Gautam Narang of Gatik, chief executive officer and co-founder, joins NYSE Wired at theCUBE Studio at the New York Stock Exchange to discuss Gatik's approach to scaling autonomous dock-to-dock regional trucking. Narang describes the company's evolution from teenage robotics enthusiasm to a commercial leader in autonomous middle-mile logistics and outlines the technical architecture of the Gatik Driver and key commercialization milestones; they also discuss partnerships with fleet operators and manufacturers.
The conversation covers dock-to-dock regional networks, safety and validation frameworks, fleet deployment strategies and business models that support rapid scale. Narang explains that Gatik's competitive moat combines an artificial intelligence-first perception and decision stack, redundant Level 4 hardware and the Gatik Arena simulation and validation platform.
Narang highlights that Gatik demonstrates driverless operations across multiple states, holds over $600 million in contracted revenue and has closed a $200 million Series D led by Qatar Investment Authority. They emphasize AI driven perception, rigorous third-party safety audits and an asset-light leasing model as factors that enable rapid commercialization and scale in middle-mile logistics.
theCUBE Research hosts the discussion with Gemma Allen, alongside co-hosts John Furrier and David Vellante.
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Gautam Narang, Gatik
Gautam Narang of Gatik, chief executive officer and co-founder, joins NYSE Wired at theCUBE Studio at the New York Stock Exchange to discuss Gatik's approach to scaling autonomous dock-to-dock regional trucking. Narang describes the company's evolution from teenage robotics enthusiasm to a commercial leader in autonomous middle-mile logistics and outlines the technical architecture of the Gatik Driver and key commercialization milestones; they also discuss partnerships with fleet operators and manufacturers.
The conversation covers dock-to-dock regional networks, safety and validation frameworks, fleet deployment strategies and business models that support rapid scale. Narang explains that Gatik's competitive moat combines an artificial intelligence-first perception and decision stack, redundant Level 4 hardware and the Gatik Arena simulation and validation platform.
Narang highlights that Gatik demonstrates driverless operations across multiple states, holds over $600 million in contracted revenue and has closed a $200 million Series D led by Qatar Investment Authority. They emphasize AI driven perception, rigorous third-party safety audits and an asset-light leasing model as factors that enable rapid commercialization and scale in middle-mile logistics.
theCUBE Research hosts the discussion with Gemma Allen, alongside co-hosts John Furrier and David Vellante.
>> Palo Alto Studio connecting, Silicon Valley and Wall Street.
Gautam Narang
>> I'm John Furrier, co-host here with David Vellante, my co-host.
Gemma Allen
>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired, Physical AI and Robotics. And my next guest is a true lifetime robotics enthusiast. From teenage robotics to autonomous trucks carrying real commercial freight, Gautam Narang has spent his career putting AI into the physical world. Today, his company Gatik has tens of thousands of driverless deliveries under its belt, more than $600 million in contracted revenue, a waitlist that he can't quite keep up with, and just raised another $200 million to scale. The question now is whether the hard part is still making the trucks drive themselves or making autonomous trucking work at massive scale. Gautam, welcome to NYSE Wired.
Gautam Narang
>> Thank you so much for having me. Great to be back.
Gemma Allen
>> it seems as though every time we catch up with you, something new and fast-paced and accelerated is happening, right? You are certainly living an exciting life in the world of 2026. Just raised $200 million. I mentioned in my opening there that you have a waitlist. I know you spoke publicly about how demand is definitely starting to outpace supply. Talk to me a little bit about what has been happening in the market for you since you were last on and signed the deal with PepsiCo.
Gautam Narang
>> Absolutely. So it's a very exciting time at Gatik. Where we are today is we have proven that the core technology works and is scalable, right? So today Gatik has truly driverless trucks on public roads across multiple markets doing daily driverless operations for multiple customers. Right. So we talked about this. At the start of this year, we're doing operations on public roads without a driver or an observer in Texas, in Arkansas, and in Phoenix, Arizona. And more recently, we announced a multi-year partnership with PepsiCo. PepsiCo has been a great partner. We have been working with them since 2022, and then over the years, we have scaled with them. And today we have dozens of driverless trucks with them. And then we're actively working on scaling that footprint in existing markets and new markets as well. And then the Series D financing of $200 million is also a very exciting signal. Obviously, the investors that are backing us, the round was led by Qatar Investment Authority. These are the kind of investors that invest in category leaders, especially when the commercial proof points are there, right? So QIA recently doubled down on Anthropic. And then alongside QIA, we have Koch Disruptive Technologies. They have been a long-term investor in the company and then the rest of the syndicate is super exciting as well. So we have Cathie Wood's ARK Invest and Millennium and Intact. So these are very strong, long-term financial investors that usually get involved once the economics of the business are proven out.
Gemma Allen
>> So if we can, let's talk about the investor thesis for a company like Gatik and let's drill into it a little bit, right? Yeah, because typically, like you said, it's late stage. These folks know that the commercial viability is there, right? It's about really understanding how quickly you can scale. And, make the money roll in, right? Which you guys are already proving to a certain extent. To start, though, let's understand the technical moat that is Gatik. Help me demystify exactly what is a Gatik Driver, like what is happening behind that wheel, what systems, what technology, and what's the defensible technical play that you guys have built?
Gautam Narang
>> Yeah. So think of Gatik as automating regional logistics networks for some of the largest retailers, grocers, and CPG companies. So back when we started in 2017, Gatik took a very contrarian approach towards trucking. We said that it's better to start with the regional logistics versus going after long-haul trucking, right? So, and then we said, okay, designing these networks and automating these networks would be faster from a commercialization and scalability standpoint. And that bet has now proven out, right? So Gatik does end-to-end or dock-to-dock deliveries for the customers, right? So What it looks like is our driverless trucks pick up customer goods from their distribution centers. We navigate their logistics yards and hubs. We get on surface streets, so navigating intersections, traffic lights, pedestrians, bicyclists. We get on the highway. So scenarios like on-ramp, off-ramp, high-speed cut-ins, all of that is fair game and what we have solved for today. And then what's unique about Gatik is we deliver products all the way to the customer's docks. So dock-to-dock delivery is something that is unique to Gatik. Majority of the peers in the trucking space, they have been focused on what they call hub-to-hub deliveries, which is highway only. And that model frankly has struggled to take off, right? Because the end consumer expectation is you will do dock-to-dock deliveries like me at Pepsi, me as a grocer, I would want my transportation provider to pick up products from my distribution center and then deliver all the way to my retail stores. The whole idea of switching from automated tractor to manual tractor close to the highway does not scale as everyone expected it to scale. So that's basically what is unique about Gatik. And then think of this as more of a whole systems play. Obviously, the core autonomous driving software stack that we have running on the truck is unique and proprietary to Gatik. But it takes more than just cool AI to really build the business of autonomous freight, right? So our customers, they need a very reliable solution. So frankly, at the end of the day, they don't care if there's a driver on board or not. What they care about is, are the goods being moved on time and in full and is that happening at a high reliability? And that's basically what Gatik unlocks for some of the largest fleets in the world.
Gemma Allen
>> So talk to me about a typical use case and journey because you go 10 miles, but you also go 400 miles, right? There is a vast range there. Talk about how the technical capability and requirements change from these kind of loading dock to loading dock to large distribution center for like a Walmart or a PepsiCo or wherever it is, right? Where is the kind of technical use case differentiation there?
Gautam Narang
>> So think of this as the operational domain that Gatik is solving is highway driving, surface streets, and logistics yard. So we today take on distances that are up to 400 miles. It's more of a strategic decision versus anything to do with technology. We believe that the business ROI is there if you focus on all things regional. So that's basically how we define it, which is 400 miles. The technology is designed to enable day and night operations. Light rain, light snow is also within the scope of what we do on a daily basis. Extreme inclement weather is what we believe we can unlock in the near-term future. And the way we have designed the Gatik Driver, that's the core technology that is enabling the driverless operation, is across 4 different pillars, right? The first one is the software stack. That is what we have running on these trucks. The perception layer of the stack is fully AI-first, meaning how we perceive the environment and how the decisions are being made on board the truck. All of that is learning-based, but then it's not a monolithic black box, right? So we have ways to verify the output of the AI models and ensure that we have confidence and we can somewhat rely on the decisions that the models are taking, right? So unlike the LLMs, hallucinations are not acceptable for our safety-critical application. So that's basically what the onboard software stack enables for the Gatik Driver. The second pillar is the hardware layer, right? So the truck itself has to be capable of Level 4 driverless operations, meaning there cannot be a single point of failure anywhere across the system. Anything that is safety critical has to have a redundant and a backup system in place. So today our Generation 3 platform has a redundant steering, brake, compute, power distribution, sensing, and connectivity. And the platform has been designed in very close partnership with Isuzu Motors and our Tier 1 partners, and it's been designed to enable driverless operations across thousands of trucks. And that's basically what we are scaling with. The third pillar is what we call the Gatik AI Foundation. Think of that as the infrastructure that is not onboard the truck. So everything that is enabling the scale-up that we are discussing. So this contains our simulation platform. We call it Gatik Arena. So Arena is an end-to-end neural simulator that allows us to do a full validation, safety validation for a new market. It allows us to unlock new features in an expedited fashion. It also allows us to do data augmentation so we can augment thousands of miles of real-world data to tens of millions or hundreds of millions of very high fidelity synthetic datasets across the three different sensor modalities that we use, which is LiDAR, cameras, and radar. So the cost of data for us has come down drastically. So it allows us to expand to new markets and go from driver in to driver out within a few months. And that's basically what our Gatik Arena and our AI Foundation has enabled in terms of scaling. The fourth pillar, which is very important, is the safety framework, right? So obviously, we're talking about driverless trucks on public roads at highway speeds. So every safety-related claim that we have is backed by hard evidence. And Gatik is unique in the sense that our safety framework underwent a very rigorous and comprehensive third-party safety audit. So this was the commitment that we made very publicly in 2024. We said we'll only pull the driver out once third-party auditors have independently reviewed the system and given us the green light. That's basically what we concluded mid-last year, and that's when we commenced our driverless operations.
Gemma Allen
>> Was that a national safety framework? Has there been some sort of agreement in terms of what the regulation on the safety side looks like for this world, or is that still somewhat of a conversation gaining traction?
Gautam Narang
>> Yeah, so there are no standardized regulations when it comes to safety. Obviously, there are frameworks and policies that you can adhere to. But there is no one framework when it comes to, okay, if you hit this KPI, you are good to pull the driver out, right? So this was more voluntary that Gatik did. It was not a regulatory requirement. Now, in terms of regulations, for us, our focus is intrastate. Today, the AV regulations are very much at the state level. So there are 29 states where we can deploy the trucks, pull the driver out, and commercialize the service. So it's not a bottleneck for us at all. Now, that said, there's a very strong momentum at the federal level, at the national level, to unlock a national AV framework. So we do expect that to be rolled out in the near-term future.
Gemma Allen
>> So if you're talking to Sean Duffy, you're like, Sean, regulate this industry, because if anything, it helps our commercial timeline even further, right?
Gautam Narang
>> Yeah. Yeah.
Gemma Allen
>> Okay. I want to go back to Arena because I find this interesting. So how many scenarios do you guys simulate which have never happened in the real world? Like, what is the breakdown of the millions of things that can happen? Like a rock hits a windshield, sun casts its light in a particular way on, what sorts of, and is that synthetic data and those synthetic scenarios like fully proven? Like, what do you say to that question?
Gautam Narang
>> So it's a, so think of this as augmentation for the real-world data that you have. It's also an augmentation for the safety validation that you need to do before you can deploy the trucks in real-world environment and pull the driver out. Said otherwise, it's not a replacement for real-world experience and real-world data, right? So there is still a sim-to-real gap, meaning, all the validation that you do, all the training of the models that you do in simulation, obviously that helps you get closer to what you need to validate, what you need to prove out to, deploy the systems on, real-world environments and pull the driver out. But it's not a replacement for real-world experience, right? Obviously we can recreate a lot of exciting scenarios or edge cases that you would never encounter in the real-world environment, or it's just very expensive to try to collect data against those scenarios.
Gemma Allen
>> So what percentage of the tech is deterministic versus neural? Like, you know, break that down.
Gautam Narang
>> So the full performance layer is learning-based. It's fully AI-based, meaning we have two main AI models. The first one is what we call scene representation. So that's basically one model for us to understand what's happening around the truck and make sense of the world. The output of that flows into what we call our scene reasoning model. So that's our decision-making and reasoning model. And this is tied to the operational domain that we focused on, which is highway driving, surface streets, and yard navigation. So these are the two models that allow us to keep the core technology scalable and generalizable. So when we expand to a new market, we don't have to retrain the weights. We don't have to, I would say, collect data for that new market. It's all one model, one core stack that enables driverless operations in a new market. Now, that said, it's not a black box, right? So there is a parallel safety layer that is more heuristic-based that allow us to verify the outputs from these models to ensure that we're not compromising on safety. So think of this as the hybrid approach where the performance layer the regular operations of the truck is fully AI-based. But then we have guardrails that allow us to ensure that, uh, the outputs from the models are trustworthy and then traceable.
Gemma Allen
>> So going back to the investment thesis, let's talk about money in, money out, right?
Gautam Narang
>> Yeah.
Gemma Allen
>> So you guys have had kind of two key partnerships thus far. You have Isuzu on the OEM side, on the truck side, and really NVIDIA on the tech ecosystem side, right? Talk me through how those conversations are evolving, especially as you think about building your own proprietary technology, ensuring that there isn't any sort of long-term, dare I say, vendor lock-in. You know how you think about scalability. You've said demand is really like right now outpacing supply, right? So is— where is the bottleneck there? Is that on the Isuzu side? Like, help us understand the partnership side of it.
Gautam Narang
>> Yeah, great question. And yeah, so our focus remains on meeting the customer demand, right? So at the start of this year, we announced that Gatik has over $600 million in contracted revenue, right? This is multi-year commitments from some of the largest fleets in the world. And these are take-or-pay commitments, right? So these are real. These are not, I would say, non-binding LOIs or MOUs. And that number has increased significantly since the start of the year. Right. So we have added new customers. Each of these customers makes multi-year commitments on the go-to-market and the scale-up. Gatik's business model is also very unique. The two key highlights of the business model are one, we own the customer relationship. So it's us working directly with the PepsiCo's of the world, Loblaw's of the world, and the other customers that we work with. There's no middleman between us and the customers. And that kind of direct customer ownership adds to long-term stickiness. So that's one of the things that we feel very strongly about. And the other aspect of the business model is we're fully asset light. So all the equity dollars that we have raised go towards scaling the team and scaling the operations. We do not have to buy assets, which are the base vehicle or the AV hardware, using our equity dollars. So we have leasing partners publicly. We have shared ITOCHU and a few others. They own the trucks and the AV hardware. They lease the trucks to us and then we provide a service to our customers. So as revenue comes in, we make payments on the lease. And these are multi-year commitments. So our leasing partners are very comfortable underwriting the base vehicle and the AV hardware. So that is also something that is very unique about Gatik's go-to-market. And what the investors saw was we have completely de-risked the technology, we have de-risked the commercial traction. Obviously, we have a very strong ecosystem of strategic partners. Isuzu and NVIDIA are some of the partners that we have shared publicly. Gatik also works very closely with other OEM partners and other technology partners that are helping us with the scale-up that we are anticipating for this year and beyond.
Gemma Allen
>> I'm very interested in the decision to lease as opposed to sweating the asset, right, which a lot of folks do too, because amortization is also very attractive, right, in terms of the investment thesis. But from your perspective, is that because it gave you guys a level of agility as technology evolves, as hardware evolves, as software and tech evolves? the CUDA system has obviously been a system we've all known and loved, or, yeah, pick your fancy there. But what was the rationale behind that? Is it just it gives you more adaptability.
Gautam Narang
>> So it gives us flexibility around what the capital is used for. So it allows us to have a very asset-light and capital-efficient business model, right? So the scale-up that we are working towards, which is today hundreds of trucks, eventually thousands and tens of thousands of trucks. Imagine the kind of capital you would need to enable that scale-up, right? So for us, all of these assets are off our balance sheet, right? That's where the leasing comes in. And it's not just for the base vehicle. It's also for the AV hardware. So we are talking about our leasing partners underwriting the LiDARs, cameras, radars, the compute, and other components that enable driverless operations. And one of the things that got our leasing partner comfortable was our customers make multi-year non-cancelable commitments, right? So there is revenue certainty that we have for the next few years. That is what got our leasing partners comfortable to underwrite not just the base vehicle, but also the AV hardware. And this is something that again is very unique to Gatik. So if I compare this to, let's say, the long-haul model, Uber, DoorDash, there you get paid per mile or the robotaxi model where you get paid per ride. And in trucking, there's always spot pricing. So there is fluctuation around the revenue that each truck can generate. So for us, we do not have that problem, right? So the fact that our customers are making real commitments, take-or-pay commitments, multi-year commitments gives us full visibility into what kind of revenue can we expect from each of the trucks. And the good thing is customers also make minimum utilization commitments. So Gatik does not take any utilization risk either. And that's basically what got our leasing partners comfortable to underwrite the whole driverless capable truck, not just the base vehicle.
Gemma Allen
>> Well, it's a very smart model. I want to ask you about EV, right? Diesel prices have been skyrocketing. They're all over the news week on week. There's all sorts of crisis happening in that space. Talk to me about the decision to remain diesel, truck-based and not actually go the electric route. Is that something you guys have explored? What sorts of feedback do you get from customers on that and what is the true bottleneck there?
Gautam Narang
>> Yeah, so many of our customers, they have shared sustainability goals that they want to hit certain numbers by 2040. We are in a position that will support when the ecosystem is ready. So from our point of view, the technology is already ready for us, meaning the core technology is platform and powertrain agnostic. So the same tech works on an ICE vehicle as it does on an electric vehicle. And in the past, Gatik has experience running autonomous electric trucks for customers like Walmart. And this is us understanding how long does it take or what does it take to run autonomous electric trucks and then what we realized is we wanted to wait for native EV platforms to be available versus doing ICE to EV conversion. So our stance has been once the OEMs are ready with their electric platform that meets the customer performance requirements and once the charging infrastructure is there, we will be ready from our autonomous driving capabilities. Now, I also believe that the adoption of electric trucks will happen first in middle mile or regional transportation that Gatik focuses on versus long-haul routes, right? So if you think about our target market, we're looking at a very complex logistics network, right? So we pick up products from our customers' distribution center and warehouses and we deliver to a network of hundreds of retail stores. So it's basically us running our truck across those networks. So we have an opportunity to do an opportunistic charge let's say, while the goods are being loaded or unloaded at the pickup or the drop-off location. So electrification will truly first happen in all things regional, given the range performance that you see from electric vehicles. And then eventually the distances will get longer. So we are ready once the platform and the infrastructure is ready and once the market is ready and mature enough to catch up with you.
Gemma Allen
>> Yeah. Last question. $200 million raised. you guys are on quite the trajectory. What does the next 6 months look like for you? is there another PepsiCo on the horizon, even someone bigger perhaps? What are the priorities between now and 2027?
Gautam Narang
>> So the next chapter for Gatik is all about scale, right? So the demand from the customers, and these are customers that have very large private fleets. So PepsiCo is just one example. In addition to Pepsi, we are working very closely with the other major retailers, grocers, and CPG companies, and they have tens of thousands of trucks. That we can automate within their supply chain. And that's the kind of demand that we're looking to fulfill. Right. So our focus is very much on keeping our heads down and getting more trucks on the road. The $600 million backlog that we shared continues to increase. So the demand and the pull from the customer side is immense. And that's basically what we're busy with and focused on. And this capital will help us expedite that rollout. Right. So it's an execution story from here. All the other building blocks are in place— core technology, real commercial demand, very strong strategic partners that are enabling that scale-up. And historically, the company and the team has executed quite well, and we remain bullish on the future of Gatik and on the future of autonomous trucking as well.
Gemma Allen
>> But we certainly also remain bullish on this company and this thesis. Gautam, thank you so much for joining us on NYSE Wired.
Gautam Narang
>> Gemma, thank you so much for having me.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired: Physical AI & Robotics. Thanks for watching.
>> Palo Alto Studio connecting, Silicon Valley and Wall Street.
Gautam Narang
>> I'm John Furrier, co-host here with David Vellante, my co-host.
Gemma Allen
>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired, Physical AI and Robotics. And my next guest is a true lifetime robotics enthusiast. From teenage robotics to autonomous trucks carrying real commercial freight, Gautam Narang has spent his career putting AI into the physical world. Today, his company Gatik has tens of thousands of driverless deliveries under its belt, more than $600 million in contracted revenue, a waitlist that he can't quite keep up with, and just raised another $200 million to scale. The question now is whether the hard part is still making the trucks drive themselves or making autonomous trucking work at massive scale. Gautam, welcome to NYSE Wired.
Gautam Narang
>> Thank you so much for having me. Great to be back.
Gemma Allen
>> it seems as though every time we catch up with you, something new and fast-paced and accelerated is happening, right? You are certainly living an exciting life in the world of 2026. Just raised $200 million. I mentioned in my opening there that you have a waitlist. I know you spoke publicly about how demand is definitely starting to outpace supply. Talk to me a little bit about what has been happening in the market for you since you were last on and signed the deal with PepsiCo.
Gautam Narang
>> Absolutely. So it's a very exciting time at Gatik. Where we are today is we have proven that the core technology works and is scalable, right? So today Gatik has truly driverless trucks on public roads across multiple markets doing daily driverless operations for multiple customers. Right. So we talked about this. At the start of this year, we're doing operations on public roads without a driver or an observer in Texas, in Arkansas, and in Phoenix, Arizona. And more recently, we announced a multi-year partnership with PepsiCo. PepsiCo has been a great partner. We have been working with them since 2022, and then over the years, we have scaled with them. And today we have dozens of driverless trucks with them. And then we're actively working on scaling that footprint in existing markets and new markets as well. And then the Series D financing of $200 million is also a very exciting signal. Obviously, the investors that are backing us, the round was led by Qatar Investment Authority. These are the kind of investors that invest in category leaders, especially when the commercial proof points are there, right? So QIA recently doubled down on Anthropic. And then alongside QIA, we have Koch Disruptive Technologies. They have been a long-term investor in the company and then the rest of the syndicate is super exciting as well. So we have Cathie Wood's ARK Invest and Millennium and Intact. So these are very strong, long-term financial investors that usually get involved once the economics of the business are proven out.
Gemma Allen
>> So if we can, let's talk about the investor thesis for a company like Gatik and let's drill into it a little bit, right? Yeah, because typically, like you said, it's late stage. These folks know that the commercial viability is there, right? It's about really understanding how quickly you can scale. And, make the money roll in, right? Which you guys are already proving to a certain extent. To start, though, let's understand the technical moat that is Gatik. Help me demystify exactly what is a Gatik Driver, like what is happening behind that wheel, what systems, what technology, and what's the defensible technical play that you guys have built?
Gautam Narang
>> Yeah. So think of Gatik as automating regional logistics networks for some of the largest retailers, grocers, and CPG companies. So back when we started in 2017, Gatik took a very contrarian approach towards trucking. We said that it's better to start with the regional logistics versus going after long-haul trucking, right? So, and then we said, okay, designing these networks and automating these networks would be faster from a commercialization and scalability standpoint. And that bet has now proven out, right? So Gatik does end-to-end or dock-to-dock deliveries for the customers, right? So What it looks like is our driverless trucks pick up customer goods from their distribution centers. We navigate their logistics yards and hubs. We get on surface streets, so navigating intersections, traffic lights, pedestrians, bicyclists. We get on the highway. So scenarios like on-ramp, off-ramp, high-speed cut-ins, all of that is fair game and what we have solved for today. And then what's unique about Gatik is we deliver products all the way to the customer's docks. So dock-to-dock delivery is something that is unique to Gatik. Majority of the peers in the trucking space, they have been focused on what they call hub-to-hub deliveries, which is highway only. And that model frankly has struggled to take off, right? Because the end consumer expectation is you will do dock-to-dock deliveries like me at Pepsi, me as a grocer, I would want my transportation provider to pick up products from my distribution center and then deliver all the way to my retail stores. The whole idea of switching from automated tractor to manual tractor close to the highway does not scale as everyone expected it to scale. So that's basically what is unique about Gatik. And then think of this as more of a whole systems play. Obviously, the core autonomous driving software stack that we have running on the truck is unique and proprietary to Gatik. But it takes more than just cool AI to really build the business of autonomous freight, right? So our customers, they need a very reliable solution. So frankly, at the end of the day, they don't care if there's a driver on board or not. What they care about is, are the goods being moved on time and in full and is that happening at a high reliability? And that's basically what Gatik unlocks for some of the largest fleets in the world.
Gemma Allen
>> So talk to me about a typical use case and journey because you go 10 miles, but you also go 400 miles, right? There is a vast range there. Talk about how the technical capability and requirements change from these kind of loading dock to loading dock to large distribution center for like a Walmart or a PepsiCo or wherever it is, right? Where is the kind of technical use case differentiation there?
Gautam Narang
>> So think of this as the operational domain that Gatik is solving is highway driving, surface streets, and logistics yard. So we today take on distances that are up to 400 miles. It's more of a strategic decision versus anything to do with technology. We believe that the business ROI is there if you focus on all things regional. So that's basically how we define it, which is 400 miles. The technology is designed to enable day and night operations. Light rain, light snow is also within the scope of what we do on a daily basis. Extreme inclement weather is what we believe we can unlock in the near-term future. And the way we have designed the Gatik Driver, that's the core technology that is enabling the driverless operation, is across 4 different pillars, right? The first one is the software stack. That is what we have running on these trucks. The perception layer of the stack is fully AI-first, meaning how we perceive the environment and how the decisions are being made on board the truck. All of that is learning-based, but then it's not a monolithic black box, right? So we have ways to verify the output of the AI models and ensure that we have confidence and we can somewhat rely on the decisions that the models are taking, right? So unlike the LLMs, hallucinations are not acceptable for our safety-critical application. So that's basically what the onboard software stack enables for the Gatik Driver. The second pillar is the hardware layer, right? So the truck itself has to be capable of Level 4 driverless operations, meaning there cannot be a single point of failure anywhere across the system. Anything that is safety critical has to have a redundant and a backup system in place. So today our Generation 3 platform has a redundant steering, brake, compute, power distribution, sensing, and connectivity. And the platform has been designed in very close partnership with Isuzu Motors and our Tier 1 partners, and it's been designed to enable driverless operations across thousands of trucks. And that's basically what we are scaling with. The third pillar is what we call the Gatik AI Foundation. Think of that as the infrastructure that is not onboard the truck. So everything that is enabling the scale-up that we are discussing. So this contains our simulation platform. We call it Gatik Arena. So Arena is an end-to-end neural simulator that allows us to do a full validation, safety validation for a new market. It allows us to unlock new features in an expedited fashion. It also allows us to do data augmentation so we can augment thousands of miles of real-world data to tens of millions or hundreds of millions of very high fidelity synthetic datasets across the three different sensor modalities that we use, which is LiDAR, cameras, and radar. So the cost of data for us has come down drastically. So it allows us to expand to new markets and go from driver in to driver out within a few months. And that's basically what our Gatik Arena and our AI Foundation has enabled in terms of scaling. The fourth pillar, which is very important, is the safety framework, right? So obviously, we're talking about driverless trucks on public roads at highway speeds. So every safety-related claim that we have is backed by hard evidence. And Gatik is unique in the sense that our safety framework underwent a very rigorous and comprehensive third-party safety audit. So this was the commitment that we made very publicly in 2024. We said we'll only pull the driver out once third-party auditors have independently reviewed the system and given us the green light. That's basically what we concluded mid-last year, and that's when we commenced our driverless operations.
Gemma Allen
>> Was that a national safety framework? Has there been some sort of agreement in terms of what the regulation on the safety side looks like for this world, or is that still somewhat of a conversation gaining traction?
Gautam Narang
>> Yeah, so there are no standardized regulations when it comes to safety. Obviously, there are frameworks and policies that you can adhere to. But there is no one framework when it comes to, okay, if you hit this KPI, you are good to pull the driver out, right? So this was more voluntary that Gatik did. It was not a regulatory requirement. Now, in terms of regulations, for us, our focus is intrastate. Today, the AV regulations are very much at the state level. So there are 29 states where we can deploy the trucks, pull the driver out, and commercialize the service. So it's not a bottleneck for us at all. Now, that said, there's a very strong momentum at the federal level, at the national level, to unlock a national AV framework. So we do expect that to be rolled out in the near-term future.
Gemma Allen
>> So if you're talking to Sean Duffy, you're like, Sean, regulate this industry, because if anything, it helps our commercial timeline even further, right?
Gautam Narang
>> Yeah. Yeah.
Gemma Allen
>> Okay. I want to go back to Arena because I find this interesting. So how many scenarios do you guys simulate which have never happened in the real world? Like, what is the breakdown of the millions of things that can happen? Like a rock hits a windshield, sun casts its light in a particular way on, what sorts of, and is that synthetic data and those synthetic scenarios like fully proven? Like, what do you say to that question?
Gautam Narang
>> So it's a, so think of this as augmentation for the real-world data that you have. It's also an augmentation for the safety validation that you need to do before you can deploy the trucks in real-world environment and pull the driver out. Said otherwise, it's not a replacement for real-world experience and real-world data, right? So there is still a sim-to-real gap, meaning, all the validation that you do, all the training of the models that you do in simulation, obviously that helps you get closer to what you need to validate, what you need to prove out to, deploy the systems on, real-world environments and pull the driver out. But it's not a replacement for real-world experience, right? Obviously we can recreate a lot of exciting scenarios or edge cases that you would never encounter in the real-world environment, or it's just very expensive to try to collect data against those scenarios.
Gemma Allen
>> So what percentage of the tech is deterministic versus neural? Like, you know, break that down.
Gautam Narang
>> So the full performance layer is learning-based. It's fully AI-based, meaning we have two main AI models. The first one is what we call scene representation. So that's basically one model for us to understand what's happening around the truck and make sense of the world. The output of that flows into what we call our scene reasoning model. So that's our decision-making and reasoning model. And this is tied to the operational domain that we focused on, which is highway driving, surface streets, and yard navigation. So these are the two models that allow us to keep the core technology scalable and generalizable. So when we expand to a new market, we don't have to retrain the weights. We don't have to, I would say, collect data for that new market. It's all one model, one core stack that enables driverless operations in a new market. Now, that said, it's not a black box, right? So there is a parallel safety layer that is more heuristic-based that allow us to verify the outputs from these models to ensure that we're not compromising on safety. So think of this as the hybrid approach where the performance layer the regular operations of the truck is fully AI-based. But then we have guardrails that allow us to ensure that, uh, the outputs from the models are trustworthy and then traceable.
Gemma Allen
>> So going back to the investment thesis, let's talk about money in, money out, right?
Gautam Narang
>> Yeah.
Gemma Allen
>> So you guys have had kind of two key partnerships thus far. You have Isuzu on the OEM side, on the truck side, and really NVIDIA on the tech ecosystem side, right? Talk me through how those conversations are evolving, especially as you think about building your own proprietary technology, ensuring that there isn't any sort of long-term, dare I say, vendor lock-in. You know how you think about scalability. You've said demand is really like right now outpacing supply, right? So is— where is the bottleneck there? Is that on the Isuzu side? Like, help us understand the partnership side of it.
Gautam Narang
>> Yeah, great question. And yeah, so our focus remains on meeting the customer demand, right? So at the start of this year, we announced that Gatik has over $600 million in contracted revenue, right? This is multi-year commitments from some of the largest fleets in the world. And these are take-or-pay commitments, right? So these are real. These are not, I would say, non-binding LOIs or MOUs. And that number has increased significantly since the start of the year. Right. So we have added new customers. Each of these customers makes multi-year commitments on the go-to-market and the scale-up. Gatik's business model is also very unique. The two key highlights of the business model are one, we own the customer relationship. So it's us working directly with the PepsiCo's of the world, Loblaw's of the world, and the other customers that we work with. There's no middleman between us and the customers. And that kind of direct customer ownership adds to long-term stickiness. So that's one of the things that we feel very strongly about. And the other aspect of the business model is we're fully asset light. So all the equity dollars that we have raised go towards scaling the team and scaling the operations. We do not have to buy assets, which are the base vehicle or the AV hardware, using our equity dollars. So we have leasing partners publicly. We have shared ITOCHU and a few others. They own the trucks and the AV hardware. They lease the trucks to us and then we provide a service to our customers. So as revenue comes in, we make payments on the lease. And these are multi-year commitments. So our leasing partners are very comfortable underwriting the base vehicle and the AV hardware. So that is also something that is very unique about Gatik's go-to-market. And what the investors saw was we have completely de-risked the technology, we have de-risked the commercial traction. Obviously, we have a very strong ecosystem of strategic partners. Isuzu and NVIDIA are some of the partners that we have shared publicly. Gatik also works very closely with other OEM partners and other technology partners that are helping us with the scale-up that we are anticipating for this year and beyond.
Gemma Allen
>> I'm very interested in the decision to lease as opposed to sweating the asset, right, which a lot of folks do too, because amortization is also very attractive, right, in terms of the investment thesis. But from your perspective, is that because it gave you guys a level of agility as technology evolves, as hardware evolves, as software and tech evolves? the CUDA system has obviously been a system we've all known and loved, or, yeah, pick your fancy there. But what was the rationale behind that? Is it just it gives you more adaptability.
Gautam Narang
>> So it gives us flexibility around what the capital is used for. So it allows us to have a very asset-light and capital-efficient business model, right? So the scale-up that we are working towards, which is today hundreds of trucks, eventually thousands and tens of thousands of trucks. Imagine the kind of capital you would need to enable that scale-up, right? So for us, all of these assets are off our balance sheet, right? That's where the leasing comes in. And it's not just for the base vehicle. It's also for the AV hardware. So we are talking about our leasing partners underwriting the LiDARs, cameras, radars, the compute, and other components that enable driverless operations. And one of the things that got our leasing partner comfortable was our customers make multi-year non-cancelable commitments, right? So there is revenue certainty that we have for the next few years. That is what got our leasing partners comfortable to underwrite not just the base vehicle, but also the AV hardware. And this is something that again is very unique to Gatik. So if I compare this to, let's say, the long-haul model, Uber, DoorDash, there you get paid per mile or the robotaxi model where you get paid per ride. And in trucking, there's always spot pricing. So there is fluctuation around the revenue that each truck can generate. So for us, we do not have that problem, right? So the fact that our customers are making real commitments, take-or-pay commitments, multi-year commitments gives us full visibility into what kind of revenue can we expect from each of the trucks. And the good thing is customers also make minimum utilization commitments. So Gatik does not take any utilization risk either. And that's basically what got our leasing partners comfortable to underwrite the whole driverless capable truck, not just the base vehicle.
Gemma Allen
>> Well, it's a very smart model. I want to ask you about EV, right? Diesel prices have been skyrocketing. They're all over the news week on week. There's all sorts of crisis happening in that space. Talk to me about the decision to remain diesel, truck-based and not actually go the electric route. Is that something you guys have explored? What sorts of feedback do you get from customers on that and what is the true bottleneck there?
Gautam Narang
>> Yeah, so many of our customers, they have shared sustainability goals that they want to hit certain numbers by 2040. We are in a position that will support when the ecosystem is ready. So from our point of view, the technology is already ready for us, meaning the core technology is platform and powertrain agnostic. So the same tech works on an ICE vehicle as it does on an electric vehicle. And in the past, Gatik has experience running autonomous electric trucks for customers like Walmart. And this is us understanding how long does it take or what does it take to run autonomous electric trucks and then what we realized is we wanted to wait for native EV platforms to be available versus doing ICE to EV conversion. So our stance has been once the OEMs are ready with their electric platform that meets the customer performance requirements and once the charging infrastructure is there, we will be ready from our autonomous driving capabilities. Now, I also believe that the adoption of electric trucks will happen first in middle mile or regional transportation that Gatik focuses on versus long-haul routes, right? So if you think about our target market, we're looking at a very complex logistics network, right? So we pick up products from our customers' distribution center and warehouses and we deliver to a network of hundreds of retail stores. So it's basically us running our truck across those networks. So we have an opportunity to do an opportunistic charge let's say, while the goods are being loaded or unloaded at the pickup or the drop-off location. So electrification will truly first happen in all things regional, given the range performance that you see from electric vehicles. And then eventually the distances will get longer. So we are ready once the platform and the infrastructure is ready and once the market is ready and mature enough to catch up with you.
Gemma Allen
>> Yeah. Last question. $200 million raised. you guys are on quite the trajectory. What does the next 6 months look like for you? is there another PepsiCo on the horizon, even someone bigger perhaps? What are the priorities between now and 2027?
Gautam Narang
>> So the next chapter for Gatik is all about scale, right? So the demand from the customers, and these are customers that have very large private fleets. So PepsiCo is just one example. In addition to Pepsi, we are working very closely with the other major retailers, grocers, and CPG companies, and they have tens of thousands of trucks. That we can automate within their supply chain. And that's the kind of demand that we're looking to fulfill. Right. So our focus is very much on keeping our heads down and getting more trucks on the road. The $600 million backlog that we shared continues to increase. So the demand and the pull from the customer side is immense. And that's basically what we're busy with and focused on. And this capital will help us expedite that rollout. Right. So it's an execution story from here. All the other building blocks are in place— core technology, real commercial demand, very strong strategic partners that are enabling that scale-up. And historically, the company and the team has executed quite well, and we remain bullish on the future of Gatik and on the future of autonomous trucking as well.
Gemma Allen
>> But we certainly also remain bullish on this company and this thesis. Gautam, thank you so much for joining us on NYSE Wired.
Gautam Narang
>> Gemma, thank you so much for having me.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired: Physical AI & Robotics. Thanks for watching.
Gemma Allen