There are moments in time where it's

very smart to be contrarian.

>> And there are moments in time where

being consensus is the smartest possible

thing you can do. And I think right now

we're in a moment in time where being

consensus is very right. You know, you

can really overthink it. And what's a

contrarian thing? We should go do a

bunch of hardware stuff cuz blah blah

blah. You like maybe buy more AI. You

know what I mean? I think people make

these things way too complicated.

>> Yeah. True. In every aspect of life

probably. All nice to see you. Thanks

for making the time. appreciate it.

>> Yeah, as always

>> and I thought we could begin with

something we were chatting about or you

were explaining before we started

recording which is a new phenomenon of

sorts. Could you explain what we were

just talking about?

>> Oh yeah, we were just talking about some

of the acquisitions that are happening

in the AI world. We saw that XAI just

got an option to effectively purchase

cursor. It looks like obviously scale

was sort of partially taken by meta.

There have been a variety of these sort

of deals that have been happening over

the last year or two. And separate from

that, we're just talking about what does

that mean for the AI research community

and the AI community in general. And I

think the most interesting or one of the

interesting things that's happened over

the last year or so is Meta really

started aggressively bidding on AI

talent, which was a very rational

strategy, right? They're going to spens

dollars on compute. So, it made sense to

have a real budget to go after people.

And normally what happens in tech is a

single company will go public and a

bunch of people from that company will

be enriched and then a subset of them

will continue to be heads down and

working really hard and focused on their

original mission and a subset of people

start to get distracted. They may go and

work on passion projects for society.

They may get involved with politics.

They may go start a company. They may

just kind of check out and hang out or

go to the beach kind of thing. And what

happened recently is because of the meta

offers and then all the other major tech

companies having to match offers for

their best researchers somewhere between

50 and a few hundred people effectively

had an IPO but as a class of people. It

wasn't like they were at one company.

They were spread across Silicon Valley

but all of their pay packages suddenly

went up dramatically and they

experienced the equivalent of an IPO.

And that's really unusual. It's kind of

the personal IPO. And the only time in

history I can think of where I've seen

it happen before is in crypto where a

bunch of the really early crypto holders

or founders suddenly as a class all went

effectively public in 20 I guess 17ish.

>> Mhm.

>> And then again more recently. But this

is really interesting. It's kind of

under discussed. It may not have huge

long-term implications, but it does mean

a subset of people will change what

they're focused on. Try and do big

science projects to help humanity work

on AI for science. Maybe maybe some

people will go off and do personal

quests or you know things like that

>> or just quiet quit and do lots of drugs

and chase vices. I mean there's that

too. Definitely not.

>> In that case, you look around say

Austin, you've got the Delionaires,

which refers to Dell post IPO early

employees and so on. But as a class of

people, when that happens, I suppose we

don't know how how large or how

long-term the implications are, but

there seem to be implications. And I

don't know anyone well I know only a few

people who I would go to as

technical enough and also kind of broad

enough in their awareness and networks

to watch AI to the extent that someone

can watch it comprehensively. I would

put you in that bucket. And you wrote

this week just to talk about some of the

other kind of elements at play here, the

compute constraints that AI labs are

facing and the implications and maybe

for the next one to 5 years. This is in

a piece people should check out random

thoughts while gazing at the misty AI

frontier. Good headline by the way.

>> Very dramatic.

>> Yeah, very dramatic. I love it. It's

very evocative. Would you mind

explaining actually before we move to

the compute constraints because I do

want you to to hop to that next but for

people who don't have any real context

on the talent wars and what you were

just mentioning earlier with meta like

on the high end what does some of these

pay/equity

packages compensation packages look like

that are getting offered?

>> I don't have exact knowledge of the full

range and everything else the rumors and

the things that have kind of made it

into the press. The claims are that

these things are between tens of

millions and hundreds of millions of

dollars

>> per person. And again, it's a very small

number of people who would get anything

that's quite that upsized. But I think

the basic idea is we're in one of the

most important technology races of all

times. And the faster that we get to

sort of better and better AI, the more

economic value will effectively show up.

And therefore people were really willing

to pay in an outside way for the handful

of people who are the world's best at

this thing. And 5 10 years ago these

people were like well compensated but it

was a completely different ballgame.

They just wasn't the core of everything

that's happening in technology but also

honestly society and politically and you

know for education and health like it's

going to have all these really broad and

I think largely positive implications

for the world.

>> Mhm.

>> But it is the moment of transformation

and so suddenly these pay packages are

going way up. what are the compute

constraints that you discussed in your

recent piece?

>> So basically all the different people

call them labs now. That's open AAI,

that's Enthropic, that's Google, that's

XAI, etc. All the labs are basically

training these giant models and

effectively what you do is you buy a

bunch of chips from Nvidia and you're

actually building out a system. So you

have tips from Nvidia, you have memory

from Highex and Samsung and other places

and you're building a data center.

There's all these things that go into

building these big systems and data

centers and everything else. And you

basically have clusters of hundreds of

thousands or millions or the scale keeps

going up of systems that you're buying

from Nvidia and from others. Google has

their TPU. There's other systems as

well. And you're using that to basically

train an AI model. And what that means

is you're running huge amounts of data

against these these big clouds. And

eventually the crazy thing is your

output or your model is literally like a

flat file. It's like almost like

outputting a text dock or something. And

that text back is what you then load to

run AI, which is insane if you think

about it. You use a giant cloud for

months and months and months and your

output is like a small file. And that

small file is a mix of representing all

of humanity's knowledge that's available

on the internet plus logic and reasoning

and other things built into it. And you

can kind of think about that in the

context of your brain, right? You have

three or four billion base pairs of DNA

and that's more than enough to specify

everything about your physical being but

also your brain and your mind and how it

works and how you can see things and

talk and taste things and all your

senses and everything's just

encapsulated in these very small number

of genes actually. And so similarly you

can encapsulate all of human knowledge

into like the slot file effectively.

>> How do you think about the constraints

then? What are the constraints? every

year the constraint on building out

these big clouds to train AI and then

also what's known as inference where

you're actually using these chips to

understand to run the AI system itself.

You need lots and lots of chips from

Nvidia to do this or TPUs or others but

then you also need other things. You

need packaging to actually be able to

package the chips and so there's a whole

supply chain around building out these

systems and different parts of that

supply chain have constraints of them at

different times. And so right now the

major constraint is memory or a specific

type of memory that's largely made by

Korean companies although there's some

broader providers of it and people think

that that memory constraint will exist

for about 2 years maybe plus or minus

because ultimately the capacity of those

companies has been lower than the

capacity for everything else in the

system. People think other constraints

in the future may literally be building

out the data centers or power and energy

to run these things, right? But for

today, it's this memory. And so

everybody in the industry is constrained

in terms of how much compute they can

buy to throw out these things. And so

what that does is it creates a ceiling

on top of how big you can scale these

models up in the short run because every

lab is buying as much as it can. A bunch

of startups are buying as much of this

computer as they can and everybody's

constraint. What that means though is

you have an artificial ceiling on how

big a model can get in the short run and

how much inference can run or how many

things you can actually do with AI right

now. And that also means that you're

effectively enforcing a situation where

no one lab can pull so far ahead of

everybody else because they can't buy 10

times as much compute as everybody else.

>> And there are these scale laws that the

more compute you have, the bigger the AI

model you can build. In many cases, the

more performant it can be eventually.

>> Mhm. And so that may mean that over the

next 2 yearsish all these labs should be

roughly close to each other because

nobody has the capacity to pull ahead.

And when the constraint comes off there

is some world where you could make an

argument that suddenly somebody can pull

far ahead of everybody else. So right

now open AI anthropic Google you know

they're reasonably close in terms of

capabilities although some will pull

ahead on one thing versus another that

should roughly continue everybody thinks

for the next at least 2 years because of

this. Google is also constrained by the

memory from Samsung, Micron, etc.

They're they're similarly constrained as

the other players. Right now, everybody

is similarly constrained and you know a

subset of these labs either are already

making their own chips or systems like

Google has TPUs and other things. Amazon

has actually built its own chips called

traniums. And so there's basically like

different systems for different

companies, but fundamentally all of them

are are limited in terms of how much

they can either manufacture themselves,

purchase themselves. And a year or two

ago, the main constraint was packaging.

Now it's it's memory. Two years from

now, who knows? Maybe it's something

else. We constantly are hitting

bottlenecks as we're trying to do this

build out. This is probably going to be

a naive question because I'm a muggle

and not able to write technical white

papers or anything approaching that, but

it seems to me that I'm the first person

to say this, we're better at forecasting

problems than solutions potentially. And

so, for instance, way back in the day,

the price per gallon of gasoline or

petrol goes above a certain point. Okay,

people are forecasting doom and

destruction. But past a certain price

per barrel, suddenly new means of

extraction became feasible and there

were investments made in things like

fracking and so on. Is there sort of a

plausible scenario in which there is

some type of workound

>> along those lines if that makes any

sense? I don't know. Maybe there isn't.

As far as I know there so far at least

is not.

>> Mhm. Part of that is because the way

that some of these things are built and

it's basically the capacity that you

need for example for memory is basically

a type of fab

>> and so you need time to build out the

fab and to get the equipment and put the

lines in place.

>> Right.

>> So it's a traditional sort of capex and

infrastructure cycle.

>> Mhm.

>> And these companies basically

underinvested in that because they they

didn't quite believe the demand

forecasts that other people had around

this stuff.

>> Mhm. And so now they're trying to catch

up. And so it's it's one of these things

where everybody keeps saying, "Well, AI

is growing so fast. How can it possibly

keep growing at this rate?" But it keeps

growing at this rate. It just keeps

going. And that's because its

capabilities are so impactful and so

important. And so you look at the

revenue of these companies. And it's

interesting. I I can send you the chart

later, but Jared on my team pulled

together a graph of how long did it take

for companies to get to a billion

dollars in revenue and then from a

billion to 10 billion and then from 10

to like a hundred. And there's only a

small number of companies that have ever

done that. And you can literally look by

generation of company how long it took.

And so for example, I can't remember

it's ADP or somebody it took them 30

years to get to billion in revenue or

whatever it is. Enthropic openi did that

in like a year. For Google it took four

years or whatever. I don't remember

exactly what the numbers are, but it was

kind of like as you go through these

subsequent generations, it gets faster

and faster to get to scale. Right now,

OpenAI and Anthropic are each rumored to

be roughly around $30 billion run rate,

which is insane. That's crazy.

>> That's.1% of US GDP. So AI probably went

from 0 to half a% of GDP at least as a

revenue contributor. And you extrapolate

out and if they hit 100 billion in

revenue in the next year or two years,

whatever it is, then we're getting close

to a place where each of these companies

is a percent or two of GDP. That's

insane if you think about that.

>> It's bananas. Yeah, it's bananas.

>> Is really actually important when

useful. That doesn't include like the

cloud revenue for Azure for doing AI

stuff or you know Google GCP or like

it's just those two companies. It's

insane.

>> Mhm. I would love to dig into your

thinking because you're you're one of

the best kind of first principles and

also systems thinkers I've met and

I love having conversations with you

because I always learn something new and

it's not necessarily a data point but

often it might be a lens or a framework

for thinking about different things and

that framework evolves for you as well

but for instance if I was looking at

this interview you did this is a while

back with first round capital and you

were talking about sort of market first

and then strength of team second, but

you talked about passing on investing in

lift series C. This was at the time and

ultimately part of it seemed to hinge on

winner take all versus oligopoly versus

other. And I'm curious how you are

thinking about that within the AI space

because I mean you started skating for

that puck before almost anyone I know,

if not everyone I know. And how are you

thinking about that? And this ties into

something that you mentioned in your

piece that I haven't heard anyone else

talking about, but I'll give the

sentence as a cue. I don't think you'll

need it, but founders running successful

AI companies should all take a cold hard

look at exiting in the next 12 to 18

months, which might be a value

maximizing moment for outcomes. And you

sort of went back to the dotcom bust and

the sort of survival rates and then

breakout rates. Could you just explain

that sentence and then also explain how

you're thinking about whether you think

this will be winners take all igopoly

like what type of dynamic you think

emerges

>> in terms of the precedent and that

doesn't mean it's going to happen here

but if you look at every technology

cycle 90 95 99% of the companies in that

cycle go bust

>> and that dates way back even to what was

high-tech a 100 years ago which was the

automotive industry

>> in Detroit dozens of car companies and

hundreds of suppliers s and it collapsed

into a small number of auto companies

virtually. And so this is not a new

story. During the internet cycle or

bubble of the '90s, 450 companies went

public in 99. 450 or so companies went

public in the first few months of of

2000. And so that was 900 companies. And

say another 500,000 went public in the

couple years before that. So you had

somewhere between 1500 and 2,000

companies go public go public. So that

means they kind of made it.

>> Mhm. And of those, how many have

survived? A dozen, maybe two dozen.

>> Yeah.

>> And so out of 2,00 companies, 1,980

or so went under.

>> Mhm.

>> One form or another. Or maybe they got

bought for a little bit. And so there's

no reason to think the AI cycle will be

any different. And every cycle is like

that. SAS was like that and mobile was

like that and crypto was like that. So

most companies are not going to make it.

A handful will. And we can talk about

those. And so if you're running an AI

company right now, you should ask

yourself, what is the nature of the

durability of your company? And are you

one of that dozen or two that are going

to be really important 10 years from

now? Or is now a good moment for you to

sell because what you're doing will

start to get commoditized or will be

competed by a lab or will be something

that the market will shift or the

technology will shift and you'll become

obsolete. And there's a handful of

companies that will continue to be

great. They should never sell. They

should never exit. they should keep

going. But there's probably a lot of

companies that now or the next 12 to 18

months is the best moment for them

possible in terms of the value that

they'll get for what they're doing.

>> And for every company, there's a value

maximizing moment where they hit their

peak. And it's usually a window. There

usually, you know, 6 12 months where

what you're doing is important enough,

you're scaling enough, everything's

working before some headwind hits you.

>> And sometimes it's very predictable that

that headwind is coming and you can see

it. And often you see it in the second

derivative of growth, like how fast

you're growing starts to plateau a

little bit and you're either going to

keep going up or you should sell.

>> And so that's really what that's meant

to be. I'm incredibly bullish around AI

as you can tell from the rest of the

conversation.

>> And so it's it's less about the

transformation that's happening overall

because of this technology and more that

only a handful of companies are going to

continue to be really important. And so

are you one of them or not? If you're

one of them, you should never ever ever

sell.

>> So what are the characteristics of that

handful? the handful that have durable

advantage because you look back at 2000

it's like man what would you have used

to try to pick out Google and Amazon

>> and I'm not saying that's the best

comparator but within the many just

avalanche of AI companies

>> which are those that you think have

durable advantage I mean of course some

of the name brand labs come to mind

maybe they become the interface for

everything else who knows but How would

you answer that in terms of either

shared characteristics or actual names?

What sets apart the handful that you

think will make it?

>> I think the core labs will be around for

a while. So that's open AI, anthropic

Google, barring some accident or

disaster, some blow up, but it seems

like they're in a really durable spot.

And to your point on like market

structure, I wrote a Substack post, I

don't know, 3 years ago or something

predicting that that would probably be

an igopoly market and there'd be a

handful and be aligned with the cloud.

That's roughly kind of what happened. I

mean, there's Meta and there's XAI and

there's other players that may change

this. It didn't exist when I wrote that

post, but it feels to me like in the

short run that's an igopoly. Like

there's no reason for that to be a

monopoly market unless one of them pulls

ahead so much in capabilities that it

just becomes the default for everyone.

And that could happen, but so far it

hasn't. And again, this computer

constraint may prevent that in the short

run or at least provide an asmtote on

it. As you move up the stack and you

see, well, there's different application

companies. You know, there's Harvey for

legal, there's a bridge for health,

there's decagon and Sierra for customer

success. You know, there's these

different companies per application.

There's three or four lenses that you

can look at. One is if the underlying

model gets better, does your product or

service get dramatically better for your

customers in a way that they still want

to keep using you? Second, how deep and

broad are you going from a product

perspective? Are you building out

multiple products? Are they all

integrated in cohesive hole? Is it

really being built directly into the

processes in a company in a way that

it's hard to pull out? Often the issue

for companies in adoption of AI isn't

how good is the AI, it's how much do I

have to change the workflows and the

ways that my people do things in order

to adopt it. It's about change

management usually. It's not about

technology. And so if you've been able

to embed yourself enough into workflows

and how people do business and how they

work and how everything else kind of

ties together, that tends to be quite

durable.

>> Mhm.

>> Are you capturing and storing and using

proprietary data? Sometimes it's useful.

I think data modes in general are

overstated, but I think sometimes it can

be actually quite useful and that's

usually the system of record view of the

world. So, you know, there's a handful

of criteria around like will this thing

be long-term

defensible or not and the application

level that's often one potential lens on

it.

>> Mhm. So question if if people are

listening to this and they are in the

position of perhaps a founder who should

consider identifying their kind of short

period of maximum valuation and perhaps

hitting the parachute in some way. What

are the options? Because I think of some

of these companies I'm not going to name

them but there are multiple companies

that have multi-billion dollar

valuations. There's seems to be again

from a mostly lay person perspective

i.e. me

that that the labs

probably can build what they are

currently selling without too much

trouble. Do they aim to be acquired by a

lab in which case there's sort of a

build versus buy decision for the lab

itself? Are they aiming for one of not

the open AIs or anthropics, but maybe

somebody who's trying to get more skin

in the game like Amazon or fill in the

blank? What are the exit options? I

think there's a lot of exit options. And

the thing that's crazy right now is if

you go back 10 or 15 years, the biggest

market cap in the world was like 300

billion.

>> Mhm.

>> The biggest tech market cap was, I don't

know, 200ish or something. I think the

biggest one at the time was Exxon or

somebody like 15 years ago. Mhm.

>> And over the last 10 or 15 years, what

happens is we suddenly ended up with

these multi-trillion dollar market caps,

which everybody thought was nuts at the

time, but things will probably only get

bigger. There'll probably be more

aggregation versus less into the biggest

winners. And there's more and more

companies who have these market caps

between say 100 billion and a few

trillion

>> in a way that's just unprecedented. And

that means there's enormous buying power

because 1% of 3 trillion is 30 billion,

right? you can get 1% and pay $30

million for something which is insane,

right? That's that's pretty

unprecedented and that means that these

really big acquisitions can happen

>> for the companies that I'm imagining

again I don't want to name names that

may have seem to have a limited lifespan

right when I'm in these these small

group threads with friends of mine who

are often time not always but I'm in a

bunch of them and when they're

tech investors very successful tech

investors and I'm like okay these five

companies you've got 10 ships how would

you allocate your 10 ships there's

certain companies that consistently get

zero even though they're reasonably

wellnown. Why would one of the labs buy

one of those?

>> Depends on what it is. And it may be a

lab. It may be one of the big tech

incumbents and Apple, Amazon, right?

>> Google's kind of both things. There's

Oracle,

>> there's Samsung, there's Tesla, there's

SpaceX now in the market doing things.

There's a bunch of different buyers of

different types. There's Snowflake and

Data Bricks. There's Stripe. Coinbase if

you're doing financial service there's

just a ton of companies that actually

are quite large that's kind of the point

and so often you end up selling to one

of four things you can sell to one of

the big labs or hyperscalers or giant

tech companies you can sell to somebody

who cares a lot about your vertical so

for example a Thompson Reuters if you're

doing legal or accounting or things that

are kind of related to that

>> I mean I think actually one thing that

doesn't happen enough is merger of

competitors particularly private

companies where you can do that because

ultimately if your primary vector is

winning and you're neck and neck with

somebody and you're competing on every

deal and you're destroying pricing for

each other. Like maybe it's better to

just merge. It actually was X.com and

PayPal in the '9s, right? Elon Musk were

running different companies and they

merged because they said we're people

doing this. Why fight?

>> Yeah. Or Uber Lift way back in the day,

right? That might not have been a

merger. It might have been an

acquisition, but it's like

>> Yeah. And the rumor is that that almost

happened and then you know the Uber side

walked away from it. Mhm.

>> But all the money that Uber spent on

fighting Lyft for all those years maybe

would have been better spent just buying

them. Maybe not. I don't know the exact

math.

>> But often it actually does make sense to

say, you know what, like we'll just stop

fighting it out and we'll just combine

and just go win. Cuz if the primary

purpose is to win the market, you're

already fighting all these big

incumbents that already exist anyhow. So

why why make it even harder? as you

know, and we talk about this a lot, but

we'll talk about you with your investing

hat on. But before you even put that,

let's call it full-time investing hat

on,

you had a lot in your background that

may or may not have helped you. And I'm

curious if you look at your biology

background, the math background. Do you

think any of those things or other

elements materially contributed to how

you think about investing that has given

you an advantage in I suppose there are

different stages to kind of winning

deals but sometimes they're not crowded

but let's just talk about the selection

process the math stuff helped me I think

in two ways one is it's helped me with

certain aspects of like technical or

algorithmic CS and understanding And

sometimes that's useful

>> in the context of how certain things

work in AI or things like that or just

fluency of numbers and data and I to

call it nerd language or something.

>> And I did the math degree honestly just

for fun. And I think that's actually the

thing that was helpful.

>> We did an undergrad degree in math so I

didn't go that far with it.

I did the very sort of abstract pure

math stuff and I think that was a good

forcing function of how to really think

logically step by step about things

because roughly the way that at least I

learned how to do proofs was you do the

logical sequence but then some times you

do these intuitive leaps and then go

back and try and prove it to yourself

>> or flesh out the

>> the reasoning behind that intuitive leap

and I think sometimes investing is a

little bit like that. When did you first

have the inkling that you could be good

at investing? And that could be

investing at large. It could be maybe

within the context of our conversations,

startups and angel investing. When did

you first kind of go, "Huh, yeah, maybe

I could be good at this." Was there a

moment or a deal or anything like that

that comes to mind?

>> Not really. I'm really hard on myself,

so even now I second guess myself a lot.

Mhm.

>> Somebody was telling me that the two

people that always beat themselves up

the most in hindsight is me and this one

other person who's another well-known

founder/investor. And so I don't think

there's a single moment where I'm like,

"Wow, this makes sense for me to do." I

think it just kind of organically kept

going because I was getting into some

very strong companies and then, you

know, that allowed me to sort of

continue what I'm doing. But

>> okay.

>> Yeah. Wish I hadn't done it like that.

>> God damn it. You need to revise your

Genesis story like every every good

founder.

>> Yeah.

>> Yeah. I mean, ever since I was seven,

I've been thinking about investing in

technology.

>> All right. Now we're talking. So,

getting into those deals, right?

What allowed you to get into those

deals, right? Because some people have

anformational advantage and they put

themselves in a position to have

anformational advantage, right? And I

think that had I not I don't want this

to be a leading question. It's like had

I not moved to Silicon Valley

>> when I did like 2000

and then subsequently you know stayed

there moved to San Francisco

specifically like nothing that I was

able to do in angel investing would have

been possible. So but there's more to

your story because a lot of people moved

there with hopes of startup riches in

whatever capacity. Not saying that

that's why you moved there, but what was

it that allowed you to get into those

deals? There are certain things that

come to mind based on our prior

conversations, but I'll just leave it at

that. Like, why were you able to get

into or select those deals? I think

there's what happened early and what

happens now. And I think those two

things are different. I think to your

point, the single most important thing

for anybody wanting to break into any

industry is go to the headquarters or

cluster of that industry. Like move to

wherever that thing is. And all the

advice of you can do anything from

anywhere and everything's remote is all

BS. And you see that for every industry,

not just tech. You know, if you wanted

to get into the movie business, people

wouldn't say, "Hey, you can write a film

script from anywhere. You can digitally

score from anywhere. You can edit it

from anywhere. You can film it

anywhere." They're like, "Go to Dallas

and join their burgeoning, you know,

film scene." They'd say, "Go to

Hollywood." And if you want to do

something in finance and you're like,

"Well, you could raise money from

anywhere and come up with trading

strategies and a hedge fun strategy from

anywhere and you could do it from

anywhere." People wouldn't say, "Hey, go

to, you know, whatever, Seattle." They'd

be like, "Go to New York or go to XYZ

financial center." So, the same is true

for tech. And Shan and my team has been

performing this sort of unicorn analysis

of where is all the private market cap

aggregating for technology. And

traditionally about half of it's been

the US and then half of that has been

the Bay Area. But with AI 91% of private

technology market cap is the Bay Area.

91% of the entire global set of AI

market cap is all in one 10 by 10 area.

Right? So if you want to do stuff in AI,

you should probably be in the Bay Area.

Mhm.

>> Probably the secondary place is New York

and then after that it just drops off a

cliff. And really it's the Bay Area. If

you want to do defense tech, you

probably should be in Southern

California close to where SpaceX and

Anderl are and sort of Irvine, Orange

County, etc. or Elsaundo. There's a lot

of startups there. If you want to do

fintech and crypto, maybe it's New York.

But the reality is these are very strong

clusters. So to your point, number one

is I was just in the right location.

>> Mhm. I was in the right networks and I

default was I was running a startup

myself. I was at Google for many years

and then I left to start a company and

people just started coming to me for

advice and the way I ended up investing

in Airbnb is I was helping them when

they were eight people or something

raise their series A and I introduced

them to a bunch of people and help with

some of the strategy there in very light

ways right they would have done it

without me but and they said hey at the

end of it do you want to invest a little

bit I said great that sounds wonderful

so it's very organic or the way I

invested in Stripe is I'd sold sort of

infrastructure early API company to

Twitter and when Twitter was say 90

people or

And I sent an email to Patrick, the CEO

of Stripe, just saying, "Hey, I've heard

great things about you and I really like

what Stripe is doing and I want to use

it for my own startup." And I I sold

this API company myself. Do you want to

just talk about this stuff? And so I

went on a couple walks and then a week

or two later, he text me and he's like,

"Hey, we're doing a round. Do you want

to invest?" So the first few things that

I did were very organic where the

founders were like, "Want you on board?"

>> Mhm. I didn't think, oh, I should be an

investor and I'm going to chase things

and I just really liked talking to smart

people and I liked working on certain

business problems and I love technology

and his translation the and so it was

very like you know I was just a nerd and

I I met other nerds and we hit it off.

It's kind of the early like story for

me. It just struck me that I'm sure

people have heard or I'm sure you've

heard this before but you know if you

want money ask for advice and if you

want advice ask for money. It just

struck me that it it kind of goes the

other way around too. It's like if you

offer a bunch of advice, often times you

get to give money and if you try to give

money, you might get solicited for

advice.

>> Oh. Yeah, that's a good point.

>> When did you write the high growth

handbook? When was that published?

>> It's a while ago now. It's probably like

sevenish years ago. Something like that.

>> Seven years ago. All right. We're going

to come back to that in a minute

because you you were in the right place

geographically speaking, right? You were

in the center of the switchboard

and like you said, these some of these

initial kind of standout investments

came about very organically. And what

I'd be curious to hear because you also

said yourself not too long ago that

there's there's what I did then, there's

what I did now. There's also what you

did in between right along the way. And

I'm wondering for instance if you would

still stand by this. This is from that

first round interview I was mentioning.

As a general rule when I make

investments it's market first and the

strength of the team second. And there's

more to it. But would you still agree

with that?

>> 90% yes. every once while you meet

somebody exceptional and you just back

them or something maybe so early like

when I led the first round of perplexity

>> like the very very first round and the

way that came about was Arvin the CEO

just I think he like pinged me on

LinkedIn literally and this was when

nobody was doing anything in AI and he

was like an open AI engineer or

researcher and he's like hey I'm at open

AI which nobody cares about at the time

and I'm thinking of doing something in

AI and I heard that you're talking about

this stuff and nobody else is talking

about it and can we meet up and So we

just started meeting every two weeks and

brainstorming, right? And then that led

to like investing in that. And that was

kind of a a people first thing where he

was just so good and every time we

talked, he'd show up a week later with a

thing that we discussed built. Like who

does that?

>> Yeah, that's a good sign.

>> So good.

>> Or the way I ended up investing in

Anderil was Google shuts down Maven,

which was their sort of defense project.

And so I think well if if the incumbents

aren't going to do it what a great place

for startups to play because there's

been a long history of Silicon Valley

and the defense industry that's HP and

that's a lot of the you know early

brands and so I was just looking for

something there or somebody to work on

this area and it was very unpopular at

the time and I ran into I think it was

Trey Stevens who's one of the

co-founders of Vanderel who's also a

founders fund it's lunch or something

else again right city to be in and he

said oh I'm working on this new defense

thing and I said amazing let's talk

about about it. Sometimes it's just

looking for these things too in a market

and sometimes it's people. So Andrew was

looking for a market and then finding

amazing people. Perplexity was kind of

in between where it was like I was

looking at everything in AI cuz I

thought it was going to be incredibly

important but not very many people were.

And then I just ran across an

exceptional individual and that's when I

funded Open AI. That's when I funded

Harvey which is the early legal. I

funded a lot of really early stuff

because they were the only people doing

anything

>> in this market that I thought would be

really important. Let me come back to a

few things you said. So you mentioned

the Perplexity founder or later the

founder who said you're you're talking

about this stuff, right? Or he heard or

read or found you talking about this

stuff.

>> Where was that? Was that posted on your

blog? Was it somewhere else? How did he

actually find you talking about

anything? I mean, I think he pinged me

in part because I was involved with a

bunch of the prior wave of technology

companies. Airbnb, Stripe, Coinbase,

Instacart, Square, a bunch of stuff like

that. And so I think at that point I was

already known as founder and investor.

But then on top of that I was just I was

trolling AI researchers and just asking

them about what's going on because it

was so interesting. There's a bunch of

art that was being done with these

things called GANs at the time these

generative adversarial networks. And so

I was playing around with that. I tried

to hire engineers to build me

effectively wasn't mid Journey because I

just thought it'd be really cool to be

make it easy to make AI art. Okay. So

let me let me pause for a second because

this is my second question and it's a

good time.

when you mentioned, you know, AI, I

thought it would be incredibly

important.

>> Yeah.

>> What were the indicators of that, right?

What was the smoke in the distance where

you're like, "Oh, that's an interesting

direction." I think there was two or

three things. AI was one of those things

that people have always talked about.

So, when I was doing my math degree, I

took a lot of kind of theoretical CS

classes. There were the early neural

network classes and things like that and

the math behind it and and so there's

always this promise of building these

artificial intelligences of different

forms. And one could argue Google was a

first AI first company and back then it

was called machine learning and it was

different technology basis in some sense

and I think 2012 was when Alexet came

out and there's this proof that you can

start scaling things and have really

interesting characteristics in terms of

how AI systems work. And then 2017 is

when the team at Google invented the

transformer architecture which

everything is based on now or roughly

everything. And so for example, if you

look at GPT for chat GPT, the T stands

for transformer. And around 2020ish, I

think was when GPT3 came out and that

was such a big step from GPT2. And it

still wasn't good enough to really do

stuff with, but you you're like, "Oh

[ __ ] the scaling wallpapers are out.

The step function and capabilities was

huge." You suddenly have a generalizable

model available via an API that anybody

can ping. And so just extrapolate that

out to the next step. And this is going

to be really important. Mhm. So it's

basically looking at that capability

step and playing around with the

technology and then reading the scaling

law papers or just in general the the

scaling laws seem to work for everything

and you're like wow this is going to be

really really important so let me start

getting involved with it.

>> Do you think you would have or could

have done that without a mathematics

background? I mean I'm guessing there

were probably some other folks but that

leads me to the question of like how are

you finding and ingesting that right?

Was it the talk of the town? So it was

in a sense like within your social

circles and the networks that you're a

part of it was a open discussion so you

were engaged with it or are you

ingesting vast quantities of information

from different fields and this happened

to be something that really caught your

attention.

>> I guess it's three things. I mean I've

always ingested a lot of information

from a lot of different fields just cuz

I like learning about stuff and I was

always this mix of like math and biology

and anime and art and other things. So,

you know, it was always kind of a mix.

And then it was something that my

friends were talking about, but it was a

bit more like toy like, oh, this is cool

and look at what came out, but most

people didn't then extrapolate. It's

kind of like early crypto or Bitcoin.

Like, everybody was talking about it,

but very few people bought it.

>> Mhm.

>> And so, I think that was part of it. And

then third, honestly, I just thought it

was really neat stuff that I kept

playing around with. This is back to the

GAN stuff and the art where these

different models would come out and you

could mess around with them. And one of

the things that's really under discussed

in terms of the importance of it

relative to this wave of foundation

models and AI and everything else is the

way AI or machine learning used to work

is your team at a company or wherever

else would go and there'd be what's

known as an ML ops team operations team

whose whole thing was like helping you

set up all the data and the pipelines

and everything to train a model and you

train a model that was custom to your

use case and what you were trying to

accomplish. And then it was you had to

build a bunch of internal services to

interact with that model. So it's a huge

pain to get to the point where you had a

working ML system up and running in

production

and then suddenly you have a thing where

you just do an API call. So with a line

of code or a few lines of code, anybody

anywhere in the world can ping it. But

not just that, it's generalizable. So

it's not just specialized to one use

case like spell correction or whatever.

You can use it for anything. M

>> and it has all of the internet embedded

in it in some sense in terms of the

knowledge base

>> and it can start having these advanced

reasoning capabilities. But one of the

most important things is hey you can get

it with a couple lines of code. You

don't have to go and build an MLOps

team. You have to host it. You have to

interact with it. You don't have to do

all this extra stuff. It just works.

>> Mhm.

>> That's really important.

>> It's huge.

>> Yeah. It's kind of hard to overstate. I

have a million questions for you. The

problem with this is like the

embarrassment of riches of directions

that we could go.

>> Mhm. So I am using in my team claude

code and assorted tools for all sorts of

stuff right now, right? And one of them

it just so happens overlaps with an area

of great skill for you and experience

which is angel investing. So this is the

first time where I feel really enabled

to do and there is some manual effort

involved as you might imagine but to go

back and do an analysis of 20 years of

angel investing

>> to try to do any number of things and I

suspect that a lot of what interests me

is not particularly useful like doing

some counterfactuals what if I had held

each of these for three years for 5

years for whatever I mean that's kind of

like just opus day whipping myself in

the back. Yeah,

>> for the most part. But in doing an

analysis like that, there are certain

things that immediately come to mind for

me that might be of interest. And I want

to hear what you would do, if you would

even do this. I mean, part part of it is

frankly just curiosity, right? Are the

stories I tell myself about this

>> true or not?

>> And so I'm interested like who made

certain introductions? Are there certain

people who just took me their basically

people on in hospice care and like

shipped them over as like a last ditch

effort? Are there people who actually

sent me good stuff consistently etc etc.

>> So there are a million and one ways I

could try to interrogate the data and

enrich it. We're doing a pretty good job

of enriching it. I mean Claude is and

other tools you know OpenAI is very good

at this. What are some of the more

interesting questions or lines of kind

of examination you think looking back

like whatever it is in my case it's

roughly 20 years of stuff. The weird

thing I've been doing is uploading

pictures of founders and asking the

models to predict if they'd be good

founders. Oh wow. Okay. Because if you

think about it, we do this all the time

when we meet people, right? We quickly

try to create an assessment of that

person

>> and their personality and what they're

like. And there's all these micro

features like do you have crows feet by

your eyes which suggest that your smiles

are genuine and what does that imply

about the sense of humor you have or

fured your brow over time and what does

that you know so there's all these like

micro features

>> and when you meet people you actually

can get a pretty quick impression of

them pretty fast it doesn't mean it's

correct right

>> but we actually do this really fast as

people

>> so I have this whole like set of prompts

that I've been messing around with just

for fun

>> around can you extrapolate like a

person's personality based off of a few

images

>> and therefore can you be predictive

about their behavior in any way? I think

that's fun, right?

>> Yeah. Are you finding any signal there?

>> Yeah, it works pretty well.

>> Wow.

>> So, I've been doing the weird [ __ ]

right? Like

>> practice smiling people.

>> Yeah. Yeah. I think it's interesting,

right? Because we do this all the time

where we read people, right? And that's

part of the prompt. It's like you're a

very good cold reader of people based on

micro features and etc etc. kind of

spell it out and then based on that, not

only give me your interpretation of this

person, but explain the specific micro

features for each thing that you're

stating about the person

>> and it'll break it down for you.

>> It's amazing. Like, imagine what this

technology is. It's crazy. And again,

I'm not saying it's fully accurate and

I'm not saying it'll be predictive and

but it's done pretty well in terms of

nailing people. It's even done things

like, "Oh, this person probably has this

type of sense of humor," or, "This

person probably holds themselves back in

most social settings and then chimes in

with a witty ride thing that nobody

expects or what." I mean, it's very

specific.

>> It's very specific.

>> Wow, that's amazing. Right. And so, I've

been doing stuff like that, which may

not be your question, but I've been

finding it really fun. It's related,

right, in the sense that and I'm sure

I'm missing some steps, but I I love

angel investing and I the dose makes the

poison. So, there's usually a case to be

made when I get to a certain threshold,

I'm like, "Okay, this isn't fun

anymore." Like, I love dark chocolate,

too. But I don't want just to be

force-fed dark chocolate all day. But,

and you and I have talked about this,

right? But I really do enjoy the

learning and the sport of it frankly and

interacting with some very very smart

people. Not not all of them work out as

far as founders of companies, but

ultimately I'm trying to figure out how

to separate signal from noise. And also

it's fun to try to use anything but in

this case investing to sharpen your own

thinking, right? and to stress test your

own beliefs and the assumptions that

undergur some of your predictions things

like that. I'm just wondering if you've

ever done like sort of a retrospective

analysis of your startup investing or if

you're like no more market reason style

only forward.

>> Yeah. Early on when I was first starting

to invest, I would have this long grid

of things by which I would score each

company

>> and then I'd go back and see if it was

correct.

>> It was roughly correct. I think the hard

part is there's a lot of like randomness

in outcomes.

There's the company that sells for a few

billion dollars that you thought was

dead or whatever it is.

>> Sure. And so how do you score things

like that? Right now we're in this

really weird market moment where

trillions of dollars of market cap are

all chasing the same prize and so

they're going to do all sorts of stuff

that wouldn't happen normally.

>> Mh.

>> So it's really hard to account for that

kind of thing, right? Relative to all

this. I'm much more in the merk and

recent camp of like I think very little

about the past. Mhm.

>> I think close to zero about my own past.

I just am like, let's keep going.

>> Mhm.

>> And maybe that's bad and there should be

dramatically more self-reflection. And I

try to self-reflect in the moment, but I

don't try to re-extrapulate and examine

my entire life and decisions. And

>> Mhm.

>> If anything, most of the decisions have

been ones where I'm really upset with

myself for not being more aggressive on

something. Mhm.

>> In other words, I invested in the

company, but I should have tried even

harder to invest more even if I tried

really, really hard because there's a

handful of companies that really matter

and that's all that kind of matters as

an investor. Obviously, as a person, I

enjoy getting involved with different

companies and different founders and

helping them whether the thing works or

not or I think the technology is

interesting or whatever. But the reality

is from a returns perspective, there's a

very clear power law that people talk

about and it's true. And I remember a

friend of mine did this analysis. I

think it may have been

Drew Milner or someone where it's like

look at all the companies from like I

don't remember the exact states 2000 or

2004 until today in technology.

>> Mhm.

>> And it was something like a 100

companies drove like 90 something% of

all the returns.

>> Mhm.

>> And 10 companies total drove like 80% of

all returns over a two decade period in

technology.

>> Yeah.

>> If you weren't 10 companies, you were a

bad investor. Mhm.

>> And once you start dealing with these

power laws and these outcomes, how can

you rate that? Right. It's basically,

did you hit one of 10 things or not?

That's really the rating. That's

probably the correct rating for

investment.

>> I'd love to try to focus on some

earlyish decisions

on this podcast, right? Because like you

said, there are the earlier decisions.

There's how you did things then, there

you

say that one is better than the other,

but certainly what you do in the past

tends to inform what you're able to do

and what you do in the present. And what

I'm curious about, and we won't spend a

ton of time on this, but it might be

interesting to folks, is to discuss when

you moved from purely doing angel

investing yourself to

involving other investors in your deals,

right? And there are multiple ways to do

this, but the reason I want to ask this

is because you did a number of SPVS.

I'll explain what that is. Special

purpose vehicle, but for folks, you

might be familiar with venture capital

firm. They have funds and they raise,

let's just call it $100 million for a

fund. It can be more or less of course.

Then they invest in a bunch of different

companies. And then you sort of see who

wins, who lose, and then if their

profits, I guess conventionally, let's

just use the textbook example. The

venture capital firm takes 20% of the

upside, and then the the LPs, the

investors get 80%, and the venture

capital firm takes a management fee to

keep the lights on. Although it usually

does a lot more than keep the lights on.

With the SPVS, you're investing in,

let's just say for simplicity, a single

company, right? Mhm.

>> And there are advantages to that in

simplicity for somebody who's putting

together the SPV, but you also have a

lot of reputational risk cuz if you have

a fund and you have a couple of losers,

your investors don't automatically go to

zero, right? But even SPV and it goes to

zero,

that could really hurt you

reputationally. And when I look at some

of your early SPVS, which I think

included certainly a number of name

brands like Instacart and so on, how did

you choose

which companies to do the SPVS with,

right? Because it seems like a very

important set of decisions to lay the

groundwork for creating optionality for

what you do after that.

>> I think to your point, I've always been

terrified of losing other people's

money. Like I'm fine if I lose my old

money.

>> It's my decision. I'm an adult. It's

okay. But I've always been and people

giving me money are adults or

institutions etc to invest on their

behalf. But similarly there I was just

terrified of ever losing money for

people. And so I've tried over time to

be judicious behind the SPBs that I did

early on. And the focus was on things

that I thought would really be outsized

companies. And so that was to your point

Instacart. It was early Stripe. It was

Coinbase. It was a couple things like

that that were amongst my very first

SPVS. And the emphasis was very much on

do I think this can be a massive thing

and also do I think there's enough

downside protection in some sense that

even if it didn't work as well as I

thought it would still be a good outcome

for people. So yeah, I try to do that

very diligently. It's interesting

because a lot of people ping me for help

as they think about becoming investors

or they're scouts for a fund which means

basically they're given a small amount

of money by a venture capital fund.

Sequoa famously has this program. They

give people money and then those people

invest money on their behalf. And some

of the scouts that I've talked to

basically treat it like free money or an

option. They're just kind of like, I'll

just wrote a bunch of stuff. Maybe

something works. And I pointed out to

them, hey, if you actually want to

become a professional investor at some

point, this is kind of your track

record.

>> Mhm.

>> A, you're a fiduciary in some sense, so

maybe I'll be more careful from that

perspective. But B, you know, this will

establish like your track record. And do

you want to have a good one or bad one?

And how do you think about that? And

again, sometimes people just get lucky

and they hit the one thing out of a

hundred, but that more than returns

everything and they look great.

But it's hard to be consistently good at

this stuff or consistently hit great

companies.

>> I want to double click on a few things

you said and maybe you could walk us

through a pseudonmous example. It

doesn't need to be a named company, but

when you're talking about setting your

track record, right? You did an

excellent job of that before you then

went on later to raise funds and so on.

And I would love you to perhaps explain

some of the things you do in diligence

or how you weight things differently and

also how you think about like the capped

minimum downside. I'm not sure that's

the exact wording that you used in

selecting those deals, right? Because

you could have selected any number of

deals on a sort of due diligence level.

What's the kind of stuff that you focus

on maybe more than others? And what are

the things you pay less attention to

than others? I think there's a big

difference between early and late

things.

>> On the early side, there's a point

earlier I tend to spend a lot more time

in the market than most early stage

investors. Most early stage investors

say, "I just care about the team and how

good are they?"

>> But I've seen amazing teams crushed by

terrible markets and I've seen

reasonably crappy teams do very well.

And so, you know, at this point, I think

the market is more important. Although I

think obviously great teams can find

their way if they decide to shift around

a bit.

>> I index a lot on market early and that

may be customer calls. That maybe is

trying to understand, do I think

something could be big? It could just be

some intuition around, hey, you know,

defense is really important. Nobody's

doing defense. Let me find a defense

company. Right? I tend to index a lot on

that. And relatedly, I've tended to

avoid science projects. And there's some

people who get really distracted by,

wow, this is really cool. It's quantum

and it's this and it's that. And I've

largely avoided those things. And, you

know, sometimes I miss things that were

really good. But often that was the

right call. I actually think spaxs saved

sort of hard tech and science-based

investing industry because if you look

at what happened basically at the market

peak a bunch of spaxs took a bunch of

companies public that would not have

been able to raise money in private

markets later and they gave them enough

money to keep going but more importantly

they returned a bunch of money to these

hard tech funds and that saved them from

going under. It gave them all the

returns was basically the spack era. So,

Chimath basically saved hard techch. I

mean that seriously, not cheek. And I

largely avoided that kind of class of

companies. And I'm not saying it was

smart. I would have made money off of

it. I just thought there was all sorts

of capitalization issues and science

risk and market risk and other things to

them. For later stage stuff, the hard

part often is everything on paper gets

modeled out for a late stage company as

a 2 to 3x from that investment point,

>> right? because all the funds that are

driving the rounds underwrite against

some IRRa clock 25% IRRa whatever it is

and so they all come up with these

models and the models all say all these

companies are basically going to two to

3x and the art there or the science

there whatever you want to call it is is

that a.5x company is it going to drop in

value or is that a 10x and how do you

know it's a 10x versus a 2 to 3x versus

a.5 and that's the harder part of growth

investing and there's a subset of things

that you're like this thing will just

keep going and here's why but often it's

not mathematical often that's just like

some market dynamic or some core insight

or some market share question and people

tend to make that stuff really

complicated and they have these really

complicated multi-page models and

50-page memos and all the rest and often

these things boil down to one single

question. What is the one thing I need

to believe about this company that makes

me think it's going to continue to be

really big?

>> If it's three things, it's too

complicated. It's probably not going to

work. If it's no things, then it doesn't

make much sense. So usually there's one

or two things that are really the core

insights you need to understand like the

outcome for something.

>> Could you give an example of one of

those beliefs for any company that comes

to mind?

>> I'll give you two or three of them. I

mean Coinbase part of it was just hey

this is an index on crypto and crypto

will keep growing because if Coinbase

trades every main cryptocurrency and

they take a cut of every transaction and

have enough volume to effectively bought

a basket of every cryptocurrency by

investing in Coinbase.

>> Mhm.

>> That was the premise there. Stripe it

was they're an index on e-commerce and

e-commerce will keep growing back then.

Now it's much more complex and there's

all sorts of great drivers of its

performance. Android was hey machine

vision and drones are going to be

important. AI and drones are going to be

important for defense.

>> That's it.

>> I mean it was more complicated than

that. I'm just saying like that was the

fundamental

>> well that was it for the belief the core

belief

>> there was like cost plus model versus

hardware margin. You know, Andrew

actually had four or five things that

were important there that were kind of

like a checklist for a defense tech

company, but for a lot of the other

ones, it was like e-commerce is good.

>> This is probably two inside baseball,

but what were the stages of the

companies that you mentioned when you

created the SPVS?

>> Roughly.

>> Well, I first invested in Stripe when it

was like eight people and then I kept

following on and I ran out of my own

money, frankly, and that's when I

started doing SPVS. So, I think I did my

first SPB and Stripe around the series

Cish.

>> Mhm.

>> We're in there.

>> Mhm.

>> Something like that.

>> Got it. And were the others more or less

similarish? Instacart, etc.

>> It was probably roughly in that

ballpark, Ced D, kind of that that

range.

>> I didn't have funds and everything else.

And, you know, I was putting as much as

I could personally into these things

both earlier, but honestly, I just kept

going when I could. When you're looking

at trying to determine if something is

a.5x or a 10x in addition to the core

belief, what are other layers of due

diligence that you bring to bear on

trying to ascertain that where something

falls on that spectrum?

>> Oh, I mean I do enormous due diligence.

So meet with the CFO multiple times,

walk through all the financials, walk

through the financial model, walk

through customers, call customers, look

at executive team, you know, it's it's a

bunch of stuff. Mhm.

>> My fund is the only one I know that

actually does like cash reconciliations

where we'll go through and do a cash

audit to look at cash flows for later

stage things. So I do enormous diligence

cuz I want to make sure I'm not doing

something inappropriate. But the flip

side of it is most of it just collapses

into like what's the one thing? Mhm. So

when I work with a company, I actually

try to be very fast and straightforward

on the diligence in terms of saying

let's just talk about a we need to just

make sure financials are correct and you

know like there's the basics but like

let's collapse it down into one or two

core questions right that help us

understand if this thing will keep going

not here's 30 pages of questions that

don't matter

right

which is what a lot of people they're

like hey we need to know the secondary

cohort on this [ __ ] thing that's like

a tiny product that who cares they just

waste time. They waste the founders time

and the team's time. And I try very very

hard not to do that. As a former

entrepreneur myself, I know how precious

the time is and I know how annoying

those questions are.

>> I was actually going to at one point ask

you about this, but we don't need to

spend too much time on it. You have a

post, this is from a while back, 2011,

listing questions a VC will ask a

startup. You omitted some of the

questions like the one that you just

mentioned, but I am curious if any of

these questions or additional questions

come to mind when you are talking to

founders. could be early stage or later

stage that you actually apply yourself

and I know it's from 2011 so I'm not

expecting you to remember the post

itself. I haven't looked at that post in

a really long time. I'm actually writing

another book now that is sort of the

0ero to1 startup phase and it gets into

some questions like that.

>> Mhm.

>> I think the reality is venture capital

has changed dramatically since I wrote

that post. Right. Because in 2011

>> the venture capital funds were largely

doing seeds through series D maybe and

then companies would go public. Mhm.

>> Yeah. This whole 20-year private company

thing didn't exist. Do you know why

there's a four-year vest on stock?

>> No. Why is that? I can kind of guess now

that we're talking about IPOs, but go

ahead. Why?

>> In the 1970s, they came up with a

four-year vest on stock options for

employees because companies would go

public within four years. And so then

you're done.

>> Yeah. Yeah.

>> Literally, right? And so it's like a

four-ear clock usually. And then when

Google took six years to go public,

everybody's like, "Oh my gosh, it took

them so long to go public. six years

like they just sat on their hands. Do

you know what I mean?

>> Literally people would say that, right?

>> And so what happened is venture capital

used to be very early stage and then

what we now call growth investing was

public market investing, right? That was

a stop that

>> people in the public markets would do

after four or five years of a company's

life. And so public markets used to be

involved very early. And then as Sarbain

Oxley came out and companies decided

they didn't want to go public and

there's more private capital available,

the timeline until going public

stretched out, right? And so suddenly

venture capital firms are doing all the

growth investing that used to be public

market investing.

>> Mhm.

>> And in 2011 that really wasn't happening

much. It was kind of Yuri Milner from

DST and a few other folks, but it wasn't

that much of an industry. And so the

nature of venture capital has shifted

radically over the last 15 years. And

that means those questions that I listed

there didn't include what I'd consider

more growth centric questions because

there wasn't a lot of growth investing

in venture.

>> What would be examples of growth centric

questions?

>> Honestly, it would overlap with some of

the early stages. You know, by the time

you hit a very late stage, it's very

financially driven.

>> Mhm.

>> And so often what at least I and my team

look at is what is just the core

business and how do we extrapolate that

going and then what are these ancillary

things that the company's doing that are

almost like options in the future that

may or may not come through. And so

usually we base our investment on that

core. Can they just keep doing the thing

they're doing forever? Cuz most

companies mainly get big off of one

thing at least for the first decade,

right?

>> Yeah.

>> There's very few companies that end up

with multiple things that all work

usually with one thing and then 10 years

later you maybe come up with the second

thing that really works, right?

>> Mhm.

>> It's like Google Cloud for Google,

although obviously there's YouTube and

there's a bunch of other stuff and Whimo

and all these interesting things now,

but it took a while, right? For a long

time just search search and ads.

>> Mhm. But then sometimes there are these

extra things that are potential really

interesting drivers on a business. Like

SpaceX was launch and then it became

satellite, right? It became Starlink.

>> Yeah, man. Starlink, what a thing. It's

too bad I have so much tree cover here.

Can't use it anywhere I spend time. But

let's turn to the high growth handbook

for a second. So that that was let's

just call it 7ish years ago. It is an

outstanding book. People should really

check it out, especially if you're

playing in the ventureback game. What's

the subtitle? The subtitle is scaling

startups from 10 to 10,000 people.

There's a lot of good advice in this

book. I wanted to ask you if there's

anything in this book that you wish

startup founders the book was intended

for would pay more attention to or if

there's anything that you would add or

expand to the book. So, when I read the

book, I had an outline for it that was

two, three times the length of the

actual book in terms of chapters. So

there's a lot of stuff I didn't write

about sales and marketing and growth and

a bunch of other other stuff. But the

book was basically written as sort of

like a tactical guide. It wasn't meant

to be read it from start to finish.

There's a bunch of interviews with

different people who are think amongst

the best practitioners in the world at

those areas. But fundamentally it was

meant to be more like you're suddenly

involved with the M&A, jump to the

chapter and read that and then put it

aside until you something else comes up

around hiring that you need to look at

or whatever. And so it it really is

meant to be like a handbook or guide or

companion to a founder versus, hey, I'm

just going to read it start to finish

and there'll be some pathy quotes in it

or whatever or one concept over 500

pages. You know, I try to avoid stuff

like that. It's very tactical. It's very

tangible. It's very specific. And this

new book that I'm working on is

basically the zero to one version of

that.

>> Mhm.

>> It's like how do you hire your first

five employees as a startup? How do you

somebody tries to buy you, what do you

do? How do you raise your first round of

funding? You know, it's that kind of

stuff. So, it's kind of like the 0ero

to1 technical guide.

>> Let me ask you about one specific

section. I think this is chapter two.

This is on boards.

And if this is getting too in the weeds,

tell me. We can hop to something else.

But I am curious if you could talk about

there are two things. Take a better

board member over a slightly higher

valuation. And if you want to revise

these, that's fine, too. There are two

things I'd love to hear you talk about

just because this is something that you

know founders I've been involved with

bump up against constantly take a better

board member over a slightly higher

valuation and then write a board member

job spec and then it specifically for

independence maybe we I would love to

hear you

maybe just elaborate but could you speak

to either or both of those a bit and if

you want to take it a different

direction I mean it's really just boards

writ large

>> so I think when founders pull together

boards Often the early boards are

investors because the investors ask for

a board seat as part of it or as part of

the investment and sometimes the

founders want somebody on board who's

really committed to the company and will

help out extra. And to some extent when

somebody takes a board seat it really

means or it should mean that they're all

in to help you versus you can have lots

and lots of investors if you have very

few board members. Reed Hoffman has this

thing which is like a board member at

its best is like a co-founder that you

wouldn't be able to hire otherwise and

so you bring them onto your board. It's

somebody that you want to spend more

time with on specific issues related to

the company.

>> Mhm.

>> Fundamentally, your board should be able

to help with different areas of the

company. It could be strategic

direction. It could be closing

candidates. It could be product areas.

It could be customer intros. It could be

a variety of things. And usually, you

want to kind of think of your board

members as a portfolio of people. It's

going to change between an early stage

company and a late stage and a public

one. You only need different types of

people over time usually.

But most companies are very reactive on

their board versus proactive.

>> And so they tend to end up with a couple

investors and then they kind of add

somebody from an industry seat and they

don't really think through like who they

want and why. And

>> if your co-founder is kind of like your

spouse, your work spouse, your work

husband or your work wife, your board

members are like your in-laws. You know,

you have to see them at Thanksgiving and

you have to chat with them all the time.

And so hopefully you have somebody you

want to steal all the time and who's

helpful and wonderful. And the bad

version is like gh it's the like