Why the AI Boom Is Just Getting Started

When you get the right part of the

S-curve, you get exponential unit

growth. If you have a very strong

business model, your earnings don't grow

linearly, they grow exponentially. You

know, the world doesn't think

exponentially. Very few people believe

you can accurately predict 2 3 4 years

out. But if you follow and understand

the Scurve and you you know the moes and

you know how to model, you really can uh

predict these these great things. the

enterprise AI or enterprise application

AI market is less than 1% penetrated and

we've never seen, you know, we talk

about S-curves, we call this an L curve,

just straight up.

Alex, you were saying that your highest

conviction position is anthropic right

now. Can you tell the story of

discovering it, making the investment,

using this anecdote as an excuse to talk

about all the things that I think you

and I are mutually interested right now,

investors like you, investing in private

markets, anthropic, the business, AI,

everything. It's a great great way to

zoom in. Why is it your highest

conviction? And how did you get started?

>> Yeah. Well, when when the gun went off

with OpenAI chat GPT in November 2022,

we immediately took the firm and did a

massive deep dive with our 10 person

team. And we anytime you have a new

compute paradigm, there's a new stack

and on the and and that creates new

winners and losers on the old stack. And

in this stack, you know, it's now Jensen

talks a lot about it, but it's power at

the bottom, chips at the bottom, the

clouds, and then the foundational

models, and then the applications on

top. And at that time, this was 2023

early, we said, we want to be in the

chips and the infrastructure first. And

not only do they get the uh demand

first, but we know who the winners are.

And no matter who wins above, which we

weren't sure at the time, we know we're

going to need tremendous amounts of

compute. And we did a deep dive into

that, which we can talk about later, but

over the next 2 or 3 years, we started

to get more clarity on how the

foundational model, the layer would

evolve. And at the time, two or three

years ago, there were 60 different

companies going after it. OpenAI was

kind of in the lead. And we did a

webinar in April 2023. We said, look,

this might be a winner take all. It

might be a total commodity because

there's open-source players. It might be

a race to zero or it might be an

oligopoly where there's three or four

leading players. And what we saw over

the following, you know, 3 years was

that almost all the startups

fell away and died. And then some of the

largest companies in the world including

Amazon and and Meta. Amazon really never

really showed up. We'll see what happens

with Meta, but they were they came in

strong and then basically their effort

faltered and they had to do a total

reboot. In the meantime, Anthropic kind

of was this dark horse candidate, the

startup and um they focused uh really

purely on the enterprise and OpenAI had

kind of won the consumer and then Gemini

can never be counted out. We we love

Google as well. It's one of our largest

positions. So it really started to look

like a three-horse race and somewhat of

an oligopoly

very similar to how the uh cloud market

evolved where three companies underpin

the entire SAS cloud world and and have

really excellent businesses and then we

also were aware of the open- source risk

um from China and we started to get

comfortable that the quality of the

tokens from the leading edge were

superior because if you're 80%

close to the top of the benchmarks going

from 80 to 85 is a huge unlock and the

um the open- source guys they don't have

as much compute so they can come close

to the leading edge but they can't

leapfrog it and then they kind of

falter. Meanwhile, the scaling laws and

other

means of improving the models, the

feedback loops, etc. Uh we saw that

there was a very strong runway and

everyone we talked to close to the

industry saw that the scaling laws would

continue. So we developed this thesis

that it would be a three-horse race. And

then the big kicker was code. And this

is the true unlock of AI. In the first

few years, we knew AI would would be

big, but we were skeptical. Also, we

made large investments because we knew

the training was would be there, but we

weren't sure how much revenue might come

and if it could truly replace labor

because if you remember the early

versions of the models were good, but it

there was a lot of uh some negative

feedback from corporates and could they

be truly agentic? We realized in 2025,

the first cloud code and and the coding

tools really began to explode and you

saw

the first gen was like Microsoft C-Pilot

which is like $20 a month and then and

then it started and that could sort of

improve your grammar of coding, maybe

find a bug, maybe make a block of code

like a paragraph and then Anthropic came

out sometime in in the middle of the

year and it could do so much more. Um,

and it started to get to this point

where it could run agentically and we

kind of saw that happening and the

coding market just exploded and then we

started hearing that people who could

use it unfettered. We heard we heard

that you know even within Anthropic at

that time people were spending $100 a

day on tokens which if you do the math

comes out to 20 or $30,000 a year. And

if you think about how many coders there

are in the world, 20 million, you've got

a half a trillion dollar market just

from coding alone. And mind you, that

was on 7 8 9 month old technology. We

could see just on the coding market

alone that Anthropic had a tremendous

opportunity ahead of it. So I think at

the time, this is pretty funny, we wrote

in our letter, you know, we made the

investment um at the 180 valuation. And

we said, and I think they were

hoping to get to a nine billion

>> one to nine. Yeah. And and then the

numbers were like nothing we'd ever seen

before. 100 to a billion on the way to

9. But when we did it in August of 2025,

we nobody had any idea what 2026

could be. the the the second big unlock

lately which is that you know claude

code has gone to almost completely

agentic um where you had Andre Carpathy

and Lionus Torvalds last year saying two

of the smartest people in coding and

they completely flip-fpped and Karpathy

said you know last year's code tools

could write 20%

and 80% would be handwritten that

flipped when the the latest model came

out and now he hasn't written a line of

code not except in English and not to

mention the pure unlocked that we're

going to get for the people that never

knew how to code. So just coding alone

has completely taken off. Anthropic has

been able to stay ahead in coding. And

so one difference between the cloud,

GCP, AWS, and the AI companies is the

cloud's generally it's commodity.

They're they're selling you servers and

storage. You know, they have a lot of

software on top and there is stickiness

to it. But in the AI models, everyone

thought it would be pure commodity. But

there's tremendous differentiation with

within. There's different training

methods and different skills that

they're good at. And a lot of people

have routers that switch in between,

which sort of makes it sound like

they're commodity, but anthropic,

they're very good for anything that has

to do with private equity and finance.

Google's very good for ingesting PDF.

And so there's a lot of like

differentiation critical IP, which is a

great competitive advantage.

and companies many companies have come

after the coding franchise and Anthropic

has been able to keep ahead.

The other thing that's good about the

foundational models and anthropic is

it's not just the API or the model.

They're building a whole monopoly or

whole ecosystem of products around the

API. So we've got the SDK claude for

co-work uh orchestration layer and and

all the tools and and they call it sort

of a harness which is the software

around the API that gets the most out of

the model. This was one of the things we

saw with AWS really early on in 2013 was

oh people thought it was a commodity

server up in a warehouse big deal and

what they they saw this was a new way of

do doing computing. So they had they

invented all these products that they

could see before everybody else that

slowly built lock in. The other way we

think about this is where are we on this

scurve and we have this infrastructure

layer scurve which we think is somewhat

like 10% penetrated. And by the way we

think it's still uh one of the best ways

to play AI and we'll talk about how that

feeds back through. Um but if you think

about it, um even though you know 200 or

I don't know how many 800 million people

are using AI, they're just using AI 1.0

which is like a a search engine on

steroids. But now with these new

primitives where you have claw on your

computer linking it in, then you build

skills. Companies are going to build

people and companies are going to start

building skills and then they're going

to build true AI bots and then big

corporations are going to build much

larger but where are we in terms of the

amount of people doing that? I mean

Sunder said it's 10 bips of the uh

knowledge workers the world. So

Anthropic has something like 14 or 15

million DAUs. Probably a small portion

of those are truly doing AI the way you

can do it. So that 10 bips, it's classic

Scurve where these are the tinkerers and

then it's going to go to the early

adopters, then it's going to go to the

early mainstream. But you're going to go

from 10 bips to one to two or 3% to 5%

to 15% in the next four years. And kind

of a light switch this year went off in

the enterprise where everybody realizes

they need to do this now and do it fast.

It's still

>> like internet 1.0 I know when it's like

you knew you needed a website in 1998

but it's like hard to build that website

but this is coming together fast and so

you know we think the I don't the

enterprise AI or enterprise application

AI market is is like less than 1%

penetrated and we've never seen you know

we talk about S-curves we call this an L

curve just straight up and then we'll

take this to the infrastructure

which is even we're at 10 basis points

of people really using AI and we're

already sold out of all the there's not

enough compute in the world. So

Anthropic has half of what they need

right now and that's before this huge

takeup. So Mark Andre said in the next

four years one thing he's sure of is

there's not going to be enough compute.

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I'm so curious when an investor like you

who historically was a public markets

investor, you could hit buy and buy

whatever you want, is now operating in

lots of the most important private

market companies. We can talk about

Stripe or Data Bricks or OpenAI or

Anthropic. How do you get the positions

at the size that you want coming from

the legacy of been being able to just

buy? How much of it is um creativity

just directly with the company? If it is

directly with the company, so they have

it's a double opt-in. they have to

decide to let you in too. How do you do

that? Like what what have you learned

about getting the allocation you want or

the amount of equity you want in a

private company given that you know that

wasn't your original background?

>> In that case, you know, we we got to

know the company. One of our analysts

knew people in in the finance group

there and we actually we had a look at

the at the $60 billion round and we we

didn't do it. we didn't know the company

as well and we um the gross margins were

negative and and and frankly we hadn't

seen coding explode the way it had and

and one thing about public markets is

you get to know companies

over a long period of time and you can

kind of invest on your own schedule. I

got a chance to spend some time with

Daario. I obviously listen to on

podcasts and it I started to realize

these guys their management team is

excellent. the focus, the dedication,

they had almost no turnover, the quality

of code, and then the business plan was

really starting to play out. And uh it's

one thing to grow from, you know, 100 to

a billion, but it's another to do nine.

And then so we reached out to the

company as much as we could. They took a

meeting with us. We did a 90page

PowerPoint deck where we used Claude

Code to scour the internet for all the

feedback we could about the coding

market. and their and what their

products were good at, where they might

need to improve and we also did our

whole overview of what the coding market

would be. They welcomed us into this

round and then we stayed close with the

CFO and uh it's it's been great to build

a relationship with them and I think we

p punched above our weight in terms of

the allocation. So that one was a total

home run. In the rest of the the world,

we are in this period where the unicorn

market is bigger than most stock markets

in Europe, maybe even combined. It's

definitely bigger than Germany. It's

definitely bigger than the UK. And we

even before we invested in privates, the

first one was 2020. We meet with these,

we have to know these companies and you

really have to know them now because

sometimes they're the biggest companies

in the space and and have huge impact.

So we, you know, we do two to 3,000

face-to-face meetings with management

teams a year and about 10 or 15% of

those are with privates and then we kind

of focus in on the companies that we

really want to learn about and find ways

to meet with them and uh get involved in

their rounds. And our first one was

Stripe.

And we had a large investment at the

time. This is 20 2018 2017 1819 we and

2020 we own Audion which is a fantastic

payments company and they're a next-gen

cloud payments company taking from world

pay and you know the cloud the cloud

modern payments was 5% of total you know

$80 trillion market or what have you but

you can't invest in aud unless you know

Stripe like the back of your hand so we

did tremendous amounts of due diligence

talked to 200 customers in Audon but

when we asked about audio and we asked

about Stripe and we realized this is

Coke and Pepsi and um we said we got to

find a way to invest and I finally got

to meet the Coulson brothers in 2019 and

so that was our first one. We weren't

really known for privates. I've got a

friend um who's who has a involved with

a a venture firm that has tremendous

amounts and I talked to him about it and

I said let me know if you ever want to

sell some and then I get a call from him

during co in April

of 2020. We knew a lot about Stripe. We

didn't have the full financials, but we

knew enough that at that valuation, I

think it was 35 billion. We knew they

had they disclosed we had over half a

trillion of TPV. And we knew that

Audience's take rate was 25 or 30 bips

and we knew Stripes was 40 or 50. And we

knew how many employees they had. So, we

could kind of get at the profitability.

It turned out the take rate was higher.

It turned out they were being modest

about their TPV. It was much higher than

the 550. It was closer to the one 1

trillion. And you know, we underwrote

the thing under our assumptions and it

was much better. And then we were able

to upsize that from the seller to a $100

million block. Sometime they like it

that you know the VCs are going to own

and then most of them are going to sell.

They like it that we'll own and own in

the public market which we did with new

bank uh as well. all owned it for a long

period of time in the public market as

well.

>> Maybe now's the right time to lay out

everything you've ever learned about

S-curves. Obviously, your firm is sort

of predicated on this idea of technology

adoption life cycles

>> and investing in companies at the right

time amidst a certain platform change or

S-curve change.

>> And I think everyone knows the basic

idea of an S-curve and and the sort of

uh the stages you mentioned, tinkerers

and and early adopters and early

majority. But I'd love you to go into

the the super deep detail of what you've

learned since this is the lens through

which you've viewed markets and stocks

for a long time. Bring us into like the

nitty-gritty fine grading nuance detail

of why S-curves can be so useful for

investing. We have an investment

framework. It's

>> S-curve

and we'll dive into each one.

Competitive advantage and then

underappreciated earnings power. And

when you get the right part of the

S-curve, you get exponential unit

growth. If you have a very strong

business model, which in tech there's so

many of those for so many different

types of modes, uh your earnings don't

grow linearly, they grow exponentially.

And that's the last piece. Invest when

there's underappreciated long-term

earnings power. And very often the

earnings can grow from $1 to $10.50 to

20. And it happens way more than you

think. And it allows you to buy some of

the best companies in the world for

extremely low pees. When we were buying

Nvidia in 2023,

we were paying four times earnings. When

we bought Tesla in 2019 for the car

scurb, we were paying five times

earnings. When we were owning Apple, we

were paying four times earnings. When we

bought Amazon for AWS, we we were

getting it for free. And you know, the

world doesn't think exponentially.

and they're so focused on the next year,

the next quarter. Very few people

believe you can accurately predict two,

three, four years out. But if you follow

and understand the S-curve and you you

know the modes and you know how to

model, you really can uh predict these

these great things. So let's go to the

scurve. So the S-curve is crucial

because every technology follows this

pattern where it comes out,

you know, the I the smartphones were out

10 years before the iPhone. The internet

was out 20 years before Netscape. AI has

been out hidden inside of these

companies, but it wasn't until Chachi PT

took it public uh and ignited what it

was. So electric vehicles, Tesla went

public 15 years before 2019 when it went

vertical

it because there were so many barriers

to adoption. The first smartphones, you

know, they were clunky, they didn't have

touchscreen, not Apple, there wasn't a

wireless data system. And then uh and

they were too expensive. They were $500

or $600. Steve Jobs got the price to

200.

There was AT&T had a 3G network. It was

touchscreen. It was so easy your

grandmother could do it. So Annie built

an ecosystem and made it simple. So all

the barriers to adoption were eliminated

and then you rocket when those barriers

are removed. That's the tornado of

demand that everybody in the world knows

they need this right away. And so that's

the flip that happens. It happened with

electric vehicles. The price was too

high. Elon got the price to 40,000.

Range anxiety was there. he got the the

range to 300 miles. The supply chain was

was finally in place so he could churn

out millions of these things. So that

triggers the inflection. Now the other

nuance, it's not just, oh, it's taken

off now. It's how tall, how big is this

S-curve, how tall it is, so you know

when to sell, how long to hold on, cuz

we're underwriting out 2 or 3 years. We

have to know what the growth looks like

thereafter. And these S-curves can be

dynamic. So when Amazon had AWS and it

was a hidden line item inside of Amazon

covered by retail internet analysts, not

hardware chip, it was a new business

model, what have you. But we realized

the TAM for this, it was the largest TAM

in enterprise IT ever because previously

the TAM was routers, memory, storage,

Dell, EMC, but they were doing it all.

And so we figured out, you want to know

how tall the S-curve is. So we figured

out they were addressing 600 billion of

IT systems directly addressing that. And

then we said it's probably going to be

50% deflationary. Therefore, we're 1 or

2% penetrated. But then over time, we

realized it it actually wasn't

deflationary. If you talk to anybody

now, they say if you build it yourself,

it's about the same price. So that means

the TAM was so much bigger. So there's

mega S-curves and there's subass curves.

You know, we've been lucky that we've

had, you know, internet 1.0,

uh, mobile, cloud, e-commerce,

and now AI, which we can confidently say

is the biggest and all these things

build upon one another. So, you know,

with with the electric vehicle S-curve,

you you you have to pay attention too

because, you know, at the time we we

thought probably maybe 40 to 50% of the

cars would go electric, but it did hit a

big wall at 10 or 15%.

Usually the S-curves go kind of all the

way. Um, but in this case, for a variety

of reasons, it didn't. So, you have to

adjust and you have to stay on top of

it. And generally you want to um when

something gets to sort of 30 40%

penetrated then you stop having

exponential growth which means the sell

side catches up and there's no longer

big beats

>> and is that when you sell typically

>> generally we like we like the high

growth and it was a mistake with Apple

because in the first five or six years

of Apple um it was awesome. I mean it

was our largest position. and it would

go up 50 70% a year um except for '08

and then we sold in 20 2012 when it got

to sort of 50% of the US had a

smartphone and with Apple you know they

maintained their leadership position it

had a couple years of underperformance

and then the multiple got low and they

added several ancillary things and then

they al also got to play in the uh the

application because they get 30% % of

the app. So they were able to compound

very nicely, say 20%, but the the big

years were in the 50, you know, the the

0 to 50% part of the curve.

>> I'm so fascinated by this uh you know,

sometimes decade plus long flatline at

the beginning of one of these curves,

which makes me wonder what you've

learned about the right moment to buy or

even start paying attention before you

buy.

>> How do you measure that? Is it always

different? What are the pitfalls that

you've fallen into? How do you know when

when to we talked about when to sell,

but how do you know kind of when to

start thinking about buying in one of

these things?

>> Yeah. And you know, Andy Grove says sort

of when you have strategic inflection

points, you can't trust the data. And

and strategic inflection points are

about intuition, anecdotal evidence. I

love this book called The Towel Jones

Averages, a guide to whole investing,

which is rightrain and leftrain. And the

best investors have the right the

creative side where they it's visual.

It's connecting the dots. Um you know we

invested in the mobile video game

S-curve for so long. Mobile video games

were just the screens were small on the

phones and the processing power wasn't

good. So you had all these uh casual

games. But then I was in China and I saw

this little 12-year-old boy with a huge

phone and he was like playing a awesome

video game. I'm like oh my god it's now

coming to the phone. So, it's visual.

Um, enterprise is hard cuz you can't see

it. We go to the Gartner IT Symposium.

30,000

American CIOS go there and like we saw

this happen with Splunk where that used

to be an amazing database company and

like their their room where they were

explaining was like standing room only

or we saw that with VMware you know I'm

talking like 30 years ago where they

virtualized the server and like there

was standing room only and you could

just see the corporate demand just

beginning and with AWS

We went there and the grand ballroom was

completely packed and that was at nine

o'clock and at 10 o'clock the grand

ballroom was completely packed 11:00. So

you could you could actually see the

demand exploding before it happened. So

um we look for for all kinds of clues

and there's a whole pattern recognition

that happens. And by the way it's okay

to be late. It's okay to miss the first

one, two, three years in a lot of cases

because if the top of the S-curve is

half a trillion, um the growth can go on

for a long time. So, you don't always

have to be right there. It's okay to

miss the first 100%. Peter Lynch, I

started at Fidelity and he loved to

mentor the young kids. So, I got some

time with him. He said, "Wite out the

chart.

It's all about the future." Um, and so

it's okay to miss, but but what helps

about the S-curve is is sort of how long

it goes for. Then there's sort of the

the slope of the Scurve, which is

important. And a lot of people think cuz

we're in a modern world, everything's so

fast, but there's a lot of factors that

determine the pace of the adoption. And

we we um commissioned this gentleman,

Horus Du used to work with Clayton

Christensen, to go look in history. And

we have the big S-curves on our wall

over the last 100 years. And the radio

Scurve is one of the fastest ever. It

took 7 years to reach like 100%

penetration. But the dishwasher Scurve

is like that because it needs to be

plugged into the back end.

>> What are some Yeah. What else did you

learn? That's fascinating. What else did

you learn?

>> So like the B2B stuff can take a long

time because it needs to be plugged into

the existing systems. It's like it's got

to be put

>> the dishwasher

>> inside the house and then um and

consumers generally tend to go a lot

faster. Um

>> I love that the radio and the

dishwasher, the two models for adoption.

>> Yeah. And and I I covered internet of

fidelity. I c, you know, my first stock

was Amazon. That's a whole other story

which is a lot of fun. But I also did

B2B internet and you know there was a

whole huge bullcase on that. But the

basically the underlying infrastructure

wasn't in place for B2B to happen.

Ultimately happened 20 years later with

SAS. And so that is a risk with AI in

that you know these big companies are

very security conscious. Uh they're can

be slow to move. There's a lot of

cultural issues with a with AI where you

know you really need a few evangelists

to push it through and the top

management needs to push it through but

then the IT's saying this is this is

risky and that happened with cloud too

that was one of the big things with

cloud where it was too it was everybody

was afraid it's unsecure to have your

data in the cloud and then we saw the

CIA do it and we saw Capital One and we

talked to the Capital One CIO we that

it's more secure in the cloud and then

it really started to take off. But but

those takeoffs

maybe because SAS is like the dishwasher

and because cloud is like the dish it's

got to be plugged in

it it meant that yeah it was growing but

it was sort of a 30 to 40 maybe a 50%

growth rate but what's amazing about AI

is you just at least with consumers or

even business you just open up

>> the browser and it's there

>> and so that's why we're getting this

straight up

>> and I think there's enough runway in the

me in the near term going from 10 bits

of people really using it to two to five

or whatever which is going to cause it

to keep on going straight up. So this we

this we call it a backwards L curve. Um

so it's really pretty exciting.

>> What have you learned about uh when the

group that ends up being the leaders

separates itself from one of these

competitive packs? So you're talking

there mostly about overall growth of the

S-curve and demand. There's always

multiple players fighting for it. You

know, you've invested, it seems like you

kind of invest after someone has

separated themselves from the pack, not

try to pick the winners from the pack.

Is that is that like roughly?

>> Well,

>> correct?

>> Well, we're definitely So, you look for

the S-curve, then we do an exhaustive

study of everybody with exposure in that

area and try and find the one with a

very powerful competitive advantage. And

a lot of people didn't like tech. Warren

Buffett didn't like tech because he

couldn't predict the future too fast.

Yeah. And so the Scurve is our map for

looking in the future. Now a lot of

people were worried about tech because

they thought there was so much

disruption you could never trust a

company to be a longived asset. And what

we've found over the years is some of

the competitive advantages

within the digital world are more

powerful, if not equally or more

powerful than than in the offline world.

You've got the network effect that was

so powerful for LinkedIn, Facebook,

Alibaba, you name it. Then you can

become an industry standard. Oracle and

Bloomberg are the industry standard.

Oracle, you know, they charge a lot and,

you know, there's free versions, there's

open- source Oracle, but they had all

the database administrators. They they

had all the software that was tuned to

work with them. So, they they basically

had a chokeold on the relational

database market forever.

um you can get to scale very quickly

because these scurves grow and all of a

sudden Anthropic is doing 90 30 billion

in sales or Amazon you know had so much

scale and they got it quickly. So they

got a Walmart size scale advantage in 5

years versus 40 years for Walmart. So

you can have network effects scale you

can become industry standard. You can be

a platform that people build on top of.

You can have critical intellectual

property, which was what Qualcomm had.

You couldn't make a phone without paying

them, or ASML has critical intellectual

property. You can't make a chip without

their lithography. And I think what's

interesting is maybe these AI

foundational companies, you know,

they've got scale. Oh, you can also have

brand. And brand's very important

because Google, Amazon, they got to

grow. They never had to advertise.

Elon's never had to advertise for

anything. and cost to acquire versus

lifetime. It's the whole business model.

And so almost all the companies I

mentioned have Apple, they have all of

these rolled into one. Um, so we can

sometimes we can notice these things

before the rest of the world. And one of

our high points was we pitched Amazon

for AWS at 2013 at the Robin Hood

investors conference and we said the

bulls have no idea what they're sitting

on. Amazon's won the war but before it

even started and at that time we said

there's Coke and there's no Pepsi. Did

turn out there was Pepsi but it was big

enough to last. And we could see they

had a seven-year lead. So first mover is

important. Then they became a whole

ecosystem and a platform. Then they got

scale. So they were 10 times the size of

everybody else. Nobody could invest in

the R&D to to catch them. So um but

you're right that if you don't have a

competitive advantage, you can be in the

best S-curve of all time

>> and still lose out.

>> But if your name was Rim, Palm, Nokia,

HC, LG, Motorola, I can go on forever. 0

negative negative negative negative. And

that's what we saw at the foundational

model layer where there's like 50

companies trying to do that and they all

have fallen away and two or three have

emerged at the top and there's a lot of

reasons to think they will continue to

hold their position.

>> So to take Google's a little trickier

because they have this other huge

massive complex business attached to the

Gemini business. But if you take

anthropic and open AI as pure plays and

you dig through those and you reason

through their competitive advantages,

why aren't they susceptible to erosion

of those things in the fullness of time?

>> Yeah. Of all the S-curves we've we've

done, AI is by far the most complex and

the fastest changing. So it can be we

have to keep in mind that there are

risks

but also the rewards are the highest cuz

we're talking about a market in the

trillions. You know we we just said

cloud you know maybe cloud's 800

billion. This might be you know we now

think 3 to five but there's higher risk

higher reward. But let's just say with

anthropic now they have it looks like

they have critical intellectual property

generally they've been able to maintain

their their high market share and code.

Number two is uh they've built a a

strong brand for enterprise to where go

talk to any CIO and they'll just the

first thing they'll say is claude.

They're going to have escape velocity

and scale. And what was scary for OpenAI

and Anthropic fighting these big

companies like Google was they had these

huge cash cows. And to both of the

management teams credited Open and

Anthropic, they were able to

work in these super capital intensive

industries and find ways to raise

capital. And certainly with Anthropic,

with their 10x sales growth, it looks

like and their fundraising ability, it

looks like they've reached escape

velocity. So now they have scale.

And the other thing that Anthropic and

OpenAI could have is Anthropic now that

they're leading in code, they set that

code back onto their model and it's this

concept of the recursive improvement.

And if you look at the pace of their

innovation, it's accelerating.

>> Um, and so maybe they can have this

liftoff stage. you know, Open AI has,

you know, they they were focused on so

many different other sectors, but

they're starting to do better in

enterprise and their coding tools good

and they're starting to see accelerating

growth on that side. And then look, the

consumer franchise,

it it looks like enterprise right now is

much better because you're, you know,

you and I, we're willing to pay a lot

because it's replacing human beings. you

know, consumer, maybe you can get

advertising, but maybe they would pay

for a a clawbot type assistant if you

could make that perfectly well for them.

Um, but they have gazillion eyeballs

there. But you're right, things do

shift, but it usually on the we have

this

charts that we almost do for all of our

pitches. On the internet, the leader

goes bigger, faster, and wins. And it's

it's it's happened you know most of the

time the leader gets it. Shopify becomes

the leader. It just keeps on going.

Amazon the leader keeps on going. SAS

company XYZ just you get the lead. It

compounds on internet company compounds

on itself. And and another thing is you

need to be big. Another is scale. You

need the compute and you got to pay for

the compute because so there's only so

many people that can do that. So that

those are some of the modes that we

think are now showing up. Now there are

some exceptions to that rule. usually

with the paradigm shifts AOL and then

dialup went to broadband and and they

didn't make make the change. You know,

Netscape came out early and it wasn't as

strong of a business model. But I think

if you talk to anyone in the valley or

any startups, you know, they'll tell you

that they're building on top of these

three and the world's a huge place and

the economy is a huge place that that

they'll be able to differentiate within

those. I'm so curious then what you

think all of this means for software. Um

when I look through your portfolio, I

don't see a ton of uh big software

companies, enterprise software

companies. I I don't know if you once

had them and sold them or or how you

thought about it, but it's hard to have

the experience of building really

useful, cool little tools, even if

they're still toys, and not have the

thought of, wow, you know, like if I

spend enough time on this, even if I'm

not technical, maybe I could build a,

you know, an ERP equivalent replacement

or something for my company. There

doesn't seem to be a fundamental reason

why that's not possible and and then

those companies could be in lots of

trouble. Seems like everyone has a a

strong view on this one way or the

other. I'm curious how you've approached

those sorts of companies given that you

don't seem to own a ton of them.

>> We were at certain points maybe 5 years

ago, we might have had 40 or 50% of our

portfolio in software. And early on in

in our April 2023

seminar, we said definitely invest in

chips first and and we said but at the

application layer initially we thought

these companies are huge. They have huge

sales forces. They can take these AI

APIs and and build products and they

have the data. This is going to be

amazing for software.

And pretty quickly we realized their AI

products were not very good. They

weren't moving the needle. Nobody could

charge for them. We basically sold

almost all of our software, almost all

of our application software. We still

have one or two small ones, but entering

this year, we were actually net net

short. And uh it really helped us in the

first quarter. There's so many layers.

The old way of software is like using a

pen and paper or it's like a horse and

buggy. The new way of software is like a

jet engine or frankly like the

transporter from Star Trek. It's so

revolutionary changing that it feels

like it has to be disruptive

now.

even if it's not disruptive now or right

away. Uh so the software companies have

another problem which is

their list on the to-do list or priority

list of any CIO has fallen a lot. So

even if AI is not going to be

disruptive, they're spending it on

anthropic tokens because there's faster

ROI there. Um, second,

um, if they're spending all that money

over there, it pushes on the budget, so

that hurts them. Third, a lot of

software companies were able to raise

price every year. Um, and now they're

probably nervous about doing that. Then

fourth, we'll see what happens with jobs

cuz I don't, you know, there's smart

people on both sides of that, but we are

seeing some companies really gut their

jobs or whatever,

>> freeze hiring and so that hurts on

seats. Maybe in terms of them building

their own apps, maybe it just um

>> you know, if you want to be optimistic,

it's it's taken them a while to do that.

We talked about how early the primitives

of AR are. So maybe they have just taken

a while to get to something they can

commercialize, but

you know, they might not have the right

people. They might not know it's a

different selling motion from selling a

fixed system versus, you know, if you're

installing something that does human

work, you got to be right at the side to

make sure it's really getting done. So

you need the FDE

for deployed engineers and they might

not have the right people internally to

do that. Then of course there's the the

risk of you can build it yourself. The

bulls will say, well, they're never

going to build their own ERP system. And

that's probably right. And it is true

that technology, old tech is very

sticky. Like mobile video games didn't

hurt console games and uh the tablet

didn't hurt the PC and the smartphone

didn't hurt the PC and uh there's a lot

of integrations and work that goes into

these software. So that's all true and

companies do like to buy from they don't

like to build themselves that much. So

that's all true but you can't imagine a

world where in 1 2 3 4 5 years um you

could have a brand new AI native company

going after each one of these very

strong incumbents and it might their

data advantage could get obiated. it

might be easy to take it out and put the

new one in with AI and such. So, what's

good if you like so is the valuations

are very high and everybody knows

they're under pressure. Some people are

tempted to buy these, but the AI um

coding tools are just getting better and

better. So, we'll we'll have to wait and

see. And we're we're watching these

software companies very closely to see

if they're getting any revenue that can

change that trajectory. But it's hard

because if you're a company like

Salesforce, you've got 40 billion in

sales

and now you you might have 500 of ARR

700 of AR of AI. So you've got this huge

base. Now maybe this starts to work but

it takes a while. And in software

there's the rule of 40 which is your

growth rate plus your operating margin.

And if you've got a 20% growth rate and

20% that's good. For AI, we have a new

kind of rule of 40. We call it well,

it's really for chip investing. But if

what percent of your sales are AI, say

30%, and what's your market share in

that category? Say 30%. You'd be 60.

That's a great place to look because

you've got exposure and you've got a

strong market position. Problem with

software is their AI is 1 or 2% at this

stage and it's a long way to go. Um, one

thing we are picking up though now

lately and this is halfbaked, but AI

could make some of these software

platforms more important because what's

the first thing you do with claude? You

plug it into Slack. If that can become a

key repository, that will make Slack a

permanent fixture within the

organization. And so maybe these agents,

maybe the next wave of AI will be these

agents that use tools and they might

operate inside of the existing incumbent

software tools to use them like a human

being would.

>> Just to pull in that thread, uh it seems

like the commonality of the tools they

might use that are the most sticky would

be network-based tools. Uh so Slack is a

great obviously a great example of the

software in Slack itself is I don't know

leaves something to be desired. It's not

the software is not the special part.

It's that everyone is there,

>> right? But I'm curious yeah what kinds

of things you would want. Is it just

network you know the presence of a

network effect? Is that the only thing

that really matters?

>> It's still early in our thinking here

but I don't know even even even maybe

you know workday or the HR systems or um

the big systems of record

you know the agents may be running on

top of on top of them. CRM is going

headless or they're making a headless

version and that's sort of the bare case

too that you get relegated to just being

a database but you know there's a human

interface to it then they need to make

the AI interface which is no interface

it's just them going right into the data

and so you know you lose that customer

interaction but if if the

agents are going right to right to CRM

and doing the work inside of CRM M that

that will solidify CRM so you won't have

to think it's going away.

>> Can we talk about chips? You've

referenced them a few times.

>> Inf infrastructure chips, you know,

everything around the data center maybe.

I don't know how you conceive of it.

>> Why is this so interesting to you? I

love the the modified rule of 40 for

percentage that's AI and percentage

market share in the category. That's an

interesting stat.

>> What companies shine on that today? What

are what are lagards, you know, that are

surprising? For the past 40 years,

nothing has changed in the data center.

Even with cloud, we're basically Intel

x86.

It became the data center chip sometime

in the '9s. And um and compute grew in

the cloud era and it grew compute

workloads grow you know 25 to 40% every

year but Moore's law is improving at

that rate.

So it didn't require tremendous

innovation

and there really was almost no growth in

hardware for years and years and years

and the whole industry basically

commoditized every part every chip every

part of the server the printed circuit

board to the memory to the enclosures to

the networking

you know there was no innovation

you would go from one gig to 10 gig

That would take 7 years. And when you do

switch in the first year, it does take

some innovation to get to 10 gig and

would create a little cycle, but then it

would commoditize.

And now you go to AI and

the workloads are growing 10x every year

and they're pushing every single aspect

of this hardware to the physical limits

of what it can do. And so, not only are

you creating tremendous unit growth, but

the industry, we call it the

decommoditization of the hardware

industry. And I I met with Shawn Maguire

like 3 years ago, and he said, "I wish I

could come back and be a a hardware

hedge fund because all the companies are

public and they all have powerful IP."

And Sequoia made some of their best

investments back in the hardware day

with Apple and Cisco and others. And

we're in this renaissance of chips. So

not only do you have tremendous unit

growth,

but you it's requiring tremendous

innovation and what that means, you

know, at every aspect of the server. And

so you know memory which used to be a

pure commodity, this high bandwidth

memory is stacked 10 chips on top. you

know the input outputs are 10x what they

were before like took Samsung for years

to do it and it's a critical critical

piece and then that is constantly

upgrading so they're on the same you

know they've got to be working with

Nvidia for three or four generations in

advance we we had this with Celestica

Celestica

was a contract manufacturer and this has

been a disaster industry since 1999. It

went all offshore to China. It was

commodity, but they hung on and they

they kind of kept

Celestica's heritage was IBM

supercomputing and they kept all that

talent and skill. And then we noticed

they were the sole supplier of the

Google TPU server. We're like, "Oh my

god, this was like three years ago. The

stock was trading at eight times

earnings." And they had this whole and

then they also had this whole business

of selling Ethernet white box which is

code word for commodity white box

Ethernet switches into the clouds.

It it turns out that these are excellent

businesses. Not only do they have

tremendous growth, but to do an AI uh

server computer, it's it's liquid

cooled. It's running so much hotter and

you know it's two or $300,000

piece of machinery whereas an old server

was $5,000. If it breaks you just throw

it away. If this thing breaks the whole

thing goes down. So you become like

critical infrastructure like selling a

critical part on a plane. You'll never

get swapped out. And then they they it

turned out they were quite good at

liquid cooling and you know a lot of

other people tried to do it and failed

and so they've retained that position.

Then it also turned out that the

Ethernet market was because you were in

the old days you would go from 100 gig

to 400 to 800. It would be a 7-year

cycle to upgrade. Now they're upgrading

every year and that's really hard to do.

Then there's a whole software layer, the

open source sonic layer. The the guys at

at Celestica invented were some of the

people that wrote that open- source

software. They work very closely with

Broadcom. So what we thought was just a

great growth driver turned out to be

great competitive advantages and they

have like 50 60% share of the cloud

Ethernet switch market which is a

crucial market for um AI because AI is

incredibly network intensive. And then

even something like the printed circuit

board. I mean a regular server you need

10 layers. These AI servers you need a

40 layer and there's very few PCB

suppliers that can make this. And um

there's all kinds of complexities in

there. And we also own Elite Materials

which makes the leading ingredient which

is copper clad laminate which goes into

these boards. And so the PCB

uh units are growing, the layer counts

are rising. So you've got like a

50 to 60% keer just in the units and

then the ASPs are rising and then the

gross profits are rising and your

visibility which used to be hey we'll

call you next week if we need you to

like hey we need you for the next four

years to be like designing this road map

with us. So you've you've gone from a 5%

grow or low margin to you know a 35% 40

50 topline kager for the next four years

with rising margins

and then on top of that there's

shortages of everything. So even if it

is a commodity it's going to be a great

cycle. So we see that up and down the

supply chain. You find these companies

like Corning like they make the fiber.

Um they've got some ridiculously high

share of the fiber. I was reading this

uh Microsoft

data center they just built. There's

enough fiber to circle the world four

and a half times in that one thing.

And their fiber is thinner and more

bendable and can be specially

manufactured to the exact specs. and

it's higher margin and it's the fastest

growing part of their business. And then

they're doing, you know, in networking

there's scale out which is kind of

connecting all the server racks

together. Then there's scale across

which is connecting the data centers

together. And when you want to build one

of these huge clusters and you can't get

all the power in one place for training,

you want to wire them together. But the

wires you need like 10x the wire has to

be so much thicker. So that's creating

huge growth. And where the real kicker

comes in is when you do scale up. That's

connecting every GPU in the rack to the

other ones. That's done over copper.

Eventually that'll be done over fiber.

when that happens that two to three X's

Corning's opportunity. So you just have

at every layer of of the rack,

>> everyone's overwhelmed.

>> Everyone's overwhelmed. But the story

like in the power supplies, every Nvidia

chip or rack uses

n 50 to 125% more power. And like

literally that drives the ASPs of Delta

and Advanced Energy. I just I think it's

it's I can't believe these stories when

I hear I'm like wait so your ASPs are

going to like go up 40% for the next

four years in a row and it's higher

margin. The broader picture is like

we're going to be the AI demand if we're

right with this L curve. We're already

short, you know, the DRAM market, the

NAN market, the PCB. We're already like

30, we're 30% short all these things as

we are now.

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>> The measure of percent AI, percent

market share. Do you care more about the

absolute or the rate of change of those

metrics?

>> It's good because I took we did this

presentation in two 2024 where we

actually listed everybody's market share

and everybody's and then I I asked

Claude to plot plot it to a thing and it

actually didn't get it right because

what it didn't get is the rate of

change. So the rate of change is

important and that's incredible too

because you go from 10% to 30% and your

growth rate accelerates and your margins

accelerate. So rate of change is very

important.

>> Why don't more people get this right in

public markets? Like if your whole

framework is S-curve, competitive

advantage, underappreciated earnings

power. It feels like the movie's been

played out a lot over the last 25, 30

years.

>> My mom said, "Why do you tell everyone

your secret? It's like it's why does the

casino teach people how to play

blackjack? It it's harder. It's really

hard to do. It's it's you have to have a

a deep you have to be comfortable

investing. You know, we've been doing

I've been doing tech for 20 years at

Whale Rock. We've got a team that's been

doing this, covered many cycles. We know

the different. So, very few people, no

one's paid attention to hardware and

chips at all. So, you've got all these

newbies coming into it.

>> You and Gavin, that's it. and Gavin's

done a great job. people weren't

comfortable with it and it's it's harder

to do than it seems and the chart you

know a lot of these companies their

charts are up so it's scary can I buy

and then you also have to have the

holistic view because if you don't have

conviction so every you know every time

with Nvidia over the last four years

it's like oh they had a great year oh my

god it's got to be a bubble and then

they had another great year and it's

like 6 months of marking time it's got

to be a bubble this is like getting out

of hand this is pretty scary like and

the the bare cases are not like totally

without merit but if you can see the

whole picture and understand how these

things are unfolding and gain conviction

in that frankly if you're just a semi-

analyst so many semi analysts missed it

because they didn't see what was really

happening at the foundational model

layer so it helps to have have the big

picture it helps to have you know

decades and scores of scurves that

you're looking at and and where it plays

in different things.

>> What what in this whole picture, you

know, I would describe your your stance

so far in the first hour discussion as

like very bullish on on the impact that

AI is going to have and the returns

available as a result. What makes you

the most concerned or uncertain or is it

just the rate at which all this stuff

changes and like what keeps you worried

amidst what seems like pretty extreme

bullishness? I mean, one thing that

bothers me is there's a lot of

negativity in the general population

about AI and there's a lot of negativity

in some aspects of the government. You

know, I think Maine just banned data

centers and

80 only 20% of the people are optimistic

about AI and potential for negative

regulation. But I do think kind of the

genie is out of the bottle. Another risk

is that if AI sort of slows down in its

improvements, I think there's a whole

lot of AI adoption to happen even if the

models didn't improve. But Jensen said

this, you know, years ago when he was

talking about his GP crap, just the

graphics chips. If good enough is good

enough, I won't have a business. Now

every year he made the graphics a little

bit better and people always wanted the

best in AI. If anthropics sort of hits a

wall and stops improving or open AI then

the open source models will catch up and

um and then it might be a race to the

bottom and it might be you know it won't

be good for the stocks probably. It

could be good for the chip companies.

chip companies don't care

>> who's winning tokens, right?

>> Who wins.

>> So, that's another positive and they'll

benefit if if open source, you know,

Jensen really wants open source to like

take off. It's all he kept on mentioning

at at his last GTC. So, that could be a

risk. Another thing is if one or two of

the players falters and loses its

position and can't compete, that could

be like a lot of compute that they don't

need in the future. Now, if AI is so

big, somebody else will suck that up.

And we saw that with, you know, Oracle

cancelled a big deal and then Meta went

right in. But let's just say Meta

decided not to be involved

with AI. Hey, we can't keep up. It's

just going to be a waste of our

resources. So, we we watch that very

carefully and um in general, we see

more, you know, more more companies

truly going after this and even

Microsoft going trying to build their

own. So I think those are those are some

of the key risks.

>> Seems like you really have done very

little in the application layer of AI.

Historically the apps ended up being

most of the market cap you know not not

the infrastructure and there wasn't

really a model layer in the past. I

guess you could say it was the clouds or

something.

>> Yeah.

>> Why focus so much on the bottom layers

of Jensen's five layer cake versus

things in the application layer that are

actually getting used by consumers?

Well, we do, you know, part of OpenAI is

they have chatbt which which is an

application, but we think the

application layer well a it always comes

later. So, you know, the first three or

four years of the iPhone and then the

applications really took time. So, maybe

it's just starting. Um but to date um we

found that area to be pretty risky

because where does the where does the

foundational model end and where does

the application begin and can can the

applications build enough of a moat um

where they can fend off and um and build

and build businesses in that. and um and

we we thought we would see it in some of

the incumbents like a a CRM and they're

starting and maybe just a matter of time

but we really haven't seen it in the

enterprise world and there there are

some you know very good

startup application companies out there

but the ecosystem is not clear you know

like when we started the ecosystem and

chips was clear when we started the

foundational model ecosystem wasn't

clear now it's clearer to us and at the

application layer it's still kind of

unclear and a little bit dangerous

because um but there will be great

application companies built you know we

really were watching Brett Taylor at

Sierra Brett was CEO of CRM he wrote

Google Maps he was CIO of Facebook and

uh he he's building this fantastic

company called Sierra we're not involved

but that's where the rubber hits the

road will he be able to turn this into a

huge company and he's doing quite well.

We'll see. It's a matter of timing when

these things really start to to to come

in into their own and prove they're

sustainable. It usually doesn't start in

the first 3 or 4 years. It comes a

little bit later.

>> At your office, you have this this giant

uh award wall for the research. I can't

remember what it's exactly. It's for the

best research job or project of the year

given to an analyst. And I think you won

it. you gave it self awarded in their

own when you're by yourself, but you've

got this now long 20 year history of a

year one or one or more people, you

know, put their name on this wall for

having done the best job on a research

project that year. I'm so curious about

the nature of that research and how it's

changing as a result of all of this.

Say, you know, the person that's going

to win the award this year and the sort

of work that that requires a human to do

when so much of the work that probably

would have won you the award in, I don't

know, 2009 or something could probably

be fully automated or done in an hour

with cloud code or something today. How

is the nature of research and what gets

you on that whale rock award wall

changing in real time? I would like to

say that we're so advanced in our AI

systems that it's a huge change so far.

I mean, it's it's helping us get up to

speed and we have a handful of of great

apps, but it's not yet it's not

supplanting the job of the analysts. And

so much of what we're doing is we're

meeting with as many companies as

humanly possible. We're developing

relationships with with the management

teams that we cover. We're talking to

the competitors. The system we use is

right out of common stocks and uncommon

profits which was written by Philip Fish

Fischer in the 1950s. And it's the

scuttlebutt approach. It's growth

investing. It's it's get out there and

talk to suppliers, uh, customers,

competitors, looking for the key

characteristics of these leading

companies and really developing

conviction in them. Now, if it's a new

complicated area like ABF substrates or

PCBs, we're able to get up to speed on

those things quickly, but it can't pick

stocks for you in any kind of a way. I

will say that, you know, if you're an

analyst who's good at the blocking and

tackling and there's a role for that,

but that role is you need to have

obviously the insight on top. So, we're

now like using AI to write notes uh or

you know review the quarter or and those

notes are much better but there better

be a really good paragraph on top which

is the wisdom. What does this mean? How

does this deal with our thesis? Um what

changed? You know, don't just be a

reporter. Um so the AI can be a great

reporter. It can't it can't quite pick

into the future. And like the job that

the guys did on app 111 two years ago. I

mean I think we got two of the best adte

guys around and they you know they

convinced me to buy I knew adte I

started actually nearby here in New York

at at that internet advertising startup

and after I did banking.

So I knew internet advertising and ad

tech which is historically a terrible

industry. Um, but Michael and Sam really

figured out the Apploven story like

before anybody and they followed it when

it was private. They know all the

competitors. They know all the

intricacies of, you know, there's all

this terminology and um they, you know,

Sam went to the Las Vegas app

advertising conference and we went to

con and, you know, we talked to scores

and scores of of people. So um and they

did the work on the model and developed

a great relationship with Adam Ferogi.

He's one of the best managers out there.

And um I don't see AI doing that.

>> What role does talking to other

investors outside of your firm play in

your life? Like

>> I one of the great things is just the

friendships I've built with so many

smart investors

and and frankly Philip Fischer said part

of his process was like get to know a

good 10 or 15 like-minded people around

the country and share ideas and um

and a you know they're great great

friends to make a lot of them have been

on your podcasts and uh and and you

develop good friendships and and you you

share ideas, talk ideas. It's important

that it's a two-way street. Um, I call

it the tripod. When I like something

and then my analyst likes it and then

somebody who I really respect also likes

it. That's three legs of the stool can

really help the conviction.

>> What have you learned about shaping the

products that you offer your investors

across the history of the firm? It's not

just one monolithic structure anymore.

>> There's there's several things that if

I'm an investor and I want to give you

money, I can there's a couple ways I can

do that.

>> How did you arrive at those things? And

and how do you how could you turn that

experience into um advice for other

investors that are trying to provide

their LPs with the right set of options?

>> For the first 15 years, it was a long

short fund and we you know, you want to

be focused and if you defocus that can

be hard. So we we grew that and we got

that to the scale that we wanted to.

We're 20 years old, maybe 10 years in,

people started to ask for a long only

product. And so in 2020, we we launched

uh the long only fund. So we're 6 years

on that. And um that's now larger than

the long short. The bulk of the assets

are in these two. In maybe 2015, we we

formalized that we might be doing

privates. And so we gave investors the

option to opt in or opt out and you

could do 15% or 25%. So, but we didn't

break the seal on the privates until

2020. In 2021,

we offered um a hybrid fund that could

be 80%

uh into privates. sort of similar

approach but if you wanted more exposure

to privates. Um and then very recently

we launched the whale rock meggaap tech

fund and we just think there's a huge

structural underweight of the largest

tech companies in the world because a we

also realize that a a lot of our

performance over the years was from some

of the largest companies whether it be

Apple or Amazon or Tesla and and people

just it's hard to overweight these to

the to the amount. And so a lot of our

largest pools of capital endowments or

what have you, they realize they they

have been massively underweight, the

largest tech companies in the world for

the last because they only have, you

know, they have a lot of privates. They

don't have a ton of public and then

maybe half the public is international.

And then of their public bucket, they

don't want to they there's a belief that

there's no alpha in large cap. So they

underweight large cap and they have a

lot of small and mid managers that are

stock pickers because it's intuitive

that large cap can't have alpha. Um and

then in their hedge fund portfolio, even

if it's long bias, they're not going to

have 15% and Nvidia and all these other

things. And we realize that there's a

huge that this people are worried that

there's these big companies. This is

just a product of the digital economy in

that, you know, in tech, the leader

usually grows bigger and wins and

develops very high market share quickly

and and there's great competitive

advantages and and they're also selling

around the globe. So, this is going to

lead to massive profit pools and massive

market caps and it's just going to

happen into the future. And so, most

endowments are betting against this.

They're they're because they're

completely underweight this. And finally

somebody came to us and said you know

what should we do which index we got to

and I'm on the board of Hamilton College

and they were trying on their investment

committee they were trying to figure out

this and so we kept on hearing it and

finally uh one of our clients was like

we said we'll we'll do this for you

because there's a lot of alpha to be had

and the mag 7 or the fang or whatever

it's going to be different and you know

in 2022 they all rallied but like last

year they were very divergent and this

year they're down and so we created the

the Whale Rock Mega Cap Tech Fund which

is the top 30 the universe is the top 30

market caps globally and then we pick

you know the 12 or 13 that are the best

and I think there's tremendous alpha in

the largest cap because if you think

about it a small cap it just takes one

person to to figure out it's good and

move it up but it takes a hundred

people, 100 diversified PMs to realize

Google's not a loser, it's a winner. And

can we figure that out before

95% of those generalist PMs

do it? And you know, we've been able to

do it.

>> We like your odds in that.

>> Yeah, we like your odds in that. And so

there is alpha to be had there. And then

as an asset category, it's great because

these companies by definition have

wonderful modes and maybe they're not

the super S-curve, but sometimes they

are. I mean, Nvidia sure is, and TSM is

really levered to it, and Heinix is

extremely levered to it, and ASML is

levered to it. So, it's a great um so

that's a new we're four months into that

one. And so the right way maybe to think

about it, it it it sort of sounds like

really what you've built is a research

machine to understand the world through

the lens of companies and that the thing

you're constantly trying to improve is

that research machine and then the way

that you would then express that through

products is multiplied. But if I was to

try to understand Whale Rock, it would

be to investigate the research machine

first and foremost. We call it the whale

rock learning machine and it's a group

of 10 highly experienced individuals

that you know Warren Buffett reads books

and we read books and we read blogs and

we but we're also in tech you got to go

out and talk to people. So we do 2500

3,000 facetoface meetings with

management teams and you know Mer and

Buffett talk about compounding

knowledge. We've been compounding that

knowledge for 20 years. you know,

there's changes to the team, but broadly

there's a lot of um consistency to it.

Um Andrew and Michael have been with me

for 19 and 18 years and the average

experience level on the team is 10 or so

years and that includes some of the new

newer people. And uh yeah, that research

engine can support all these all these

products and it's the same people that

do publiclix and the private. So, we're

not going to scour the world and turn

over every A B. But when we see

something that fits into our system,

we're able to act on it.

>> It's so much fun to do this with you.

When I do this, I ask the same

traditional closing question of

everybody. What is the kindest thing

that anyone's ever done for you?

>> Well, I got to say it's definitely my

father who, you know, I was super lucky.

My father um graduated Cornell a double

e electrical engineering, pivoted to

Wall Street and um uh had a great career

at Goldman Sachs and he was um he ran

corporate finance in the 80s and then

ran private equity as chairman in the

90s and uh he was just whips smart but

he he had such humility and was such a

great gentleman and uh when I started

Whale Rock, you know, friends and

family, he was the first call, but he

said, you know, I've been at Goldman for

for 41 years. How about I come and join

you? I'll be the gray hair. I'll be the

oversight. I'll be the chairman. You do

what you do. You build the firm in uh

Boston. Build the team, run the money,

I'll help raise some money. And we got

to work together for 6 years until he

passed away in 2011. But I just feel so

lucky to have worked with him. You know,

it's not easy running a fund. We never

raised our voice. And he was just an

amazing mentor to so many people. And

when he passed away,

um I got so many letters from people who

said, "Your father was just such an

influence on me. He was such a

gentleman. He was such a great mentor to

me." And so I just feel so lucky uh to

have worked with him. And if I could be

half the person that he is, I'd be

completely winning. And

>> how did he do that? How did he What was

his method? Why did so many people say

that?

>> Um

I don't know. He He just He was He was

modest. He was whipsmart. He was wise.

He was also known as a um a great

investor, which isn't the most common

thing at a lot of investment banks. He

also was on their commitments committee

and kept him out of a lot of tougher

situations and yeah he was very warm and

he people would could go into his office

with with with problems and he handled

it handled it with grace and um whether

it's a personal problem or a work issue

or what have you and he just had this

soft way and he also had a great sense

of humor.

>> Lucky.

>> Yeah. I'm so lucky. So Alex, thanks so

much for your time.

>> Thanks so much.

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