So alpha in 2013 is different than alpha

today. Alpha in 2006 is different than

alpha in 2013. Alpha moves around,

right? Alpha is excess return above

market return or beta. That's what

everybody in active management on the

buy side is really focused on and it

really comes from having a

differentiated view. I got to spend the

last 8 and 1/2 years of my career on the

street working at 72 for Steve Cohen.

Then competitive advantage when you go

to a place like 72 it was about

understanding when stories changed one

degree instead of 10. Understanding

change in value chains and in tickers

and in stories. Where is the market?

Where is the world? And where is the

story relative to that? At a place like

72, it's a hit rate game. Steve Cohen or

Ken Griffin, you know, or Izzy or

Dmitri, they're kind of not just picking

great stocks, but building phenomenal

businesses.

>> You want to get it right, not be right.

What was great about Larry, Steve, and

Jimmy, just maniacal about getting it

right. Everything that happened on Wall

Street is going to happen on Main

Street. Fact that we're debating about a

bubble means there's a bubble. That's

it.

>> Kirk, thanks so much for coming on the

pod.

>> Hey, thanks for having me.

>> You've been blessed to work with some of

the greatest hedge fun managers of of

our time. What was that like? What's

that been like?

Uh it's frankly been uh a wild

experience over a 20-year period and I

got to learn so many different things

from uh the different seats I was in

both where I worked but also when I

worked there. So I started my career uh

in 1999. I was a junior year intern at

Tutor Investments in Boston uh working

in the venture group but rolled up to

Jimmy Palada and uh I got to see uh what

one of the early hedge funds on Wall

Street looked like at that time. hedge

funds were still in the first seven to

10 years of being in existence and it

was during the internet bubble and I'm

showing my age a little bit here but uh

I got I learned I learned I learned a

ton about uh risk-taking and research

and uh you know sort of executing um in

in in markets at one of the biggest

shops at the time in Boston and then I

go back to business school and I I land

uh post business school working at Glen

View Capital for Larry Robbins and it

was, you know, very different money

manager than Jimmy was, uh, but sort of

incredible research shop. Uh, I learned

very much about sort of, uh, deep

tissue, uh, understanding of market

structures and business models and how

things worked. And we were very big in

healthcare services at that time. And

Randy Simpson headed up that business

uh, for Larry. and I got a chance to

work hand inand um on understanding the

thematic investments uh that that the

firm was making. Uh we were a two-year

time horizon shop um with, you know,

sort of 50% of committed capital in the

top 10 positions. Uh and so you had to

know your names and you had to know them

cold, but not only that, you had to

understand what what the thematic

drivers were in those businesses. Um you

know, really understanding how the

businesses made money. And so it was an

amazing time there. And then I got to

spend the last uh you know, eight and a

half years of my career on the street uh

working at point 72 for Steve Cohen. And

uh you know, sort of again uh a very

different way uh as a as a firm of

deploying capital. um you know sort of

uh multimmanager uh is much more about

catalyst driven uh variant view

investing um and turning the book far

more than we would have at a Glen View

um but you know sort of learned very

much about really understanding event

driven catalyst driven impacts uh

probabilities and decision trees um and

working with uh you know a broad swath

of investors because really you might be

working for Steve at 72 But there are

120 uh teams inside that organization

that all invest in different ways, have

different uh coverages, uh different

approaches, different styles, different

personalities. And so learning how to

navigate uh and build uh businesses

inside of those firms um was a

remarkable study of you know knowing

your competitive advantage and knowing

your lane and being as good as you could

be in your lane to help other people be

better. And so it was it was I was

really lucky to work with these uh with

these with these organizations which

were a reflection of the people running

them.

>> Three great firms. What were the

differences in their approaches to

finding edge and really I guess

squeezing the juice out of it?

I think the way to think about it is um

you know sort of the way I think about

the word edge um I would say um alpha in

that frame right what what and what

alpha is is competitive advantage you

know so for for anybody you know sort of

um

not familiar with the term alpha and I

think that's probably very few people

watching this pod um you know sort of

alpha is excess return above market

return or beta

Um and that's what everybody um in

active management on the buy side uh is

is is really focused on and it really

comes from having a differentiated view

um from the market view um to create an

excess return above the market. Um

that's a that's a construct that

actually evolves through time. So alpha

at in 2013 is different than alpha

today. Alpha in 2006 is different than

alpha in 2013. Alpha moves around,

right? Um, it's always there, but

sometimes it's in sort of speed to

information, sometimes it's in, you

know, sort of just the information

itself or access, sometimes it's in sort

of the organizational ability to process

something and turn it into a trade. And

so the you know sort of working back at

tutor in9 in 2000 you know I was in the

venture group and I was cold calling

CEOs and CFOs of companies to see if

they needed money. Um but the public

markets business which was set next to

um were sort of you know it was the

internet it was the rise of the internet

and so it was um it was it it was really

having you know sort of um you know you

were competing with far less firms at

that time. Um so competition lowers

competitive advantage because more

people in um the arbitrage and spreads

get tighter. Um but in 99 it was really

about um you know sort of a you know a

team you know at tutor that uh were

incredible analysts right Mike Stansky

covered healthcare at that point. Um you

know you had you know sort of Nina

Hughes uh was covering tech, Rob Broie

was covering tech. you had these uh

prolific uh analysts um that were sort

of building good cash flows and staying

close to their names and at that point

it was really about um just being

experts right and being experts in what

you were doing and you know then you

know Jimmy was one of the best risk

takers ever right and um he was also a

trained accountant so understood from a

bottoms up how everything worked so it

was really important to have domain

knowledge and that was that was

something that created a competitive

advantage in 2000. You know, by the

time, you know, sort of moved over to to

to work with with with Larry and Glendy,

which was several years later, you know,

I I actually built out sort of the

primary research business there. Um, and

that was, you know, collecting

information in a compliant way to help

the investment teams uh and investors at

the firm understand their businesses

better and understand what was going on

in the world, right? And so it at that

point it was you know a combination of

um they were really strong modelers. So

everybody that worked there had done in

banking they had done their you know

sort of private equity experience and

they were really comfortable in Excel.

It's actually one of the reasons I

started the primary research business

there is because I wasn't as comfortable

in Excel. Um that's a longer

conversation but um you know sort of

fundamentally um the team the team was

incredible at breaking down a P&L

understanding the businesses um and and

the unit costs and the unit drivers of

the businesses and having really good

relationships with management teams

because uh they were known we were known

for being really really you know sort of

value added um you know sort of partners

as as investors.

Then competitive advantage when you when

you go to a place like 72 which was SACE

when I got there um it was really you

know sort of it was about understanding

when stories changed one degree instead

of 10. So, you know, sort of when when

understanding change in value chains and

in tickers and in stories, you know,

understanding when those stories were

changing was like the the competitive

advantage I think of that firm, which

was like when you know, sort of where is

the market, where is the world and where

is the story relative to that and is it

still in line with where the story was

or has it changed based on new

information that you get um or a change

in tone with the management team. So,

it's really around capturing the nuance.

Um, it's about having a scaled

organizational framework where you have

a repeatable process that ends up being

the thing that creates the alpha. And

what I would say is the thing about sort

of um point 72 that I always uh really

loved was uh the rigorous approach from

a top down on repeatable, transparent,

rigorous process.

um you know, sort of being sort of um

really bent on um you know, doing the

right things right, hiring the best

people and then executing at an

organizational level on processes that

were repeatable and transparent. Um

because, you know, at a place like 72,

it's a hit rate game. At a place like

Glendu, it's a slugging game. They take

less bets. They have to be bigger. you

know, at a point, you know, or any

multi-manager, um, you're you're you're

it's it's it's a hit rate game, so

you're taking a lot of at bats. If

you're taking a lot of at bats, you need

to make sure your swing is really tight.

And so, fundamentally, that's what I

think is really interesting about the

alpha creation mechanisms at those

different shops is um they're sort of

they they really optimize around the

driver of the alpha, whether it's a hit

rate or a sizing. um you know sort of

let's be clear at Glen View if you're

you're making you know a select number

of bets you have to have a high hit rate

but you also want to get you know sort

of the sizing right and so you know

fundamentally um alpha is competitive

advantage and you know there there there

are ways to manufacture those from an

organizational level from a per people

level and then from a process level and

so it's figuring out how to optimize

against all three of those for it to be

sustainable over time against your peers

And I loved it because I spent a lot of

time studying competitive advantage

because I was in a middle office

function and I had to create content um

for folks who were in the markets that

they found to be differentiated, value

added and competitive.

>> I want to zero in on the competitive

advantage that you've outlined

specifically for 72 because I've heard

similar stuff to the, you know, for the

other firms. um and definitely

exceptional, but it's just that I find

the multi-manager multi-manager meta

where these top firms are playing the

game um I would say at the highest

level. They're currently in the meta and

they've, you know, Steve or Steve Cohen

or Ken Griffin, you know, or Izzy or

Demetri, they're kind of building

phenomenal, not just um not just picking

great stocks, but building phenomenal

businesses and uh you know, a business

process to, as you say, extract that

alpha or that edge. And I think

specifically what you said about point

72 and I found this very interesting is

really zeroing in on that process and

trying to find

those catalysts for change. Um and you

mentioned you were in a middle office

function. I guess how did you tangibly

assist the portfolio

function or the portfolio you know the

PMs the pods there to I guess to build

out their edge and to extract as much

alpha as they possibly could. Well, at

the end of the day, you know, sort of

when you're in a middle office function,

you know, you have to always know where

you sit, New York, right? When you don't

have a P&L, the key that you're So, so

any any PM at any firm on the street is

going to have um uh a hit rate and a

process orientation without any

incremental inputs um that is going to

be X. And then every input that comes

into the conversation

um needs to create lift in X or there's

no value in that content.

Right? So think about it like this. At

the end of the day, every PM on the

street is being evaluated on three

things. Number of at bats, hit rate

against set at bats, and then sizing

against that hit rate. Right?

If you're building a function to inform

a PM's process, and I'm I'm genericizing

it because it's actually universal,

you need to help them generate more

ideas,

have a higher hit rate against their

ideas, or help them improve their

slugging percentage and or conviction.

If you're not creating lift in one of

those three buckets for a for a hedge

fund when you're running a research

business, you don't have a research

business.

Okay? So fundamentally

where you where middle office can help

is creating products that help drive

lift in one of those three modalities.

Those are three vectors that you can

really tweak um with a PM who's

operating at efficiency is generating

incremental ideas, improving the hit

rate against ideas andor helping with

slugging i.e. conviction on a beat or a

miss or on something going well or

poorly so on and so forth. Um where I've

always found

um the best place to try to play there

is in the middle bucket hit rate being

right more than wrong. Why? Because it's

also something you can measure very

cleanly. Right? If you say if if if

you're doing the work and it sounds like

something is slowing down and you name

it in the world and then it slows down,

you can sort of have you you you're

never going to get the attribution from

organizations around how much your

research mattered. Was it 5% of a

decision or 100% of the decision? And

that's okay. It's like that's a process

and an orientation and the separation of

church and state is really important.

um because it also protects the sanctity

of the research, right? You don't want

to be you don't want to bias is another

thing that starts to bleed into these

conversations. You want to be really

focused on getting it right. And you get

it right through process. You don't want

to be right. You want to get it right.

And so, you know, sort of what's really

important is, you know, sort of being,

you know, building a research process

and a research product that is

accessible and differentiated for

consumers to use as a decision-making

tool or part of a decision-making

framework to make better decisions in

the markets. And then you want to be

able to track it maniacally and be

brutally intellectually honest about

whether it was right or wrong because

you scoring yourself hard is better than

you know better than anything else. And

oftentimes you can be right and the

market might not pay you for it. And so

you can't tie it you can't tie back to

returns either. So when you're running a

research business you can't say hey the

stock was up 10%. That's actually not

the right metric. You could say the

management team said that the Grinch

launch was slower than expected. Um, and

if you called that out, that's a win,

but it might not man it might not show

up in the stock. It might not show up in

the atbat, right? But like you're not

getting paid on the return. You're not

getting paid on the atbat. You're

getting paid on the Grinch,

>> right? So, it's really important to keep

those separate. And that's one of the

things I loved about working at Glend

Point 72 is like we were able to build

these confined spaces in like

centralized functions that you know sort

of service the investors

but you know sort of uh we were building

into them to help improve you know sort

of the research hit rate. Um but at no

point were we ever paid on P&L or

anything like that. um because there was

a separation of church and state because

you can get confirmation bias, right?

You can start to sort of think that

you're sort of in the P&L framework and

you're not and you want to stay in the

right place because it keeps the

research clean and I think it's really

important and clean from like a bias

perspective. It's always compliant. It's

always done with the right, you know,

sort of legal framework and everything

else, but it's really important to have

that separation of church and state. And

then you can actually enhance your

competitive advantage as a researcher

which might be different than what a PM

and analyst have as a competitive

advantage because they're they're

they're solving a different problem

right because a company is a company

that stocks a stock in a research

function at one of these shops you're

very focused on what's going on in

companies value chains supply chains you

know sort of fundamental really deep

tissue and and while PMs and analysts

care about that too they're also

thinking about what does that mean for

everything else. They're putting it all

together, you know, and that's that's in

a lot of ways it's it's it's a lot more

nuanced and difficult. Um, which is why

the separation is so important.

>> What are some of the ways that you

increased your competitive advantage

within the research function um that led

to I guess outsized gains for what you

were doing.

>> Oh, okay. So what's interesting is this

this answer the first answer is actually

really boring which is um I just

outworked people

right um and I I I think it's important

because when I was a young when I was a

young guy um and I worked at tutor I was

I was still I was the youngest guy when

I got there and the youngest guy when I

left and I was the only one my age in

the office.

>> How old were you? I was 23

>> when you got in and then when you left

celebrated

>> 25 26. So I was I was the only age. I

was the only analyst my age there. Yeah.

So like um

>> and and and so I went back to business

school and then I went to go work at

Glen View and and

>> what started at Glen View was um I

started working Sundays and

I worked Sundays uh from 2006 2007 uh

till about 2020. Um, and I'd work like

six hour Sundays on average. And what I

learned was if you work six hours a

Sunday, 50 Sundays a year, right? That's

300 hours,

right? 300 hours on a 50-hour work week

is six weeks.

So, if I'm working

13 and a half months a year to somebody

else's 12 months, it doesn't matter how

good your process is or how smart you

are. you're not gonna beat me to the

ball.

And in a knowledge research business,

um,

you end up compounding knowledge value,

which makes you faster.

And so your 13 12 months in 2011 is not

as effective as in 2013 is not as

effective in 2015 because you're

compounding knowledge which shortens

your time to answer and shortens your

time to alpha or relevance

really quickly because knowledge

compounds. That's one of the things that

scares me the most about all these tools

that are proliferating for young people

on the street is that nothing beats

doing the work.

You can get a plugin for Excel that, you

know, sort of gets you to get gets your

Excel built in 30 seconds. And, you

know, there's some really cool tools

that are super value ad and automating

and enriching, but like if you haven't

pulled apart a three sheet model and you

haven't spent the time trying to figure

out the, you know, MDNA and a 10K,

there is value in that apprenticeship.

there is value in that swinging the

hammer that I think is going to be is is

has the risk of being lost if people

don't figure out how to train against

you know sort of both time and seat and

sort of the doing the manual labor right

and like I sound like an old man there

like I do I'm 49 years old but like

those extra 300 hours a year uh we're

the difference between good and great

and and not only that you walk in

prepared on Monday you're ready for

bear. Um, you know, sort of you get uh

you just get all this this time

orientation. There are there are

meaningful trade-offs.

Meaningful trade-offs. Um, but you know,

sort of generating alpha is about

trade-offs. Creating competitive

advantage is about trade-offs. Unless

you are the best basketball player on

earth and even those guys were in the

gym at 6 a.m. you know you read any book

about any great uh athlete and they made

their they they they made their craft

their life and they put real work into

it. So first order my developing edge on

that front was um literally just time in

the seat. The second was I read

everything.

I read everything I could about how

things worked and then I did a lot of

field research. So I've been down in

coal mines in Australia. I've walked

factory floors in Taiwan. Um I've walked

malls in Germany, you know, I've been in

pubs in the UK. Um

>> great spot.

>> Yeah, great spot. Um you know, sort of

I've visited hospitals and distribution

centers. um you know sort of I've walked

corn fields um all to understand how it

all works. Taking apart the lawn mower

is a great way to know how to run a lawn

mower. Um and what it does is when you

are doing the domain knowledge work,

when you are trying to understand what

is going on with things, the nuances and

the the signals before the actual signal

um are really powerful um you know

potential sort of this heads up um that

something is about to change, right? Uh

when you're using data, most data is a

now cast framework. So, you look at a

credit card panel and it tells you

what's recently happening and gives you

some predictive capability on, you know,

what the next quarter might look like or

how things are changing. Um, but what

gets really interesting is when you

start to recognize the signals of

something slowing before it shows up in

the world. Like for example in 2009

um I was running call center business uh

working for Larry and we're doing checks

and supply chains and you know I got a

phone call from um private company um

who was doing sort of like connectors

and um they got a phone call from TSMC

and TSMC called them and they said hey

um do you want do you want some do you

want some capacity? we have some

capacity. And all I said to him was,

"What when was the last time you got a

call from TSMC for capacity?" And he's

like, "I haven't heard from them in like

three years." And um I was just like,

"Oh." And um when does that usually

happen? And he's like, "It usually

happens when people are canceling

orders."

And so wasn't I hadn't even heard of

cancellations

in the channel, but I heard that, you

know, sort of TSMC had called this guy

and offered him a line, which suggested

there were cancellations.

Now you got to read that there might be

cancellations. So you're already on your

front foot. So the first time you hear

it, you can feel really good about it

because it's the second time you heard

it. And you know, sort of that only

comes from being a gym rat. That only

comes from building out the ecosystem

and understanding how these, you know,

sort of how these places work, how they

run their business. So when you get the

headlines, you already know what's going

on under the water. And that's all in a

legal and compliant way. And but

fundamentally it's like becoming an

expert and a domain expert and a

knowledge expert. I think that's even

more important today with everything

going on because what all of these tools

are doing is flattening access to

information,

right? And if everything gets flattened,

you got to figure out how to create

breath and depth in your knowledge base

to compete with everybody else because

otherwise everybody's got the same

stuff.

>> 100%. I mean,

when I look at the world today and as

someone who's in grad school right now

and as a young person who's trying to

find my own personal edge, right? Trying

to build out that own that personal moat

or this is no one can compete with me at

something I'm doing. I look around at um

at everyone, myself included, just and

evaluate how people are trying to build

out their own personal edges. And it

just feels like it's very difficult, you

know, um to build out that that mode um

for most people because the

I guess

all these new tools and technology

kind of

commoditize the edge that was once there

for most people. So say you studied at a

good school, great at knowledge work,

right? You can code very well and

obviously that's still very valuable,

especially at the highest level, but the

level that you need to be at in order to

provide that edge or provide that value

to the firm you're working at or a firm

that you're starting um you know is much

higher than it was in three, four years

ago. And I guess

if you had I don't want to spend too

much time on this. I want to get into

your research

um you know more about your research

function but what would you say is the

number one ways or like the top the best

ways for young people today to build out

that unique edge that I think is

increasingly more challenging to get

your hands on get access to. It's it's

it's a it's a it's a great question and

I mean I think

where I'm finding differentiation in my

own life now right and where I'm finding

that I think I have an edge that is um

sustained and you know whether right or

not is a different thing but there's an

edge there right um one of the

competitive advantages I think I have is

that I have figured out how to

create a library of historical

situations, let's call it, um,

and

analogies,

and I'm able to apply them to other

historical situations and analogies

in a pattern framework

that allows for contextualizing what's

happening and being able to say, "Okay,

these are the three things that could

happen off the back of this, right?"

Example,

um,

everybody's focused on

the AI economy

and whether we're in a bubble and all of

these there's a debate around all of the

things.

>> Open up X, you see it all the time,

>> right? and

I have my views and they're sh they they

move around and like one of the things

is never get too married to a view.

That's the other thing I learned about

all the guys I work for. You want to get

it right, not be right. You know, at the

end of the day, alpha rewards those who

value assets in a cold way, right? Be

cold about the analysis and you know, be

focused on getting it right rather than

being right. don't write fight. So

that's one thing I would add to what was

great about Larry, Steve, and Jimmy is

they just they just they just wanted to

get it right and just maniacal about

getting it right. Um and uh through best

through best process and best practice.

Um but you know, sort of fundamentally,

you know, like I look back and I'm like,

okay, what's my body of work that I can

look back to from from bubble dynamics?

And it's obviously the internet bubble,

the housing bubble. Those are two. Um,

you can look back to the Great

Depression. Sorcin just wrote a piece on

it. It's a natural. Um, and you look for

parallels.

And you know, sort of and then once you

look for parallels, you sort of look at,

okay, now I'm creating decision trees,

you know, and the what what what I'm

really getting at is you need a corpus

of knowledge that you can draw on. You

don't have to ask a robot for.

So read read the books, do the work,

right? Like you know sort of you know

having a competitive advantage takes

work, right? And it's not asking a

chatbot.

It's reading a book. It's you know sort

of like it's it's it's it's it's having

these conversations. It's listening to

these conversations. Not necessarily

mine, but like you know you're going to

have somebody on was traded the 90s. And

when you have that person on people

listening, they be like, "Okay, what

were they looking for?" Right? The fact

that we're debating about a bubble means

there's a bubble. It's the way I look at

it. If people are trying to explain away

a bubble, there's a bubble.

It's that simple. The word bubble's

coming up, it's probably there. Like

look throughout history, right? But the

second part is like history rhymes

because people are animals and animals

do the same thing over and over again

expecting a different result and it

never happens. And it happens when

people who are doing it this time, most

people aren't around from last time, so

they forget, you know. Um, you know,

World War II was supposed to be the

World End of Wars,

you know, that's just what happens. And

it's like, you know, and and and and by

the way, that was there's probably other

wars that had that same label. Um, but

like, you know, you look at sort of like

what's happening. um you know so for

example when I look at the AI economy

and how the ecosystem is forming I look

back to quant on Wall Street and when I

talk to people about AI I'm like this

has already happened

we went to a model driven framework on

Wall Street between 1984 and 2025

everything that happened on Wall Street

is going to happen on Main Street

now it's not an apples to apples but

it's not apples to frogs either it's

probably an apple to a banana but it's

this They're both fruit, you know, they

just they're just going to feel a little

different. They're going to look a

little different, but at the end of the

day, I can point to a number of

different parallels that make it

meaningful. And that comes from and and

that's and that's about pattern

recognition. It's about a corpus or

inventory or library of uh incidents and

situations of historical significance

and and then sort of applying those to

what's happening now to be able to make

predictions on what's going to happen in

the future. And the more domain

knowledge you have and can say this

reminds me of this thing that happened

five years ago or this looks like 2014,

you have a backdrop and an analog that

allows for you to start to make

decisions around what's happening in the

future, right? And until

that is mapped and structured and you

know sort of there's perfect information

um in sort of the the the the the matrix

that is being built um that is a

competitive advantage I believe because

you're able to have sound reasoning and

judgment against what has gone on before

and what could likely happen again with

probabilities baked in right um you know

sort of like when I say to people you

For example, like one of the things I

pitch is like, you know, sort of models

proliferated Wall Street between 84 and

90 and returns were crazy for those

model driven businesses. There's PhDs

competing with PhDs on tick data. And

then in the 90s, everybody started to

build model companies and you're the the

number of hedge funds trading with

models went from 40 to 4,000 over the

90s and then they had to create ETFs,

right? And so and then they created

factors, right? So like and and along

the way there were blowups and you know

sort of alpha degrades and all these

other things but you know sort of like

you see like everybody talks about meta

buying these researchers from open AAI

and I was like Millennium buys

researchers from SA from Citadel.

Balosnia buys people from point 72. I

was like how's that any different? So if

it walks like a duck and it talks like a

duck, maybe it's a duck. And so what

does that mean for market structure for

data? What does that mean for um you

know sort of the evolution of you know

tokenization for model inputs? Do are

they going to need portfolio

construction theorists from Wall Street

at the LLM shops and at the model shops?

Is there a convergence between Wall

Street and Silicon Valley? Is there

regulation that comes in?

That's where the conversation goes. But

it's all based on I'm just looking at

history. If I look at history, it tells

you the future. And like I'm a big

believer in that. It's been right

because like you can look at sort of

names too and you can look at sort of

research processes. I'm like, "Oh, last

time I saw this, last time I like when I

used to I used to do calls. I used to do

thousands of calls talking to CEOs and

CFOs of private companies and tracking

change in supply chains. And one of the

questions I always ask, when was the

last time you saw this?

2016. Oh, what happened after?

Okay, so you saw cuts and then this

happened and then what was the timeline

on that? 3 weeks, 3 months, three years

and then I had a I had a playbook and

then you're just matching against the

playbook. You're just seeing if the

collection is going against your

framework. So you start thinking in

frameworks. Once you start thinking of

frameworks, stuff really scales

because when you have a competitive

advantage, you want to scale the [ __ ]

out of it. Pardon the language

for sure. And I'm going back to what you

were saying that, you know, when you

were talking the earlier parts of what

you were saying were about building out

that own, you know, that personal

competitive advantage. And I think it's

such a crime that the top tech execs and

AI execs are saying to young people that

the way they are going to build out

their own competitive advantage is by

using the models better and just

focusing all their time on modeling like

using those models and not doing what

you touched on that analog training that

deep research that reading that grunt

work which is required for learning and

I guess now I think is a is a great time

to talk about some of the takes you know

that you had there and why that and how

that translates into your company Carbon

Arc.

What happened on Wall Street that is

going to happen on Main Street and what

are you how are you capitalizing on that

I guess?

So if you if you look at sort of um so I

want I want to double click on on on one

thing you just said about sort of young

people and what have you and and

training and everything else which is

like

>> one of the things we're seeing and it's

20 years in

>> is that social media is now being banned

>> in countries because of the damage it

does to young people.

Um Australia, the UK is talking about

it. Uh I think New Mexico sued Meta a

couple days ago. Um

uh

you know sort of one of the things that

I think has happened over the last 20

years um is uh we have been quick to

launch tech without totally

understanding the impacts of the tech.

And by the way I'm a I'm a capitalist. I

believe in sort of, you know, sort of

but I'm also I worked on Wall Street so

I believe in regulation. I think it's

important um to have guard rails that

are in, you know, sort of structured in

a way that, you know, sort of protect

everybody involved.

And

I think, you know, sort of we're now

seeing, you know, frankly, like, you

know, like I, you know, I have three

daughters. Two of them have cell phones,

one of them doesn't. And the one that

doesn't isn't going to have one for a

while. she's going to go longer than her

sisters did getting access and we have

strong curbs on you know what and how

things can be accessed from a time

perspective and it's just a you know so

it's a it's a mental brain development

thing

I would sort of argue that and I don't

know how firms are dealing with this so

I'm saying this without having any

knowledge of how Wall Street is dealing

with tools internally I would have

people learning the oldfashioned way for

the first six months 12 months even

longer,

>> you know, whatever that is, right? And

like to your point and, you know, sort

of like they have AI agents running

calls now and like it's, you know, like

like for for like expert calls and stuff

like that and like everybody's talking

about the N and the quality, but like I

actually don't even know how the quality

is or isn't, but it's actually not about

the quality. It's just about sort of

like who's going to audit and we're

going to have AI auditing, you know,

sort of agents and everything else. You

know, again, I sound like an old man,

but I'm like, let's walk in the room

rather than run. These tools are

meaningful. We're using them in our org.

Like, I'm a believer in them, but I also

think that stunting the growth of this

generation 22 to 26 is is really

dangerous because it will it'll lead to

degradation in our third in their 30s

that like they won't be able to come

back from. Uh because if you don't know

how to do anything, you don't know how

to do anything. And competing on, you

know, your raw smarts isn't enough

because everybody's smart, right?

Everybody's at a certain level. Um

getting to the question that you asked

which was what you know what happened

that you know if if if Main Street is

becoming Wall Street, how are you

looking to capitalize on it? I think

>> I guess first off,

>> yeah,

>> what did you mean by that specifically?

you know, how does that what do you mean

by main street is becoming Wall Street

first and then we can go into how you're

capitalizing on that.

>> So, if you think about Wall Street and

you look at a little history in 1973,

um

Fischer Black and Myron Scholes wrote

the Black Scholes paper and published

it. And that was a it was it was a it

was a watershed moment for financial

engineering for quant during the 60s.

MIT and University of Chicago were

competing on the capital asset pricing

model and portfolio theory and the

evolution of

uh statistical understanding and

quantitative understanding of why

markets move the way they move and how

they move the way they move. Bodiglani

and Miller Black Scholes um all these

incredibly Merin, Bobby Mertin, all

these incredible guys were doing crazy

work, winning Nobel prizes and black

shows you can price any option

stocastically.

Um and it was it involved advanced, you

know, math and and and sort of in that

plus the rise of the pro personal

computer,

>> right? um ushered in the quant age on

Wall Street in 1983 around the time

Renaissance was formed and Goldman

launched the first quantesk.

Okay, between 84 and 90 you saw an

explosion of quant firms starting to

trade uh you know sort of model driven

portfolios

uh based on asset prices, not based on

fundamental cash flows and all these

other things and had incredible returns.

And then you started to see everybody

start to proliferate. Two Sigma was

formed in the 90s. De Shaw probably came

in the late 80s. Um you know sort of all

of these really bright um you know sort

of PhDs who traditionally weren't Wall

Street guys

started to make money in the markets

with models.

Over that time, what you saw was that

market structure changed

and all of the frictions started to be

removed to automate trading. So, you

went from paper

to to sort of uh to electronic trading

over a 15-year period. You went from

teen stocks used to be priced, you know,

two and a quarter,

>> you know, two and 78.

>> It went to decimals. I don't even

remember it being like I didn't even

know it was fractions ever.

>> Oh yeah. Yeah. Um there used to be guys

wearing vests on the floor of the New

York Stock Exchange trading stock.

Commissions were priced in dollars, not

pennies or millipennies or

right. Um the New York Stock Exchange is

now a museum for all intents and

purposes. Right. you know, the pit in

the Chicago stock exchange. They all

used to be full of guys, uh, mostly men,

some women, but mostly men, like, you

know, wearing vests and yelling at each

other to trade stocks and making

markets, trading pork bellies and what

have you. Um,

in 1984,

number of block trades on the New York

Stock Exchange was 50%.

50% of volumes are block trades. That

number today is seven.

Everything's lots. So the and by the

way, volumes have gone up a thousandx,

right? So what happened as models

proliferated proliferated Wall Street

was that commissions collapsed, volumes

exploded,

latency collapsed,

and the number of products being traded

exploded.

And that was all to create surface area

for the models to drive alpha.

90% of volumes right now are traded um

machine to machine at less than a penny

a share. So in that same frame, if you

believe that we are going from a

human-driven main street to a model

driven main street, the feed stock data

needs to follow the same path. needs to

go from being big expensive blocks that

cost millions of dollars or that have to

be litigated

um to bite-sized ratable

inputs that allow for models to show up

and uh and pay for what they consume and

get paid on what's consumed. And that's

what Carbon Arc is fundamentally doing.

We're building the market structure

tooling to allow for data to be bought

and sold

as units of, you know, sort of like

tokens and inputs to drive model driven

decisioning.

We're not living in a space where like

we're we're like we're we're doing that

for credit card data or inventory data

or trade claims or healthcare claims,

things that inform how the real economy

works. And we're breaking it down in

bite-sized chunks because organizations

make decisions based on uh expected and

desired outcomes.

And if you're running a model to figure

out if you want Olivia Rodrigo or

Sabrina Carpenter as your brand

ambassador, you want data to inform that

based on your customer base. um what

else they're working on and you know

sort of and having that data and those

inputs need you need to be able to pay

for what you need get what you need and

pay for and and have the supplier get

paid on what's consumed. So it's the

decimalization

of market structure because if you think

about the AI economy, it's models which

is the application layer. They do the

work, chips, they power the work. And

then data structure that feeds the work.

So data structures, electricity or oil.

You look at electricity, you look at

oil, they're all priced per barrel, per

kilowatt, what have you. Data should be

priced per megabyte.

>> Is that what that's what you're doing?

>> Yeah.

And so your thesis and correct me if I'm

wrong is that because the application

layer is increasingly commoditized as

the LLMs get better and as

and even with a human overlay you know

that part is getting a whole lot more

commoditized the edge becomes in or or

the value rather is in the data layer

>> in the data layer and how you interact

with the data And that's where domain

knowledge comes in, right? So like let's

say you and I get in a room and let's

let's start in the stock market, but

then we'll we'll go we'll move over. But

let's say we get a fin we get we get we

get we get a we get we we're both given

a 10K a couple of 10 Q's the last two

transcripts and a comp sheet maybe an

illance report and it's on Lululemon,

right? and you look at everything and

based on your view, um, you're like, I'm

going to take a two-year time horizon on

this. Lululemon's a great brand. They've

been on their, you know, they've been

they've been on their they've been on

their backside. They haven't, you know,

sort of they haven't executed. There's a

new CEO in here over a two-year time

horizon. It's a $10. I think it can be a

$25 stock. based on closing

underperforming stores, right sizing

their product portfolio and executing

against it. It's a two and a half bagger

from here and I would buy on dips and I

would own them for two, you know, two

years, right? Um and and and and you the

edge or the competitive advantage you're

bringing to that is like you've got a

framework around a two-year turnaround

story and you're basing it on what

happened with Abbercrombie and Fitch in

2017, right? I could come in, I could

look at it and be like, you know, sort

of like it always takes longer. The next

catalyst, you know, sort of Aloe and

Vori just launched their new products.

Um, you know, sort of it's going to take

6 to 12 months to turn this thing

around. It might go to 25 in two years,

but it's going to six from here. So,

that's down 40. And I can short it. You

can be long. We could fundamentally both

be right.

And we're looking at the same

information and we're bringing a

different context to that information to

create a competitive advantage. And a

lot of it comes down to sort of the

ability to you need persistence of

capital and I need timing. And that's

what we're playing for.

What becomes more interesting is we're

if we're both competing on my frame

because if you have the same view I

have, which is

it's going from 10 to six and we both

have the same information,

we're now both on the same side of the

trade and we're both working on the same

information. So there's no edge.

We both have the same view,

but the same view is the same view. If

you then started doing calls and mall

walks

and you were like, um,

oh wow, Lulu inventories are building in

the stores. I can see it. And promotions

are worse than expected. And that's a

reaction. And you know, I talked to some

folks at Aloe and Vori and those weren't

expected promotions. Now you have edge.

Now you size up and you go from being

short $100 to being short $200. Does

that make sense?

>> Yeah.

>> Right. So, what I'm trying to get at is

data structure

um can sort of if you have a framework

in a context, data structure can drive

um

stronger conviction on hit rate improve

your probabilities. You just took the

probability of Lulu having a near-term

problem from 6040 to 8020

and you would size it appropriately.

Right?

When you think about the models, the

models are all going to be largely the

same. Assuming that every time you query

it with the same prompt, it comes back

with the same answer, which I don't

think is true, but like it's close

enough. If you have the same level of

compute, compute is just a utility,

right? So, it's that there's an access

thing there and a pricing thing there

because you need money to run it. But

like, let's say we have finite, then it

comes down to the data structure. And if

you have credit card data and I don't

and you're making a decision on how your

consumers are spending in a zip code and

I'm guessing you have a competitive

advantage,

right? So accessibility becomes a

competitive advantage. When I worked on

Wall Street, one of the things that was

really interesting it was um so when I

got when I got my job at Glen View, I

was an analyst. I was covering long I

was I was a long short analyst. Went to

Europe. I covered consumer and tech. But

what I was really good at was getting on

the phone and collecting information in

supply chains. I I had cold called for

years. So I was very comfortable just

talking to anybody. I get on the phone

with somebody who ran a trucking company

and I can get on the phone with a you

know sort of you know sort of like a

doctor. I could get on the phone with

anybody and

you know just like it was it was I was

just really good with it. And um and and

Larry put me in charge of building out a

primary research team. Uh, and so, uh, I

looked at the space and I was like,

okay, there are all these expert

networks. Everybody can call an expert

network and do an expert call. How do I

create a competitive advantage? Cuz like

if if if you can go do bunch of calls

and I can do a bunch of calls, how am I

going to beat you? And I was like, okay,

so how how does the street how does the

street usually do it? And analysts

usually did three or four calls at the

end of a quarter on their names and they

talked to Gerson or Guidepoint or

something like that. And I was like,

okay, so they do four calls a quarter.

I'm going to do 20 calls a month.

I'll do the four calls they're going to

do and I'll do 12 of those in a quarter

and then I'll do another, you know, I'll

do I'll do the rest and at the end of a

quarter I'll have 60 calls to their four

and I'll know if something's changing

before they will and I'll have a better

sample and so I'll have less bias and

over time it'll compound and that's that

was how I created a competitive

advantage in a commoditized channel

check world and then you know then you

go sort of a layer deeper and you build

your own network and do all these other

things. So there's other things you can

continue to do to get depth.

Um but I think you know sort of it's

it's it's really important to to

understand

that differentiation

of data doesn't come from a single input

or a single opportunity. it comes from a

systematic framework around collection

and synthesis

um that you know sort of uh

is required for sustained competitive

advantage. And so so I went to points of

me too and I was building the thing and

I got involved in the data there. And um

one of the things I realized when I was

working there was that we were able to

go get this this data like was credit

card data and clickstream data all the

stuff people talk about for exhaust data

and you know we were doing that and we

were early because we're big and

realized that a lot of people didn't

have access to it and and corporates

didn't have access to it because it's

really hard to work with and it's really

expensive and all these different

things. inside of you that if you could

smash down the cost of the insight, sell

the insight instead of the the whole

asset, you could get more people in to

do more work, right? You could get it to

people who only do projects, not

systematic trading. You could you could

get it into the hands of folks. You

could democratize access to the inputs.

And then the competitive advantage

becomes who's creative enough to do

something with it, right? So like what

questions because like here's the thing

we're all in the content business. This

is a content business right now right

here. You want it to be relevant,

different and accessible. You want

people to watch and accessibility is

asmtotic. You want people to watch and

be like this is really this is really

easy for me to consume. You know you're

putting it on YouTube, you're putting it

on LinkedIn, you're putting it where you

put it because those are the places

people can access it in a clean way.

You're not putting it on Telegram in

Turkey, right? this it would be an

accessibility issue, right? Um

relevance. You have a you have a you

have a group of people that want to

watch this and it's relevant to them

because they care about sort of people

who worked at funds because there's some

experience there and they want to talk

about competitive advantage and event

driven catalyst driven research and all

these other things. So there's relevance

there and you're bringing people on that

are going to you know drive with your

audience. And so differentiation is the

thing, right?

Differentiation is made up of two

things. More data and better questions,

right? And so my goal is to get people

more data so they can ask better

questions,

right? And accessibility comes into

price and usability and all those things

when it's a data thing. But here, you

know, you're asking the questions you're

asking. you you came in with a a set of

questions that you wanted to ask. But

then there's also the oh I want to

double click on that because that's a

different statement and let me let me

pull the string on that and ask the

question there. So you're not only

asking the questions you came in with

new questions are coming in here and

like and I'm just giving you more data.

Right? The the Washington Post competes

on differentiation.

If you watch the movie uh the Post, it

was about the Pentagon Papers and them

publishing the Pentagon Papers. the way

that they did was differentiation

was relevant and they're accessible.

They're in everybody's house. It was

really about differentiation.

So like the the thing is every content

business on earth competes in those

three pillars and everything is a

content business

and differentiation is where that

competitive advantage lives.

and you're saying it now and I fully

agree and it's I mean I like the analog

to the podcast because it allows me to

see it very clearly the I agree and I I

look at you know I watch a lot of

podcasts like I study the especially the

big ones just to see what they're doing

what do they do differently right and

like I draw like 20 VC look at that I

look at invest like the best right what

how do they ask their questions how do

they pace the camera um how do they

select content that fits their their

their audience and that's all stuff that

with enough effort you can figure that

out right but what you say there about

differentiation and I'm seeing it now um

the questions you were able to ask in

the moment when you provide me with data

right so Wall Street Main Street like

you know everything that happened on

Wall Street is going to happen to Main

Street like I was sitting here and you

know thinking I haven't heard that for

let me go deeper into that and I think

you've nailed it on the head when you

talk about how the data leads to

differentiation and I find it very

interesting that you're trying to build

a business to help people capitalize on

that and not even specific to

financial markets trading. I mean what

you said there that it's it's for small

businesses as well credit card data as

an example to help them make decisions

on I think you said Taylor Swift or

Olivia Rodrigo or was it Sabrina

Carpenter not sure

>> I would Taylor wins you know

>> we all live in a Taylor world so but but

but think about it like this right like

at the end of the day everybody asks

like you know sort of how you building

this thing and and how do you think

about it it's a two-sided platform right

and it was a cold start problem hardest

thing I've ever done. Um, you know, I I

I built businesses um at Glen View and

in and in Point 72 and when I when I

started Carbon Arc, I was like I was

like, I've done this before. It's I'm

going to figure it out. And what I

didn't realize and it was naive T on my

part was that I had three advantages

building inside of those firms that

didn't get replicated when I started on

my own. Those three advantages were I

had captive customers,

I had permanent capital, and I had a

mandate from the boss.

Those three things are incredible,

you know, sort of like, you know, sort

of like tailwinds and and and they they

they build so much uh, you know,

foundational framework into what you're

building that like um, you know, sort of

I I have so much more appreciation. I

always had a ton of appreciation for

where I worked and who I worked for, but

like it's it's it's leaps and bounds

because um they you know the thing the

thing I'd say about you know sort of

Larry and Steve you know sort of is that

specifically um with what Steve's built

is that like um he is you know visionary

and is a builder. Everybody thinks of

him thinks of him as a trader. He he

builds he's built a platform that allows

people to do great things and build big

things and um and that's what he that's

what he does. He's just like he's just

he's he's incredible and you know sort

of uh I have a lot of respect for it and

and appreciation for it. But what's

crazy is like if you think about it,

right? People ask me what we're doing

and how you build the thing. Like we

bring in all this data, but we're

building against three problems that

everybody's trying to solve.

At the end of the day, everybody's

trying to solve demand problems,

logistics problems, and supply chain

problems. Main Street is trying to

figure out those problems and optimize

against them. And Wall Street is trying

to figure out if they're any good at it.

That's it. And it's all about moving

inventory. And all of my views on

inventory come from working in supply

chains and being the biggest channel

check guy out there on supply chains.

And that all came from a thing called

the beer game at MIT. So when you go to

MIT

and it was 2003 and I I was I was

probably too young to go. I probably

should have had another couple years of

seasoning. Super green. Um I went to MIT

in the first week of orientation. They

they sit you down with all of your

classmates. It's like 350 classmates.

nice small class, 40% international, and

we're sitting in a big auditorium in the

gym or something like that. And

everybody's sitting at tables, and on

one side of the table, you have four

folks, and the other side you have four

folks, and they have a notepad in front

of everyone, and you have uh betting

chips, poker chips,

and you can't talk to the person to your

right or your left. And the front of the

chain is retail, and then it's

wholesale, and then it's processing, and

then it's raw materials. And the orders

come from a professor at the head of the

table.

And it's called the beer game. And

you're like the ca they the the chips

are cases of beer.

And um they start putting orders in. And

they do it like this. And it's like four

four

eight four four. And what you're

supposed to build is safety stock and

manage the inventory and the supply

chain. And everybody's supply chain

blows out.

everyone's supply chain blows out. It

goes like this. They're all boom bust.

They look like the 1999

Lucent or Alcatel um you know sort of

charts, right? And what you learn is

that in you know with when there's when

there's uh siloed information and a lack

of visibility,

the human condition does everything the

same.

the the eight made the front person in

the front order more who made the other

person order more who made the other

person order more and then it it just

becomes this really fundamental you know

sort of boom bus cycle in supply chain

and what I learned in that game and what

then I learned when I was a researcher

on Wall Street is it's all about

inventory

you get inventory right you get the

organizational situation right? You get

you end up getting the stock right and

inventory is lead times shortages and

surpluses

because it leads it drives what you do

in the P&L is how your inventories are

managed and it's this fascinating study

and so like what's underpinning how we

built the system at carbon arc is demand

management logistics management and

supply chain management and then how

organizations people locations manage

against that and so that's the

underpinning frameworks that then drive

all of the things we build and how we

curate our partners.

And so because top of funnel is

targeting, acquiring, upselling,

retaining, and forecasting. The

logistics are lead times, simulations,

alerts, and supply chain is, you know,

build schedules, shortages, surpluses,

and you just and then once you lay all

those out, you structure and the data,

and then people are just pulling Legos

out of there to build their analyses on

top of those frameworks.

>> So that's how you think about any

business.

>> Yep. And like then so here's the thing

now we'll get into like some if you want

some like advanced research theory which

is like you can factor anything. So

everybody knows factors is momentum

growth value.

>> Yeah. Break that down for me.

>> Yeah. Like Taylor Swift you can factor.

>> How's that work?

>> She's got momentum. She's got resonance.

She got persistence

right. You can factor Proctor and

Gamble. Gillette. You can factor

McDonald's on a one to negative one. I

actually think the world moves to

factors over the next three to five

years. I actually don't think, you know,

the LLM's, you know, buy credit card

data. I think they just go into a factor

framework and buy factors. I think

they're factor factories,

but like I think that's what happens.

Like think about it like this. You know,

when I when I worked for Larry, at the

end of the day, we were investing in

market structure and business model,

right? Um, you know, sort of like, you

know, sort of market structure, uh,

business model, right? There in the

global 2000, there are four market

structures and then business models.

There aren't 2,000 companies. So,

decompose the company and just get into

the framework and you've got four market

structures. Because here's the thing, if

somebody's in an oligopoly, like a

duopoly, they're going to act

differently than if they're branded

commodity.

They have different rules, right? Macy's

competes differently than Netflix,

right? And those drivers are dictated

more by market structure and business

model than they are by any near-term

dynamics, right? So, you know, if you

think about sort of a company and you

put and you decompose a company into its

sort of fundamental factors, what have

you, you got themes.

There are probably nine themes in the

world. technology shift, demographic

shift,

um act of god,

a hurricane is a theme, right? Then you

get into market structures, there are

four. You can argue five with monopsin,

but like nobody really talks about that

one. It's monopoly, commodity, branded

commodity in oligopoly, right? Then you

got business models. How many business

models are there? You can probably get

nuts and go to like 30. I think they're

under 10. retailer, wholesaler,

processor,

platform is for, right? And they have

rules. That's why comparable company

analysis works. That's why when you're

comparing Macy's, you don't compare it

to Netflix. You compare it to J. C.

Penney, Kohl's, Walmart,

right? Because they're comparable. They

have comparable cost structures. They

have comparable ways to market. They

manage inventory very similarly. And

then you get into sort of like

management team and culture and that's

that you can get really nuanced there.

There's probably a regulatory factor you

can look at like meta's regulatory

factor is quite high right now, right?

But like you know sort of um and then

you get into once you get into

management team you can start with good

and bad. Are they good? Are they bad?

Right? Uh but you can get into like

strategic vision like for example this

is a great you know this is a great

scale point once you start getting into

thinking about things like this you can

go to like okay like when I look at the

cruise lines and people trade the cruise

lines and the thing about cruise lines

is they're a capacity driven model with

an event driven framework

so it's doubly hard

right because they have to fill and then

they then they leave port right so it's

It's like selling tickets. So, a stadium

is actually very similar to a cruise

line in terms of like that the

obsolescence of a ticket price. And so,

but here's the thing.

Ticket pricing is taught several

colleges in the United States. The guys

who run the ticketing

um the ticket pricing frameworks learned

it from the there are different theories

around ticket pricing and you learn it

at Stanford, Chicago or MIT. Knowing

where the guy who works at Carnival went

to college

helps you understand how they're going

to price tickets.

That's management. That's understanding

how people are making decisions because

it's like the optimization. There's

still competing factors, right? Um, you

know, I was I was I was talking to

somebody the other day, but like you

know, sort of looking for scale points

in businesses helps with the research

ethos too, right? So like you know sort

of Tractor Supply for example um is like

this esoteric sort of like it's a

homegoods

um

home center type name sits in between

because you can buy some different

things there right but like they're

overexposed to Texas and they sell a lot

of animal feed

when Texas's uh weather is really cold

like it's been a couple times the last

couple years. You know what? You know,

animal feed, you know what's interesting

about animals? They eat more when

they're cold.

They eat twice as much. So, if you got

an outsized portion of your business in

Texas and an outsized portion of your

business in animal feed and animal feed

is low margin,

you're going to have better comps, low

margin.

And then you're just checking to see if

that's happening. So, you actually know

what would happen. Because like the key

in research is not figuring out what's

happening. It's knowing all the possible

things and then figuring out which one's

most likely.

>> The key in research is not figuring out

what's happening. It's knowing all the

possible things first and then figuring

out what's happening.

>> Which one?

>> Which one among the

>> Yeah.

>> Because then you prune,

>> right? like

you know we're getting into like you

know sort of like factor frameworks and

scale points and I'm like it's almost

it's like pigeon null hypothesis right

like it's like um like you know I I did

a ton of work in China studying China

over the years and I found the best way

to get a read on China is to call

Australia

>> why 45% of the GDP is China

It's not it's not it's and by the way

there are different types of signals.

It's not binary.

>> Yeah.

>> It's it's a grade. It's on a spectrum.

Right. So like

>> you know so so so you know you can have

low grade signals, medium grade signals,

high grade signals and that can change

over time

>> depending on where you are in the story.

But, you know, one of the first things I

would do if I was trying to get a read

on how things were going in China

um was uh I would call Australia. You

know, the other thing so like

going into the Lehman crash in 2008 in

August of '08, there were a couple

things going on. Number one, the

Olympics had just China had the

Olympics, Beijing Olympics.

And so not only did you have the

financial crisis going on and housing

sort of really rolling over here in the

US

and it was a rolling crisis. But like

the other thing that happened which was

pretty wild is um they shut all the

ports in China

going into the Beijing Olympics. And so

there was a pre-by

of Caterpillar and all these equip all

this equipment to fund the construction

on the other side because they were

going to shut down the ports for like

three or four weeks and so nothing could

get in. So everybody had to get in

before. So they bought everything ahead

of time

and so once like once once everything

opened back up there was nothing to buy.

So it creates an air pocket. Again, it's

inventory, but it's also understanding

the scale point and figuring out sort of

like the nuance around the thing. And

this all just comes from like sort of

like just trying to be a student of how

things work

and um and so the reason the factors

matter, the the reason like hotels and

hospitals are the same business. So like

you know figuring out the hack so that

when you get on the phone you know

really quickly what the answer is or

what the answer could be or what the

outcome should be. TSMC and US Steel are

the same business. High fixed cost

volume businesses, different end

markets, different product types. Um,

you know, sort of different different

equipment inside, but they run the

business the same way.

>> So, there's a factor for that.

>> Yeah. Because they're the same business

model,

>> right? U different market structure

because TSMC is I wouldn't say they're

comm a monopoly, but they're close,

right? They certainly have monopolistic

opportunities. Um

uh but you know sort of again you can

come up the curve really quickly on what

it's supposed to sound like and what

it's not supposed to sound like as

you're doing your work. The key in all

of this is to improve your conviction

faster

on everything decisioning, right? So

like if you know, okay, there these are

the five possible outcomes. So like one

of the things that I do all the time to

a fault is I will look at every

situation that I'm in personally and

professionally and I'll map the decision

tree

and then I'll and then everything's just

a data point. It's tiring. it's not

necessarily healthy, right? Um,

you know, like I meditate and do all the

things, but like I'm still a I'm still a

planner and a thinker and you know, sort

of um but like with in terms of research

and thinking through, you always have to

be thinking steps and steps ahead and

then the likelihood of those things

happening um to achieve the outcomes you

want to achieve, right? And then if you

you know, sort of and like by the way,

no matter what happens, you're going to

be okay. We're not we're not in that

space, but like, you know, sort of the

the factor framework. What I love about

that is

it scales. What we're talking about with

all this stuff is about scale.

You know, like I I built scale research

businesses. There's a lot of people a

lot of research that you've seen on Wall

Street historically has been small

groups or teams or people who do really

great work, but that doesn't scale. I

I've been lucky and and have built scale

businesses because I I got to a place

where I was researching frameworks and

situations rather than specific

companies at a specific point in time

spatially

has

you building out this business

and specifically building out factors

for things that typically wouldn't

normally have factors. I mean, we talked

about Taylor, like a Taylor Swift

factor.

I think this is an interesting angle.

Has that

given you a differentiated view on where

the world is going unrelated to finance,

so unrelated to investment. Do you have

any differentiated or contrarian takes

about the way the world is going

culturally,

economically

because of your unique vantage point?

>> I

I would say I don't know like I think my

view is informed by my unique

experience,

you know, in terms of like the factor

frameworks. We've done it in places like

music because we have a lot of data

there. Um I frankly don't think we have

enough data to do it broadly in a safe

space. That's where I think the world is

going. But I think you know sort of

frankly like my my experience

where

um I had to be conversant and structured

and thoughtful about several hundred

companies and bunch of verticals

domestically and globally

working in

high fidelity high velocity transaction

data on Wall Street when Wall Street was

first to do it. I think it I think I

have this like my vantage point is

informed by having a unique seat at a

unique time in history where like like

you know we were we were working in

credit card data on the early side

relative to everybody and I had a lot of

experience producing that inside of a

large organization that you know valued

quality. So I learned a lot and I made

some I made a lot of mistakes and I was

able to do that inside of an

organization that like you know required

like a lot of process and a lot of

process orientation and stuff like that.

And so I think my vantage point is

informed by my experience and it's

unique because nobody's had the

experience I've had. And I don't want to

sound like I'm the only guy in the world

that's done the thing, but like I have I

have had the opportunity to do some

really cool um stuff in in in places

that placed a premium on a lot of at

bats. So I I got a lot I got a lot of

turns,

right? Like it wasn't a lab environment.

like it wasn't academic,

right? Like it was actually like the

feedback loop was material and real and

um it was from folks who were very, you

know, sort of um

discerning consumers,

>> super valuable

>> of knowledge, right? and like you know

and I and I I was facing up with them

every day you know as a as a partner and

a sometimes a competitor and sometimes a

you know sort of a service provider and

sometimes a counterparty right and so

learning all of those things like I