0:54

How's it going , david ? It's great to finally

0:56

get you on the podcast . I think that we've been planning

0:59

this thing for quite a while , you

1:01

know back in 2024 . And now you're

1:03

the . You're the first episode of 2025

1:06

. Because I burned myself

1:08

out and had to take like six weeks off

1:10

.

1:11

I am honored no-transcript

1:38

feelings about that , actually and

1:40

so there's so many people who

1:42

bill themselves as cyber security experts

1:44

you're probably having some on the show

1:47

and some of them . When you drill down

1:49

on what they , why they think they

1:51

are , it's because they took a bunch of microsoft

1:54

certification classes , right . So

1:56

that's virtually useless

1:59

in any real world scenario , because

2:01

the really bad stuff is stuff

2:03

that people have never seen before , and

2:06

no amount of Microsoft licensing

2:08

or certification is gonna prepare

2:10

you for some wacky denial

2:12

of service attack that nobody's ever seen . That's

2:15

just basically having a brain and

2:18

being able to think through it . So the best

2:20

cybersecurity people are people who

2:22

are not formally trained to be cybersecurity

2:25

people .

2:26

Yeah , yeah , no , that's , that's

2:28

very true . You know , it's always interesting

2:31

when , when

2:33

I bring people on and you know I try

2:35

and find fantastic guests , you

2:38

know like overly qualified , very

2:40

experienced people like yourself and whatnot and

2:43

every once in a while this happened

2:45

. This happened , you know , maybe

2:48

in the middle of last year , right , where someone

2:50

came on and we started . I started to get

2:52

a little bit technical , because I'm technical , you

2:54

know like I'm . I'm in the weeds

2:56

, you know . I , I , uh , I

2:59

wake up and I get you know into log

3:01

files and I'm figuring out what's going

3:03

on . I'm reverse engineering systems

3:05

and whatnot , like all day long and I look up

3:07

and it's 6 PM , you know that

3:09

sort of thing , and we started getting a little bit technical

3:12

and I immediately reach

3:14

their technical quote-unquote

3:17

expertise limit and

3:20

then I push a little bit

3:22

farther and come to find out , oh

3:24

, you're not technical at all , you

3:26

kind of stumbled your way into

3:28

this thing and someone promoted you early

3:30

and that's what happened

3:32

, right , which is it's frustrating

3:34

for myself .

3:35

I deal with this all the time

3:37

, given the kind of things I do and where I live . I've

3:40

been asked by some VC firms and

3:43

other firms to do

3:45

vetting of people , and about

3:47

80% of the people who call themselves cybersecurity

3:50

are actually lawyers

3:52

and they're no

3:54

, I'm serious . They're people at a law

3:56

firm who were involved in

3:58

one case involving

4:01

some kind of aspect of cybersecurity

4:03

and now they're an expert . It's like

4:06

you know , I go into CVS and buy a

4:08

bottle of aspirin and now I'm a doctor . Yeah

4:10

, it doesn't help

4:13

our profession because it

4:15

downplays . It makes it look like it's

4:17

easy to be this .

4:19

Yeah , yeah , I mean , you

4:21

know , when someone is trying to

4:23

get into cybersecurity

4:25

, right , and they're reaching out to me , they're asking for

4:27

advice and whatnot really

4:29

the very first thing . And some people , some

4:32

people that I've had on and I said this too they like

4:34

took offense to it and like were appalled by how I approach it . But

4:36

I this to , they like took offense to it and like we're appalled by how I

4:39

approach it . But I try to convince people to not get

4:41

into security . Right , because if

4:43

I can convince you just through words

4:45

to not get into cybersecurity

4:47

, you're not going to

4:49

be successful in this field . Right , like

4:51

, because you have to have a curiosity that

4:54

cannot be itched . Right , and you

4:56

need to be the expert like , not

4:58

just an expert in security . Like

5:00

. You need no networking pretty well

5:03

. You need no system design pretty well

5:05

. You need to know , you know the different

5:07

processes and services that are talking

5:09

behind the scenes on your windows device

5:11

, right , what they're linked up to and

5:13

where they're actually configured and all that sort

5:15

of stuff so I've .

5:17

I've been doing this literally for half

5:19

a century in some form

5:22

or fashion with computers . The

5:24

thing when I look at a problem , I

5:26

think of it at multiple levels and

5:29

sometimes it's literally at the bit

5:31

level and I'm thinking , okay

5:34

, what's on the heap , what's in the stack

5:36

, what are

5:38

the bits ? And without

5:40

even articulating it , this

5:42

helps me do things like I

5:44

repaired a remote control fireplace

5:47

the other night because I just knew

5:49

what was wrong with it without having ever

5:51

touched the thing . And this is

5:53

a skill set that

5:55

I'm sure you have . I have Many

5:57

people don't . You cannot

5:59

teach this and it's some kind

6:02

of weird survival trait that

6:04

I don't think people recognize

6:06

for what it is . But you can throw anything

6:08

at me that's a computer-related thing

6:10

and I can figure out what's

6:13

wrong with it in a couple of minutes .

6:14

Yeah , wrong

6:18

with it in a couple of minutes . Yeah Well , david , you know , we kind of we kind of just like dove into

6:20

the deep end here without you know you talking about your background

6:23

, so why don't we backpedal a little bit

6:25

and talk about ? You know

6:27

how you got into IIT . What was that

6:29

start like what ? What

6:31

intrigued you right about the

6:33

field that kind of propelled

6:36

you into this world that you're in now ?

6:39

Okay , well , I mean , we have to go back a ways for

6:41

this . So I mean , I went

6:43

to high school in Pennsylvania in

6:45

the mid-70s early 70s

6:47

even and we actually had a computer

6:50

and it was an IBM 1130 or something

6:52

like that , and it had punch cards

6:54

, if you've ever used those , and you

6:56

had to mark , sense the cards and then you

6:58

run them through . And if

7:00

you did assembly programming , which is what you normally

7:03

had to do , there were 16 switches

7:05

on the CPU and you would configure

7:07

the switches for a binary number

7:09

and you'd hit the button and that was one

7:12

machine instruction and then you would do that

7:14

for the entire program you just wrote . So

7:16

that's like days to do that . So

7:18

I was intrigued by it and

7:21

then I sort of let it go

7:23

for a while . I got a degree in philosophy

7:25

, taught some symbolic logic

7:27

and other things , and then I

7:29

, through a bizarre set of circumstances , I

7:32

ended up being an intelligence agent for

7:34

a number of years . I had to go in

7:36

the military . I did

7:38

, and they , because of my test

7:40

scores , they trained me to be

7:42

a cryptographer . They sent me to

7:44

Russian school for a couple of years and I

7:47

ended up on submarines , and

7:49

so I did that during the height of the Cold War

7:52

and it was fun . I actually had a

7:54

really good time . It's not like anybody got

7:56

killed , you know . You didn't have to worry about

7:58

bombs blowing up a Jeep or something

8:00

, it was just the Cold War was a very different

8:02

kind of thing . But computers

8:04

first time I've seen computers play

8:07

a part in the real world because

8:09

submarines at the time were heavily

8:11

computerized . I mean , given what

8:13

was there at the time , what

8:20

was there at the time . So the computers were called YAK20s , u-y-k and I think

8:22

it was a deck computer and they used to send out this is I always thought it was hilarious

8:24

. They used to send us out with a repair kit and

8:26

it was a big brown plastic case

8:28

and if you opened it the only thing I

8:30

had in it was a rubber mallet . And they

8:32

said you'll never fix anything

8:34

. Just start whacking the crap

8:37

out of the boards and

8:39

something will go back in place . And

8:41

you know what it did . I had to do it like three

8:43

times . So I mean

8:45

, that's kind of the early days

8:47

. I did some other work . I went to NSA

8:50

, I worked at Cosmonaut , the Cosmonaut

8:52

program , and then I got some more

8:54

degrees from UMBC in computer

8:57

science with a math concentration , did

8:59

grad work at Hopkins and then I was

9:02

at a crossroads because I

9:04

either stayed as a professional intelligence

9:06

agent or I went into this

9:08

fledgling world of computers

9:10

in the early 80s and it was a no-brainer

9:12

for me and I mean I knew where

9:14

things were going and I got out and

9:16

started programming

9:18

a number of different languages

9:20

and within a few years I

9:23

was designing systems and

9:25

then I ended up . I was , I

9:27

ended up running research at Booz Allen

9:29

and Hamilton . The consulting firm IBM

9:33

hired me to be the chief scientist

9:35

for the Internet Information Group , which

9:37

is all their Internet-related software

9:39

, basically Not networking , but

9:41

anything above that . And that was

9:44

pretty cool . Actually at the time I'd

9:47

never played in the big leagues like that before

9:49

. I got a lot of job

9:51

offers this was like 95

9:53

, 96 . And I decided

9:56

. The one I wanted was this little little

9:59

company in Herndon , virginia . It was an 8A

10:01

firm and it was called Network Solutions

10:03

and the only thing they had going for them

10:05

is they had a locked contract

10:08

with the National Science Foundation to

10:11

basically run the internet . So

10:13

they it was called a cooperative agreement

10:16

, so they ran

10:19

the whole domain name system

10:21

, all the root servers

10:23

, tcpip allocation

10:25

for North America and the CDPD

10:27

, the cellular data network . So

10:29

I came in as CTO and

10:31

then I ended up running all that

10:34

. So that was pretty cool . And I

10:36

got to deal with crisis after crisis

10:38

because from 96

10:40

, 97 on that's basically the

10:42

dot-com bubble . So all

10:45

of a sudden people actually gave

10:47

a damn what was going on in the internet

10:49

, and up until that they didn't . It

10:51

was a curiosity . In the early

10:53

90s it was like labs

10:55

, and by the end of the 90s

10:58

it was billions

11:00

, tens of billions . So

11:02

I went through that . My company went public

11:04

, I did an IPO , a couple secondaries

11:06

and I was running all this stuff during

11:09

Y2K and I was on President

11:11

Clinton's task force representing the internet

11:13

during Y2K and

11:15

that's a whole story right there . I didn't like

11:17

where some things were headed . I left

11:20

and started writing books on privacy

11:22

and wrote for a couple of magazines . Nobody

11:25

cared really that much about it at

11:27

the time . It was some kind

11:29

of like weird conservative thing and

11:31

the liberals didn't want to have anything to do with it because

11:34

privacy seemed to run smack into

11:36

First Amendment issues , and

11:39

so my natural constituency

11:41

were people I didn't actually want to deal with

11:43

. So then I got into some

11:45

other things . Story's almost over

11:47

here . Sorry the staking's long , no worries . I

11:50

did politics . I was a CTO

11:53

for Senator Bayh when he ran for president

11:55

for two years . That was actually

11:57

a paid gig in Arkansas . And

11:59

then I was the head of security for

12:01

General Wesley Clark when

12:04

he ran for president , and

12:06

so I got some other

12:09

exposure and at

12:11

this point I was pretty cynical

12:13

about almost everything . And

12:15

the thing I was cynical about was the

12:18

people who should understand what was going on did

12:21

not understand what was going on , and

12:23

this was a huge . I mean I knew where

12:25

things were going . I mean what we're seeing today

12:28

with cybersecurity , for instance , you

12:30

know , and data breaches . I mean the writing

12:32

was on the wall for that 20 years ago and

12:36

it's now . Anyway , we can talk about that . So

12:39

I started doing . I started traveling

12:41

the world . I hit 85 countries in a couple

12:43

of years and then I came back and

12:45

I started working with very

12:47

early blockchain companies all

12:49

in Europe , because none

12:51

of them wanted to work in the United States

12:53

because they were terrified of the

12:56

Security and Exchange Commission , especially

12:58

when they're doing ICOs for tokens . I

13:01

mean it's still not clear

13:03

how US tax law

13:05

treats that stuff . So I worked

13:07

with a number of those companies and

13:10

I'm still working with a couple , and then

13:12

I got into post-quantum encryption . So

13:14

now I'm doing sort of Web3

13:16

non-centric security

13:18

with post-quantum encryption . So that's

13:21

kind of a long story . Wow

13:23

that is .

13:26

I mean , that's really fascinating . You

13:28

know , it's just

13:31

where this field has taken you . I

13:35

mean , did you ever think that you would

13:37

taken

13:39

you ? I mean , did you ever think that you would , you know , be on President Clinton's cabinet ? You

13:41

know , like no , no , Like starting all those years ago . You

13:44

know , did you ever have that in mind as

13:46

that even being a possibility

13:49

?

13:49

So the truth is I was

13:51

a single parent , I was raising five

13:54

children , I couldn't even afford daycare , and

13:56

I'm sitting on all this stock in

13:58

a company that might go public , so

14:01

that was very good motivation

14:03

for me . And finally , when

14:06

the stock did do well , I mean I didn't

14:08

get rich , but my kids all

14:10

went to college and I bought a Porsche

14:12

. So what

14:14

?

14:14

kind of .

14:15

Porsche . I got a 911 . Okay

14:17

, which I'm now feeling really embarrassed

14:20

about because I sold it and bought a Tesla

14:22

and I really love . Yeah , I know

14:24

, I love , love my Tesla and

14:27

I just feel , I feel like such

14:29

you

14:34

for the Tesla .

14:35

We we bought two Teslas in 2024

14:38

and I love them . I absolutely

14:40

love the car . Recently

14:44

bought myself a Model X and , uh , I've wanted that car since it was

14:46

announced , right Like . I just love

14:48

everything about it , but

14:51

I couldn't imagine selling

14:53

a Porsche , even for a Tesla . I would

14:55

just like have both .

14:57

Well , you know , here I live in , I live in the city

14:59

Parking spaces are at

15:02

a premium . I actually

15:04

have two spaces , which is like two

15:06

more than most people have . So

15:08

when my wife and I bought this house , that was

15:10

one of the reasons we bought it . But

15:12

we have an SUV too , and

15:14

the Porsche was just sucking up money

15:17

and

15:20

every time something happened it was thousand dollars . Oh

15:22

yeah , I mean everything . You can't you know ? Cigarette

15:25

lighter , three thousand dollars , yep . So

15:28

I got tired of paying it . The dealer

15:30

here sucks so whenever they

15:32

couldn't get parts . So , and especially

15:35

during the pandemic . So anyway

15:38

, that's , that's why I did it but I got

15:40

to drive it I got . I had

15:42

to drive it for 20 some years . It's

15:45

uh , it's an incredible machine .

15:46

When you're dating , oh yeah yeah

15:49

, I , um , I I just sold

15:51

my audi s5 and it was my

15:54

. It was my first sports car and

15:56

that's a good car . What a , what

15:58

a fun vehicle . But

16:01

when it breaks a man , when something

16:03

goes wrong on that car , it's I

16:06

mean , like you said it , I just got

16:08

to the point where I assumed you

16:10

know I'm going in for an oil change and

16:13

I assume they're going to find

16:15

three thousand dollars worth of stuff that's

16:17

broken that I don't even know about

16:19

. Oh , I'm sure .

16:21

So you know , going off that , going back

16:23

to the other thing I said when

16:25

I was a kid , growing up , this is , like

16:28

you know , pre-psychedelic

16:31

era , going through the Beatles and all that

16:33

. So my friends who are

16:35

good with mechanical stuff were

16:37

highly in demand . Women

16:40

liked them , guys liked them . They

16:43

could change your spark plugs . They

16:45

didn't have to go into the gas station , they would

16:47

go , yeah , it's your timing

16:50

, and they would get in there and they would fix

16:52

it . They could fix TV

16:54

sets , they could fix washing machines

16:56

. Guess what ? You can't fix a goddamn

16:58

thing today . So now

17:01

it's the person with the skills that

17:03

I was just talking about . It's

17:05

it's the person who I used

17:07

to go to dinner parties with people and a

17:09

lot younger than me and I would

17:12

have an iPod and a CD and

17:14

I would say , hey , I'll give 20 bucks to anybody who

17:17

can take the songs off this CD and

17:19

put it in this iPod . Nobody ever

17:21

knew how to do that

17:23

and to this day , when I deal

17:25

with politicians and multi-hundred

17:28

millionaire VIPs , they

17:31

don't know how to do anything either . And

17:33

they all have like nephews and

17:35

like eight-year-old , nine-year-old

17:37

nephews and they do the work for

17:39

them Like printers , like configuring

17:42

a printer is still way too hard

17:44

. Way too hard , yeah

17:47

, and it should be easy , and if

17:49

you're lucky it will be , and

17:52

if it doesn't configure in the first two minutes

17:54

, you're in for a bumpy ride .

17:56

I hate printers . I really do . I really do . I really

17:59

hate having them . I only have it

18:01

because my wife is a she's an early

18:03

childhood teacher and so

18:05

she has to print a whole lot . So we have like

18:07

a very robust printer and

18:09

it's just . You know it doesn't

18:11

work . You know very like

18:13

fluently with a Windows

18:16

PC and MacBook laptops

18:19

and you have to reinstall the driver all

18:21

the time and it's so

18:23

, so dumb . But so

18:25

go ahead . Yeah

18:27

, I was going to ask you what

18:29

, what your time was like at

18:32

the nsa . You know , I've had other people

18:34

on from various agencies CIA

18:36

, nsa , dia and

18:39

they all tell me roughly

18:41

the same thing . And

18:45

I have a good friend who's in the military

18:47

and he said that if I ever do make it

18:49

into the NSA , that

18:52

first month when you're being

18:54

read into 90%

18:57

or 80 , 80 of what you need to be read into

18:59

and whatnot , it's

19:01

gonna like the capability side of it

19:03

. It's kind of just gonna blow your

19:05

mind right , like you wouldn't even

19:07

realize . Oh , you can use that for for

19:10

this thing over here , right ? Well

19:12

, I'm I'm wondering did

19:15

you have that same kind of experience back

19:18

then ? Because you were

19:20

really , I mean , at the

19:22

beginning of this digital

19:24

era , right ? I mean it didn't

19:26

really even start . You were at the very foundation

19:29

of it . Was that experience

19:32

true for you as well , or what was that like

19:34

?

19:35

Well , when I got to NSA it

19:37

was early 80s and

19:39

they had a couple of supercomputers

19:41

, like really expensive ones Cray's

19:44

, cray 2s is what they were and we

19:46

didn't have access . Nobody had access to them , so

19:48

they had like PCs . They

19:50

were like 83 , 86 machines or

19:52

something . So if I wanted software

19:55

I had to write it . So people used to

19:57

come to me and I would write a Turbo

19:59

Pascal program to do

20:02

some intelligence thing , because you couldn't

20:04

bring stuff in from the outside , and

20:06

so that was kind of fun . And when

20:08

I was in the submarines I had some

20:11

of the deepest security clearances

20:13

you can get . I mean things that are only like

20:15

that are still classified and

20:17

only like 30 people in the world

20:19

could read the material . It

20:22

was like there is stuff like that . But

20:24

in the end in CIA

20:27

what that means typically is

20:29

it means they have an asset , a human

20:31

asset , like Putin's

20:34

hairdresser . So Putin's

20:36

hair I'm just making this up I hope if

20:38

he gets killed tomorrow I'm going to feel really bad

20:40

. But so let's say Putin's

20:42

hairdresser gets turned you know

20:44

, happens all the time . So that

20:47

would be very , very carefully

20:49

protected because they're

20:51

going to shoot him in the head if they find out . For

20:54

NSA . It's

20:56

almost identical to

20:59

what hacking is . In fact , now it is hacking

21:01

. It's like you

21:03

know . It's like basically it's zero days

21:06

. Before there was even a term zero

21:08

day . Nsa was looking for zero

21:10

days . They were looking for defects

21:14

, bugs , some kind of malfunction

21:16

in any mechanical or electronic

21:19

device that they could turn into

21:21

an acquisition thing . So that's

21:23

why and this stuff's not classified anymore

21:25

, I think but that's why they were doing things

21:27

like bouncing laser beams off windows

21:29

. So you could I mean you could

21:32

hear what was being said in the room , and

21:34

there's crazier things than that in

21:36

the room and there's crazier things

21:38

than that . And so that's the secret . The

21:43

secrets in NSA were mostly that kind of stuff . And then a bunch of boring stuff

21:45

to most people , like what frequency

21:47

a satellite downlinks on . You

21:50

know what I mean ? It's it

21:53

. Most people could really give a damn and

21:55

wouldn't even understand if you

21:57

told them . But if the Russians

21:59

got it it would be a big deal , right

22:02

?

22:05

Yeah , that is , that's really fascinating . You know you talk about having that

22:07

clearance and you know only 30

22:09

people in the world are

22:11

even allowed to read that document . I always

22:14

wonder how , like

22:17

, the level up from

22:19

that even works

22:21

right , because someone

22:24

I'm just trying to think of you know

22:27

least privileged permissions , right From my

22:29

perspective . From my perspective

22:31

, if I want to give someone else access to

22:33

a system or whatever it might

22:35

be right , it doesn't matter the sensitivity

22:37

of that system , I have to have access to that

22:39

system right , in some way , shape

22:42

or form , I have to have that access

22:44

. And so it's just interesting

22:47

to me for how agencies

22:49

deal with that , because obviously you

22:52

don't want everyone knowing you know nuclear

22:54

secrets or you know whatever

22:56

that might be , and you have to really tightly

22:58

control that information

23:00

. It's just fascinating for

23:02

me to you know , think about

23:04

it , how you would do it , even with a people

23:06

like a physical , you know person

23:09

right , like how do you control that

23:11

, how do you monitor what they're doing , and that

23:13

sort of thing 10

23:28

years from now , nobody's going to be doing that .

23:29

Maybe six or seven years , nobody's going to be doing that . And the reason is because

23:32

both defense and offense

23:34

is going to shift

23:36

over to AI AI-driven systems

23:38

because they move much faster

23:41

than human beings . So if

23:43

an AI is running some kind

23:45

of denial of service act or some

23:48

kind of penetration hit on your network

23:50

, it can make like a million

23:52

hits on every single address

23:54

in your network just like that . So

23:57

no human being will even see

23:59

it coming , let alone stop it

24:01

. So you need to have some kind

24:03

of AI-driven defensive system

24:05

. On the other end , and that's one of the reasons

24:07

I'm working with a company or two that's

24:10

doing Web3 decentralized

24:12

stuff , because I think the

24:14

biggest damage that's been done in security

24:16

in the last 20 years is

24:18

deferring things to centralized companies

24:21

and that's where all the breaches happen

24:23

. They're service providers . I

24:25

mean you know Equifax and SolarWinds

24:28

. You look at any of those , it's never

24:30

the company with the name on it

24:33

, that's they're not responsible

24:35

. It's some idiot third party that

24:37

they hired to do credit card processing

24:39

or something and they

24:41

got hacked . And then it happened

24:43

with AWS too . So

24:45

I mean that's the hole . So

24:47

in the future , when it moves into an

24:50

AI driven system , that hole

24:52

, those holes , will go away . Hmm

24:54

.

24:56

Yeah , I , you know , I always

24:58

talk about planning

25:00

for the future on the podcast

25:02

and and you kind of seem like someone

25:04

that that thinks into

25:07

the future . Right , then you start . You

25:09

start working towards it immediately because it's

25:11

like , hey , if we're going into a post-quantum

25:13

world like we are , I

25:16

need to be experienced with

25:18

it , I need to have some level of expertise

25:21

with it , otherwise in 10 years I'm

25:23

going to be obsolete and I won't

25:25

be able to do anything . Right

25:27

, how , how do you determine

25:30

? You know where things are

25:32

going , where to spend your time

25:34

, what to really focus on ? Because you

25:36

know for myself , right 10 years

25:38

ago , I knew I wanted to get into cloud security

25:41

, right , and now I've been in

25:43

cloud security for a while and now

25:45

I'm shifting gears , getting a PhD

25:47

in how to

25:49

secure satellite communications in a post-quantum

25:52

world . That's a good one . Using

25:55

zero trust principles right .

25:57

Good yeah .

25:59

So I'm also someone that looks towards

26:02

the future and then acts on it and says

26:04

, well , what's going to challenge me , right

26:06

, what's going to make me grow ? And those

26:08

are typically the most rewarding , probably

26:11

most arduous tasks , right , how do you

26:13

approach it ?

26:14

Well , I have some old friends

26:16

who are very , very

26:18

senior tech people and

26:21

sometimes we talk . I

26:23

just had a long call with an old friend of mine

26:25

yesterday who used to be the chief scientist

26:27

at Amazon in the early days In fact

26:29

I had a fellowship there at the time and

26:32

we had this futuristic talk

26:34

because we both were kind

26:37

of laughing about it , because we both see very

26:39

similar things coming two

26:42

years , five years , 10 years . I

26:44

mean there's nothing we can do about

26:46

it . And I found a long

26:48

time ago that if

26:50

you invest in the future you

26:53

will go broke so fast , because

26:56

I tried this , because I always saw what was coming

26:58

and I was almost always right . But

27:00

you can't just because you know something I like 3D

27:02

printing . I saw that coming years before it happened . I saw that coming years

27:05

before it happened . So when the 3D printing

27:07

companies came up , I said , oh , I'm going to buy stock

27:09

in this stuff . Well , guess what ? I was

27:11

right about the industry , wrong about the companies

27:13

, and I mean that's you

27:15

know that's the kind of stuff that happens

27:17

. But I think futurism

27:21

that's another

27:23

word I mean I sometimes call myself that

27:25

, but many people who

27:28

call themselves futurists are

27:30

frauds . I mean just flat

27:32

. They're like hella evangelists

27:34

, like that level of

27:36

fraud . And when you talk to

27:38

many of these people they

27:40

have a marketing background . They're

27:43

not people like you and I

27:45

. That could you know in a pinch . You

27:47

know , dig into a router and try and

27:50

figure out what's going on . I haven't done that

27:52

stuff in years but I could still do it . They're

27:55

not like that and

27:57

you know that goes back again to

27:59

the kind of the theme that I didn't know I

28:01

had here , but that these skills

28:03

are changing and

28:05

they're going to be less useful . Like

28:07

I tell you , you know you were saying about cybersecurity

28:10

and you give people like a

28:12

test question to see if they're

28:14

serious . I try to talk people out of

28:16

going into computer science and

28:18

I've been doing that for seven or eight years and

28:21

I often give talks to grad schools and

28:24

they get angry , usually because they're , like

28:26

you know , one year away from getting

28:28

their doctorate in computer science . The

28:31

argument I have is computer

28:33

science today and tomorrow will

28:37

be mostly algorithm development

28:39

, and there is only so

28:41

many algorithms that

28:43

you need people to develop , and it's

28:45

a very small subset of

28:48

the number of people running around today with

28:50

graduate degrees in computer science . So

28:52

most people who call themselves computer

28:55

people or technologists they're

28:58

kind of , you know not to be offensive but they're

29:00

kind of webmasters . You know

29:02

they put up a website , they know

29:04

how to do some Java , javascript

29:06

. I mean they know what JSON is , maybe

29:08

. I mean they know stuff , but it's

29:11

very , very narrow . It's

29:13

not the way things used to be , where

29:15

you had to know all of this stuff . It's

29:17

like the mechanic guy who can do your spark

29:19

plugs . It's not just General Motors

29:22

. He had to work with Fords and Chryslers

29:25

and you know else , because

29:27

it was they were principals .

29:29

So yeah

29:33

, that is a really good point . You

29:36

know , I don't even know what , like

29:38

what they would get a phd in

29:40

computer science and like what does

29:42

that even look like ? Because in your bachelor's

29:44

you're learning , you're

29:46

learning , you know the bits and

29:48

you know hexadecimal , you're learning

29:50

c plus plus and all

29:53

that sort of stuff and I I didn't get my bachelor's

29:55

in that area . I actually got my bachelor's

29:57

in criminal justice and you know

29:59

, wanted to , wanted to go the federal agency

30:01

route and I kind of stumbled into

30:03

it and found it to

30:05

be a lot more interesting in some

30:07

ways . But what

30:10

does that even

30:12

look like for a PhD in computer

30:14

science ?

30:15

Yeah , I think your point's a good one . I never thought about

30:17

that . Basically , everything

30:19

you need to know about computer science you can get

30:21

as an undergraduate Right

30:24

. That's kind of what you're saying and that's absolutely

30:26

true . The stuff that paid

30:29

off for me in the long run was

30:31

stuff like knowing how to build a compiler

30:33

. So I took a couple of grad level

30:35

classes in that and I

30:38

did build compilers , but they

30:40

were like natural language compilers , so

30:42

you can apply that technology

30:44

to many other things if you understand

30:47

what that technology is and that

30:49

kind of thing . Like , I was a

30:51

Lisp programmer for a while if you know

30:53

anything about Lisp . So

30:56

Lisp was the language for AI for many

30:59

years . But it's a

31:01

crazy programming style . It's

31:03

all recursion , so you have to be I

31:05

mean all of it , that's what it does . So

31:07

you have to understand recursion or mean all of it , that's what it does . So you have to understand

31:09

recursion or you cannot possibly

31:12

program unless . So those programmers

31:15

are pretty much gone now , but that

31:17

was a skill I had to learn from school

31:19

.

31:22

Huh , I guess it makes sense

31:24

. For , yeah

31:27

, I guess , just thinking about

31:29

from an education perspective , right , it

31:32

makes sense to get that undergrad degree

31:34

in computer science , if you're going to go down

31:36

that path and whatnot , and then it

31:38

probably makes more sense to get you know

31:40

these onesie , twosie classes of

31:43

developing core technology

31:46

types right , rather than

31:48

even going down the path of getting a master's

31:50

like a full master's . Just get those

31:52

courses , get that skill and

31:54

then build from there . You know

31:56

, because those are skills that

31:58

really you know you can build off

32:01

of right and it'll transform into

32:03

something else where you're using it with

32:05

AI and building a model .

32:07

Well , every once in a while , because of the kind

32:09

of stuff I do , I run into hybrid

32:11

people . Now I mean younger

32:13

, typically like computers , people

32:16

that have an undergraduate degree in computer science

32:18

and then they get a law degree . I've

32:21

run into half a dozen doctors

32:23

who started off as IT

32:25

people and then they went to medical school

32:28

. And these people are they're killers

32:30

because they can do

32:32

stuff none of their colleagues can do . So

32:35

when they get out into that world , the

32:37

legal world , the medical world , everybody

32:40

relies on them for anything that looks

32:42

like a computer , and I'm

32:44

talking like litigation . Is the hospital

32:47

going to buy a new $150 million

32:49

automated surgical robot arm

32:51

? Well , let's ask Joe , because

32:53

he's got the computer science degree , although

32:55

you said you didn't , but even so . So

32:59

I mean , I think that's very

33:01

powerful . I don't see the

33:03

specialization requirements

33:05

anymore specialization

33:11

requirements anymore .

33:12

Yeah , that's actually very true . You know , I think this is kind of how I approach

33:14

it . You know , when I was getting started

33:16

, I wanted to learn as much as I

33:18

possibly could about everything

33:21

. There wasn't a specific technology

33:23

that I wanted to focus on or a specific

33:26

domain or anything like that , and so I got

33:28

experience , you know , with

33:30

WAFs , right , and then regular

33:32

firewalls and EDR systems . I

33:34

have experience with all of the big

33:37

EDR systems . You know , when

33:39

a lot of people , a lot of people , will

33:41

say I only know CrowdStrike or

33:43

I only know X EDR

33:45

, right , I have a full spectrum

33:48

of experience across almost

33:50

every single domain in security . And

33:53

then I went through and

33:55

I decided , okay , I'm going to specialize

33:57

in cloud security . And now I'm kind of taking

33:59

a step back and I'm upscaling right

34:02

on the PhD side with post

34:04

quantum encryption on satellites , two

34:07

things that have

34:09

so many different facets to it that I've never

34:12

touched before , right , while

34:14

I'm also going back in my career

34:16

and getting more broad , getting more

34:18

generalized and specializing

34:21

in a few niche areas , but

34:23

still building , you know , a

34:25

stronger I just I say building

34:28

a stronger overall experience , right , because

34:30

you know something I'll learn

34:32

in network security or

34:35

with a WAF or whatever it might be , will

34:37

benefit me in vulnerability management

34:39

and it'll benefit me in other areas

34:42

.

34:43

Knowing the concepts will

34:45

pay off throughout your entire lifetime . The

34:51

concepts will pay off throughout your entire lifetime Far more than memorizing

34:53

tables or something like that . Understanding the concepts is really

34:55

, really important , and I think that gives people

34:58

survivability in the marketplace

35:00

. So you know . Something else to consider

35:02

. When I went to college and

35:05

my first degree was in the 70s

35:07

, late 70s , there

35:09

was no computer science degree . You couldn't

35:11

get one . You had to get a

35:13

math degree , carnegie Mellon . I've got a couple

35:15

of friends who went there . They got math degrees

35:18

and then they ended up being computer programmers , but

35:20

that's all they could do . So we

35:23

changed job titles , especially

35:25

in this country , every seven

35:27

or eight or 10 years . As look

35:29

at what a lot of people do now in

35:32

LA and New York and Chicago

35:34

, and you , if you go to a room of millennials

35:37

, you know at a bar or something , and say , what do

35:39

you do ? I guarantee you

35:41

at least a third of them were professions that

35:43

I may not even know what they are and they did

35:46

not exist 10 years ago . I'm

35:48

an SEO specialist , okay , well

35:51

, what is that ? I mean

35:53

I know what it is . I'm exaggerating , but

35:55

most people my age wouldn't

35:57

, and it's because the professions

36:00

have changed . So you want to be futuristic

36:03

for a while . Put your hat on

36:05

and think five , six , seven , 10

36:07

years . What kind of professions are

36:09

we going to look at ? Well , I bet a lot of them

36:11

are going to have the word AI in them , and

36:14

they're not going to be building AIs

36:16

, they're going to be training

36:19

AIs or they're going to be servants

36:21

to AIs . So when the AI

36:23

needs like a cup of coffee or something , you'll

36:25

metaphorically , that's what

36:27

you'll do , because they don't need

36:29

us to do anything like this . They

36:32

need us to feed them data , but they've

36:34

already eaten all the data . Openai

36:36

announced , I think a week or two ago , that

36:39

they've now looked at every single piece of data

36:41

that they could possibly look

36:43

at and they're now building systems

36:45

that generate false data

36:47

that they can use for training . The rest

36:49

of the systems Sounds goofy

36:52

, but what that ? I mean ? What

36:54

that is is those are machines that are now

36:56

training themselves . I mean

36:58

, look at programming . I mean open

37:00

AI , like the chat , gpt stuff . I'm

37:02

sure you've tried to write programs with it

37:04

. Everybody has . Oh , yeah , they're not

37:07

bad . Yeah , I mean , they're not

37:09

the most clever thing I've ever seen , but

37:11

they work , they compile and

37:13

they do the thing they're supposed to do . So

37:15

you know we're just . And then you

37:18

know not to get you know too spiritual

37:21

here or anything , but you take that idea

37:23

of technology and then you put

37:26

drones and robots

37:28

and Tesla . My

37:31

Tesla has a summon feature that

37:33

I am terrified to use . I

37:35

tried it once in the middle of DC and

37:38

we were like two blocks away and I hit

37:40

the button and then it comes barreling down

37:42

Connecticut Avenue with nobody

37:44

at the wheel . And this

37:47

isn't Waymo , this is like a car driving

37:49

itself with no real particular direction

37:51

in mind . So when you start

37:53

looking at that , I mean what

37:56

are we looking at ? We're kind of looking at Skynet

37:58

.

37:59

Yeah , yeah

38:01

, that's a really good point . Where

38:05

do you think AI

38:07

security fits into the

38:09

development of AI ? I

38:12

know that we talked about that offensive

38:14

and defensive component , but when

38:16

we're talking about models , it's

38:19

a little bit different , right , because

38:21

you almost have to , you know . It's

38:23

like you have to monitor what

38:25

the model is consuming . It's like you

38:28

have to monitor what the model is consuming . And

38:30

you know this is the thing , I

38:32

don't know how else to explain it Right , you

38:35

wouldn't want the model right

38:38

to look at Nazi Germany and

38:40

say that is good

38:42

, that's something that we want

38:44

to propagate , but you don't

38:46

want to keep that information from

38:48

the model Right , and

38:53

so you get into a weird dilemma

38:55

.

38:55

Do you remember there was an AI that Microsoft

38:57

did about 10 years ago that

39:00

they had to pull off the market because it

39:02

became a racist ?

39:04

Yeah , Do you know what I'm ? I'm just looking it up because

39:06

I'm here , yeah , t-a-y

39:09

.

39:09

So it did exactly what you're

39:11

saying . So they fed it a bunch of

39:13

stuff and they didn't really constrain

39:15

what it ate on the websites

39:18

and it hit a bunch of white supremacist

39:20

sites and then basically it

39:22

was saying Sieg Heil and it

39:25

was saying like a whole bunch of anti-Semitic

39:27

stuff and it used

39:29

the N-word with people in casual

39:32

conversations . So Microsoft

39:34

shot it in the head and they never revived it again

39:36

. Wow , that's

39:40

not programming . That's not programming

39:42

. That is its interpretation

39:44

of the data that was input to it

39:46

.

39:47

Right , right , so

39:49

like that . That's that's kind

39:52

of where I think AI security like comes

39:54

into , into play , right

39:56

, where it's kind of more about monitoring

39:58

what the model is consuming and

40:01

trying to figure out . See , I

40:03

always view it and people with at nvidia

40:05

they argue with me on this as

40:07

like a ai model hierarchical

40:09

system where you have

40:12

an overarching ai model that you want

40:14

people to consume and interact with

40:16

and whatnot , and then that ai

40:18

model is fed off of other models

40:20

that is looking at specific

40:23

topics . So it's almost like that model gets

40:25

specialized into a

40:27

certain area , like maybe world history

40:30

or European history or

40:32

, you know , sports , right

40:34

, the finance industry , and when

40:37

that model reaches a certain level

40:39

of maturity , it starts feeding that upper

40:41

level model that information

40:43

to query , to interact

40:46

with , for users like us

40:48

to start querying it and building

40:50

different things from . I think that that

40:52

might be the only way to do it . But again

40:54

, you know NVIDIA , those geniuses over

40:56

there they argue with me that that's not a great

40:58

way .

41:00

Yeah , I would argue that too . Actually , I mean

41:02

part of the problem here . Here's another

41:04

weird comment that I

41:07

don't think a lot of people make . We have hit , for

41:09

the first time in

41:11

the computer lifetimes , we have

41:13

hit the point where you can no longer backchain

41:16

why a computer had an

41:18

answer . We could always

41:20

do that before . It might take a while

41:22

, but if somebody said , god damn it

41:24

, why did the computer do that , why

41:26

did it shoot down that airplane , you know

41:28

, a week later somebody's going

41:30

to tell you why that

41:32

day is gone . And generative

41:35

AI systems . It is completely

41:37

impossible to backchain

41:39

those guys and to get like a

41:41

stack dump and find out exactly

41:44

why they did what they did . So

41:46

as that is I mean that's like

41:48

pervasive across this industry

41:50

. So as that continues

41:52

, you know you're going to . Well , that was like

41:54

the racist , the racist plot at Microsoft

41:56

. They knew what it was because

41:58

they went back and looked at the websites that

42:00

it was it was looking at . But in

42:03

the future there's already so many

42:05

of them . How would you know ? And it's not

42:07

like they have to look at whitesupremacistcom

42:11

that pulled in . They can just go to Twitter

42:14

or X or any of a number of other ones , and

42:19

they can find all of that crap in

42:21

free speech forums . So

42:24

, from a cybersecurity viewpoint

42:26

, you go back to your question . I

42:28

don't think you can look at the input , I think

42:30

you have to look at the output . So

42:33

I think cybersecurity for AIs

42:35

is going to be like

42:37

it's like you have an attack dog

42:40

and it's been trained and you're

42:42

walking around with it on a leash to make sure it doesn't

42:44

bite anybody . And I think that

42:46

is what AI is going to be like , because

42:48

you won't know why it's doing it and

42:51

there will not be a human

42:53

understandable correlation

42:55

of causality between reading

42:57

this post on X and

43:00

deciding to use the N word

43:02

in a forum . You just won't know . So

43:05

you just have to wait until it screws

43:07

up and then you have to roll up a newspaper

43:09

and hit it in the nose . Huh

43:11

.

43:13

That's . It's fascinating . I feel like

43:15

we could go for another hour just talking

43:17

about any of the 10 topics we

43:20

just dove into . You know , unfortunately

43:22

we're almost at the end of our time , but I

43:24

really want to dive into the

43:27

stuff that you're working on

43:29

now , right , so you're discussing

43:31

about building or

43:34

working on , you know , web3 and

43:36

post-quantum . So talk

43:38

to me a little bit about that , because I don't want to butcher it

43:40

and this can get pretty complex .

43:42

No , I , actually I wanted to do that . So

43:44

I'm working with a company called

43:46

Neoris . It started in Portugal

43:48

, but it's a global company and it's

43:50

a it's a web three company and the

43:53

founder developed some really

43:55

cool security approaches to

43:57

where you have these little lightweight processes that

44:00

can be very quickly ported

44:02

to any device you know routers

44:04

, computers , whatever , and even IoT

44:07

devices and then , when

44:09

there is a possible attack

44:12

or there's some suspicious

44:14

looking , you know , packets start coming

44:16

in . Instead of going

44:18

out to like Microsoft and saying , is

44:20

there a CrowdStrike or something saying Is

44:22

this okay ? What it does is

44:24

it takes a , it has a blockchain

44:27

attached to it and it has a vote

44:29

, but not just its computers

44:31

, but other networks that are

44:33

in , like the big meta network . So

44:36

some computer you know in

44:38

Berlin will vote on this because

44:40

their profiles and you know , like

44:42

virus profiles , right . So there'll

44:44

be that kind of thing and and it works really

44:47

, really well . And the demo

44:49

we've been using is we

44:51

have a robot arm and we hit it with a

44:53

, with an attack , put

44:55

a virus on it , and then our

44:57

system is able to . When

45:00

we do it again with our system running , it

45:02

deflects the virus and it won't accept it

45:04

as input , and so now we've added post-quantum

45:06

onto that . So the attractive

45:09

part about this system , to me at

45:11

least , is it's decentralized

45:13

. So if you're a company

45:15

and you buy a system like this and

45:18

you run it and something goes wrong

45:20

, it's your IT guy's fault , it's

45:22

not Microsoft , and I think

45:24

that's very empowering . That's

45:27

interesting .

45:28

We spent the whole time talking

45:30

about how that control

45:32

or that empowerment is going away

45:35

from us and more towards

45:37

technology or these thousand-pound

45:39

gorillas in the industry and

45:42

that's interesting how it's bringing it

45:44

back , how it's bringing that

45:46

ownership back to us almost in

45:48

some ways .

45:56

Well , I think I mean I'm kind of an anarchist at heart really . You would never know from my background

45:58

, but whenever I see things getting too institutionalized

46:00

it gets my hackles up . And

46:02

the government never bothered me

46:05

because I'd been in the government and the

46:07

government's fundamentally incompetent , no

46:09

matter who's president , and they can't really

46:11

do things . They say they're going to do things

46:13

, but it takes them like a decade

46:16

to do almost anything . The

46:18

thing to worry about is guys like Zuckerberg

46:20

, you know , and those people , the

46:22

billionaires , that are not stupid

46:25

, that have lots of

46:27

assets . Elon Musk is probably

46:29

a better example , because he'll

46:31

do almost anything , potentially , if

46:33

it suits his interest . I

46:35

worry about those . So the more

46:37

we take our technology

46:40

out of these people's hands , the

46:42

better off we are .

46:43

Yeah , well , it also enables us

46:45

to maintain our own privacy right

46:48

, which has been something that you

46:51

know doesn't really exist .

46:54

I wrote a couple of books in this . I wrote a book called

46:56

Privacy Loss , still on Amazon

46:58

. It's not there for 14 years . I predicted

47:01

a lot of this stuff and

47:03

I think that the trick to privacy is

47:05

you have to accept the idea that

47:07

the old definition

47:09

of privacy is irrelevant . Privacy

47:12

is not binary , it's not , and

47:15

baby boomers talk about

47:17

it in Gen X people . They go oh , I lost

47:19

my privacy , oh , I got my privacy back

47:22

. It's not virginity , it's

47:25

not like that . It's not binary Privacy

47:28

. It's like uptime on a network . You

47:30

know 99.9999

47:32

. It's like four nines or three nines or two

47:34

nines . That's what privacy is , and

47:37

you have to expend a certain amount of energy

47:39

and time and money to achieve

47:42

each granularity level on privacy

47:44

. But people don't want to

47:46

spend that money because they think they're entitled

47:48

to it anyway . So

47:50

that's going to be a problem too .

47:54

Yeah , yeah , that is definitely going to be a problem

47:57

. I feel like to some extent

47:59

it's been that

48:01

whole debate , that whole talk has

48:03

almost kind of been pushed to the back burner

48:06

in some ways . You know

48:08

, I I always remember the

48:10

first time I went to to germany

48:12

for a study abroad in college , it

48:15

when I was on the plane it

48:17

had just broke from the snowden

48:19

leaks that we were spying

48:21

on germany , right . So when

48:23

I get off this plane , I have a connecting flight

48:25

to make it to Berlin . I'm in Germany

48:28

, I'm in Dusseldorf , right , and I'm

48:30

going through customs and this guy doesn't want to stamp

48:32

my passport and I'm sitting here like like

48:35

hey , man , I have a flight in 20 minutes

48:37

. I have to run across an airport in

48:39

Germany . I don't know where I'm going to

48:42

catch this flight that I hopefully get the

48:44

right one , right . And so

48:47

I started to argue with him , right , and

48:49

it eventually because there was no TVs

48:51

around me or anything like that he

48:54

eventually just stamped it . His boss came over

48:56

and stamped it and by the time I get to my

48:58

gate , I sit down for five minutes and

49:00

I see America spying on Germany

49:03

since whatever year , and

49:05

I was like that's not good for

49:07

me , because now I just came here and

49:09

I yelled at that guy and they're

49:11

probably looking at me a different way now , but I

49:13

mean of course we were .

49:16

Everybody spies on everybody , and

49:19

you know it's like this TikTok thing

49:22

which is absolutely ludicrous . And you know

49:24

it's like this TikTok thing which is absolutely ludicrous . I mean , it's not ludicrous

49:26

to think that TikTok is gathering personal

49:28

information . It's ludicrous to think

49:30

they aren't , and in fact

49:32

, I would be shocked if they weren't doing that

49:34

. And guess what ? I bet

49:36

Meta does it , and Instagram and

49:39

Facebook , and I bet Elon

49:41

Musk does it with X , and

49:45

I bet , you know , every one of these social media platforms does

49:47

it . Microsoft does it , even if you surely you've noticed

49:50

this . But if you buy stuff

49:52

, software you used to buy , like Microsoft

49:54

Office or Adobe Photoshop they

49:56

have switched to these serialized

49:59

license models which require

50:01

a lot more information from you

50:03

, and so not only

50:06

do they want the money , they also want the

50:08

information .

50:09

Yeah Well , these products

50:11

, you know they can be free to

50:13

some extent because we're

50:15

the product . You know they're taking

50:17

our data and they're selling it to

50:20

whatever broker and you

50:22

know it's a mess . And I don't know

50:24

how we come back

50:26

from this perspective without

50:29

having something like Web3 , you

50:31

know , widely deployed , widely accepted

50:34

and , you know , building from there

50:36

.

50:37

That's why I'm interested in Web3

50:39

. I mean , my

50:42

basic meta thought on this is

50:44

I think individuals need

50:46

to be armed with cyber weapons

50:48

and , like when I was

50:50

at Network Solutions , I was running a

50:52

thing called the internet , which is the DNS

50:55

system and other stuff , and I had

50:57

to defend the first . As far as I know , institutional

51:00

denial of service attacks Big

51:03

. No one had ever seen one before and

51:05

they were really stupid and anybody

51:08

today could have stopped it . They were just like

51:10

smurfing on

51:12

some broadcast address

51:14

. But we had to decide what to do because

51:17

there was no precedent and no policy

51:19

. And I made the decision let's

51:21

find out the IP address and let's

51:24

, like , blow them up out of the water

51:26

. And we did , and that

51:28

was my approach . If somebody did that

51:31

to us , I would find out what network

51:33

they were at and I would blow their

51:35

network out of the water . And then I didn't

51:37

have to worry about Smurf attacks . So

51:39

I don't even think you can do that now

51:41

, but yeah .

51:44

It's like Battleship . Yeah , it's like playing

51:46

Battleship .

51:47

It absolutely is , and

51:49

that's how things are with the Chinese in the US

51:51

right now too , right .

51:54

Well , david , you know we're

51:56

at the top of our time . I try to be very conscious

51:58

of my guest's time . You know , when I say it's an

52:00

hour , it's an hour . But

52:02

I mean this conversation has been very

52:05

fascinating , very engaging , and

52:07

I definitely want to have you back on .

52:15

This is a fantastic time . Yeah , well , thank you . I've enjoyed

52:17

it too , and I think the stuff you I looked at

52:19

some of your other ones that you've had guests on , I mean the stuff you're doing

52:21

is really relevant right now .

52:23

Yeah , I try to be . You know I don't want to put out like dated

52:26

, dated information . I want the podcast

52:28

to actually , you know , have value and

52:30

show value to to my listeners

52:32

. Well , you know , david

52:34

, before I let you go , how about you tell my audience

52:36

you know where they could find you , where they could find

52:38

your company , that you're , that you're doing this great

52:40

work with ? Uh , if they wanted to learn more , the

52:52

company is called Naoris .

52:53

It's a Portuguese word , it's N-A-O-R-I-S dot com . N-a-o-r-i-s dot com

52:56

. You can find me at DavidHoltzmancom

52:58

or GlobalPOVcom is another website I use

53:00

and my email address is on there

53:02

and if anybody wants to reach out , I'm

53:05

I'm pretty accessible .

53:07

Awesome , awesome , well , well , thanks David

53:09

for coming on . I'm definitely going to have to have

53:12

you back on , and you

53:14

know . Thanks everyone for listening . I hope

53:16

you really enjoyed this episode and more

53:18

to come in 2025 . Thanks .