Why the Next AI Revolution Will Happen Off-Screen: Samsara CEO Sanjit Biswas

0:00If you think about it, there's like a third shift between midnight and 8 a.m. roughly, right, that people tend not to work because they're sleeping. Imagine if operations like logistics could still run during that shift, right? And then same thing, imagine you're a field service technician, you need a part. Like how amazing would it be if the part could just get delivered to you? Like that is something that's going to be a nice augment to operations. So it's interesting because typically when you see automation kick in, again, volume increases, right, because costs come down. there's way more demand out there than people realize because sometimes you'll say yeah i could use that part but i don't need to deliver it if it's going to cost 50 bucks for someone to drive it to me if it costs five bucks or or no bucks like how awesome would that be so we kind of view it as like it will increase the speed that the world operates at

1:01Sanjit Biswas:In this episode, we talk with Sanjit Biswas, founder and CEO of Samsara. Sanjit formerly founded Meraki and has a legendary reputation amongst Sequoia-backed founders, so I'm excited to welcome him today for a conversation about physical AI. Samsara is a$20 billion market cap public company with sensors deployed in streaming data from millions of vehicles capturing 90 billion miles annually. Sanjit shares his insights about the constraints of physical AI from running inference on on 2 to 10 watt edge devices to why the messy diversity of real-world data is both the biggest challenge and opportunity for embodied AI.

1:36Sanjit Biswas:If you're building in robotics or physical AI, this conversation offers a rare perspective from somebody who's actually scaled it. Enjoy the show. Sanjit, thank you so much for joining us today. You are a legendary Sequoia founder, and it is a delight to have you back at Sequoia. Thanks for having me. It's great to be back. I want to start with your background. So you went from MIT's RoofNet project to co-founding Meraki through its$1.2 billion acquisition. And you are now the founder and CEO of Samsara, a$23 billion market cap company with the best ticker on the public markets. What's the through line?

2:15Tell me about your personal passions and experiences and what the through line is between all of that. Yeah. So I'm an engineer by background, so studied EE and CS. I went to undergrad out here at Stanford, went to MIT for grad school, and that's where we worked on this project called RoofNet. So the through line for me has been about building cool products, cool technologies that have real world impact. And RoofNet, this is like over 20 years ago, the idea was could you build really big wireless networks? Because, you know, kind of early 2000s, Wi-Fi was not mainstream. It was a brand new technology.

2:47Internet access was just becoming mainstream and is still pretty expensive. And so we saw this opportunity to take Wi-Fi chips and all that technology and use it to build really big networks. And so we kind of had this idea that Internet access should be everywhere, right? It should be in the air. And how would you do that? And we need to build a big network. So that was RoofNet. And then with Samsara, it's a bit of a different sort of focus. We focus on the world of physical operations. So think all the infrastructure companies, whether it's energy utilities, construction, logistics, like all these real world physical industries.

3:16And the idea has been real world impact through things like risk reduction, improving efficiency, improving sustainability, just using all this data and now AI. Physical AI feels like it's finally going through an inflection moment. You've been building Samsara for the better part of a decade now. What did you see at the time and like how has the field changed? Why now? Yeah. So if I rewind 10 years to when we were founding the company, we had a couple of sort of intuitive bets or guesses. And the why now for us at that point was connectivity. So we'd been through the Meraki journey and we'd seen internet access go from being kind of rare and expensive to being ubiquitous.

3:54So this is 2015 for reference. We saw basically the ability to process large amounts of data really coming online. So the cloud had matured. We were seeing the beginnings of the GPU wave. And if you remember back to 2015, NVIDIA was a player and they were doing a lot of interesting like embedded GPUs. So if you picked up a Nintendo Switch back then, it had amazing graphics. but it fit in your hand. And so we saw compute was getting really good. And then we saw sensors, really specifically cameras, were getting really good because this is probably seven, eight years after the iPhone launch. So cameras have gotten extraordinary.

4:30And you combine all these three things together. You've got connectivity, you've got compute, and you've got sensors slash cameras. And we said, this is the sort of like makings for a total sea change when it comes to ability to process data in real world context. Wonderful. Okay. I'm excited to nerd out more about technical questions on the frontier of physical AI. Before we get into it, maybe can you just say a word on Samsara for our listeners? I guess how much of the business is, you know, I think of it as very much having had roots in commercial trucking. How much of a business is that today?

5:01And what do you see the ultimate vision of Samsara being? Yeah. So we really focus on the broad world of physical operations. So think about all these different kinds of industries. Trucking is definitely one of them. It's about 20, 25 % of our business, so logistics and big trucks on the road. A lot of our business now is related to field service and construction, so other big kind of frontline industries. But we also are starting to work in public sectors, so we work with local governments, we work with student transit. So we just signed the largest yellow school bus operator in North America, which is pretty cool.

5:32And we work in industries like aviation. So think about labor-intensive, asset-heavy industries that really power the infrastructure of the planet. Wonderful. Can I go back to your comment on, Sue, at the time there was a why now around bandwidth, compute, and cameras. Yeah. And it sounds like you may not have necessarily had a crystal ball on what was going to happen with AI. Yeah. But you kind of felt like you're on the right side of history. And that with those raw ingredients, you'd be able to do increasingly sophisticated stuff over time. Yeah. What I'm curious about, I feel like there are a lot of founders today who are kind of in a similar position where nobody has a crystal ball.

6:08We don't really know what's coming. but you kind of know that whatever capabilities you're going to have tomorrow are very different and better than whatever capabilities you have today. So I guess the question is like, since you kind of had a directional sense for where the world was going, how did that influence the way you built the business? Like, was there anything specifically you did just kind of in anticipation of this inevitable direction that the world was going? Well, actually the historical context is important. So our first company, Meraki, which was funded by Sequoia, we were domain experts.

6:38So we knew a ton about networking because that's what we've been working on in terms of our PhD. With Samsara, it was kind of the opposite. We knew nothing about this domain. We'd never driven a commercial truck before, never worked in a warehouse. And so we were sort of eyes open about it. What we did have was that intuitive sense of the compounding rate of those underlying technologies. So we said, okay, there's this really interesting problem space, this world of physical operations. It's kind of overlooked, especially 10 years ago, no one was really talking about infrastructure, the way they are now.

7:06But there are, you know, things are changing very quickly behind the scenes in terms of tooling. So that intuition is exactly sort of what we were powered by. And we said, even if it's not mainstream yet, or it's not ready yet, certainly in five to 10 years, which is about now, it will be possible to do this stuff. So I think for a lot of the current founders, it's kind of like if you look at AI model capabilities, even when like, you know, the chat GPT moment happened, these models weren't perfect. They've gotten a lot better in the last two, three years, and they're going to get even better in the next two to three years.

7:36I think technical people understand that in a way that consumers and customers often may not see yet. So I think you have an embedded systems background, and you're one of the unique people that's operated at the intersection of the hardware and software worlds. I'm curious, what are the things that make building AI in the physical world different than running AI in big data centers? A couple of things. Well, it actually is a lot of fun. So the physical world is very diverse. You know, you see a lot of companies now working on physical intelligence and world models. And it's because the training data set is really broad and vast.

8:13So if you think about our products, we have products like dash cams that end up on the roads on millions of vehicles. They see like 99 % of the U.S. roads. It's just an incredible data set. You've got urban, you've got rural, you've got residential, you've got weather. And so we see all these like interesting exceptional cases. So the training data is really interesting. And then what we can apply, all the inference and basically pattern matching to is also interesting. So I think that's the most fun part. The most challenging part, though, is how messy it is and how distributed it is. So for our products, it's not practical for us to just stream all the data to the cloud.

8:50It would be like a crazy bandwidth bill. You need pretty massive data centers if you think about millions of video streams, like constantly running inference. And so we have a much more distributed architecture where we actually run in the cameras themselves. And that changes your compute and power footprint. You know, we're talking about 2 to 10 watts, not like kilowatts, right? But you can do a lot more because you've got millions of them. And so what is it? Like, how do you run, I'm thinking, some of these large LLMs? Even the image models are very large right now that people are working with.

9:24Are you running just very bespoke small models on two to 10 watts? That doesn't give you much of a footprint. It doesn't give you a lot of room. And that's a fun engineering problem. So if you think about it, these state-of-the-art models, they are very large. So you're talking about like hundreds of millions of parameters or billions of parameters. That is simply not possible. So our footprint is much more similar to what you can run on like your mobile phone, right? So it's not tiny. It's not a microcontroller. It runs Linux. It's got like hundreds of megs of memory, maybe gigs. but it's not like a big data center, right?

9:56So what we tend to do is we will train models in the cloud. We'll basically distill them down or use teacher models. So we'll use a big model to basically instruct a small model that's really designed for our use case because we don't need to be able to answer what the capital of France is, right? Like that's not something that Dashcam has to encounter but we do need to be able to understand what is the risk profile on the road. So we train it with the data that's relevant for the task. How much of the data, you see 99 % of US highways or US roads, how much of that data can you make use of? How much of that data do you make use of?

10:29Yeah, we can make use of a lot of it. And we basically have the ability to train over like this entire data set. There is a very practical question of like, okay, you run a tokenizer at the edge, you send all these to the cloud, what do you do with it? And what's cool about that is what we do with it this year is so much more interesting than what we could do with it two, three years ago. So two to three years ago, and these products really started around this idea of reducing risk. So if you think about the problem we're trying to solve, it's that our operations customers, they operate on these roads every day.

11:01It's actually the riskiest thing that they do is more so than construction or working in oil and gas. Driving on the highway, getting to and from the job site is where they incur most of their fatality or kind of high severity risk. So the question is, how do you go take all these images and tokens and turn it into a risk signal? Right. A couple of years ago, we said, you know, the biggest risk we are seeing right now is mobile phone usage. Right. Like people are on their mobile device while driving a big truck. And that's super risky. So we built a detector for that. You do that and you say, OK, we can solve this problem.

11:34We can detect mobile phones. What else drives risk? Now we're seeing things like weather. Right. And weather has always been a risk factor. It's not a brand new one. But it's now something we can detect using these pretty sophisticated models. Training a weather detector using like old school convolutional networks, like an Alex Net style model, you would have gotten a lot of things wrong. You couldn't tell the road conditions. Once you use more sophisticated models like the ones we have today, you can really figure it out. So that's the cool thing is there are these unlocks that happen every couple of years as model capabilities increase and our data set increases.

12:06So these two things like really work in our favor. Is there an upcoming unlock that you are most looking forward to? In terms of our product set or just in general? A new capability that's going to unlock some new use case or some new feature for your product? You know, I feel like we are seeing just such incredible foundational model capabilities that are making it possible to just inference over huge amounts of data. So historically, what we did is we understood like what was happening in the moment, right? So like I said, mobile phone detection or not wearing a seatbelt or following distance.

12:38Now we can start to really look over the course of a trip. And we're not only detecting like negative, like risky downside events, but we can actually detect good behaviors too. And I'm really excited about that because frontline workers, 80, 90 % of the time they're doing a great job. No one's able to recognize it because no one sees it. So what's awesome is we can now see that someone's doing awesome and give them a high five or like some kind of recognition or kudos that is like making people's day. And it's a cool, like silver lining side effect of having all this stuff running. So anyway, it's kind of a unexpected upside sort of thing.

13:11And do you think it'll be video reasoning models that sort of empower that? I know you can't run giant models at the edge, but are you doing stuff server-side that takes advantage of LLMs? Yeah, I should have mentioned that. So the model's connected. We have a ton of inference running at the edge. It's running continuously because when you're driving, there's a continuous risk. And then we're taking those tokens, we're streaming them up. And in addition, we have images, we have video, We have other kinds of telemetry. And then we can go and run all kinds of sophisticated things in the cloud. So if we need to understand when an accident happened, what really happened, we can run a full video language model, like a reasoning model, essentially, in the cloud.

13:51And that can say, oh, this was actually defensive driving, and this guy got cut off, or these were the conditions. So that is really cool. We couldn't have done that five years ago. Do you believe in world models? Loaded question. I do. I'm cautiously optimistic about them. but I think you need a tremendous amount of data. Yeah. Are you guys training your own world models? We are not building our own world model. And I think that requires a very specific kind of focus. But in the same way, we don't train our own like base foundation models, but we are looking forward to using them at some point.

14:24Yeah. And I imagine you have an incredibly rich data set that might be useful. We do. Yeah. We see about 90 billion miles on our system every year. So it's a lot of driving. Yeah. It seems like the sensor footprint you've built out is like a tech nerd's dream, right? Most people dream of a connected world and you should be able to have so much telemetry on all these different attributes of the physical world. But as far as I can tell, you're one of the only companies that's really gone out and, you know, put sensors on the physical world in a really meaningful way. Why do you think that is? And what's the key to actually be able to make that dream happen versus have it just be a tech nerds dream?

15:05Yeah. First of all, it takes a village to actually get the stuff out there. And I think that's maybe one other big difference between just pure software and physical world is we have to get the products installed. So they're installed on millions of vehicles. We have to train frontline workforces on like what the stuff is and what it's doing. And then we have to provide value to all these customers kind of from day one, right? Like they have to get something out of it. You combine all this together and you get this big footprint. But it's been hard because you need thousands of people at our scale now to do this and to do the change management, like the installs and all that kind of stuff.

15:41You know, there are a few companies that have data sets of the scale, but it's like Tesla and probably us. Right. And then Waymo, there's thousands of Waymos, but not millions. And maybe it will be millions in the future, but we're not there yet. So that gives you a sense of how much effort is just like sheer willpower is required to get this stuff out there. Speaking of which, I think there are a lot of founders right now who are technical founders like yourself who've built something cool and are now encountering this crazy supercharged race to scale that the AI wave seems to have brought. And so I guess the question is, you are a technical founder.

16:24I think both Samsara and Meraki have been known for go-to-market execution. And so maybe the question is like, how important has go-to-market execution been to your success? And as a technical founder, was it obvious to you at the beginning that it was going to be that important or kind of what was your journey like in appreciating the importance of go-to-market execution, if that makes sense? Yeah. I'm replaying like 20 years in my head really fast. So when we started Meraki, at that point in time, I had never sold anything in my life. In fact, as an engineering nerd, I avoided any situation where there was like, you know, there's like fundraiser where you have to sell candy bars at school.

17:07I was like, does anyone need a website for this thing? You're just trying to find some way out of it. So I really was not like a salesperson in terms of background. And no one in my family had done sales. So it was very foreign. The thing that turned me on to it was this idea of this is what it takes to get the product out there. And if the product's not out there, it's not having impact. So if you're driven by impact, if that's what motivates you, it's fun to see people using it. Right. And then this is what makes it sustainable. So with Meraki, we were growing the company between 2006. It was acquired in 2012.

17:37In the middle of that was a great financial crisis. Right. There wasn't a lot of funding at the time. Risk capital was just turned off. So we basically had to make the company operate at break even. right or thereabouts and that's what really convinced us like we have to figure out how to have sustainable sales execution and a model that's highly predictable and as engineers we're like hey this is actually a big engineering problem right um and then that stuck with us with simsara we were talking about impact at scale uh we raised capital along the way but actually we reinvested way more just from the revenue of the company in the gross margin so if you look at our numbers we're public now so you can kind of go back through the balance sheet And you can see we've invested probably close to$3 billion just in getting the stuff out there, right?

18:21R &D, customer success, all that stuff. That is only possible with a lot of sales, right? So once you understand the why, you can kind of buy into it and say, I'm going to figure this out. It was not natural for us, but it was a pivot that ended up being something we had to do. And I'm really glad we figured it out and have been getting better at it each year. Meraki, you were a domain expert. it's Samsara you were not when you started the company why why go and pick that domain I think it was curiosity um and this is a little bit of like going back to sort of curious nerd roots like you just find yourself like reading books and wondering how stuff works um so after Meraki we actually didn't have a plan to start another company uh there was a while I thought I was going to go back to grad school finish the PhD kind of thing uh my co-founder John Bickett he's like way smarter.

19:11He's like, that's never going to work, but you go do that. And in that period of time, I realized that academic research is very long feedback loop, kind of slow cycle, but there were a lot of other interesting problems that caught my attention. So I got interested, I think, in energy at that time. So I was like learning about how the electrical grid worked or the time didn't work because photovoltaics and renewables were coming online. I started getting curious about nuclear, about satellites and things like that. So it's kind of fun to be able to just open your mind up to everything when you've been like laser focused on one thing.

19:43And then over and over, I found myself and then John found himself like attracted to this world of infrastructure. And so it was just curiosity about this part of the world that felt pretty overlooked. Very cool. What do you think of autonomy? And that might be a loaded question. Yeah. But, you know, I two years ago, I avoided getting in Waymo's. Now I don't think, no, I don't think twice. I feel safer in a Waymo than not in one. What's your point of view? Super excited about it. Very bullish. I think it's been a long time coming. When I was an undergrad at Stanford, they were doing the first like DARPA Grand Challenge cars.

20:17So this is like 20 plus years ago now. And like you said, Waymo's have gone from kind of like prototype tests to like, I prefer Waymo, right? It's super consistent. You know, there's lots of things to like about it. So our view on it is autonomy happens and it actually increases the operational intensity of the world, right? So if you think about it, there's like a third shift between midnight and 8 a.m. roughly, right, that people tend not to work because they're sleeping. Imagine if operations like logistics could still run during that shift. And then same thing. Imagine you're a field service technician.

20:49You need a part. Like how amazing would it be if the part could just get delivered to you? Like that is something that's going to be a nice augment to operations. So we're a fan of it. Our view on it is we think it's an and, not an or exclusive. and it's interesting because typically when you see automation kick in, again, volume increases, right? Because costs come down. There's way more demand out there than people realize because sometimes you'll say, yeah, I could use that part, but I don't need to deliver it if it's going to cost 50 bucks for someone to drive it to me. If it costs five bucks or no bucks, like how awesome would that be?

21:21So we kind of view it as like it will increase the speed that the world operates at. You think it's happening on roads only or you have customers with warehouses and forklifts and all the above. Like you think autonomy will hit all those sectors? So I think autonomy already hit the warehouse. We have a lot of customers with big like, you know, logistics warehouses. And really about 10 years ago, they started getting automated in a meaningful way. And it's pretty rare for me to go into like a heavily, you know, industrialized environment without seeing automation. And that's everything from like lift systems to big arms moving things.

21:53And it actually is welcomed by the people in the warehouse because it helps reduce injury. So if you think about it, frontline workers are putting themselves at risk when they do their job every day. It is not a great outcome to get hurt lifting a pallet or doing something like that. So that is, I think, a good sort of preview of what we're going to see out on the road. And then I think after that, there's a construction site and job site. Humanoids, yes or no? Cautiously optimistic, a little bit scary. I won't lie. They feel like they're in that kind of creepy, uncanny valley, like when you see them walking around without heads or hands or something.

22:29Have you seen Neo? I have. That's a friendly one. But I think it reminds me of where self-driving was about 10 years ago. So probably not a tomorrow, but it does feel inevitable. So as the capabilities increase, it's going to be really exciting. How does the role that Samsara plays in the world change as we have more and more autonomy over time? Well, I kind of think of it as digital transformation. So if you zoom way out, that's what customers are excited about is how do we digitize these operations that have been around 50, 100 years in some cases. And most of our customers, they welcome new technology.

23:03So they adopted computers for route planning like in the 1970s or something like that. So they're not against technology. Is it going to help? Is it going to be relevant? So our take is you're going to want like a platform to see all of your operations for all of these different operations to interact. So you can see your frontline workers, you could see all your vehicles, you could see your assets, know what needs maintenance. All of these problems will be evergreen. You're going to want to maintain your assets like 20, 30 years from now. Maybe they're robots and maybe they move on their own, but they still need maintenance, for example.