On this episode of the Futurum Tech Webcast – Interview Series, I am joined by Ross Sabolcik, SVP, Industrial and Commercial at Silicon Labs for a conversation on Industrial and Commercial IoT, AI, as well as smart cities and agriculture.
In our conversation, we discussed the following:
- Most people think of “Smart Homes” and consumer electronics when it comes to the Internet of Things or IoT, so what exactly is Industrial and Commercial IoT?
- The current state of smart city concept adoption and how successful deployment is right now
- What the impact AI is on Industrial IoT
- What market segments might surprise viewers about large scale IoT
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Transcript:
Daniel Newman: Hi everyone. Welcome back to another episode of the Futurum Tech Podcast. I’m Daniel Newman, host, CEO of the Futurum Group. Excited for today’s Futurum Tech Podcast, where I will be joined by Ross Sabolcik. He’s the SVP of Industrial and Commercial at Silicon Labs. We’re going to be talking about IoT, industrial commercial applications, smart cities, AI, agriculture, and a whole lot more. Ross, welcome to the show.
Ross Sabolcik: Hey, thanks for having me. I’m excited to be here.
Daniel Newman: Yeah, it’s great to have you here. First time on. I’ve had your colleague, Matt Johnson, a couple of times join the show, been following Silicon Labs for a long time, doing very interesting things in a unique spot in market. And of course, riding the wave of silicon as we see the world being transformed and hey, you can’t do all this cool generative AI stuff without lots of sensing, lots of data. That’s what makes it all happen. And I feel like Silicon Labs is right in the heart of that particular intersection with the technology. But we’re going to dive into all that here on the show today. But let’s start off with just a quick introduction about yourself and your role before we dig into the harder questions.
Ross Sabolcik: Sure, yeah. So I am the SVP and general manager in charge of our industrial and commercial IoT, and our IoT business is broken up in the three primary segments, in the INC, and that is smart cities. So think of things like electric meters, water and gas meters, street lights, things that would cover a metro area. We also have a true industrial segment, so think traditional industrial automation of factory and process control, human machine interfaces, things like that. And then we have a commercial business, so commercial lighting, retail systems like electronic shelf labels and things like that. So those are the three major segments that we have, that we cover in the industrial and commercial space.
Daniel Newman: Well, there we go. And let’s run into that. We’re going to talk about… Let’s start off with IoT. I know the smart home is the rage, or at least it’s been. Everyone likes to talk about the things through whether it’s going to be your smart speaker, your wearable consumer technologies, but kind of like the metaverse, the big real application for it and the one that tends to be the most monetizable right now, and especially most profitable, tends to be in industrial and commercial applications. Not to say the other isn’t big, just saying that this is one of those things where, when it comes to yielding the most off your farm, or it comes to the smart robots that are going to actually help a factory create greater productivity and lower error and fail rate, it seems to be in the industrial space. So talk a little bit about what that is, the industrial commercial IoT and what you’re focused on there.
Ross Sabolcik: Yeah, so I think you really hit on an important concept when you think of these industrial and commercial applications, or even these more professional applications. The motivating factor for customers to adopt IoT is return on investment. So convenience, in the home side, a lot of it is convenience and security and those types of care abouts for the end customer. In the INC space, it’s typically what’s the return on investment? How am I going to save energy? How am I going to make my process more efficient? Where am I going to be able to identify and eliminate waste? So those are some of the big drivers. And if you think about it, some reports have said that there could be about 8 trillion dollars of value unlocked by those types of efficiency improvements from the industrial and commercial space.
So when you think about, let’s say a smart meter, so an electric meter on the side of your home, traditionally if you go way back, somebody had to walk out to your house and read the numbers on the meter and that’s how you got billed. And about 15 years ago or so, those started to move to be wirelessly connected. So you didn’t have to roll a truck out to read the meter. And that was a real ROI improvement there.
Daniel Newman: Oh yeah.
Ross Sabolcik: As you see what’s happening now though, once customers see I have this wireless connectivity, they start asking, what else can I do with it? So while billing may have been that killer first wave app for something like electric meters, what you’re seeing now is the utilities are saying, I have all these sensors that are across my entire electric grid. How can I use them to better understand the health of the grid? Not just to send you a bill at the end of the month. So that efficiency, that improvement in your operations, that kind is a thread that runs throughout.
And you could see examples of that in retail, electronic shelf labels, for example. Previously someone had to go out and change the tags on the store shelves if the pricing changed. Now with electronic shelf labels, you can have real time updates. And we saw a lot of retailers deploying this during the pandemic when let’s say pricing was really volatile. I saw in my local home improvement store, electronic shelf labels on lumber. And you might remember, lumber was moving up and down on a daily basis, 10, 15%. When you have an electronic shelf label, you can respond to those kinds of price changes in real time.
So be it metering or those retail applications or even in industrial, being able to better monitor your processes to see where you have waste or doing predictive maintenance to see when a machine is potentially going to fail, those are all the drivers that are making customers really look long and hard at IoT, and how to digitize their operations.
Daniel Newman: Yeah. So clearly these things, I remember when it became a big thing with the energy companies and the smart meters and the readings, that was one of the ones that came on really strong. When I talked to Matt Johnson recently, we talked a lot about how there was a huge wave and a period of time where IoT was huge, just such a rage. And we kind of alluded to early Ross, that it was all about consumers. But it really was these applications where it was all about getting data, being able to optimize, streamline operations, increase productivity, deliver analytical value, inside especially the industry for the manufacturing space for instance.
And then of course things like, I don’t know, smart cities. And that was another one that really came up and I think we heard a lot about. And it’s probably a little bit washed out right now by everybody wanting to talk about Chat GPT, but there are other big technology things going on and I think smart cities are still a really big one. But I’ll also say that I think we were really promised a lot. And then I think at times either A, we’re not being told about all the advancements. B, it’s seemingly slowed down. Has it slowed, is it still moving? What’s the state of smart cities and what are the opportunities there?
Ross Sabolcik: Sure. Yeah. And if you look at smart cities, smart cities are in a market where some countries or some regions were really early adopters. So if you go back, as I mentioned 15 years ago, AMR was the term, automated meter reading. And the idea was, just being able to bill your customers without having to roll a truck out to read the meter. So that had been really established, and in some of the early adopters, those networks have been built out. The coverage of smart meters in certain regions, if you look at the UK, is about a hundred percent. So those meters have been deployed.
What you’re starting to see though that’s really interesting is now, 10, 15 years on as utilities have seen the power of getting more data throughout their grid, they’re asking, what else can I do? So there’s a big push now in metering for what’s called grid Edge intelligence. And what’s driving it is, the load on the electric grid is becoming much more complex. So if you think today, if you have solar panels on your roof that are generating electricity that you’re feeding back into the grid, and you maybe have now an electric vehicle charger that’s 10 kilowatt or more load on the grid, these are big new demands that are being put on the grid, that the grid really wasn’t engineered to handle. So what the utilities and metering companies are starting to do is saying, I want my electric meter to be a grid Edge intelligence point, to tell me what’s happening on the grid besides discounting how many electrons you’re using, to look at what is happening with the voltage and current on the grid.
Is it stable? Is there a brownout in your neighborhood? Did you just turn on your electric vehicle charger and you’re at risk of maybe overloading your circuit, and the meter can talk to your EV charger and say, okay, back off a little bit right now.
So those are much more sophisticated use cases. After that early, let me just read the meter use case, utilities and metering companies started to look at how can I leverage this connectivity to do even more? And outside of the electric grid, you’re seeing other areas like street lighting. So being able to control the street lights dynamically. Being able to detect, is there no traffic on this road? Maybe I can dim the lights. Detect traffic, bring the lights up. Those are other use cases that we’re starting to see in smart cities.
The one thing I’ll add is there’s a wireless technology we’re a big proponent of, called Wi-SUN, and Wi-SUN is a wireless utility network that is used for a lot of these metering applications. And I tell people, this is the largest IoT network that you’ve never heard of, because there are hundreds of millions of nodes of Wi-SUN smart devices that have been deployed in the field. But unlike a home protocol, which is much more tangible to a lot of people because they feel it and touch it, this is a massive network that’s also touching their lives, but they’re just not aware of it.
Daniel Newman: Yeah, I think that’s right. Part of good technology, Ross, is when it’s transparent and ubiquitous, meaning there are some types of technologies we want to experience. If you look at some of the trend lines and things like Apple and what they’re trying to do with the headset, while right now very heavy and clunky, I think part of the problem they’re actually trying to solve is that, we’ve actually changed our entire, the physiological appearances because we’re so busy hunched over, looking at our phones all the time. You know what I mean?
Part of the societal norm is like, hey, I’m going to put a wearable on that, that maybe is no larger than that pair of eyeglasses that you’re wearing and allow us to have that, call it, the Robo-cop, terminator mode, where we can have data, but we’re also more engaged in the physical world together. Like I said, the early showings, whether it’s been Oculus or it’s still a long way away, but you can start to see, hey, how can we make our technological worlds and physical worlds more united? I think that’s where the Metaverse application becomes more sensible. And I think with things like smart cities, it’s like, the ability to manage water with technology, the ability to manage energy, it doesn’t necessarily need to be visible. So maybe the actual smartest cities are the ones you don’t really see are smart at all.
Ross Sabolcik: Right. Yeah. For example, we talk about electric meters. If you look at water meters, there’s a really mind-blowing stat, somewhere between 20 to 45% of water that’s shipped in a utility to your home is lost due to leaks in the systems. That’s just a huge amount of waste if you think about it. And the problem is how do you detect those leaks? Even if the leak is in your house or in your sprinkler system, or even in the main pipe feeding your house, how do you detect that?
So with these next generation water meters, they’re also making them connected. So cloud-connected. And they can do things now, like detect you have a slow leak in your house, because the meter sees that it’s constantly running at a very slow rate, and now alert you that hey, you might have a leak. And that that’s something that you will probably not catch. If it was large enough, maybe you’d see it in your bill and say, something’s going on here. But if it wasn’t, you might be leaking hundreds of gallons and never know it. So again, the fact that it’s instrumented in collecting all this data, you can start to find these inefficiencies in the system that you probably wouldn’t be able to do otherwise.
Daniel Newman: So let’s talk about AI. I’d like to because why not, right? It’s a requirement.
Ross Sabolcik: It’s hot.
Daniel Newman: Red hot. But on a more serious note, talk about the intersection, because I think one of the biggest opportunities with all the ambient data, the smart city data, the industrial data is not just generative, but also general AI, is this is a really significant inflection.
Ross Sabolcik: Yeah. For sure. And I think the way I describe a lot of these industrial and commercial networks is: customers, maybe for a decade have been collecting huge data sets about devices or the environment they’re operating in, but extracting knowledge from those data sets, actionable knowledge has often been really difficult. And I can give you a couple of examples. If we stick with the electric meter example, you’d like to know how much electricity you’re spending in your business or in your home, but you can’t instrument everything that’s in your house, that every device can say I’m using this much. So there’s some really cool AI techniques now. They call it load disaggregation. So you can measure with an electric meter the voltage and current that your house is consuming, and you can look for signatures that would indicate certain types of devices, like your washing machine turned on, or your air conditioning turned on, or your electric heater turned on.
And what these systems can do is say, okay, here’s how much energy you’ve used in a month and here’s how much went to each of those devices. So you can start to identify, what is using most of my energy and where can I be more efficient? So that’s all brought about by having AI that’s able to take all this data from the Edge, and do that load disaggregation.
Another big one we see is in predictive maintenance. So if you think about rotating machinery, it is a real common one. Bearings wear out in pumps. If a pump fails and you weren’t planning for it, if you are a manufacturer, that could bring your production line down for hours or days. And if you look at the cost per minute of having a production line down, it’s pretty damaging. So there’s a lot of techniques being done around monitoring rotating machinery to identify early stage failures. So you can go and do maintenance and not have to be worrying about downtime. And a lot of those systems rely on wireless connectivity to get that sensor data, and get it back to the cloud, or even analyzed at the Edge. So again, both of those really high ROI applications that are using AI to mine insights from all of these Edge devices.
Daniel Newman: Yeah. I think that the applications are going to continue to rapidly emerge where IoT and AI intersect, because it’s really always been about data. And the Edge is the absolute fastest producer of data at scale, and much of the Edge data is IoT. Lighter weight devices, lower power, sensing and such. So as you think about how companies are going to optimize things like production, it’s going to be a combination of sensor data and AI. And it’s going to come together, and generatively, it’s going to start being able to create plans for uptime and upkeep. And we talk about precision of maintenance. You talk about the best time to water your crop. All these things are going to be down to extraordinarily good, extraordinarily defined and prescriptive numbers that will make everything produce at greater rates.
So speaking of greater yield and production of crops, you sort of alluded to it with Ai, but we just both did with production on a farm. But smart agriculture, that’s a pretty big application. I know when I lived in Texas, so my daughter lives in Lubbock, she’s going to grad school there, and we were driving between Austin and Lubbock.
Ross Sabolcik: Lots of cows.
Daniel Newman: Yeah, there’s cows, and fields, and farms. And Look out there and you just go, this is most of the country. Agriculture’s big, whether it’s farms and steer, or it’s where I was living before Ross, in Illinois where it was corn and wheat. Big business, and technology and IoT for instance, have a pretty significant role to play.
Ross Sabolcik: Yeah. So a great example with cows, just livestock in general. If you look at livestock, there can be really devastating infections that can happen to your herd, that can really quickly take down a lot of your herd. And the question is, can you get a real early indication that something may be going on with a particular animal and you need to quarantine them? So we’re working with several customers who are building, we call them the Fitbit for cows. So they’re callers that they’ll put on livestock, and it’ll measure a whole bunch of biometric data for that livestock, and then identify for example, well this animal looks sick, or this animal may be calving right now, giving birth. All of these metrics about the herd, so that you can better manage the herd overall, by having that real time insight into what’s going on.
And again, the ROI, if you can limit spreading an infection by a couple of days, the impact that could have on preserving your livestock is massive. We have another really interesting application that a customer is building, that is about beehive monitoring. So there’s a sensor that they put in the beehive and the bees will actually build the honeycomb around the sensor, but they can measure the temperature of the hive, the vibration that the bees are doing in the hive, and all these types of things to get early insight that maybe something’s happening and they need to go and check the hive, get a better sense of if the hive is being really productive, with real time data.
But you made a comment earlier that it’s always been about data. I would tweak that a little bit and say, it’s really about mining insights from the data, making the data actionable. And it’s never been easier to collect a lot of data from massive sensor networks. The real challenge now is how do you mine something out of that data that’s an actionable insight for you to go and do something. And that’s where this intersection of IoT and I think AI both at the Edge and the cloud, becomes incredibly powerful.
Daniel Newman: So Ross, it’s a funny story, but I woke up today, worked out, I had a meeting, jumped in the shower, water pressure, super low. And I came to find out that here in Austin we had drained the reservoir because everybody had been watering their lawns, and with the heat wave here, and so this is kind of a crossover, it’s a little consumer, it’s a little industrial. But it’s a really great example of where sensing IoT, we should be ahead of this stuff. This is where technology should have, long before we got to a point where now we’re in a boiler, long before any of this stuff happens, smart city, smart technology, Edge, and IoT applications should be solving this problem.
So we’re just about out of time, but one other thing that I did want to pick your brain on is, what we proverbially call AI at the Edge. What are you seeing, thinking and about that? Because it’s not just IoT, it’s bigger than that, but kind of the whole Edge application and where do you see Silicon Labs fitting in?
Ross Sabolcik: Yeah, so the data in a network or data in one of these sensor systems is going to come from the Edge. The Edge is going to generate the data. And so if you can do some processing of that data at the Edge, there can be a lot of advantages. You can have lower bandwidth of what you got to upload to the cloud, and you can also take care of perhaps privacy concerns. So an example I see in, for example, smart buildings. So commercial buildings. We’ve all had that situation of you’re in a conference room with a motion sensor and you’re having a meeting and no one’s moved and the lights turn off, even though you’re still in the room. That’s because it’s a motion sensor. It’s not really an occupancy sensor.
So how do you do better sensing? Well, one of the ways that people want to use cameras to detect that there are actually people in the room, but you may have privacy concerns, I don’t want to broadcast this video out to the world on a cloud somewhere. So there’s been some applications where there’ve been cameras involved, and that data has made it out into the wild. And from a privacy concern, that’s really not what you want to have. So we’ve actually had applications using our IoT end node processing, where you can count the number of people in the room using a video stream, but that video never has to leave the camera. So the camera really becomes a person-counter, to help with privacy concerns.
So that brings that all together, you reduce the amount of data you’re sending to the cloud, you process it at the Edge, you take care of any privacy concerns. It’s really powerful, a lot of these applications that we’re seeing on the Edge.
Daniel Newman: Yeah, I like that example. And vision is such a good example of an Edge application that’s creating scale, it’s a higher capacity workload. It definitely requires more compute to do at any sort of scale, but it does have very similar attributes to a lot of what IoT. And I do think obviously with the, as we continue to move process to process, higher performance, lower power, we will see a point where the ability to do pretty sophisticated vision with pretty low power, is going to quickly find its way into the marketplace. And I’m sure Silicon Labs will have its place to play in that.
Ross, I do have to say thanks, I do have to let you go. We are at time here, but I really do appreciate you tuning in with me today. Joining me here on the Futurum Tech Podcast.
Ross Sabolcik: Thank you. I appreciate the opportunity.
Daniel Newman: All right, everybody, stay with us. That was a great conversation. So much is going on with IoT. And yes, we found a way to talk about AI as well. You can be pretty sure that is a horizontal technology, and that’s going to be impacting every different technology adjacency and every vertical. And we hit on a lot of those today. But I got to go for now. Hit that subscribe button, join us for all of our shows. We appreciate you tuning in. Bye-bye now.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.