SaylorCorpus

Michael J. Saylor Keynote

Saylor University · 2019-11-14 · 41m · View on YouTube →

0:11

How's everybody doing everybody? Enjoy

0:11

your lunch.

0:13

I think we're getting people gathering

0:14

in. Um, just want to say good afternoon

0:16

again for those who are just joining us.

0:18

My name is Jeff Davidson. I'm executive

0:19

director at Sailor Academy. If you

0:22

weren't here this morning for a little

0:23

presentation, just as a reminder, we're

0:25

a nonprofit global initiative educating

0:27

people around the world, mostly

0:28

underserved communities. We're offering

0:31

courses in very key foundational

0:33

subjects like English and and tech and

0:36

all those sorts of things, but also

0:37

career focused opportunities like

0:39

decision-m, leadership, and software

0:42

engineering. All really interesting

0:44

stuff. We've got students in 185

0:46

countries who have collectively earned

0:48

over half a million certificates across

0:50

those areas. And I think I have do I

0:53

have a couple slides

0:56

there

1:09

Well, there were supposed to be a couple

1:09

slides of showing you a couple stu

1:11

actual student um uh that that are

1:14

earning these certificates and that we

1:16

can we can kind of skip over that. But

1:18

whether they're around the world or

1:19

right here in DC, there are large

1:21

numbers of people who simply just don't

1:23

have access to education.

1:32

So real quick

1:32

here in Washington DC uh a group

1:36

faith-based group that works with

1:37

homeless people transition back in

1:39

society.

1:42

[applause]

1:51

we have a video coming out soon on that

1:51

where there's really life changing

1:53

stuff. was very affirming for us to see

1:55

the order in the cloud. We got people

1:57

taking you see the people signing up and

2:00

earning certificates. It was very great

2:01

to go meet some people and uh so very

2:03

excited to see see the students who are

2:05

benefiting you know again we also have

2:07

students from 185 countries. So you see

2:10

some of the quotes we get emails that we

2:12

get from places around the world.

2:39

for learning the excitement because

2:39

again it's about access for a lot of

2:41

people. There's a ton of talent, a ton

2:43

of potential, a ton of great people

2:46

around the world who need, you know,

2:49

that access. Um, you know, there's

2:51

future future engineers, future doctors,

2:53

future everything out there. Um, if they

2:56

can just get that chance and that is

2:58

what open education is really all about.

3:01

And very few people have championed that

3:03

ideal more than our next speaker. Uh,

3:06

it's my distinct pleasure to introduce

3:07

the trustee of the Sailor Academy,

3:09

Michael Sailor. Michael's the chairman

3:11

and CEO of Micro Strategy, a global

3:13

leader in software, excuse me, and um in

3:17

enterprise analytics and mobility

3:18

software. We've got 2500 plus employees

3:21

around the world. So, Michael's very

3:23

going to bring a very global employer

3:25

perspective. Earlier, someone mentioned

3:27

lack of employer uh perspective in the

3:29

morning session. Well, we've got a

3:30

little bit of that coming through

3:32

Michael. Michael's full bio is in your

3:34

program, but you'll note that he went to

3:36

uh MIT and earned a couple scholar

3:38

earned a couple degrees um through an

3:41

ROC scholarship. Uh and so he came out

3:44

with no debt. And he's mentioned more

3:46

than once that that was his impetus to

3:48

try to give back uh to make try to offer

3:51

a first class education to people who

3:53

can't get to a place like MIT, but also

3:56

anywhere just without without the debt.

3:57

So you can free people up to go tap into

3:59

their potential.

4:01

um his philanthropy extends to many

4:04

areas, but his uh commitment to

4:05

education is just really extraordinary.

4:07

Not just giving resources, but time,

4:09

energy, and ideas. So, we're very

4:11

appreciative of Michael helping us put

4:13

this event on and just for his um

4:15

continued support through our whole

4:17

enterprise and and reaching all these

4:19

students around the world. So, please

4:20

join me in welcoming Michael Sailor.

4:22

[applause]

4:33

Thank you, Jeeoff. [clears throat] Um,

4:33

and thank you for coming here today. Uh,

4:35

I thought I'd uh share a few uh a few

4:39

observations of mine about um what's

4:42

going on in the marketplace uh from a

4:46

technology

4:48

uh employers point of view. some

4:50

observations I see looking at big tech

4:54

right now and then the implications for

4:56

what we're all doing together in open

4:57

education. Um first uh a couple of

5:01

trends you if you look at the year 2019

5:06

and you ask how has the world changed in

5:07

the past 20 years? Well, there's a lot

5:10

more computing power. Like a like a lot

5:14

more like a thousandx more, maybe

5:16

10,000x more on average per unit.

5:20

There's a lot more memory,

5:23

computer memory. That is uh I have a

5:26

this is an Apple Watch, but it's a

5:29

series 5 Apple Watch. It's got 32

5:31

gigabytes of RAM in the watch, and the

5:34

phone has 500 gigabytes. 500 gigabytes

5:38

is um when uh when we used to do data

5:41

analytics for the Target corporation

5:44

back in the mid 90s, the entire retail

5:47

chain could store everything they did

5:49

for like three years in 200 gigabytes.

5:55

So to give you a sense of of what we've

5:58

got, um we're rapidly able to uh keep

6:01

track of a lot more information and the

6:04

processing power increased. So I got to

6:05

say it feels like it's like an order of

6:08

a th00and to 10,000. There's a lot more

6:11

elasticity now in the marketplace and

6:13

especially elasticity as um typified by

6:17

uh cloud offerings like Azure and AWS

6:20

which are are generating a huge amount

6:23

of excitement and interest. Uh what does

6:25

it mean? Uh it means that um instead of

6:30

taking a year to hire 50 employees and

6:34

spending $20 million to set up a data

6:37

center in Singapore, you could do up the

6:40

entire thing in 12 minutes with a click

6:42

[clears throat]

6:47

and then if you made a mistake, you can

6:47

unwind it. So,

6:49

uh what is the implication of all that?

6:52

Well, I mean, clearly um

6:56

if you've got a piece of software that

6:58

does something, you've now got 10,000x

7:02

the resources to run the software on.

7:05

So, that's really good for people that

7:07

have good software. Now, if you happen

7:10

to be in the business of manually

7:12

laboring to replace a piece of software

7:15

or doing something uh using your hands

7:18

instead of a computer,

7:21

it's just getting thousands of times

7:23

more painful for you. Uh it's uh and

7:26

it's not going to end. And so what

7:29

happens? Um well, what one thing that

7:32

happens is the rise of big tech. This is

7:34

why Google, Facebook, Apple, and Amazon

7:36

are in essence crushing everybody. And

7:40

you know, they're rolling over and uh

7:43

rolling up any kind of competitor

7:46

because they've got the software and

7:49

they and they keep doubling down the

7:51

amount of software engineers to make it

7:53

better. And as the cost of running the

7:55

software falls, the people Why is

7:58

Microsoft worth nearly a trillion

8:00

dollars today? If you have a lot of

8:03

software and the world relies on more

8:05

software then you have an advantage. So

8:08

so software laden companies are being

8:12

lifted and unautomated companies manual

8:16

bricks and mortar companies just keep

8:18

keep getting hammered and hammered and

8:20

hammered and so while we have um more of

8:24

one thing we have 10,000 times more

8:26

computing power than we had. What do we

8:29

have less of in 20 years? And uh this is

8:33

best illustrated by the observation that

8:36

when I was in high school, there were

8:38

three channels and there was prime time

8:40

TV from 8 till 11 and there was like one

8:43

TV show between 11 and 11:30 and then

8:45

there was just a bunch of bars on the

8:47

screen.

8:49

And if you were technically apt, you

8:51

could actually figure out how to get

8:52

like the PBS station. So you might get a

8:54

fourth

8:56

and that was pretty much it, right? And

8:59

so at 11:30, you probably just went to

9:01

sleep because there was no Netflix,

9:04

there was no Amazon Prime, there was no

9:06

Disney Plus, there was no HBO Go, there

9:11

was no Apple TV Plus,

9:16

there was no free infinite, there was no

9:20

YouTube. If you actually look at the

9:22

most popular websites in the world 20

9:25

years ago, um at the top you had AOL

9:31

and uh then there was a burst where

9:33

Yahoo came on and then you start to see

9:38

uh this uh this little engine that could

9:41

which is Google that comes out of

9:43

nowhere and just starts to get more and

9:45

more powerful. And then right behind it

9:47

in the caboose is YouTube.

9:50

And Google and YouTube run all the way

9:52

to the top. And eventually Facebook, the

9:55

prodigy comes along. And today it's like

9:57

Google, YouTube, and Facebook.

10:00

But uh

10:02

and it's not like Google's actually

10:04

slowing down. Google's actually widening

10:06

the gap. In the last three to four

10:08

years, they went from being the top with

10:11

a lead of this much to being on top with

10:13

a lead of this much. In fact, twothirds

10:16

of all of the usage as far as I can see

10:19

are Google and YouTube. Like this one

10:21

company and Google owns YouTube.

10:25

Yeah. So this one company has like 70%

10:27

of all the views,

10:30

right? And and uh [clears throat] it's

10:33

it's got a lot of implications like

10:36

what is YouTube? Well, YouTube is just

10:38

millions and millions of hours of

10:40

streaming video, but a lot of it's

10:41

didactic. There's a lot of educational

10:43

content if you want to learn anything,

10:45

right? I mean, a lot of it's some of it

10:47

is deep education. I can find a woman in

10:49

India that'll give me a six or eight

10:51

hour tutorial on selenium and automated

10:54

testing if I want. By the way, I'll see

10:57

16 other competing tutorials on

10:59

selenium. Uh I can take, you know,

11:03

courses galore on nutrition or the like.

11:06

You know, there's a guy called the

11:07

aimator. He's uploaded 1,000 videos on

11:11

chess. Thousands and thousands and

11:13

thousands. So, sometimes it's pop

11:16

culture, it's like look at me, this is

11:17

fun. Sometimes it's travel log.

11:19

Sometimes it's like serious.

11:22

Some uh sometimes it's con academy type

11:24

stuff. It varies. But what you can see

11:27

is just this enormous thirst for

11:29

thematic uh knowledge coming in a in an

11:33

easily consumable uh format.

11:36

[clears throat] So what do we have more

11:39

of? We have a lot more compute and

11:40

memory. What do we have less of? We have

11:43

less attention span and less patience.

11:47

You know, I joke with my customers that

11:49

um it used to be if you roll the clock

11:53

back 10 to 20 years, a business person

11:56

would run a query and they would wait uh

11:59

and they're waiting a minute for the

12:00

answer and there's two other things

12:02

they're thinking about. And today, if

12:05

they have a question, they might wait

12:07

five, three seconds, a couple of

12:10

seconds, and there's 20 things they're

12:12

thinking about. Your phone's ringing,

12:14

your computer's ringing. There's Apple

12:16

like has, have you noticed like they

12:18

have all these notification alerts, and

12:20

they're like, "Well, you can't we're

12:21

turning your notifications off and then

12:23

you're gettingounded by every possible

12:26

app to get permission to turn

12:28

notifications back on." your bank wants

12:30

to notify you and your your Facebook

12:33

wants to notify you and every other

12:35

insta something wants to notify you and

12:39

at some point you're like getting

12:40

notified about everything everywhere all

12:42

the time and you can't keep up.

12:46

So what's the most valuable resource on

12:48

earth? It's the number of milliseconds

12:50

between when somebody wants something

12:52

and when they get it. and uh and the

12:55

[clears throat] competition or or the

12:57

expectation is being set by Google and

13:00

Google has created something which is uh

13:04

smart and fast. So successful products

13:08

everywhere in the world, things that are

13:10

working, they they have a couple

13:12

characteristics. One is they're smarter

13:15

and the other is they're faster and the

13:17

last is they're flawless.

13:20

And Google is an a kind of example of

13:22

that. And that when you start to ask

13:24

Google a question,

13:26

you know, like uh

13:29

who's going to win the baseball match

13:32

and you misspell baseball,

13:34

you know, Google says, "Did you mean

13:36

who's winning the World Series right

13:39

now?" You know, and the answer is the

13:40

Nationals,

13:42

you idiot. You know, like you asked the

13:44

wrong asked the wrong question the wrong

13:47

way, but I inferred what you wanted to

13:49

ask and it's and you're like, "Oh, yeah,

13:51

that's what I really wanted to say."

13:53

Right? You know, and it kind of, you

13:55

know, it's kind of amusing, but if

13:57

you're if you study as computer

13:58

scientists, you know, you kind of

14:00

scratch, you think, man, those those

14:03

engineers are so quick, right? like and

14:06

then you just kind of feel sorry for

14:08

their competitors

14:10

because as you're typing sometimes

14:13

you'll type three letters. If you were

14:16

to go on Google during the World Series

14:18

and type B,

14:21

it would probably say who's winning the

14:23

baseball game right now, Washington

14:25

Nationals, you know, like you probably

14:26

wouldn't get past B before it's giving

14:30

you that thing. It's not waiting. And uh

14:34

uh it it takes me to an interesting

14:36

point which is if you've got a if you've

14:39

got a product or a computer program,

14:41

it's waiting for the user to ask the

14:44

question or to do the thing

14:47

and it's sitting idle.

14:50

Like in the amount of time that I've

14:51

spoken, my watch, you know, burned off

14:55

enough cycles to have like launched 10

14:57

trips to the moon, calculate the

14:58

ballistic trajectory and come back,

15:00

right? Like but it didn't.

15:02

You see, like it's just pouring

15:05

computing power onto the floor, right?

15:10

This is like more computing power than

15:11

the Walmart Corporation had 20 years

15:14

ago. And as I'm talking to you, what is

15:16

it doing? Nothing.

15:20

So then you end up with these companies

15:22

asking, "How do I get the computer to

15:25

work 100 times harder?"

15:27

Because the only way to get ahead in the

15:30

economy or ahead in the world is I have

15:33

to tap into the unlimited infinite free

15:37

exploding magical resource of the age.

15:41

You know, 100 years ago was oil,

15:44

gasoline, internal combustion engine.

15:47

Now it's compute engines. How do I tap

15:50

into that? Well, how do I make the

15:53

computer work a 100 times harder? Well,

15:55

how about how do I make the computer

15:57

work a 100,000 times harder,

16:02

right? How how do I build uh something

16:07

which is going to think and then do

16:11

before you ask it to do something what

16:14

you should ask it to do if you were

16:16

sophisticated enough to know to ask.

16:19

Well, there are examples of of companies

16:21

that the companies that are doing that

16:23

and they're making

16:25

incredible large heckloads of money.

16:29

Google is one example we just talked

16:31

about. Uh Google does it in their search

16:34

engine and YouTube they do it again.

16:36

It's like Lord help you. So if you click

16:38

on one one little video on Franklidd

16:42

Wright houses and then you look in the

16:44

right it's like here's 18 Franklidd

16:47

Wright house tours and then here's a

16:49

documentary on Franklidd Wright and

16:51

here's a documentary on houses built by

16:53

acolytes of Franklidd Wright and here's

16:55

discussion of architecture and you know

16:58

if you click on two it will feed you a

17:01

thousand hours worth of this stuff and

17:04

it and it you got to be careful what you

17:06

click on because it will just overwhelm

17:07

you with pet videos, videos on the Ketto

17:11

diet, videos on chess, videos on

17:15

whatever. And it's infinite, right? It's

17:17

like uh yeah, there are guys that do uh

17:21

they now do live chess commentary. So I

17:25

want to paint this picture for you that

17:27

you know two world champion chess

17:29

players will watch a game which is going

17:32

to take four hours

17:34

where the players are moving every 10

17:36

minutes and for that nine hours they

17:40

will give you a color commentary.

17:46

Now, think about how many thousands and

17:46

thousands of hours of chess video you

17:49

can watch.

17:50

And think about what you would

17:51

accomplish in the year during which you

17:54

watch that. Because there's 10 to the

17:56

120 different chess games out there. You

17:59

could watch it 5,000 hours a year for

18:02

the next 50 years and you won't get

18:04

through 1%. It's just

18:07

frightening, horrifyingly, terrifyingly

18:11

entertaining, right?

18:13

However you think about it, you really

18:14

want to think what does that mean to

18:16

you? Well, um another example

18:21

of of um of this uh is Apple,

18:27

you know, and it used to be Apple

18:29

thought, well, we'll put the camera on

18:31

the phone and then they thought, well,

18:34

we think we'll take the the picture and

18:37

then we'll do a little bit of software

18:39

editing on the picture to make it look

18:41

better. and then we'll give you a little

18:42

filter. And then they thought, well,

18:45

what if we actually took two pictures

18:48

and then we went through pixel by pixel

18:50

and we picked the best pixels of each of

18:52

the two, you know, less shaky and we put

18:55

together one.

18:57

And then they thought, well, why don't

18:58

we just take 16 photos? Each one is five

19:02

megabytes, and we'll just go and make 80

19:04

megabytes. And then in the background,

19:05

we'll just go ahead and shuffle it all

19:07

together and create this composite. But

19:08

we're not going to tell you. And as you

19:10

can see, and you can tell this is going

19:12

on because in the iPhone 10, you take

19:15

the photo and if you click the trigger

19:17

too shutter too many times, it's the

19:19

machine or the the the camera kind of

19:21

stops and it delays. And then the iPhone

19:24

11, they improved the chip. It's like I

19:27

had to invent some software which is

19:29

insanely expensive so that I can give

19:31

you or sell you a chip which is insanely

19:33

fast so you would upgrade and throw away

19:36

your old phone right now.

19:40

What's the key to that? Right? You have

19:42

to get away from analog and machines and

19:45

you have to get into uh software so that

19:49

you can make the software work harder.

19:51

Right? This

19:53

right this has got enough horsepower in

19:55

it to listen to me speak. Right.

19:59

When is sunset?

20:07

Sunset will be at 4:55 p.m. today.

20:07

a fishly expensive

20:10

use of computing power, right? To listen

20:13

to me speak. And you know, like, by the

20:16

way, you want want to see something even

20:17

more expensive?

20:19

What time is it?

20:22

It's 1:25 p.m.

20:23

Yeah. Yeah. Right. Like I can answer the

20:26

question by just glancing at it or I

20:28

could look at an analog or I can create

20:30

a chip which processes a billion

20:32

instructions a second. Listen. convert

20:34

your voice into some stream, parse it,

20:37

and [clears throat] then reverse it back

20:39

into some some English semantic

20:41

representation. Look at the thing and

20:43

then and then I can do a voice decoder

20:46

thing and I can synthesize speech and I

20:48

can tell you what time it is.

20:52

Is that good for our society?

20:54

Interesting. [clears throat] Well, it

20:57

has implications.

20:59

What are the implications? Right. Well,

21:01

first of all, um [snorts]

21:04

you really better be educated.

21:06

[laughter]

21:07

You really better be educated. And uh

21:09

there are certain things that we want a

21:11

lot more of right now. Um if you want to

21:15

if you want to produce this sort of

21:18

product

21:20

uh which uh which keeps getting better

21:22

and better and better which is drives

21:24

the engine of the economy then you have

21:26

to be very precise

21:29

and uh and very orderly in the way you

21:32

communicate. [clears throat]

21:34

Our company's values are precision,

21:37

transparency, engagement,

21:39

and agility,

21:41

but they're they're very much related to

21:44

what you'd have to do if you want to

21:46

write a piece of software or you want to

21:48

get a lot of people to work together.

21:58

it's pretty Thank you. That's nice of

21:58

you. Uh it's it's pretty clear that

22:03

if you don't uh if you don't get through

22:05

all the basics uh all of the the English

22:09

and math and basic sciences and then and

22:12

then follow with logical

22:16

thinking processes

22:19

uh that drive intellectual rigor then

22:22

you're not going to be able to

22:23

contribute in any of these enterprises

22:26

uh that are building these things

22:29

because they all are defined by lots of

22:32

moving parts like lots and lots of

22:34

moving parts at um

22:38

at Micro Strategy

22:40

um I think we

22:42

well the CEO's control freak uh and

22:47

and so one thing that the CEO did was he

22:50

implemented a rule whereby he has to

22:52

approve every single hire and I think we

22:55

hired five six hundred people last here

22:58

something like that and uh I don't have

23:02

time to interview them all but I want

23:04

some degree of control. So we

23:06

implemented a set of precise assessments

23:09

and whenever you want a job with us

23:11

doesn't matter where you are if you're

23:12

in Poland or if you're in Nigeria or

23:16

South Africa or or Malaysia it could be

23:19

anywhere Japan Korea we give you um

23:22

[snorts]

23:23

uh assessments uh that give us an

23:26

insight as to your analytic skills, your

23:28

business judgment, your coding skills,

23:31

your design or or creativity

23:33

freethinking and and graphical design

23:35

skills.

23:36

and um and uh then English and then

23:40

after that we might give you a French,

23:42

German, Spanish, Japanese, Korean or

23:45

Portuguese

23:46

test to assess that and then we put that

23:49

into the um into the record and then

23:52

that flows all the way up the the entire

23:55

organization and there's some interviews

23:57

like four or five people might interview

23:59

someone and the HR people opine on it

24:02

and then the manager and their boss and

24:04

the department head and it gets all the

24:05

way to me.

24:07

And uh

24:10

what's interesting is

24:12

what you find is that the resume very

24:15

rarely is that um useful. Um in fact,

24:18

the better the resume, sometimes the

24:21

more skeptical I am.

24:23

Like if you have that good a resume, why

24:25

are you looking for a job? Like why do

24:28

you want to work for me? Like I don't

24:29

want to be a member of a club where they

24:31

would have me, right? Like so some I

24:34

mean sometimes that's good to have a

24:35

resume but sometimes it's not good

24:38

because the people that have failed in

24:40

the last six jobs always have great

24:42

resumes because they had lots of

24:44

experience

24:46

you know so maybe uh and then the

24:48

interviews well I mean some people

24:50

interview well but of course the more

24:51

you interview the better you get at that

24:53

right

24:55

and uh

24:57

and so what I see is the resume not so

25:00

useful the interviews may or may not be

25:03

useful. You read a lot of stuff, you

25:05

can't tell anything. The assessments, I

25:08

look at the assessment and about one

25:10

second I can say, you can't write.

25:15

You're going to be a great designer. You

25:17

should build the graphic interface. You

25:19

have really poor sense. You you know,

25:21

given given 50 choices of what's

25:24

beautiful, you made the wrong choice 25

25:26

times. Right. Right. If you if you like

25:30

I can see that or I can see like we ask

25:32

business questions like you have a

25:34

meeting do you show up five minutes in

25:36

advance 15 minutes in advance or two

25:38

hours in advance right and if they say

25:41

15 minutes in advance we we're like okay

25:43

that's good two hours that's too soon

25:45

five minutes is like cutting it way too

25:47

close right do you have common sense

25:52

bottom line is uh oh by the way what

25:54

didn't I mention I didn't really mention

25:56

the university

25:58

In fact, uh I don't pay attention to

26:00

what university went to. I don't pay

26:02

attention to what degree they got and I

26:04

don't pay attention to what grades they

26:06

got. Uh because they're all

26:09

non-comparable. You can't tell. Uh what

26:12

I found is that the best single

26:14

indicator of success is uh the one

26:18

second of the objective assessments that

26:21

we have. And uh the uh the negative of

26:26

what we do is we make we make people

26:28

take two hours of or two and a half

26:29

hours of assessments to actually give us

26:31

that one second insight. The ideal

26:34

situation, as I've spoken about in these

26:36

previous events, would be if there was a

26:39

universal set of assessments that

26:41

someone could take once and they could

26:43

submit those credentials to a thousand

26:45

different employers and then instead of

26:47

taking two hours to go through the

26:49

employer's battery of assessments, you

26:51

could go through the two uh the two

26:52

hours of assessments once uh put them on

26:54

the blockchain or upload them, you know,

26:57

and then uh you know, if you think about

27:00

the implication, it doesn't just speed

27:02

up your process by two hours because if

27:05

you did it 10 times, it would cut 20

27:08

hours out of your life of waste. But it

27:10

doesn't just make you 20 hours faster.

27:13

When you actually get to something which

27:15

is um a public uh a public uh shared

27:20

standard, then you could go and post

27:23

those assessments online and be

27:26

considered by 800,000 employers in five

27:29

minutes.

27:32

Right? So, you've securitized the

27:34

talent, right? Like if you uh

27:39

if you tell me you have a bunch of Apple

27:40

stock and you'll sell it to me for $120

27:42

bucks a share and it was trading at $260

27:44

a share, it takes me one second to

27:47

decide I want to buy, right? Because

27:50

we've got a very precise definition of

27:51

the security and the price. So, if you

27:55

want to actually buy and sell talent and

27:59

you're you're a wouldbe employee or a

28:01

wouldbe employer,

28:04

right, then um then the best way to uh

28:06

to speed that up and create a liquid

28:08

market is is to post a public credential

28:12

that is well understood and shared. And

28:15

uh the the real interesting thing is if

28:18

I could go uh if I could go online and I

28:21

could pick my city, if I could go to

28:24

Hanjo or go to Warsaw or go to Paris and

28:27

just hire 27 people that hit these

28:31

credentials,

28:33

I might do that in a day.

28:36

I would do that in a day and I would pay

28:38

for the 27 people as opposed to take two

28:42

years and hire three recruiters and pay

28:46

half a million dollars in head hunting

28:47

fees in order to get, you know, half of

28:51

that. So the um the market is becoming

28:56

uh more liquid and um I'm I'm enormously

29:00

excited by um the potential of these uh

29:04

public credentials. You know what a

29:06

credibly is doing, what we're doing at

29:08

the Sailor Academy. It's very very

29:10

interesting. Put your credentials on the

29:12

blockchain and um you can see um this is

29:17

being driven by uh big tech in a lot of

29:20

ways.

29:22

Google and uh and uh Microsoft and

29:27

Amazon are all driving these big cloud

29:29

initiatives and one of the interesting

29:33

characteristics of that is so a

29:35

corporation like Fizer wants to put all

29:37

their processing in the cloud once it

29:40

goes into the cloud in AWS or Azure

29:43

let's say they need people to tinker

29:45

with it and administer it and run it

29:48

well how do they find those people.

29:50

Where are those people? Well, it doesn't

29:53

matter where they are, right? Because

29:55

it's in the cloud anyway. They could be

29:57

in anywhere. They could be in Africa.

29:59

They could be in Asia. They could be in

30:01

America. So, the migration of all of the

30:04

computing to the cloud is a

30:07

globalization of the labor pool. Now it

30:10

also turns out that um that when we want

30:14

to run uh a customer's uh intelligence

30:17

environment in the AWS cloud, we have to

30:21

take our people and put them through an

30:22

AWS set of classes. So we go online in

30:25

AWS and they go through like 40 hours or

30:28

80 hours worth of classes in Linux and

30:31

system administration AWS and the like

30:33

and at the end there's a certification

30:34

and you take a test and you submit it

30:37

and you get graded and if you're

30:38

successful you get a certificate and

30:41

that certificate you can then post on

30:43

your LinkedIn and uh and that uh becomes

30:46

a pretty big uh career stamp

30:50

and uh you know what happens next our

30:53

person gets a certificate and then they

30:56

get head-hunted away by somebody else

30:59

[laughter]

31:00

because there's massive demand for

31:03

people that have that skill. So, so, uh,

31:06

that's the first order effect. And then

31:08

we end up sitting and talking about it.

31:10

We go, well, gosh, I guess we better

31:12

give them a raise as soon as they get

31:14

that certificate. Yeah. So we had a

31:18

situation where we were um slow as an

31:23

employer. We go to some place China or

31:26

Warsaw or anywhere in the world then we

31:28

hire a lot of people and then we train

31:31

them and then uh they get skills and

31:34

then uh you know other companies

31:36

competing with us including you know big

31:38

tech and all the integrators they hire

31:41

our people because they think well this

31:43

this is great. You trained all these

31:44

people we can hire them. This is good.

31:46

So when we're when we uh give them

31:50

raises of 5% a year or 3% a year, we

31:53

have 25 or 30% turnover. So then we

31:56

start thinking, well, we better give

31:58

them a career path that's a bit more

32:01

precise

32:03

uh and say, well, when you hit this

32:04

level, we'll move you to here, and when

32:06

you hit this level, we'll move you to

32:07

here and and here and here. And if we do

32:11

that and if we're rigorous about it,

32:14

then our turnover goes from 25 or 30% to

32:21

So, uh what is that? Well, that is the

32:24

visible hand of the market spanking us

32:28

for being bad employers and reminding us

32:31

that if we don't actually cultivate our

32:34

talent and take care of them and give

32:36

them an inspiring path and then make

32:38

sure we pay them, then some other

32:41

company will and they will solve the

32:43

problem.

32:45

And uh

32:48

when you put together the idea of

32:50

objective credentials with the idea of

32:52

globalization with the idea of liquid

32:55

you know uh crossborder flows of labor

32:59

with the rise of big tech it really

33:03

means that somebody in Nigeria can get

33:05

their AWS certificate and potentially be

33:07

just as employable as somebody in

33:09

Manhattan. and maybe I want them more.

33:13

Um, and that uh what does that mean?

33:16

Well, that means that uh an AWS or Azure

33:20

or the like certification is becoming

33:22

more important than a Harvard or a Yale

33:24

degree because I can tell you like in my

33:27

uh meetings when I sit when we sit

33:29

around and talk what I hear is well we

33:32

need 50 cloud engineers, we need 30 of

33:34

these people. We need 25 of these

33:35

people. We need 50 of these. There's no

33:37

one that ever sits in a meeting with me

33:38

and says, "We need five more Harvard

33:40

grads.

33:42

We need 25 Yale grads." Like, nobody

33:46

ever asks for a credential by the name

33:49

of a of a traditional institution.

33:51

They're wanting a tech capability or and

33:54

it's either a basic or more likely it's

33:56

an advanced certification. So, that is

33:59

driving and you know, uh it's going to

34:02

ripple through everything, right?

34:03

Because the numbers we're talking about

34:08

they're not tens of thousands. They

34:10

might be hundreds of thousands, but

34:11

they're really likely millions and

34:13

millions and millions of uh people being

34:16

infected and going to tens of millions

34:18

and then hundreds of millions. And uh

34:21

the um the motive uh or the motor on

34:26

this becomes uh a company like Cognizant

34:30

with 250,000 knowledge workers about to

34:33

hire 150,000 more.

34:37

You know, they're uh they're really

34:40

wanting to go at this in a in a

34:42

machine-like way. So, you know, what

34:45

have we done? uh it's driven us to uh

34:49

define our roles much more precisely

34:51

like now we're all the companies that

34:53

are successful they're building software

34:55

that's reusable and then building roles

34:57

and certificates uh that that are

35:00

essential for the ecosystem so that you

35:03

can train and certify practitioners to

35:05

use those tools and then you want to

35:07

build your own ecosystem up and uh after

35:11

you've defined the roles you can define

35:13

the credentials then you can issue the

35:15

credentials then you can issue to them

35:16

publicly and uh and then you end up with

35:21

you know 10,000 micro strategy

35:23

architects or 50,000 hyper intelligence

35:27

engineers

35:39

the the first order observation I have

35:39

is that if you've got a product or a

35:41

service offering like that that you can

35:43

automate and then you can uh massively

35:45

we scale up uh the creation of that

35:49

talent then you can grow and you can

35:52

prosper. If you if you can't then you

35:55

just keep getting squeezed every single

35:58

year

35:59

by someone else that is uh the second

36:03

order observation I have is like if I

36:04

think about every internal project at my

36:06

company like everything when we sit

36:09

about and we talk about what are we

36:11

going to do to make 2020 better than

36:13

2019 or 2021 better when if you define

36:16

the word hope

36:18

every every hopeful project consists of

36:22

in essence the relentless drive to

36:24

organize and automate

36:27

using some software tool.

36:31

We're either trying to build software

36:33

faster or writing specifications in a

36:36

more orderly uh automated way or

36:38

documentation or courses or

36:40

certifications, managing projects,

36:42

opportunities,

36:44

contracts, quotes, marketing campaigns,

36:47

schedules, any miscellaneous activity,

36:50

any purchase, any hire decision, any

36:52

promotion decision, any transfer

36:54

decision. They're all just many software

36:58

projects using a tool. It's either a

37:01

compiler, it's GitHub, or it's

37:03

Salesforce, or it's Workday, or it's

37:05

Micro Strategy, but invariably we're

37:08

building something to build something

37:10

else in order to create 10,000 of the

37:13

something so that we can do a 100,000.

37:16

So, we can squeeze something down from

37:18

taking 47 minutes to 4 minutes to 40

37:21

seconds to 4 seconds to point4 seconds

37:24

till we get to the point we could do

37:25

27,000 of them in 4 seconds, right? And

37:28

and the only way you do that is just

37:31

relentlessly

37:33

model,

37:35

test, machine,

37:38

install, deploy, pilot, rebuild.

37:43

If you're not manufacturing

37:45

an outcome, then you just literally

37:48

can't keep up in the modern world

37:50

because your competition is some

37:54

enterprise that is manufacturing an

37:57

outcome.

38:01

I guess that uh takes me to my closing

38:03

thoughts. Um

38:06

it seems pretty obvious uh that if you

38:09

want to do a good thing for the world,

38:12

it's undebatable that if we can increase

38:15

the level of education everywhere on the

38:18

planet and whatever it is, if if you

38:21

don't have a high school degree, get you

38:23

on. If you don't have a college degree,

38:24

get you on. If you don't have a master's

38:27

or a technical skill get you one, if if

38:30

we can create PhDs, create PhDs. We need

38:33

to create more and we need to do it

38:35

cheaper and and in the limit where we

38:39

can offer any type of education you

38:43

might want at the cost of zero. And and

38:46

by the way, I I can't see any reason why

38:49

we shouldn't get there because the cost

38:52

to provide

38:55

all all the critical types of education

38:57

is got to be 1% of what we're spending

39:01

right now on on similar things or

39:04

irrelevant things. So, and the cost of

39:07

the compute power is just falling

39:09

exponentially

39:11

uh and will continue to fall

39:13

exponentially. So,

39:17

This is a straightforward thing that we

39:19

all I think we're all joined here

39:21

together in a belief this is a good

39:23

idea. I think uh the success comes one

39:28

part marketing. How do we actually

39:31

market this and communicate this to the

39:34

world so we can do that? And part of it

39:36

is politics.

39:38

How do we get political accredititors to

39:41

embrace uh open learning and endorse it?

39:45

And then part of it is the product if

39:48

you know it used to be that everybody

39:51

used Netscape and then 96% of the planet

39:53

used explorer but today 80% of the

39:56

planet uses Chrome. Why? Smarter,

39:59

faster, better, right? Make the product

40:02

better. Make everything better. You

40:05

know, you look at your courses, you're

40:06

like, are these boring or these fun?

40:07

These are boring. We need to make them

40:09

fun. Right? It's fun. Is it in black and

40:12

white or color? Okay. we love is black

40:13

and white. We can make it color. Can we

40:15

make it easier? Can we make it uh more

40:18

uh more elegant? Can we give it more

40:20

feedback? And we're always trying to

40:22

figure out how do you create that

40:23

awesome

40:25

incredible product?

40:27

And uh and then ultimately we've just

40:29

got we've got history on our side. Bit

40:33

by bit every quarter, every year more

40:36

and more in the world embraces this. The

40:38

world opens up. And I think that uh I

40:41

think there's no doubt where the trend

40:43

is going. If when we look at our own

40:45

stats on the Sailor Academy, it's clear

40:48

there's more enthusiasm every quarter,

40:50

every year. We're picking up momentum.

40:53

So for everybody that's involved in the

40:55

in the space or in the ecosystem, uh I

40:58

want to uh thank you for having an

41:01

interest and and for your commitment. A

41:03

lot of people making big sacrifices to

41:05

support this particular initiative. Um,

41:08

we're happy uh that you joined us today.

41:11

We'd love to help and partner with you

41:13

any way we possibly can. And uh I can't

41:17

think of a better way to spend our time

41:19

and our energy than do something like

41:21

this. Ultimately, I can't see that

41:24

anything but good will come of it. So,

41:26

thank you.

41:28

[applause]

41:42

Well, very good. If you look at your

41:42

agenda, we're going to take a break for

41:43

about 15 minutes or so and then come

41:45

back here at 2:15 for the next panel.

41:48

So, thank you everybody. Appreciate it.

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