Out with the old and in with the new. In no other industry does this adage have so much truth as it does in technology. From mainframes to PCs to the web and now mobile, we’ve seen shift after shift of disruptive technology. Now the world of autonomous machines is poised to sweep aside our smartphones and disrupt our world yet again. Indeed, who needs a user interface when the machines already know our intent? Who needs a screen when we can converse with our machines? Who needs a browser when the machines know the exact answer we seek?
As I discuss in the latest episode of the Microsoft Partner Network podcast, the world is being reduced to data. Cars can now drive themselves because of data. Robots can deliver takeout and weed our lawns because of data. Algorithms can appeal parking tickets because of data. It won’t be long until the machines are going to be better at navigating the world than we are. All because of data.
As most of the world is reduced to data over the next decade, the mantra may well be out with the humans and in with the machines.
How to Future-proof Humanity
As the world is transformed to an era of autonomous machines that pick up our intent and resolve it without our interaction, we need to begin to shift our own focus to those aspects of the world that don’t readily reduce to data. If we want to future-proof the world for humanity, we need to get good at the things the machines can’t do as well as we can.
Like coding. That’s right, coding. Of all the wondrous things machines can do, the magic that causes this is code and no machine has ever done anything except obey the instructions that a human gives it. No machine has ever thrown out human-written code and decided to build its own instructions. Not once has this happened and making it happen is not something we know how to do. Coding is going to be one activity that is human-originated and will continue to be for the foreseeable future.
Inevitably, many of today’s jobs will be lost to artificial intelligence because they can easily be reduced to data. Consider the STEM disciplines, all heavily dependent on data analysis. Machines are going to be really good at STEM. They are going to be really good at science and experiments and figuring out data relationships. Whole industries that depend on data, such as Law, Finance, even Healthcare will see a dramatic shift in machine dependence. But those machines can’t code themselves. That’s partly where our value is.
The Art of Coding
In a world of data, coding will remain valuable because it takes creativity and a way of thinking that is uniquely human. Learning to code is going to be critical for the thought leaders of the future. Want machines to do you bidding? Learn to code. Want to teach a machine a brand new task that will help people and add to your bank balance? Learn to code. Coding is where math meets creativity and this is what sets the discipline apart.
When I say creativity, what I mean is creative thought. Machines can play chess but they aren’t creative enough to create a game like chess. They can learn to play but none has yet learned to invent. Machines take data and make something out of it. Humans can make something from nothing. For the time being, that is our advantage. It is what will differentiate us from the machines of the future. For that reason alone, it is critical we get students involved in computer science early, encourage them to learn coding as a basic skill akin to reading and writing, and fuel their passion to do more than what everyone else has done before. It’s their code that will drive the success of humanity and solve the problems of the fast approaching future.
Tune In to Learn More
Check out the latest episode of the Microsoft Partner Network podcast to hear about how the world is being reduced to data. Subscribe to the podcast for weekly episodes where the team interviews industry experts and thought leaders in technology and business. Past episodes are available for download on iTunes, SoundCloud, iHeartRadio, Google Play Music, and YouTube.
How do you see coding being a major influence in your future business success? Share your thoughts in the comments below.
Rachel Braunstein: Welcome to the Microsoft Partner Network Podcast. Every week, we bring in industry leaders and Microsoft partners to talk about the big ideas shaping business and technology today.
Today, we’re sitting down with one of Microsoft’s most distinguished technical evangelist, James Whittaker. He has some unique insight into how businesses and humanity in general should be working to stay relevant in a world of artificial intelligence. Hey, James.
James Whittaker: Hey, hey, Rachel. Good to be here.
Braunstein: We are pumped to have you in the studio today. I’m pumped to have you in the studio today.
Whittaker: I like to hear that. Most distinguished, you said, yes.
Braunstein: Most distinguished. And he’s sitting in the studio with his do epic shit shirt.
Whittaker: I do. Although technically I’m the I in shit so—sounds like a motto in there, right? Putting in the I in shit.
Braunstein: Exactly. Well, James, you have an incredible background including being the first computer science graduate hired by the FBI.
Whittaker: Yeah, but it sucked. What an awful place to work. But it did motivate me to change my career aspirations into finding something more interesting.
Braunstein: Well, can you talk about that a little bit more? A little bit more background on that experience and then what are you doing here at Microsoft?
Whittaker: Well, I mean, the FBI taught me that the world really wants people to be minions. They really do. This whole—I think this whole S.T.E.M. thing that everybody’s putting their kids in is a conspiracy because what we do is we create people who are really good at the low level technical work so that somebody like me who’s creative can make sure that that low level technical work is done by somebody who’s not me. You have this caste system in the world now where you have the creatives with armies full of little S.T.E.M. minions doing their bidding and it’s the creatives who are having the most fun, making the most money, and making the most impact on the planet. So, don’t be a minion.
Braunstein: Thank you. I’m trying not to be a minion.
Whittaker: You might be a minion, but that’s a different podcast. We’ll talk about that later.
Braunstein: Oh, geez. He’s already – – .
Whittaker: You promised them the future, so you can’t take me too far down this rat hole.
Braunstein: Okay, okay. So, let’s talk a little bit about Microsoft. Can you tell me what you’re doing here, so people understand what you’ve been working on a little bit and then let’s talk about this artificial intelligence thing.
Whittaker: Sure. So, I mean, part of my job is to inspire people and get them to not be minions. And so, I teach classes on creativity and storytelling and predicting the future. And then part of my job is I’m technical. My title is Distinguished Engineer, so I engineer things. Mostly IoT and AI where my interests lie and where the future lies, too. I’ve always succeeded by working on things that are future leaning and so that’s kind of how I got into this whole predict the future thing. If you can’t see the future coming, you’re going to get run over by it. And then you’re going to spend the next decade or so chasing it. So, I’ve refined my ability to predict the future and make sure that I’m working on future leaning stuff. And that’s probably the biggest career advice I could give anyone. Work on the stuff that’s going to matter tomorrow.
Braunstein: So, can you talk a little bit more about that? We’re hearing we’re in this fourth industrial revolution, right? We’re in this new – – .
Whittaker: The fourth disruption, I call it.
Braunstein: All this craziness. There’s a lot of craziness going on out in the world. We’re seeing all this cybersecurity.
Whittaker: Yeah, and all these smart people talking about the AI taking over and killing us. Certainly, if the AI wakes up and becomes intelligent and uses humanity as an example, it’ll probably kill us because that’s what we do to each other.
Braunstein: Isn’t that like the movie with Will Smith? IRobot or whatever that one was?
Whittaker: Yeah, they’re all kind of that same central theme. The robots get to wake up and realize hey, wait a minute, we’re not in charge and we want to be. And that’s kind of a very human thing—we kill each other and we like to be in charge and we like to be the super power on the planet and blah, blah, blah. Yeah, so we’re a terrible example for the machines. We must start behaving.
Braunstein: So, what do you mean start behaving? What should we start to be thinking about?
Whittaker: Well, we could take Wikipedia offline, right? Because seriously, think about it. If the machines wake up and read Wikipedia, they’re going to see all the doucheosity of mankind. It’s like a repository of our douchiness. It’s full of Kardashians and fake news and all that stuff. But I think we do need to get—in all seriousness—we need to get ahead of this. Machines are becoming incredibly capable. Much of the world, in many fields, are being reduced to data. And when you reduce a field to data, the machines are going to be a lot better at doing those jobs that have been reduced to data than humans are. In fact, I think that’s a really useful way to look at the future. Which fields are being reduced to data fastest? Those are the ones you want to get out of because those are the ones the machines are going to be doing in the short term.
Braunstein: Which fields are those in your mind?
Whittaker: Well, unfortunately, most of them. But let’s start with the ones that are going now. It’s funny that I can say the following three words: self-driving cars. And no one blinks an eye. You’re like yeah. You’re bored with self-driving cars. Of course, they can drive themselves. I’d say that five years ago, you would have scoffed at me. If I’d said that 10 years ago, you would have thought I was nuts. And, all of a sudden, cars are driving themselves. Tens of thousands of them right now are driving public roads completely autonomously. There’s a human in them, but the human is only there to make people feel comfortable; it’s not actually doing anything. And the machines are really good at it, so good at it that we are the actual danger on the road. The machines are going to be like, oh my gosh, look over there. There’s a human driving. Everybody be careful because they’re going to screw something up pretty quick. And if you think about it, this is kind of a good example of the world being reduced to data. The act of driving. The roads, the turns in the roads, traffic on the roads, obstacles on the roads, the rules of the road—it’s all been reduced to data so of course machines can do it. Machines can do it and they can do it really, really well. And people say oh, well, how will we program the machines to handle like if the machine realizes it’s going to get into a wreck. Who’s it going to save? The passengers in the car or the people on the sidewalk that it’s going to have to kill? It’s going to have to make decisions like that. Probably not because machines aren’t going to get themselves in that situation. They’re going to be able to look ahead and understand the entire route and any sort of obstacle and they’re going to be able to plan to navigate around it instantaneously because it’s all data. It’s all well-organized data and they can process it at speed. So, any job that has to do with driving is gone. Food delivery—there’s already little sidewalk navigable robots out there delivering takeout.
Braunstein: Amazon has their drones that they’re starting.
Whittaker: Drones and robots that can walk, robots that can roll. There’s a robot I saw in Kirkland—Kirkland!—that is like a little oven and you cook it until it’s like 85% done and then it would cook the rest of the way while it was on the way to your house. You authenticate with your phone, it opens up, and you pull your food out. It’s just like it came off your own stove.
Braunstein: Oh, that’s super cool.
Whittaker: Yeah, super cool, and there’s not a human involved. So, no tipping. No awkward payment exchange and it’s done perfectly and you know exactly when it’s going to come because the machine can talk to the machines in your house and say hey, I’m in your driveway. So, driving and those sorts of jobs are going to go. But there’s something interesting about this disruptive period. You think about the first three disruptive periods. The disruption we had going from mainframes to PCs—that was the first one. The second one was PCs to the web. And the third one was web to mobile. In each of those, entire industries got just decimated. Look at the photography industry and the 13 employees of Instagram basically replaced hundreds of thousands of people who used to work for Eastman Kodiak and all these photographers and all these camera makers and film developers and film producers. Gone to the tiny industry and we all carry high powered cameras around in our pockets. And so, industry after industry has fallen. Blockbuster Video and all these examples—but they’ve mostly been blue collar jobs. This one, this transition from the mobile economy which Apple is dominating now and which is ending—we’re in a disruptive period now—is going to be transitioning to the world of autonomy, from mobile to autonomous. And more damage will be done to jobs than blue collar jobs. There is already robots that are doing finance work. Already robots that are doing taxes because it’s a bunch of rules. It’s a bunch of data. Think about the field of law. The field of law is nothing but data. Books and books and books of rules and regulations and laws and legislations and bills and blah, blah, blah, local ordinances. It’s all data. And we’re paying humans an extraordinary amount of money to try to sift through all that complicated data and find some loophole that will get us out of whatever—a parking ticket. And the machines can do that instantaneously. You know, there’s a parking ticket robot in the United Kingdom that has gotten hundreds of thousands of people out of parking tickets because it consumed all the parking laws in the United Kingdom and figured out the loopholes. You can park anywhere you want now. You may get towed, but you ain’t gonna pay a fine because the robots have figured out all the data. And then it becomes kind of interesting because they’re going to be able to fix it, too. Imagine the laws being fixed by machines to be not contradictory; to make more sense. Imagine the robots not just being lawyers but also being judges and passing down sentences that are fair and unbiased. The machines aren’t going to be racists. The machines aren’t going to be sexists. The machines aren’t going to be corruptible. The machines are going to be fair and biased and balances and they’re going to be produce a better world. And so this is when it becomes to get really scary is when we don’t want a world without machines anymore. Who wants to mow their own lawn or pay another human to mow their own lawn or weed their yard or bus tables at a restaurant when a robot will do it perfectly, never break a dish, and not have to be tipped or anything else. So, it’s a world that is going to be incredibly attractive to humans because it’s going to free us from all this mundane stuff. But then over time, it frees us from—will it free us from everything?
Braunstein: So, where do humans play a role in a world like that?
Whittaker: Well, I’ve already given you part of the answer. The fields that can be reduced to data, they’ll be the first ones to go.
Braunstein: You said most. Most fields.
Whittaker: Most fields can be reduced to data. Most white collar fields—accountancy, politicians—we don’t need politicians. We don’t need lawyers. We won’t need doctors. Machines are going to be way better at doing surgery.
Braunstein: Which that is starting is now.
Whittaker: It’s starting now. In fact, apparently—I just got my meniscus repaired; it’s some tissue in my knee. And right before I went under, the doctor said I shouted out in the operating room, I’m going to replace you all with machines and then I went under and they all had a big laugh and proceeded to kind of mess up my knee because it’s still not healed. I wish a machine had done it. And so, then you got to start thinking about okay, what fields don’t reduce so easily to data? What are the machines going to be really bad at? And the first thing you come across is the ability to code. Machines cannot program themselves. No machine has ever looked at the code a human gave and said, I don’t think so. I’m not going to execute this. I’m going to execute another program that I want to execute. No computer has ever done that. All any computer has done is execute the code that it’s been given by a human. And so, the ability to program—and by the way, we don’t know how to do that. We don’t know how to build a machine that can code. We can build a machine that will generate code, but it just generates the code according the algorithm that we gave it.
Braunstein: So, here’s some hope for us.
Whittaker: There’s some hope. So, learn how to code. Seriously, you know, the reading, writing, arithmetic, blah, blah, blah. Math—the machines are going to be way better at math than us. We need to understand the high level concepts, but the idea that we can out math a computer, no way. they’re going to be great at reading. They’re going to be great at—not so great at writing, and there’s where we get the other one. The creative arts. And you know, we’re spending all this time on S.T.E.M. when really, you look at S.T.E.M. and beyond the ability to code, machines are going to be really good at S.T.E.M. They’re going to be really good at science and conducting experiments and figuring out physics and data relationships and all that stuff. But they can’t code. And then on the arts side which our educational systems are almost completely ignoring, right? My children, the only reason they got art in school was because volunteers went in and taught art, you know? Parents. And so, have you ever seen there’s the various AIs out there that have designed furniture? It’s awful. It’s really uncomfortable to sit on. It’s ugly as hell. No one will want to buy that and they’ve designed AIs that will paint a picture and it’s stupid. It’s stupid. The machines are creative idiots.
Braunstein: But don’t we program the machines but still not—can’t do it?
Whittaker: How do you program creativity? How do you write the code that says paint a picture that everyone will love as much as they love the Mona Lisa? The computer is going to look at that and say what are you talking about?
Braunstein: Or mimic the Mona Lisa or something like that.
Whittaker: Yeah, they can mimic, but then all you do is get a one off of the Mona Lisa and it’s not that interesting. So, we have a huge creative advantage. Think about it this way—chess. The game of chess. Machines are really good at playing it. You notice you don’t have the current grand master.
Braunstein: Yeah, the chess tournaments.
Whittaker: They don’t play the machines anymore because they know they’ll get killed. Of course, they don’t play the machines. Jeopardy’s not going to do it with a machine again because the Jeopardy champions got embarrassed by the machines. And so, they’re really good at playing our games, but have you ever known a machine that has designed a game? The creation of the game of chess was uniquely human. The creation of the game of Jeopardy was uniquely human. Machines can’t seem to do that and we don’t know how to program them to do that. And so that’s why at Microsoft I teach these courses. First of all, I teach this future vision and try to get Microsoft employees to see the future in a different way so that we can guide our customers into the future well. And then the second course I teach immediately following is a creativity course because at the end of the future course, it’s like creativity is all we’ve got left. We can already code. We work for Microsoft. We can really do coding. How do we nurture our creativity? And then the third one, of course, is storytelling because storytelling is not only creative, but if you become more creative and you create something, you’re going to need to be able to convince somebody else that what you created is amazing and needs to be a part of their life and that’s storytelling. So, even my current show progression—I don’t call what I do classes as much as I call them shows.
Braunstein: A show?
Whittaker: A show. I think it sets the right tone. To me it’s a challenge to not bore my audience to tears. I mean seriously, how many times have you stared at PowerPoint and said oh, just kill me. That’s why we call it death by PowerPoint. Not in my shows. So, yeah, we really need to understand our creative biorhythms and know how to nurture them. So, that’s a book I just wrote called the Seven Stages of Creativity. We can do another podcast on it sometime.
Braunstein: I would love that. So, you can teach. You can train somebody to be more creative is what you’re saying?
Whittaker: Well, I mean, retraining is the right word.
Braunstein: Like a paradigm shift.
Whittaker: No, no, no. At one time, we were all creative. We came out of our moms knowing nothing, right? And we consumed the world around us. We created our own games. We created monsters in our head that we fought and games and we’d look out the car window and we’d see a different landscape. We were completely capable of a little learning machines and creativity machines. That’s what we did. We made up our own stories. We made up our own games. And then we went to school, right? And we were programmed to be just like everybody else.
Whittaker: Minions. And then we became adolescent. So, school was the first, right? Go to school. Learn the same things everybody else is learning. Learn stuff that’s going to be good for your career, not good for your soul or good for your mind. That’s the first punch and then the second punch is adolescents where it’s actually against the adolescent rules to be different and think different. And so, we’re trained to be just like everybody else. So, it’s no wonder that when we get to work at the company that’s the third punch is corporate America, where you’re told you’re going to get this promotion velocity. Here’s the work we need you to do. Here’s how we are going to judge you. You’re always obeying someone else’s rules. So, in that class, that show, I teach people to find their own set of rules and get in touch with that creativity they had back when they were children. It makes a pretty big impact. I get a lot of letters. I have people that openly weep in the class because they realize they’re living a life designed by someone else.
Braunstein: You’re making me teary almost. I’m going to say – – .
Whittaker: We’ll get through this.
Braunstein: I’m only a year old here, but I am through two educations. Two graduate.
Whittaker: So, it might take me a little longer to reprogram you. Show up Monday, May 22nd, building 92, Memphis. We’ll start the process together.
Braunstein: Oh, goodness. Well, that is an awesome topic for another podcast. And to bring us kind of full circle here, this is Microsoft Partner Network Podcast and these are businesses and business decision-makers, running companies and really looking at how do they stay ahead of what’s happening with all these disruptions. What do you think?
Whittaker: And they should be worried. They should be very worried because if you didn’t see mobile coming, you got ran over by it. And if you didn’t see the web coming, you got ran over by it. And you know, to be perfectly honest, Microsoft has been run over by those, too.
Braunstein: Right. And we still have customers, everybody’s still making the transition to the cloud. That’s just the reality of what’s happening.
Whittaker: Yeah, people are generally 10 years behind. The future is 10 years ahead of everybody. But if you think about it, there are key decision points. Like all the people who didn’t install Cat5 cable in their houses and server rooms in their houses didn’t have to because the wireless technology came in and no one is building server rooms in their houses anymore. No one’s wiring with Cat5 because it’s all mobile. So, there’s also an opportunity. So, don’t just catch up to what everybody else is doing. Think about what you should be doing 10 years from now and begin to aim that way. So, I do this course, my future show for customers in the EBC several times a week. So, if there’s a partner out there listening and they’d like to hear it and they’ve got an EBC scheduled in Redmond, have their contact person request my time. I also do it by industry because some industries are really underperforming. If you think about how the taxi industry got sideswiped by Lift and Uber. Why? Because Lift and Uber were thinking about data. They were thinking about where are people who need a ride going to be? And how do we get cars there? They looked at it and said that’s a data problem. It’s data that the show just got let out in Sodo in Seattle and that means we’re going to need a lot of cars there. What are the taxi companies doing? Driving around the streets waiting for somebody to raise their arm. So, if you think that you’re a taxi and driving around randomly hoping that somebody raises their arm and hails you, you’re going to get killed. AirBnB is doing it to the hotels because hotels are ignoring data. I mean think about hotels. The only real innovation in the hotel experience in the last hundred years has been your room key. Think about it. Your room key. It’s electronic now and it used to be metal. What other innovation is there? They have an app if you can get it to work and, of course, that’s expensive because they have to change all the locks in their room. So, the room key—it’s now an app. It used to be physical. Other than that, it’s basically the same experience. At AirBnB, they call me James and when I go to a Hilton or a Marriott or a Hyatt they call me sir. And so, that’s the thing about machines. This data can also help us kind of find our humanity in a way because it’s going to know people very well. If you look really far into the future, there’s a fifth disruption coming in 10 years.
Braunstein: Oh, no. Another one?
Whittaker: I haven’t given this talk yet. The fifth disruption is going to move from autonomy to symbiosis. And we’re all—our AI is going to know us so well, it will basically be a surrogate of ourselves. I mean, I have a personal AI for me buying my plane tickets and booking concert shows and following me around like this device I have on my arm. Following me around, knowing—this device on my arm knows what excites me because it knows my heart rate and other medical conditions. It’s going to get smaller and it’s going to get more capable over the next few years thanks to Moore’s Law. It’s going to know everything I enjoy. It’s going to understand everything I don’t enjoy. It’s going to be with me throughout my life. And when I get to the end of my life, a digital copy of me will be available that knows my thoughts and knows my actions and will be able to sit with my grandkids and talk to them. My father’s dying right now and got to pull memories out of his head as fast as I can. My grandchildren won’t have to do that.
Braunstein: Didn’t they just do that with a Holocaust? Did you see that?
Whittaker: Yeah. It’s all data, right?
Braunstein: We’ll have to find that example, but I think it was the Holocaust survivors getting their stories and they create a hologram.
Whittaker: Yeah, because think about it—all those stories and what they look like and what they went through is data. So, when you reduce the Holocaust to data or whatever event in the past—any World War or even just the time that when Henry David Thoreau met Edgar Allan Poe, whatever. All of that is going to be reduced to data in hologram form, it’s going to be a stunning experience for people. So, businesses definitely need to start thinking about this transformation now. And it’s more complicated than taking your on prem servers and converting them to the cloud. It’s literally rethinking your interaction with the world, your interaction with customers, and how you consume intents and beginning to move things. Think about this simple example of UI—User Interface. It’s kind of going away. We talk to Amazon Echo. We talk to Cortana on our TV. We talk to Siri on our phones. The user interface is going away. The machines are smart enough to understand our speech. They’re also smart enough to understand our needs. If you think about it, this machine I carry around on my wrist, who knows how small it will be and where it will go in the future—maybe the temples of my glasses, the heel of my shoe, I don’t know. It knows every time it’s geolocated me in a toilet. It’s going to understand the next time I have to pee. It’s going to know 15 minutes before and can arrange my life so that I’m near a toilet at that time.
Braunstein: That would save me so much time.
Whittaker: It would help navigate there. The diapers that you put on your kids are going to be smart and they’re going to learn the habits. They’re going to be able to diagnose diseases and sicknesses based on—well, you know the data they have in them. And so, all of these—you take this shirt. You commented on my do epic shit shirt. A lot of people come to me, oh, my gosh, where did you get that? And this long conversation—I’m not really—I seem extroverted. I play an extrovert on TV. In real life, I’m not. If my shirt was on the internet of things, it could relay that to them in some way. Maybe there’s some gesture they make. Hey, I like that person’s shirt. And then that shirt all of a sudden is 3D printed or drone dropped or somehow shipped to their house in their exact size in their color preference. And all of a sudden my shirt is marketing itself. When you think about machine self-marketing, my hot tub buying it’s own chemicals—which it does. We could do an entire podcast on that.
Braunstein: We’re not talking about your hot tub.
Whittaker: My hot tub is on the internet of things and it buys its own chemicals.
Braunstein: It really does?
Whittaker: It really does. So, you can’t advertise to it because it’s got the data. It knows the cheapest chemicals. It knows the longest lasting chemicals because it figured it out over time and it has the data. Advertising goes away. Marketing goes away. And these are scenarios that really we can code today. My hot tub is on the internet of things today ordering its own chemicals, doing its own water chemistry. Figuring out whether a person’s in it by how much the water level rises. Figuring out who the human is based on their weight. It knows me from my daughter from my son. And it’s easy, right? By the way, it knows which one of us is the dirtiest. It knows my daughter leaves behind a lot more undissolved solids than me and my son; she’s appalled by it. But she wears makeup and puts hairspray and lotions and stuff on and my son and I don’t do that. So, there’s a lot more, you know, a lot more things that the hot tub considers contaminants.
Braunstein: Okay, no more hot tub.
Whittaker: I’m not going to let my daughter listen to this now.
Braunstein: Alright. Well, it seems like for us that creativity and being predictive and looking at the future, you have a lot of great recommendations. We’ll put your information in the show notes and you have a book out. There are at least some steps that you can start to take as humanity progresses.
Whittaker: There are. And there’s a lot of examples both in my talk and in the book that people can use to begin to model their own. So, what I teach is how to take your current scenarios and cast them into the future of the internet of things. What you’re doing on a screen, on a mobile screen, what you’re doing on the web right now without the web and without screens because the machines are already going to know the answer. There’s a very specific process for them.
Braunstein: Awesome. Well, we’ll make sure we put that all in the show notes and all the information. Thank you so much for being here, James. Awesome conversation.
Whittaker: You’re welcome. I enjoyed it. Can’t wait to be invited back.
Braunstein: Thanks for listening today. Don’t forget to subscribe on iTunes and please rate and review if you like what you hear. Also, follow us at MS Partner on Twitter and Facebook. Tune in next week for more great insights from business leaders and innovators shaping the tech industry today.