 Okay. Thank you everybody. I'm going to get started again. It's my pleasure to introduce the trustee of the Sailor Foundation, Michael Sailor. 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 out the program a little short on time, Michael's full bio is in your program, but you'll know he's a graduate at 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 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 Sailor. Thank you. 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 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 changes 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 so many things that used to be a product like a camera, like a printer, like a typewriter. Now they're 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 Ram McNally Atlas, which once was a product that we printed, eventually became Google Maps. But then Google Maps went from dematerialization of Atlas to 200,000 pages of satellite images, which you never could add in 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 intelligence 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 that 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. It's 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 100,000 places. And as the cost of hardware gets cheaper and the cost of RAM gets cheaper and RAM is 10,000 times cheaper than it was 15 years ago. Computers are a thousand 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 9am and at 923am 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 a 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 on the back end 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 the fragile world, you wanted to try something that cost you $40 million in four years. And if you screwed it up, you're out for years and you're out $40 million. In the agile world, you wanted to try it, you tried it in four hours or in 40 hours, you spent no capital, if you screwed it up, you throw it away and you try something else. Right. 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 capital involved and bricks and mortar involved, they're just inherently more fragile. It's going to be much harder for us to do things. And one thing that's fragile and expensive is a traditional education at a bricks and mortar institution in a traditional fashion. It takes too long, it costs too much money. This digitization, digital revolution, 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. He said, well, no, our drones are flying LiDAR arrays. What is LiDAR? Anybody know what LiDAR is? Anybody heard? 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 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. 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 $1,000 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, I had someone that wanted to, 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 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, right? 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. There's a tendency to think, well, we're going linearly. We used to do that and we're doing this. But in fact, my observation in my industry is we're an expanding universe. And it's an expanding universe of expectations, of demands, of requirements, of aspirations. Everybody wants more of everything in every direction. 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 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, 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 and Wikipedia, Google, Facebook, Twitter, everything else. So you go to the customer and 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 to know Windows? No, then 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 wanted to run on all these clients and I wanted to run on smartphones. Smartphones instead of MacBooks? No, smartphones and MacBooks. I wanted to run on every client and you wanted to run and no, I wanted to run everywhere. Well, everywhere? Everywhere. Well, everywhere means I have to, well, 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 got a million problems. We can, and every one of them, you know, how do you find sharks from Lidar? Right? You know, that's a problem. We got a million problems and it's pretty clear in the tech world, 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 or the classical definition is, is you know, someone who is capable of contributing, 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, five 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 conclusion you probably need a billion PhDs. There's probably, we need, we don't need seven billion but we need more than 10 million. You probably need like a billion people on the planet that they're 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 and 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 like a, it's just expensive, right? It's a quarter million dollars to get through college. It's a quarter million dollars to get K through 12. It's another quarter million dollars for the rest. Maybe someone can figure out how to do it for 200,000 dollars, but 200,000 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, 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 has 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 a 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. I don't think we're going to solve the problem of how do you create great, great cheap education in the 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 a classroom and as a practical matter, and I get a kick out of this, on sailor.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's 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 chalk board, it was very uncomfortable. Today you would get a better education for nothing with a $500 computer logging into sailor.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 from 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 2 plus 2 equals 4. You don't need the opinion of someone that smells 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 that 2 plus 2 equals 4 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 they lead to better cheaper more comprehensive education right that's 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 hacker rank and what that and we use a product called smart recruiters to 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 we give them an A, B, C, D, E diagnostic A analytics it's like the math version of the SAT it takes about 20 minutes to 30 minutes and we figure out what your symbolic reasoning is. B, business 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 etc. A lot of a lot of standard business test just questions to see if someone has common sense C, coding can they code and we used hacker rank to see if they could code it's a quick evaluation D design we actually give people about 25 different systems who 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 minister 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 and in fact the last one by those English E but we also have an F for French a G for German and M is Mandarin right and 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 the 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 that 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 could have a master's degree in computer science from Harvard or from MIT you get a 50 on the hacker rank 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 right and so it's it's a very interesting they were lurching from a subjective traditional set of credentials to objective universal set of credentials and by the way these aren't perfect but I tell you what 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 100 by the way and if if you if if you come out of school and you're a bright whipper snapper 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 so they fail at their job or they get frustrated or people get frustrated with them and they get pushed out and and in the last I 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 and to the credential everybody's got good credentials by the way they 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 a lot of different places yeah so it's 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 you know 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's 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 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 your 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 thinks 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 it or what we'll call collaborators do they find a constructive and then what about the two or three superiors did they find it constructive now for 2,000 people instead of 2,000 data points a year I end up with 2,000 data points times 10 or 20,000 data points a quarter 80,000 a year okay and and if you come into the company and you're just driving everybody crazy yeah 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 a veils as word for it um 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 everyone of you whether you enjoyed my speech or didn't enjoy my speech every time we have a meeting we ask people whether they thought the meeting was constructive you know this is concept of radio 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 and February and in March all your meetings went negative now that's it's a very interesting world but 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 some of the master's degree in computer science from MIT 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 and 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 wouldn't 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 there are massive pulls 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 little pull of capital but to put this in perspective i would hire 100 people like in 30 seconds if you gave me that a certain objective capability that i want and i would hire them in Warsaw or in china or in some place i've never been i'm not going to go but i could instantly create a hundred jobs i could probably create a hundred jobs two three hundred 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 e skill and hence the rise of hacker rank and the like and 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 that are economically feasible are manipulating that data with these information machines those skills you can't you can test you know like there's a there's a big data set from uber they'll just send you the data set okay you've got the data set fixed traffic okay a million people can take the data set and it's like they're owning the traffic network of the united states fixed the traffic what do you need you don't 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 one hundred will fit will succeed and they'll be really good so our ability to find the talent is 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 can 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 i have services i could sell here in the u.s. 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 problem so i think in that regard now i'll end with this thought right the the architecture for success in the civilization is we start with cheap slash free computer technology and sensing technology which just creates the world a wash of data and then we augment that with cheap free digital education create software that digital software that 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 and that's what's been missing right 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 something some are not good at some things so it used to be this idea of i'm just going to give you yeah 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 right if you went to whatever stand for business school you know you want to just accept the stand for brand and let us do anything we want with any of your money and i i tell you the problem with that um i have found um that there's a theme and the theme is business school grads that have been that have training and product management or training and 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 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 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 phd's 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 i'm using examples that have been around for a long time right isaac newton gave us a 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 it's a very rapidly expanding universe it's getting more complicated at a rapid rate and um what we want is we want to very precisely know what someone can do not so what what they could do 20 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 and i 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 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 zero to 100 i don't there's a big difference between do you want the doctor that was 99 or 100 do 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 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 they want they want the extreme and they want to stamp out a million copies of it and uh 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 and uh and 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 can move faster they can change jobs they can re uh redeploy themselves and that's agile and um that that'll create a massive output in goods and services everything that we might want will produce more of it and that creates an increase in productivity as a second-order effect as we find the most talented and then we replace the crappy program with the better program with the better program with the better chip and uh and that of course is going to increase everybody's quality of life and uh that will result in a general advance in human knowledge and uh 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 um 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 uh i i see that as our best route in the 21st century to make the world a better place and i know we can't do it alone um we need to harness the power of every organization for-profit non-profit governmental we possibly can but um 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