Don, you want to do a mic check?
Testing. Yep.
There you go.
Sounds good. Great.
All right. Let me know when we're ready to go, guys. Good? All right. Hello, everyone. I'm John Sager, Director for Global Automobility here at Evercore ISI. With me today is Kodiak's Founder and CEO, Don Burnette. Don?
Awesome. Well, thank you. It's great to be back and excited to give you guys the updates and talk a little bit about what Kodiak's been up to over the last year or so. Very exciting times in the self-driving space, particularly for us. For those of you who don't know, we're a self-driving company working on the software aspect, AI-powered, intelligent driving. We really want to focus on the world's toughest jobs. That's kind of our guiding light here at Kodiak. We often talk about the dull, the dirty, and the dangerous. If it's dull, dirty, and dangerous, people either don't want to be doing those jobs, or they shouldn't be doing those jobs, and that presents a massive opportunity to inject autonomous technology. We focus on three primary verticals within transportation. We have long-haul trucking. Talk a lot more about that later. We have industrial trucking.
This is think about everything that's off-road, where we've now deployed over 35 fully driverless trucks as of the end of last quarter. These trucks are out there operating with nobody in the cab 24/7 for our launch customer. This is a DaaS business model, meaning the customer owns and operates those trucks, and we support that operation. We carry multiple types of loads, all the way up to triple trailers, which is very challenging, even for human drivers, and that allows our customer to increase their ROI in that area. Then, of course, we have defense. We believe that Kodiak has the most commercially mature technology that can be applied to off-road and defense applications as well. Show a little video here. Oh, nope. Can we play it? Maybe not.
Can we play the video?
Will it play? We will skip the video. Most recently, Kodiak has been focusing on our driver- out launch, our commercial driver- out launch for highway operation. We set up shop in Texas back in 2019, and we have been running commercial loads there ever since. We work with multiple carrier partners across all of the Southern United States. Most recently, we have been really focusing on our launch lane, which is going to be Dallas to Houston, which we announced a few days ago. We are now seeing consistent intervention-free deliveries from Dallas to Houston. We have some videos. This is a long one. We put up some videos, a time lapse online, where you can watch the Kodiak Driver system from depot to depot, end-to-end, deliver freight commercially with no interventions, no interactions from the safety driver at all.
We are doing this on a regular daily basis now from Dallas to Houston. We just announced this morning, IKEA has been named our driverless launch partner. We have been working with IKEA for well over four years. We have driven over 750,000 mi together. That is not the total that Kodiak has driven. That is just what we have driven with IKEA over those four years, and we have delivered over 1,300 loads. That includes to retail destinations where we actually deliver to an IKEA store. Really excited to partner with IKEA and finally be able to announce that they will be our driverless launch partner at the end of the year.
On that note, we have been tracking our progress through what we call our Autonomy Readiness Measure, which is effectively a measure of all the claims in our safety case that we need to complete before the system is validated to a safety level where we can safely pull the driver. As of the end of August, we are at 93%, and we will be giving monthly updates between now and December as we reach 100%, close the safety case, and initiate those driverless commercial runs. It is hard to see on the screen, but we have a network of over 26,000 mi that we operate on. The ones you see in yellow are the ones we do on a daily basis. We are operating freight daily out of Dallas to Atlanta, Dallas to Oklahoma City, Dallas to Houston, Dallas to Laredo, and Dallas to El Paso.
Our testing and operations facility is based out of Dallas, which is the most convenient location for us. But we do have an extensive network going coast to coast, including up to the North in Michigan, where we do a lot of winter and snow testing. Our primary operations are still in the Sun Belt, which is where we will launch at the end of the year and continue to expand from there. We are accelerating a deployment through our partnership with Roush. We have selected Roush as our upfit partner, that they integrate all the AV technology that we need onto a truck and provide that for our customers. We have already set up a bespoke line with them, which we have been operating for well over a year now. I think last year at this conference, we were just announcing Roush. Roush is in full speed.
We've brought on a new truck platform, the Daimler Western Star platform, in addition to our previous PACCAR platform. Again, really stressing the modularity of our hardware kit and system that will work on multiple makes and models of vehicle. We're ready to accelerate that deployment into 2027 as we achieve driverless on highways and begin to expand commercial operations. I think with that's what I wanted to share.
All right, great. Thanks, Don. What safety cases are left to be solved for the ARM that closing that final 7%?
Yeah, it's a complicated set of claims. We've talked about it in the past. There's all these different aspects of safety culture, safety management system. There's functional safety, there's safety of the performance of the system. People tend to focus on safety of the performance of the system, meaning does it execute lane changes, does it hit the brakes, does it avoid collisions? There's a lot more to a safety case than just the performance. That is one big aspect of it. Really, it's a lot of the validation aspects when it comes to testing in closed- course conditions. There's three ways that we build evidence to support our safety case. There's on-road operation. Obviously, you go out, you drive your vehicle, and you see what happens. That also helps with exposure. Part of the safety case is knowing how often things happen.
In terms of performance, you have on-road development, then you have simulation. Simulation picks up the bulk of what we do at Kodiak. We believe that simulation can only take you so far. If you truly want to validate your system in the real world, you have to test it in the real world. The third avenue is to go to the track and to test under closed conditions. Here, you can set up a lot of diagnostic tests. You can set up a lot of challenging tests that you otherwise are unlikely to encounter in the real world. You can also set up tests from simulation to verify that simulation is giving you good results.
Now, for us, the primary validation stages that we're finishing up are on the closed- course tests. Those are the ones that take a lot of time, and unfortunately, AI still doesn't help you with that today. There's a lot of manual work. You have to send a team out to the track. You have to find tracks that allow you to go to highway speeds with a semi-truck. There are not many of them in the United States, right? You have to make sure you have time. We have now three different locations that we're testing on a regular basis in order to kind of close out all those cases.
A lot of it comes down to the functional safety testing, validating minimum risk maneuver conditions, fallback safety controllers, backup safety controllers to basically ensure that no matter what goes wrong, that there's always a healthy system that can be there to take over and control the vehicle in a safe manner, whether that's to continue driving, whether that's to pull the truck over, whether that's to call for help. We want to test each and every scenario to make sure that we've checked all the boxes, and that's primarily what we're doing over the next couple of months.
Yeah, makes sense. It feels like largely the technical and the regulatory issues have kind of been solved, right? At our AV trucking tour earlier this year, one of the things that we were kind of surprised by is that even though this makes a lot of sense from a total cost of ownership perspective, it came pretty clear to us that the OEM is kind of the industry's bottleneck. To your credit, you've got 35 customers and I think more than 40,000 hours of paid driverless operations. What are the key milestones that demonstrate that you're successfully moving from that early commercialization phase towards scale?
I think that our industrial deployment with Atlas Energy is a really good case study to understand what is the evolution of a commercial deployment. When we talk about this sort of in a vacuum, again, most of the industry for the last two decades has focused on the AI, the software, the performance. But I often talk about the three pillars of autonomy. It's become very clear that the technology is only one of three very important concepts that you need to deploy a successful operation. Technology being the one, safety being two, and safety should not be overlooked. We already talked a little bit about that. But the third pillar is the product itself.
If I were to hand someone in this room a driverless truck today, and I said, "This truck is yours. Go forth and make money." You'd probably ask, "Well, what do I do with it?" "How do I interact with it? How do I turn it on? How do I get support? How do I tell it what to do? What's the interface? Do you interface with my software system that I have today? Do I have to have a new software system? What kind of training is involved?" Not just training at the corporate level, but also training all the individuals who interact with the trucks on a daily basis. Because we're very focused on a Driver-as-a-Service or DaaS model, we really want to put the assets in the hands of a customer as soon as possible. And that involves a lot of knowledge transfer in terms of how these vehicles actually operate.
That's all to say we've been through that path over the last almost two years, 20, 22 months or so, with our deployment with Atlas. Starting at just two trucks in December of 2024, we've built that up to 35 trucks. And over that period, we've learned a tremendous amount about how customers ultimately use the product. We've been able to refine our processes. We've been able to refine the training. We've been able to refine the touchpoints, the controllability. And I don't mean of the performance of the vehicle; I mean the actual interface to the vehicle. The remote support, remote assistance, all of those are sort of the unsexy aspects to making this a real business and providing real ROI to customers.
And I think Kodiak is in a unique position because we have so much experience doing that once we achieve driverless for highway operation, we'll be able to hit the ground running a lot faster in order to get this in the hands of customers sooner.
Yeah. I think a lot of times for analysts and investors, it's like we get this hockey stick and we forget about the real-world deployment issues that you actually have to go through, and bump your head on different things, and encounter new scenarios before you're able to see things actually take off. Atlas has selected Daimler Truck. It's North America's Western Star platform for additional driverless deployments. But Kodiak is introducing its, your Gen 7 autonomous system.
Yep.
Can you just talk about the next phase of deployment and scale at Atlas?
Yeah. We are really excited to be deploying those trucks this quarter, and it has been a really fun and exciting opportunity to kind of walk the walk, so to speak, in terms of the modularity of our system. We have talked about the modularity of Kodiak system for many, many years. The idea that we can quickly put this onto other makes and models of vehicles, we can interact and integrate into any truck. This was an opportunity for us to truly prove that. In a matter of 9- 12 months, we were able to go to customer request, like, "Hey, we want a new platform. We want an additional platform. We want it to be the Western Star platform, this specific make and model." We said, "Okay, we are going to integrate that onto the new truck. We are going to validate that truck.
We are going to bring it not just to, "Hey, we have implemented, we can now test." This has to be driverless- ready, which is a much, much higher bar than, I have a safety driver in that I can fall back on taking over whenever something does not go wrong, if there is any hardware issues, et cetera. We brought that truck up in, I think, very, very short order. I am extremely proud of the team. That builds on our experience to be able to deploy to different future trucks makes and models, which is really great. We were able to bring our hardware and software and sensor kit up to a new generation. This is the 7th generation, as you said. So we have been iterating pretty much on almost a yearly basis since we started the company, refining the design. We are starting to refine things like aerodynamics.
It is always about reliability. The 7th generation, we have taken all the learnings from Gen 6 that we deployed in practice with full driverless in one of the harshest conditions in the U.S., being the Permian Basin, and I often say, I challenge anyone to find a more harsh environment, at least in the U.S., than the Permian. I do not think it exists. But if it does exist, please let me know. These trucks are getting beat up day in and day out, and they have performed incredibly well over the last two years. We have taken those learnings and put them into Gen 7. We have shrunk the package. We have made it more efficient from a power consumption perspective. We have doubled the performance of the actual underlying hardware. We have streamlined the sensor suite, and we have made it even more reliable than before.
We've taken all these kind of learnings and put them into our latest- generation package. We've put that into a new make and model of vehicle. We're excited to be working with Daimler on this program, and we think that ultimately our customers are going to really enjoy the new product.
Yeah. Automotive-grade quality is not something to be trifled with.
That's right.
It's a very high bar. Can you walk us through your go-to-market model? How do you envision the economics of DaaS playing out?
Sure. We want to be asset- light to the extent possible. Obviously, that comes with some testing, some demonstration, some knowledge transfer. But ultimately, if you look to our industrial deployment, we have a fixed- cost basis where we charge a monthly fee for operation of the trucks. In industrial applications, customers tend to, not always, but tend to already operate around the clock, and a lot of the promise of self-driving in commercial has been to increase the asset utilization for the end customer. We definitely see that as a potential in the highway environment or over-the-road environment. But in industrial, that was already the case, and for them, it does not make sense so much to pay by sort of the usage.
They are always in use, rather, they want to just pay a known fixed fee and then be able to deploy their assets as efficiently as they possibly can to whatever customers need them. In the highway environment, obviously we want to meet the customer where they are. Most of the conversation these days is around a per mile fee, and of course, we want to incentivize these fleets to be able to use the technology as much as they can. So, we are targeting about that $0.85 per mile fee structure, and then we expect our trucks to operate at least 200,000 mi- 250,000 mi per year, increasing the ROI for the end customer and driving significant revenue and leverage for Kodiak.
Okay, you are bringing in the $0.85 per mile, but how are you paying out your integrators or your suppliers?
Right now, we cover the CapEx of the sensor suite and the hardware that we integrate. The end customer purchases the truck themselves. So they buy the truck, the truck gets taken to Roush, Roush integrates all the systems, all of our hardware, sensor pods, compute stack, it is all built by contract manufacturers offsite at scale. Those are also sent to Roush. Roush has a dedicated assembly line for Kodiak's technology. They integrate the system, and then the system gets returned to the customer. Kodiak is actually not involved in that process at all. We do a final kind of validation check just to make sure all the systems come up and healthy. That takes only a couple of hours. Everything is pre-calibrated, so our pods come pre-calibrated from the contract manufacturer. They get put on the truck, and they are basically ready to go.
This also makes it easy to change out pods or swap out sensors if you have any failures or if there's anything that happens in the field that requires some maintenance. We don't have the complicated calibration system that you have to run on a daily basis or anything. We calibrate our system once. It's good. We've had these systems running for multiple years without having to recalibrate anything. In that sense, the customer pays for the truck. We currently cover the cost of the upfit and the equipment, and then we amortize that over the operational lifetime of the vehicle.
Can you discuss a little bit about the value proposition for the customer? You mentioned the utilization's already very high in some of these instances. What are the things, the key metrics, that they're looking at when they're evaluating your opportunity?
First and foremost, we want to make sure that they're saving money straight off the top. We want to charge them less than they would pay a driver today. We think that that's an important first step as early adopters of this technology. Beyond those benefits, you get the asset utilization, which we just mentioned. We're seeing fuel economy improvements for our system relative to human drivers. Fuel's at a record high, so that's starting to become very meaningful for fleets. Of course, you've got insurance savings. We're already seeing insurance in our driverless deployment on par with human drivers, and we expect that to come down over time. For the last eight years of operation and all the miles and all the hours we've driven, we've never had an at-fault accident with our system, whether that's driver- in or driver- out, supervised or unsupervised.
Insurance companies are working with us. They're listening, they're paying attention. They need the data in order to truly set market rates appropriately. We think that those insurance costs are going to come down over time. You also have the ability to expand your business. We often talk about drivers and jobs. We see autonomy as augmentive, at least in the near to medium term, for our customers, allowing them to capture more market share to grow their fleet as opposed to replacing drivers. That also means you can grow without the headaches of having to recruit, train, retain, pay bonuses, et cetera, to drivers, which I think simplifies the overall operational aspects of the whole logistics industry.
Yeah. We're kind of, as investors, steeped in Porter's Five Forces. Can you talk a little bit about what you think is your most durable or more durable differentiator from a tech perspective?
Yeah. I think the experience of operation is incredibly important. It's much more valuable than anybody truly understands. I think Waymo is a great case study in this particular example. They've taken longer than most people would have liked to get their technology out there, arguably years and years, and it's like, "Why don't they build more vehicles?" But you're seeing the safety benefits of patience, and ultimately, riders are responding overwhelmingly positive. Everybody I know who lives in a Waymo-enabled city uses Waymo almost exclusively and absolutely loves it and never would use anything else. That's not just technology. That's the entire experience. That's entire ROI for a customer.
I think there's a lot of first-mover advantage to kind of getting your leg in, not only from a customer interaction perspective, but also from a general experience in building the product and iterating that others can't simply replicate by training an AI agent.
Yeah. There's something incredibly valuable about real-world deployment. We're seeing that across not only autonomous vehicles, but earlier today in humanoids, right?
It's a very similar case of getting that data into the data flywheel, then that becomes the advantage and the differentiator. What allows you to operate across industrial, defense, and long- haul kind of simultaneously?
This is a great question. We've always believed that ultimately, we want our technology to serve the entire transportation space. While we started the company focused on long-haul trucking, long-haul trucking is merely an application within transportation. We recognized that we wanted to be able to serve all kinds of customers in all kinds of areas, not only just in commercial, but ultimately in personal transportation as well. That meant keeping a very open mind and being very flexible with the technology we built. The first important decision we made on that path was not adopting a high-definition maps-based approach. Back in 2018, pretty much, that was unheard of at the time. Now, that's very common, but then it wasn't. We were one of the first, if not the first, company to make that decision back then.
That alone has allowed our AI to have the flexibility to go in and learn arbitrary environments. We were really pushed into the generalized solution by defense. We recognized there would be opportunities in defense. I think we won our first defense contract back in 2021. The technology was still relatively in its infancy at the time, but we were very surprised, actually, to learn how well the system could adapt to what I could now call unstructured environments. You can roughly break down driving into two categories, structured and unstructured. Structured is man-made, so it could be highways, could be urban, could be anything where there's buildings and lampposts and lane lines and pavement. Unstructured is everything else. It could be the forest, it could be a sand dune, it could be a river crossing, it could be a dirt road.
If I put you behind the wheel and asked you to drive through an urban center, you could do that. If I put you out on a dirt road or put you in a forest and I said, "Don't hit the trees," you could also do that. Humans have this amazing ability to sort of generalize around the concepts of successfully make progress down the path or go in the direction of your intended target while simultaneously avoiding obstacles and being safe, conservative. That was something that we had to embed into the system at a very early stage. Luckily for us, it ended up generalizing, particularly as these new sort of transformer-based model approaches became the new powerful technique that we all use to train these systems.
Train these really large, unsupervised models in the data center and then distill them down into smaller models that run at the edge. Well, it turns out that if you have the data to put into the system, if you have enough data across a generalized corpus of environments, you can in fact train a generalized driver that will understand all these different environments. We do not have separate AIs that do the different structured versus unstructured driving. The Atlas trucks have the same AI that our highway trucks have, which is the same AI that our defense vehicles have.
Yeah.
The mission profile is a little bit different and the configuration is different, but the underlying neural nets and all the algorithms that run are exactly the same across all three.
How do you think about the size and attractiveness of each of those three end markets, and how do you guys make a decision to go after one versus the other, or prioritize one versus the other?
Yeah, I get this question a lot. It is like, how does Kodiak continue to compete at the highest level when you do a variety of applications with the technology? The truth of the matter is you are not always running full speed at every one simultaneously. The trick, the key to successful diversification is to, one, have synergy, of course, between them. So single dataset, single AI. That is really important. If you had three different teams working on three different AIs on three different datasets, three different times a day, then it would be incredibly inefficient. So you need to find that synergy across the applications that you decide to go into. That is the first step. Then the second one is to strategically sequence your focus, right? You only get $100 to spend. How are you going to allocate those $100?
Sometimes, over the last couple of years, we've definitely focused more on the industrial unstructured driving problem because we wanted to release that product to Atlas. We wanted to get driverless trucks out there into the hands of customers, and we wanted to start learning. That was the fastest path to get there, and we've deployed more driverless trucks, I think, than anybody else at this point. We've certainly learned more about interacting with a customer-owned fleet than anybody else, and those are incredibly valuable learnings. Over the last year now, we've shifted the focus back. As the industrial product has become mature, we've shifted the focus back towards our highway ambitions because, as everyone knows, long-haul trucking is the largest of all the TAMs.
It's the largest opportunity, and we certainly want to be playing in the game where investors are excited about the large market that we're going after. Today, our primary focus and what the team focuses on is that long-haul application as we march toward 100% for our Autonomy Readiness Measure, as we close the safety case, as we deploy driverless, with IKEA later this year. Defense kind of sits in the background where defense is very contract-based. Sometimes you surge into a program, sometimes there's a little bit of a lull that allows you to focus on some other things. They're all kind of existing at the same time, but your focus shifts strategically, sequentially from one to the other as necessary in order to continue to push in all directions.
Yeah. Correct me if I'm wrong on any of these numbers, but I think as of the end of Q2, you had $150 million in cash and equivalents. You're burning around $37 million per quarter. That should give you enough liquidity into the second half of 2027. Is there anything you can tell us about your future fundraising plans or how you're feeling about the balance sheet today and opportunities going forward?
Yeah, I think there's nothing specific I can say other than, we've stated publicly that we're going to be opportunistic in terms of raising capital, recognizing that, excuse me, we will need to raise more capital. We think about what we're achieving in terms of milestones, and we're really excited about that. We think the market's going to be really excited about that as we get closer and closer to our driverless launch. It's something that only one other company has been able to achieve at this point. We want to prove that we can compete in the arena at the highest level. Obviously we want to be conservative with cash. That's something that's in our DNA. It's in our culture. It's something that we've always done since the very beginning of the company and something that I'm very proud of.
I think we're the most capital-efficient autonomy company ever. That's something that, yeah, we're very proud of, and we will continue to be prudent with our spending and raise capital opportunistically at the right times based on the milestones that we achieve and the market.
I don't know about the most capital- efficient ever, but you're very capital- efficient, especially compared to some of your competitors. What is it about your approach that allows you to be that way and to be so? $37 million is not a lot of money—
Yeah
—for you to be spending per quarter.
It starts with hiring the right team. You need to hire people who kind of embody that ethos, right? It also is really important to be able to say no. I think saying no as a leader is one of the hardest things that you possibly can do because you have all these really smart people who want to do all these really great things and they want to try all these really great things, and it's all very well-intentioned. The easiest thing to do, especially putting on my researcher hat and my academic background is to say, "Heck yeah, let's throw everything at the wall and see what sticks, and then we'll know that we have the best possible solution." The problem is that's a very wasteful way to get to the solution.
You need to be able to say no, and you need to be able to force people to focus, and you need to make sure that they understand that this is the way it needs to be, and this is the way that the best businesses are run. I do not think that the self-driving industry historically has represented the most efficient business structures relative to other industries where there is a lot of cutthroat competition, and market forces force you to be as efficient as possible. We kind of wanted to, from the very beginning of the company, recognize that. Or set out to prove that this does not have to be, quote unquote, a marathon. You do not have to spend billions of dollars and hire thousands of people.
If you have the right people with the right focus and the right mission, then you can actually deliver this technology safely and broadly and bring it to customers at a reasonable expense.
Yeah. There is kind of an old- school mindset and approach that is required from an ROIC, hurdle rates, and everything that you need to have up front as your discipline. Otherwise, you are just a bit freewheeling. We have about five minutes left, so I do not know if anybody in the audience wants to ask a question. Go ahead, Alex, I will just repeat it. Hang on, Alex, we have got a microphone for you.
Thinking about Daimler Truck and the annual launch there, how does that compare to other OEM platforms? Are there any strengths, weaknesses for that truck? Is that truck ready for driverless operations?
The truck is ready for driverless operations. I don't want to nitpick between the OEMs, in terms of the capabilities of the truck. By and large, the trucking industry is an amazing industry, and these machines are pretty incredible in terms of what they do across the spectrum. What I can say is the customer is very excited about the Western Star platform. As you know, of course, there's the autonomy component of what we deliver, but then there's the base truck. The base truck has to perform, right? The suspension has to perform, right? The transmission has to perform, day in and day out in a very grueling, dusty, dirty, harsh environment. What I can say is the customer is very excited about that, and we've had a very good relationship working with the Daimler team to get the product to an autonomy-ready state.
One more over here.
Yeah. I'll just ask, the other public companies in this space, they've kind of laid out a path for commercial partners. It's not like the economic model or revenue plan is similar. Do you just see the market super big? Are you going to pursue different commercial partners? Do the commercial partners want to have multiple DaaS providers? What's kind of your expectation about how the commercial market unfolds for you guys as you expand into that?
Yeah, so we'll definitely be in a position to share more of our commercial roadmap as we kind of march toward our driverless launch. What I'd broadly say is, we think this is a massive market. We think there are advantages to being a first mover. But I don't think this is a winner-take-all space. From a customer standpoint, generally speaking, the trucking industry is one that likes to multisource. Now, whether every trucking carrier will multisource on autonomy remains to be seen, but certainly we work with very similar customers to our competitors. We have a lot of overlapping customers that are working with both technologies. I think if you talk to those customers, they will say great things about Kodiak. They may say great things about others as well.
We have a handful of customers that are exclusive at Kodiak, and I think our competitors have some exclusive relationships as well. That being said, to evaluate the state of the market when there is effectively zero trucks deployed is, I think, generously premature. This is a long path. The trucking space is massive. There are thousands of trucking companies, big and small. Even though we would like to think about the big players, even the biggest players are only 20,000 trucks. Our ambition is to be significantly larger than that. It is not enough just to go after the top two, the top three, the top five, the top 10. We have to think about the long line of trucking operators out there that are ultimately our customers. The truth of the matter is, there is so much business to go around.
I cannot imagine this ultimately being a one- supplier race. In fact, I do not believe the market will tolerate that. I think the market will demand competition as they have in other areas. We feel like we are in a very strong position, given our commercial launch later this year and given our plans to scale that commercial business throughout 2027 and beyond to go and capture a pretty significant portion of the market.
Maybe I will wrap it up with one final question.
Sure.
Just about the regulatory environment. Where does it stand for autonomous trucking today? Are there any meaningful gating factors, from a regulatory perspective, that would prevent you from scaling further?
The short answer is no. We have no gating factors preventing us from deploying today. Obviously, we are based in Texas, in Dallas, where we have a very favorable regulatory environment. Broadly speaking, it is a patchwork of state legislation that governs autonomy today. There is lots of talk about federal preemption, and there is a bill going through Congress today. Whether or not that passes in this current form or whether it passes in some later incarnation, I think the pendulum has swung significantly far to one side, to the point where we are over the hump. There is nothing that is going to prevent this technology from having an impact because the benefits are so numerous, right? It is vital to our economy, it is vital to pricing, and it is vital to meet the demand that we as a society are demanding. There are no roadblocks for us today.
There are no impediments in place for us to expand across 29 + states, including coast to coast now that we have opened up California. You are going to see that expansion pretty quickly and broadly across new lanes after we open Dallas to Houston as our launch lane. We will quickly be moving to other lanes as well, and then eventually larger corridors throughout the United States. We feel very good about the regulatory environment. We work with all levels of the government, at the local municipal level, at the state level, and at the federal level. I think regulatory is one where the industry, generally speaking, works together very positively, all for a common purpose, which we think is a good one. We are all set. Everything we need to actually launch and scale this business is in place today.
Okay. We have maybe a minute left. Is there any parting shot or final words that you want to leave investors with?
I think this is one of the most exciting times in the autonomy industry, right? We are finally here. We have the proof points in our current deployment. We have learned the important lessons for actually getting to the other side. We know how to close the safety case. We have been updating the public on a monthly basis, and we will continue to do that as we close out September on our progress. We feel very confident that we are on track to achieve driverless commercial deliveries by the end of this year. Then throughout 2027, we will be growing the fleet, expanding that coverage, bringing on new customers, bringing on new lanes. I think we are really off to the races. This is a great time in the autonomy industry.
As somebody who's been in it for 18 years, it's been a long time coming, and I can't be more excited about this year.
All right. Awesome, everybody. Thank you very much, Don.