Futurist Keynote Speaker Gerd Leonhard At KPMG D A Insights Event In Berlin

Yes. Good morning. Good Morgan. Good morning. Bonjour. Salamat Bagi. It's really great to be here. A great pleasure to be here. So my name is actually Gerd Leonhard with an h, but we can skip the H. It's fine. I was born in Bonn, Germany. So if my presentation is perfect, that's my German background. I live in Switzerland, in Zurich. So if there's no risk in my presentation, that's why, because of my Swiss background. And if it's awesome, that's because I lived in the US for 15 years. It's also because I speak rather fast sometimes. So please, I apologize. Because if you speak in the US and you speak slowly, then you get kicked off the stage. It's just no point in it. So first of all, I wanted to say briefly what a futurist is. As a futurist, I don't predict the future. Some futurists have done that, like Alvin Toffler and Isaac Asimov and brilliant people like this. I basically observe the future and this is something that all of us are doing, but I do it about 95% of the time. So I don't operate a business while I have a future business. But it's basically thinking about the next five years. And I want to share some of my key insights with you today. So my real job is listening. And I would definitely recommend that we start thinking about this as being part of your job as well. Because it's so important to listen to the future, to the next five years. In China they say, if you want to know about the future, ask your children. And if we're open to understanding what the future brings, the next five, seven years, not 50 years, then things change for us. And the most important thing that's happening today is the convergence of man and machine, technology and humanity. I happen to have a book on this. I just wrote Technology versus Humanity. It's been out for three weeks. So all of your questions you may ever want to have, they answer in the book. Just kidding. This is the most important curve of the day. Exponential technological change. It's very hard for people to understand exponential because obviously humans are not exponential. We do learn. Occasionally we get a little bit linearly faster. We go step by step. But we're not computers. What's happening now in computing, and pretty much everything around computing is that it's mind bogglingly explosive. Not just Moore's law and computers, but also artificial intelligence. Now we're seeing things around us that used to be science fiction. They're becoming science Fact, self driving cars, automatic language translation, intelligent machines, robots that people fall in love with. You've seen the movie, heard it's actually happening already. I'll talk about that a little while. So we're now moving into a world where you say, this can't be true, this is actually working. I mean, we have two years away, pretty much less than two years, until language understanding of computers becomes completely normal. You could speak in 50 languages, even mixed languages, and command computers, natural language understanding. And this has always been tested, but finally it's here. So the mistake that we often make is that we say, okay, it hasn't happened and it won't happen because we were here, right? We were doubling 0.01. It's still nothing. But now we're at 4. The next step is 8 and 16. So we're going to be at the top of this curve in roughly seven years. That's 30 times as much as we're here. So my kids that are millennials, they will probably never know. The kids of my kids will never know how to drive a car without a computer running it. They will never know what a CD looks like. They will never be offline in their lives. Is that a sad thought or not? I don't know, but it's more like a mental state of being offline. Basically, our future is we're at the pivot point. And if we keep thinking in a linear way, we will see catastrophic results for businesses, but also for politics and culture. Because thinking linear means that we say, okay, we wait and see. But now, pretty much in this kind of world, wait and see means wait to die. Because things are moving so quickly that we don't have time. The car industry is the best example. Six years ago, I had a workshop with some of the major German carmakers, all of their leaders, and we talked about self driving cars, autonomous vehicles, car sharing. Right? And what did we get in a room like this? We got laughter. No, we literally got laughter. Nobody wants to share their car. Are you kidding? That's a crime in Germany. Share my car. Think of that. So today, the number one initiative of all carmakers is electric vehicles, self driving vehicles, car sharing, mobility. Imagine if we would have thought of that earlier, so we would have had possibility of changing things. So today on this curve, we have exponential connectivity. The world is connecting at really high speed, mobile and otherwise. We have data as the new oil. We talked about this for a long time. It's finally here. The data companies are the most powerful companies in the universe, as far as we know. So no longer oil, but data. Intelligence. What can you do with lots of data when you don't have intelligence? Well, a doctor can't possibly remember 140,000 cases of cancer of the same patient that he's seeing. The machine can. That's turning us into a bit of a superhuman, you could say. So this is also the key question. How do we keep the human inside? I would propose to you that it's actually totally meaningless if your business is really great with technology, if you have no meaning, this is probably not news to you. But basically, people don't make decisions based on data. I mean, we do all kinds of things. What we do as a human is vastly more complex than saying, okay, the focus group has proven xyz. And so that's what we do. Data is really crucial to what we do in business. It's a big decision point. But just like you don't eat in every place that TripAdvisor tells you, TripAdvisor is a great tool, but, God, you'd be in deep trouble if you just eat what TripAdvisor tells you. It's a data point. So what's really important is how do we keep the human inside? And I've developed in my book these two terms. One is algorithms, of course, you know what that is. And the other one is andro rhythms. The things that humans do. And there are some little things that humans do. Mystery, secrets, lying, deceptions, humor, empathy, compassion, the list goes on. And this is very important for business because business is about trust. You could say in a way that the only currencies that really matter in business are trust and data. But sometimes they are, of course, at odds because you don't really know what to say. Ginny Rometty, the CEO of IBM and IBM is one of my clients. Just to divulge this, but Ginny said one day that in a speech that basically, in the future, major decisions will be made by algorithms and data, not by intuition and experts. I don't know if you agree with this. I would say I hope not. I think basically, data is becoming increasingly powerful and important. But will we remove the human part of decision making based on data? Will you let an AI decide whether you should have a baby or not based on the reading of DNA of that baby? Or would you let an AI run the government? That would be a logical conclusion. Well, at least in Germany, it may not be useful. So we have to think about what that means. Basically, this is bringing home an interesting thing called the Moravec paradox. And that paradox simply Put says that whatever is really easy for computers is hard for people. And what's hard for computers is very easy for people. In other words, the stuff that we do as people is almost impossible for computers. For example, when I meet you in the hallway, it takes us about one second, sometimes less, to figure out if we're basically in the same tribe, if we can talk to each other. And, and that's without saying anything. So there's a huge amount of information that takes place between people that is sort of unquantified. And never mind those issues like ethics, morals, values, understanding, trust. So at that point we can say, well, that's kind of an interesting angle. For example, you remember that story on Facebook, This Pulitzer Prize winning photographer Nick ud, he had put his photo on Facebook and Facebook kicked them out because Facebook's algorithm said no nudity allowed on Facebook. And well, I took out the part here, but basically it's more or less a new child. But this was a photograph that has won all kinds of prizes and he was kicked off Facebook for the picture. That's how smart the algorithm is. Did not recognize art from pornography. That's the Moravec paradox. So we have to do better than that in the future because basically the bottom line is machines don't think like humans do. We think with our bodies. Daniel Kahneman, the Nobel prize winning psychologist, once said, we think with our bodies, not our brain. So I would submit to you, when we talk about data and analytics, that's the good thing. Let the computers think like they're going to think like machines. Let them think an entirely different way. They don't have to be like us, they shouldn't be like us. It's fantastic. If a computer can do its own thing, its own thinking, its own cognition, do we need a computer to really understand what we feel like or what kind of ethics we have? Do we need a computer to decide who to kill when an autonomous car has an accident? I would submit to you that 99.999% of what we're talking about here could be very well achieved with computers being cognitive without going anywhere close to this. And that's the low hanging fruit of what you guys are doing in business. We don't need computers to think like us. We can take care of that. There's a few things that we should keep to think about the possibilities that may leave us baffled. Like this one is a software made in Switzerland by accident called Emotion Advisor. Check out the video and then let me know what you think. On YouTube. It's called Anviso. Okay. This software analyzes people's facial movements as they're looking at financial products. So if you. That's kind of funny, isn't it? It would not be happy with. Mostly we would discuss, I suppose, but so you're looking at things and then. And the software says this is what people are really thinking. It's like a lie detector. Right. So very interesting angle. So this, this is kind of baffling because maybe very useful. Do you believe that this is a useful product? Maybe, or maybe not. You know, it's actually an interesting angle. We really hard to understand where it goes. Just take it one step further. Yesterday Newsweek was running an article saying that some people had proposed the next president of the US should be an artificial intelligence. Because the argument was an artificial intelligence. A machine would not have misgivings about things, wouldn't lie, you know, wouldn't have any bias, you know. So after Trump, it's going to be, you know, IBM, Watson. Yeah, or something like Watson. I suppose you could say that Trump already is an AI, but a badly programmed one, and that that really wouldn't make any difference. In any case, I think that's probably not a good idea. Right. But here are some of the mega shifts I think that are underpinning the DNA revolution. If you understand those. I don't have time to go through all of them, but I'll show you a list and you can download the PDF later. If you understand those, then you can look forward and say, okay, how do we actually use this? Here's the list. They all end with Asian. So that's why I used to call them the Asians, but I live in Switzerland, so I use the datafight cow as a symbol because now the cows are actually getting wired in Switzerland to track them, to see what they're doing, how much they're eating, how often they have sex and things like that. And so the megashit underpinning this is really very complex scenario of many things you've seen before. Let's just look at one. Datafication. That's kind of a fake word, but datification means something like your doctor that you used to go and see. He used to scribble things with hand and put it into his folder with a bad handwriting. Today the doctor has all the data about you from your Fitbit and from your remote diagnosis and writes on an iPad, so everything you talk about is recorded. Well, some doctors at least not my doctor. So that data is turned into actual data streams. Now we have a hiking boot from an American company called rei. The hiking boot connects to my app while I'm walking and it says 43km. You need new shoelaces, because it can analyze this. Everything is turned into data. Everything is becoming automated and everything is becoming virtualized and robotized. We put all these trends together. We get things like the blockchain, which I'm sure you heard about. This is the current hot topic of the day. Wouldn't be possible without all those trends. We get the Hololens, which you see outside the possibility of seeing things differently. We get the fourth industrial revolution and we get intelligent digital assistants. The last one is a biggie. Very soon, we're not going to have websites, we're not going to have apps. Well, we're still going to have those. We're just going to talk to computers wherever and whatever shape they are, in the car, on the mobile, at home, and we'll just say, book me that flight to Morocco, my favorite Riyadh, and off it goes and takes care of things for us. So you've heard this before. You know, data is the new oil, but now intelligent data is the petrol, is the gas that we drive on. So data is one thing, but, you know, garbage in, garbage out. You have to be intelligent about the data. And there's no way that we humans can actually take all that data and say, okay, let me work on 100 billion data sets. That's just not going to happen. So what happens here is we have all these helpful tech companies who are now the new oil companies. You know, if you look on the left, just 10 years ago, the big companies in the world were the data, the oil companies, and of course, some banks. That has changed. Too bad. Now we're over here, all the big companies, market caps, our technology companies, data companies, social networks and platforms. You know where this is going, right? The rest of this pyramid, if you look a little bit further down, is Chinese companies. That's the next edition of all of these guys, right? In fact, Baidu and Alibaba are already in the. In the upper piece. So that's really interesting to see where things are going. Facebook, for example, has a great correlation to this whole discussion about data oil. You can see, for example, the market cap of Facebook is higher than the market cap of Walmart. Connecting people and being a pleasure trap makes more money than selling stuff. It's amazing. I mean, we are the content of Facebook. Maybe we should get a piece of this. But you can bet that regulation is a certainty, just like the oil companies were regulated. I mean, it's interesting to see. The most powerful companies of the world used to be very regulated, but now they're not. Or very little. That's going to change. This is Facebook's battle plan. 10 years roadmap. We can learn something about DNA here. That is pretty much all they're doing. They're mining us. I'm not saying this badly, I mean, I'm a Facebook user. But you do feel sometimes like, you know, you're being mined. But look at the key point here in Facebook's roadmap, right? Connectivity, that's obvious, right? But artificial intelligence, DNA, the global brain. So how do they do it? I used to be in the music business as a musician and producer. So I for a while there tried to help the record labels to understand the Internet, which of course was a useless undertaking. In fact, the most incomplete transition to digital is the one of the music business. But you see on this really happy chart here, I have kind of a laugh and a look at this. Is the music business actually not doing so bad because we're kind of in the valley of death, you know, where it's gone down a lot. But all of a sudden, streaming, right? All of you guys are probably streaming music. Spotify or YouTube or, you know, the difference is, of course Spotify is like €8amonth for 20 million songs. And it used to be, what, €20 for one CD for 12 songs. That's kind of an issue on a per song basis. But do you see this happening here? Who are all these new guys, the streaming guys? Well, they're not the record labels. They're Spotify and they're Baidu and the Alibaba and maybe Facebook eventually. And companies like YouTube, of course. So the learning from the music business is, well, using data analytics. Spotify makes it work and the money is there, but you're not getting it. Somebody else is getting it. Same goes for print newspapers, you see in these scopes. I mean, if you're in a publishing business, you know how painful this is, right? The advertising, look what happened here. The Internet and then of course the mobile phone and before you knew it, deep dive into the valley of death. At the same time, the new guys, Google and Facebook just eat everything. There's a great saying on the Internet. Facebook is eating the world. 92% of digital advertising in news is run by Google and Facebook. And what do they do? Answer is data analytics, artificial intelligence. That's their tool. So these are the areas that are ripe for disruption by intelligent data and analytics. There's four areas. One is the complex environments and then trust issues and obsolete intermediaries to access. And then we can clearly say that banking and financial, there's quite a few banks I think in the room. I think banks qualify for all four. That's good news, isn't it? Just kidding. It means that you're ready for disruption. But looking at this, for example, obsolete intermediaries, media, travel, transportation and government and military. And again behind all of this is this. It's taken data analytics to the next level by using computers that can think about the data. I think this is actually all good news because quite clearly the summary is, you know, Sundar, the new CEO of Google says basically we're going from mobile first to AI first. Computing will be universally available like air. We interact with it naturally. We're descending into a world of digital everywhere. There's fantastic opportunities here. I would say at this point is 90% positive. There's 10% of issues, you know, privacy, application losing control, all of those things that we're currently already seeing around us. But this is really a question of governance, of policy. But really great to see that this sort of global brain is emerging. And that's what we talked about earlier. Basically what we're talking about, a place where we keep all data and analytics and intelligence is kind of a global brain. It all flows together. We're not talking about Skynet here, just in case you're thinking about Skynet, thinking about much more positive way. But this is a place where intelligent assistance is the number one thing that happens. I turn this around the world rather than artificial intelligence. I call this intelligent assistance ia this is an example your car finding its own way into the garage like pretty much every car will do in the near future. That is the low hanging fruit. So to me this is a really powerful thing that's happening all around us. For example, the email service that Google has just launched that can answer the own messages very simply, quickly or Slack where you can set up a meeting instantly by automating it. And this is basically the reverse of artificial intelligence. Some examples include Spotify playlist great called Discover Weekly. Here's a funny story on this. I used to be a musician and producer, but under a different name. 20 years ago and the other day I got the list by Spotify, curated by the robot, essentially by artificial intelligence. And on the playlist, six of the 20 songs were mine. Of course Spotify didn't know that it was me, but recommended my own songs to me which was kind of interesting. So was pretty smart. That's disgusting. Let me get rid of these songs. And then we have automated insights where you can write articles based on computers. Write articles, Right. We have IBM Watson doing the things for the for the jacket company the North Face. Right. I'll show you a short clip on this. Todd Spoleto, president of the North Face we are working on the prototype to match customers to gear. Watson, let's give it a try. Say it's mid June and I'm backpacking in Yosemite. Of our 353 jackets I can recommend nine. Watson, what if it rains? There is just a 3% chance of rain so I recommend the breathable stretch fleece fuse form dolomite jacket. A perfect choice. Wats no wonder our customer loyalty numbers keep climbing. I believe we can do even better. I like the way you if you go to the North Face website you can actually try out this engine by IBM Watson. It's really funny, they actually make recommendations based on where you are and how old you are and stuff. But I think the bottom line is we shouldn't confuse a clear view with a short distance. This is working somewhat today, but it's not entirely here. It's not like we can just go there and do this. It still needs a bit more time. So regardless of this, we're heading into a new relationship of man and machine between the algorithm and the humorhythm. And this is happening in financial industries. For example, the Robo advisor. Every bank in the world is looking at automating financial advice on the bottom line level. And that's happening everywhere that's actually working. I've tested quite a few and it's really interesting angle. And then there's some people saying that maybe we shouldn't risk putting our knowledge in the hands of machines. It's kind of this back and forth between those two things. But one thing that's quite clearly happening is that we're moving from apps to the global brain. From the mobile phone to a depository of concepts and ideas and information that we access with digital assistants. Imagine if you have a real estate company and you're planning to build something and now you do all your research online. You have people doing the research in the future, just in a few short years, you sit down and say give me analysis of the city of Frankfurt. The need for this kind of housing in this area. Very complex question. And the assistant will get Back to you 14 seconds later with a report that used to take three weeks and €50,000. That's becoming possible. That's super power for us. Obviously Commanding the global brain via voice control. That's right here. I mean, if you try to use voice control now on a Mac, for example, it works pretty well. There's a few things that don't really work too well, like mixing German and English words and names, you know, not so good in a year over. Most of our kids will never actually type with a computer. They'll just speak like a friend. This is, of course, what companies say that make these tools, right? Like a lover, I guess. Talk about that in a second. So you have apps like the Amazon Echo. Four and a half million Americans are using this at home. The Echo listens to you and then you say, echo. Please turn on the bedroom light. Play a song. Buy me some new toothbrush. Alexa, order me some pens. I found multiple matches in your past orders. The top one is via culture. Round stick grip ball pen, medium point, 1.2 millimeters black ink, pack of 28. The order total is $9.99. Should I order it? Yes. You could say this heaven or is this hell, Right? It's hard to tell. I mean, this device is listening to you the entire time, literally the entire time, just paying attention to whatever it is you're saying. It will know if you're drunk or tired or upset, or it will know all these things about you. Right. This is a minor infraction on privacy. So we're talking about these intelligent agents that can do exactly this. These are not dumb machines like Siri and Cortana used to be. They're actually getting very smart now. They're not like you speak to Siri and you say, I think I have alcohol poisoning. And Siri suggests that you would go to the next liquor store to get some more. And today you can ask really complex questions and you'll get an answer from this global brain. So now we have these intelligent agents deep diving into our heads, finding out what's inside of us. Extremely useful, but also extremely open to all kinds of abuse. And this is something we have to think about when we think about data. Because this is only years away, not decades. This merging of man and machine. That's really what we're talking about here. This is only years away, not two years, but most people say the singularity. Called the singularity. The place where man and machine have the same capacity. Seven to 10 years. In 2050, experts are saying that one machine will have the capacity of all human brains. One machine. So at that point, the ethics are no longer nice to have. Imagine if we weren't thinking about the rules of how we use data when we get this. We already have quite a few issues about that today. But why would we go down a road of the Internet of things, of connecting everything, if we don't have a way of saying, well, who's actually in charge? How does it work? So when we think about data analytics, it's inevitable. And this is the power of commerce driving it, clearly. So we have to think about what is the social contract in what we do, who agrees to what? What's the deal in this global super brain? I mean, let me make no doubt about this. There is about a chance of some 90% increased efficiency in having a global brain. For example, in logistics, there's some estimates saying between 40 and 65% of cost of logistics could be diminished by using a global database, the Internet of things, and this kind of concept. So then we have to think about, what does it mean? Where do we go with this? What is the next step in this whole debate? And that brings me to what I call the era of digital ethics. Everything that we do today about data analytics, artificial intelligence, cloud computing has an ethical component, values understanding. This is why KPMG and other companies like KPMG are very heavily talking about what does it mean for human life, for our culture and for work? I mean, let's take the wrapper off. We're going to have a lot less jobs if this works. Every large company wants to have less jobs, less employees. That's the nature of the beast. What do we do with this? There will be new jobs. Yes. So you can be a drone operator rather than a taxi driver, but still, where do we go? What's the next step in this chain? So now, just a few days ago, the major tech companies, six of them, announced this new collaboration called the Partnership on AI to benefit people and Society. Now that's an interesting term. Who would have thought about six tech companies having a partnership to benefit people and society? It's kind of interesting to see that this is the whole idea of saying what we do has responsibility. Finally, it actually changes the world, what we do. And here's a key question. We have to actually put human flourishing first, because a customer that likes you, that is happy, is the best customer you can expect. Ultimately, the goal of business is not efficiency. That's just one of the goals of the cfo. The goal of business is customer happiness. And then there's a question of control. When we talk about all these things, you know, what about our sovereignty, whatever, that's a difficult word. You understand what I'm saying? Who is mission control for humanity? Where is that data being kept? This is a very important question. There is no doubt we can do all these fabulous things and we can do stuff like solve cancer, end dying, increase longevity, solve climate change, fix the energy crisis, have abundant energy. We can do all these things. But how do we figure out who's in charge of this? This is a very important question. Who is mission control for humanity? Is it going to be Silicon Valley? Or even worse, China, or the two together? So when we talk about data analytics, we have to think about examples like this one. This is the first example. There's been a case, a trial in the US that says if we have to release prisoners that are out on bail, who decides if they get released or not, because they may actually do it again? So the judges went and did this and their percentage was pretty good. So they 18.6% failed and came back or were repeat offenders. But the computer fared better. The computer actually made better decisions on if people should be released or not than the judges. Well, in this trial, I don't know who ran the trial. I'm not going to talk about that. What do you believe? What is better to do? Well, you would say it depends. It's not that black or white, this case, automated driving. I think automated vehicles and autonomous driving will be in all cities, all major cities around the world. It'll be a long time before on the German freeway in Bavaria, we can drive in an automated car because there's a substantial difference in how we drive and why we drive and what the purpose of driving is. In fact, just now, the German government yesterday announced that Tesla should no longer use the word autopilot, because it's not. It's not an autopilot. Call it a assisted pilot. And this is the problem with data, right? This is actually a fantastic tool. I mean, I've been in a Tesla with autopilot. It rocks, right? It's fantastic, but it's not driving by itself. I'm not turning the seat around and eating a hamburger. Well, if I did, I would probably not be here to show you this. It's called judgment erosion. Judgment erosion happens when you do things based on data that isn't actually so. And that, I think, is a problem. For example, that was pointed out in the Guardian last week. This psychologist pointed out that when the algorithms are getting better, making decisions, people often stop working to get better. We actually take a seat back and let the algorithm take over. It's called automation bias. And so what we need to do with data analytics, we have to think about, well, how do we go in the future that's not going to be like this, where we think of this as being a solution. I call this machine thinking, or even worse, wormholing. Finding a shortcut through space and time. That's like saying, you know, I don't actually have to build a relationship with a person. I can just marry a robot. Believe it or not, there's people proposing this as well. So this whole idea of saying what should not be automated, that's a key question. When we think about data analytics. We have to think about what should be automated, what should not be automated. Because there are some things that are not automated, like these minor things on this side here. You cannot automate trust of the customer. You can destroy it by automation and you can further it by automation. That's completely different. Trust is not generated with a mouse click, but it can be helped with a mouse click. It can be increased or destroyed with a mouse click. So those human things are becoming much more important. In fact, I would say anything that can be automated will be. That is an assumption. I think that's completely clear. Let's say anything that can be digitized, automated, virtualized, or robotized will be. That's digital Darwinism. That is going to happen. The flip side is also true. Anything that cannot be automated becomes extremely valuable. And that's us, hopefully, you in this room, your kids. Some people like to say that basically, if you can describe what you're doing for work, then chances are you'll be automated. I'm faring pretty well here because I can never describe what I do to my kids. They're like, you make money telling people about the future. That's a crazy idea. So this is an interesting angle I think, that we're pursuing here in this conversation about. I like to say that every great algorithm needs a great algorithm, a great human thing. So when you use data analytics to make your business better, you also have to think of a purpose and your brand and what you're saying in the context of all these things. And rather than firing everyone that is now being replaced by algorithms, maybe you can put them to good use somewhere else to create the algorithms, the human part. I work with a very large international chain of retailers, and what they do is they say, okay, we don't need people at the cash register, but now we're going to use the same people to do customer service advisory in the store or online. We train them to do something different to create more value. I'll finish with this Quote and then we'll have some questions. Very important quote from Alvin Toffler that talks about our future says in dealing with the future it is far more important to be imaginative than to be right. To say that here, this would be interesting angle. We don't really like imagination in Germany so much. We think about this as kind of a dangerous thing. Productivity, that's better. But this is really important for us. I mean, look at this chart. The World Economic Forum says, what is the future of our skills? It's totally obvious. Of course complex problem solving is always on top of that list. But there's a bunch of stuff. Sorry, that's new. Sorry about this, people. Management. Emotional intelligence. Remember when the time when emotions were a total. No, no. At work. Emotions don't exist at work. And now we're talking about emotional intelligence. That is the prime new thing on this chart is emotional intelligence and of course creativity. So here's the bottom line when we think about this. What is your choice? Are you going to be on Team Robot or on Team Human? Here's the choice that you're going to make there. Ultimately, using data and being exponential with analytics is a clear requirement for the future. At what point does it overlap with being human or not being human? Here's a great list of these things that I enhanced or diminished by my Frank Diana. He's talking about all the things that technology enhances and all the things that it diminishes. And we have to think about what is the ultimate middle ground. And I think that there's some CIOs here among you. I think CIOs may very soon need to become Chief Understanding Officers, not Chief Information Officers. This is not really about information. It's about understanding. It's about this thing that makes us human. So as a summary, right? Data analytics and intelligence and humanity. That's our future. The founder of the World Economic Forum says we must put human beings at the center of our systems. Now this means the center of DNA is human flourishing is put the human inside. And to find a balance between the two. I think that's ultimately the best value that we can generate. Albert Einstein said years ago, already computers are incredibly fast, accurate and stupid. Well, they're no longer stupid, but okay. Human beings are incredibly slow, inaccurate and brilliant, to which I would add expensive. And together they are powerful beyond imagination. I think that's the ultimate destination of data and analytics that we want to get to. Thanks very much for listening and hope you enjoy the rest of the day.