[Jeffery Davidson] Okay thank you everybody, I'm going to get
started again. It's my pleasure to introduce the trustee of the Saylor Foundation, Michael
Saylor. For his day job, he actually runs a very large company called MicroStrategy.
He's the chairman and CEO, they're a global enterprise -- leader in enterprise analytics
and mobility software. They have over 2,000 employees worldwide. Since we're changing
up the program, a little short on time, Michael's full bio is in your program but you'll note
he's a graduate of MIT with two degrees, both earned on a full ROTC scholarship, and upon
graduation he was commissioned as a Second Lieutenant in the United States Air Force.
When I've heard him speak, Michael often cites his ROTC scholarship as inspiration for his
willingness and passion for paying it forward and help finding a way to help others get
a first-class education without having to take a lot of debt in order to do so. His
philanthropy extends to many areas but his commitment to education is extraordinary,
giving not just his resources but his time and his energy for new ideas as well. So with
that, please join me in welcoming Michael Saylor. Thank you.
[Michael Saylor] I want to thank everybody for being here today
and thanks for the time and commitment you've given to open education. As I know, everybody's
got their own passions but it's so exciting to see everybody coming together at this event.
I know to a certain extent I'll be preaching to the choir, but Jeff asked me to share a
few thoughts on my observations about technology trends in the marketplace and the views of
various employers in the tech business toward education and toward open education. And as
I have observed, things are changing very rapidly this year; in fact they're changing
rapidly every quarter. It seems like for the past three or four years, the pace of change
has accelerated and of course it's been accelerating for quite a while. Every day I get up and
I see a new and interesting thing that is just another brick in the architecture of
open education so I thought I'd share a few observations with you. For those of you who
know, I wrote a book called The Mobile Wave in 2012 and one of the themes of the book
was the dematerialization of products and services from the physical world into the
cyber world. So many things that used to be a product like a camera, like a printer, like
a typewriter, right, now there are actually software applications or icons on your iPhone
and oftentimes as they dematerialize they became not just a piece of software but they
became entire software networks. So something like the Rand McNally atlas which once was
a product that we printed eventually became Google Maps but then Google Maps went from
dematerialization of the atlas to 200,000 pages of satellite images which you never
could add to an atlas and then it went to real-time traffic and then it went to an intelligent
advisor telling you how to drive to work. And so what started as a physical product
in the real world became an intelligent service, which is software running on a network, with
hundreds of billions of lines of telemetry and we never go back. Now that, I mean, that's
a really interesting trend, I mean this dematerialization, and I mean the world continues to digitize
at a more rapid rate. Why is it important? Because in a world of physical things if I
have a wind tunnel and I want to teach someone how to build an airplane I need to produce
the wind tunnel and there's a physical limit to the number of people that can fit into
the wind tunnel and use the facility. As an undergraduate at MIT, we didn't get to use
the wind tunnel it was too expensive. Now, in the world where you create a cyber wind
tunnel, you can produce a million copies of the wind tunnel and give it away to people
in a hundred thousand places and as the cost of hardware gets cheaper and the cost of RAM
gets cheaper -- and RAM's 10,000 times cheaper that was 15 years ago, computers are a 1,000
to 10,000 times more powerful than they were 20 years ago -- the advent of things like
AWS have resulted in a world where we have software you can punch a button at 9:00 a.m.
and at 9:23 a.m. you can spin up a dedicated environment to support 87,000 users in Singapore
and you can run it for the next four hours and turn it off. Now, four years ago, that
would have cost you 150 full-time employees and 30 million dollars in capital. Today,
you could do it in 37 minutes and the significance is as we get to that world where you've got
infinite capacity on the iPad and infinite capacity on the laptop and massive capacity
in the backend servers -- as everything gets 10,000 times cheaper, better, faster, and
as we digitize, we're able to move from a world of fragile to a world of agile. And
in the fragile world, you wanted to try something, it cost you forty million dollars and four
years and if you screwed it up you're out four years and you're out forty million dollars.
In the agile world, you wanted to try it you tried it in four hours or in 40 hours, you
spent no capital, and if you screwed it up, you throw it away and you try something else.
And if I can try 150 things fast, then I don't have to be perfect, I just have to find one
out of 150 things that works. I discard the other 149 things, civilization advances. In
the world of the wind tunnel, you can't, right? Things that have physical physical capital
involved and bricks and mortar involved, they're inherently more fragile, it's going to be
much harder for us to do things. And one thing that's fragile inexpensive is a traditional
education at a bricks and mortar institution in a traditional fashion, it just takes too
long and costs too much money. This digitization, digital revolution, you know, we tend to -- we
were big on it in the year 2000, but there's a better story every single year that goes
by. I was sitting with a friend of mine the other day, a very successful DC businessman,
and he just took on a job running a drone company. And I said, "What do you do?" He
said, "Well, we've got these drones." I said, "Drones like the ones that fly around and
take like a photo?" And he said, "Well, no, our drones are flying LIDAR arrays." What
is LIDAR? Anybody know what LIDAR is? Anybody here? You're advanced, right? Light detection
and ranging. It's the technology that lets cars drive, self-drive, but what it means
is I put this array on the bottom of a drone and it flashes the room and it takes a perfect
image of everything. You put it on a drone above a beach, it'll tell you everything.
What is everything? It means you now have data that will tell you where the sharks are,
you can put it over agriculture, I can tell you where all the plants are, what's living,
what's dying, how fast it's growing, is it safe, is it not safe, count the number of
cats running through the field. Now, why is it interesting? Well because it's like a million
times more data than a photo. Like, we think, oh digital photo, that's a lot. Well this
LIDAR thing is this array and now we've got these drones that are a thousand dollars flying
around doing this array, that are collecting -- we're not talking about gigabytes or terabytes
of data anymore. I don't even -- you know exabytes, exabytes a minute of data, and it's
a business that didn't exist two years ago. And he goes yeah, well I just had someone,
an insurance company wanted to hire me to fly over every single roof -- whatever -- in
the country or something and figure out, you know, what's dangerous or not. Now, why is
that interesting? In every single field, there's something like that that's driving the digital
revolution and the world that was physical is digitizing and that means all of the skills
and all of the goods and services that were physical are on this path to digitizing. And
whereas it used to be I had to put you in a lab and hand you some tools to learn to
do something or to prove you had the capability to do something, now I can fly a drone over
a beach, I can hand the data to a programmer, and the programmer can write algorithms to
spot sharks. And you don't have to fly in a plane and you don't have to be on the beach
you just simply need to have a set of skills, right, and what are they? Math, science, engineering,
coding, a whole set of technical skills. And every single industry that rolls over to become
digitized creates a ton of jobs for people that actually have these kind of STEM skills
and techniques. Now, why is that interesting? Well, because in a world of digital the machine
is now software. Software is an information machine and there are more and more information
machines that we need to build. We don't have enough people to build them. You know there's
a tendency to think well we're going linearly, we used to do that we're doing this, but in
fact my observation in my industry is we're in an expanding universe. And it's expanding
-- it's an expanding universe of expectations, of demands, of requirements, of aspirations.
Everybody wants more of everything in every direction, right? It's like, Elon Musk wants
to go to Mars and now people don't laugh at that. They're like, they're wanting to do
it. We want to cure every cancer and people don't laugh. It used to be people wanted to
make you comfortable. Now people think you can cure the cancer, right? We want to get
rid of sharks on the beach. We want everything to run perfectly. There is no no tolerance
for imperfection. I say there's -- in my industry we used to, like, build software running on
top of three databases: Oracle, SQL server, Teradata, or something. Now they want to run
software on top of 48 different relational databases, 10 different OLAP databases, 50
different big data databases, 100 different enterprise applications simultaneously, and
also splice in Wikipedia, Google, you know, Facebook, Twitter, everything else. So you
go to the customer, you say, "Well which of these new things do you want to do?" They're
like, "Well, I just want to do 187 things simultaneously." Okay, we used to deploy it
to DOS and then it was Windows. Do you want it on Windows? No, it's the Web. Okay, so
you're going from Windows to the Web. No we want Windows and the Web. What's new? Well
now we want it Windows and the Web and iOS. So do you want to go from Windows to iOS?
No, I want iOS and Windows and Android, and I want it to run on all these clients, and
I want to run on smartphones. Smartphones instead of Macbooks? No, smartphones and Macbooks.
I want it to run in every client. And you want it to run in -- no, I want it to run
everywhere. Well, everywhere? Everywhere. Well, everywhere means I have to -- well yeah,
you have to comply with Chinese privacy policies in Beijing. But it's different in Singapore.
And that's different in Germany, which is different in Ireland, which is different in
Brazil, which is different in the US. Which is probably different in California of the
US. So everything is -- it's an expanding universe in every direction and we've got
a million problems. We could -- and every one of them, you know, how do you find sharks
from LIDAR, you know? That's a problem. We got a million problems and it's pretty clear
in the tech world [if] you want to solve the problem you need to have mastered all of the
undergraduate skills and then probably a bunch of master skills and the definition of a PhD
-- the classical definition -- is you know someone who is capable of contributing and
making a unique seminal contribution to the body of knowledge of the civilization. If
you're able to make a unique contribution then you probably deserve a doctorate. If
you're able to do algebra you probably don't deserve a doctorate. We have about 10 million
PhDs the last time I checked, five million in the US, 5 million everywhere else. There's
like seven or eight billion people on the planet. If you think about it a bit and you
think about what skills are required to, you know, put chips underneath the skin that,
you know, solve diabetes or cure cancer or do whatever, you come to the conclusion you probably
need a billion PhDs. There's probably -- we need -- we don't need seven billion but we
need more than ten million. You probably need like a billion people on the planet that are
able to make a unique contribution, because if you want to create an airplane that flies
15,000 miles or turn it into a rocket ship, you're not going to do it with algebra. You
know, you're not going to do it without having mastered postgraduate thermodynamics and coding
and probably plugged into someone's network. And we desperately need more sources of new
engines, new propulsion, new breakthroughs in medicine, new breakthroughs everywhere.
And that'll be done with a lot of very sophisticated, educated people. Human capital. We don't have
enough human capital, right? I mean the cost to create a PhD is a million dollars a year.
I mean, sorry, a million dollars total. It's just expensive, right? It's a quarter million
dollars to get through college, it's a quarter million dollars to get through K through 12,
it's a quarter million dollars for the rest, right? Maybe someone can figure out how to
do it for two hundred thousand dollars. But two hundred thousand to a million dollars
to take a person and convert them into a PhD, if you do it the traditional way, and so I
don't think we can do it the traditional way. I mean we -- the only way we're going to create
the kind of human capital we need to solve all of these problems and move the civilization
forward is if we provide 100X more education even if -- even if I said I got a hundred
billion dollars and I'll pay everybody's education everywhere on the planet, we still don't have
the capacity and the bricks and mortar institutions to train these people, right? So if I paid
everybody's Harvard education, Harvard still will cap you at whatever the number of students
is they want on the campus. So we need 100x more capacity and we need 100x cheaper price,
right? The cost per education unit delivered has got to go way down, the capacity's got
to go way up. To do that, you need to create a machine and in this case a machine to manufacture
education. And software that teaches is that machine and we know that you can create software
that will teach people. Now, we also know if I want to teach you ballet, right, if I'm
working in a skill like golf or ballet or cooking, right, you're in the physical world,
in the analog world, and all of a sudden you're dealing with fragility and capital intensity.
It's really hard to do that with a piece of software, right? So I don't think we're going
to solve the problem of how do you create a great, great cheap education in physical
world so easily; we will struggle with that. But on the other hand, if what I wanted to
do was generate someone that knows Calculus, in theory, there's no reason why you can't
learn Calculus in front of a computer just as well as learning Calculus in the classroom.
And as a practical matter -- I get a kick out of this -- on Saylor.org we have lectures
from MIT that were uploaded from the time that I was at MIT okay? But here's the joke,
right? When I was at MIT, my entire family savings for the last 200 years would have
been slurped up by the first six weeks of my education there as a student, right? It
was too expensive. And I sat in a room bigger than this where the lecturer was as far away
as you are my dear in the back, right, and I squinted to figure out what was going on
on the chalkboard and it was very uncomfortable. Today you would get a better education for
nothing with a $500 computer logging into saylor.org, and I know because I was there,
right, I lived it. So just because something is expensive it doesn't mean it's better.
The world is full of products that are manufactured, the iPhone for example, and there's nobody
that thinks that if they spent a hundred thousand dollars and built their own phone it would
be better than the one that they got from Apple. And if you spent a hundred million
dollars to build your own phone it still wouldn't be better [than] the one you got from Apple.
So productizing education, I think, is critical for us, and I am optimistic because it's a
lot easier to productize the teaching of algebra or Calculus or coding. You can objectively
determine that someone knows two plus two equals four. You don't need the opinion of
someone that smelled them in the classroom. They don't need to show up and sit in the
front row, on time. All they need to do is prove the two plus two equals four and that
hasn't changed for thousands and thousands of years. So one would think that you could
automate it and slip the copyright by now. I think open education plus computer power
plus cheap ubiquitous networks, right, that they lead to better, cheaper, more comprehensive
education, right, that's the magic the magic there. Now if you look at it from an employer
point of view, I think employers are becoming much more data-driven today, and I mean that's
a buzzword, so let me let me tell you what I mean by that. We've been in business 27
years, I've hired 20,000 people over the course of 27 years. In the year 2016 for the first
time we started to administer diagnostics and we used a platform called eSkill and HackerRank
and that that -- we use a product called SmartRecruiters that recruit the people. So here's how it
works. For every single person that goes to an interview with our company -- and this
means the last 5,000 people -- and we probably hired a thousand, so probably a thousand people
have been hired in this technique. Everybody goes through the company [and] we give them
an ABCDE diagnostic. A: analytics. It's like the math version the SAT; it takes about 20
minutes to 30 minutes and we figure out what your symbolic reasoning is. B: business. We
actually -- we actually give people a business exam to figure out if they have common-sense
reflexes about how to do business in the enterprise, like you have a meeting how early should you
show up for the meeting -- five minutes before, 15 minutes before, or..etc. A lot of a standard
business test is just questions to see if someone has common sense. C: coding, can they
code? And we used HackerRank to see if they could code, it's a quick evaluation. D: design.
We actually give people about 25 different systems, you say here's some information,
here are five different ways to present the information, pick the best way. And you know
what? Like, I can administer that in half an hour and I can tell -- in one second I
can tell whether someone has the aptitude, the talent, to be able to design and create
application interfaces. And I -- and that's more important to me than knowing that they
have a degree in fine arts from Harvard, right? In fact, the last one of those is English.
E. But we also have an F for French or G for German and M is Mandarin, right. We check
some other languages. But the general idea is, let's just figure out do they have these
basic aptitudes and it's like the SAT, but you know what, it used to be people would
say, well, the SAT, that's no longer relevant and you've been out of work -- in the workforce
for 15 years and so we won't look at that. We'll just do a bunch of interviews and we'll
look at your resume. And we have a lot of things we take into account when we hire someone.
We do look at their resume, we do interview them. But we actually found a better strategy
is we put the diagnostics up front. We screen, and then we interview, then we look at the
resume. I could tell you -- you could have a master's degree in computer science from
Harvard or from MIT. You get a 50 on the HackerRank and then I have to compare you to someone
from a university in China I never met before in my life, who's 8,000 miles away I will
never meet. And in one second -- they're 98, I will hire them. And so it's a very interesting
thing. We're lurching from a subjective, traditional set of credentials to objective, universal
set of credentials. And by the way, these aren't perfect, but I'll tell you what's interesting
here, is we applied them and we found that the leading indicator of failure of all executives
in the company is a low analytic score. Like -- like if they if they rated below 50 -- and
it's zero to a hundred by the way -- and if you, if you come out of school and you're
a bright whippersnapper you're going to be 90 to 100 and then over time those skills
kind of start to languish a little bit, and they'll fade. And 25 years out, maybe you'll
be 80 instead of 100, but you'll still be 80 and if 25 years out you're 50 or 40 or
30 or 20 or whatever the number is, we found that those people come into the company, they
just don't understand what's going on or they can't communicate fast enough. They can't
learn fast enough. Ao they fail at their job or they get frustrated or people get frustrated
with them and they get pushed out. And in the last -- say I have 20 executives I hire,
10 of them fail and all 10 of them, you know, had -- had diagnostic scores which were really
low and we looked past it to the resume and to the credential. Everybody's got good credentials,
by the way, they've all got a lot of experience. And so the problem with experience is if you're
really bad at your job you end up having a lot of experience at a lot of different places.
Yeah. So it's -- that's really difficult and there's an interview bias which is, you know,
I meet with someone -- we all like the people we meet with because it's a human nature thing
to like them. But I, you know, there's a phrase in aviation -- I learned to fly when I was
in the Air Force -- you know, it's like trust your instruments, don't trust what your brain
is telling you. Trust your instruments because --because your brain is going to kill you.
The instrument tells you that you're upside down, right, pay attention to the horizon
instrumentation. The same is true in our business. We're recruiting so fast that we're better
off to trust our instrumentation. Now of course we're always tuning it and these aren't perfect
diagnostics so we augment them with capability assessments once people join and performance
assessments. I tell you -- fascinating -- with performance assessment we used to we used
to take one assessment per year. Your boss gives you a rating once a year. And then we
said well how about once a quarter. Your boss gives your rating once a quarter. And then
we thought, well wouldn't it be good to see what the rest of the people on your team think?
So then we roll out 360-degree where we ask all the people that work for you whether or
not they find the engagement constructive. And what about all your peers and your counterparties,
did they find -- what we'll call collaborators -- did they find it constructive? And then
what about the two or three superiors, did they find it constructive? Now for two thousand
people, instead of two thousand data points a year, I end up with two thousand data points
times ten, or twenty thousand data points a quarter. Eighty thousand a year, okay, and
if you come into the company and you're just driving everybody crazy, it's not going to
matter what your academic credentials were right? Maybe you have a degree in how to get
along with people from Harvard. But then everybody hates you, right? Well, so we now have data
that tells us. We don't -- we don't have to guess, we don't have to take everybody else's
word for it. You know, we take this to the extreme. There's engagement analytics now
where where we actually start to get ratings every time you teach a class. We ask every
student whether they enjoyed it. Like, we would ask every one of you what do you -- enjoyed
my speech or didn't enjoy my speech. Every time we have a meeting, we ask people what
they thought the meeting was constructive. You know, there's this concept Ray Dalio has
called radical transparency: what if we rate every single meeting? You have 200 meetings
a year and everybody -- and everybody finds they get a lot from each one of them or you
have 200 meetings a year and you were doing great in January, in February, and in March
all your meetings went negative. Now that's -- it's a very interesting world, but here's
my point. In a world where we didn't collect the data and we couldn't manipulate the data,
we had to rely upon traditional credentials that came from political -- the established
institutions, right, and my best bet is I get someone with a master's degree in computer
science from MIT. Well that's the old world, that's a fragile world. And it's a fragile
world because MIT only produces a few hundred of those things every single year and that
cuts out seven billion people who just don't have a chance. The new world is -- we, we
put in place a more objective set of diagnostics. I would love, right, for someone to put out
like a branded thermodynamics diagnostic where they tell me this person certainly knows thermodynamics
as of yesterday. Not -- I know thermodynamics as of when I was at MIT, but I can tell you
right now, you would want to trust your life to my thermodynamic calculations because I
forgot, okay, so how do you actually figure out in five seconds that someone didn't forget?
Now, it might take the, the applicant half an hour or an hour or whatever, but it only
takes the employer a second. And this is -- this is the important point. Because we -- we want
to sift through 18,000 people in 30 seconds. Like, there are massive pools of capital right
now controlled by executives who are trying to solve a big problem in the market or pursue
an opportunity in the market. Now I -- I am a little executive with a little pool of capital,
but to put this in perspective, I would hire 100 people like in 30 seconds if you gave
me that certain objective capability that I want. And I would hire them in Warsaw or
in China or in some place I'd never been, I'm not going to go. But I could instantly
create a hundred jobs. I could probably create a hundred jobs to 300 jobs with nothing more
than a few numbers. What we need is liquid competence information. And it is -- it is
forming,right, hence the rise of eSkill and hence the rise of HackerRank and the like.
And when you consider the Googles of the world and the Amazons of the world, they will hire
a million people in a heartbeat if they actually can get that kind of objective talent. So
that, I mean, the trend -- the trend is going to continue to capture this mega amount of
data and as the data forms and the world digitizes, the skills that are economically feasible
or manipulating that data with these information machines -- those skills you can't -- you
can test. You know, like, there's a big dataset from Uber. They'll just send you the data
set. Okay, you've got the dataset. Fix traffic. Okay. A million people can take the dataset
and it's like they're owning the traffic network of the United States. Fix the traffic. What
do you need? It used to be you needed a country to fix the traffic and you needed to be able
experiment on the country. Today, you know, maybe you might not need more than a thousand
dollar computer to fix the traffic and maybe, maybe nineteen thousand people will try but
100 will succeed and they'll be really good. So our ability to find the talent is much
greater. Our need to find it is greater. These, these approaches to finding talent, they're
all about agile and agile is all about speed, and speed is life. And if I can do things
fast and I can do them everywhere, you get -- you can see a world where we want to trade
more, we want to tap into labor pools everywhere, in China and India, wherever the labor might
be, we want to create the talent and then we want to repurpose the talent. I have services
I could sell here in the US or in Europe but I don't have the human capital necessary to
create it and maybe the human capital doesn't exist anywhere. And we need to create it,
right, so I think as we go toward more automation we'll be able to create that human capital
and we can then manipulate it or distribute it in order to solve the world's problems.
So I think in that regard, now, I'll end with this thought, right, the architecture for
success in the civilization is we start with cheap slash free computer technology and sensing
technology which just creates the world awash of data, and then we augment that with cheap,
free digital education. Create software that -- digital software that will provide education,
right, automate that, the education machine. Once we've done that, if we've got -- if we've
got an education software apparatus that can be made freely available or cheaply available,
the next element is free and precise digital certifications of capability and that's what's
been missing, right, if we produce all these people but our certification is very imprecise.
Everybody from MIT has the same degree in aeronautics and yet they don't all have the
same skills in aeronautics. Some are really good at some things, some are not good at
some things. So it used to be this idea of I'm just going to give you -- you know there
are business schools that they have this presumption that, well, we're not going to give grades,
okay, we're too good for that. If you went to whatever -- Stanford Business School -- you
know, you ought to just accept the Stanford brand and let us do anything we want with
any of your money. And I'll tell you the problem with that. I have found that there's a theme
and the theme is business school grads that have been -- that have training in product
management or training in project development all these things -- they're all failing at
a massive rate in the real world because that person at Google wants the car to drive via
LIDAR. They don't want like a theoretical plan. If you don't know what LIDAR is you're
going to fail no matter how good your b-school degree is. We don't need general skills and
general problem-solving. What we need is someone that knows that Amazon hasn't deployed its
full stack of services in Beijing and so your product will absolutely crash and burn in
Beijing until they do that. There are very particular things that we have to do. And
if you're going to actually solve these problems in the real world you have to have technique.
And the technique means, well do you or don't you have enough mathematics to solve this
problem? If you haven't mastered the calculus of variations you cannot -- you will never
solve this problem. Doesn't matter whether you have a prestigious degree. I don't care
if you have a -- four PhDs from the best school on earth. I need to know whether you've mastered
the calculus of variations. We need that very precise and -- and by the way, I'm using examples
that were around for a long time, right, Isaac Newton gave us the calculus of variations.
You want to solve some problems in the tech world, there's stuff that Amazon invented
last year they're putting into the market this month and if you don't master that particular
technology you will absolutely fail, right, so it's -- it's a very rapidly expanding universe,
it's getting more complicated at a rapid rate, and what we want is we want to very precisely
know what someone can do. Not what -- what they could do twenty years ago, I want to
know what they can do now. And here's the issue: I don't have eight hours to talk to
you. I -- I don't have six months to find out, right? I can't afford to take six months
to try it out. I actually need to scan 937 people in one second and find the one person
that actually can really do that. Then I'll spend the next six hours figuring out what
you need to be happy in the organization, and help you transition, and negotiate with
you, but -- but I need to find the the human capital and we need to create the human capital.
So -- so when I talk about certification, I mean, we need it precise in an area and
we -- in our case, like, we rate everybody 0 to 100. Well I -- there's a big difference
between -- do you want the doctor that was 99 or a hundred you want the doctor that was
50 on the scale of solving the problem that's about to kill you? I mean you really want
the best, right, especially if I'm going to write a piece of software that does that thing
19 million times a minute I really want the best. I don't want the average, right? I don't
need the average. The world doesn't need a hundred thousand average algebra teachers.
The world needs one really, really good algebra teacher, then to be automated and manifested
in software which then delivers an algebra education to the next 10 billion people. We
just need the best. Nobody wants the average phone, right? The iPhone that you have in
your pocket is better than every device ever created in the history of man. Nobody wants
the average one they don't want the best [sic], they want, they want the extreme and they
want to stamp out a million copies of it. And that gets problematic again as the world's
products get so diversified. So, I mean, the first step is cheap and free digital education,
the second is precise certification -- and ideally free precise certifications. That
should result in a massive increase in human capabilities. Give that to eight billion people
and we ought to be able to double, triple, quadruple the amount of talent or the amount
of capability out there. And that creates an acceleration in -- in the rate of human
capital allocation, right? If we could precisely describe what those billion people could do,
then we can move them between companies and employers. They could move faster, they can
change jobs, they can redeploy themselves and that's agile. And that will create a massive
output in goods services. Everything that we might want, we'll produce more of it. And
that creates an increase in productivity as a second-order effect, as we find the most
talented and we replace the crappy program with the better program, with the better program,
with the better chip. And and that of course is going to increase everybody's quality of
life. And that will result in a general advance in human knowledge. And if you sum it all
up, right, I mean, that's the formula for making the world a better place. And there
are other things people are doing to make the world a better place but, I mean, I think
we all share an enthusiasm for open education and I see that dynamic of cheap education,
to more human capital, to more precision, to more agility, to more -- to more engagement
-- I see that as our best route in the 21st century to make the world a better place.
I know we can't do it alone. We need to harness the power of every organization -- for-profit,
nonprofit, governmental -- we possibly can. But I want to thank everybody for being engaged
in the process and -- and let you know I do appreciate, you know, your support. And anything
that we can do to help, we will. Thank you. [Applause]