0:02

Bloomberg Audio Studios, Podcasts,

0:05

radio News. This

0:10

is Master's in Business with Barry

0:13

Ridholts on Bloomberg.

0:15

Radio this

0:17

week on the podcast, Boy

0:19

Do I have an extra special guest.

0:21

I know Jim O'Shaughnessy.

0:23

For I don't know, maybe twenty plus

0:25

years something like that. We actually first

0:27

met in the green room at CMBC

0:30

like early two thousands

0:33

and found we shared some similar

0:35

likes and philosophies. And I've

0:38

been a fan of his book What Works on Wall

0:40

Street pretty much from when it came out.

0:43

This is a fascinating conversation about a person

0:46

who has worked through multiple

0:49

locales and seats in

0:52

finance, not just running

0:54

a systematic investing at bear Stearn's,

0:57

but creating O'Shaughnessy asset

1:00

management, creating a unique

1:03

custom index product that

1:06

ended up attracting the attention of Franklin

1:08

Templeton, who paid some

1:10

undisclosed and ungodly amount of money

1:13

for the whole firm, and now

1:16

in a later phase of his career

1:18

doing O'shaughnessee ventures and the

1:20

O'shaughnessee Fellowship. I first

1:23

know him from really the first pant

1:25

book What Works on Wall Street? That

1:28

was a half a century of data analysis.

1:31

Really was never accessible to.

1:32

The public before.

1:33

I found the conversation to be fascinating and

1:36

I think you will also. And

1:38

at this point I am obligated to do

1:40

a disclosure. My firm Retults

1:42

Wealth Management has been working with O'Shaughnessey

1:45

on their direct Index platform. Really

1:48

we were one of the first beta testers. We now

1:50

have over a billion dollars on that platform,

1:53

maybe coming even closer to another

1:55

big round number with no further

1:58

ado. My discussion with

2:00

O'Shaughnessy ventures Jim

2:02

O'Shaughnessy.

2:03

It's great to see you, Berry and

2:06

congratulations. Wow, that's a.

2:07

Well congratulations to you. I'm

2:10

still My firm just had its tenth anniversary.

2:13

You guys, anytime I see

2:15

the phrase for an undisclosed

2:17

amount, my brain automatically

2:19

says.

2:19

Wow, that has to be a lot of money. If it's if.

2:21

They're not disclosing it, it's material,

2:24

but undisclosed, that's a lot of casts.

2:26

Or it could be like trading places

2:28

and the normal bet of a dollar.

2:32

The usual bet Mortimer one dollar.

2:35

So we know each other from

2:37

way back when you first came

2:39

into my orbit from the book What Works

2:41

on Wall Street. I read it from

2:44

cover to cover. I was on a trading desk when

2:46

that came out, and I'm like, huh,

2:48

so there's some science and math behind

2:50

this. It's not just rumors and whatever

2:53

happens to cross TV that

2:55

day.

2:56

I'm intrigued.

2:57

Before we get there, let's

2:59

talk a little bit about what you were

3:01

doing prior. Tell us about the

3:03

early Jim O'Shaughnessy.

3:05

Well, I was always fascinated

3:08

about the markets in general,

3:11

which stemmed from a very

3:14

angry conversation between

3:16

my uncle and father about

3:19

IBM. And I

3:21

had just been allowed to go to the adult

3:23

table, and I was sitting

3:25

next to my dad and he

3:27

and my uncle John were going hammer

3:29

and tong about whether IBM

3:32

was a good company or not. And I was listening, and

3:35

it was all about the chairman. It

3:37

was all about, you know, things

3:39

that I looked at as kind of soft intelligence,

3:42

squishy squishy, and

3:44

so I just thought I asked at

3:47

the dinner, I said, well, would

3:49

it make more sense to like look at how much

3:51

money they're making and what their earnings

3:54

are and how much you have to pay for that?

3:56

And they both just literally glared at

3:59

me. That's hilarious

4:01

kids. They don't know anything exactly

4:04

exactly. It's the chairman. How tall is

4:06

he? I like to cut his gips. It's

4:09

almost as if you were there that

4:11

bug got implanted, that mind

4:13

worm got implanted in my brain.

4:16

How old were you when that? I was seventeen.

4:18

Oh so you're just going into college?

4:21

Yeah?

4:21

Absolutely, And you were a

4:23

Minnesota kid, Is that right? I grew up in Saint

4:25

Paul, Minnesota and beautiful

4:28

country. Certainly in the summer anyway, gorgeous.

4:31

The winter's tough.

4:32

Yeah. Yeah. Well, if this were

4:34

the old USSR, that

4:36

is where all the political prisoners

4:38

would be of Minnesota.

4:42

But so I started doing research

4:44

on essentially the Dow thirty

4:47

because it was manageable, thirty

4:50

stocks I could list by hand

4:52

showing how old I am, because you literally

4:55

there were no computers that we could use

4:57

at the time. Simple

4:59

things like like what's the price, what's

5:01

the dividend, what's the price to earnings,

5:04

book value, etc. And I

5:06

found a definite trend,

5:09

right. I found that buying

5:11

the ten stocks and the diw with the

5:13

lowest pees from nineteen like

5:16

thirty five. I think I started through

5:19

when I was doing it, and this would have been about

5:21

nineteen eighty, absolutely

5:23

decimated the ten highest

5:25

PE stocks. So wow,

5:28

I love this. In the meantime, I had

5:30

computers, and the only reason

5:33

I actually got to write What Works on

5:35

Wall Street was because Ben

5:37

Graham didn't have computers.

5:40

If he had had them, I would have had

5:42

no chance because he would have done it. Basically,

5:45

what I wanted to see was,

5:47

is there any rhyme or reason to all

5:49

of these reasons people say they like

5:52

or hate a stock? Right? Where is the

5:54

proof? Where is the empirical

5:56

evidence that say, buying

5:58

the low PE stocks from the die works

6:00

very well over many market cycles.

6:03

So I wrote a first book called invest

6:06

Like the Best, in which I basically

6:08

showed you how you could clone your favorite

6:11

portfolio manager by taking

6:13

his or her stocks, putting

6:15

them on a big database like Compustat,

6:19

seeing how they differed from the overall

6:21

market, and then using those

6:23

as factor screens to get

6:25

down to a portfolio that looked,

6:27

acted, and most importantly, performed

6:30

like your favorite manager.

6:32

Now, the average investor typically

6:35

didn't have access to Compustat, to

6:37

big data, to big computers, and

6:39

so they relied on you who did.

6:42

And if I recall what Works on Wall Street.

6:45

You back tested like half a century

6:47

worth of data something like that, and it was

6:49

the full market, not just the

6:51

thirty dove stocks.

6:53

Yeah. Absolutely. And also

6:55

not just the full market, it was also

6:57

any company that had been but

7:00

went bankrupt or got taken over, the

7:03

very very needed research

7:06

database on compustat. So

7:09

no survivorship bias none, You

7:11

back that out, Yeah, great, Yeah, because

7:13

some of the early academic studies

7:16

were they

7:18

had a lot of survivorship

7:21

bias. They didn't properly

7:23

lag for when you actually

7:26

knew a number, so they just assumed,

7:28

right, well, there's the number on March

7:31

thirty first, I'm going to use that number.

7:33

Well, you didn't really know that for most

7:36

of history until maybe May or June.

7:39

Really interesting, so you run

7:41

these numbers. What sort of strategies

7:44

do you find perform best. Well,

7:46

we found that on the

7:49

value side, smaller value

7:51

stocks that had some catalysts

7:54

and had turned a corner and their

7:57

prices had started to go up.

8:00

A beautiful strategy.

8:02

Small cap value with a touch

8:04

of momentum.

8:05

Yes, okay. On the growth

8:07

side, we found momentum works

8:10

really really well. As

8:12

we continued the research, we found

8:15

okay, there's all sorts of caveats. So,

8:17

for example, we learned after

8:20

a severe bear market i e.

8:22

One in which the market had declined by

8:25

forty or more percent, not a

8:27

lot of those, not a lot, thank god, but

8:30

momentum inverted, and

8:32

the stocks with the worst

8:34

six or twelve month momentum actually

8:37

did vastly better than

8:39

the ones with the best. And if you think about

8:42

it even for a minute, it makes sense, right deepest

8:44

value. But what happened

8:46

was a lot of really great stocks during

8:49

the bear market got pushed

8:51

way low in price, and

8:54

so people when the market was

8:56

recovering jumped on those

8:58

stocks. They were like, I can't believe I'm getting

9:00

you know, these earnings at six times

9:02

earnings for an IBM or

9:05

a you know Qualcom, Right, that's the

9:07

baby with the bathwater strategy exactly.

9:10

And so but we

9:12

found, you know, that value actually

9:15

works now. It hasn't for a long time.

9:18

But we also found

9:20

that large stocks with high

9:22

shareholder yield i e. Dividend

9:25

yield plus buyback yield

9:28

was an excellent way to

9:31

identify big stocks that

9:34

are obviously much more conservative

9:36

than the smaller fry in

9:38

the small cap world.

9:40

Interesting, So let's talk a little

9:42

bit about your work at bear Stearns.

9:45

Really, where I first met you in the two thousands,

9:48

you were head of systematic Equity

9:50

at bear Stearn's asset management. I'm

9:53

assuming you were applying a lot of the lessons

9:55

you learned in what works on Wall Street to

9:58

the Bear institutional and tel

10:00

investing strategies.

10:01

Absolutely, and you

10:03

know, let me just say Bear was

10:06

really a great company, very unfortunate

10:09

what happened to it during the financial crisis.

10:12

But the reason I love Bear is,

10:14

you know, a lot of big banks talk about

10:17

being entrepreneurial. Bear Stearns

10:19

really was. And essentially,

10:22

if you were doing your thing and playing

10:24

by the rules and doing well, they

10:27

let you alone. Which

10:29

was pretty important for me because when

10:31

I got there, it was right after

10:33

the dot bomb, and

10:36

a lot of the brokers had done

10:38

pretty poorly because they were in a lot of those

10:40

names. And so I convinced

10:43

Steve Dantis, who was then head

10:45

of Private Client Services, that

10:48

wouldn't it be better if we did a

10:51

packaged portfolio, a

10:54

separately managed to count and we

10:56

offered at one time

10:58

I think we were all the way up to

11:01

the brokers so that they

11:04

could use a more systematic

11:07

time tested way of investing

11:10

for their clients.

11:11

Bringing a little discipline into

11:13

what had been, at least in the nineties

11:15

very much a cowboy type of environment.

11:18

And I'm not just for fron of Bear. The

11:21

entire retail stock brokerage

11:24

was wild totally.

11:25

He was very open to it.

11:28

We ended up putting together a separately

11:31

managed to count platform that they brokers

11:33

embraced. They loved it because literally

11:36

they did what they did well, which was

11:38

calm the client during bad times,

11:41

try to keep them from getting too excited

11:43

during great times. But they also loved

11:45

the idea that it had a very

11:48

explicit explanation for

11:50

why they were putting that client in

11:52

that portfolio. So that

11:55

was a lot of fun. By the time I left

11:58

Bear, my group controlled

12:00

about seventy percent of Bear

12:02

Stearn's asset management long only,

12:04

and that was a lot of money, wasn't it. It was

12:07

It was about fourteen billion dollars.

12:09

Okay, so you mentioned you

12:11

left Bear. Let's put a little flesh

12:13

on those bones. Your

12:15

timing was perfect.

12:17

You exit Bear in two thousand and seven,

12:19

Is that right?

12:20

To set up O'Shaughnessy's asset

12:23

management was the thinking,

12:25

Hey, I want to do this out on my own shop,

12:28

or were you sniffing something

12:30

out in O seven that's like, hey,

12:33

maybe I don't want to be attached to

12:35

a giant ocean liner taking

12:38

on water.

12:39

You know, that's funny. I spent the next

12:41

two years after that trying

12:44

to convince reporters that I

12:46

really didn't know anything. Why

12:49

I left Bear was because I felt

12:52

that I really wanted to be on my own

12:54

again. I really wanted to

12:56

be able to just talk about

12:58

quantitative investing. Bear

13:01

was a boutique, so there were a lot of different

13:03

managers. Liked them all.

13:05

I thought they all were great, but I

13:08

really really wanted to focus just

13:10

exclusively on quant And

13:13

secondly, we had upgraded a lot

13:15

of our systems to the

13:17

idea that would become canvas,

13:20

right, because remember Netfolio was

13:22

our first try at that. That was nineties

13:25

or ninety nine. Yeah, really, well,

13:27

of course, you know the really funny

13:29

story here is in April

13:31

of nineteen ninety nine, I

13:34

wrote a piece called the Internet

13:36

Contrarian, and in that piece

13:38

I said, eighty five percent of the companies

13:41

currently extant in the Internet

13:43

space are going to be carried out of the market.

13:45

Feet First, I've never

13:48

seen a bubble like this in my history

13:51

of investing. And what did I do

13:53

next? Berry? I started an Internet

13:55

company.

13:57

Well just because the stocks or a bubble,

13:59

does I mean this internet thing isn't

14:01

going to catch on?

14:02

That's true, right, it's

14:05

there are you know?

14:05

It's funny we forget in the

14:08

thirties, forties, fifties there was only ma

14:10

bel Every company used telephones.

14:13

Yep, the way we describe internet

14:15

companies, if you use

14:17

the Internet as a core part of your

14:19

platform. There's difference

14:22

between the dot.

14:22

Coms and the nineties and people.

14:24

Who have just really integrated

14:27

the technology into their business. Right,

14:29

So I think Netfolio is not

14:32

a dot com but a comm

14:34

that used the net as a way to reach

14:37

more people and give them access to data.

14:39

Well, it's really funny because I made

14:41

a couple well I made more than a couple of mistakes,

14:44

but one of the big ones I made was

14:47

we designed Netfolio as

14:49

a B two C company, so

14:52

we called we were taking on at

14:54

the time mutual funds, which were dominant.

14:56

We didn't have ETFs while we

14:58

had them, but they were there very early day,

15:01

very very early days.

15:03

And so what did the spiders just turned

15:05

twenty five recently, Yeah, I think of something

15:07

like that. Yeah, So ninety nine is

15:10

like it was really the

15:12

beginning.

15:12

Oh totally. And basically

15:14

the idea was it was the first

15:17

online investment advisor.

15:19

And the reason that we

15:22

thought it would work so well was personalization,

15:25

tax management, all of

15:27

those things. So, for example, we

15:29

would they were all run by quant models

15:31

that we had developed, right, but

15:35

it gave the user the ability to say,

15:37

let's say they're anti smoking, right, and

15:39

Philip Morris is one of these selections

15:42

they could just check nope, don't want it.

15:45

Up comes the next stock that meets

15:47

the criteria, and so it had

15:49

a lot of really great features, but

15:51

the tech was not quite there yet.

15:54

You were twenty years ahead of

15:56

where you would end up in

15:59

the late tens, right.

16:01

I.

16:03

Was.

16:03

I really do have to give my son

16:06

Patrick the credit for resurrecting

16:09

the idea because when we were

16:11

at OSAM, I said,

16:13

listen, we left Bear

16:16

right into the Great Financial Crisis, and

16:18

I put the team together and I'm like, I don't

16:21

think that we're going to be able to sell many long

16:23

only portfolios after the market

16:25

has collapsed by nearly fifty percent,

16:28

So let's spend our time developing

16:32

internal technology that works

16:34

the way we work. The office

16:36

shelf stuff really wasn't cutting it, and

16:39

so the project to get there was

16:41

multi year and Patrick

16:43

oversaw that, and then he walked into my office

16:45

one day and he goes, you

16:48

know, Dad, we've been using the desk star

16:50

to kill a mouse. And I'm like, okay,

16:52

I like the metaphor, but what

16:54

do you mean? And he started talking about AWS,

16:58

talking about netfolio and he's like, we

17:00

have the perfect tech now

17:03

that our clients results being

17:05

one of them, could use and

17:07

I'm like, brilliant, let's go

17:09

with it.

17:10

So we're going to talk a little more about Canvas,

17:13

but I want to stay with the

17:16

launch of O SM and O seven. So

17:18

A, you don't need to disclose

17:21

this, but I'm going to assume you had a

17:23

lot of bear Stern stock options that you had

17:25

a vest on your exit, so

17:27

you probably had a pretty good sale,

17:29

pretty good print on those

17:33

when you first set up O'Shaughnessy, you're

17:36

running your traditional models, things

17:38

like Cornerstone value and Cornerstone

17:41

growth, and I'm a big fan

17:43

of your microcap

17:45

sleeve, which really operates

17:48

parallel to venture capital returns,

17:50

only using public stocks.

17:53

Am I getting that more or less right? Actually we use

17:56

that also. Yeah, we wrote a paper

17:58

saying that it was the poor man's

18:00

way to get exposure to private equity.

18:03

Private equity or venture capital are both both

18:05

really private equity closer because

18:08

the the microcap I love microcap

18:11

investing. The only real reason that

18:13

we offered that was because I loved it

18:16

so much. Well, and the data backs

18:18

it up, Oh, totally, totally it

18:21

is. Microcap is an

18:23

amazing place if

18:26

you've got the right tools to sort

18:28

through the thousands of names

18:30

in the microcap universe, because

18:33

you would not want to buy an

18:35

index of microcap stocks.

18:38

For the most part, there are microcaps

18:40

because they kind of suck. However,

18:44

there are so many diamonds

18:46

in the rough in microcap

18:48

that if you have a strategy, like

18:50

a quant strategy that can sort

18:52

through these thousands of names,

18:55

you can do extraordinarily well.

18:57

I love the strategy and I

18:59

know.

19:00

Oh the OSAM microcap

19:02

sleeve is what I call. It has

19:04

just really shot the lights out, especially

19:07

last year when the market was having.

19:09

A pretty good year. They did pretty

19:11

well, didn't they They did? They did. Now,

19:13

remember you introduced me as chairman of o

19:15

SAM, I'm no longer no longer. Yeah,

19:17

they let me retire and

19:21

actually Patrick is now chairman

19:23

emeritus over at OSAM.

19:26

Let's talk a little bit about Canvas, and again full

19:28

disclosure, we're a client.

19:30

We were a beta test. Do we love the product?

19:32

Which is kind of ironic because I

19:35

used to hate direct indexing every

19:37

time I would demo

19:40

or see a product. It was clunky,

19:43

it was cludgy. You would get these

19:45

statements that were like hundreds of pages

19:47

long.

19:48

You guys kind.

19:49

Of figured out the secret

19:52

sauce for how do we make this

19:54

clean, usable and easier

19:56

to understand. Tell us a little

19:58

bit about the genesis

20:01

of Canvas.

20:02

Well, first of all, we call it

20:04

custom indexing as opposed

20:06

to direct and the reason

20:08

I make that distinction is because,

20:11

as you point out, the direct

20:13

indexing products of that time

20:16

were clunky, they were difficult.

20:18

You got reams and reams of paper

20:21

reports, and they were really only

20:23

focusing on tax benefits.

20:26

Right. What we wanted to

20:28

do with Canvas, which

20:30

is custom indexing, is, as the

20:32

name implies, give you, as

20:35

the advisor, full control

20:38

over what your client portfolio

20:40

wanted to look like. You got the advantages

20:43

of tax harvesting. You got

20:45

the advantages of being able to mix

20:48

indexes in with active strategies,

20:51

but you could also do a social

20:54

investing fund if you want it. But the

20:56

way we did it was we didn't presume

21:00

what your client was going to think

21:02

of as good social investing.

21:04

So often when you see some

21:07

of the ESG portfolios,

21:09

they've been predetermined as to what

21:11

is going to be included. We give

21:14

you the tools to

21:16

turn a dial up or down on whatever

21:18

you want. I think last I looked, there were

21:20

over fifty eight separate

21:22

things that you could find tune around on

21:25

the idea of ESG. We wanted

21:27

to give the tools to you because

21:30

you knew your client vastly better

21:32

than we did, and we thought,

21:34

let's try. As you mentioned you were

21:36

one of the beta testers. That was actually

21:39

one of the smartest things we did, I think,

21:41

because we had really good

21:43

advice from a lot of people that we knew

21:46

in both venture and other places.

21:49

The first thing that many of them said to us

21:51

was do not try to go

21:53

big with this. Originally, find

21:56

advisors who you trust who

21:59

will give you you real feedback.

22:01

In other words, they won't shine you on if

22:03

they didn't like you. Guys were very good

22:06

at times.

22:06

And Michael batt nicking my office, one

22:08

of my partners, who was

22:10

over the moon when he first saw this. Every

22:13

time another product came in, it would

22:15

take me thirty seconds to poke holes

22:17

in it. And he came breathless

22:20

into my office, Dude, you got to see

22:22

this. And I'm like, yeah, yeah, okay, another

22:24

garbage right, tee it up. And

22:26

it took about thirty seconds to go, Oh my god,

22:29

how do we get a piece of this?

22:30

This is fantastic.

22:31

The interface, the design, all

22:34

of the bullet points that all the boxes

22:36

checked were great. Let's stick

22:39

with what we no longer call esg

22:42

and Meyer Statman famously

22:45

called values based investing. Some

22:48

people have called it woke investing, but that's

22:50

really the wrong phrase. I'm fascinated,

22:53

for example, by the Catholic

22:55

bishops whose endowment says,

22:58

look, we don't want any aboard of any

23:00

drugs that do that. We can't invest in those companies.

23:03

We can invest in hospital chains

23:05

that perform these sort of surgeries,

23:08

or insurers. You have the ability

23:10

to say, whatever your personal

23:12

preferences are, you could just tune

23:15

those out of pick an index,

23:17

the S and P five hundred the Vanguard Total

23:20

Market. You could say, I don't

23:22

want X or Y or Z, and

23:24

how it comes tell us a little bit

23:26

about that.

23:27

I felt that that was really really

23:29

important because everybody has

23:32

different ideas. As you point out, the Catholic

23:34

bishops wanted to exclude

23:36

certain things. Others might want to include

23:39

certain things. Actually felt it would

23:41

be very arrogant of us to

23:44

determine what good social investing

23:47

was because we had managed

23:49

money for a variety of religious

23:51

institutions, and guess

23:53

what, they all have different

23:55

takes on what they want to see.

23:57

We did one where, for example,

24:00

well you couldn't buy any company that

24:02

did anything with animals with eyes.

24:05

That was an interesting one. But then

24:07

on the other hand, we had a client who

24:09

wanted to see more female

24:12

board members and females in the

24:14

C suite, and you could you could screen for that.

24:16

You can screen and there's a bunch of research that shows

24:18

those companies. Now you don't know if it's posative

24:21

or just merely correlated, but those companies

24:24

tend to outperform. The request

24:27

we probably hear the most is

24:29

no gun stocks, no tobacco stocks.

24:31

Yeah, kind of interesting. Yeah, the tobacco

24:34

guns, those are pretty

24:37

large groups where majority

24:39

of investors want nothing

24:41

to do with them. But the other thing that's

24:43

cool about our dials on

24:46

canvas you Let's

24:48

say that Ritholtz has a wild

24:50

eyed libertarian walk in who

24:52

happens to have a billion dollars and

24:55

he says, you know what I want

24:57

the gun manufacturers. I want

24:59

I'm a big like an amendment guy, right,

25:01

Or I want the pharmaceuticals,

25:03

or I mean the sinstock, I mean gambling

25:05

and alcohol. Well, and you know the joke

25:08

there was that my first company, O'Shaughnessy

25:10

Capital Management, we used to keep

25:12

a joke portfolio which was called

25:14

the Eat, Drink and Be Merry for tomorrow

25:16

you die Berry. It

25:19

killed me sure.

25:20

So what ends up happening very often

25:23

is when there's a non financial

25:26

reason for kicking a stock

25:28

out out of a lot of portfolios.

25:30

Eventually a company with

25:33

still having decent financial prospects,

25:36

it becomes cheap.

25:37

Yep. Absolutely, But

25:39

the thing with the social style

25:42

investing, we wanted you

25:44

to be able to reflect your client's

25:47

unique needs. And

25:49

there really wasn't anything like that. I

25:51

don't know if there is now, but I haven't

25:54

seen anything like that.

25:55

Well, certainly not to this degree

25:57

of granularity. By the way, when

25:59

we first we're beta testing

26:02

Canvas. Internally, my

26:04

view was, Hey, people

26:06

are going to want to use this for value

26:08

based investing. Then they're going to want

26:10

to deconcentrate. If

26:12

I work for Google, do I really need all

26:15

this tech exposure My income is

26:17

coming from there, Let me diversify that way.

26:19

And then tax loss harvesting was going to

26:21

bring up the rear. I had it exactly

26:24

backwards, in large part

26:26

because I don't know, maybe a year into

26:28

it we had the COVID crash

26:31

market falls thirty four percent and

26:33

coincidentally bottoms just near

26:36

the end of the quarter. That

26:38

rebalance. You know, typical

26:40

tax loss harvesting own a

26:42

dozen mutual funds. You pick up

26:45

ten twenty basis points against

26:47

the portfolio of losses to offset

26:50

gains. The hope with this was

26:52

it would be fifty sixty. We

26:54

had clients getting two hundred, three hundred,

26:56

four hundred basis points. And I've talked

26:59

to some of your staff or

27:01

former staff, and they've told

27:03

us some unique use cases where

27:05

the numbers are bonkers. First off,

27:07

explain to the audience who

27:09

may not be familiar with this what is

27:12

tax loss harvesting.

27:13

So essentially what it does is

27:16

we had to build a non trivial

27:18

algorithm that could monitor

27:21

every portfolio we were managing

27:23

on behalf of clients and

27:26

as you know, they can go all

27:28

the way up get maximized

27:30

tax losses or all the way

27:32

down don't worry about them. So,

27:35

for example, you wouldn't care about it in an IRA

27:37

right right. But

27:40

the purpose was that we

27:42

found through our research that

27:45

a tremendous amount of alpha

27:48

was being left on the table, and that was

27:50

the alpha from tax lost harvesting.

27:53

When you're in a market like the market

27:55

we had when we went into COVID

27:57

and the bear market ensued, and

28:00

under other circumstances, well

28:02

kind of you're out of luck. But in

28:04

this particular case, that creates

28:07

the kick in for harvesting

28:09

the losses, reducing

28:12

the overall tax needs

28:14

for the portfolio, and you could

28:16

really look at that as that's money in your

28:18

pocket. By the way, we had

28:20

the benefits completely backward

28:23

too. A tax loss harvesting was

28:25

at the bottom of our list as well.

28:27

It's arcane and technical

28:29

and you don't really think about it, but

28:32

we have clients who were either you know

28:34

startup founders that cashed out, or

28:36

they inherited or or just

28:39

owned stock with a very low cost

28:41

basis. You know, it's always funny when you

28:43

see a five million dollar portfolio

28:46

and some stock has blown up

28:48

where it's eighty percent of the holdings.

28:51

Hey, if you have five million dollars

28:53

and four million of it is Apple

28:55

or Amazon or some combination

28:57

of big stocks, that's a lot of

29:00

angle stock risk. And to a

29:02

man, every person says, hey, you

29:05

should diversify. The answer as always, I'm

29:07

gonna get killed in capital gains taxes.

29:10

This worked out to be a really good way to say

29:13

we're gonna work out of your concentrated

29:15

position over three, four or five years,

29:18

and then twenty twenty comes along and what

29:20

should have been a five year process

29:23

took half as long because you had so

29:25

many losses. So for those people

29:27

who may not be familiar with this, let's

29:30

say you own ten mutual funds, right

29:32

and some are up, one or two are

29:34

down. You sell the ones that are

29:36

down, you replace it with something very

29:39

similar. Hey, now I got a little bit of

29:41

loss even and my portfolio looks the same, but

29:43

I have an actual realized loss that I

29:45

could use to offset my real

29:47

gains. But those losses are three

29:50

five ten percent.

29:51

They're nothing.

29:52

On the other hand, if you have a direct

29:55

index or a custom index that has

29:57

a couple of hundred stocks, well,

30:00

the worst stocks in those portfolios,

30:02

they're not down three four five percent, they're

30:05

down forty sixty seventy

30:07

five percent. You sell the ones

30:09

that are down, you replace them. And this is one

30:11

of the things I like about canvas. You

30:14

identify the replacement stocks

30:16

that are is it fair to say

30:18

mathematically similar?

30:19

They look, well, they come

30:22

from the same strategy, so yeah,

30:24

you could say they were mathematically similar.

30:27

So the overall portfolio more or less

30:29

retains the same characteristics.

30:32

You're just realizing losses deep

30:35

losses on some stocks and replacing

30:37

them with something relatively

30:39

similar exactly. And you know,

30:42

we're just basically making math

30:44

work for us. And because

30:46

the entire thing is operated

30:49

within the canvas architecture. After

30:52

getting the algorithm, which was non trivial

30:55

what do you mean by non trivial outbum, it took a

30:57

hell of a lot of work, okay to be able

30:59

to make that function properly, And

31:03

as we worked with firms like yours,

31:06

it became very very clear to us

31:09

that that was going to be a big deal in

31:11

canvas, So we wanted

31:13

that algorithm to work perfectly.

31:16

But as you also note, we wanted

31:18

the nearest neighbor, if you will, that

31:20

would replace that stock to

31:23

not affect the overall metrics

31:26

of your portfolio. So it's going

31:28

to look, act and perform very much

31:31

like the earlier portfolio, but

31:33

you've already taken that wonderful tex loss

31:36

so that you can offset the gains from

31:39

elsewhere. The other use

31:41

case that we thought would be number one

31:44

was, you know you have a concentrated

31:46

position. Let's say Google

31:49

right, don't give me any tech

31:51

exposure, or give

31:54

me tech exposure only in this

31:56

tech which is like hardware for example,

31:58

right that I can do and

32:01

that type of use case would

32:03

work hand in hand with the

32:05

tax loss, making it a much

32:08

much more efficient, more money

32:10

in the investors pocket. In

32:12

terms of final outcomes

32:14

with the portfolios.

32:15

What was the uptake on that approach? People

32:18

enthusiastic about.

32:19

It, they were, but they

32:21

were not nearly as enthusiastic

32:24

as we anticipated they would

32:26

be. There were a few advisors

32:29

that we were working with who worked

32:31

specifically with founders

32:34

and early employees who

32:36

had a lot of options in that particular

32:38

and usually tech, but

32:40

we also did work and do work

32:43

with a lot of people who just

32:45

amassed through employment a

32:47

huge position in their particular

32:50

company, and they wanted to

32:52

have the rest of the portfolio be built

32:55

to complement and offset,

32:57

if you will, any further invent's

33:00

over there. So it's worked actually quite nicely.

33:03

And then in twenty twenty one, Franklin

33:05

Templeton comes knocking at the door.

33:08

They're an investment giant with a

33:11

trillion plus dollars on their books

33:13

and they've been pretty acquisitive over

33:16

the past few years. Tell

33:18

us a little bit about how that transaction

33:21

began. If I recall correctly, you

33:23

guys weren't out shopping the firm to.

33:25

Be sold, were you not? At all? We

33:27

were. It's a funny story. We almost

33:30

got kind of a cold call from a

33:32

gentleman at Franklin Templeton. I

33:35

was sort of like, give it to Chris

33:37

Lovelace or you know who's the president of the

33:39

firm, And ultimately

33:42

Patrick spoke with him and came into my office

33:44

and he's like, hey, Franklin

33:46

Templeton is really interested in

33:48

canvas. I'm like, okay,

33:51

they want to use it. No,

33:53

no, they want to buy it. And

33:56

I'm like, okay, well, let's do

33:58

a due diligence on Franklin Templeton.

34:01

They're massive, as you know, I think trillion

34:03

and a half in assets under management,

34:06

and we were really having

34:08

great results, as you know, with Canvas

34:11

on our own. We thought about

34:13

it for a long time, and you

34:15

know, we really wanted custom

34:17

indexing to be a new category

34:20

of asset management, and we felt

34:22

really proud about that because it isn't

34:24

too often that you're able to invent

34:26

kind of a new category of

34:28

investing. And as

34:30

we chatted about it and talked it out,

34:33

we're like, you know, we're at an

34:35

inflection point here. We

34:37

are relatively small boutique,

34:40

even though this is working really really

34:42

well. If we want custom

34:45

indexing, custom portfolio creation

34:48

to really make the big time, it

34:50

probably makes sense for a

34:53

much larger asset manager

34:55

with all sorts of advantages

34:58

that we did not have to

35:00

take it and run with it. So

35:03

we let that be our guide, and

35:06

after doing quite a bit of due diligence on

35:08

the people at Franklin, we were like, okay,

35:10

let's negotiate about selling

35:12

the firm to them.

35:13

Talk about good timing. Morgan

35:15

Stanley bought one of your

35:18

competitors in that space. Vanguard

35:20

rolled out their own product,

35:23

which quickly amassed you

35:25

know, billions and billions of dollars on it. So

35:27

this has worked its way into

35:30

the mainstream, even though it's still

35:32

relatively I don't want

35:34

to call it a niche product because it's bigger

35:37

than that.

35:38

But it's not ETFs.

35:40

It's not giant yet, but

35:42

it's still growing at a pretty rapid clip, isn't

35:45

it Totally? And I think that

35:47

ultimately we might

35:50

look back ten years from now and

35:53

have the thought, can you imagine that people

35:55

just bought packaged products? I

35:58

mean, like, my god, no

36:00

tax advantage, none of the customization,

36:04

none of the immunization for concentrated

36:06

positions that I have. And

36:09

so we definitely think that

36:11

this is a way of investing

36:14

that well, you know, once a

36:17

client sees their portfolio

36:20

under canvas and with the

36:22

customization, it's

36:24

really really hard to go back

36:27

to thinking, ah, you know what, I think I'll

36:29

just go with five mutual funds or five

36:31

ETFs. I don't really care about

36:33

much of the other I think that

36:35

you know, these things take time. But I

36:38

mean again, your firm is a classic

36:41

example here. You were able

36:43

to use custom in a way

36:46

that was good for your firm,

36:48

good for your clients. And

36:50

you know, the clients that we speak

36:52

with love it. You know, they

36:55

all love it.

36:55

That's been our experience. It's really

36:58

Mark Andriesen's software is the

37:00

world writ large. Because

37:03

there are two aspects to this, and I'm going to circle

37:05

back to the database part of it

37:07

in a bit. But the front

37:10

end, the user interface and the software

37:13

that allows a very simple

37:16

set of choices and that you could

37:19

go increasingly down the rabbit hole and

37:21

find more and more and more issues certainly

37:23

is a big factor a lot

37:26

of what is done. The

37:28

technology just wasn't quite

37:31

mature enough fifteen twenty

37:34

years beforehand. And when you

37:36

look at it, it's just well, this is just software.

37:38

It's just a user interface and a

37:40

way of organizing it. But now

37:43

let's circle back to the database,

37:45

which I recall you saying was

37:47

the secret sauce. Tell us a little

37:50

bit about the database that you've been working

37:52

on for a quarter century. That

37:55

drives canvas.

37:57

So we use the copystat

38:00

universe. They cover virtually every

38:02

company that trades both here

38:05

on American exchanges and elsewhere,

38:08

and it is kind of

38:10

the gold standard really in terms of

38:13

databases.

38:14

How does it compare to something like CRISPER

38:17

or some of the other.

38:18

Well, so, CRISP. It comes to us from

38:20

the University of Chicago Center for Research

38:23

and Security Pricing. The downside

38:25

of CRISP is it's, first

38:27

off, I love chris We used it in

38:29

the most recent edition of What Works,

38:32

but it doesn't have enough

38:34

of the fundamental factors

38:37

attached to it. In other words, it's mostly

38:39

price history rice history. And

38:42

it also tries and

38:44

generally succeeds to

38:47

include all of the names

38:49

that might have been around trading

38:51

on the AMEX or the New York Stock Exchange

38:53

or NASDAC. But the challenge

38:55

is a guy by the name of Macquarie wrote

38:58

a really compelling paper talking about

39:00

how a lot of the historical data

39:03

not compustat, but further back

39:05

right in the twenties and thirties, come

39:07

from the papers. Yeah, and also

39:11

wasn't nearly as thorough as

39:14

say the compustat is. In

39:16

fact, one of the things that we were doing

39:19

before Franklin Templeton approached us

39:21

is we were literally digitizing

39:25

old Moody's manuals. They go

39:27

back to nineteen hundred, and what

39:29

we wanted to do was marry

39:32

into the CRISP data all

39:34

of the fundamental factors that

39:36

would have given us the ability to run

39:38

a nineteen hundred through nineteen

39:41

fifty five when Compustat begins

39:44

test We ran some test runs.

39:46

We did price to book, and we did a couple

39:48

others. And what we were finding and

39:51

won't surprise you, generally

39:53

speaking, same kind of results,

39:55

right with the exceptional price to book, we

39:57

actually took price to book of

40:00

our composites. You know how we have

40:02

the composits for value and momentum

40:04

and all of those things, and we took

40:06

price to book out because of the research

40:09

that we did that covered the great depression

40:11

from the thirties. You know, and I

40:13

know if you've taken any finance courses,

40:16

price to book previously had been used

40:18

as a proxy for likelihood

40:20

of bankruptcy. Right, Well,

40:23

guess what during the thirties,

40:25

a lot of those low price to book companies

40:27

went bankrupt. Well, when your book value

40:30

collapses, exactly, it's the book

40:32

isn't much value exactly exactly.

40:34

So we did find some learnings

40:37

where we jiggered with the

40:40

composites that we use. That's another thing

40:42

we do. We don't use a

40:44

single factor. In my first version of What

40:46

Works on Wall Street, we would sort down

40:49

for the final portfolio on a single

40:51

factor, and we found that that wasn't

40:53

nearly as effective as

40:56

a composite of factors. Again,

40:58

a lot of people the old joke about quants, right,

41:01

what do you guys do golf all day? You

41:03

know you're just running your models. Well,

41:05

we don't golf all day. But what we do

41:08

do all day is research

41:10

the underlying models. What

41:12

we're always trying to do is

41:14

improve them. But it's

41:17

evolutionary, not revolutionary.

41:20

Listen, the foundations are very, very

41:22

similar. By the way. They make a lot of

41:24

sense too. I say, if

41:26

we changed it and walked out

41:29

onto Lexington Avenue here and we

41:31

found a food truck, right, and we

41:33

went up and long line. Everything

41:35

looks good. And we talked to the owner

41:37

and we said, how about you clearing

41:39

a year And he says, well, I'm clearing

41:41

one hundred thousand. And we're like, well,

41:44

would you take a buy offer from us? And

41:46

he goes, yeah, you can buy it for ten

41:48

million. You and I are going to go get

41:50

out of here. There's no way we're going to buy this

41:53

right, Well, change it to a stock

41:55

ticker. There's a lot of stocks trading

41:58

at that kind of multiple, and so

42:00

when you look at the underlying strategies,

42:02

they make intuitive economic

42:05

sense, and so the

42:07

data set that you're using becomes of

42:10

paramount importance. The other

42:12

thing I found was that, and

42:14

this one disturbed me a little. I haven't looked

42:16

at this recently, but when I was doing

42:18

it several years ago, you could

42:20

get really different numbers if

42:23

you went to Bloomberg, or if you

42:25

went to Reuter's, or if you went to Dow

42:27

Jones or any other innumerable

42:30

providers of data. And so that

42:32

was another huge project for us,

42:35

and also part of the data set

42:37

that we're talking about. One of

42:39

the other things that I was widely hated

42:41

for by my research team was

42:45

we went on a multi year data

42:47

cleansing exercise because

42:50

we found that a lot of it had a

42:52

lot of hair on it. And

42:54

so I'd made no friends

42:56

on the research desk when I said,

42:59

listen, we've got to get this

43:01

pristine. And so our

43:04

data cleansing of the universe

43:06

also is another real important

43:08

distinction between just

43:11

generally available data and

43:13

that which we are using.

43:14

Huh, really really interesting. Let's

43:16

stay with price to book because

43:19

I want to ask your opinion on something,

43:21

and you're the perfect quant to bring this up

43:23

to. Which is all right? So

43:25

we're talking about price to book. Back

43:28

in the day when manufacturing

43:31

required a lot of men and material

43:34

and capital, and you had big factories

43:36

and railroads were laying thousands

43:39

of miles of steel, and you

43:41

know, you were building these forges and foundries

43:43

to make cars. The modern

43:46

era, especially with technology,

43:49

there are a lot of intangibles that don't

43:52

seem to find their way to book

43:54

value. Things like patents and copyrights

43:56

and algorithms and processes

43:59

that are prepared rietary that really

44:02

are the whole value of the company,

44:04

but somehow never show

44:07

up in metrics like price to book,

44:09

which has led to some people

44:12

and I'm not positive who

44:14

to name. I don't want to mischaracterize anybody,

44:17

but some folks have said we're

44:19

mispricing companies

44:22

that operate in the tech space because

44:24

we're not giving them the appropriate credit

44:26

for all of this intellectual property. Is

44:29

that an overstatement or is there

44:31

some truth there?

44:32

I think there's more than some truth

44:34

to that. We published a papers

44:37

called the Veiled Value, and it looked

44:39

at the idea that brand

44:41

value, that all of the items

44:44

that you just delineated, we're

44:46

not being captured in Trademark's

44:50

research and development straight

44:52

across the board. When we took

44:54

a look at that, we found that

44:57

you could figure out a way

44:59

to price that into the model. So

45:02

you are absolutely right. This

45:04

is one of my bugaboos, things like GDP,

45:07

all of the metrics that we continue

45:09

to report and get obsessed about.

45:12

Basically they've lost a lot of their

45:14

meaning because they were designed

45:16

for the world you just articulated.

45:19

They were designed for manufacturing.

45:21

They were designed for physical things,

45:24

and we moved off that for many

45:26

many decades. Atoms to bitts was a

45:28

big transition, huge transition, and

45:31

so we think that we another

45:34

aspect of research right when

45:36

when we got the idea, you know, we think

45:38

we're missing something here. That's

45:41

what resulted in the paper about

45:43

brand value and goodwill

45:46

and all those things not being taken into

45:48

account by investors at all.

45:50

And so we found ways

45:53

we could do that with factors and

45:56

improved the efficacy of the underlying

45:59

modelsificantly.

46:00

I think one of the greatest quotes ever

46:03

issued by a statistics professor

46:05

is George Bachs All models

46:08

are wrong, but some.

46:09

Are useful, exactly. I quote him

46:11

all the time because he's

46:13

absolutely right. The idea

46:16

that you're going to get anything to

46:18

perfection is a fool's errand right.

46:21

I have a writer that we're working with

46:23

under O'Shaughnessy Adventures, one of our

46:25

new verticals, which is Infinite Books,

46:28

and he's got a great quote, which is perfection

46:31

is a one hundred percent tax.

46:34

Really interesting. Let's talk a little

46:36

about O'Shaughnessy Adventures starting

46:39

with your mission statement. Osv's

46:41

mission is to fuel creators in the

46:43

worlds of art, science

46:46

and technology with the advice,

46:49

data and resources they need to

46:51

stay focused and get great

46:53

ideas out of their heads,

46:56

off of their whiteboards, and

46:58

out into the world discuss

47:01

I had.

47:01

A thesis that started to develop

47:04

around twenty seventeen twenty

47:06

eighteen as I watched old

47:09

playbooks that used to work beautifully

47:12

stop working, and so

47:14

I came up with this idea that we

47:16

were in a great reshuffle

47:19

where all of the old models

47:21

were collapsing and people

47:24

were kind of freaked out. They were like,

47:26

this has worked for decades, Why

47:29

doesn't it work anymore? And I think

47:31

that one of the reasons it didn't work

47:33

anymore was because the tools,

47:36

the tech tools, and the platforms

47:39

and the Internet and all of

47:41

that put together allowed

47:44

for much more innovative

47:47

business models in a variety

47:49

of industries. Right, So if

47:52

you look at the verticals of O'shaughness

47:54

adventures, you'll see what we think. Right,

47:57

So we have what we call infinite

48:00

adventures. That's venture

48:02

capital. But I love in the old days

48:04

they used to call venture capital adventure

48:06

capital. And the one I really loved

48:09

liberation capital. Uh,

48:12

well, to find that what is what is liberation? And

48:14

I've heard the phrase, yeah in the old days,

48:16

the so called hateful eight that wanted

48:18

to leave shockly.

48:20

Right, the early days of semiconductors

48:22

and the pre in Fairchild

48:24

semi conductor.

48:25

Exactly exactly right, good

48:27

call. And back

48:30

then, the idea that a group of

48:32

engineers or even you

48:34

know, regular business people would leave

48:37

a big company that was well

48:39

funded by a bank or a series

48:42

of other investors was almost unthinkable.

48:45

And so what came to be known

48:47

as the hateful eight who created Fairchild

48:50

got pitched by a variety of

48:53

investors, external investors

48:55

saying why don't you guys to start your own

48:57

company. He finally talked them

48:59

into it, and that's when we

49:01

use the term this is your liberation

49:05

capital, where you can focus

49:07

on just what you want to focus

49:09

on, making better semiconductors.

49:12

You don't have to play any of the politics

49:14

of the big company, you don't have to answer

49:16

to people who don't really understand

49:18

what you're doing. Right, the people

49:20

in New York that might have owned it or financed

49:23

it had very little understanding

49:25

of what semiconductors were all

49:27

about in the fifties and sixties, and

49:30

so I like that part very

49:32

very much.

49:33

That's the genesis of Intel right,

49:35

as well as the whole run of other

49:37

semiconductors, can trace its roots

49:40

back to fair Child right

49:42

exactly.

49:43

And so there we're looking for

49:46

companies that we think will

49:49

expand the opportunity set

49:52

for very clever entrepreneurs

49:54

and creators. Another vertical

49:57

is infinite films. Why that, well,

50:00

I think we're approaching a period where

50:03

you can make films, documentaries,

50:06

You can use AI to augment your

50:08

filmmaking in such a way

50:10

that the people who couldn't make movies

50:13

in the past are going to be able to make

50:15

them in the future. You could legitimately

50:17

make.

50:17

A film with an iPhone. Now that you

50:20

can you couldn't do even five

50:22

years ago. Is kind of on the border.

50:24

Barry. Some of the things that I've seen as

50:28

submissions to infinite films, Oh

50:31

my god, Like literally,

50:34

I'm sixty three. If I had

50:36

seen that as a trailer for

50:38

a movie at a movie theater like

50:40

ten years ago, I would have thought, Wow,

50:43

this is amazing, this is cool. And

50:45

then the guy at the bottom says, by

50:47

the way I made this on my iPhone, that's crazy.

50:49

That really is great, And so that Unlock's

50:52

tremendous talent that never

50:55

had access to the Hollywood

50:57

infrastructure. So

51:00

our thesis is there are tons of

51:02

really creative people out there

51:04

who now have the tools to

51:07

make great movies. Another thing

51:09

I wanted to do was where are

51:11

the Rudies of movies today?

51:13

Now? Rudy's, of course, is about the

51:16

kid who goes to Notre Dame and he's five

51:18

foot nothing and weighs a buck nothing

51:20

and he gets on the team, the

51:22

Notre Dame team. Why was

51:25

that such a great movie? Because it's incredibly

51:28

inspirational, It gives

51:30

the viewer like, you know what, I can take

51:32

a shot at it, I can do it. Hollywood

51:35

seems to have completely forgotten about making

51:37

these types of movies.

51:39

And just for people who might not

51:41

remember the movie Rudy, it's

51:43

the story that drives the whole thing

51:45

and the characters. There's

51:47

not a whole lot of expensive special

51:49

effects or you know, they're

51:52

not flying out to Nepal. It's all done

51:55

pretty much on the cheap. And

51:57

that's the area of film you're looking

51:59

to explore, or narrative driven

52:02

accessible story.

52:04

Narrative driven accessible

52:06

stories that we're also

52:08

changing the underlying economics on.

52:11

So here's how we're gonna do that. Everyone

52:14

who comes and works on one of our films

52:17

is going to own a piece of

52:19

that film and back

52:21

end points, back end points. But for

52:23

everybody, we're not going to use Hollywood

52:26

accounting. Our accounting is very, very

52:28

straightforward. Here's what it

52:30

cost us to make it. What happens

52:32

after we recover those costs, You

52:35

own x percent. If we manage

52:37

to sell it or generate revenue from

52:40

it through the multiple platforms you

52:42

can put it out on, you're going

52:44

to benefit from that. The other thing

52:46

that we're going to do is we're going to give young

52:48

people a shot right

52:51

now, if you want to try to beat

52:53

Let's say you graduate from NYU Film

52:55

School and you decide you're gonna go out

52:57

to Hollywood and you're going to pitch all of these

52:59

student video its, say you want to

53:01

get good luck, because it ain't

53:03

gonna happen. Right There is almost

53:06

a guild like system out

53:08

in Hollywood where you know, it's

53:11

kind of the idea that, yeah,

53:13

I want to get in the Screen Actors Guild. How

53:15

do I do that? Well, to get in

53:17

the Screen Actors Guild, you have to be in three

53:19

movies. Well, wait a minute, how do I

53:21

get in the movie if I'm not in the Screen Actors

53:24

Guild. So there are a lot of

53:26

really old fashioned rules. And it's

53:28

not just Hollywood, by the way, it's

53:31

much of media. It's much of

53:33

all of the things that we consume every

53:36

day. And so basically

53:38

what I did was say, what

53:41

industries that I find fascinating

53:43

that I'm interested in have the

53:45

greatest arbitrage ability.

53:47

Huh.

53:49

I love that concept. And you know

53:51

it's funny you mentioned films because

53:54

that dynamic tension of

53:56

indie films. Look at how great A

53:59

twenty four has been doing amazing as

54:01

a as an independent studio.

54:04

The timing is really good, and the

54:06

technology tools, the ability

54:09

to film on a phone, edit on

54:11

your laptop and then distribute

54:13

it by uploading to YouTube

54:15

or wherever.

54:17

Barry, that's the key. There's

54:19

always cultural lag, right, you

54:21

know the S curve tech

54:23

adoption, right, it's real,

54:26

And let's change industries

54:28

and let's look at publishing. Right,

54:31

So we are launching Infinite

54:33

Books. Why well, because

54:35

the current publishing industry is

54:38

still playing under nineteen

54:40

twenty rules, not twenty twenty

54:43

rules. We no longer

54:45

have to have minuscule amounts

54:47

going to the author. We can,

54:50

because of the tech, because of our ability

54:52

to produce that book, give the

54:54

author much more of

54:56

the upside. So, for example, we're

54:59

going to give any where between depending

55:01

on what the author wants us to do for

55:03

them. It's going to always be

55:05

above fifty percent. Mostly it's going

55:08

to be seventy percent. But that's

55:10

just the start. Imagine Berry, you

55:12

write a book, you bring it to Infinite

55:14

Books, and I say, hey, Barry, what

55:16

other languages do you want this published

55:19

in? And You're like, I don't know, maybe

55:21

Spanish, maybe French. Maybe

55:24

done. Because of AI, we

55:26

can translate the entire book and

55:29

have it available for the French or Spanish

55:31

speaking markets. Even better,

55:33

let's say you want to do an audiobook and you

55:36

want to read it because you've got a great voice. I

55:38

say, Berry, do a minute on

55:40

this for me, say Express

55:43

Surprise or Anger or whatever.

55:46

It will model your voice

55:48

and you can read your book

55:50

on all the audiobooks. But what's really

55:52

cool is we can translate

55:55

your voice into French,

55:58

into Spanish, in to Russian,

56:01

into anything. Wow. And so

56:03

all of these tech advantages

56:06

are being left just lying around

56:08

on the floor, and we think that's crazy.

56:11

We're still early days of the transition,

56:14

oh very early, to technology,

56:16

to AI, to all these

56:18

changes in platforms. It's

56:20

amazing how slowly it

56:23

takes place. I think our mutual

56:26

friend Morgan Housel described

56:29

how long it took from

56:32

the Wright Brothers doing

56:34

the first test flight in Kitty Hawk before

56:36

it even made its way into newspapers.

56:38

Exactly takes forever, and it does.

56:41

And this lag, even in

56:43

our twenty four to seven always online

56:46

environment remains right.

56:48

It's like, if you think about it, it makes tons

56:50

of sense. People are habitual,

56:53

right, they get into habits, they

56:55

do all of these things. Now, I

56:57

think that the pandemic really

57:01

sped up a lot of these trends,

57:03

things like work from Anywhere. O'shaughness

57:06

Adventures is a work from anywhere enterprise.

57:09

We have people in Singapore, India,

57:13

UK, all over the world because

57:16

we can, and the

57:18

idea that we have to have

57:21

a traditional office, the

57:23

idea that we have to do any of those

57:25

traditional things goes right out the

57:27

window. It becomes a much

57:29

less costly enterprise when

57:32

you can do it this way. But we

57:35

back to infinite books like we

57:37

also are going to at the

57:39

author's decision. Right, We're not going

57:41

to force anything on our authors. But

57:44

if the author wants an

57:46

AI agent to Let's

57:48

say, for example, your new book, Let's

57:51

say if it were an Infinite Books publication

57:54

and you noted that it

57:57

quadrupled sales in Omaha, Nebraska,

58:00

how about having an AI agent find

58:02

out what podcasts in Omaha

58:05

are interested in the subject Berry's written

58:07

about. How about sending them a query

58:09

letter. How about setting them a clip from the book

58:12

and saying you really ought to have him on

58:14

your show or podcast or

58:16

write about them. In your substack. All

58:19

of the tools that are available to

58:21

us work today, and

58:25

people aren't using them, and so we

58:27

suspect that this is going to really

58:30

I hate the word revolutionize because

58:32

that's, you know, come on, but

58:34

it's certainly going to accelerate. That's

58:38

a better ride.

58:39

So I want to talk about another aspect

58:41

of O'shaughnessee ventures, which

58:44

is the fellowship program, which

58:46

I find to be absolutely

58:48

fascinating. How does this work

58:51

tell us a little bit about the O'shaughnessee Fellowship.

58:53

For most of history, a genius

58:55

could be born, live and die without

58:58

even knowing they were a genius, far

59:00

less other people knowing it. Right, We

59:03

were really bound by our geography

59:05

and by our networks, and those

59:08

networks were pretty small. Like who'd

59:10

you grow up with, who'd you go to school

59:12

with, who'd you mary? Where are your kids

59:14

going to school? What church do you go to? That kind

59:17

of stuff pretty random. You're just random

59:19

where you were born. I was just dumb. Luck was

59:21

kind of dumb. Luck. You could move, of course,

59:23

but changing your digital zip

59:26

code is a hell of a lot easier than changing

59:28

your physical zip code. But

59:30

more importantly, we now are

59:32

interconnected. I can

59:34

find somebody who's a genius who

59:36

happens to live in Bangladesh. I would

59:38

have never under the old system ever

59:41

known about that person. Now

59:44

I have the ability to know about

59:46

that person and find and

59:48

fund them. The whole idea

59:50

behind the fellowships was we

59:53

wanted to come up with something that highlighted

59:55

the fact that there are tons

59:58

millions of brilliant

1:00:00

people who in the past

1:00:03

just didn't have the right connections, didn't

1:00:06

have the right credentials, you'd name

1:00:08

it, to get into a place

1:00:10

where they could get funding, they could

1:00:12

make their idea come to life. And

1:00:14

so the idea is quite simple. We're

1:00:17

gonna find and fund them and

1:00:19

see what comes from that. I

1:00:21

think that it allows for

1:00:24

so many things, Like it allows we

1:00:26

have a guy who got one of our grants,

1:00:28

which is the smaller amount. It's ten

1:00:30

thousand. The fellowships are one hundred thousand

1:00:33

over a year, no strings.

1:00:35

No strings attached. He has a check for one hundred

1:00:37

k. Go do something interesting.

1:00:39

We don't care what it is exactly.

1:00:41

And we wanted to do no strings

1:00:44

because like we don't want Gotcha's

1:00:46

we don't want. But you've got to do

1:00:49

You've got to give us right of first refusal.

1:00:52

The way I look at it is if if we got

1:00:54

somebody so wrong that they're

1:00:56

going to take one hundred thousand fellowship

1:00:58

from us, develop something really

1:01:00

cool, decide to start a company

1:01:02

around it, and then take it to a different person

1:01:05

for funding, well, we made the mistake

1:01:07

right, because generally speaking, what

1:01:09

we're finding is they love being part

1:01:12

of the community. Because I'm

1:01:14

also a huge believer

1:01:16

in cognitive diversity, right,

1:01:19

there's a great quote that is like, no

1:01:21

matter how smart somebody is, no matter

1:01:23

how insightful, no matter

1:01:26

how brilliant, you

1:01:28

can't ask them to make a list

1:01:30

of things that would never occur to them.

1:01:33

And so essentially what happens when you

1:01:35

get all of these really bright people

1:01:38

in our fellowship and grant community

1:01:40

communicating with each other. Wow,

1:01:43

the ideas that come out of those cross

1:01:46

pollenization of ideas are

1:01:48

really extraordinary.

1:01:50

But this sounds like this is really an

1:01:52

incubator of sorts.

1:01:53

It can be, but it needn't

1:01:56

be. Here's a great example. One

1:01:58

of the guys that we gave a great aunt too, his

1:02:00

name's just that's

1:02:03

his stage name, was an

1:02:05

accountant in India who decided

1:02:07

he really had music in him

1:02:10

and he really wanted to do a

1:02:12

musical video using traditional

1:02:15

Indian songs and singing in

1:02:17

Hindi and other Indian dialects.

1:02:20

He went super viral,

1:02:23

tens of millions of downloads

1:02:25

of his song. He's being put on

1:02:27

all of their Good Morning India.

1:02:30

You know, we have Good Morning America being

1:02:32

written about in all of their newspapers.

1:02:35

And essentially that was because

1:02:38

we thought, Wow, this guy's got talent. Let's

1:02:40

see what happens. We're not incubating

1:02:43

him for anything. Right, if he goes off

1:02:45

and signs a deal with a music company,

1:02:47

we don't do music, so God

1:02:49

bless.

1:02:50

This sounds a little bit like the MacArthur Genius

1:02:53

Awards, where here's a chunk

1:02:55

of money, go be a genius.

1:02:57

There's just so much potential

1:03:00

around the world, Barry, that

1:03:03

I feel compelled to

1:03:06

amplify. Everybody loves to bag

1:03:08

on the generation before or after

1:03:10

them, Right, Listen, the

1:03:13

kids today, young people today

1:03:15

are digital natives. They

1:03:17

know how to use these tools in

1:03:20

ways that we boomers probably are

1:03:22

never going to get to. And I say,

1:03:24

let's empower them. Let's demonstrate

1:03:27

to the world that this makes

1:03:30

real practical sense. Right,

1:03:32

Now let's take somebody else who

1:03:35

is turning his grant

1:03:38

into a company. It's a guy in

1:03:40

Africa who faced

1:03:43

a problem I knew nothing about, which

1:03:45

was the cost of sanitary

1:03:47

napkins for women who are

1:03:50

menstruating is out of reach.

1:03:52

They are all imported from the West

1:03:55

and they can't buy them because they don't have enough

1:03:57

money. Well, he came up with an

1:03:59

idea where his mostly

1:04:01

female staff and researchers

1:04:04

use banana leaves and other biodegradable

1:04:07

products that they can make on

1:04:09

the ground in Africa sell

1:04:12

for a fraction of the cost that

1:04:15

the important ones

1:04:17

work just as well. Now

1:04:19

I believe he is turning that

1:04:21

into an enterprise. He's founding a company.

1:04:24

Will take a look at investing

1:04:26

in it, because of course he's asked us

1:04:28

to. It can be on the business

1:04:31

side, definitely an incubator, but

1:04:33

on the social side, on the music

1:04:35

side, on the art side. So for example, this year,

1:04:38

I really want to have a fine artist

1:04:41

get one of these grants, because

1:04:43

again I want really people

1:04:46

to be able to see there is so

1:04:48

much talent in the world, and

1:04:51

I always try to look for things to root

1:04:53

for as opposed to against. They're

1:04:57

so easy to root

1:04:59

against something thing, right, you don't have to be

1:05:01

terribly bright to say that

1:05:03

sucks. That sucks. Here's why. How

1:05:06

about doing things the other way around?

1:05:08

How about finding things you can root for?

1:05:11

And then the results have been

1:05:13

kind of like the coolest things we've ever

1:05:16

seen, like the guy going viral

1:05:18

in India, like we have. We

1:05:20

funded a guy trying to advance open

1:05:22

source quantum computing. He

1:05:24

now is a big deal in quantum

1:05:27

computing. It's a great

1:05:29

thing to do in general.

1:05:30

Tell us about some of the first few

1:05:33

you tried. Who were the

1:05:35

people that were the first couple of recipients

1:05:38

of.

1:05:38

The guy I just mentioned with

1:05:41

the quantum computing. He had me at hello

1:05:43

because I love that stuff.

1:05:44

What about people who are looking at

1:05:47

markets in the economy, I know that that's

1:05:50

a pieve of yours. Oh.

1:05:51

Absolutely, the thing there

1:05:54

is we wanted it to be significantly

1:05:57

different than our

1:06:00

traditional quant One of the reasons

1:06:02

I became so interested in machine learning

1:06:04

and AI was I viewed that as

1:06:07

the next frontier for quant The

1:06:09

dirty little secret of week quants

1:06:11

is if you really press us

1:06:14

and ask us to really explain

1:06:16

your model like you would to a five year old

1:06:19

we're using pretty much the same stuff,

1:06:22

right, So what we wanted

1:06:24

to do there was push

1:06:27

the needle as far as we possibly

1:06:29

could. But then one of the

1:06:31

first people to get one of the fellowships

1:06:34

was a married couple, Matt and Martha

1:06:36

Sharp, and what they wanted

1:06:38

to do was make a documentary about

1:06:42

non traditional schools for their

1:06:44

kids. They have a bunch of young kids below

1:06:47

the age of seven, and they

1:06:50

put out a great documentary about

1:06:52

a particular school, which was really novel.

1:06:55

And so we really are all

1:06:57

over the map in the type of per

1:07:00

groups that were willing to

1:07:02

consider. Yet another was a

1:07:05

refugee in Ireland who

1:07:07

found that she couldn't figure out

1:07:09

a way in her native language

1:07:12

to work her way through the halls

1:07:15

of the bureaucracy, to

1:07:17

figure out how do I get a place to live?

1:07:19

How do I do all of these things? So

1:07:22

we funded her to make an app. And then

1:07:24

finally another one that I just

1:07:26

love is we have a doctor who

1:07:29

came to us and said what he wanted to

1:07:31

do was make an app for an iPhone

1:07:34

or an Android where you could

1:07:37

completely non invasively, I

1:07:39

could point the phone at you, get

1:07:41

your vitals on the phone, just

1:07:43

by the camera on the phone. Really

1:07:46

yeah, And what was cool

1:07:48

for us was we really pushed

1:07:50

him. We're like, why, why, why why?

1:07:53

And finally at the end of

1:07:55

our interview with him, he

1:07:57

was near tears and he went the

1:08:00

real reason for this is my dad

1:08:02

died of a stroke and I was in medical

1:08:04

school and I didn't save him.

1:08:07

I didn't even know that he

1:08:09

had a problem. And so this

1:08:11

is why I'm so passionate about

1:08:13

this. To get a life saving

1:08:16

thing in the hands of an

1:08:19

on something that we all carry with

1:08:21

us, these smartphones, is

1:08:23

what motivated him. And on top

1:08:25

of that, looks like it could also be a great

1:08:27

business.

1:08:28

Well that's really interesting. Let's stay

1:08:31

with AI and talk about

1:08:34

medicine in particular. I'm

1:08:36

fascinated by the concept of AI

1:08:39

running through billions or even

1:08:41

trillions of molecular

1:08:44

combinations to identify

1:08:47

promising drugs, some

1:08:49

of which are already out there, some of which haven't

1:08:51

been created. But it really gives

1:08:53

us the ability to take millennial

1:08:56

worth of experimentation and do

1:08:58

it in a really very short period of time.

1:09:00

It's a world changer. The ability

1:09:03

to, as you mentioned,

1:09:05

take different molecules where there isn't

1:09:08

a drug addressing a certain problem

1:09:10

and or taking existing

1:09:12

research from drugs and repurposing

1:09:15

it. AI can go

1:09:17

into all of those spaces that

1:09:19

we humans simply can't do and

1:09:22

find the connections on an existing

1:09:24

drug. You know what, this drug

1:09:26

was originally done for malaria,

1:09:30

Well it doesn't work for malaria, but

1:09:32

it works really well for this

1:09:34

disease over here, and then

1:09:37

new drugs that the discovery

1:09:40

is going to be amazing. And you've got to

1:09:42

remember a lot of this stuff can be done

1:09:44

what they call in silico. You

1:09:46

don't have to test it on humans

1:09:49

or animals. You can test it on

1:09:52

the clone of we humans

1:09:54

that you set up in the computer.

1:09:57

And so these types

1:09:59

of thing things like I honestly don't

1:10:01

think it's an overstatement to say,

1:10:04

like this, this AI and

1:10:07

it's many use cases belong

1:10:09

up there with the wheel and fire

1:10:12

and the printing press, because

1:10:15

it is a multi use

1:10:17

technology that's going to affect everything

1:10:20

from drug discovery to

1:10:23

financial analysis. What

1:10:25

about we had trained an AI to

1:10:29

generate nothing but null sets? Right,

1:10:31

Like, if you're a medical researcher

1:10:34

and you're trying to get funding, what

1:10:36

do you want to do? You want to prove something new?

1:10:38

Right, you don't. You're not going to get funded to prove

1:10:41

you know that aspirin works, but

1:10:43

you want to find something new and you also

1:10:45

want it to be a positive

1:10:48

finding. So what happens is

1:10:51

the incentives preclude

1:10:53

a lot of brilliant scientists from looking

1:10:55

for things that don't work. And

1:10:58

yet, like the dog that didn't

1:11:00

bark in Sherlock Holmes, there's

1:11:02

a lot of really cool

1:11:05

information, useful information

1:11:08

via negativia. And so

1:11:10

one of the things that we want to do is

1:11:13

just have a large language model,

1:11:15

churnout hypothesis

1:11:17

after hypothesis that is

1:11:20

going to generate a null set, publish

1:11:22

them to a database that all scientists

1:11:24

can have access to. Because there's

1:11:26

a wealth of information in

1:11:28

the stuff that doesn't work. Here

1:11:31

are things you don't want to waste your time exactly.

1:11:33

Let's talk a bit about stability

1:11:35

AI. You're on the board of directors,

1:11:38

you're the executive chair, and you started

1:11:40

back in September twenty twenty two. Pretty

1:11:43

good timing. Tell us a little bit about

1:11:45

what stability AI does and

1:11:47

how does this relate to the rest of O'Shaughnessy

1:11:50

ventures. So stability AI

1:11:53

builds foundational open

1:11:55

source models. I had

1:11:57

a very pointed

1:12:00

point of view that with

1:12:02

a technology this powerful, I

1:12:05

did not want it controlled by

1:12:07

a panopticon controlled by

1:12:09

a few, and

1:12:11

I saw that with that kind

1:12:14

of power could come some pretty

1:12:16

negative externalities. And

1:12:19

so Stability AI was

1:12:22

the one that really caught

1:12:24

my eye because they really were the ones

1:12:26

who shot the gun. Back in the

1:12:28

summer of August of twenty

1:12:31

two, they released a stable

1:12:34

diffusion model which generates images,

1:12:36

right, but they did something

1:12:39

that no one had done before. They released

1:12:41

that model with all of its weights.

1:12:44

Now, not to get too geeky

1:12:46

here, but the only way people

1:12:48

can build on that type of model is

1:12:51

to know what the weights are. And

1:12:53

so what they did was

1:12:56

show it all. They released the whole thing,

1:12:58

full open source, driven source,

1:13:01

fully transparent, and bury

1:13:04

the Cambrian like explosion

1:13:07

of creativity that happened

1:13:09

almost immediately really

1:13:12

proved to me. Yeah, back

1:13:14

to cognitive diversity, right, when

1:13:16

you allow all of these clever people

1:13:19

the ability to play with it, to

1:13:21

tinker with it, you get

1:13:23

a much better model. For example, that's

1:13:26

why Linux runs the web.

1:13:28

Linux is open source, right, and

1:13:31

it does so because a bunch of

1:13:33

different people work on different problems.

1:13:36

And so my point of view

1:13:38

was I'm all for the open I use open

1:13:40

AI. I use all of the commercial

1:13:43

Uh.

1:13:44

What are some of the commercial apps?

1:13:46

So perplexity, I love perplexity.

1:13:49

It's on my phone's open

1:13:52

AI. I'm looking at

1:13:54

Claude, the new Claude that you

1:13:56

know.

1:13:56

Perplexity can be driven by either

1:13:59

Claude or there's like

1:14:01

four different engines.

1:14:02

That which really interesting. One of the things

1:14:04

I love about perplexity.

1:14:06

It's just so great and it's cheap

1:14:08

and it's so useful. Exactly every

1:14:10

interview I do, I don't

1:14:13

start with perplexity, I finished

1:14:15

with perplexity and what did I

1:14:17

miss?

1:14:18

What did I get wrong?

1:14:19

Although you still have to be careful because every

1:14:21

now and then, like O'Shaughnessy

1:14:24

is not the rarest of names, you

1:14:26

know. I had Bill Dudley, former

1:14:29

New York Fed chair, and I learned

1:14:32

that he was a running back in the NFL

1:14:35

in the forties, which is kind of interesting

1:14:37

because he wasn't born till the fifties. But

1:14:40

every now and then something will pop up that

1:14:44

is a little off. I love the phrase hallucination

1:14:46

for that. What else do you use besides

1:14:48

perplexity and chatching.

1:14:50

The stability

1:14:52

AIS for his models. Are

1:14:54

they available? Are

1:14:57

they accessible to the lay person? Like, that's

1:14:59

the beauty of they are,

1:15:01

but through different APIs. We

1:15:03

really wanted to focus on being

1:15:05

the builder, right, so we

1:15:08

did not want to try to compete in

1:15:11

the direct to consumer space.

1:15:14

And so what we're focusing on is

1:15:16

multimodals, including generative

1:15:19

models, including specific

1:15:22

models for medical research, obviously,

1:15:25

generative art models, movie

1:15:28

models, et cetera. The

1:15:30

thing I wanted to mention when

1:15:32

you were talking about perplexity in it coming up

1:15:34

with I also passionately

1:15:36

believe that the

1:15:39

models that are going to win, or

1:15:41

not the models, the approach

1:15:44

that's going to win is human

1:15:46

plus machine, so

1:15:49

called Senator model. I

1:15:51

think that you're going to see, you know, we're going

1:15:53

to see a deluge of AI

1:15:56

only generated stuff, content,

1:15:58

movies, et cetera. And to be honest, most

1:16:00

of it's gonna suck. Right. The

1:16:03

magic comes when you add

1:16:05

a human in the loop. The magic

1:16:08

comes by being able

1:16:10

to partner with that and co create

1:16:13

and sometimes iterate on your own stuff.

1:16:16

Like you said, the ideas

1:16:18

that you can generate through putting

1:16:21

your own stuff into the

1:16:23

various models is really cool. We

1:16:25

invest in a

1:16:28

startup called wand and what

1:16:30

they do is it's for graphic artists

1:16:32

and it's an AI, but it has an actual

1:16:35

tool, thus the name wand And.

1:16:38

What the artist is able to do is feed

1:16:40

their own work into

1:16:42

the model and then ask it hey,

1:16:45

spin out variations on it, and

1:16:47

then the artists will look at it and say, wow,

1:16:49

I never thought about it that way. That's really cool.

1:16:52

And then he or she will iterate,

1:16:54

iterate, send it back and this is an

1:16:56

iterative process. But what's

1:16:59

really cool is they end

1:17:01

up in places. We had one artists say

1:17:03

to me, I would never have

1:17:06

thought to do it this way, but

1:17:08

I absolutely love it. It's

1:17:10

his work. He's iterating on his own

1:17:12

work, but he's using a tool,

1:17:15

the WAND, that makes it infinitely

1:17:18

easier for him to get these great

1:17:20

ideas. Huh.

1:17:21

Really interesting. Last

1:17:24

question before we jump to our

1:17:26

favorites, we ask all our guests, which

1:17:28

is I want to bring this back to

1:17:31

stocks. I know thanks

1:17:33

to perplexity as an example, but

1:17:36

there are lots of other tools.

1:17:38

I find myself going to Google

1:17:40

a whole lot less than I used to, and

1:17:43

in fact, the Google

1:17:45

search results like, suddenly

1:17:48

you realize these are crude.

1:17:50

They're much less useful

1:17:52

than they used to be. They're festooned

1:17:55

with a lot of advertising and

1:17:57

a lot of Google in internal

1:18:00

products dominate that first

1:18:02

page.

1:18:04

What else is AI? What other

1:18:07

companies?

1:18:07

What other sectors might

1:18:09

AI affect, either positively or

1:18:11

negatively?

1:18:12

Well, honestly, how much time

1:18:15

do you have it? I think that AI is

1:18:17

going to transform virtually every

1:18:19

industry. And one

1:18:21

of the things that people they

1:18:23

get afraid when they hear that, and my

1:18:26

view is quite different. It's going

1:18:29

to transform for a lot of industries,

1:18:31

the pure drudge work, the

1:18:34

pure copy and paste stuff.

1:18:36

What do you want? Do you like copying and

1:18:38

pasting? I hate it? And so

1:18:41

it also is going to be able to

1:18:43

create jobs that we can't even

1:18:45

conceive of right now. Right

1:18:48

like two years ago, would you have known

1:18:50

what a prompt engineer was? No,

1:18:52

I certainly wouldn't have, right, and

1:18:54

yet there's a lot of people doing really

1:18:57

well pursuing that is a

1:18:59

career. And so I

1:19:01

think that entertainment is going

1:19:03

to be materially affected

1:19:05

media, materially affected search,

1:19:08

as you well point out, like you

1:19:11

can do a customized search just

1:19:13

for Barry and it, you know, depending

1:19:15

on how much information you want to give that

1:19:17

AI about yourself. You're going

1:19:20

to be at a place where you're going to be able to say, hey,

1:19:22

what was that place that I had lunch with Jim

1:19:24

last time? We both really really liked it. I'd

1:19:27

like to go there again and guess what, it's

1:19:29

going to give you the name and address of

1:19:31

that restaurant. Because

1:19:34

it has access to your calendar,

1:19:36

It has access to all of that type of

1:19:38

stuff.

1:19:39

It feels like I'll

1:19:41

never forget. I tweeted out this

1:19:43

really interesting Roman

1:19:46

Pizza place, and Roman Pizza is

1:19:48

a different type of and

1:19:50

I just you know, I used Sarah

1:19:53

to speak into the iPhone. Hey

1:19:55

we had a fend. This is really different

1:19:57

than your usual pizza. And

1:19:59

some how it showed

1:20:01

up on Twitter as woman Pizza

1:20:04

and like, wait, I'm standing right in

1:20:06

front of the place. Any correlation

1:20:09

between my geotag

1:20:11

and business I'm in front of it just

1:20:13

felt like technology should

1:20:16

have figured that out. Yeah, what you're saying

1:20:18

is that sort of access

1:20:21

to your contacts, access to your where

1:20:24

you are, access to your calendar, once

1:20:27

there's an intelligent agent running all

1:20:29

of that. A lot of these sort

1:20:31

of silly why can't Siri

1:20:33

talk with this person, Why can't

1:20:35

Alexa? It just seems like the

1:20:39

pre AI era was

1:20:41

filled with a lot.

1:20:42

Of pretty dombai. It's

1:20:44

starting to get smarter. Yeah,

1:20:46

And that's the thing going back to your Right

1:20:49

Brothers example. You know when

1:20:52

the Right Brothers did that very

1:20:54

brief flight, it was only a

1:20:56

matter of eight yeah, I think it was twelve

1:20:58

seconds, and I think they went like one hundred

1:21:00

and odd feet. Like

1:21:03

you could see why a lot of people would going, Eh,

1:21:06

they didn't accomplish much, but I

1:21:08

like the person who was watching and

1:21:10

said, this changes everything,

1:21:13

And so that's kind

1:21:16

of how I see AI. Of course,

1:21:18

we're in the early innings of this, and

1:21:20

of course it's going to this is

1:21:22

the worst you're ever going to see it. It's going to

1:21:24

improve, improve, improve. But

1:21:26

the other thing I want to really underline here

1:21:29

is it's the quality of the data

1:21:31

that you train your AI on that

1:21:34

determines its value to you. And

1:21:37

one of the big reasons I'm a huge believer

1:21:39

in private AIS is

1:21:41

that you will feel if you

1:21:43

know that no one else can have access to

1:21:46

that right, you're going to give it a

1:21:48

lot more access to things

1:21:50

than you might otherwise. That's

1:21:52

happening right now. Wow. And

1:21:55

so one of the things,

1:21:57

you know, a lot of people see this as, you know,

1:21:59

like the great model

1:22:01

that will figure everything out. I

1:22:04

don't see it that way at all. I see it

1:22:06

as a lot of smaller but

1:22:08

incredibly useful AI

1:22:11

agents doing specific

1:22:13

things for each of us. Again, Canvas

1:22:16

fits in beautifully. Here. We are now

1:22:18

in an era of mass customization.

1:22:22

We are in an era where it's going

1:22:24

to be able to design it just

1:22:27

for you and your likes and dislikes.

1:22:30

That's really profound when

1:22:32

you think about.

1:22:33

It, really fascinating. So let's

1:22:35

jump to our speed round, our favorite

1:22:37

questions. We ask all of our guests, starting

1:22:40

with what has been keeping you entertained

1:22:42

these days? What are you either watching or

1:22:45

listening to?

1:22:46

So we rewatched

1:22:48

True Detective, my wife

1:22:50

and I I would highly recommend

1:22:52

rewatching the first season

1:22:55

of that. It was brilliant. It

1:22:58

led us into a reward of the

1:23:00

entire series. And now

1:23:02

we're on number three, the second one.

1:23:05

Here's one of the funny things, like in memory,

1:23:07

I kind of my wife and I were both kind of like, yeah,

1:23:09

that second one wasn't very good. It was

1:23:12

good, and so we're

1:23:14

doing that Masters of

1:23:16

the air that's on. Yeah,

1:23:19

great, really loving that. I loved

1:23:21

Band of Brothers, so we're

1:23:23

both really really liking that.

1:23:26

And then we are also

1:23:29

watching a series,

1:23:32

or I guess I should say rewatching

1:23:34

a series which kind

1:23:37

of kicked off the idea of

1:23:39

the Golden Age of television. It was one of the

1:23:41

earlier ones. I'm not the Sopranos, but

1:23:44

The Wire.

1:23:45

Now, I recall The Wire being

1:23:47

very brutal and difficult.

1:23:49

It watch it is, But

1:23:52

what's so cool if you choose to watch

1:23:54

it again, you see that

1:23:57

the reason it kicked off that kind

1:23:59

of tea was because it

1:24:01

was brutally honest about

1:24:04

things. It wasn't trying to lie to you about

1:24:06

anything, and the characters

1:24:09

are incredibly complex,

1:24:11

even though even the evil

1:24:13

guys are incredibly complex,

1:24:17

And so watching it now

1:24:19

from the vantage point of like twenty

1:24:22

years or more, it's

1:24:25

really amazing.

1:24:27

Really interesting. Tell us about your

1:24:29

mentors who helped to shape

1:24:31

your career.

1:24:32

Primarily, I

1:24:34

would list my grandfather. I

1:24:36

was lucky enough, he was very successful

1:24:40

in the oil industry, and I

1:24:42

am the youngest of the third generation at

1:24:44

least the males. I have one younger

1:24:47

female cousin and she's just a few

1:24:49

months behind me. But I

1:24:51

lived in the same town my grandfather did,

1:24:54

and after my grandmother died, he

1:24:56

would come to our house twice a week for

1:24:58

dinner and literally

1:25:00

I would literally sit at his knee.

1:25:03

And he was a wonderful storyteller.

1:25:06

He was a wonderful teacher,

1:25:10

and he taught me this idea of

1:25:12

premeditating that I have

1:25:15

written a lot about and use all

1:25:17

the time. Another was

1:25:19

a wonderful man, not related to

1:25:21

me at all, by the name of Jim Myers.

1:25:24

Any entrepreneur, you hit some rough spots,

1:25:26

sure, and I had hit a really rough

1:25:28

spot and was basically broke and

1:25:31

trying to pay for a house

1:25:34

because we'd moved to Greenwich and

1:25:37

keep my business afloat and all of that,

1:25:40

and the banks are like, dude, like, you're

1:25:42

an entrepreneur. This is back in the nineties.

1:25:45

Yeah, sorry, we're not going to give you a

1:25:47

mortgage. He stepped in and he's

1:25:49

like, Jim, I believe you're going to be tremendously

1:25:52

successful and gave

1:25:55

me one on handshake, which I

1:25:57

was able to repay rapidly.

1:25:59

But more than that, just being

1:26:02

a super high quality man, he

1:26:05

taught me more about real business

1:26:08

than any textbook because

1:26:11

I was young, right, and I

1:26:13

started with him when I was in

1:26:15

my early twenties and

1:26:18

just an amazing man.

1:26:20

And then finally the other

1:26:23

mentors that I would say are like

1:26:26

the greatest minds of history. I love to read.

1:26:28

I particularly like to read

1:26:31

biographies about people I admire.

1:26:33

And you know what, Barry life was

1:26:35

not easy. We remember them now, right,

1:26:38

like, oh, they were this huge success.

1:26:41

When you read their biographies, you see

1:26:43

they went through a lot of muck

1:26:46

to get where they got and so kind

1:26:48

of universal lessons.

1:26:49

So perfect segue. Let's talk

1:26:51

about some of your favorite books and

1:26:54

what are you reading right now?

1:26:56

So right now I am reading

1:26:58

about four different books

1:27:01

and I which.

1:27:03

By the way, is an occupational hazard

1:27:05

for folks like us, because there's

1:27:07

always a book I'm prepping for a podcast,

1:27:09

there's a book I'm reading for work,

1:27:12

and then there's a book I'm just like, I'm going to relax

1:27:14

and read this.

1:27:15

Yeah, so for fun. Right

1:27:17

now I'm reading Burned book by

1:27:20

Karra What's her last Swisher?

1:27:23

Which I find very interesting. She's

1:27:25

always fascinating, yeah, kind of an inside

1:27:28

look. My only comment

1:27:30

there was she might be a little

1:27:32

guilty of the things that she accuses

1:27:34

the people she doesn't like are. But

1:27:37

other than that, it's a fun and kind of a rollicking

1:27:40

read. I am reading

1:27:43

or rereading several of the

1:27:45

books from Will Durant's Story

1:27:47

of Civilization, which

1:27:49

I read as a kid or a young man,

1:27:52

loved and thought,

1:27:54

you know what, we moved recently,

1:27:56

and so I was going through all my books and

1:27:58

I found that I'm like, I should reread

1:28:01

some of these just to see if it still stands

1:28:03

up. Barry, It's still stuff,

1:28:06

really really stands up. And

1:28:08

then just finished an

1:28:11

additional biography about

1:28:13

Teddy Roosevelt Teddy Rex

1:28:16

And then finally I'm reading

1:28:18

a lot about AI

1:28:20

and scientific development. The

1:28:22

book I'd recommend there is written

1:28:25

by a pair of authors, one

1:28:27

an AI expert, the other a great

1:28:29

storyteller, and it's called AI

1:28:32

twenty forty one, Ten

1:28:35

Visions of Our AI Future.

1:28:37

Huh, highly recommend.

1:28:38

I'm going to check that out. We've been talking

1:28:40

about the Ripe Brothers. Did you ever read the David

1:28:43

McCullough biography of the Ripe Brothers?

1:28:44

I did.

1:28:45

Fascinating, right, really really really fascinating.

1:28:48

And our final two questions, what

1:28:50

sort of advice would you give to a recent

1:28:53

college graduate interested

1:28:55

in a career in either quantitative

1:28:57

analysis, finance as

1:29:00

management.

1:29:00

What's your advice for them? My advice

1:29:03

is to focus

1:29:05

on the parts of learning

1:29:08

that might not be

1:29:11

included in a business or finance

1:29:13

degree. My line is that

1:29:15

markets change second by second,

1:29:18

but human nature barely

1:29:20

budgees millennia by millennia.

1:29:23

Arbitraging. Human nature is the

1:29:25

last sustainable edge

1:29:27

in investing. And so if

1:29:29

you read about evolutionary psychology

1:29:32

and biology, regular psychology

1:29:34

and biology and history, what

1:29:36

you're going to see is no, history

1:29:39

doesn't repeat, but it rhymes,

1:29:41

and you can see in you know, all you

1:29:44

got to do is go read a book about the south

1:29:46

Sea Scandal where Isaac Newton,

1:29:48

one of the most brilliant guys of his era, lost

1:29:50

a fortune, causing him to lament

1:29:53

that he could measure the motion of heavenly bodies

1:29:55

but not the madness of men. And guess

1:29:58

what we're not changing. So

1:30:00

you can read it in a market related

1:30:03

way, or just understand

1:30:05

human nature better, you're

1:30:07

going to be miles ahead of

1:30:09

the people who are just studying

1:30:11

math or finance or economics.

1:30:15

Really interesting, and our final question,

1:30:18

what do you know about the world of investing today

1:30:21

you wish you knew forty or so years

1:30:23

ago when you were first getting started.

1:30:26

I think maybe just the advice that I just gave,

1:30:28

I wish that I would have known forty

1:30:30

years ago that markets

1:30:33

are market prices are

1:30:35

determined by human beings,

1:30:39

and if you are ignorant

1:30:41

of all of the ways that

1:30:43

we let things affect us,

1:30:46

from whether we're hungry or not, or

1:30:48

whether we're angry, or whether we're

1:30:50

calm, I would have understood

1:30:54

that it was not just numbers on a

1:30:56

page, that markets are

1:30:59

full blooded, almost

1:31:01

human like things because they're

1:31:03

driven and created by humans.

1:31:06

If I could have told Jim

1:31:08

of age twenty three that

1:31:11

it would have hastened but also improved

1:31:15

the pretty circuitous path that

1:31:17

I took to becoming a quat

1:31:20

really interesting.

1:31:22

Thank you, Jim for being so generous with

1:31:24

your time. We have been speaking with

1:31:26

Jim O'Shaughnessy, founder

1:31:28

of OSAM Asset Management

1:31:30

and currently CEO and founder of

1:31:33

O'Shaughnessy Ventures and host

1:31:35

of the Infinite Loops podcast. If

1:31:38

you enjoy this conversation, well.

1:31:41

Be sure and check out any of.

1:31:42

The five hundred previous discussions

1:31:45

we've had over the past ten years.

1:31:47

You can find those at.

1:31:49

iTunes, Spotify, YouTube, wherever

1:31:52

you find your favorite podcast.

1:31:55

Be sure and sign up for my new podcast

1:31:57

At the Money, where we speak with

1:31:59

an X and give you information

1:32:02

on a topic relative to your money

1:32:05

in short eight to twelve minute

1:32:07

batches. You can find those in

1:32:10

the Masters in Business podcast

1:32:12

feed, or wherever you get your

1:32:14

favorite podcasts. I

1:32:16

would be remiss if I did not thank the Cracked

1:32:18

team that helps us put these conversations

1:32:20

together each week. My audio

1:32:23

engineer is Sebastian Escobar, My

1:32:25

producer is Anna Luke. Sean Russo

1:32:28

is my head of research. Attika Valbrun

1:32:31

is my project manager. Sage

1:32:33

Bauman is the head of podcasts.

1:32:36

I'm Barry Riddolts. You've been listening

1:32:38

to Masters in Business on Bloomberg

1:32:40

Radio.