The Biggest AI Deployment Nobody Talks About | Samsara CEO Sanjit Biswas

These are not the tokens you're going to find online. Like you can't crawl Reddit and find out about what happened on a construction site. The Samsara system in a given day were driving 99% of the US roads usually multiple times a day. I was just in the field last week with a large energy utility, and they shared with me a really interesting stat. They said over the last 125 years we built a certain amount of grid capacity. In the next 5 years, we're going to triple that. We're talking about millions and millions of vehicles. We believe we helped prevent about 380,000 car crashes, road accidents in the last year.

Yeah, absolutely. Well, first Matt, thanks for having me on your show. I would say physical AI is really the application of AI to the physical world. So, if you think about the infrastructure of our planet, it's way more than just the roads where you might see a Waymo robotaxi. It's the construction sites, it's the electrical grid, it's really kind of like all the plumbing that's under the street. It's everything out there. The interesting challenge, I think, with physical AI is that it's not digitized, right? Like this is the frontier where you don't have decades of bits that you can reason over and tokenize and quickly ingest, and that makes it really fascinating because the amount of value that's trapped is really significant. So we think about it in a few different ways. We think about digitizing things like location from GPS tracking. Of course we think about using cameras as sensors. So you can use that to understand the physical world in a pretty rich way, especially when you ingest lots and lots of basically video footage. If you think about how many frames you get from that, it's really significant. And then there's a lot of other kind of data sources, whether it's like weather sources of like what happened with precipitation on all the roads. You can think about speed limit data. Like there's lots and lots of physical aspects of the world that when you put them together, when you fuse them together, there's a huge value unlock.

Yeah. Well, it's interesting you mentioned centuries because that is like the right time scale to think about physical infrastructure, right? All the way back to like the Roman era, like there's pretty significant infrastructure out there. So many of these processes of like how do you maintain a roadway have been in place for a very, very long time. A lot of that process was manual, right? Like let's go inspect the condition of the road, let's understand when it was last worked on, you know, let's kind of dig it up and see what we find. If you think about the ability to digitize that and then use sensor data, it's a huge unlock. So the question becomes how do you get the data? Then how do you process it? And then how do you come up with a meaningful insight or really an action, like what should we do about it? And that's I think now possible. The last call it two decades was around reporting, like how do we ingest the data and give you a really cool like table so you can look at it and reason about it, figure it out. Now what's awesome really in the last two, three years is the AIs are able to reason about this kind of information. They can look for other context clues and then give you the insight, and now we're actually seeing agentic AI, of course, which is it can take an action for you, can maybe schedule the work to be done or start performing some of the work itself.

You know, I think it makes complete sense where all of this AI wave has started, which is in the digital world. We had all the bits. We had, you know, petabytes of data to reason over and there's a great training set. So, you think about all the trillions of tokens that were needed to get these models bootstrapped. The physical world is much messier and there's also a hardware component to it. And there's kind of this saying of like hardware is hard, right? Like this stuff has to hold up in the environment. It's got to relay the data over like unreliable networks. It has to be deployed to the front lines and that requires a lot of sort of messy work, right? Physical hardware installations, getting millions of frontline workers to adopt new technologies, like integrate it into their day-to-day work, and it's basically not as much low-hanging fruit as what we've seen kind of in the digital world. All that being said, it's a massive part of the global economy, right? All these industries, they make up about 40, 50% of world GDP. And so it's an area where you can have tremendous impact, but you have to really roll up your sleeves and get much more involved than kind of connecting to a large database that may have already existed.

Absolutely. So of course you're dealing with human life in a lot of physical operations and that's an area for tremendous impact. So if you can build safety systems that keep workers safe, that's a great thing. But you also have to be careful that you don't somehow introduce risk into the picture. There are other sides of that too, which is like these are digital technologies so we want to make sure they're hardened from a cyber perspective, like they're not introducing cybersecurity risk. But really practically, the physical world is a pretty dangerous place. Think about a construction job site, right? There's a lot of like earthmoving equipment, multi-ton, right? Like really dangerous stuff. It's kind of low visibility. And so you know the operators that are operating that equipment are taking some risk. The people on the site are taking a lot of risk. So it's inherently a risky environment and our question has been can we find ways to make it less risky using data. So we see the risk as the opportunity as opposed to the challenge.

Yeah. So Samsara is a technology company serving the world of physical operations. So think about those construction companies, the energy utilities, the supply chain and logistics companies that power the planet. We help digitize their operation. So that's a combination of hardware. So think GPS trackers, dash cameras, asset trackers, all kinds of different devices. Cloud services to ingest all the data and then now AI and applications to really close the loop, right? To help people take some kind of action or ideally automate the action that's needed. What we found is it's helpful to start with just tangible real-world problems and then expand over time. So where we started was around fleets of vehicles, almost all of these industries that have, you know, tens of thousands of vehicles that they need to perform their work, but over time we've expanded now into those frontline operations and we're able to fuse all this data together from different sources on our platform, third-party sources, and unlock tremendous amounts of value for the customer.

Yeah. So on the data points side of things, these are numbers that feel abstract even to me and I live them every day. But we're talking about 25 trillion data points. GPS, video, third-party API integrations, all kinds of data flowing into the system. Millions and millions of vehicles, for example. We're talking about, you know, millions of frontline workers that are using our apps every day. And in terms of impact, that's the other sort of set of data points we look at, which is like, well, how is all this technology having impact in the world? We believe we helped prevent about 380,000 car crashes, road accidents in the last year. That's meaningful to us because as engineers and product builders, we are able to have like significant impact in the world this way. We've helped avoid the emission of billions of pounds of CO2 by helping do things like optimize routes and reducing, you know, engine idling, things that seem simple but technologically simple perhaps but the execution matters a lot. What's cool is you see that real-world impact.

You got it. Yeah. So there's so many different factors that produce risk. And you know, we're very excited about autonomy and robotaxis and everything that we're seeing sort of on the frontiers, but there are a lot of these industries like in heavy-duty trucking or construction, people work very long shifts. You know, maybe they've been out in the field for 10, 12 hours, it's been hot and so they're exhausted. So you know, fatigue is definitely one of them. You also tend to see more accidents in general at night, right? Because the roads are less visible. You see accidents in foggy conditions, after it snows or rains, things like that. So, we're able to help prevent a lot of risk by warning the driver of when we see, you know, kind of the risk increasing. We can provide some real-time feedback and that helps them be much more alert, much more aware. And we can also coach away some of the bad habits that people develop. This is an interesting stat, but in the US, approximately 10% of people don't regularly wear their seatbelt. And that varies by state, it varies by industry, but that's like one of the biggest things you can do to improve your risk outcome is just simply put on the seatbelt. And it makes sense because sometimes people are doing a quick trip or, you know, they're distracted. But that little reminder helps save lives, right? So that's one simple one. Putting down your mobile phone is the other one, right? When you take a look at your mobile phone, like lots of people have this habit, your car if you're driving can move the length of a football field and that is hard to think because you're like I'm just taking a quick look to see what that message was about but then you look up and you've moved 100 yards, right? That's the kind of risk avoidance that we can create with real-time alerting.

That's correct. Actually during our PhD. So my co-founder John and I, we met at MIT as PhD students over 20 years ago. I now cap it because we're just old. But it was a fun sort of research project that we worked on, which was this was around the time that Wi-Fi was emerging as a new technology. We built a research project called RoofNet. We covered essentially the city of Cambridge, the area between MIT and Harvard, with free Wi-Fi in the early 2000s. So that was really exciting. It's like a hands-on, kind of very practical research project. We did a bunch of academic research on routing protocols and, you know, how to build the network. But the first company, Meraki, came out of that project, which was we thought it was tremendously cool this idea that Wi-Fi could connect so many people, just incredibly useful. We wanted to help other people build big networks. And so we essentially took that research and now I would use the word distill, like we condensed it down to run in a box that other people could build networks out of, and then we started essentially making that product available. So that was Meraki. To be honest we kind of thought of it as a project, like we weren't even thinking of it as a company. We kind of bootstrapped the business in Boston. We ended up moving to California. And it was fascinating because this is 2006, like 20 years ago. Wi-Fi was a brand new kind of nascent technology and there were some real challenges, right? How do you do guest access? How do you do networks at scale? How do you deal with people starting to use YouTube which was brand new back then? Like hard to imagine, right? But that kind of exposed us to how fun it was to solve real problems. And we had a huge, you know, kind of deep background in networking. We had a lot of friends from grad school that we recruited to start that company. And so we got off the ground quickly. We started seeing these devices get out in the world and Meraki ended up kind of just growing and growing and growing. It was doubling in revenue every year. So that was the beginning of our entrepreneurial journey. It was a little bit of an accidental start.

Yeah, you're very right. We had never spent time like in a loading dock or warehouse or like in a construction yard. But we were always fascinated by them and I think that was really the key, is this is just like nerdy curiosity of like well how does the electrical grid really work? Right? I have an electrical engineering background. I was just always kind of like fascinated by this. Or like supply chain, if you're just curious about like well that Amazon package, like how far did it travel, like where were the goods stored, like all of those kinds of questions were fascinating to us. And similar to Meraki, we weren't intending to start this company right out of Cisco, like we had been kind of on this pretty intense run, but the curiosity kind of got the better of us and we started reading lots of books about this and, you know, just trying to learn about the world. The challenge with physical operations though is you can't learn about it in a book, like you actually need to go on site, and to do that you need a reason, you need an excuse essentially. And so we said well maybe we can be helpful to these industries, right? Because like I was saying earlier, it's the infrastructure of our planet, so massive, there have to be interesting problems to solve there. So we kind of started this company market-first, very steep learning curve. And I have to say as a second-time-through entrepreneur, I'm really glad we had that experience because had we gone back and done it, we probably would have overweighted our prior experience and said, 'Hey, this is how it's done or this is how we did it at Meraki.' With Samsara, it's a different customer. We serve the world of operations much more than the kind of technical buyer. We sell direct, so we interact directly with our customers versus via channel. And that reset was enough for us to kind of go back to beginner's mind, which I think is also very important for most companies.

Yeah. And I would say, you know, every single one of those layers has some connective tissue attached to it. So if you think about the hardware, I've got hardware on my desk of course, and so this would be an example of one of our sensors.

This is what we call an asset tag. So, you could put this on a piece of construction equipment, right? It's got an accelerometer in there, so it can tell, you know, how much it's been moving. It's got a Bluetooth radio that's a little bit more powerful than what you've probably used on the consumer side. So, you know, your AirPods have Bluetooth. This is industrial-grade Bluetooth. It's got a battery inside and then it's built to be super tough. So, you can like beat this thing up. You can drive over it with a truck and, you know, it'll continue to operate. That layer has hardware, but also has firmware that's running on it. It's got network connectivity. I mentioned Bluetooth. So, this connects to the millions of Samsara gateways, tens of millions of phones and handsets that can act as kind of a relay point for us. And then we're able to get that to the cloud in a secure way. So, that's the data capture side, right? Going from motion like the accelerometer into the Bluetooth layer into the cloud. But from there, you need to organize it because like you got signals coming from all over. You need to be able to like operate on it in a pretty methodical way and that's what's going to feed the AI because if you give the AI pretty noisy data you'll get, you know, it's like less signal-to-noise ratio. So we need to get clean data in. And then to use your analogy, that's the brain, right? Like that's where we store it, we operate on it. You can surface those insights to the end user or the agentic piece is you can just take an action, right? Maybe change a safety setting, right? Like say hey we're going to ask our entire fleet in New York because it's raining to increase the following distance versus on a bright sunny day, right? That kind of change would have normally required a human in the loop. We're now finding that the AI can do it very consistently and can do it at scale that people wouldn't be able to get to because you'd need someone just sitting there monitoring all the settings for thousands of vehicles. Not very practical. So, it just doesn't get done. And that's maybe the arms, the kind of action side of things.

Yes, this is Bluetooth Low Energy. If you're familiar with the BLE that you would see on your fitness device or your AirPods, that kind of thing. So, Bluetooth has come a long way over the years, it's kind of gotten added on to and it's picked up a lot of the great characteristics that many of these other standards had. So, we can get a lot of range out of these trackers. And then we add a layer on top of that of security. How do we make sure that we preserve the privacy and the security of the tag that it's being applied to?

This specific one would last about 3 years. We have others that last 6 plus years. We even have a really small form factor one. I've got these on my desk as well. And I don't know if you can see them, but this is like a tracking label. So we're talking about a sticker.

Exactly. So these last about 45 days or so. So long enough for shipments to kind of go one way. And then these are disposable. So, they don't have lithium-ion batteries in them, for example. So, you can just peel them, stick them, track them, and then dispose of them.

Yeah. This is a...

What's in it? It's hard to make out on camera, but there's basically some batteries and then, of course, the Bluetooth chip, right, running our firmware. And that is how it beaconizes essentially its signal of where it is.

I would say for us it's systems innovation. We did not build the battery. We don't make the silicon or the chip but we work with partners to integrate all this together. And then we have the network which is essentially think about the millions of vehicles on the road, all the people running the Samsara stack, they form a community and relay signals for each other. And you've actually probably seen this on the consumer side with the Apple AirTag, kind of that concept of an ecosystem. We apply basically the industrial-strength version of that.

Yeah. Unfortunately, I don't have all the hardware that we make on my desk, but the vehicle gateway, think of it as a black box that goes on a truck or a piece of construction equipment or basically any kind of moving asset. That is a different type of collector, right? So, it collects diagnostic information from the engine computers and that's everything from, you know, how much fuel is it consuming to does it have fault codes to was the driver's foot on the accelerator or the brake. That's all there on the diagnostic port. So we're able to ingest that information.

Yes. Well, we have to collect it and organize it but it forms a long time series of many, many different signals. And even something that sounds as simple as a fault code, really there's a lot of depth and richness to that too because if you read the fault codes very carefully you can understand very specific dynamics about different kinds of engines and fuel types and, you know, air pressures and so on. So we take all of that in.

That's right. So I think you're familiar with dash cams. You've seen them in Ubers, right? And super valuable, useful for drivers because if something happens on the road, you can exonerate yourself very quickly. So we have a connected version of that. So it records HD video, it has some storage, it's got the ability to send that to the cloud. And we run AI models at the edge. So we can do things like provide you feedback to increase your following distance like I said based on the weather condition. Now we're also seeing the driver side of that camera. So it's like outward and inward facing. The driver side of the camera can do things like detect fatigue or mobile phone usage, provide real-time feedback to the driver. And the idea is they can self-correct, self-coach, and that is like the aha, is it breaks the cycle or the bad feedback loop of I'm going to look at my phone. If you get a sort of like audio alert in the moment and it happens many, many times you tend to break the habit because it's sort of negative reinforcement. That was a huge breakthrough for us six, seven years ago as we introduced AI at the edge and now we're kind of going further with that concept finding other forms of risk and so on.

Exactly. Because now that you've got the technology in the cab, what else could you do with it? Could you give the driver a briefing in the morning as they start their shift about where all they're going to go and traffic conditions and weather? We have a little button. They can use that to, you know, call dispatch, for example, and say, 'Hey, I'm going to be late because I need to go pick up some tools or something like that.' So the connectivity layer just got enhanced with the presence of all this technology.