Gerd Leonhard & Peter Vander Auwera: Chapter 3: The Megashifts

Hello, this is Gerd Leonhardt, futurist, author of Technology versus Humanity in Zurich, Switzerland, doing another podcast with Peter Van, our content curator, who's helping to reshape some of the ideas that I set forth in my book. And just a few years ago, technology versus Humanity, but still very much timely this time, Chapter three. Can you. Hello, Peter.

Hello, Garrick.

Good morning. Hey, Peter, can you give us a brief overview of what chapter three is actually all about?

Chapter three is about what you have called the mega shifts. And you have identified a number of mega shifts. I'm going to quickly go over them, and then we'll dive into a couple of them.

So the mega shifts that you have identified are digitization, mobilization, screenification, disintermediation, transformation, intelligization, automation, virtualization, anticipation, and robotization.

Can I jump in there for a second? It's important to realize first, we've changed some of the mega shifts in my work subsequently. And, for example, screenification has moved out, and we're now calling this intelligization. We call it cognification. And you can find all that stuff. It's actually now Chapter three is public on the web because there was so much demand on people finding out about the mega shifts. So we have now a separate website, Megashifts Digital, and we also have one called megashifts.com which is a blog. But anyway, if you want to download, the whole thing is Megashift Digital. And there you see all the megashifts laid out just as an update from the book, you know, which has mutated quite a bit.

All right, so the first one is digitization. Can you say just some of the highlights of what this Mega Shift is about?

Yeah. First, a small anecdote. You know, the work that I started on this was roughly five or six years ago after I talked about data as the new oil and that sort of thing. And we jokingly used to call those the Asians, because, you know, they all end on Asians. And that was actually a good term that kind of stuck with people. And then I started compiling all the.

Asians that were floating around.

And the work I did on this came out of this realization that many clients come to me and say, okay, what is digital transformation? We want to transform. And then it dawned on me that this is not just about digitization or digital digitalization, as Germans say, but it's about everything that's caused by going digital. So when things go digital, we can automate because we have the data. When we can automate, then we can Also make it smart, which is cognification, so that we can actually connect stuff. And computers can start to sort of think in parenthesis along with what we want. And we don't have to program computers anymore, which is machine learning, deep learning. You know, they're kind of learning in a new way. And then if we do that, then we can virtualize, which is virtualization. And so basically things can move in the cloud that used to be on earth, so to speak, or physical, like digital music that's now in the cloud, or movies, which are in the cloud, or even transportation, which is in the cloud. So that, that's virtualization. And then if we can virtualize, then we can make a robot do it. That's called robotization. So the robot can finally be instructed by the cloud to do xyz. So Baxter, you know, the most popular robot for small factories and stuff used to be 200,000 K, now it's 12,500.

Wow.

And it can actually learn from a human because the human information has gone in the cloud, the cloud has become smart. The robot gets smart because it's connected to the cloud. And of course, physically speaking, it can actually do the work now, which is.

Quite different as well.

So they all hang together. And it all starts with data vacation, which means to generate data where there wasn't any before. So that's, for example, human resource analytics, that's gathering data for later use in.

Some mostly nefarious ways. But still, you know, that's, that's a good example or datafication of music. Now we didn't have the data of who was listening in cup verder, you.

Know, to what music. Now we do, you know, it's been data fight. And that's both good and bad, depending how you look at it.

Yeah.

In this first part of this chapter, you make a comparison or at least a critique on what most people are used to work with, like KPI's performance indicators. And you were suggesting key human indicators. Can you expand a bit on that?

Yeah, I think the philosophy of work in companies has been largely based on some sort of, you know, implementation of being functional and efficient and optimized. And that's machine thinking, in my view. And that worked fine as long as the machines are stupid, because then we have to be the machines and do all the monkey work, so to speak, and just be better machines. And that was the philosophy of a lot of corporate work, you know, that we are better machines. Unfortunately, now your machines are finally getting it and they're no longer that stupid. And in 10 years, they'll be pretty damn smart. In 20 years, they'll be beyond smart in the human sense. So better machines won't cut it. And that's why we don't need KPIs in the sense of our performance index. That's like measuring how many Coke lids the machine can press on in half an hour. We need human indexes, which means value, trust, relationships, thinking, innovation, entrepreneurship, imagination. And that's, you know, I've kind of dropped the ball a little bit on this and talking too much about it. But I think that's kind of what Jack Ma calls, you know, the EQ and the iq, the emotional quotient versus the iq. And so, you know, being a better machine is just not a good future.

For us because it worked fine when the machines were as dumb as they were.

Yeah. But on the other hand, do you think that machines are going to develop eq?

I think the good thing about machines is that they will, of course, attempt.

To understand our eq.

You know, attempt.

In the sense of learning what we mean. The big difference is that machines will, I think, be able to simulate EQ because they've learned so much about how we do it. So they will watch us, they will say, okay, that's imagination.

And they will.

Be able to fake it.

Yeah. So it's almost like us humans today, which is one of the other Asians. Like we humans now with our smartphones, we are taking photographs and recording everything around us and putting in everything in the cloud. And you raised the question in your book, does that make us look and feel more like machines or more like humans? So it's like, on the one hand we are recording, but on the other hand, the machines are also recording and simulating and faking our behavior.

I think we need to be very good at understanding the difference between a simulation and a copy than the original. You know, I think we quoted this last time, you know, Nick Cave said about computers making music. Yeah, they can make music, but they can't make great music because they don't have the nerve. So we can have a machine that is human in the sense of human function, simulating functions of humanity. But we have to understand that this is still not the same. That's like saying, okay, you can, you know, you can call fast food food, or if you like food, then you know what the difference is. Right. It's kind of like, I think ultimately we have to. We have to be able to understand the difference between the simulation and the reality. And we have to maintain that also.

In Some way, yeah.

So the question is really is what is real and what is not?

And you used in your book a quote from Dave Eggers book, the Circle, which in, in essence is saying that we are becoming utterly socially autistic.

That's what you put. So.

And if I think about, if I try to mix that, to relate that to Kevin Kelly's latest post on mirror world, it's like there is an alternative second mirror reality and we are getting really confused about what's now real, the person in front of the mirror or the picture of the person in the mirror.

Yes. I think this is of course one of the ongoing challenges of technology.

It's that we confuse the realities and that we may prefer this other more simplified reality. And I think this is part of the social contract in the discussion about valuing the reality versus the simulation and also maintaining the difference. I think it's very important for us that we maintain the difference between humans and machine. And we say, okay, it's very useful, but you know, it is really just a snapshot of reality.

And can a computer make you feel welcome and stuff? Yeah, it can do all those things. But I mean compared to a real human, it's still just a 0.5% snapshot. And it has its use. It's not a bad thing. It's just as long as we don't.

Start saying no, that's so much better and much more convenient. Let's just grab reality and, and take this for real. That's kind of like TripAdvisor. You can stand in front of the restaurant, you can look up TripAdvisor. TripAdvisor says, oh, it's the best place, period. And so on and so on. But you look inside and there's nobody there. And whoever is there looks very dissatisfied.

And then you look right next door.

And the place is full. And TripAdvisor doesn't even list it. Okay, then that's machine smartness. That's kind of like, okay, it could.

Be, well, the other way around. It could be completely accurate, but you.

Don'T know which one is which. And this is where women come in.

And TripAdvisor is operating here as a sort of a platform, so called platform for disruption. And you spend quite some time in this chapter on articulating disruption and it's great and it's needed, etc, etc. But you also suggest that we need construction. Can you elaborate a bit on that?

Yeah, I think the obsession with Silicon Valley and now China, of course, is to disrupt things fast. Like Facebook mantra used to be, you know, move fast and break things, right?

Used to be.

Yeah, used to be. And now it's like it has moved too fast and it has been broken. But, you know, you can appreciate that mantra when it's like 10 years ago because. Because there was so much that wasn't working. But today, technology has to allow us to build new logic, a new ecosystem, something that actually hangs together, something that makes sense, not just makes a difference. So when it's Airbnb and Dropbox and Uber and so on, we have to ask the question, how will this work to satisfy almost every angle in this new business? So what's the social implication? Do they have to pay taxes? Do they have to make up for the rise of rents? Should they build their own houses, whatever the deal is? So it's about construction, not just disruption. I think this area of saying that you're going to just disrupt, and then after you have disrupted stuff, you move on to the next disruption.

So it's almost like. Or in addition, it's not only extraction, if there would be an opposite of the word extraction, it's also giving value back to the community, the society that you operate in.

Yeah, I think a lot of disruption is really feudalism, because what it does, it says, okay, we're going to disrupt this, how this works, that say banking, right? And then we're going to put this in place, and once we have it in place, everybody will love it, and then we're going to own the place and we could do whatever the hell we want. That's the Facebook problem. They have disrupted how this all works, and now they are it. And now they are the ones that need to be disrupted because they've become a cartel. And that's really what happens when you disrupt stuff like Uber. You know, you also have to take into account what the social implications, you know, the externalities of your business model are, and then you have to address those as well. You can't just do one thing and then, you know, now Uber is going to go and disrupt the truck and trucking industry. Right. I mean, there's responsibility that comes with building stuff that lasts.

You mentioned transportation, you mentioned banking in your books. You said that one of the biggest areas for change will be in health and energy. Is that still the case and. Or why do you pick those two industries?

Yeah, I think we're currently seeing. We have the waves of what I call the waves of disruption.

First the music industry, the media industry, the publishing industry, and now transportation industry, and now it's insurance and banking. That's right. Now we can see that changing quickly with on demand insurance or pay per use insurance and things in banking, for example, the whole fintech and the crisis of major banks, that's coming. And then the next step, of course is health care, which is ridiculously expensive in almost all countries. And the pharma industry has done a very good job at essentially selling stuff that either doesn't work at all or seems to work sometimes. But everybody gets it right. It's like, I mean, the costs have exploded. So now we need to switch in healthcare from treating the sick to preventing the sickness.

And that's what technology allows. We can actually monitor and we can understand and then maybe we can do something about it before it happens. Or for example, with diabetes, it would be very hard to fix diabetes today using genetic engineering, or it's impossible, but we can treat and prevent and also alert people to diabetes. That would make 90% of the hospital stays, the sort of catastrophic incidents go away. So that's medical. And the next one is energy to.

Where we're essentially, in 15 years we're going to be in a position to supply the world's energy completely.

You believe that?

Yeah, by solar or by other means. And we're still going to have oil and coal, but coal is already becoming less economically feasible than renewable.

I mean, those are huge steps that require like in the case of oil, $35 trillion worth of sunk assets from the oil industry that will have to just be eaten up somehow.

I would like to build upon what you said for the health industry and related to one of the other megashifts, transformation.

You said that the mother of all transformations will be our own mega shifts.

From being physically separate to being directly connected to computers and devices. So how would that compute in a health industry scenario?

Yeah, I think this is of course a critical debate in the healthcare industry because the more data that we share, including our DNA, our biomes, you know, all of the information that we are basically, if we can share all of that in some way where a machine could compare and find patterns, you know, then we would find obviously dramatic solutions and possibilities of logic. But then the problem is of course, considering 200 trillion genes and 300 trillion biomes, this would be rather complex. And then the question of data protection, ownership and security will feature large. But clearly, currently I think we have like 2 million genomes that have been analyzed worldwide in depth. And to really make sense out of building a health cloud and understanding how people work, we need 2 billion. And that's going to happen so that will change everything because we'll be finally understanding stuff.

We probably won't be able to really change stuff out of the understanding anytime soon, but we'll be able to say, okay, if this and that, like what computers do best. Right. Then to actually go in and change it, that's probably going to be further away. But healthcare will get a lot cheaper because we'll be smarter about how we live.

Yeah, one more thing on this. I was really intrigued by the sentence of being physically separate from being directly connected to computers and devices. I didn't get that from the book. Can you explain it a little bit, please?

Yeah, I think one of the things that we're seeing already today is this trend towards this convergence of mandate machine. So we already have cyborgs, you know, people with chips and implants and those kind of things. But the question is how far would we want to take this in return for some benefit? Folks up. I believe that brain computer interfaces would be very beneficial for handicapped people and people who have issues controlling things, of course. But would they be good for healthy people to do really far out stuff like directing robots and things? Yeah, I think in some cases. Okay. In other ones, I would say that's probably going to be quite confusing and potentially dehumanizing. So I think on that interface issue, I think that I wouldn't give a black or white answer. I would say that basically we need to figure out what is still good for humans and what is not. I mean, the ultimate judgment really should be whether that provides human flourishing or not, and not whether it provides entertainment or superiority or some sort of omniscience. You know, I think that if we're just going for power, like a lot of omniscience is essentially about power. You know, you want to have superpower, you want to be better than the others, or you want to make more money. You know, if that's the final argument, then I would say that's probably not a good thing because the motivation is, is inferior. But then who's to say that, right?

Who's to judge that?

That's a difficult part. And I think in the medical field the low hanging fruit is really completely different. Lowing your fruit is. Let's reduce the unnecessary hospital and doctors visits by having remote diagnostics for the really simple stuff. And technology can do that today. That's already happening with scanadu and with of course Apple sooner or later. And, and if I can prick my finger and cough into the device and connect my wristwatch device says, you know what you're not in good shape, but you have to work out more, but you don't have a major problem. It could be potentially dangerous, of course, because it's not the same, but it would capture a lot of people who are doing unnecessary medical, who are covering those unnecessary medical expenses.

It's a convenient way of creating awareness about your personal health.

I think that's good and bad as well, because I use it, you know.

I use the Apple Watch and I find it's quite useful. It can be used as a giant excuse, given that humans are primarily not moving until it's really needed. You know, we are. It could be used as a way of saying, like, okay, I'm monitoring this, and that's the first step, and it pretty much stays there, you know.

Yes.

And so that's. I think the motivation to really do.

Something about your body and your food.

And stuff does not really come from monitoring alone.

It comes from awareness or sort of a, you know, realizing that this is important. Yeah, so that's a little bit different.

Anticipation is one of the other mega shifts, and it's probably one of the most elaborated sections in this chapter of the book. So you start there by giving examples of simple, basic anticipations, like calendar management. Less basic is crime prevention and drug discovery. And then you imagine a number of scenarios that could happen when the Internet of things really rolls out globally. But the key part in this section is about the ethical issues that this brings. And you have listed four of them. Dependency, confusion, loss of control, and abdication. Let's start with dependency.

Yeah. You know, the anticipation megashift has actually.

Been morphed into the cognification in the latest edition. So it's basically when technology allows us to anticipate or even predict things, it can be quite harmless, like anticipating traffic, which there's patterns for. So we know that at five o' clock it would be jammed.

Right.

But anticipating human needs, like, I know you will like this, so click here. Sort of thing that impacts a lot of things that humans do, like serendipity, excellence, discovery, you know, the sort of natural process that we have that's kind of disturbed by anticipation. For example, in a way, you could say that Facebook is anticipating what we want to read by the patterns of the past and therefore disturbing our discovery process.

And that's called manipulation.

Right.

But.

But it would also lead us to sort of abdicate stuff that we would basically say, okay, why should we do this ourselves? We just let the machine decide.

I mean, it has all kinds of.

Side effects that we have to Keep an eye on. But the first one, what is it again?

The first one?

Dependency.

Yeah, okay, dependency. I mean, I put them all to the same pot because basically when we use technology to take over functions that we used to do, then of course we develop dependency like Google Maps, you know, with Google Maps. But on the other hand, you know, when you're using Google Maps in Brussels and you know your way around just a little bit yourself, then many times you would question Google Maps, you know, or you would switch it off or you say, you know, that's, you know.

I can go somewhere else.

Right, Yeah, I know where I am now, so.

Right. So you do learn actually even when you use Google Maps that to go this way is not good because the park isn't safe at night and things like that, which Google Maps just doesn't know very often. Just doesn't know. I mean, it's a machine, it doesn't read the stuff that's not expressed, you know, so it's not data. So when you get dependent, then it's okay in some instances. But when you have too much of it, like, you know, you get dependent on a dating app because you are too lazy to figure out how to.

Actually talk to a person, you know.

Or you have become used to shortcutting, you know, by essentially matching physical properties, you know, before, before you go on a date, you know, that sort of thing is it's both application as well as dependency, which in the end could be a pretty dangerous mix.

I was having a conversation with my.

13 year old daughter on something very.

Much related to this. So she had a sort of exam on geography today. She says, why do I have to learn all these things? Because it's all in Google Maps. It's that sort of dependency you talk about. Yeah.

I think there's a vast difference between using these tools as a background information.

Provider or a reminder than to use them as your external brain.

Yes.

For example, in the case of my daughter, learning geography is learning the context of what the app later will provide you. Would that be a good way of putting it?

Yes, Yes. I think this is the human thing that we do with information is not like we're going to go to the back of our head and retrieve a jpeg. That's not how we do things. We always have context, we have questions, we have assumptions, we have feelings about certain things. I mean, this is much more complex than pulling up a JPEG or web link. If you were to say that we're going to drop all these things because the computer gives Us, the language, translation, it gives us the location information, it gives us everything on demand. Why should we bother? Bother. Then I would say, well, you know, we're gonna miss 95% of what it actually means. And I would say that's not important in some circumstances, like, you know, if I'm gonna drive a car and then the machine would eventually drive me, would that be a big loss? I don't think so. You know, driving a car is not a human, is not human nature or human Right. But contextual awareness and orientation. Orientation meaning, you know, I mean, imagine you would marry a woman from Siberia and you would only speak to her through the say hi or whatever app in Russian, and she would do the same. That relationship would be functional, but it would not be at all the same than having some sort of way to talk to each other directly because it's intermediate, it's mediated.

I like it. I don't know what the second part.

Of the phrase would be, maybe it.

Pops up in your head. But it's. What we want to do is, or what we want to encourage is from functional to something that is more human, more contextual. That would be an interesting meme to develop.

Yeah. I think that we are so obsessed with functionality and optimization and efficiency that.

We would say, okay, the woman from Siberia, I love her and it's functional, it's efficient to talk to her through my app. And so I don't bother. But it's also a total simplification. And as Marshall McLuhan says, you know, any extension of man is also an amputation. Right.

So we are amputating certain things of ourselves. And sometimes I think it's fine. Like I said, you know, a casual conversation with a woman from Siberia would be just fine. But if I'm going to marry her, I should probably know how to talk to her myself, you know. And so, yeah, I think it's from functional to sort of. You say contextual or deeper than that.

Right.

And what technology does is it focuses on functional and quick, convenient. And like I said, it's okay. Many times it's fine. But we have to realize, you know, functional would be being a musician on an iPad in a 10 hour learning curve.

That sounds like me.

You can be half a dj, right?

Yes.

And it's a lot of fun, it's great. Nothing wrong with it. But then you call yourself a musician, which is fine with me, I don't care. But I think that if you realize that playing music is a 10,000 hour process, you know, to really get there, you can't just say that you have achieved the same in 10 hours. You know, that just isn't true because that's not how humans work. That's how machines work.

That. That's a very nice segue into one of the last mega shifts. I hope it's still called that way. Robotization. Yes, you mentioned, like, amputation. So we have talked about embodiment before, but robots are, as you said in your book, the embodiment of all these mega shifts. Why is that?

Well, let's put it this way. I mean, the robots that we've seen in the past weren't much of a threat to us in terms of their humanness or intelligence because first of all, they couldn't really move far enough. They didn't have the constant Internet connection. They were crude. And now they are becoming constantly connected. They're learning from each other, they're being fed by the cloud that are coming mechanically superior to us. So an 800 kilo robot can do a backflip, which I can't even do without going to hospitality.

Can now skate. Has learned to skate.

Yeah, I could do all of those things. Right. And so this is really one of the things where we have to say, well, robots are very likely to be everywhere. And so this is something where I'm saying, okay, in 10 years, this robotization of society will not just be in factories, it'll be pretty much anywhere where routine job with some minimal intelligence is required. I think it'll be sort of a bonus, you know, if we have a person like checking in the hotel or so. And it's interesting, it's a great example of the hotel. There are many hotels where the check in is an app or even a bot, like in Japan. But the better hotels still have people because that's added value, that's not efficient. Definitely not, because I have to wait for that person. And I find myself sometimes thinking like, yeah, if I can have a nice app that does this for me and I can pick my room, I can talk and I can, you know, maybe massage the person into an upgrade and that sort of thing. You know, you can't do that with an app. But sometimes I wish I would just.

Be an app for the checkout or so. Right.

So it's clear this robotization of that's not just going to be hardware. Also robotization in the sense of software, robots, intelligent agents. And like I said before, I think this is kind of the hell. Then it could be great, it could be terrible. And we have to measure what exactly do we want the outcome to be.

I think the bottom line of this chapter is that there are a number like 10ish mega shifts that are moving forward at an exponential rate, but all at the same time. And I mean, it's the key subject of the book is that you say we must also collaborate to address ethics, cultures, culture and values. And so you wrap up this chapter with making some observations about becoming the purpose of our lives rather than the tools to discover the purpose. I think that was a very nice landing in this book. Maybe it gives some ideas or sparks here to wrap up this chapter before we move into the next one.

Yeah, I think that's, you know, the conclusion is in the end, which I didn't express in the book, I don't think to the very end, but I.

Think the mega shifts, because they're all happening at the same time, they're creating this huge combinatory change, combinatorial, so where everything is being combined into new things. And that sometimes tends to be very tough for humans because it's based on.

Machine logic and what machines can do.

And so at the end of the chapter, I kind of point towards what I now call the rehumanization. The purpose of all that stuff is not that we have more tech. The purpose is that we have a gain, and if possible, a collective gain.

To our happiness and our fulfillment and our way of life and our economic troubles and our environmental issues and all of those things that we actually gained something that is the purpose. The purpose is not in itself. And this is what's been getting lost in the last couple years is because everybody's going crazy about the Internet of things and AI, as if that was something that would basically be the new humanity. But I say basically it doesn't matter. None of that stuff matters if it doesn't do anything good for most of us. And with good, I mean, people, planet.

Purpose, prosperity, quadruple bottom line.

Yeah. And that's, I think, why your focus for this year is to focus on the things that really matter, like what you just expressed.

Yeah, And I think that, you know, let's not forget that I think prosperity and, and money matters to many people. And that's also really important. We shouldn't just forget that. But it's in the context of everything else. Like if you make $75,000 a year in average, most people are happy in most Western worlds in that range of 75 to 150k. If you make 40 million a year, you're not 47 times as happy. So, you know, prosperity is a relative term that if you don't have it and you don't have it, then it's. It doesn't make you happy.

Right.

But in general, you know, people, planet, relationships, purpose, you know, that's all part of this package. And I think technology needs to acknowledge that, that its purpose, it's not celebrating itself. And the same goes for science. You know, the purpose of science is not to celebrate that we've just cracked another huge miracle. But how will it be applied to actually doing good things?

That's very cool. Where can people find more about this?

Well, I think Megashifts. Megashifts Digital is the download page. Megashifts.com is a small blog where you can find lots of other things around this and of course featurevistgurd.com and my updated website and the book itself, of course, tech versus human tech versus human.com and it's now available in 11 languages. Just yesterday on Italian, but what is the 11th language? Italian. Yesterday Italian.

Okay.

And so it's still screaming. And megashits will be expanded on.

They are a core topic for my year as well. And this is an ongoing thing. When you look at megashifts moving towards this idea of rehumanizing what we do, it ties nicely into what we discussed last week, which is a digital ethics council and all that sort of stuff. So thanks very much, Peter.

Yes. Next week we have another chapter. It's chapter four, Automating Society. That's the title of the chapter. So we're going to dive into that one.

Okay.

A newsletter, I think.

Yes. So Peter is Etervan, I think, on Twitter, right? Yes, like Van, the V A N. And our newsletter is Digitalethics Co. And it's free to sign up.

It's a weekly newsletter.

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Okay, thanks for listening and ping us anytime with questions and it's a pleasure to have you listening to us.

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Bye.

Thank you.