Cracking the code on autonomous trucking

Summary

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Catalyst with Shayle Kann

Episode: Cracking the Code on Autonomous Trucking

Date: May 7, 2026
Host: Shayle Kann
Guest: Eyal Cohen, Founder & CEO, Humble Robotics
Produced by Latitude Media

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Episode Overview

This episode dives deep into the current state, challenges, and future potential of fully autonomous trucking—particularly with a clean-sheet design and the latest AI/vision models—through a conversation with Eyal Cohen (Humble Robotics). Cohen, a longtime veteran of autonomy in the automotive sector, shares why trucking autonomy has lagged passenger vehicles, what new AI paradigms unlock, and how Humble Robotics is rethinking the freight vehicle itself for a decarbonized, automated logistics future.

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Key Discussion Points & Insights

1. Why Is Autonomous Trucking So Hard?

• Historical Context:

  • Early industry efforts (Otto, Starsky Robotics) began around 2016, aiming to adapt advances from self-driving passenger cars to trucking.
  • Initial assumptions held that trucking, especially highway driving, would be easier than messy city streets.
  • Passenger car autonomy (e.g., Waymo) has reached commercial deployment in many cities; trucking lags behind.

• Challenging Realities:

  • Fewer but riskier edge cases on highways (e.g., high speed, much heavier vehicles, longer stopping distances).
    • “The passenger cars have a lot of structural advantages in developing an autonomous system that were very difficult for trucks on highway.” — Eyal Cohen (05:53)
  • Unlike a passenger vehicle, a stopped 80,000 lb. truck on a highway is a severe safety hazard, not just an annoyance.

2. Evolution of the Tech Stack—The New AI Era

• First Wave:

  • Early autonomy involved hand-crafted rules and HD mapping—“hand coding” behaviors into lane-keeping and perception.

• Impact of Vision-Language Models (VLMs):

  • Recent AI advances (VLMs) enable deeper scene understanding from cameras, reducing reliance on expensive lidar-only approaches.
    • “Today for me at Humble, we flipped that and we do a lot more on vision algorithms. We're camera first. And that's because of just the evolution of this technology.” — Eyal Cohen (13:32)
  • Easy access to highly capable, open-source VLMs speeds development.

• Sensor Debates:

  • While some (Tesla) press for camera-only, Cohen argues trucking requires maximum safety:
    • “For a vehicle, a truck for example, you would want it to have camera, lidar and radar to do a level four ... our margin for error is very low here. It's a very heavy vehicle.” — Eyal Cohen (19:03)

3. Why Design a New Vehicle? What’s Different about Humble?

• Cabless, Integrated Approach:

  • Humble builds an electric, cabless Class 8 truck—no driver space at all.
    • “The thinking that got me here was ... what is the simplest possible vehicle to move freight? And it's like a box with wheels.” — Eyal Cohen (23:49)
  • Merges tractor and trailer into a single, intelligent platform for operational efficiency and design elegance.

• Technical and Operational Advantages:

  • Full-vehicle intelligence allows for sensors all around (including rear), enabling safe backing, better incident management, full automation, and no need for workarounds like remote-deployed warning triangles.
  • Helps realize the long-term vision: hands-off, automated loading/unloading at warehouses with no human interaction.

4. Electrification & The Automation Link

• All-Electric from the Start:

  • Electric platforms are easier to automate (charging can be managed robotically; diesel fueling is much harder to automate).

• Industry Challenges:

  • High up-front costs for electric trucks.
  • Charging infrastructure remains a bottleneck, especially for long-haul; easier for short-haul/drayage and in markets like China.

• Who Builds the Charging?

  • The classic chicken-and-egg: “There has been quietly an industry built up on electric trucking ... but I feel like it's inevitable.” — Eyal Cohen (31:35)
  • Humble will help customers get access but won’t become a charging company itself.

5. How Will Autonomous Trucking Roll Out?

• Passenger Model Doesn’t Quite Fit:

  • Robotaxi rollouts start with geofenced city zones and gradually expand—for trucking, it's likely to appear first in limited, controlled hub-to-hub “rails.”

• Short vs. Long Haul:

  • Long-haul efforts are best capitalized but face depot and operational hurdles (who builds and manages aggregation depots?)
  • Humble is focusing on short-haul/local, where electrification is easier and operational pain points are different.

• Economic Realism:

  • “In this B2B market... ultimately, it's the cost of moving freight. And it has to be advantage to a shipper that's moving freight to want to use an autonomous service...” — Eyal Cohen (38:50)
  • Removing the driver only offsets about ~$1.10 per mile (out of ~$2.30-$2.40 total); sensors and extra tech add cost, so tight economics matter.

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Notable Quotes & Memorable Moments

• On Early Misconceptions:
  “When I started with Otto and Starsky, we thought [highways] were easier, it was going to be an easier road, and just turned out that we had some misconceptions about the space.” — Eyal Cohen (08:54)

• On AI’s Leap in Perception:
  “A VLM brings understanding right from the jump ... you had to sort of code in all the intelligence ... We didn’t get this intelligence layer for free [in 2016].” — Eyal Cohen (16:19)

• On the Importance of Full-Vehicle Sensing:
  “If you’ve made the tractor smart ... and you’ve left the trailer conventional ... you can’t see behind you. If you can’t see directly behind you, you cannot handle, for example, an accident where somebody just drives right into you.” — Eyal Cohen (25:39)

• On Economic Constraints:
  “You have to save money, I assume, in trucking in order for it to be adopted at any meaningful scale. ... I doubt that same dynamic exists to the same extent in trucking. It’s like a classic B2C versus a B2B market.” — Shayle Kann (38:38)

Timestamps for Key Segments

• 04:19 — Why passenger vehicle autonomy arrived first; hidden complexity of trucking.
• 12:34 — Transition from hand-coded autonomy to neural nets and VLMs.
• 23:39 — Rationale for Humble’s fundamentally new vehicle design.
• 28:28 — Electrification: benefits, hurdles, and international context.
• 33:48 — How autonomous trucking rollouts will likely occur vs. passenger autonomy.
• 36:10 — The hard economic reality: driver savings as cap for the tech premium.
• 41:29 — Regulatory landscape: cooperation, evolving legal ground, and international competitiveness.

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Closing Thoughts

Eyal Cohen’s core message: Trucking autonomy has proven harder than it first appeared, but advances in vision-based AI, along with a willingness to rethink the vehicle at a fundamental level, are opening new frontiers. Unlocking the scale benefits—in safety, cost, and carbon—will require not just tech progress but new business models, infrastructure, and regulator-industry collaboration.

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For links to referenced topics and further resources, visit Latitude Media.

Transcript

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[Shael Kahn]

I'm Shayl Khan. I lead the early stage venture strategy at Energy Impact Partners. Welcome to catalyst. So Waymo's first public ride service was in December 2018, years before the meteoric rise of LLMs that we've seen since then. And it's interesting to think about what's happened with autonomous vehicles, which were capable of driving on public roads, at least in some limited capacity, even back then. Not all that limited, I should say, but limited nonetheless. Before the current wave of AI, anyway. I was listening to an interview recently with the co CEO of Waymo who described how they've been able to integrate the newer AI capabilities into their system and how that's allowed them to basically supercharge their growth and to deal with a wider variety of edge conditions than they would have otherwise. Obviously, Waymo now is ubiquitous where I live in the Bay Area and increasingly is becoming so in many other cities. So the path to autonomy for light duty vehicles seems very clear. Less so at this point. For trucking, though, the market is huge. Trucks hauled over 11.2 billion tons in the last year in the US alone. There have been a bunch of attempts at autonomy for trucking, and in some ways it intuitively seems like it should be an easier challenge, but in reality turns out maybe to be a harder challenge. We'll get back to that. Anyway, we just don't have this equivalent of Waymo in trucking yet. So what would it look like for someone to start fresh now, fully immersed from day one in the new wave of AI, Particularly in this context, having benefited from the advent of things like Vision, Language, action models, which is sort of an offshoot of LLMs aimed more at the physical world. By the way, I find autonomy interesting in its own regard, given that autonomy really goes hand in hand with electrification. We've seen that already in the light duty vehicle world. I think we're starting to see it now in heavy duty. In any event, Eyal Cohen is the founder and CEO of Humble Robotics, a company that we at EIP just announced we invested in a couple weeks ago. Eyal is a veteran of the autonomous trucking world. He worked at Apple, Uber, Spark, AI and Wabi before starting Humble. And his view is that starting with a clean sheet, both from a vehicle perspective and from a tech stack perspective, will allow Humble to dramatically accelerate adoption of trucking autonomy. So I talked to him about the broader world of autonomous trucking, where we are today and where we're headed, and what he's building at Humble. That's coming up after the break.

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[Shael Kahn]

YAL welcome.

[Eyal Cohen]

Thanks for having me.

[Shael Kahn]

I have a bunch of questions for you. Let me start with this one. You know, waymo has done 200 million miles now on public roads and is driving me all over the complicated and messy streets of San Francisco. So, you know, passenger vehicle autonomy is clearly here and now across a bunch of cities. And yet we're not there on trucking autonomy. Why is that?

[Eyal Cohen]

Well, that's a great, that's a great first question, you know, so I think we need to go back a little bit to kind of when autonomous trucking started and the history there. And it'll help explain a little bit of that. So there's been unmanned military defense experiments for a long time, but from an industry that's not in defense. Autonomous trucking really started in 2016. There was two companies, Starsky Robotics and Otto. And I was part of Auto. I joined pretty early and I joined Otto at the time. I think there are some fallacies. I think maybe that we had at the time or misunderstandings about the space at the time. That'll help explain the answer to your question. But when I joined, I had been working on passenger car autonomy with other efforts. I was at Apple for a bit. And the challenge that I saw was cities felt very hard at the time given where the tech was. We were just like, how are we going to solve San Francisco and everything that happens in San Francisco? And the way the tech worked at the time still for a lot of companies, works this way is we were doing this very complicated HD mapping procedure where basically vehicles would drive and try to capture the world in 3D and try to maintain this updated high definition map in 3D to locate the vehicle across to. It all felt very hard at the time. And when I learned about auto doing trucking, I was like, hey, this feels. This feels like a more straightforward problem has great commercial application. Why don't we go tackle that? I didn't know too much about trucking. I like trucks. Most people are in awe of big vehicles, but the highways felt easier. And they felt easier because when you drive on a highway, mostly what you do is go straight. What it turns out is that actually the passenger cars have a lot of structural advantages in developing an autonomous system that were very difficult for trucks on highway. And so even in 2016, when we were doing at auto, we did this beer run 100 miles long with nobody in the front seat. And everybody felt like, okay, autonomous trucking is right around the corner. What we found over time is the highways were way more difficult than we expected. They're challenging. I mean, by the way, my premise here is that everything will get automated. And passenger cars just happen to be first, but everything will get automated. It's just sort of the march of the technology, the progress. But the highways we thought would be easy, they turn out to be hard. Because of edge cases, you're driving at a higher speed. You're talking about a vehicle that could weigh 80,000 pounds in some cases if it's at full gross weight and your stopping distance is long. Trucks just have challenges on highway that make them more difficult.

[Shael Kahn]

Would it be, would it be accurate to put it as like on a highway, you probably have fewer edge cases than you have on like a San Francisco street. But it's harder to manage. It's harder to deal with those edge cases because you're going at high speed in an 80,000 pound truck, et cetera.

[Eyal Cohen]

Yeah, yeah. I think probably like different kinds of edge cases. The way I would put it, you see them less often which is one. One key issue. Like a lot of the times we're driving on the highways, nothing happens. And I used to actually joke that you could just take a truck with no perception system and put it on the highway. And as long as it stayed in the lane line and stayed at 55 miles an hour, it could probably complete 200 miles with no issue. It's just that occasionally things happen on the highway that are very difficult to manage, and you don't see those events very often to be able to handle them and learn from them. So that's one challenge. Another challenge is that the truck, the passenger cars like a Waymox, when they are confused about what to do, when the robot doesn't know what to do next, it can stop obsess. And it's annoying. And I'm sure you've seen videos of Waymo stuck, right? But it's mostly annoying, not a hazard. So in San Francisco, when a passenger car stops, people go around and they honk, they call annoyed their local politicians, whatever. But mostly it's a safe situation. You don't have that advantage on a highway. So stopping a truck on the highway is actually extremely dangerous and could easily cause an accident. Especially if there's a bend or a hill where you don't see it's trucking. The margin for error is less. So all these things just started to pile up in trucking, and I think it just took longer to solve. It will get solved, but I think we. In 2016, when I started with the Auto and Starsky, we thought they were easier, it was going to be an easier road, and just turned out that we had some misconceptions about the space.

[Shael Kahn]

And so where are we today in terms of autonomous trucking? I mean, you said Otto and Starsky were the first two. There have been a bunch of other, I guess what I would call serious attempts since then. How far have we gotten?

[Eyal Cohen]

Yeah, many serious attempts, I would say. And by serious, I mean real technology development, real capital, real efforts. They've all kind of followed a similar blueprint. I would say they take existing tractors that have been manufactured by oem, sometimes in partnership with an OEM like Volvo, sometimes just sort of buying from a lot and doing a quick retrofit. And the attempts so far, there have been a few driverless runs. And by driverless, I mean nobody in the front seat. There's a lot of debate about what driverless even is. Right. For example, I think Aurora, which is one of the largest players in this space, they do what they call driverless runs. But they sometimes have a safety observer. This is from their public writings, somebody watching the system. But they call it driverless, as in maybe they don't touch the wheel or engage in any way. So there have been driverless runs even going back to that 2016 auto. There's a company named Bot Auto that just, I think yesterday did a commercial, what they call the first commercial driverless run. I saw that on LinkedIn. But I would say there's not a regular driverless service for trucking on highway that exists today. And that's 10 years after we've really put serious effort into the space and

[Shael Kahn]

five years plus after passenger vehicles really hit the inflection point where those rides are getting taken all over the place in multiple cities now, and even on highways now as of more recently, of course. So it's interesting and I think when you take a step back, surprising, because intuitively I would have thought the same thing, I guess that you intuitively thought, which is that all things equal, you would think trucking is easier, there's just less, fewer things going on on a highway than there are certainly in the streets of a dense city like San Francisco. But yeah, it turns out that those things are harder to. Harder to handle. One thing I'm curious about though, is that this first wave of autonomous vehicles, both trucking and passenger vehicles, I would say was built pre LLMs, pre current wave of AI. And Waymo essentially made it to market before, certainly started offering commercial passenger rides before GPT3, for example. And so they probably weren't leveraging the latest and greatest from Transformer World at that time, but obviously have been incorporating it since. And I'm curious what like. So if you're. The difference is between evolving a tech stack for autonomy that was built in 2016 or 2018 or 2020, and then layering on whatever you can do today versus starting fresh today, like what is the difference between those two?

[Eyal Cohen]

Yeah, great question. So the landscape obviously has changed a lot. And again, if I go back to that 2016 era, this was even before ML had really taken off en masse in at least autonomous vehicles. So to stay between the lane lines, we might just actually be hand coding. Look at these two different pixels. Is one yellow and is one not. That's where the lane line is. Very, very simple handcrafted kind of algorithms. So the whole space has evolved tremendously. And I think a lot of the companies have had to break down their stacks and re engineer them multiple times. And I'm sure Waymo's had to do the same. But today, I'll give you one example for Humble, my company, most of my time in this technology space, we've been pretty lidar heavy, like LiDAR first, I would say, and even for trucking on highway. And there's been attempts to develop these very long range lidars that could see 300, 400 meters, so you can handle the stopping distance of a truck. The LIDAR technology for a long time was really good and it was a little challenging to make it robust and reliable and at scale, but the technology itself was very good and cameras kind of lagged behind. We weren't doing so much with the vision side of it today for me at Humble, we flipped that and we do a lot more on vision algorithms. We're camera first. And that's because of just the evolution of this technology. It's just changed so much where now you get all this intelligence kind of. I wouldn't say for free, but when I take an open source, the corollary to an LLM is a vlm, like a visual language model. And if I take a off the shelf vlm, something that understands an image and tells me what's in it, it's very, very good. There's a lot of intelligence baked in there. I think that every company has to rethink their stacks as the technology evolves. What really hasn't changed that much though is some of the validation that you do, some of the safety engineering that you do, the evolution of the hardware technology, because that's gotten a lot more robust. So when you start a new effort now, you're using a pretty different technical approach for the brain, the AI, but you're using a lot of very well understood techniques for the rest of it, for how you develop a safe system that's evolved tremendously in 10 years, et cetera. Yeah.

[Shael Kahn]

For those who aren't already familiar, what are these vision language models, these VLMs that you referred to, what is it and what does it actually enable?

[Eyal Cohen]

Yeah. So a VLM is like a LLM corollary is the way I think about it. And it's taking an image as an input and providing some interpretation of what that image is happening in that image. And it's not something that really I had used in my past. This is very new to me also. When we started Humble and our head of autonomy, Drew Gray, has been really, really in depth in the VLM space. But what I've seen coming out of it, it's like what I think a lot of people experience now, if you go to ChatGPT and you upload an image and you say, hey, what's going on in this image? It has a pretty good understanding of what's going on in that image. And sometimes you will find intelligence baked in there or some. I guess intelligence is a hotly debated concept here right now. But bear with me, some understanding of the image that goes beyond there's just a dog in here. It's like there's a dog that may have come from that door on the left. Or this construction cone is sitting on a pickup truck, not on the highway, and therefore is probably not an important construction element because it's just being carried by a truck. Just to give an example of an edge case. And so the VLM brings some of that understanding right from the jump. And that's really cool. Pretty novel. When I started 10 years ago, nothing like this existed. And so you had no idea. You had to sort of code in all the intelligence, really try to understand what the features were. We would take imagery or lidar data. We would send it to get labeled. People would put box. I'm sure you've seen images of boxes around it that say dog or human or whatever. And we would say, if you see a human, you probably want to do this kind of thing. It's more complicated than that. But we didn't get this intelligence layer for free. And there's open source technology out there that is very, very good. You'll find free VLMs or open source VLMs that have a lot of intelligence just available, which is also kind of a wild place to be. So, yeah, quite an evolution.

[Shael Kahn]

My understanding of this sort of sensor debate in autonomy world, at least for passenger vehicles, right there's the lidar radar fusion side, which seems to be Waymo does a little bit of everything. Then there's Tesla, which is camera first and camera only, basically, maybe potentially a little bit of radar. But as I think about it. So I can see how these VLMs as an extension or corollary to LLMs, allow you to do a lot more with video and just get further, faster on camera data. But my understanding was always that the limitations on camera for autonomy are more around. It can get obscured in certain conditions and things like that. And that's where you want your LIDAR or whatever, which doesn't have the same set of issues. Is your view like you can go camera only as a result of the VLMs, or is it you can lean more heavily on camera, but you still want this sensor fusion approach?

[Eyal Cohen]

I think especially for Trucks. And given what I was talking about earlier with this 80,000 pound vehicle that you might be moving on the road, you want it to be as safe as it can possibly be. And so, you know, in my mind it's not a dogmatic debate about camera or lidar. It's what's the best technology for the moment that makes it the safest. And for my perspective, for a vehicle, a truck for example, you put on the road, you would want it to have camera, lidar and radar to do a level four. Level four, being driverless to do a level four truck today. And the reason I say that is because you want to be able to see the world in multiple ways. But there's always a sensor doing the most heavy lifting. There's not sort of this co equal democracy between the three sensors. It's usually like some sort of priority is put on some algorithms depending on what you're doing. Lidar doesn't see traffic lights very well, or it doesn't see the red green. So you would need to use camera primarily for that. Lidar can see better at night, Radar can see through weather in some cases. And so if you want to make a safe product and trucking in particular, you want to really take advantage of all those today, a human, and when you think about the end state, a human is effectively two cameras, two eyes, two cameras, and they're able to navigate the world fairly well. You could make the argument that over time trucks would go to a vision only system. But I think the debate sometimes you see around Tesla and Waymo and others feels a little dogmatic. I think most of us on the trucking side have taken just the approach that hey, our margin for error is very low here. It's a very heavy vehicle. We have to be extremely safe. So we just put as much equipment as we can to try to guarantee that safety and get very creative on the algorithm side and how to take all that data from the three different modalities and put them together.

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[Shael Kahn]

so I think of humble as having at least two things that are kind of new and different in this space. One is just to some extent it's like timing. You're starting fresh now and so you get to like build from the ground up using VLMs and these things that didn't exist before and benefiting from all the learnings that we've seen on passenger autonomy and so on. On the other is the form factor of the vehicle. So you mentioned that most of the attempts in autonomous trucking have been attaching a bunch of sensors to an existing truck, which is also, by the way, how it's been mostly historically in passenger autonomy. That's what Waymo's doing on these Jaguar vehicles and these other ones. Tesla maybe is a little different with the robo taxi, but those aren't really out on the market yet. So that is similar. You are taking a clean sheet approach and building a vehicle that doesn't even have a cabin. I guess in the long arc of history of course that's how it's going to end up. Right. When we don't need a driver, we shouldn't have a space and a driver for a vehicle. But apart from that, give me the thinking that led you to building a new vehicle.

[Eyal Cohen]

Yeah. So right at Humble, we have this cabless autonomous electric Class 8 truck. Right. And the, the thinking that got me here was a couple of things. It's kind of interesting. First, I was like, okay, if you were to imagine that long arc of future, like you're saying, what does that vehicle look like? What is the simplest possible vehicle to move freight? And it's like a box with wheels. Right. Basically, it's a platform concept where either a container is being loaded onto that platform, or maybe it's just a box of the box truck moving. And that would be, in theory, the lowest possible cost of moving freight. Right. It can't possibly get lower than that. I don't think maybe there's some new mechanism to do it, at least for on road trucking. So that was the first thought that got us here. And the second was like, okay, is this possible today? Can we do this with the technology and where it is today? And that's where we started exploring it with Humble. And the answer became like, yeah, the technology is there. Basically there's enough here where we can take that long arc of history and move it in a little bit, move it forward. And then along the way, because you're doing a clean sheet design, you could think about all these problems that you run into into the space. So I'll give a couple of examples of what you can just rethink with this kind of vehicle. So one way to think about our vehicle is that it combines a tractor and a trailer together into the single platform. Right. And a lot of challenges with doing this, by the way. So it's right, because part of the

[Shael Kahn]

market is like the tractor and the trailer are owned by different entities as it stands today. Right. It's not the entire market. Sometimes it's the same player, but they get separated sometimes. And sent off in different directions.

[Eyal Cohen]

They get separated and sometimes for very good reasons. Like trailers are relatively inexpensive, tractors are relatively expensive. So there's a lot of kind of interesting thinking around trying to combine this concept. But just from the technology side, for example, if you've made the tractor smart like the other autonomous truck players, and you've left the trailer conventional or a dumb trailer, you can't, for example, put sensors on the back of the trailer. You have a smart tractor, but you're just taking trailers from everywhere. So you can't see behind you. You can't see directly behind you. If you can't see directly behind you, you cannot handle, for example, an accident where somebody just drives right into you. And I think that's. There's maybe some clever ways to do that, but that's tricky. You cannot back into a dock because that requires some amount of understanding of what's going on behind you. One interesting. This is just an inside baseball thing, but one interesting challenge we've had in the industry is that trucks today, if they're pulled over, they're required by law to deploy warning triangles. Those warning triangles go behind the vehicle. If you only have made the tractor smart and the trailer has not been touched and you don't have a driver there, how do you deploy those warning triangles? And so companies like Aurora and I think, Waymo, when they were working on trucking, they proposed using lights that are high up on the vehicle to indicate that the vehicle stalled as a replacement for the triangles. But there's advantages and disadvantages to that. And so it's just being able to access the rear of the vehicle and have this full vision from a technology perspective just allows us to do more. And when I was thinking about what is the real future here for freight, again, so now a long arc, but what you would want is all freight to be fully automated, completely hands off, nobody touching it. It goes to a warehouse, it loads up, it loads goods from a warehouse into that vehicle and drives its destination and unloads. Right. And completely hands off, no human. I think that's where we want to get to. But to do that, you have to kind of treat the whole vehicle as smart, not just the tractor. And so that's how we got. So it's kind of this interesting combination for us of the technology is ready. The simplest concept that we can come up with for moving freight is a capitalist vehicle. And there's actually real structural technology advantages to doing it in the long term. And so that's where Humble is today.

[Shael Kahn]

You mentioned your vehicle is electric, autonomous. There's this very interesting and very appealing direct correlation between autonomy and electrification that you see broadly. I presume you sort of knew from day one this was going to be an electric truck. But electric truck, I mean, setting aside autonomy even, it's got its own set of open questions on, of course, things like range and weight of the battery and charging infrastructure and charge time and so on. So how do you think about the electric component of your vehicle?

[Eyal Cohen]

Yeah, and I think it starts with that vision that I talked about of like having fully hands off freight. If you were going to have fully hands off freight, you would want it to be electric to charge, to handle the charging in an automated way. It's really hard to do that with diesel. Like how are you going to get a diesel pump? You know, like intuitive. I mean you could maybe do it with robots. That could be challenging. So part of the electric story for me is just like, how do you get to this fully automated freight vision? So electric is good. From that perspective, there are challenges. Charging. Forget the autonomy side, right? Electric trucks in the US have had a challenging rollout. The electric trucks themselves have been fairly expensive. In some cases, 4, $500,000 a truck today. A tractor today is somewhere between 150 and 250 depending on what you're buying. Tractor is a very expensive electric truck. There's charging infrastructure that needs to exist. Electric trucks are having a moment again now because of the volatility in the world and some of the challenges that are going on. But in general it's been a tough rollout. But what we have seen in countries like China is that electric trucks are really taking off because the infrastructure developed and once it's there and the costs are where they need to be, it makes a lot of sense. It's harder on the long haul. I think that's something that as an industry we have to reckon with a bit. You have to put a very large battery to do long haul. It is very heavy. You need a lot of power to charge. You need a lot of charging infrastructure. But for use cases that are more local, short haul Drayage. Drayage is moving freight out of ports. Electric makes a lot of sense. And so my career started in electrification. Bay Area was very big for electrification for a long time. That kind of moved out a little bit. But I loved working on motors and batteries and the technology there. So I always in the back of my mind wanted to get back to it. And this was an opportunity for me because of that vision, that long term vision, because it makes a lot of sense for the short haul moves that we're aiming to do with Humble. And like I said, electric trucks are having a moment. It is the technology that some countries have adopted en masse. It just needs to be applied in the right way. And there's always going to be challenges in tech. Rollout charging is certainly one of them. But they're solvable. They're all solvable.

[Shael Kahn]

Do you think you need to solve them? There's a chicken or egg challenge. Often in things like this where it's like you need that charging infrastructure to be there in order for your customers to buy and utilize your truck. You don't necessarily want to be an EV charging company, I suspect, but somebody's got to do it and there has to be enough of a demand signal from your customers such that somebody will build the charging infrastructure and if it's third party owned charging infrastructure, make money on it. Right. There's got to be high enough utilization and so on. I think we're starting to see this happen at ports and things like that to some extent where you've seen a little bit more electrification already. But is your view kind of like build it and they will come sort of a thing? If we build the vehicle, our customers will want the vehicle. Charging will show up or do you need to be more proactive?

[Eyal Cohen]

Yeah, I think it's a little bit of both. So there has been quietly an industry built up on electric trucking. There are a few startups and companies out there that have been working on depots and solving electric trucking. And I think there's been some shift to more short haul. Tesla semi obviously, I think just rolled out of production, which is really great for the industry and that'll induce some charging efforts. So it has been happening, it's just been happening relatively slowly, I would say, compared to what the industry expected. But I feel like it's inevitable. And as Humble grows, my conversations with customers around this is we will help you solve it. It might not be that Humble itself is developing technology around charging. We have our hands full with autonomy and, and a clean sheet design. There's a lot there. Right. But we will help the customer solve it because we know everybody in the industry, we've been working with them for a long time and there's a myriad of solutions. Right. You could do charging at the ports and the warehouses themselves. You can have depots, there's public infrastructure, private infrastructure. But I think what we'll see is again, especially with what's going on in the world right now, I think we'll see, we'll see a steady improvement in that and eventually you'll have real uptake on electric trucking. Because from a cost structure perspective, once the charging is in place and you've sort of changed your operations and navigated that a little bit, there's a lot of advantage to it.

[Shael Kahn]

What do you think the rollout of back to autonomous, sorry, not just electric, but what's the rollout of autonomous trucking? Going to look like. I think people now have an intuition for what it looks like on the passenger side. It's sort of like, okay, company X usually Waymo, maybe it's going to be Tesla soon, maybe it's going to be Uber at some point, you know, says, okay, we're now offering publicly available rides in city X and it's ring fenced. You can only go, you know, within these boundaries and they do that for a while and then they expand the boundaries and then they go to a new city and so on. So now we have a sense of like, here's what's coming in terms of passenger autonomy. I imagine it can't quite work the same in trucking. So what's that, what's that going to look like? Paint me that picture.

[Eyal Cohen]

Yeah, I'll put humble aside for a second and just look at the industry as a whole. You have a number of very large players, very well capitalized, some public companies, all kind of going after the on road, long haul autonomous trucking and they will continue to work on that. And you will see autonomous trucks on the highway and I think you'll see them very soon and they will be driverless. All the sort of pieces have started to come into place like hardware and supply chain and regulatory efforts that took a while to sort of make sure state and federal regulators understood kind of the technology and how to deploy it. So those large players, I think they will start deploying, I mean traditionally they've been calling it a hub to hub model. Right. Where it's like you sort of, you can imagine at either end of a highway segment a destination for autonomous truck to go and drop off rates. Sometimes you'll see it go right to customers. I don't think you'll see generalized solutions where there's just autonomous trucks zigzagging everywhere for a while. But I think what you'll see is some specific segments, mostly in Texas, where you'll see occasional autonomous trucks and some scale applied there. That's for the long haul side, at least in the space that Humble plays in for class A trucking heavy haul. In the short haul, there really hasn't been a whole lot of effort in that space yet. So we're one of the first companies to tackle that. So we'll see what our rollout looks like over time and how that goes. We're fairly young, we're less than a year, but we're moving very fast on that front. But I think you'll see long haul Thomas trucks moving. I think the question will shift to can we get a truck safely across the road in most cases? To can we handle the operational concerns? Can we handle the unit economics? That's a challenge. If you take a very expensive truck and then you make it significantly more expensive and then you're saying we've saved on some labor costs, you have to make that argument work really well.

[Shael Kahn]

I was going to ask you that question on unit economics. I guess one way to get at it is in a normal truck move, this is going to vary substantially, I'm sure, in terms of long haul versus short haul and other things. But what portion of the fully loaded cost of delivery is the driver? How much savings do you get economically removing the driver?

[Eyal Cohen]

Yeah, so don't quote me, but about. We use atri. It's an industry guide for trucking costs. And trucking costs in 26 or 25 looks at about $2.30, 40 cents a mile, at least for the long haul side. And I think the driver wages are about a $1 $10 of that per mile.

[Shael Kahn]

So fairly significant, 30, 40%, something like that is the driver. So that's maybe if you're trying to save money and go autonomous, that's like the headroom you have to play with to make the vehicle more expensive. If you have to make the vehicle more expensive because you're adding sensors and things like that.

[Eyal Cohen]

Correct? Yes, exactly. So it's like you have to offset that, that labor reduction or you have some room to play with there, but you'll have some remote assistance. The vehicle will be a lot more expensive because of the sensors. There's no getting around that. A lot of the current OEM efforts, basically the way it works a lot in industry now is that an OEM will make a tractor and will partner with an autonomous provider to put the sensors on it. And they try to do this at the factory. That's the goal. But often they're using fairly expensive tractors like the higher end models because they have more room in the tractor for equipment. They call those sleepers where a driver might sleep and they replace some of the bed with a computer, for example. Those vehicles are expensive. They become very expensive until they're at scale. So there's a challenge for sure in the unit economics. I think you even see that with WAYMOS today. They have to get the unit economics down low enough to. To make the labor argument worth it. So I think that'll take time. That'll take time. And part of the story for Humble and the reason that I started thinking about the lowest possible cost of Moving freight was the unit economics. It's like, well, if you remove a cab, you've taken out some significant cost from the vehicle. You've also taken out significant weight from the vehicle. And that allows us to kind of rethink the unit economics a little bit.

[Shael Kahn]

Yeah, it strikes me that at least for a period of time, Waymo is getting away with inferior unit economics. Like, they definitely have inferior unit economics. And I can tell you from personal experience, I'm sure you have too. I ride Waymos around even though they're more expensive than Ubers right now, because it's cool and it's novel and yeah, I doubt that same dynamic exists to the same extent in trucking. It's like a classic thing of a B2C versus a B2B market, 100%.

[Eyal Cohen]

It's.

[Shael Kahn]

You have to save money, I assume, in trucking in order for it to be adopted at any meaningful scale. Maybe people will pilot something, but I don't know why else they would do anything at scale unless they can save money.

[Eyal Cohen]

Yeah, I think that's a real challenge. There's a real challenge in what you're saying, and it's very true. It's like in this B2B market, when you're dealing with freight, there's a safety component, there's a reliability component. Is my freight coming on time? There is, but ultimately it's the cost of moving freight. And it has to be advantage to a shipper that's moving freight to want to use an autonomous service in some way. I'll give you an example of a challenge that the industry faces on the long haul side. Originally, a lot of the ideas were basically make a rail, like I said. So you can imagine at the highway, there's a depot right at the highway, and you would aggregate freight at this depot and an autonomous truck would take it hundreds of miles, maybe 1,000 miles, like a long distance, and drop off the freight at the other end of that segment at another depot. And the thinking is like, yeah, we will do these sort of short moves to the depots. And then an autonomous truck, with its cost advantage structure, would move the freight the long haul. And then you do these short moves again. Well, the challenge in that is today you don't have those depots and somebody has to build those depots. And those depots are friction, like who is moving the freight to that depot and why do they want to move it to that depot, as opposed to just what they do today, which is just take it from their warehouse to the other end and so you can see that some of the. I think the economics here are tough. And in this industry in particular, the economics matter a lot. So I think Waymo has managed the cool factor and people are willing to pay more. And I think there is an experience component to it. I think people sometimes say they prefer Waymos for the experience. I think Weymos did an excellent job with the interior and experience. Right. So I think they have a little bit of an advantage there. I don't see that as much in freight. It's hard to. What's a premium service in freight? I mean, one example. I mean, to give the counterargument, you could say, okay, this truck could go day and night, right? There's no restriction on how often that vehicle can go. Maybe there's some argument there, but in general, yeah, you have to get the cost structure down. Part of the reason I started humble in a way is like, I was just really thinking about the customers, what they need, like, what their challenges are and how to just get the cost of freight to be as. As. As well and meaningful as possible, I guess.

[Shael Kahn]

Final question for you. What's the regulatory landscape like? Like, what do you, you know, are we allowed to run driverless vehicles on? And I guess, is it. Is it tied to. Are you on a city street or in a drayage situation versus a highway? And are those regulated differently or is trucking regulated as one?

[Eyal Cohen]

Yeah, that's a great question. So the regulatory landscape is interesting and fun. I actually kind of really enjoy this part of the industry, and I love talking to regulators about it because I kind of feel like we're all breaking new ground, the regulators and the industry. And it's good to do that hand in hand and have good dialogue on it. The situation today is we work with nhtsa, that's a major regulator for the trucking industry. We work with fmcsa, that's another regulator. Regulator for the trucking industry. On the federal level, there are state laws that are state by state. A lot of the testing for autonomous trucks has happened in Texas because the regulatory landscape there has been favorable to autonomous trucks. In California, a week ago, you would not have been legally permitted to do a driverless truck, and today you are. So there was some legislation or some new rulemaking there that allows for it with certain conditions. So it's an evolving landscape for us developing this catless vehicle. It's got a sort of additional regulatory challenge in that you're changing what the truck looks like. There's no steering wheel, there's no Windshield. Windshield is required by law. So how do you navigate that? So the way that works is that we talk to the regulators about what we're trying to do, and we come up with a good plan for how do we do this? How do we test it to be safe? Our vehicle. Our vehicle looks both like a tractor and a trailer. Like, it has. It has in some ways, like, you could think of it as like a smart trailer that just drives around by itself. Right. So if it's a smart trailer, how do you regulate a smart trailer versus a smart truck? Right. There's a lot of questions like that that we just work with the regulators on. But, you know, the. I would say that the regulators in general in the space have been very good to work with, and everybody kind of sees where the technology is going, and they just want to make sure that it's done in the safest way possible and thoughtfully, safely. And there's also this other component the regulators are thinking about, which is what's happening around the world. We see actually a lot of driverless efforts deploying in places like China. So are we being competitive in that regard while also being safe? But, yeah, it's an interesting question about the regulatory side. Actually a really fun one to kind of figure out hand in hand with them.

[Shael Kahn]

All right, Eyal, thank you so much for doing this. Super interesting.

[Eyal Cohen]

Thanks for having me.

[Shael Kahn]

Eyal Cohen is the founder and CEO of Humble Robotics. This show is a production of Latitude Media. You can head over to latitudemedia.com for links to today's topics. This episode is produced by Max Savage Levinson. Mixing and theme song by Sean Marquand. Ann Bailey edits the video version of the show. Stephen Lacey is our executive editor. I'm Shael Kahn and this is Catalyst.