let's

55:31

continue to be at the very bleeding

55:33

edge. Let's continue to produce the very

55:35

best models. And because we

55:37

have an advantage in aggregating capital on talent,

55:40

let's press that advantage and use it to

55:42

stay on the absolute bleeding edge. There's

55:45

an alternative version of the world. And I'm not

55:47

recommending this, but I'm saying it's possible. There's

55:50

an alternative version of the world where you say,

55:52

okay, we think we're starting

55:54

to see diminishing returns to scale, which

55:57

means maybe we've squeezed about as much

55:59

juice this architecture as we can squeeze,

56:02

which means we're going to change our attention

56:05

to a few other things. Number one, we're

56:07

going to have a small team of true

56:09

geniuses trying to figure out new architectures. Number

56:12

two, we're going to move

56:14

some of the compute from training into

56:16

inference. And so instead of spending a

56:18

bunch of compute building the model, we're

56:20

going to spend a little bit more running the

56:22

model, which is what people talk about when they

56:24

talk about planning and reasoning, which

56:26

basically means that the model can do more

56:29

sophisticated things when you are asking it questions.

56:32

And then the third thing that you could do,

56:34

if you believed that returns to scale were starting

56:36

to diminish, the third thing that you could do

56:38

is just straight up optimization and try to make

56:40

it fast, try to make it cheap, try to

56:42

make it easy, and in doing

56:44

so, just run away with the developer ecosystem.

56:46

So I think you could do that, and

56:49

you would have a cash generative business overnight.

56:52

You would still have a dominant market position, but you

56:54

would be taking the risk that

56:56

we have not started to see diminishing returns to

56:58

scale, or that those returns have not diminished to

57:00

the point where it invalidates the investment. So it's

57:03

an interesting alternative version of the AI world to think about,

57:05

but it's not the one that we're living in. How

57:08

would you handicap the scale question about

57:10

whether or not we've hit

57:12

that scale wall, whether we can come

57:14

up with creative ways of gathering more

57:17

novel data, or just other means

57:19

of breaking through this? The bitter lesson seems to be

57:21

the most interesting written piece about this, that you just

57:23

need more data and more scale of data, and the

57:25

thing will keep getting better. But we've used all the

57:28

data we have on the internet, or in the written

57:30

word, or whatever. So what do you

57:32

think the odds are that Wall exists versus us

57:34

just finding a way, because we're humans, and this

57:36

is our way to push through it? I

57:39

think people will always find ways to push through

57:41

it. One of the data points that I find

57:43

interesting, and different people have framed this in different

57:45

ways, there's a guy named John Carmack, who

57:48

may be the world's greatest living engineer. Some people think

57:50

that he is. And he

57:53

has sequestered himself with a couple

57:55

of other geniuses in the middle

57:57

of nowhere, reading old

57:59

research. papers trying to figure out if

58:01

the better architecture already exists.

58:04

It just hasn't been assembled in just the

58:06

right way. And the reason he's doing that

58:08

is because state-of-the-art LLMs

58:11

are about four orders of magnitude less

58:13

efficient than your brain. If you

58:16

think about the basic input as energy

58:18

and the basic output as computation, your

58:21

brain is 10,000 times more efficient

58:24

than a state-of-the-art LLM. Now,

58:26

Andre Carpathi has actually had

58:29

the same observation, but he thinks that it's six

58:31

orders of magnitude. So one

58:33

way or the other, these models are

58:35

dramatically less efficient than the human brain.

58:37

And so the reason that's important is that

58:39

nature has shown us that a better

58:41

architecture exists. And I have

58:44

to imagine long before we

58:47

get to anything that is universally agreed

58:49

upon as AGI, we're going to end

58:51

up with just a dramatically better architecture

58:53

that's going to be far more energy

58:55

efficient. It's not going to come

58:57

out of just optimizing transformers. It's probably not

58:59

going to come by just putting planning and

59:02

reasoning on top. It's probably going to be

59:04

some different base architecture. And I'm sure at

59:06

some point somebody will figure out what that

59:08

is. This feels like the

59:10

thing you said earlier, where if you bet on

59:12

the system, you get the two and a half

59:14

X Airbnb. If you bet on human ingenuity and

59:16

the collective founder of humanity, you get the $100

59:18

billion Airbnb or something. Robbie

59:22

said, do you explain this to him using a

59:24

unit called math percido? What does that mean? Yeah,

59:29

yeah, we're talking about this. And I don't know if

59:31

I've done a good job of this today, but one

59:33

of the things that I tend to be known for

59:35

here is trying to make things as simple as humanly

59:37

possible. Maths percido,

59:40

if the input is energy, one

59:43

fairly efficient source of energy for

59:45

human beings is a bag of

59:47

Cheetos. The output is computation. Math

59:50

is a form of computation. And

59:52

so Ravi and I were saying that the metric for

59:54

the efficiency of an LLM should be maths per cheeto.

59:57

And then at the moment, humans can do

59:59

way more math. for Cheeto than your best

1:00:01

LOM. Maybe use Harvey as

1:00:03

an example of, okay, let's just zoom. All this stuff

1:00:05

is exciting and I hope we break through all these

1:00:08

walls and all the ways you described, and that would

1:00:10

be so cool. But in the version of the world

1:00:12

where we just have what we have today and you

1:00:14

just got to build a useful application and a business

1:00:16

on top of it, Harvey seems like

1:00:18

a great example to just double click on, describe

1:00:20

what it does and how it's using the model.

1:00:23

And I would love you to just explain

1:00:25

what you've seen so far, what lessons that

1:00:28

business and product has taught you just to

1:00:30

zoom in on a real tangible example. Yeah.

1:00:32

So I think Harvey's a good example because

1:00:35

I think it is the first

1:00:38

and best example of a new

1:00:40

wave of application companies that will

1:00:42

come out of this AI tectonic

1:00:45

shift. They got started at

1:00:47

exactly the right time. So they were the very

1:00:49

first company to get access to GPT-4 to start

1:00:51

building on top of it. And

1:00:53

the founders come from the perfect

1:00:56

background. Winston comes from the world

1:00:58

of law. Gabe comes from AI

1:01:00

research and said together, they

1:01:03

understand both the problem and the solution.

1:01:05

And so it's a great example of founder market fit.

1:01:07

It's a great example of the why now and being

1:01:09

in exactly the right place at the right time. And

1:01:12

what they've built over the last 18 months

1:01:15

or so is the very

1:01:17

best legal assistant.

1:01:21

So it is not an AI lawyer

1:01:23

at the moment, it is a legal

1:01:25

assistant that can basically

1:01:28

do the work of a first year associate

1:01:30

at a big law firm. And

1:01:33

partners at big law firms have actually

1:01:35

AB tested the Harvey assistant versus

1:01:37

the associate. And the Harvey

1:01:40

assistant is just as good and immediate.

1:01:42

And so the task that might have taken six hours

1:01:45

instead of takes six seconds. And

1:01:47

so it's a pretty darn powerful assistant. The

1:01:50

ambition for the company is to

1:01:52

eventually use that to

1:01:55

democratize the world of law. If

1:01:57

you think about the legal world today,

1:01:59

it's a real person product. It's

1:02:02

very expensive and so

1:02:04

whether you're a company or an individual,

1:02:07

either you have a lot of money to spend on it

1:02:09

or you're probably not going to get a very good legal

1:02:11

service or you're probably not going to get any legal service.

1:02:14

And it turns out that with AI and the fullness

1:02:16

of time, we can provide world-class

1:02:19

legal services and we can

1:02:21

do it at a tiny fraction of the price. The

1:02:24

idea for Harvey is never to replace the

1:02:26

human beings, it's to dramatically

1:02:28

expand the market to a whole bunch of

1:02:30

people who don't have access to legal services

1:02:32

today. And so for the very

1:02:34

high-end law firms and that sort of thing, we're going

1:02:37

to be an assistant. For the rest

1:02:39

of the world, we're going to be the service hopefully.

1:02:42

And if we can pull that off,

1:02:44

I think it has a chance to be an incredibly important

1:02:46

company. When you're evaluating one of these

1:02:48

products because there's just not that many of them that

1:02:50

are built and fleshed out the stuffs a year old,

1:02:53

what are the things you're looking for that

1:02:55

are distinct from the same things you might look

1:02:57

for in what I'll call a non-AI product? Or

1:02:59

is it just all the same stuff that it

1:03:01

just solves a problem efficiently and elegantly

1:03:03

and it's just the same that there's just something

1:03:05

different under the hood? Unique

1:03:08

and compelling value problems. It comes

1:03:10

back to that. Market by market, there

1:03:12

are different pros and cons to

1:03:15

the different technical approaches, but

1:03:17

at the end of the day, the

1:03:19

technical approach only matters to the extent

1:03:21

it does something unique and compelling for

1:03:24

the customer. And so

1:03:26

we try to spend less time

1:03:28

underwriting the architecture and

1:03:30

more time underwriting the customer

1:03:32

and just really understanding what problem this solves

1:03:34

to them, why it's unique and compelling, how durable it

1:03:37

is, how else they might solve that problem, where they

1:03:39

see it going in the fullness of time, all that

1:03:41

good stuff. I actually think a mistake that

1:03:43

a lot of investors make, there are

1:03:45

a lot of investors who are very technical and strongly

1:03:48

weight their personal

1:03:50

opinions of the architecture of the

1:03:52

product and that's a useful

1:03:54

input. You just have to weight it appropriately because

1:03:57

again it's only as good as its impact on

1:03:59

the customer. And so we try

1:04:01

to be more customer-oriented and let's tack out

1:04:03

more customer back. In

1:04:05

the world of venture, it seems like

1:04:07

there is this almost magic pixie dust

1:04:09

that certain firms have that founders

1:04:11

seek out. There's a handful. We could probably name

1:04:14

them on the call pretty easily together and everyone's

1:04:16

guess that these names would be the same. Squyze

1:04:18

is certainly one of them. When

1:04:20

it comes to the maintenance of that magic

1:04:22

pixie dust that a few firms seem to

1:04:24

have where the winners keep winning and winning

1:04:27

begets itself because that brand grows and the

1:04:29

reputation grows, the role of the platform, the

1:04:31

way you talk about Sequoia's platform seems to

1:04:33

play a key role in the

1:04:35

odds that that pixie dust will persist

1:04:37

into the future. Can you describe the

1:04:40

platform strategy to building an investment firm

1:04:42

like Sequoia in a way that

1:04:44

maybe others building investment firms might be able to borrow

1:04:46

some of those concepts that have been effective for you?

1:04:49

And first, we'll define what we mean by

1:04:51

platform. So when I joined Sequoia, we had

1:04:53

14 people on the

1:04:55

investment team and

1:04:57

two people who I would call front

1:04:59

office operators. We had one person

1:05:01

in talent, one person in marketing. So we had

1:05:03

14 and two. If you

1:05:05

fast forward to today, we have 27

1:05:07

people on the investment team and

1:05:11

probably about 65 people

1:05:14

who I'd say are front office operators,

1:05:16

meaning marketing, talent,

1:05:18

engineering, product, data science,

1:05:21

design, and a

1:05:23

handful of other things, customer partnerships. And

1:05:27

so that group of operators is

1:05:29

really what we mean by the platform. That's the

1:05:31

bulk of what we mean by the platform. There

1:05:34

are two key advantages we get out

1:05:36

of that group. One is they

1:05:39

dramatically amplify the efforts of the investment

1:05:41

team. So one

1:05:45

concrete example of that is the amount

1:05:47

of information that we have. We have

1:05:49

a homegrown CRM system powered by a

1:05:51

homegrown data science system. The

1:05:53

information that we have available in that system

1:05:55

for a company that we've never met is

1:05:58

more than the information. we would have

1:06:00

had on the same company 15 years ago at the

1:06:03

time of making a final investment decision. And

1:06:07

so that's a massive amplification

1:06:09

of our ability to source and pick and work

1:06:12

things through the funnel that leads to an investment.

1:06:14

So that's one concrete example. So one thing that

1:06:16

we get out of the platform is an amplification

1:06:18

of our efforts as investors. The

1:06:20

second thing that we get out of the

1:06:23

platform is advantages that have a chance to

1:06:25

compound over time. So historically,

1:06:27

the only compounding advantage that you get

1:06:29

in a venture capital business is

1:06:32

your brand and your

1:06:34

culture and your network.

1:06:37

But all those things are somewhat ephemeral. One

1:06:39

bad decision can tarnish your brand. The

1:06:41

platform team is building things that

1:06:43

can compound over time. And one tangible example of

1:06:45

that is we have a clever way that

1:06:49

one of our talent partners came up with

1:06:51

to collect signals on people. We

1:06:54

now have a couple hundred thousand

1:06:56

people in our database on

1:06:59

which we've collected these proprietary signals that

1:07:01

are not available anywhere else. And

1:07:03

so our ability to take a look

1:07:05

at a company and pretty quickly get a good

1:07:07

sense for the talents inside the building and how

1:07:09

well they've hired based on the signals that are

1:07:11

already in our system. That's an advantage

1:07:13

that's going to just keep on compounding. We have a

1:07:15

couple hundred thousand today. Over time,

1:07:17

theoretically, we could have just about everybody

1:07:19

in the technology world in that database.

1:07:22

One other point that's worth mentioning here. The reason

1:07:25

we decided to invest in our platform has

1:07:27

to do with what we saw happening outside

1:07:29

the building and a strategic choice that we

1:07:32

made. So what we saw happening

1:07:34

outside the building was the democratization of

1:07:36

the means of production. And

1:07:38

what I mean by that is any founder

1:07:40

anywhere can now go online

1:07:42

to educate themselves about the basics

1:07:44

of technology and building a business

1:07:47

and become an internet entrepreneur overnight.

1:07:49

And as a result, the volume,

1:07:52

variety, and velocity of startups

1:07:54

has increased dramatically. But if

1:07:56

we were still just 14 people or today 27

1:07:58

investors trended. do our jobs, we

1:08:01

wouldn't be able to cover the universe of opportunities. We

1:08:03

wouldn't be able to make our way through them efficiently.

1:08:06

And so the strategic choice that we made was we could

1:08:08

have taken the path of, okay, well, let's

1:08:10

not have 27 investors. Let's have 270 investors. If

1:08:14

we have a big team, we can cover everything. And

1:08:17

the reason we specifically decided not to do that

1:08:20

is because at the end of the day, there are

1:08:22

only two things that you need a

1:08:24

human being to do in the world of investing.

1:08:27

Everything else can be automated, but the two things that you have

1:08:29

to have a human being do, number one,

1:08:32

build the relationship with the founder. And

1:08:35

number two, make the decision.

1:08:39

Doesn't matter how many inputs you have, somebody

1:08:41

has to take those inputs and make the

1:08:43

decision. The declarative statement, we should invest because.

1:08:46

And so if the two things that we have to have

1:08:48

human beings do are a relationship with a founder and

1:08:50

make the decision, if we

1:08:52

disperse the knowledge and experience of the

1:08:54

partnership across a couple of hundred people,

1:08:57

any given one of them is not going to be all that special. If

1:09:01

we concentrate the knowledge and the experience

1:09:03

of the partnership on the

1:09:05

smallest possible number of people, we

1:09:08

have a chance for each one of those people to

1:09:10

grow into something really special. And

1:09:13

if we're hunting outlier founders, they

1:09:15

don't want to deal with people who are just okay. They

1:09:18

want to deal with people who are outliers themselves. And

1:09:21

if we can hire people who already have

1:09:23

outlier characteristics and then

1:09:25

supercharge them with concentrated experience

1:09:27

and knowledge, we have a chance

1:09:30

to produce the next Doug Leone, the

1:09:32

next Rulak Bota, the next Alfred Lin.

1:09:35

What Mike Moritz story most

1:09:38

stands out in your memory where the story taught

1:09:41

you something interesting. April, 2010.

1:09:45

You answer these crazy facets, very impressive. Well,

1:09:47

this line does stand out in my hands.

1:09:51

April of 2010, we had this

1:09:53

partner named Chris Olson who

1:09:55

found at that time a young man

1:09:57

named Sebastian over in South Africa.

1:10:00

Stockholm and Chris

1:10:02

built a relationship with Sebastian and

1:10:05

started to fall in love with a company named Klarna.

1:10:08

And Chris asked Michael Moritz to parachute in

1:10:10

to help him win this competitive investment. At

1:10:12

that time, it was already a big deal

1:10:14

in Europe and it was competitive.

1:10:17

And so Chris and Michael Moritz end

1:10:19

up securing the opportunity to invest in

1:10:21

Klarna. And then they bring me

1:10:24

along for a week to try to do all

1:10:26

the diligence and meet the team and polish up

1:10:28

the final investment recommendation. So I'm in Stockholm with

1:10:30

Michael Moritz and Chris Olsen for a week. We're

1:10:32

spending all day at the company and

1:10:35

Michael is not set to work. Chris

1:10:37

is leading the conversation. I'm chipping in from

1:10:39

time to time and Michael is just sitting

1:10:41

there silent, just listening, taking it all in.

1:10:44

And Chris and I are desperate to know what he's thinking,

1:10:47

particularly because asking him to spend a week in Stockholm is kind

1:10:49

of a big ask and we wanna make sure we're not wasting

1:10:51

his time. So we finally get to dinner

1:10:54

on night two or three. And

1:10:57

at that point, Chris and I thought that the

1:10:59

major issues in the investment were things like what's

1:11:01

gonna happen with interest rates because remember this is

1:11:03

a bank with a balance sheet in the wake

1:11:05

of the global financial crisis. We're very concerned about

1:11:07

what's gonna happen with interest rates. We were concerned

1:11:09

about whether or not they'd ever be able to

1:11:11

make it into Germany. At that time, they had

1:11:13

a pretty strong position in the Nordics and Germany

1:11:15

was the big market that they were trying to

1:11:17

enter. And we go to dinner with

1:11:20

Michael Moritz. Chris works up the nerve to say, okay, what

1:11:22

did you think? In Moritz

1:11:24

in typical Mauritsian fashion and

1:11:26

there's an exhale and a long pause and

1:11:30

he says, the question is

1:11:33

whether they can get to

1:11:36

a few hundred million of net

1:11:38

income and

1:11:40

the answer will come down

1:11:42

to the strength of the engineering team. Now,

1:11:46

Chris and I, I don't know if we were showing a

1:11:49

few hundred million of revenue in the model that we had

1:11:51

built and we

1:11:53

certainly had not asked that many questions about the strength

1:11:55

of the engineering team. And

1:11:57

it turned out that Michael...

1:12:00

was exactly correct. And if you look at

1:12:02

the company today, it's an absolute behemoth. And

1:12:06

the strength of the engineering team was

1:12:09

so critical because the value

1:12:11

prop for this product was very

1:12:13

strong for merchants and

1:12:16

very strong for consumers. So

1:12:18

it was a no-brainer, except it

1:12:20

was a pain in the butt to implement. And

1:12:23

so the key was gonna be, could

1:12:25

you deal with the complexity of all

1:12:27

the different e-commerce systems and all the

1:12:30

different payment mechanisms and all the different

1:12:32

preferences of the customers? Could

1:12:34

you deal with that complexity in an

1:12:37

elegant way that is product

1:12:39

driven, not brute force

1:12:41

driven, to reduce the friction

1:12:44

for people to deploy this product? And

1:12:47

if you could, you are gonna

1:12:49

become ubiquitous. And if you

1:12:51

couldn't, you weren't. And it was gonna come down to

1:12:53

the strength of the engineering team. That

1:12:55

was a lesson for me because Chris and I, we

1:12:58

had planned for this trip. We had our long list

1:13:00

of all the different questions we wanted to ask. We

1:13:02

were frantically screwing about trying to do all of our

1:13:04

work on the investment. And Michael

1:13:06

got it down to the very simplest possible thing,

1:13:08

which turned out to be exactly the right thing.

1:13:10

And so I guess the lesson you've got to

1:13:12

do was, you gotta zoom out

1:13:15

and make sure that you're operating at the

1:13:17

right level of ambition. That was just 300

1:13:19

million net income thing. And

1:13:21

that you're actually focused on the first order issue,

1:13:23

which is the strength of the engineering team thing.

1:13:26

When you think about the sensations in

1:13:28

both your body and your mind of

1:13:30

the feeling of being desperate

1:13:33

to win, how would you describe

1:13:35

what that feels like? It's

1:13:37

funny you ask this because I've been concerned that

1:13:40

as I've gotten older, I've lost some of the

1:13:42

edge or some of the killer instinct. And then

1:13:44

I was comforted by the camping trip that we

1:13:46

went on with our founders a couple of weeks

1:13:48

ago, where I felt like

1:13:50

glimmers of it were still there. And they

1:13:52

showed up in the silliest possible way, which

1:13:54

we did this set of activities, one

1:13:57

of which was axe throwing. And

1:13:59

you're on a, a clock and you had to

1:14:01

get as many bullseyes as possible before the

1:14:03

time expired. And I was

1:14:05

on a team with a couple of our founders and

1:14:08

we realized that one of them was better than the

1:14:10

rest of us at throwing the axe. And so we

1:14:12

ended up doing division of labor where my job was

1:14:15

to sprint and retrieve the axes that

1:14:17

had been thrown. And his

1:14:19

job was to keep throwing them. And

1:14:21

at one point he got a few bullseyes in

1:14:24

a row and he turned around and raised his

1:14:26

arms in victory. And there are still a minute

1:14:28

or two left on the clock. And like a

1:14:30

crazy person, I ran back to him yelling, no,

1:14:32

no, no, there's more time. Keep throwing, keep throwing,

1:14:34

keep throwing. So I guess the feeling

1:14:37

is you get a little bit carried away with

1:14:39

yourself. And actually, I think this is one of

1:14:41

the things that makes Doug Leone so special because

1:14:43

he lives his entire life this way. You go

1:14:45

into a mode where you are purely

1:14:47

driven by the objective function, whatever

1:14:50

the thing is that you are trying to achieve.

1:14:53

That is the only thing that you can think about and

1:14:56

nothing else enters your consciousness. And

1:14:58

in that case, I had lost track of

1:15:00

the social graces of yelling at somebody to

1:15:03

put their arms down and throw more axes

1:15:05

because the objective function was the only thing that I

1:15:07

could see. And then the analogy to Doug is one

1:15:09

of the things that I think makes Doug so special.

1:15:12

Anytime you ask him to

1:15:14

do anything, personal discomfort, personal

1:15:18

risk does not

1:15:20

enter his calculation at all. If

1:15:22

it is physically possible for

1:15:25

him to do the thing that

1:15:28

is required to achieve

1:15:30

the mission, he will do it. And

1:15:33

I think about that as it's

1:15:35

the ultimate humility, not caring

1:15:37

about himself, his ego,

1:15:39

his comfort at all. It's

1:15:42

the ultimate service mentality.

1:15:44

It's the ultimate mission

1:15:47

orientation, where the only thing

1:15:49

that you can see is the mission and what needs

1:15:51

to be done to achieve the mission. And everything

1:15:54

else just doesn't register. When

1:15:56

you go into that zone, the only thing that you can

1:15:59

see is the mission. else doesn't register. I

1:16:01

think that's where that killer instinct comes in. If

1:16:04

you ever do retire and you're at a

1:16:06

retirement party, what do you hope people say

1:16:08

about you? I think the

1:16:10

themes have been consistent. I mentioned with

1:16:12

Boston College, there's the Jez and Amato, Men and

1:16:14

Women for Others, and then the DC motto Everto

1:16:16

Excels. You have that concept

1:16:18

of teamwork and that concept of performance. And

1:16:20

at Sequoia, very explicitly, the two things we

1:16:23

care about most are teamwork and performance. So

1:16:26

I think the thing that I would hope

1:16:28

to hear is that I was a top

1:16:30

performer, but also a top teammate. One

1:16:33

of the ways that most manifests on

1:16:36

a day-to-day basis, the performance thing

1:16:38

I feel like we've covered, you're an animal and you

1:16:41

want to win and do whatever it takes. On

1:16:43

the teammate side, where does that

1:16:45

most commonly manifest? And what have you learned about

1:16:48

it 15 plus years into doing

1:16:50

this? Our partner, Andrew Reed,

1:16:52

had this good line the other day, which

1:16:54

was, sometimes you need less leadership and more

1:16:56

leadership. And what he

1:16:58

meant by that was, sometimes

1:17:01

people think that leadership means telling other

1:17:03

people what to do, but

1:17:06

sometimes leadership actually just means doing

1:17:09

the work so that people

1:17:11

can see how it is supposed to be done.

1:17:15

And because we're in an apprenticeship

1:17:17

business, I think a lot

1:17:19

of what we need to do

1:17:21

is to just do the work, just do

1:17:23

the basics of blocking and tackling, doing the

1:17:25

job, and that that's more helpful

1:17:28

to the other people on the team than

1:17:30

any amount of one-on-ones or mentorship

1:17:33

or structured feedback or whatever else.

1:17:36

And so I think what being a good teammate

1:17:38

means, when we construct a

1:17:40

team to go after an investment, there

1:17:43

are two roles. There's the sponsor and there's the

1:17:45

wing person. And the sponsor's

1:17:47

job is to secure the investment

1:17:49

and to make the case internally.

1:17:51

The wing person's job is to support the

1:17:54

sponsor. I don't have to be the sponsor,

1:17:56

I can be the wing person. There can

1:17:58

be an investment that somebody else is sponsoring. And

1:18:00

my role is just to support them. And that might

1:18:02

mean that I'm the one building the financial model and

1:18:05

writing the memo and calling the customers. And they're the

1:18:07

one doing the fun stuff of romancing the founder and

1:18:09

making the case in the partner meeting and that sort

1:18:11

of thing. And so I think

1:18:14

what it means to be a good teammate comes

1:18:16

back to that mission orientation that we were talking

1:18:18

about earlier. Whatever the mission is,

1:18:20

whatever the job is that needs to be done,

1:18:23

just do the job. It doesn't matter what your

1:18:25

specific role is in achieving the mission. It just

1:18:27

matters that we achieve the mission. If

1:18:29

you think about the landscape of this

1:18:32

style of investing, it's mature. There's lots

1:18:34

of firms. When you started, it was

1:18:36

much smaller, both in people, firms, assets,

1:18:39

investments, et cetera. Do

1:18:41

you still think there are open zones

1:18:43

of opportunity to try new

1:18:45

concepts and reinvent the game a little

1:18:47

bit from the investing side? I guess

1:18:49

ask differently. If I forced you to

1:18:51

go start a new firm with none

1:18:53

of the benefits of the existing firm,

1:18:55

just you, how you would

1:18:57

approach that challenge where the goal was to win

1:19:00

and be successful and back great companies. How would

1:19:02

you go to market as a

1:19:04

new investor in this more mature

1:19:06

environment? It's hard for me

1:19:08

to think of anything other than what my wife

1:19:11

Sarah is doing with her firm conviction. Tell me

1:19:13

about it. I actually think what

1:19:15

she's doing is exactly right. And I'll give you

1:19:17

the specific example, but I can also generalize from

1:19:19

there. So Sarah was

1:19:22

a partner at Greylock for about a decade

1:19:24

and then left just under two years ago

1:19:26

to start a new firm, which is called

1:19:28

Conviction Partners. And the

1:19:30

reason she started it was because she

1:19:33

saw this new crop of what she calls

1:19:35

Software 3.0, which is

1:19:37

basically AI driven companies starting to

1:19:39

emerge and wanted to

1:19:42

build a firm that could be built from

1:19:44

the ground up to service that new crop

1:19:46

of entrepreneurs. The thing that

1:19:48

I think is so effective

1:19:50

and so special about what she's doing is

1:19:52

that unlike a lot of people in the

1:19:54

venture capital world, she believes

1:19:57

that being small is a weapon and

1:19:59

that you don't. get advantages out

1:20:01

of scale in the venture capital business,

1:20:03

you get advantages out of quality. So

1:20:06

she kept her first funds much smaller than it

1:20:09

could have been. She's kept her

1:20:11

team much smaller than it could

1:20:13

have been. She's kept her portfolio much smaller

1:20:15

than it could have been. In

1:20:17

each step of the way, she's optimized for quality.

1:20:21

The benefit that you get from

1:20:23

optimizing for quality is that if you

1:20:25

achieve high quality, the growth comes to

1:20:28

you. You look for

1:20:30

growth, you're not likely to reverse engineer quality.

1:20:32

If you look for quality, you're going to have plenty

1:20:34

of choices about how much you want to grow. And

1:20:37

so she's assembled an

1:20:40

exceptional portfolio. She's

1:20:42

now starting to think about fund number two, and it's

1:20:44

going to be the easiest thing in the world to

1:20:46

raise it. She's going to keep

1:20:48

it smaller than it needs to be because

1:20:50

again, she's not optimizing for assets under management.

1:20:52

She's optimizing for quality. And

1:20:54

I think that's a great way to build a business. And

1:20:56

then the way I would generalize

1:20:58

that is not just the point on quality, but

1:21:00

also she is currently known

1:21:03

for one thing, early

1:21:05

stage AI companies, early

1:21:08

defined as series A or earlier,

1:21:10

and AI defined as AI. If

1:21:14

you are an early stage AI company, there's

1:21:16

a pretty decent chance that you're going to think of her.

1:21:20

If you are not an early stage AI company,

1:21:22

there's a pretty decent chance that you're not. I

1:21:25

was on a board with a guy named Jeff Richards from

1:21:27

GGV, and he referred to this as the chicken issue. At

1:21:30

some point, some genius at Chick-fil-A probably said, hey,

1:21:32

if we put burgers on the menu, we can

1:21:34

attract more customers. And somebody else said, yeah, but

1:21:36

the thing we're known for is chicken. If you

1:21:38

want a burger, you should go somewhere else. Most

1:21:41

companies have a chicken issue where they want to do the

1:21:43

chicken and the burger. And just focusing on

1:21:45

the chicken is important. Yeah, it's funny. It's turtles all

1:21:47

the way down. It's probably the same advice for a

1:21:49

new technology software company too. You got to do one

1:21:51

thing and do it really well to get going. And

1:21:54

that just seems to be a universally good advice for

1:21:56

sure. Is there anything else

1:21:58

about your whole world? that

1:22:00

you wish was meaningfully different than it

1:22:02

was like system settings or just ways

1:22:05

of doing things or just norms that

1:22:08

Armed with that magic wand you would change

1:22:10

drastically Yes, I probably

1:22:12

won't articulate this in the best possible way

1:22:16

But my partner rule off expresses

1:22:18

this in a pretty good way where he says

1:22:20

look venture capital is not an asset class what

1:22:23

he means by that is less

1:22:25

than 1% of the

1:22:28

companies that get started End

1:22:30

up accounting for 99%

1:22:33

of the market cap that's created and I don't know if

1:22:35

those are the exact right numbers But it's something like that

1:22:38

and so if you want to approach this as

1:22:40

an asset class and buy an index of all

1:22:43

the startups You're gonna

1:22:45

get drowned out with noise and the vast majority

1:22:47

of those investments are gonna be no good because

1:22:49

the vast majority of those companies Don't

1:22:51

need to exist. They're not solving

1:22:53

an important problem or they're not doing it in

1:22:55

a unique and compelling way And

1:22:58

so when you approach venture capital as an asset class

1:23:01

you end up with companies

1:23:03

that don't need to exist funds that are not

1:23:06

going to perform in people

1:23:09

who are attracted to Maybe

1:23:13

the fame of being a popular

1:23:15

founder maybe the perceived riches of

1:23:17

having a successful exit but

1:23:20

they're not attracted to this necessarily

1:23:22

for the right reasons and And

1:23:25

when I say right reasons everything is relative right

1:23:28

in terms of what I think of as right

1:23:30

it's not objectively, right? It's just my personal opinion

1:23:32

or my personal point of view. But

1:23:34

if you say, okay, well, what are the right reasons? I

1:23:37

think if you are a founder who

1:23:40

really cares about some

1:23:42

problem in the world that is not being

1:23:44

solved in just the right way and You

1:23:47

want to dedicate the next couple decades of

1:23:49

your life to solving that problem? That's

1:23:52

a great reason to go build a business if

1:23:55

you are an investor who believes

1:23:57

that Entrepreneurs

1:24:00

ownership more so than any other force

1:24:03

shapes the future of the

1:24:05

world that we get to live in and

1:24:08

you want to dedicate your life to

1:24:11

serving those entrepreneurs so

1:24:14

that they can realize the maximum

1:24:16

possible impact of their dream,

1:24:19

that's a pretty good reason to be an investor. If

1:24:22

you're an entrepreneur who just wants to get

1:24:25

invited to the fancy parties and conferences and

1:24:27

issue press releases about your latest funding round

1:24:29

and tell your friends you're a unicorn, that's

1:24:31

not a great reason to be a founder.

1:24:35

And if you're an investor who wants

1:24:37

to maximize assets under management so you

1:24:39

can milk the fee stream and spend

1:24:41

your time on Twitter pontificating about the

1:24:43

future direction of AI so that you

1:24:45

can show up in news reports, that's

1:24:48

not a great reason to be an investor. And

1:24:50

so if I had a magic wand and I could change one thing

1:24:52

about the industry, I would try

1:24:55

to slice off some

1:24:58

of the people who are participating without

1:25:00

the most pure motives and

1:25:03

increase the concentration of founders who

1:25:05

really care about their customers and

1:25:08

investors who really care about their founders. Amen.

1:25:11

What does it take to achieve legendary

1:25:13

potential? I love that term

1:25:15

you use all the time. That word is

1:25:17

really important to Sequoia Legendary. It

1:25:19

implies effort and scope of

1:25:22

ambition and all the things that we've talked about

1:25:24

and that you just talked about. I

1:25:26

would love you to sum it all up with what

1:25:28

you think it takes and what you've watched it

1:25:31

take. This isn't theoretical. You've seen it happen many

1:25:33

times. What does it take to achieve that sort

1:25:35

of potential? Why is that so motivating to you?

1:25:38

So we had Max Rhodes, who is the

1:25:41

founder and CEO of Fair at our

1:25:43

offsite maybe last year. And

1:25:47

he particularly in the early days of

1:25:49

Fair was just legendary for

1:25:52

his work ethic and he still is, but he

1:25:54

was very legendary once upon a time. And

1:25:57

so we asked him, what is it that keeps you

1:25:59

going? when all of your

1:26:01

friends are out having fun or when you're exhausted

1:26:03

and frustrated and just want to quit. What is

1:26:05

it that keeps you going? And

1:26:07

he said it was a voice

1:26:09

echoing in the back of his head, happened

1:26:12

to be the voice of our former partner

1:26:14

Michael Moritz, who he once asked

1:26:16

this question, what is it that separates the

1:26:18

truly legendary companies from all the rest? And

1:26:21

the voice was saying, relentless

1:26:24

application of force. And

1:26:29

I think that's it. Of course, the

1:26:31

question is, what is it that causes you to

1:26:34

relentlessly apply the force? And

1:26:37

that gets to the core of who you are and what you

1:26:39

care about and why you're building this company

1:26:41

to begin with. And to my

1:26:43

earlier comments, if your motivation is to release

1:26:46

press releases about how you're now a unicorn,

1:26:49

that's probably not a durable enough

1:26:51

motivation to really keep going

1:26:53

when things get tough. And

1:26:55

your motivation doesn't have to be obsession around

1:26:57

the customer problem. Maybe your motivation is you

1:26:59

just really love building things or

1:27:02

you really prioritize craft and you really want

1:27:04

to build just a beautiful, amazing product that

1:27:06

people are going to love. Or

1:27:09

maybe you got into it for the wrong reasons,

1:27:11

but now that you have a couple hundred employees

1:27:14

and you realize that people are really

1:27:16

counting on you, you feel a

1:27:18

sense of responsibility and you really want to

1:27:20

do right by them. And so there

1:27:22

are plenty of reasons that might cause you to

1:27:25

relentlessly apply that force. But I would say

1:27:27

the thing that probably separates the legendary from

1:27:29

the rest is in fact

1:27:31

the relentless application of force. I

1:27:34

think you might know my traditional closing question for

1:27:36

everybody, which I love and is a very appropriate

1:27:38

one, given a lot of the values you've talked

1:27:40

about and just your own past. What is the

1:27:42

kindest thing that anyone's ever done for you? I

1:27:45

had a hard time coming up with a good answer

1:27:47

to this question, knowing that you're going to be asking

1:27:49

it. And the best thing

1:27:51

that I could come up with was

1:27:53

it's going to sound very generic, but I'll make

1:27:56

it a little bit more specific. And the very generic

1:27:58

form is giving me a chance. The

1:28:00

thing that's a little more

1:28:02

specific is I had this

1:28:04

nice scholarship in college and

1:28:06

I remember the

1:28:09

wife of the director of the scholarship program who

1:28:11

oversaw all of us to make sure we weren't

1:28:13

losing our way and just Incredibly sweet lady. I

1:28:15

remember one time She made

1:28:17

the comment, you know You're

1:28:20

kind of rough around the edges, but you clean

1:28:22

up pretty nice and I think

1:28:24

that's a fair comment. I think Particularly

1:28:27

high school college earlier in my career

1:28:29

rough around the edges might have been

1:28:31

a generous statement I

1:28:33

know Doug refers to the early version of

1:28:35

himself as insufferable. Maybe I was somewhere close

1:28:37

to that I was certainly a bit

1:28:39

prickly and a bit full of myself And

1:28:42

so I guess the kindest thing that anybody's ever done

1:28:44

for me is to see through that and to see

1:28:47

Whatever goodness or whatever positive attributes

1:28:49

might have been hiding inside and

1:28:51

to help those things come out

1:28:53

and flourish over time I'd start

1:28:55

with my parents that were strict

1:28:57

but not hard on me as

1:28:59

a kid and gave me the room to figure

1:29:02

out who I was and then

1:29:04

in college the people who were

1:29:06

kind enough to Give

1:29:08

me a scholarship or spend time with me

1:29:11

my first job and I mentioned John Carroll It's

1:29:14

funny I heard after the fact that there was general agreement

1:29:16

at some of the partners that I should be hired But

1:29:18

no one person actually wanted me on their team And he

1:29:21

was like, okay, I'll take him And

1:29:24

then I think here with Doug as I mentioned, I

1:29:26

was the youngest person we'd ever hired. I was experiment

1:29:28

I was far from perfect and I almost got

1:29:30

fired multiple times after I got here But Doug

1:29:33

was the one who took a risk on hiring

1:29:35

me and Jim Getz was the one

1:29:37

who stood up for me when I was Not doing so

1:29:39

well. Why did you almost get fired?

1:29:41

What was the closest? It wasn't that

1:29:43

I did something egregious It wasn't

1:29:45

a thing that caused me to get fired the

1:29:47

context of me joining We

1:29:49

were just making growth investing a

1:29:51

first-class citizen and I was

1:29:54

hired from Summit Partners Which is really good

1:29:56

growth equity firm and I misunderstood

1:29:58

my job as teach

1:30:00

Sequoia how to invest like Summit. The

1:30:04

thing that I should have done was

1:30:06

understand who Sequoia is, and then extend

1:30:08

Sequoia into growth. When

1:30:10

I first got here, it was like oil and water, where

1:30:12

I was just trying to rinse and repeat with the stuff

1:30:14

that I've earned at Summit, which was not the right stuff

1:30:17

to do as part of Sequoia. I

1:30:19

wasn't learning fast enough. At

1:30:21

one point, apparently, five of six general partners said

1:30:23

that I should be let go, and Jim Guess

1:30:25

was the one who said, over my dead body.

1:30:28

The reason Jim threw his body across the tracks when

1:30:30

everybody else wanted to fire me was, it

1:30:33

was a little bit skills or attributes or

1:30:35

whatever, but the biggest thing was intentions. He

1:30:37

could see that I desperately wanted

1:30:40

to do the right thing. Fascinating.

1:30:43

Pat, this has been a total blast and pleasure. I've learned

1:30:45

a lot. Thank you so much for your time. Awesome.

1:30:48

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1:30:50

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1:30:52

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1:31:11

Bye.