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

24:32Because typically in operations, You'll have Ford trucks and GM trucks and Caterpillar and Freightliner and all kinds of other equipment coming together. So we act as that orchestration there. So the hardware is very much part of the story, but we also have software interfaces coming into the system. All right. So you got all of this and then you move it to the cloud. And then what do you have there? You have a gigantic data warehouse and ETL, ELT kind of data transformation. Is that how it works? That's right. From an ingestion perspective, I think you got it. So just massive amounts of data storage.

25:11This is all kind of sitting in modern hyperscaler cloud. So it's not that we have one big warehouse slash data center, but pretty large system. So we ingest the data, we're storing it and organizing it. And then we basically have a bunch of processes that are automating, like sort of automatically working on top of that data as well. And then you have the UI UX collaboration interface where you're a customer, whether you're a dispatcher or a truck driver or the business owner, like everybody has access. That's right. And there are many different personas. So you named a few of the key ones. The drivers in the frontline are very much like just regular users of the system.

25:52You do have the dispatchers. You'll have other people who'd be like safety managers or in certain cases, if they have to do paperwork, like essentially compliance managers. But you also have people that do maintenance, for example. And so they want to know what is the health of all these assets in the field? Which truck will I need to maintain at the end of the day when it comes back to the yard? And then you do have the executives and all these other business minded people who want to know, well, did we show up on time? Right. Like, what is the efficiency of our fleet? and how do we do this at scale?

26:22Many of our customers, actually most of our customers are large enterprises. So think operations have thousands, tens of thousands of people and tens of thousands of assets and so on. As a thought, so selling to a bunch of different personas, especially in like more traditional industries, especially as you've added this AI layer recently, how do you go about it? Like how do you convince people in, you know, typically non-technology industry to just to buy? Well, you know, I think the great part about this technology is very tangible. And it's the kind of thing that when you see it, you get it very quickly.

26:56So what we do is we tend to go on site, we will demo the technology and do trials. So you can easily these are plug and play. So you can easily try it out in your environment in your industry. And like I said, there's so many challenges in physical operations, it tends to never just be one thing. So yes, we want to reduce the number of accidents we get into. but I think we're also like leaving our trucks idling a lot because it's just a bad habit or we're leaving tools behind at the job site. And we'd like to get those back because we spend millions of dollars replacing them. So we will often find multiple challenges like that.

27:27And then we demonstrate to them at small scale, like maybe a team or, you know, a region or something like that, that this works. And when they see it, they get it immediately. These are people who are experts in their industry. So they would say, I immediately see the value or the ROI, but they have to see it in that kind of tangible way they're not just buying it because it's ai or big data or something like that they're like no if this solves problems for us in our construction business great let's do it when you think about the long-term defensibility of the the business especially in a world where models may or may not commoditize i think most people would say they are commoditizing is it the data layer that you that you uh feel protects samsara or how do you think about moats?

28:12Yeah, there are a few things. We see whether it commoditized or not, these models are incredible, right? And the amount of value they can unlock with their ability to ingest the data and reason is awesome. So we're very excited about what we're seeing on that front. The operational data that I was talking about, the physical world kind of digitization side of thing is where we come in, right? 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, right? Nor can you do, you know, test time reasoning about it. You can't just, you can simulate all kinds of environments, but really what our customers need to know is like, what was going on in that specific environment at that time, right?

28:52That requires this interplay of hardware and software, but also the change management. How do you get this out into the field and the partnership? So that's a unique area for us that we focus on. This is what we've been doing for the last decade plus. And it takes a lot of work. I have to emphasize that, too, is like we get out in the field with our customers, understand their business and work backwards. So it's not something that can purely be like just, you know, one click deployed. It really requires a kind of a nuanced approach. Do you have a concept of data network effect or data flywheel across customers?

29:29So does something that you learn in the context of a dash cam with customer X in geography Y also apply to customer Z in a different geography in terms of learnings? Very much. So, you know, on the dash cam side of things, the key insight there is, well, these may be all different companies. We're all driving on the same roads, right? The Samsara system and a given day, we're driving 99 percent of the U.S. roads, usually multiple times a day. So you can use that, of course, on the risk side. So we can understand where are the risky intersections or where weather conditions bad and how do we warn other drivers?

30:06So there's a network effect there. But there are other sort of side effects. Right. Because we drive all the roads and we have cameras, we can tell you where all the potholes are. Right. And that is super useful for the city. So like this city of Chicago, for example, they want to know which potholes are, you know, happened after the winter weather season. what order should we go after the men in terms of severity? You can use the camera data for that, the accelerometer data from the GPS tracker that I mentioned. So every time you see that big bump in the road, you look at the video. And the cool part about that is not only do you know where the pothole is, but we can see what's happening to it over time.

30:43Is it getting bigger? Is it, you know, cracking, all that kind of stuff. So that is another sort of data network effect that we get. And then maybe the third, since we talked about asset trackers early, you have these millions of vehicles driving around, a Bluetooth tracker on its own probably doesn't get picked up, right? Because think about a construction site. It could be acres and acres of land. But if you have, you know, one company delivering building materials, another company performing construction, the electrical contractor, one of those guys may pick it up. And that is another network effect that you get with millions of these vehicles and then tens of millions of handsets like the mobile devices.

31:20It's an incredible kind of mesh network that forms. All right. So going back to the, product and the AI stuff. So you mentioned Edge and cloud. Where do you guys do what in what proportion? We could spend an hour just talking about where what is going on. If I had to generalize, I would say at the Edge, we're typically running inference and data collection. So the data collection, of course, gets us the training data. The inference is essentially running models where we have the weights and we send them down from the cloud and they're running at many frames per second at the Edge. And this is how we do the kind of real time or the low latency detections and closed loop alerting to the driver.

31:59The reason we do it at the edge is practical, right? So sometimes you don't have a great cell signal. Many of our customers are operating in the middle of nowhere. And then also the latency matters. If you can get feedback to the driver, you know, really very soon after something happened, it's much more likely they'll change that behavior. So again, very kind of practical architecture for us, it's worked really well in terms of how robust it is and how it holds up. But that being said, it's not fixed. So if that means we need to do some inference in the cloud, we are set up to do that. We have real-time tunnels that connect these devices.

32:35Presumably what you run at the edge would be smaller models. Are those your traditional quote end of quote uh convolutional neural networks that are trained for images like a very specific tasks versus um you know other forms of like more modern generative ai there are lots of different model types so uh convolutional neural networks is very much where we started that's basically from the image net era of like okay can we detect a mobile phone right um from there these models have become more sophisticated so we run basically a model backbone with many different classifiers and heads or attention heads.

33:17So basically, once we see a device in somebody's hand, what is it? Is it a phone? Is it a vape pen? Is it a sandwich? You know, what is the activity that's going on with it? That's not a single shot detection. It tends to be a little more nuanced than that. Same thing when we look outward. We have cameras that point out at the road. We have some cameras that point at the sides or to the back. And we're trying to do things like estimate depth, right? So am I likely to run into a lamppost or a mailbox or something like that? That's a different kind of model than what a CNN would be able to do. What has generative AI fundamentally changed for you guys?

33:58Is that video reasoning? What do you use for what? We use generative in a few different ways. I would say if we think about the overall class of models, yes, you can now reason about video. So what would have required a human in the loop reviewer or, you know, and that's the kind of work that might have been done overseas in lower cost geos or something like that. You can now do at much more volume in the cloud using these models. So, for example, if someone slams on the brakes right while they're driving their truck, the naive thing to assume is like, hey, the driver was distracted and they kind of woke up.

34:36the more nuanced thing is that driver might have been avoiding a deer or a dog or, you know, some kind of defensive event. If you can watch that as a video clip, you can now say, hey, we're actually going to give the driver some positive feedback because they did a really good thing. The VLMs are able to effectively do what I just said, right? Similarly, like if you want to understand did someone run a red light, right? These things happen. You need to have a pretty sophisticated model that understands the geometry of the road and all the conditions and so on. So that would be like a JEPA style model, for example.

35:10So we're able to use a few different model families. On the generative side of like actually being able to create video, that's also very interesting because from a coaching perspective, most of our customers are bottlenecked on the number of human to human interactions they can have. For me to sit down with you, Matt, and say, hey, we need to talk about your driving from last week, we could probably do that for a small fraction. But I can't do that for every driver. It's not practical. You may have seen like, you know, AI generated avatars, like AI generated people. We can generate a coach and that can resemble the VP of safety from the company or, you know, a celebrity or who knows, you know, whatever the customer wants.

35:48But that can be a very effective way to deliver end of week coaching. So that's a form of generative video that, frankly, we couldn't have even dreamed of five years ago. Do you build some of your own models or do you take stuff off the shelf and customize it? Are you an open source shop? Are you an open AI, Anthropic shop, Gemini shop? What do you use? We are universally accepting of models in the sense of there's so much innovation happening. So, yes, the Frontier Labs are doing great work, right? And we'll use multiple models from different labs simultaneously. The open source models are pretty compelling.

36:24I think it's having its moment now, but we've been seeing, and this is really, I think, from the academic communities, open source and really open weights models really give you a lot of operational freedom. So we can do things like distill them, for example, to shrink them down to fit on a device. So we use models like that. And then there's others that we train from scratch. Those might be smaller models, call it tens of millions of parameters, but very specific to something we need to do in the field. All right, let's talk about agents. So that was the big launch that you guys had at your Beyond 2026 conference in Las Vegas, just at the end of June, so less than a month ago now.

37:04And you launched Agent Studio. So maybe walk us through this. And I think in the past you described a progression from connecting operations to understanding them to taking action. So how does that all fit together? Yeah. Well, a few years ago, we did introduce LLM's Intour product. We called it the Samsara Assistant. So think of it as like a chatbot tied to your operational data. It became very popular. We saw customers asking all kinds of practical questions like, who are my safest drivers or which trucks need maintenance, things like that. Those tended to be, you know, single turn or maybe like a few turn interactions.

37:40Like you just go back and forth with the chatbot window on the side. The breakthrough that, of course, happened last year is these agents can operate over much longer time horizons. So instead of an AI responding to a question in a second, it can like go do some work on its own, develop a plan and go after it. We've seen the impact of that in the coding world, but there's also a lot of implication for the operational world. Right. So one of the demos I did on stage is we have a warranty. agent. When it sees a fault code, it can basically crack the service manual, look at your specific like OEM negotiated warranty agreements, and then correlate the two and say, yes, this specific issue, given the age or, you know, the number of miles that have been driven on this vehicle is actually covered under warranty.

38:23And then it can open a work order, put in the steps, and also tell you, hey, do any of the other trucks have that issue? That is a, you know, like what would have been like an hour or two of human labor that we've been able to automate down to like under a minute. That is a huge kind of breakthrough unlock. And I just went very deep on warranties, but you can see how that would apply to reporting, how it would apply to, you know, briefing a driver at the beginning of the day. You can use in all kinds of creative ways. It sounds like for perhaps the obvious reasons, you're starting with non-risky kind of use cases.

38:58Is that how you guys think about it? Like something where if you make the wrong warranty claim, you know, it's not great, but, you know, nobody dies? Yeah, I think of it as we're just starting with the most practical areas we can have impact. The reality, by the way, is most of those warranty claims just are unfulfilled, right? Nobody has the time to go do all that work that I just mentioned and do the paperwork. So that's an area of tremendous interest for our customers is, you know, hey, I have all this extra work that I know would be useful, but I'm not able to get to. So how do I do that? And then, you know, over time, I think the idea will be how do we really like autonomously run parts of the operation for the customer?

39:40If that's like replanning the route, for example, before every morning shift, we now have the technology to do that. And again, it's not super risky, but it requires a lot of business judgment of, you know, that route actually is run by this person because they have a relationship. They've been, you know, seeing that customer for 10 years. You need to have all that context. So in that sense, we are starting with things where we know we can have an impact and then we're working with customers to figure out what else can we do. You use the word autonomously. I'm always fascinated for people that build religions that work in the real world, just like you guys do.

40:21What would you say is the proportion of sort of agentic reasoning versus having some good old, you know, quote, unquote, workflow and rules that's built into it for ultimate success? Like, you know, ultimately, who cares, you know, what does what as long as it works? But like, what is the recipe to make it work? Is that a combination or are we at a stage where just agent reasoning can do so much that you don't need that much like rules built into the overall solution? I think agent reasoning was a huge unlock, like I said, this ability to build plans and work over long time horizons, but you do need to outline what is it that I want the agent to do, right?

41:00And that is in some lightweight sense, like the workflow, it's also the guardrails. Like at what point do you say, hey, agent, you should ask me for some help or agent, I don't want you to go down that rabbit hole, right? Like we have to kind of keep it on track. So it's some combination of operational context, which comes through workflow and guardrails with also this now kind of new agentic reasoning ability. I don't think either really works well in sort of isolation. And the workflow side of things, by the way, we had elements of that in our product. I'll give you a very simple example. In most commercial industries, there's a walk-around inspection that you do at the beginning of your shift and end of your shift.

41:43We see about 300, 350 million of those a year. that has historically just been a workflow on a mobile device, right? Like you're kind of going step by step, taking some pictures, saying something safe. If you combine that with the diagnostic information, the location information, who last did the check, like, you know, what was in the picture, that is a huge unlock. So that's kind of what we mean by combining these two things. What do you think agents are not able to do just yet? Oh, boy, that ceiling question, it changes like, you know, I feel like every week. And there's some nuance to this.

42:21So, for example, these new models, like the kind of Fable 5 class and GPT Sol, it's hard to figure out where the practical ceiling is. But sometimes you do see them go and get distracted or like lost in a loop somewhere. Right. So I think there is some aspect of like they may find the answer eventually. But can they find the answer in 10 seconds or one minute or even one hour? Right. So that's one area where I think there is still a practical ceiling. And my guess is as these models become more and more powerful, more sophisticated, that will shrink. And then these algorithms are getting more efficient.

42:55So maybe the compute combined with the algorithm combined with just like smarter model architectures will make what would have been like a one day task, a one hour task or, you know, even faster than that. And if you suspend disbelief a little bit, what do you think you would be able to do in like a year or two? You know, not 10, because obviously, who knows? But given the progress, so right now you're able to do, you give the example of warranty. You give the example of pre-planning a day for a driver. What else do you think you can do in the next year? I think a lot of our ability to predict what happens next year or two is by looking at what is like barely possible now.

43:41And then what will the sort of cost curves look like or capability curves look like? And something that we talked about at our Beyond Conference last month was this idea of a driver ride along. So in operations, it's quite common to basically have a manager sit with you over the course of your day, like drive around with you for eight hours. And what they're doing is they're not looking for, you know, how fast you're going. They're looking for your habits of like, do you check your mirrors? Like, you know, are you alert and are you aware of that kind of thing? That's basically a massive amount of video computation, right?

44:14So you can run a tokenizer. It just turns into like a lot of compute. You can do that today. It's pretty expensive and costly, but it works and it's doable. We're pretty optimistic that the, you know, cost per million tokens is dropping so fast and the capabilities are rising, that we can deliver that at better and better cost over time to our customer. So I think in a year or two, that will be possible. And I have to say, just over last weekend, I was playing Cerebras, which is one of those big wafer scale chip companies. They have an inference model that you can run on their chip, which is basically Gemma 4, but hyper-accelerated.

44:50That's a great example of that is nonlinear in terms of jump, right? What you get out of these big models. If you run Gemma 4 on your GP, you might get like 100 tokens per second if you have a fast card. If you run it in their cloud, you get anywhere from 800 to 1500 tokens per second. So call it 10x faster. That is the kind of thing where it unlocks these new use cases that we couldn't get to because it would have been too either expensive or too slow. And not to promote the podcast on the podcast, but by the time we release this, the prior episode will be precisely an episode with Andrew Feldman of Serial Brace.

45:26Awesome. If anybody missed it. And of course, and of commercial. I'm glad you mentioned the ride along because I think there's a fascinating aspect to the whole dash cam, almost from a societal standpoint in that it could be like an interesting blueprint in terms of like how we professionally interact with AI. not just when we query it through AI chatbots, but like having AI live with us on a permanent basis. So, you know, sort of the obvious question is that there's an element of like, arguably big brother is watching you. You know, AI is watching every single move that you make and, you know, for your own good, but it's also looking at what you may not do well.

46:13I'm curious about what you've learned from the perspective of making everyone happy, if there's such a thing, whether that's the customer or the driver and, you know, people not ripping out the camera in rage. Yeah. So, you know, a couple of thoughts there. The first is we actually do spend a lot of time on the frontline with drivers and other, you know, frontline workers. So it's very important for us to get their perspective because they're the primary users and really beneficiaries of the system. Something people don't often think of is, you know, if you have a dash camera, what is it used for?

46:47The majority use case is actually exoneration. So what I mean by that is helping explain what happened if there was an accident. Because, for example, if you are the Home Depot, you're a very well-known brand. They're a customer of ours. Lots of claims, auto claims are placed against you because they'll say, hey, a Home Depot truck backed into my car on this highway, right? And that is something that really upsets a driver because they'll say, look, I was doing my job great. I didn't run into that guy. Now you can basically produce HD video evidence of where you were. And if there was an accident, who caused it?

47:20All that stuff. That's it eliminates all the ambiguity, right? Like now you can just resolve it. And look, if there was an accident, the company may choose to just settle it out and pay it out. But if there wasn't, which is like a very common case, now you can really fight it and say, look, we know exactly what happened. Drivers love that because I have to say 90 % of the time they're doing a great job and nobody's seeing it. Right. And so that has been a huge unlock is this positive reinforcement of we're analyzing the whole drive. We're seeing all these good behaviors, defensive driving or exonerations, things like that.

47:53That, I think, is what is sort of the counterweight to the, hey, what is all this for? Like, how is it being used? If you have that in your culture, if you are kind of doing the equivalent of employee of the month, but showcasing really great work, I think people get really excited about this. Also, from a safety perspective, we should remember in physical industries, the risk of injury is on the person, right? So in other words, we want everyone to go home the same way they came to work. That is an important concept that I think people don't think about. If you're working construction, you're working oil field services or something like that, you take a lot of risk when you do your job every day.

48:34So it's actually in the what's in it for me, it's like we're trying to keep you safe. If you have that and you do it in a transparent, thoughtful, respectful, from a privacy perspective way, it goes a very long way with the front line. very interesting and that makes a lot of sense just to push a little bit if i if i may um um in in some ways the ai also becomes a judge of the quality of your work i don't know if you agree or disagree uh i'm curious if there's any uh kind of like safeguards about how that happens or should happen in the future and perhaps it's a fact of life you know we just had the world cup and like we now are familiar with the VAR review and it is what it is.

49:20You were offside and that's just what it is. And technology is here to tell everyone that you are offside. Curious about like what you've learned. It seems like such an important current topic. Yeah, very much important. I think transparency again is like the key word here. It's not, and by the way, our cameras are not hidden cameras. They're quite visible. So it's not like a secret sort of recording device. And the whole idea is to bring the frontline along. So we call this change management, right? Like, hey, we're introducing these things. What do they do? What are they for? How can you use them?

49:50How can they be useful to you? If you have that conversation early in a transparent way, it tends to be quite constructive because I mentioned Home Depot earlier, they saw like a 65 % reduction in their claims, like auto claims. That was a huge win for that organization, both at the sort of executive level, but more importantly, at the sort of like regional level. Those kinds of wins are what we want to help create. Now, could you use this like in a bad guy kind of way? You could, but that would be like, who is sitting there watching like each driver? It's like super boring, by the way, to like sit and watch drivers, right?

50:25So when you kind of are transparent about what the system's doing and what it's not doing, and then how the data is used, that is like how you get the buy-in and you earn the trust of that entire organization. All right, so you sit at the very forefront of all of this in physical AI. I'm curious where you see the world going as we maybe take a step back. Are we going towards a world of like mixed fleets of just people and just robots? Is that what you're seeing? Yeah, this is the like, if we kind of imagine five, 10 years out, like, where does this go? I do think, yes, we expect there to be a lot more robots sort of involved in physical operations.

51:09You see this actually if you go into a warehouse today, right? So if you go into either manufacturing or fulfillment center, there's actually a lot of automation robotics going on. And the cool part about that is it reduces risk of injury to a lot of the human workers. Like lifting injuries are very common. 10, 20 years ago, they're way less common these days because quite literally the robots doing the heavy lifting. Now, there's still humans working there because there's kind of all the handoffs and, you know, there's some nuance to the operation. But we expect something similar to happen out in the field, right?

51:43So think about a construction site or maybe a company building a roadway or, you know, modernizing the grid. There's a lot of kind of repetitive work that has to happen. Imagine you're grading a site, like you're making it level. Could that happen during the third shift between midnight and 8 a.m., right? That could be a really cool way to do productive work on the side of the road where you're just going for like five miles, making it flat. We see robots being able to do that in the next five years. Now, all the rest of it, though, it's still pretty messy. And construction has like exception after exception, like you're solving problems constantly.

52:20That's where I think the humans offer a lot of experience and judgment of, well, how should this work? And I'm waiting on this building material while I can perform this other thing. Meanwhile, the robots like making the road flat. Right. So that's kind of what we see in the next few years. The same thing applies, I believe, to logistics and supply chain. So now, like there's a lot of kind of last mile complication that happens and you have to physically pick up deliver package. Some of our customers, they stock the shelves in the grocery store. Maybe we get there with humanoids and, you know, never say never.

52:51Like this stuff always is evolving. But in the meantime, could you automate the long haul segment between Dallas and Phoenix, right, of all of those, you know, beverage cans coming in or something like that? So we see this as an exciting like and in terms of what the future looks like. And what we've seen in operations is very diverse, lots of different types of equipment, lots of different types of labor. So it's going to be, you know, different makes and models and different makes and models of different kinds of robots, is my guess. And from your perspective as a business, you would just power it all?

53:23I guess where would automated trucks and humanoids on construction site fit in the overall picture at Simsar? Well, practically speaking, most of our customers would tell you they're supply limited in terms of labor, right? So these are labor intensive, asset heavy industries. So they welcome this idea of like, could I automate some of this labor so we can basically perform more? So that's kind of like our customers are going to be around. They have like a lot of work to do. What we want to do is provide the sort of single pane of glass so they can orchestrate the whole operation, trigger the workflow of like, OK, that truck is arriving from Dallas.

54:00Let's get queued up. So the warehouse system is ready to go and accept it. Let's make sure that we are notifying our end customer and so on. And then I showed the tracking label earlier. You could put that on the pallet of goods so you can track it end to end as it changes hands through the supply chain, as it makes its way to a job site, as it's installed. This is all very opaque today. Like if you think about your shipments that you receive as a consumer, you might get like five updates, right? Like left the facility, you know, out on the road, out for delivery, et cetera. We see like hundreds and hundreds of pings.

54:35That's going to be really important when things are moving on their own. You know, where is that aerospace assembly, right? Like we have customers who want to know where this really expensive asset is that they need to perform their job. Right now, that's sort of like untracked. Do you think automated, you know, self-driving trucking is just around the corner? It seems to be around the corner for cars. I mean, obviously, it's already happening with Waymos and Tesla's now an automated pilot. In three years, are we at 10 % self-driving trucks? Are we at 75 %? What's your gut? I think on the robo-taxi side, it's going to happen a bit faster because the operations are much more regional.

55:17They're much more similar, right? Like the way that you and I ride in a taxi across town is going to be quite similar. And so that's, I think, where you're going to see the biggest sort of like visible impact of autonomous vehicles. On the trucking side, it's also important to realize like there's long haul trucking and kind of like moving stuff from point A to point B. that tends to be a minority fraction of what the commercial vehicles on the road are doing. Most of the commercial vehicles are in industries like field service, right? So they're HVAC technicians or plumbers, electricians, so people performing some work.

55:51And they're also, they're either doing something like that or they're in industries like construction, where they're building the road. That tends to be where current day sort of like autonomy doesn't work so well. It's like the really messy long tail. So for that reason, we think the adoption might be a bit slower, but it's not like a no, it's just, it might take 10, 20 years. And these are industries where, again, the equipment's highly specialized. Like if you look at cement mixers or, you know, garbage trucks, like these are custom built. So for, for the autonomy systems to make their way out to that edge, it's just going to be a longer diffusion curve than, you know, for a sedan, which is, or a van or something like that, which is very much the same.

56:33Great. Maybe to close, I'm very curious, like you're at the heart of this real economy, as we said at the beginning of this conversation, transportation and utilities and manufacturing and all those fundamentally important things. What's your sense of the reality of American industrial power today? Are you seeing the same level of velocity? Are you seeing an acceleration? Is the AI boom and the data centers having a real impact on your customers? What's your sense of the level of just velocity? Yeah, very much. So I think our customers have, it's very clear they're busier than they've ever been before.

57:18So from an American economy perspective, we're seeing a lot of intensity. I was just in the field last week with a large energy utility, and they've been involved in grid modernization. and they shared with me a really interesting stat. They said, you know, over the last 125 years, we built a certain amount of grid capacity in megawatts or gigawatts, really. In the next five years, we're going to triple that. Like they, as a company, are going to 3X the amount of power they deliver. And that's like in five years versus 125 years. So that requires a tremendous amount of infrastructure build, even with new technologies.

57:54It's like they can't work fast enough. We are seeing that across so many different kinds of industries. And in that case, they also shared 90 % of that demand is data center related. So as the data center demand continues to skyrocket, the energy needs are all these different bottlenecks that have been appearing. So many of our physical operations companies, customer companies are involved in that directly. I know a lot of people that your customers employ are tradespeople. What's your take on evolution of trade? The idea that we've all seen in the last couple of years is that actually being a plumber, becoming a plumber might be a great idea if your lawyer job is going to get automated.

58:34Some of it is some level joke, but I'm curious about what you take. Yeah, I will warn the lawyers thinking about becoming plumbers. It's a pretty messy job, right? It's much harder. It's pretty hard stuff. So, yes, we've been continuing to see that there's basically a labor shortage, labor bottleneck in a number of different trades, but also things like long haul trucking, commercial driver's license holders, things like that. So in general, I think there's a lot of growing demand for these professions. And this data center boom is a great example. There are just like not enough electricians out there right now.

59:10So you see companies like Meta doing initiatives to like reskill people, train them on how to become a good electrician. And then how can you take the people who are trained and make their jobs as efficient as possible? So you don't want them like waiting on materials at a job site. Like you want to put them to work to perform, you know, wherever their skills are needed kind of thing. So very much kind of a bottleneck, but the trades are an incredible demand. And I also think that their jobs are getting more modernized as well, because if you think about it as an electrician, a lot of it is getting to the job site and having the materials and knowing what you're going to do.

59:46if an AI can kind of help you with that it takes a lot of the mental load off and you can focus on the really unique trade kind of value you have. Well Sanjit it's been a fantastic conversation thank you so much very excited about what you guys are continuing to build and like its sheer importance in the overall economy and it's been wonderful to learn more about it so thank you. Thank you. It's been fun. Hi it's Matt Turk again thanks for listening to this episode of the Matt podcast if you enjoyed it we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from.

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