HP is unveiling today its expectations for “megatrends,” or the major trends that will shape our world in the years to come. It comes courtesy of Shane Wall, the chief technology officer of HP and head of HP Labs. He oversees research and development at HP.
Wall periodically gets the big research team at HP to decipher the latest megatrends, or trends that may unfold over decades. And that’s hard to do, since Wall said in an exclusive interview with VentureBeat that that change keeps accelerating in our world.
But big companies like HP, which had $58 billion in revenues last year, have to try to predict those changes and adapt their business plans to prepare for the disruption — or become victims of it. Wall said that megatrends are major socioeconomic, demographic and technological shifts that will have a sustained, transformative impact on the world in the years ahead — on businesses, societies, economies, cultures, and our personal lives. They will impact our experience at home, at work, and on the go, he said.
HP has identified four major megatrends — rapid urbanization, changing demographics, hyper globalization, and accelerated innovation — that it believes will have the biggest impact. By 2030, the company expects there to be 8.5 billion people on the planet, and we’ll have more than 50 megacities with more than 10 million people each. People will live longer and be hyper aware of global issues, thanks to the internet. Global resources for taking care of people will be stressed, and inequality could grow.
The world will become truly flat when it comes to globalization, trade wars notwithstanding. And the pace and breadth of innovation will continue to accelerate. As new technology components mature and become commoditized, they transform into the building blocks that enable more frequent breakthroughs. These will include things like advanced manufacturing, artificial intelligence, augmented and virtual reality, digital health, edge computing, security, blockchain, haptics, robotics, and 3D printing.
I talked to Wall about these predictions. Here’s an edited transcript of our interview.
VentureBeat: Can you describe your overview for these megatrends?
Shane Wall: The thing about these megatrends, they shouldn’t change radically. There should be course corrections around where things may accelerate or go slower than we might expect, but in general they stay very anchored. We’ve done things like this previously before we really branded it this way.
The first one is rapid urbanization, people moving more into cities, larger cities — tier one, two, three, and four cities. Smaller spaces, new business models, and that has a whole slew of constraints on sustainability. We see space pretty solid in there, and acceleration in business models going faster.
The second one is changing demographics, populations changing in profound ways. Aging populations, what we call the silver spenders, and new generations coming up. Ninety-seven percent of the growth in the world is happening in emerging markets. That’s a key point we’ll come back to in a second on future trends. Those changing demographics, especially on the older side, are putting big pressures on health care. If anything will be accelerating in this trend and giving us more pause and thought is the health care problem becoming more acute, certainly as it relates to the U.S. That shows up a lot in politics.
The third is hyperglobalization, everything connected, digital platforms. Globalization here comes thanks to the internet. Mobile is a focus. It’s breaking down country boundaries. The slowdown here could come from things like global boundaries, tariffs, and the like.
Finally, the last one is accelerated innovation. That just keeps up. Everything will be smarter, cheaper, faster. Everything automated. That’s going quicker than you’d probably expect. Every day it seems like you get surprised about the degree of automation that goes on.
We think these four trends pretty much stay the same. What we did in 2019 is we stood back and looked at, within these four, what are the new disruptions and new things we’re seeing? We really hit three things. First is what we call economic segmentation. That’s where, we stood back and tried to take a different look at the population, rather than doing what most do with market segmentation, finding who your customer is and looking at that customer. In doing that, we tried to look out 15 or 20 years and see how economics are changing. What does that mean in terms of the haves and have-nots of the world? That’s where we’ll spend most of our time, on the profound implications of that.
The second one was on energy and sustainability. Obviously very forefront for folks. But the big a-ha, and it’s why we ended up emphasizing this, was the linkage between energy and data and economic expansion. That may not be obvious, but I think you’ll see it’s pretty profound. It could be limiting if some of these things aren’t addressed.
The third big one was, what do we see as the technology trends that are going to amplify because of these? There we picked three things. Ultra-efficient compute architectures where the client edge architectures are changing in profound ways, thanks to AI and automation. You see how that goes to energy and data. The second is the changing nature of software development, what we call software 2.0. Not a term we coined, but one we borrowed from the industry. And then finally the acceleration that this enables with what we call virtual machines.
Those are the three big ones. That’s the landscape, and we can pick each one and dive into it.
VentureBeat: Do you relate all of these to some direct investments that HP is making in products and services today?
Wall: We do. We have a couple of key objectives in megatrends, and objective A1 is that the trend we explore and put the emphasis on has to impact our strategy and choice points. The economic segmentation says we need to strategically need to grow our regions. We need to change our go-to-market motions. We need to drive to a higher degree of automation. We’ve put in place plans to do exactly [that], and then how it relates to education and reskilling. On the energy and data side, it’s dialed up our focus on edge architectures, beyond the standard x86. It’s had a very profound impact on strategy.
VentureBeat: How does something like, say, 3D printing fit into that larger vision?
Wall: As you know, previously we’ve emphasized digital manufacturing within megatrends. It intersects and cuts across a few of these, certainly as it relates to economic segmentation and the growth of population and where that occurs. Given that it’s occurring heavily in dense cities, 3D printing as a way to drive sustainability in those cities becomes crucial, because of energy, waste, how to transport materials and waste out.
The other place it hits is on the energy and data. We did analysis that looked across all of manufacturing, and it turns out that manufacturing worldwide consumes about one-third of our total energy production. If you look at end-to-end, cradle-to-grave manufacturing cycles and you can apply digital manufacturing, including 3D printing, you have a chance to substantially reduce that energy use. We’re hoping to get closer to 20 percent. That’s the promise of digital manufacturing.
In technology we start moving machine learning out to the edge. We have the emergence of the edge-based silicon architectures for machine learning. Those become really critical to 3D printing. It’ll be embedded in our 3D printers. All of the inference will happen within the printer itself.
VentureBeat: The reference to rising inequality and urbanization — it’s interesting to see that getting a lot more conversation now. You had the fellow who went to Davos went viral, the Dutch economist who was saying that if we just make billionaires pay taxes, we should be in better shape. I’m not sure where technology helps us there, or serves to be the thing that widens the gaps.
Wall: Why don’t we dive into that? This was probably the one area that we spent the most energy and time on, and the most references. Again, we call it economic segmentation. If you look at haves and have-nots, we had a rigorous definition around what constituted a have and a have-not. The data we used for it was 80 different sources and more than 20 different direct economist interviews, high-end economists with a diversity of thought. We didn’t just pick one angle. We pushed all sides of the spectrum.
A couple of things come out of it. First of all there’s this concept of haves and have-nots. That’s described most popularly by the WEF and the IMF and others with the concept of purchasing power parity. It’s a way to normalize the differences in cost of living and currencies across the world. If you make this amount expressed in U.S. dollars, just as a normalizing factor — if you make more than $35,000 in purchasing power parity you’re considered a have. If you make less than that you’re considered a have-not.
The first piece was, when you cut it that way and look at the trends over the next 15 years, you figure out that the number of haves is growing substantially over the next 15 years. It’s huge growth. In some areas the number of have-nots is growing, but in general it’s going down. That becomes really important in a little bit, this whole idea that the haves in the world are growing.
The second topic — and they intersect, but they’re different — is the notion of inequality, growing inequality. The haves can be growing substantially around the world, but they grow at a different rate, and that creates inequality between them. We didn’t focus as much on the inequality. We focused more on how much the haves are growing, because that says how much of the population is growing that we can sell to, who have the means to buy technology products.
VentureBeat: In some ways it almost seems like the trend is not necessarily in the control of technology companies. If you could control it, you’d try to minimize inequality. But that seems to be such a force that you just have to deal with it.
Wall: You got it. We might have a minor effect on it, but overall it comes down to bigger economic development and government policy, taxation policy and the like. We just looked at what happens. We looked at the growth of the haves and the have-nots, and a couple of things keep standing out. One of them was, if you look at where the haves are growing, they grow in the Americas, and they grow a little bit — not much, but a bit — in Europe. But where they really grow, huge growth, is not surprisingly in Asia.
When we think about that we tend to default to China, and certainly China has a substantial increase in the number of haves, but it’s not limited to China. It’s also in India, as they continue to advance in several ways, but it includes southeast Asia. It includes more of central Asia. It’s growing in large portions of Asia. To give you an idea, the growth in the number of households in Asia that become haves is three times the size of the North American haves. That’s just the growth.
What you end up with, if you look forward 15 years, in Asia broadly you have a size of the have population, people who can buy tech products, that’s substantially larger than what we have in North America for a base. That trend becomes really critical.
With that as a backdrop, we looked at, if you have that growth and have this rise of Asia, do we have the necessary labor and skills, based on what we assume is going to go on in tech development and economic expansion? When you dive into that, you find out that you have a very substantial labor shortage for skilled labor. That skilled labor shortage drives increase in wages and costs.
That may not be surprising, but what we hear in the broader conversation is that automation is coming to take everyone’s jobs. The research we did came out in a very different way. It actually came out that if you’re willing to continue the economic expansion we see, continue the growth going on in this region, you have to drive to much higher degrees of automation. In fact, automation won’t be able to keep up with economic expansion. That was a big a-ha. Driving to much higher levels of automation will become crucial as economies continue to expand.
VentureBeat: Do you find a way to be optimistic there, then, rather than concerned about inequality?
Wall: What it did for me is it changed my thinking. I was initially worried that this drive toward automation would eliminate such a large percentage of jobs that it would drive greater inequality. The findings that came out of it said that, because of the changing nature of jobs and the increased costs and shortage of labor, you’re going to need automation just to keep up. I don’t think you’re going to see substantial replacement of jobs, a reduction in jobs.
Now, what it does, it still requires considerable amounts of education and reskilling on the part of not only governments, but of corporations themselves. It’s going to be required that a corporation invests in that education and reskilling just to keep up with economic expansion. That was a big a-ha. That’s changed us at HP in terms of our investment in education and reskilling.
VentureBeat: Is there a point on this horizon where we see a 100 percent connected world?
Wall: I don’t know if we’ll ever get to 100 percent. If you’re over 90, that’s pretty substantial. This work on economic segmentation has hugely influenced where we thought the markets were really growing relative to our investment and how we shape our workforce, how we shape our go-to-market. It’s increased our investment in automation considerably. What it did, though, is it led to something.
If you need automation to keep up, you have to drive to a higher degree of automation. The backbone of automation is compute and the IT infrastructure. That led to us looking at energy very broadly, how energy unfolds in the next 30 years. What are the implications for that? Like I said before, one-third of energy currently is used for manufacturing. If that continues to grow, that creates a huge issue in terms of how you keep up with that.
When you look at emerging markets, in order for them to maintain their economic expansion, you have to have substantial investment in their energy production. It has to become much more efficient. Certainly when you look at China or India, not surprising, they’re just trying to keep up, and that translates to coal plants and other polluting energy. You can predict that. But here’s where it got interesting. When you then look at energy and automation, in order to automate, you need to be able to compute on the data you’re collecting. It turns out that the data we’re collecting is so huge and monumental over the next 15 years that you can only process, in a cloud, less than one percent of the data collected.
If you’re going to automate, if you need automation to keep up with the economic cycle, you have to figure out a different way to automate, because you can’t get all the data back to the cloud to run on your cloud-centered machine learning and AI framework.
VentureBeat: Even 5G isn’t going to solve that?
Wall: No. 5G is just a blip. I’ll give you an example, and there are dozens of these you can use, but let’s just take jet engines for airplanes. Rolls-Royce for quite some time has been leasing or selling engines containing more than 5,000 sensors, and those sensors are generating 10 gigabytes of data per second to improve fuel efficiency. You could never send that to the cloud, even if you had backhaul fiber. That’s happening everywhere. It’s in engines. It’s in 3D printing devices. It’s in any of these edge devices that are using sensors in large numbers for efficiency automation.
You see it in Microsoft, Amazon, and Google. Let’s take Amazon as an example. Amazon is moving to this hybrid model. It’s expressed in a software framework they call Greengrass. It’s where they’re taking programming that we know how to do in the cloud and extending that same approach down to edge devices. It’s the same programming model that goes for how you do machine learning and AI in the cloud, and it’s moving to the edge. That’s because of this data and energy constraint. You don’t have the energy or the bandwidth to move it, so you have to do the compute on the edge.
VentureBeat: That means everything has to get a lot smarter, then? All those sensors have to be smart themselves.
Wall: That’s exactly it. Now what happens is, we get to this third big point for us, which is the technology piece of this. We don’t have enough energy to put all the data in the cloud, and we don’t have enough bandwidth to move all this data to drive the automation needed to deal with economic acceleration. It all drives to the edge.
Now what we’re seeing is the emergence of these edge-based machine learning market ventures that are dedicated pieces of silicon that allow you to do AI and machine learning inference on the edge. That’s why you see this explosion of more than 50 different startup companies doing silicon machine accelerators on the edge. The architectures being put in place are 1,000 times more efficient than what you see in a traditional PC. They’re incredibly energy-efficient, dedicated to machine learning, sitting on the edge with this uniform cloud programming framework, whether it’s Azure or AWS Greengrass, sitting on top of it.
Those energy-efficient compute architectures are changing the nature of software development, which leads us to the topic of software 2.0. Software 1.0 was, you write software. You develop an algorithm, code it, comment it, test it. About 90 percent of your effort is spent figuring out the part that solves the problem and maintaining it. The actual writing of the code is a small percentage of the overall life of the code. But now we enter a world in which you don’t write code. Instead, you pull together data. You’re just operating based on data.
A good way to think about it, giving you an example, let’s take a 3D printer. Right now, instead of writing firmware for a 3D printer, I take a machine learning, edge-based, ultra-efficient architecture chip and I take all of the data that comes from that 3D printer, which has thousands of individual sensors and actuators. Which nozzle that gets fired, which loader that gets turned, every sensor for heat, I take all of those and I collect all that data, and I send that to the machine learning chip. The machine learning chip learns that, and then you run it as an inference on the machine. All you do is accumulate the data.
VentureBeat: Does this make you guys want to do your own AI processors, or drive in some other product direction?
Wall: A different product direction. What that did is three things for us. One, there’s a gold rush right now among everyone doing AI and machine learning silicon. Watching that space and tracking the investment in it to track who wins, every large silicon player is doing it. Whoever emerges there is going to be a big winner, and we want to make sure we take advantage of that.
The second piece is, for the first time ever we’re starting to see how IoT consolidates in a standard way. Right now it’s the wild west, with everyone fragmented among different security models and the like. You can start to see how things like AWS with Greengrass, and Azure with their IoT framework, create a standard way in which to do IoT. That becomes an opportunity, because suddenly you can draft off that infrastructure.
That gets to the third point, which is that we need to make sure we’re investing in the main specific AI and machine learning framework. Creating a standard for our products becomes crucial.
VentureBeat: That seems to reinforce a direction. Did you find anything here that makes you want to change the direction of something you’re already doing?
Wall: Many of those changed what we’re already doing. Rather than assuming all of this is going to happen in the cloud, it really pushed us forward to a model where machine learning will happen on the edge. It changed our thinking about what the right partners would be for some of those algorithms. It changed the architecture of some of our products in 3D printing and our graphics print business. It’s changing our thinking on architecture substantially.
VentureBeat: Logically, then, a printer should have a lot more smarts in it in the future, doing things on its own rather than trying to get its intelligence in the cloud.
Wall: I don’t know if I think about it that way. Today we already have a huge number of sensors and actuators sitting in a 3D printer. I don’t think that’s going to change substantially. But it’s what we do with the data. Rather than trying to individually control it, like what goes on in firmware and software today, we’re going to have to accumulate and act on that data, and use the data to drive the sensors more than anything.
One last concept. If you have ultra-efficient edge-based architectures, and you now have software 2.0 where you’re accumulating and using data to drive that, it allows you to go after what we call virtual machines, or digital twins. That’s where we’re really taking a bigger focus. Can we now take an entire system, like a 3D printer or a large graphics printer or even a desktop printer, and create a complete digital twin of it? I can do all of my development on that virtual twin, all of my testing on that virtual twin, and then deploy the physical hardware, doing all of that much faster.
It sounds very futuristic, but it’s what’s going in much of the automotive and self-driving car world today. All of that happens with digital twins. Then it goes out to testing and learning on the roads. We’re trying to take similar ideas and drive it into our core development.
VentureBeat: On the rise of Asia, does that cause some different thinking around technology or products?
Wall: It affects products clearly just because of differences in culture and importance. It changes our design synergies. Over time we’ve been looking to Asia more and more, unsurprisingly. I’ll tell you what it affects more. It affects us more strategically, in our market motions. If you look at HP’s business today, about 45 percent occurs in the Americas, about 30-35 occurs in Europe, and the rest, about 20 percent of our business, occurs in Asia. The growth is all going to be in Asia, so we’ve shifted our investment to strategically build out the go-to-market motions stronger across Asia. That’s a big impact.
VentureBeat: Are there any other areas you wanted to mention, anything we missed?
Wall: I gave you the broad overview, the high level, and that’s really where we’ve focused more for megatrends this year. The changing nature of economic segmentation, how that impacts energy and data, and then what that means for compute. We have some nice white papers with a lot more detail and we’ll be happy to share.
VentureBeat: How are you going to talk about this and use it going forward?
Wall: In terms of the use, a lot of that goes to our internal processes and our overall planning cycles. In terms of the rollout, we’ll be sharing our findings, because while it affects us, it also has big value outside as well.