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Yes.

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Very

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good. We're the good you're the good guys.

1:02

Exactly. There you go.

1:07

Welcome

1:10

to Your

1:12

place in the universe where science

1:14

and pop culture collide. Dark

1:18

Talk begins right now.

1:21

This is StarTalk Edition.

1:25

Today, we're gonna talk about

1:27

counting cards, stuff

1:31

that the casinos hate about

1:33

you if you have the talent. to pull

1:35

that off. I got with me, as always,

1:37

my cohost, Chuck Knight, Chuck a baby.

1:39

Hey, what's happening? Alright. Professional Standard

1:41

comedian and actor and game show

1:43

hosts. and -- Mhmm. -- brain games

1:45

on National Geographic. Very cool. Yes.

1:48

And and

1:51

the one person who has actual professional

1:53

athletic street cred among us.

1:56

Gary O'Reilly. Gary, always good to have you here,

1:58

man. Pleasure's mind. Thank you, Neil. Thanks

2:00

for making us legit. So,

2:02

Gary, what I mean, Counting.

2:04

We've all heard about it. We've seen movies

2:07

about it. we wish we had

2:09

that ability. And, again, we don't

2:11

know if you can be trained for it. And

2:13

so you decided to create a whole show on

2:15

it. So what do you have in store for us today?

2:18

A very, hopefully, a story that

2:20

will not just be fascinated but

2:23

so interesting to a number of people

2:25

We are gonna meet someone with an

2:28

interesting and amazing life story. It

2:30

basically starts as a pre team refugee

2:33

who became a professional blackjack player.

2:35

on the infamous MIT blackjack

2:38

team. So Neil, please meet our guest.

2:40

Sammy, welcome to StarTalk. Thank

2:42

you. Excellent.

2:44

So there's

2:47

been a film made and books based

2:49

on you and documentaries about

2:51

your life How do you go from being

2:53

a computer science early early

2:55

computer science geek to

2:58

card to cards or black jacks? Like,

3:00

what what is the how do you go from a to

3:02

b in that conversation?

3:04

Oh, well, one day

3:06

in nineteen ninety one.

3:08

I was walking down this

3:10

in a corridor at MIT. And

3:13

I saw a poster on the wall, and a poster

3:15

said, make ten thousand

3:17

dollars over the summer, play

3:19

black second in Vegas casinos. And,

3:21

you know, that sounded pretty good.

3:24

So

3:24

I love it. Yeah. Well,

3:29

just to be clear, MIT

3:32

you could probably get away with that. But

3:34

but there could be some places where that same

3:36

sign would say, this summer, lose

3:39

ten thousand dollars. Casino.

3:42

Correct. MIT is probably one of

3:44

the few places where the poster would be believe

3:47

For believable poster. Alright.

3:49

west. Right? Mhmm. And, you know, the truth

3:51

is, I I I'm not sure I would have believed

3:53

it except I

3:55

happen to play a lot of pac man when I

3:57

was a kid in Texas.

3:59

And,

3:59

you know, the video game. And so

4:02

one day, I went to the library and left night,

4:04

needed to make it in my quarter. Last a very long time

4:06

because I I couldn't get a second quarter. You know, there's

4:08

just one quarter that we have. And

4:10

so I went to Calabrio, and I found. I looked

4:12

at books on Pacman, and it was only one book,

4:15

but that is Guy and Kenleston. I

4:18

read the book, and I was able to play Pacman

4:20

a long, long time until I don't feel

4:22

like playing in it anymore. But then I

4:24

went back and and noticed that he had a

4:26

bunch of other books and all these other books were about

4:28

a blackjack. So

4:29

he was this one of these guys

4:31

that was able to beat, I guess, the casinos

4:33

in Atlantic City when they first opened up

4:35

about a decade before before I played.

4:37

Well, so all this started because you tried

4:39

to milk a quarter for as much

4:42

Pac man time as you could get

4:44

from it. That's

4:45

right. In the very beginning. See, but this is

4:47

this

4:48

this is so very important right now

4:50

for all of you parents out there to

4:52

to take note. Okay?

4:55

Because we all think that video

4:58

games are deleterious to our children's

5:00

mental health and to their

5:02

academics. And

5:05

we say, oh, you're gonna ruin yourself

5:07

by playing these video games. but

5:10

it's really not about the video

5:12

games. It's about the child,

5:14

the kid. So if your kid

5:16

ever says, I'm

5:17

going to the library to

5:20

get some books on video

5:22

games. They're

5:23

gonna be okay. It's I

5:25

mean, that kid's gonna be

5:27

all wrapped up. As

5:30

long as they're telling the truth, because it's the

5:32

worst time they got somewhere else.

5:34

Right. Good about it. Alright. So,

5:36

Sammy, on okay.

5:37

So, you go from Pac Man to

5:40

Blackjack. It's a pretty big leap. So,

5:42

you must have had some kind of

5:44

superpower, somewhere up to sleeve.

5:46

So so what is it? Is it total

5:48

recall,

5:49

concentration? Or do you just have

5:51

to be good with numbers. What's the deal there

5:53

for a blackjack player? None none of the

5:55

above. I mean -- Alright. -- the

5:57

super power, I think. was

5:59

twofold. But one was I guess that

6:02

I I had this notice that I might

6:04

go to library and read something that's already

6:06

read like that. That's the privilege background of it.

6:08

The example suddenly used. I think that

6:10

I had. Right? I had educated parents.

6:13

They had this superpower. Right? I think maybe

6:15

it was just not being able

6:17

to get that second quarter, like, having the motivation

6:20

to read the book, and then later having the

6:22

motivation incentivize. more of my ass.

6:24

Like, when I saw when I meant I meant

6:26

to back team having written books about it. You

6:28

know, I felt like it was a very lucky opportunity

6:30

that actually didn't meet these people, you know.

6:32

And I wanted to make money. I wanted to

6:34

make a lot more than that thousand dollars needed

6:36

to. It was a real, you know, goal for me at the

6:38

time. It's not necessarily the

6:40

best goal for the young man who happened. To me at the

6:42

time, you know, that was the goal. We were poor. Right?

6:44

Like, we lived in the process. it suck.

6:46

And, you know,

6:49

that was the main that was the main differentiator.

6:51

Just the the willingness to work my ass off

6:53

and, like, do the stuff over and over and over and

6:55

over again. There was no rocket science.

6:57

There was no math genius. You know? I mean,

6:59

coming up with systems properly required a

7:01

lot of knowledge and intelligence, but I actually

7:04

didn't come up with them. You know, I I was

7:06

taught how to do it, and then I ended up

7:08

Counting their own group and teaching a bunch of other people how

7:10

to do it. But it was all about,

7:12

like, diligence hard work, record

7:15

keeping, for sure. and

7:17

just repeating things over and over and over again

7:19

until they got it right. So

7:21

there's another thing for you parents out

7:23

there to understand. Okay?

7:26

is if you are doing

7:28

okay, maybe you are

7:30

middle class or middle middle

7:32

class, upper middle class.

7:34

Okay.

7:35

Let your children be poor. Don't

7:38

give your children any money. That

7:41

will motivate them. And

7:43

and, you know, my father used to say

7:45

all the time, I'd be like, dad, can I get to sneakers?

7:47

He'd be like, go ahead. I'd be like, okay.

7:49

Can I have the money? And be like, You you

7:51

wanna speak, because you go get them. And then I

7:53

would say and I would say, but

7:55

dad, we have money. And then

7:57

he would say, no son.

7:59

I have money. I I earn the money.

8:02

You are broke. You

8:05

are poor. You don't have

8:07

crap. See. So You

8:09

there you go. And that's

8:11

how Chuck is doing the project. It's

8:14

hard. Right? It's really hard to pull off. I mean, I

8:16

have six kids. Right? And I mean, I have a plan

8:18

now. Right? So, like, it's easy to set

8:20

them down. It sounds like they did a pretty good

8:22

job. I didn't know that. But it's not

8:24

easy. Right? It's not easy. No.

8:26

It's not. Alright. Let's let's jump from

8:29

being really incentivized to

8:31

make this thing successful. You then

8:33

have to you must have to acquire a

8:35

certain skill set.

8:37

Are we are we into game series

8:40

here? Do you start to deploy those sort

8:42

of things? Or is it something different again?

8:44

probabilities as well. It's -- Yeah. -- prior

8:46

charges out of eight of those.

8:47

There's none of those things. You

8:49

know? Yeah.

8:52

Yeah. It has to be something I

8:54

have to just give you the answer to me and make more

8:56

money. No. I just did it. I

8:58

don't know. Like twenty years ago, these

9:00

books and stories about out, and I did a

9:02

lot of interviews. And, you know, people always wanted to

9:04

hear about the messaging and stuff, and I kinda played

9:06

it along. But at this point, you know, I'll

9:08

I'll tell it to you, like, who it really was.

9:10

Okay? we

9:10

we couldn't train everybody to do it. It was

9:13

the same as, like, the math skills

9:15

required with our, you know, second grade

9:17

level, like, literally counting, like, 3455432

9:21

you know, just keep in track of,

9:23

like, one number and not having one subtracted one

9:25

and and just not getting distracted. Now

9:27

that's the level of skills that were required. The

9:29

rest of it was just memorizing a system

9:31

that other people already came up with. It

9:33

it was pretty early in the entry. it

9:35

it required discipline. It

9:37

required not drinking while

9:39

you're doing stuff like that. Mhmm. It didn't

9:41

require any great theoretical

9:44

mathematical thinking that lives in it.

9:45

So, you

9:46

know, it it gone short. I was just

9:48

gonna say it's so

9:51

I'm really attached to this

9:53

story because my father was a

9:55

gambler. And I don't mean, like,

9:57

he gambled. I mean, he was a

9:59

gambler and took

10:00

that sounds like a blue song ready to be

10:02

written. My father

10:03

was a gambling man. Yeah. Yeah.

10:05

We we went to hold him and went to

10:08

walk away. Yeah. Darno.

10:10

Darno. Yeah. My dad, it was a

10:12

gamble. Darno. Darno. Darno. Darno.

10:16

That's didn't always win. We

10:20

got a number one on our hands. Oh, yeah.

10:22

But the funny thing is that

10:25

he did count cards. and

10:27

I don't I don't know the system that he used,

10:29

but it was and

10:31

and maybe Simeon could tell me what it is.

10:34

But it basically was

10:36

there's Counting. It's plus one,

10:39

zero, or minus one. That's all he

10:41

did. And you

10:43

bet according to where the count is, where

10:45

the number of deaths in the cards that are

10:47

in the debt. And you have just

10:49

have to have the discipline to

10:51

bet wisely during the entire

10:54

shoe and, you know,

10:56

to know when to bet when the count is

10:58

up down or zero. I mean, that's I

11:00

never did it. So that's what I understood him to

11:02

be doing. But there were times I would see

11:04

him make a crap ton

11:06

of money And then there

11:07

were times that, you know, that

11:09

led to him being divorced from

11:11

my mother. So as

11:15

dog left him, I don't know.

11:18

It was

11:19

I'm

11:21

not sure those two words related and all that,

11:24

you know. is all the money

11:26

can can lead to gain the horse, but making a

11:28

whole bunch of money could also lead to gain the

11:30

horse. Yeah. Very good point. You're absolutely

11:32

right. You're right about that. You're right about that.

11:34

Okay. So if you didn't do anything, we're

11:36

asking you, what did what did you

11:38

do? That's what

11:39

I did. I did the same thing about it.

11:41

I think the difference was that we had a

11:43

group of people who were very diligent about

11:45

doing it. I keep both people

11:47

and learn sort of think they can

11:49

kind of do it. Right? We test the people all

11:51

the time. We check their

11:53

skills. Not only once,

11:55

but before each trip, it

11:57

you have a very small event. Right?

12:00

And it takes a long time to get to the

12:02

long term. And in any short period of

12:04

time, you could win a little booze. It's kinda random

12:06

to rapid. you have, like, this tiny

12:08

little one percentage or half a percentage.

12:10

And after a month, so in the years of

12:12

playing, you're gonna know it's gonna be And

12:14

-- Yeah. -- in the real world, people get

12:16

distracted. People get tired of their meds.

12:18

People get emotional about other wins

12:20

or losses. and they

12:22

deviate from the system of value and realizing

12:24

it. So that's a thing where the

12:26

distinguished us is real professionals.

12:29

from many thousands of other people who

12:31

kind of live on parts. Howard Bauchner: And

12:32

it's funny because the the the casino

12:35

does that but they don't need the

12:37

discipline because the house

12:39

rules don't change no matter what.

12:41

But the house rules are set up

12:43

so that this slight percentage

12:45

of of advantage that they

12:47

have means that over the

12:49

long term, they're

12:51

always going to win over the

12:53

long term as a casino. But

12:55

just maybe be clear. What

12:58

what he said was, Chuck, that

13:00

there are

13:01

fluctuations from hand

13:03

to hand -- Yeah. -- even from day

13:05

to day that can go up or

13:07

down. But if you stay diligent with

13:09

the system -- Right. -- the small percent

13:12

that you gain against the

13:15

average will accumulate -- Right.

13:17

--

13:17

and then you can basically bank those

13:19

winnings reliably. And that's that's the

13:21

deal. And they call that having long money. You

13:23

gotta have long money. Long

13:25

money. Okay. So to send me an in in the

13:27

intro, I talked about high low system

13:29

and card steering. So

13:31

break down card steering for me

13:33

because I think we've realized now that if

13:35

you're gonna play backjack, you have got to have

13:37

ninja like concentration and

13:39

as they say always watching.

13:41

So can you just even to just for me and

13:43

and the rest of our audience that don't know cloud

13:45

steering, please unlock that for

13:47

us. Gary will get to that question.

13:49

after the break because all the time I

13:52

know. Sorry. See what I did there. See what I did there. See what I

13:54

did there. Yeah. Keep it hanging.

13:56

So we'll be right back with our

13:58

special guests. Card

13:59

Counting Cimeon

14:03

Ducasse, unstart StarTalk

14:05

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16:02

star talk term supply.

16:09

We're back. We're semi

16:10

unique gosh. Mulegast

16:13

Tyson here, Sports Edition.

16:15

Simeon Dukacs is an

16:18

MIT computer scientist

16:20

entrepreneur. and who made

16:22

his mark counting cards

16:24

in blackjack, and we're trying to get to

16:26

the bottom of this, where how to happen,

16:28

where where it came from, what special

16:30

talents it was. And and by

16:32

the way, it's not illegal to count cards.

16:34

It's just casinos don't like it when you

16:36

do that. Mhmm. Because it

16:38

tips things in your favorite instead

16:40

of their favorite. Which

16:42

brings us to our first question, how did

16:44

you get out of Vegas with both of your

16:46

knees? Still still

16:49

in your legs. Okay. So

16:51

so, Simeon, I need you to define for

16:53

a certain terms. We went into

16:55

We went into the break with Gary asking you

16:57

about what was it Gary? So

16:59

there's a couple of techniques

17:01

and I'm assuming

17:03

I'm I'm sure we'll we'll be able to, as I

17:05

said, unlock those for us. One in particular, which

17:07

sounds very interesting, card steering.

17:10

Card steering. So if you want It's not

17:12

only that. I wanna know about quantified thinking.

17:14

There's all these terms. Are we gonna get there?

17:16

Yeah. Okay. Good. It says card

17:18

steering. Mhmm. So Sami, what do you got for us

17:20

there? So we have a

17:22

number of,

17:22

let's say, more men and a small complicated

17:25

techniques that at least in theory could

17:27

generate a bigger men

17:29

than the one percent or so advantage you can

17:31

get with card currently. And so

17:34

a card steering was a few

17:36

variations of it. it's

17:38

something to involve tracking sequences of

17:41

cards through the shackle, roughly

17:43

being able to kind of see

17:45

that there might be four or five cards in between each

17:47

and the sequence after shuffling and kind

17:49

of predicted when the

17:50

last one is gonna come.

17:52

it wasn't the lowest possible.

17:55

Right? Sometimes we could see the

17:57

last card, I wanna show

17:59

after all the shuffling is

17:59

done. And then when you cut

18:02

the cards, we we would learn to

18:04

cut a precise number of cars, like

18:06

exactly fifty two cars, let's say, exactly that

18:08

from the back of the shoe, and then we will know that

18:10

the back car would come out number fifty two. Right? And that's

18:12

exactly how when you cut, which you can

18:14

get good at by just practicing four hours, and

18:16

they're cutting executives into

18:18

cars. Yeah. I mean, don't people get

18:20

shot for this stuff. So

18:23

maybe I've seen

18:25

enough movies. where, like, a a

18:27

Smith and Wesson beats four races.

18:29

Okay? Yes. I we Smith and Wesson, not

18:31

the beats four races, but not so much

18:33

in in the corporate run public company

18:35

run Vegas of the nineteenth. In

18:37

the Vegas of the sixties or seventies,

18:39

yeah, it wouldn't wouldn't have worked

18:41

very well. And we did play a

18:43

lot internationally and there were some

18:45

places who probably shouldn't have

18:47

played. But you

18:49

think I mean, the stuff we didn't we

18:53

didn't, you know, violate any rules to the

18:55

game. We didn't cheat. And that was important because

18:57

we did get kick out all the time, call

18:59

all the time. So have it done anything illegal that

19:02

would prosecute us, certainly. So

19:04

we are careful not not to break the

19:06

law. You were running computer

19:08

simulations. you were

19:10

replicating live casino

19:12

environment. So as your Blackjack

19:15

players would be comfortable

19:17

and not dazzled by

19:19

a casino environment. I mean, this

19:22

basically is almost when

19:24

you say you analyzed results, you looked

19:26

at things, and you weren't just looking

19:28

at successes, you were looking at failures.

19:31

Now then if you

19:33

apply quantified thinking, how

19:35

do you approach this

19:37

group of data? and

19:39

then bring it forward successfully for you. Yeah. That's

19:41

a really good question. I'm getting a certain application.

19:43

That's some of the work that actually distinguished

19:47

us from some of the other groups that weren't

19:49

as successful. Right? It wasn't those

19:51

cards to Europe and in mass techniques because those

19:53

ended up not working most of the time,

19:55

honestly. They were they only worked in

19:57

certain situations. At the end of the day, we've

19:59

made the money, do it in the well known card

20:01

analytic techniques that have already been written

20:03

about, but we did it more

20:05

deliberately. And I think

20:08

writing simulations to to

20:10

predict, you know, how much it should have

20:12

won in order to kind of

20:14

track what really happened. And

20:16

then running these examinations, basically, where

20:18

in in our own sort of classroom

20:21

environment, we would try to distract

20:24

people. Right? We would have one person test, another

20:26

person, but someone else would be distracting. But I was, like,

20:28

trying to talk to them, trying to, you know, offer them

20:30

drinks to whatever happens in the real casino.

20:33

And we would track how many

20:35

mistakes people would make. People would always

20:37

invariably make some mistakes in Like,

20:39

they just weren't perfect. They wouldn't see every

20:41

single car. Right? And so we

20:44

would get a better understanding model

20:46

of of the real life performance

20:48

of of the card Counting the

20:50

casino. rather than, let's say, a radical

20:52

performance that, you know, the books that you're

20:54

gonna have. And so based on that, the

20:56

the right amount to bet and the right

20:58

way to play. I mean And that made all

21:00

the difference. Right? It's using that

21:03

data and being realistic about

21:05

our own capabilities. But

21:08

from one decade to another, computers gain

21:11

speed

21:11

and precision and accuracy

21:13

and algorithms get better. So

21:15

did any of over the decades from the nineties

21:17

to today, for example? Probably, to

21:20

some

21:20

extent, but I I would say

21:22

you know,

21:23

we've never used computers in the

21:25

casino or anything like that. Like, that will that

21:27

probably would be shooting. We we

21:29

didn't work on any of those kinds of things. we

21:31

merely use them to better analyze

21:33

and better understand how we should apply.

21:36

And I think, yeah, I'm sure there won't be good,

21:39

but for the most part,

21:41

I think the technology was

21:43

sufficient at the time

21:45

to be able to improve discipline, right, and

21:47

to be able to model how

21:49

we will perform realistically in a casino environment. And

21:52

and design

21:53

the tests that we have to apply

21:55

to each other to make sure that

21:57

we were good enough because you really have

21:59

to be very

21:59

precise. You couldn't make way too many errors.

22:02

Right? Right. So, Samiya, if if we look

22:04

at if we look at Sport, their

22:06

their quantified thinking, their data analytics

22:08

They analyze different things now,

22:10

different metrics than the ones they did ten,

22:13

twenty years ago. What

22:14

were what would you be looking at now

22:16

that you weren't looking at back then in

22:18

the nineties that would change the game

22:21

even more? Or have

22:23

you just washed it away and not

22:25

considered it? You know,

22:26

I I don't understand. I haven't given much sauce. Did

22:28

did it give a black thing? Well, I mean, the

22:30

one thing that's changed dramatically

22:33

in

22:33

many casinos. And

22:36

I'm not particularly sure if it's because

22:38

of you know,

22:40

people like Simeon, but I is

22:42

the auto shuffler combined

22:48

with eight decks and the

22:50

auto shuffler shuffles after,

22:54

like, so many hands, not

22:56

even It doesn't go an entire

22:58

shoe. It's like, you you pick

23:00

it up, you put it back in, it shuffled, like,

23:03

after three hands. or sometimes

23:05

you'll see that they do it. It

23:07

looks random. Like, I is it I'm not

23:09

even sure if the dealer is just

23:12

instructed to do it or if they're just like, yeah, just do

23:14

it, just do it, you know. But that's

23:16

that's gotta have changed things tremendously

23:18

for card counters. Well, that's right. because Simeon, I you

23:20

know, when you're just playing at home, it's one deck of

23:23

cards. And so

23:24

you can get a sense whether the

23:26

remaining cards to be dealt or

23:28

or royal heavy, or, you know, low

23:30

card heavy, you get a sense of that. But with eight

23:33

decks, how can you possibly know

23:35

that? You get a

23:35

sense of the same exact way.

23:37

you know, it's just that with one

23:39

deck of cards, let's

23:40

say, three little three little cards

23:43

came out and, you know, there are three

23:45

extra big cards left under that. Okay? So there's a

23:47

index. You're gonna wanna see a

23:49

lot more. You're gonna

23:49

wanna see, like, twenty or twenty five. Little extra

23:52

cars come out. the way it'll still

23:53

happen the same fraction of the time. Right? Like, you just

23:56

have to play long enough until that

23:58

happens. And then you bet a lot. So it's it's

23:59

not different. The numbers are just a

24:02

little larger. but you

24:03

do have to reset every time they reset your

24:06

your

24:06

brain computer every time they

24:08

shuffle. Yeah. Yeah. That's right. So

24:10

so to answer your other question, yes.

24:13

if they start traveling all the time, you you can these

24:15

techniques don't work. And -- Right. --

24:17

but, however, this isn't like a modern

24:20

pyramid. the casinos knew about Counting

24:23

since the sixties.

24:24

Right. They occasionally

24:26

make the rules. And there's a bunch

24:28

of other rule variations besides softening that they

24:30

could do to make card counting that work very

24:32

well. But the thing

24:34

is millions of people know

24:36

about card counting. it's it's marketing for the game,

24:38

like Blackjack, bidding for money than any

24:41

other card game. You know, really anything

24:43

else besides Slago change. precisely

24:45

because people know it can be beat. And so -- Right.

24:47

-- I think there's competition that I think

24:49

has seen us. They players

24:52

player players

24:53

who think they're doing well Counting, you know,

24:55

they want them as possible to rules. They don't want the

24:57

the house a little time. In ninety nine percent

25:00

of a rules, money. And so the casinos eventually

25:02

gravitate back to making the game

25:04

be beatable at the edge. As long as

25:06

it's only beatable by some long number of people,

25:08

it kinda works Right.

25:10

And as long as oh, bet. Those that's

25:12

rare people who would actually beat the

25:14

game don't start betting table last time. They

25:16

they you know, which is what we did. Right. That's

25:19

the problem. But that's brilliant.

25:21

What you're saying is that

25:22

they use their

25:25

vulnerability as

25:26

a march -- As bait tool. -- as bait. Yeah.

25:28

And swags. Yeah. You know, we're not

25:30

gonna yeah. We're not

25:31

gonna shuffle it all the time. Come on in and

25:33

play. Take, you know,

25:34

you gotta better chance here. Come on

25:36

and sit out and play because they're

25:38

still gonna win. That's exactly it.

25:40

There's most people they're still gonna win.

25:42

Okay. Sammy,

25:43

the MIT Blackjack

25:45

Club was just famous,

25:48

infamous, choose it whichever way

25:50

you wish. But

25:51

you're a professional blackjack player. You are You've

25:53

left ten grand during the summer way

25:55

behind in the rearview mirror. Why

25:57

then

25:57

if you're doing this

25:59

successfully? did

26:00

you decide to walk away? That's

26:02

a good question

26:03

too. So it wasn't because I

26:05

was kicked out of all the casinos and then

26:07

it was up. because -- Okay. -- so it kicked out

26:09

of all the casinos very quickly and continue

26:12

doing it anyway. Or

26:14

because you're missing your kneecaps as

26:16

Chuck hit Yeah. No. They're interested. There were a

26:18

few of that. here and there, but really

26:20

they're big public corporations, then they

26:22

generally just don't do that anymore. It's

26:24

not really worth it for them. Right? We

26:26

don't really win that much. I

26:29

got bored of it. Alright? I

26:31

just started feeling like it was

26:33

kinda pointless. You know, it sounds like we really made that

26:35

much was a few million dollars. It wasn't. It seemed

26:37

like a lot. Yeah. You're not. Right? But Yeah.

26:40

But I mean, I'm doing a

26:42

few a few million bucks. I mean,

26:44

it's your But that's the board line. Yeah. That's

26:46

that's that's a that's a few

26:48

million that's a few million dollars

26:50

in the early nineties. Yeah. You

26:52

know that it's not a problem. Back to that into today,

26:55

you have to look at what else happened

26:57

in in that time period. Okay?

26:59

So and where we came from. So yeah. We were the MIT

27:01

blockchain team. Right? I was studying

27:03

computer Counting little several levels. I can in

27:05

my own particular specific example,

27:08

I was in the process of

27:10

completing a PhD dissertation on

27:14

the very first way to transfer money

27:16

over the Internet. Like, that's the thing. Oh, okay.

27:18

I published it. You know, it could've put

27:20

up, like, the people who did

27:21

StarTalk a PayPal for example. Right?

27:24

they cited my papers. And literally, it worked. It wasn't

27:26

very good for us, but it was first. That's

27:28

what I dropped. Just to be clear, wasn't

27:30

that Elon Musk? Yeah. Elon Elon

27:32

and the purity. Elon and all those guys, you

27:34

know, they they better they this technology

27:37

work better, but it's because I quit.

27:39

Right? The place of the lack of that.

27:41

So no. In retrospect, the

27:44

I mean, the

27:44

one million dollars I personally made and the

27:46

five million dollars the whole group made

27:48

well, not at

27:49

all interesting and significant. And we have

27:51

multiple people come out of the group who became

27:53

billionaires in technology. Right. So

27:55

we're just waiting for a bunch of time. Financially

27:58

speaking. then it was on the other side Okay? We

28:00

didn't generate any value. We had no customers.

28:02

Nobody ever said thank you. Right? We just

28:04

moved the money from one place to another.

28:07

and we felt really smart because we thought we were smarter

28:09

than this people who worked for the casinos and, you know,

28:11

okay. So we were smarter than this people.

28:13

But, you know, so what?

28:15

like, that's not really something to be particularly proud

28:17

of. Right? It's interesting. It it what

28:20

what you're really talking about

28:22

here is but he grew a conscience. He

28:24

grew Well, no. More than that

28:26

is is the value of

28:28

purpose in what we

28:29

do. Yes. The value of produce

28:31

in in your endeavors

28:34

probably outweighs any

28:36

financial gain that you might glean

28:39

from what it is that you do. Sure. What life counselor?

28:41

I love it. Sure. And before you

28:43

see, there's a bit truthfully. Tomorrow was kinda

28:45

funny that way, right, because if

28:48

you actually follow the sense of

28:50

purpose and you

28:51

succeed succeed in a

28:53

cell of that. Right? and deliver a

28:55

lot of value to people who we want

28:57

to help and and get their credit and

29:00

exchange rate. You're gonna end up making way more

29:02

money than if you just try to make money. like, at the

29:04

end of the day, you're gonna do better or anything.

29:06

So Yeah. So so me and you you

29:08

showed there the the

29:09

the MIT BlackJack team basically

29:11

was a hot house for

29:13

future entrepreneurs? Was it

29:15

just by chance that

29:18

this happened? Or do

29:20

you think playing Blackjack the way

29:22

that you did as a team was

29:25

enabling people to develop

29:27

certain parts in the future. Wait.

29:29

Wait. Just to be clear. Wait. Gary. Gary, we're

29:31

talking about MIT here. That was a computer thing.

29:33

Computer science people at the birth of

29:35

an entire Internet. Exactly.

29:38

And and more than that, it's among the

29:40

computer science people, there's lots of people who just became

29:42

programmers with Microsoft. Right? These are the

29:44

computer science people who wanted to take

29:47

risk who wants it to be entrepreneurial, to think outside of the

29:49

box, to tackle something that's supposed to be

29:52

impossible. Right? These are all personal care

29:54

characteristics can be said to do it really, really well. Yeah.

29:56

That's pretty good. Yeah.

29:58

That's what I'm thinking was

29:59

the case here. So this this

30:03

this game of Blackjack attracted

30:06

a certain thinker, a

30:08

certain dynamic within a person

30:10

that then found another path to take it

30:12

so forward. But after

30:13

that, like, it was the commercial aspect of the fact that

30:15

we were trying to make money rather than

30:17

play chess or or

30:20

play with very closely. Right? We're

30:22

specifically motivated by generating

30:24

profit. So that combination, right,

30:26

of the quantitative thinking and

30:29

discipline and diligence and the desire to do well

30:31

financially ended up being very

30:33

lucrative for computer science. Was any

30:35

event to sticking it to the man.

30:37

You know Ted

30:37

Passeno. You know, you know Casseno. You know,

30:39

Casseno. They came into the man. Definitely. Yeah. That was the

30:42

big part. a big part of

30:43

it because you wouldn't do that to

30:45

a homeless shelter. Like,

30:47

you'd you'd do it

30:49

to a casino. Right? That's exactly right. I mean, we really

30:51

did. You know, there was also a sense of teamwork.

30:54

There was a little bit of a morgue going in. I was

30:56

like, us against them, and we went to

30:58

good guy. Right? And -- Very funny. --

31:00

people who tripped all the poor

31:02

people that don't lose, like, all that money. Right? That's

31:04

exactly what I'm getting from there you guys. There's

31:06

three drinks. And we hated them. We really

31:08

hated them.

31:08

Alright. Guys, you gotta take a a quick break.

31:11

But when we come back, more

31:14

conversation with Semion Dukats. gonna

31:16

find out what what floats his

31:18

boat today because it's not

31:19

the car counting anymore, but he's doing some

31:22

really cool stuff. we're out

31:24

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31:26

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We're

34:29

StarTalk Edition. And we've got our

34:31

our our special guest today,

34:33

MIT Alumnus, Simeon

34:36

Ducach, And

34:38

and, Simeon, do you have a social media presence? Or you or

34:40

do you lead the private life of some? No.

34:42

No. You need to finally, Simeon,

34:45

look at each other. On Twitter, sir,

34:47

SEMY0N semion at

34:50

ducach DUK

34:53

hey ACH.

34:54

We'll find you there on Twitter. So so,

34:56

Gary, take us take this thing

34:58

home. We're Okay. So we've we've walked away

35:01

from the tables with a big Counting

35:04

smile on our face, but

35:06

you weren't happy. You've become

35:09

now a mentor. why?

35:12

Was was there was the emptiness of being a

35:14

blackjack player and just

35:15

not really having any thanks and

35:18

gratitude in your life in

35:20

that regards a reason

35:22

why you went to mentoring or was there

35:24

another dynamic in play

35:26

here? Let's

35:26

see. I didn't go

35:28

straight to mentoring where I for my

35:31

technology, and I started some companies that

35:34

used more of my skills and

35:36

knowledge than

35:38

just blackjack. And, yeah, III definitely

35:40

sensed the contrast where

35:42

customers who buy

35:43

my software will not

35:44

only send money, but would also

35:48

say Thank you. Right? And and talk about how the software

35:50

helped them solve their problems. So that felt

35:52

really good, right, in a way that Blackjack

35:54

hasn't. But

35:56

then I also realized fairly quickly after a few

35:58

years by by the year or two thousand

36:01

or so that I didn't really

36:03

wanna build my company a

36:05

single company, you know, for a decade or two. I

36:08

enjoyed in many,

36:10

and I could made some money from selling my

36:12

first one. And

36:13

so I started to invest

36:15

in it for the first ten or

36:17

fifteen years personally as an

36:19

angel.

36:19

I would basically,

36:21

give a founder, the early stages, fifty

36:23

or a hundred thousand dollars of my

36:25

money. And I would try to help

36:27

them. So the mentoring became as

36:30

an add on to the investing where in addition to putting a

36:32

lot of money and I would try to help however I

36:34

couldn't I didn't often know how to help, but

36:37

the effort, the motivation really matters. I

36:39

genuinely wanted to help. It actually

36:42

ended up being better for me

36:44

financially as well because founders

36:46

would appreciate the help I gave them. And I would be referred to

36:48

other ones that would see better deals more

36:50

interesting companies than I'm I

36:52

used to see

36:54

before then. And, eventually,

36:56

you know, I got to the point, Nava,

36:58

where I run a venture capital fund

37:00

and I have, you know, hundreds of people behind me,

37:02

and it's just a much more scalable,

37:04

interesting thing. So

37:05

that's one way ventures. Correct? That's

37:08

right. Explain

37:10

to us as well as

37:13

our audience what the ethos of one

37:15

of my ventures actually is and why

37:17

it's so important to you? Yeah. So,

37:19

though,

37:19

it's one way, you know, we

37:21

have some beliefs. we really believe that people

37:24

from anywhere in the world should be able

37:26

to go wherever they want to build

37:28

businesses, to get jobs, to

37:30

create value, Right? We

37:32

fundamentally believe that. We we don't

37:34

think that the US

37:36

for

37:36

instance ought to allow more

37:39

immigrants in just because, on average, immigrants are

37:41

more likely to create jobs and to

37:43

that wealth and to to basically give

37:46

more than the

37:48

consumer services. over the long term. We may have front of it, but consume some over

37:50

the long term. There's a lot of evidence,

37:52

all economists believe that they

37:55

they are actually good for the country that they arrive in

37:58

and in America as well. So again, there is

37:59

largely because over the last two hundred years

38:02

of taking in a lot

38:04

of So that's what but that's not the reason we believe

38:06

that America should allow immigrants to

38:08

come here and build companies. The reason we

38:10

think so is that we're

38:12

immigrants. And we think it's our algorithm, right, to

38:14

compare whether you like it or not.

38:15

And, you

38:16

know, we feel very, very strongly about that. We

38:18

feel that the random documents

38:21

that you get based on where you

38:22

were born should not determine your

38:25

potential, your outcome, your

38:28

opportunity any more than the color of skin and any other arbitrary You don't control.

38:30

You don't choose to be born in America

38:32

or to be born in some other Right?

38:36

You're just born, you're born, and we think everyone's having

38:38

sex. So that's why

38:40

we we focus on immigrants. And

38:42

it also so happens that immigrants

38:46

make

38:46

the best entrepreneurs. The majority of all very successful

38:48

companies in the U. S. are started by

38:50

immigrants. That's an absolute fact

38:52

about fifty five percent of the

38:55

the social gain of arts at another pounders. And let me

38:57

I'd like to add something to that because I do

38:59

this calculation annually, and it doesn't

39:02

exist anywhere else except out of

39:04

my shop. that the if you look at

39:06

the Nobel prizes and the

39:08

sciences that are given

39:10

to American

39:13

citizens, A

39:15

third of them have gone to immigrants to

39:17

the United States. And

39:22

so that I mean, that's

39:24

an extraordinary number

39:25

far above, you know, what the

39:27

the fraction that immigrants

39:30

are in the United States when you include not only those who

39:32

are legally as well as,

39:34

you know, undocumented, you add those

39:36

to up. It's like ten percent twelve.

39:39

So factor of three higher

39:42

than what that is. So so this

39:44

is just another bit of information there.

39:46

But you're saying but

39:48

but but semi and what you're saying

39:50

there is is not entirely realistic for Let me just So

39:52

you came to the United States

39:55

from Moscow. from

39:57

Soviet Union Moscow

39:59

when basically there's a wall. Alright. I know the

40:02

wall's in Berlin, but there's a there's a

40:04

philosophical wall

40:06

if that wall did not exist

40:08

and it was still the

40:09

Soviet Union and you didn't

40:11

have

40:11

KGB or whatever else and

40:14

borders stop Do you think

40:15

everyone would have left the Soviet

40:18

Union the way you did?

40:20

No.

40:20

I I think most people would never

40:22

leave their home. Look, there's tremendous social pressure.

40:25

your network and community, everybody wants you to

40:27

stay. You In order to permanently

40:29

leave the place you grew up, you're gonna have

40:31

to betray your whole community in a sense. You have to have to Right.

40:34

Right. They're not gonna root for you. You know, they're gonna hope you fail because

40:36

they end this. And it takes

40:38

a special kind of person who is

40:42

tooling to take on that hardship, who has an unusual level of

40:44

ambition. The bread, believe it themselves,

40:46

drive an ax to grind the chip in

40:48

their Right?

40:50

These are not the word error people. Also, these these are people

40:52

who are highly upwardly mobile because of

40:54

their ambitions. And you're saying they should

40:57

be able exit their country and go anywhere in the

40:59

world, wherever they are

41:00

welcomed or or embraced. I

41:03

I just

41:04

wanna know. Why

41:06

they can't come from Norway? It's not so wonderful. Where

41:09

are the Norwegians? Where do

41:11

you need them? You know

41:13

it. I know it. American

41:16

first. Yeah. And I think in a logic,

41:18

you know, I think that the gentleman has

41:20

said

41:20

that he's severely underestimating know,

41:22

the folks that are coming here from Africa to I think -- Yes. --

41:25

it he's judging them by by

41:27

stereotypes that that failed to

41:29

take into account. that these

41:32

actually select individuals. Like

41:34

most people in South

41:36

America don't make it to the US. Even if you

41:38

take the really uneducated ones that will

41:40

require a lot more services, not the focus with

41:42

computer science degrees or whatever. Right? But

41:44

but the really important to get people

41:46

who walk, like, walk on foot and right, it to

41:48

borders in in Texas and Arizona and

41:50

California. You know, they're still

41:52

extraordinary. And

41:54

and if And I would

41:56

say if I had a a construction business and I

41:58

have to hire a bunch of people for

42:00

demolition, I would absolutely hire people

42:02

from the academy. I don't know anybody who had an

42:04

easier time who was born here because it takes very person

42:06

to to walk thousand miles and

42:08

to flood, you know. It's just most people

42:10

will not do that. Absolutely.

42:13

So So are you telling are you

42:15

saying, Simeon, that an immigrant's life

42:17

story is

42:20

more predisposed

42:22

to the probability of success

42:24

in a

42:25

startup because of all of

42:27

the things you've

42:30

just highlighted. Yeah.

42:30

That's why you that's why you that's why you get involved. And and

42:32

the reason, you know, we're able to raise the

42:34

hundred million dollars and invest it in

42:36

now show, you know, more than circling

42:39

in in in the first time, PR's kind of results.

42:41

The reason it's working out so

42:44

well is that we actually have hard

42:46

data to support that. Like, that I mean, we

42:48

got

42:48

we have theories about why immigrants

42:50

made for that. Salesman from MIT.

42:52

We got data. Right? Exactly. Right.

42:55

We believe that there's

42:57

always reasons that I'm talking about for why these immigrant

43:00

founders come all the way to build big

43:02

businesses. But it doesn't

43:02

really matter what we believe or or what those

43:05

regions might be. So if there

43:07

is, fifty five percent of the unicorns were started

43:09

by immigrants. And my

43:10

smaller percentage of all companies in America

43:12

started by immigrants fifty five percent

43:14

over half of the unicorns were started

43:17

by immigrants. that's a fact. Right? So it's

43:19

it's low coincidence

43:20

that that with our thesis, our

43:22

refund is having really great results.

43:25

what you're talking about is

43:27

a an

43:30

allocation of

43:32

resources that we are the beneficiary. Where we are the

43:34

beneficiary to this allocation of

43:36

resources, people coming here and

43:38

making a

43:40

contribution and I forget this

43:42

gentleman's name, but his his his Indian

43:44

came here and somehow

43:48

the the visa didn't

43:51

work out. And, you know,

43:53

being Indian and this was under,

43:55

you know, this particular

43:57

anti immigration

43:59

administration. And I

44:01

think it was

44:03

the, you know, his visa being revoked and had

44:05

to leave. And so he said, I Here

44:08

I am. I just

44:10

got out of Princeton or

44:13

Harvard, I in India. I'm

44:15

basically kicked out of the United States

44:17

where I just

44:20

spent like,

44:22

twelve years in school. So

44:24

what did I do? While

44:26

I started an Internet company, it's

44:29

now like the second largest Internet

44:31

company, you know, it's the largest in India and, like, third largest in the

44:33

world or in other words, what

44:35

he did was he

44:37

took everything that this country

44:40

had to offer, and

44:42

he

44:42

planted it someplace

44:44

else. So

44:45

you know, when you allow people to come here and you allow people

44:47

to flourish and contribute, what you're

44:49

doing is making

44:52

everything better.

44:52

everything better only

44:54

thing that stops that, believe it

44:56

or not, I'm sorry

44:58

to say it, is bigotry

45:01

and and and bias. That's

45:03

it. Because there is no

45:06

actual data that you can say,

45:08

for instance, one quick little thing and

45:10

I'll shut up. Oh, they're bringing crime.

45:12

They're bringing crime.

45:13

No. So

45:16

sorry, guys.

45:17

you know, these things feel good when

45:19

you say them. They're like a

45:21

rallying cry, but it's bullshit. I'm

45:23

sorry. It's it's it's not true.

45:25

So it's a Chuck Martin Luther King NICE speaking to us

45:28

now. Alright. It's so me

45:30

on. We've done

45:32

what you've your past?

45:33

We've we've looked at the

45:36

now. What's next in your

45:37

life? Have you have you got this map or

45:39

is it just, you know, one day

45:41

at a time?

45:43

Well, we have a long way to

45:45

go no one way certainly. Mhmm. We

45:47

are still growing. We are opening

45:49

more locations than than raising larger

45:51

funds down the road. But this

45:54

year, I've spent quite a bit of time

45:56

as well on the nonprofit that I actually

45:58

started with my wife

45:59

in February. that

46:00

helps refugees from the war

46:03

in Ukraine get cash in their hands

46:05

to actually improve their day to day to

46:07

build a sense of agency So

46:09

I and several trips down there and I hadn't know

46:12

there again soon.

46:14

Alright.

46:14

And beyond that, I

46:17

mean, I have six

46:17

kids. So there's plenty of stuff to keep it

46:20

busy. Wow.

46:22

Wow. Six. Six. I'm

46:24

gonna say You better be rich.

46:32

Unless he unless he's taken a it's playbook from your your daddy,

46:34

that's a that's a different Yeah.

46:40

No. So this this

46:42

has been a wonderful story just

46:44

to hear

46:45

and the story

46:46

is a certain uniqueness to it.

46:48

So take us out with some some

46:50

reflective thoughts just to make a better world. because there's a lot

46:53

of crap going on out there in the world

46:55

right now. What wisdom do you have

46:57

based on your life expectancy? I

46:59

think person, you know, I mentioned earlier that

47:02

when I stopped worrying so much

47:04

about how can I make the most money and

47:06

actually started carrying more of

47:08

our lives. doing some good to others together with my

47:10

existing skill set, you

47:10

know, I was able to do better.

47:13

And I think the party thought as

47:15

that perhaps as a nation. we

47:18

can do the same. But if we think a little bit more about how to make the

47:20

world a better place, not going to do the right thing. Right?

47:22

And when you think about foreign policy,

47:24

you know, what's actually the right what

47:27

what Maxim has a human rights, not only short

47:30

term, what's what's best for our national interest, but

47:32

what's the right thing to do? In the

47:34

very long term, we're gonna do the best

47:36

ourselves as well. in the

47:38

story

47:38

told about immigrants. It's just one example.

47:40

Let's not worry about the fact that

47:42

they create wealth here. Let's just let

47:44

them in because we recognize they're right

47:46

to come in. Right? And what will follow from that

47:49

is that this will compare and that's somewhere

47:51

else. And we've been all the

47:53

way better. Great.

47:54

Right. Love it.

47:55

Alright. Do you. Mhmm.

47:56

Sammy, and thank you for joining us on

47:58

Star Talk. That's

47:59

really fun.

48:00

the fall Alright. Alright. Gary

48:03

Chuck, it was

48:03

good to have you there. There's Oh, there's anil.

48:06

Thank you. Alright. This has been Starbucks

48:08

Edition. All about card counting and

48:11

doing

48:11

the right thing in the world before during and

48:14

after. Mhmm. You take

48:16

take the casino to the bank.

48:18

I'm Neil DeGrasse

48:20

personal astrophysicist. As always, keep looking

48:22

up.

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