Welcome to the Vergecast, the flagship podcast of swoopy cars. I'm your friend David Pierce, and here's a tiny little bit of inside baseball podcast shenanigans. I have been making more and more video stuff over the last couple of years. Podcasts are shifting to videos in ways that, frankly, I continue to be deeply conflicted about. I am mostly an audio podcast consumer. I spent most of my career making audio podcasts, and learning how to do this thing on video in a way that is both video-first and audio-first has been really interesting and really challenging. It has also meant that the way my home office looks is very important.
I want you to understand, if you're watching this, every single thing in this room that you can't see is a mess. There's a giant shelf full of just unordered crap over there. There's a bunch of bubble wrap over here from a thing that I was taking out. There's a giant load of clean laundry right on the other side of the camera. There's just a lot going on. But the newest thing is we're also spending a lot of time making more clips out of our shows because that is the main way people find stuff now is through clips on their feeds. I think that is increasingly just a kind of content, but it is also a way that people discover our shows.
So, thinking more about clips, what that has meant is that I have to sit further back from the camera because otherwise we get a lot of social videos that are just like my face sort of smooshed into the screen and it's like I'm yelling at you out of your phone. The solution is I have to sit further back so there's more room to crop around my face. Which has just led to some hilarious furniture decisions. Like I bought what I would call like a fancy TV dinner tray table. This is the sort of thing you're supposed to, you know, put next to your couch or in front of you so you can work. I now have it sitting in front of my desk at the exact height, so I can sit further back but still use a mouse and keyboard.
This is, I guess, my new podcast setup. I have my desk and then I have my tiny desk and then I have my chair and there are zero inches of space between the chair and the background. This is what we do to make video podcasts. Luckily, I bought a really great microphone arm that moves around, so that has made life easier. Anyway, hopefully this all looks good and sounds good and this is going to be a great podcast.
We are going to do two things on today's show. First, Tim Stevens, a freelance tech and automotive journalist who writes for The Verge a lot, is going to come on and talk to us about AI design in cars and the ways in which AI might be able to shrink the amount of time it takes to make cars and all the things that that might change about cars. Then Hayden Field is going to come on and talk all about just some of the goings-on in the AI business. There's a lot happening, a lot of fast-moving stories, whether it's Claude versus Codex, whether it's Anthropic versus the US government. Hayden and I just have a lot to catch up on. We're going to do that.
Then Hayden's going to stick around and talk about AI and job loss and whether those two things actually have anything to do with each other. Really fun hotline question. Excited to get to that. All that is coming up in just a second. But first, I have to figure out how to have my keyboard and my mouse on this thing because I'm realizing this tray table I bought too small. Huge mistake. Wish me luck. This is the Vergecast. We'll be right back.
All right, we're back. Tim Stevens, freelance automotive and tech journalist, frequent Verge contributor. Welcome back to the Vergecast.
Thanks for having me, David.
The last time you were here, we talked about the Slate Truck and essentially lit the world on fire. We're going to talk a little bit about the Slate Truck at the end because I feel obligated to catch people up on this weird truck that has no features and everybody wants anyway. But mostly I want to talk about a story you wrote about AI and especially how AI is changing car design. But I think the place we need to start is a little bit of a primer on how car design has worked until now because I think this is a really fascinating case for both the good and potential bad of AI. But we should understand how cars actually get made in the first place. And you made me realize that it is a much bigger and longer process than even I understood. So give me the quick sort of kindergarten-level how-a-car-gets-made kind of story.
Yeah. I mean, usually it starts with a sketch or a series of sketches. There'll be kind of a product design brief that comes from the business saying, 'Hey, we need a car that's got two doors, four doors, you know, seats this number of people, can do this sort of thing.' Designers start sketching. People will give them a thumbs up or thumbs down. They start to move forward. There's usually a couple of different concepts, but they've moved from paper sketches to digital sketches to 3D models. You know, at some point someone will carve the thing out of clay to make sure it actually looks good in the real world. There's usually some level of virtual reality design, but ultimately most designers still like to see the physical thing in action. So, they'll carve small models in clay. If that looks good, they'll move it into a full-scale clay.
And then at that point, they'll start to do wind tunnel testing, engineering testing, virtual crash testing. There's a whole lot of series of steps that need to come together before that thing can enter production, and that whole process can take upwards of five or six years, which is pretty remarkable. So, you know, the next amazing new technology cars that are hitting the roads this summer were in production, in design and development in the early 2020s, you know, back around when COVID was still going on. So, it's an amazingly long and detailed process, which makes it really hard for product planners to really kind of foresee where the market is going to be in five or six years. And it's a really huge challenge right now for all the manufacturers because things are changing very quickly right now.
So, that was one of the things actually that surprised me the most is how long this whole thing takes because, you know, I'm used to gadgets and stuff where I would say the product cycle beginning to end tends to be like 18 months, maybe 2 years on a cycle. And that has its own challenges, right? Forget five or six years. I mean, give me a sense of what that looks like in practice. Like when you look at cars now, what do you see that is sort of 2020 and 2021 that now feels kind of anachronistic?
Yeah. I think on one hand, we see a lot of manufacturers pulling back from EVs, which were really looking poised to kind of take over the market back in 2020. You know, there's a lot of enthusiasm and excitement. We had regulations in Europe and elsewhere that were saying that internal combustion was going to be dead by 2030. But now, of course, all those things are being rolled back. We're seeing a lot of anti-EV initiatives here in the US. And that I think is a big thing. We're seeing manufacturers having to pivot, right? But we're also seeing, you know, these heavy touch-focused interfaces that were all looking very trendy, you know, four or five years ago. And now we're seeing more and more people saying, 'Hey, you know, I actually really like having a volume knob in my car. I like having a couple of buttons in there.' So we're seeing manufacturers pulling back from that as well.
And you know, this is a huge challenge for the manufacturers because for a long time cars were pretty stable. There weren't really that much of a change from one year to the next. But now we're really expecting these major leaps forward in terms of technology, in terms of features. And that's something that they're struggling with.
Okay. And you've talked to some people about how AI is coming into this process. And it seems to me there are two ways if you're a car company you could think about AI. You could say we have this process that works but it takes five or six years. Can we maybe use AI to just vastly shrink that period of time? Or you're saying can we, like in the way that, you know, DeepMind is out there using AI to invent new proteins, are you GM and you're like, what wild car ideas has no one ever had that AI can come up with? It seems like you got mostly the former and maybe tiny glimmers of the latter.
Yeah, I think by and large designers are not really ready to give up the creative insight into these vehicles. So, by and large they're looking for areas to improve the process. So, right now we're seeing examples of GM doing things where they're taking a sketch and turning that into a 3D model via AI rather than hiring someone to go through a 3D modeling suite. They'll feed it over to AI, a couple different angles of a sketch and bam, they have a 3D model back out in five minutes. Whereas these take a couple of weeks for a designer to do that kind of thing. Stuff like that can really help to speed up the process and help to pull that, you know, five-six-year process into maybe, you know, three years, which is really what they're focused on right now.
We are though seeing some interesting approaches where they are trying to do more generative AI in terms of new approaches for 3D designs when it comes to improving aerodynamics of components, for example, and also we're seeing some AI design when it comes to battery chemistry engineering as well. You know, there's a lot that goes into the battery chemistry of an EV in terms of cathode and anode design. Different compositions of those materials can have very interesting different effects on the charging speed, on the lifetime of the battery, temperature sensitivity, things like that. And it's the kind of things where you have a lot of different permutations and can take a long time to test those different things. If you can feed all that into some kind of a machine learning algorithm, it can spit you back some options pretty quickly without you having to build 20 different batteries and run them all through your testing procedures.
So, the way it has worked, if I'm understanding this correctly, is like I build, let's say, one version of one component, right? I'm trying to figure out drag on one part of the car. I build a version of it. I put it in the wind tunnel, which as a budding Formula 1 fan, I've learned are like very big and expensive and important powerful things. Sometime you're going to come on, we're going to do a whole episode about wind tunnels.
But we'll save that. You run it, you get a bunch of data, you go, you build a very slightly different one, you run it again, you get a bunch of data sort of over and over and over until you have exactly the one that you want.
What an unbelievably manual process. Like, is this, I guess what I'm wondering in this front is, is this the sort of thing car companies have been doing forever because there just hasn't been a better way to do it, or because there is something, you know, slow-moving and slow to change about car companies that maybe they could have made this work faster? Like I think about this run in the tech industry where everybody went from, we make all of our models in China to we have a room full of 3D printers in our office, and that to me sounds a little bit like the same thing you're talking about where you go from the distance between I have an idea and I can hold that idea in my hands goes from literally two weeks on a boat to 15 minutes in a 3D printer. And I have to assume car companies have resources like this. So why has this been so sort of resistively slow and manual a process?
There have definitely been improvements on that front. Computational fluid dynamics, for example, which is another big thing in Formula 1 too, which is the ability to simulate wind tunnel runs effectively. And that has definitely sped up that process. You don't necessarily have to create a 3D model for everything. They do like to do actual models in wind tunnel testing just to make sure that everything is right. But typically they'll do a few rounds of computational fluid dynamics. But that typically takes, you know, a big supercomputer, it takes specialized training, it takes weeks to even run those simulations sometimes.
And so what this AI stuff can do, there's a company called Neuro Concept, for example, that's working on basically bringing that kind of computational fluid dynamics into AI to be able to simulate those runs in minutes that would typically take hours and hours on a supercomputer. So that is another step that they're being able to bring that in, make that process more quickly. And it's still, you know, a manual process. It's still taking time. You still have to do the iterations, but because they can speed that up even more quickly, they can go back and forth, iterate more quickly. I don't think there's really been a resistance on the case of manufacturers. They're pretty eager to try these technologies out.
There is still, you know, an adherence to the old-school ways of sculpting clay models and seeing them in the actual sunlight. But that has been pushed a little bit further down. They make fewer clay models than before. And that's again helping them to speed up the process.
I don't think I understood until I read your piece that there's a part of this process where they make one-to-one full-size clay models of cars. That is nuts, man. It's like a piece of engineering to try and make a car. What a huge artistic undertaking to undergo every single time.
Yeah, it is pretty incredible. And I've seen them kind of coming together and the amount of work that's entailed, and there's a lot of artistry in that, and some of that is going away as well. You know, there are now 3D milling machines, so you can basically put in a 3D model and have a full-size clay model come out of that. There's still a lot of hand tuning and tweaking that needs to be done. A lot of painting still that's done by hand as well to make those things look like real things. But oftentimes when you see a concept car roll out for the first time on stage, that's actually made of clay. It's not actually made of metal. You might be able to roll it around on the stage, but there's no engine in there. There's no interior in there. It's basically a big hunk of clay built around a frame. And it's an amazing piece of artwork.
That's not being lost, but it certainly is a little bit less common than it used to be.
Yeah. So, I think there is a certain tension in the stories people are telling you about AI between this kind of art and science, right? Where it seems like on the one hand, I can't imagine there would be that many people resistant to the idea of doing fewer wind tunnel tests, right? I think there's a lot of this stuff that is sort of pretty clearly not the work I want to be doing. It is the work I have to do in order to do the work that I want to be doing. And that's the sort of thing that's in every industry, right? It's why most people are not like worried about the idea of making fewer slide decks, right? I think there's a lot of that principle going around.
But on the other hand, there is a true creative art in every piece of this process all the way up to making the clay model, like you just said. Like being the person who can paint a hunk of clay to make it look like a real car is art, like capital-A art. And I wonder, like, have companies been reckoning with the mix of art and science here as they've been trying to figure out technology, and is AI throwing big new wrenches into that?
Not yet, but I definitely think that we're at a point where it's going to change the industry in a pretty significant way. Like I said, you know, I don't think anyone is too upset if we can take a simulated car crash or wind tunnel run from, you know, an overnight process to a five-minute process. I think that's good for everybody. But if you get to the point where, you know, new recruits, new people who are coming out of design school can't find work because a lot of those early tasks were those sorts of things that are being automated now. You know, the task of creating a 3D model from a sketch or improving a sketch and making it look more realistic. Those are the kind of things that you would take some kind of a new recruit and give to them so that they can kind of figure out the way that the process goes forward.
The more that you automate those tasks, the more that you make this process, you know, digitized and simulated, the harder it is for someone to come out of school and enter into design to the point where you've only got, you know, senior designers working on the later stages of design. And that I think is my big concern. You know, how do you maintain that pipeline of fresh new minds coming out of design schools and into these design houses while you're also taking away some of these low-level tasks that they've been basically tasked with training on? That I think is my big concern, and I haven't heard of a good answer from anybody there in terms of how they're going to keep that pipeline healthy. But it's the same thing in software development and in other areas as well where those low-level tasks are kind of going away. They're being replaced by AI. And there's really no ladder for recruits to climb up anymore.
Yeah. Yeah. It's funny, you just made me think of, I was scrolling TikTok the other day and came across one of those like, you know, how-to-make-it-in-business kinds of podcasts. And the question was something to the effect of like, how do you break into an industry? And they were like, the advice I give the same advice to everybody everywhere, which is find somebody doing the thing and figure out how to just be near them, sweep their floors, work for them, do the entry-level stuff, just try to be around. And I think you're right that in so many places AI has the potential to automate a lot of that out of existence, which suddenly means the learn-by-being-there path just doesn't exist anymore.
And I think in the car industry in particular, it seems like this has been a change for a long time. Like getting new young people to want to make cars, I think has been a challenge for a long time, right?
Yeah. And that's how I got into tech journalism was rewriting press releases basically and putting them into more easy ways to parse them for casual consumers. And you know, that's a task that AI can do very well. So yeah, how do you maintain that? You know, are these car development houses going to be willing to hire people out of college into more senior roles and skip the entry-level stuff? And will they still need the same number of people? You know, everybody across the board is saying, you know, we're not looking to cut staffing. We're not looking to change the way that we do this. We are still maintaining these staffing levels. This is just helping us be more efficient. But it's hard to imagine that still being true in two or three years' time if indeed these projections for, you know, cost savings and time savings from AI remain true down the road. But we'll have to wait and see.
Yeah, the 'we think humans should still be in the loop' is the thing you say all the way up until you lay off a lot of your staff. Like that's when it happens.
Yeah, how does software figure into all of this? I think one of the points you made is that these cars are increasingly software projects and that actually one of the things that delays cars the most is software. Which I had not realized, but also strikes me as somewhere AI has the potential to be both very useful and just massively wildly problematic.
Absolutely. Yeah, there's huge potential here. A lot of people are calling modern cars software-defined vehicles. Basically, that's kind of a big buzzword in the industry right now.
I hate that, Tim. Can I just tell you?
Most, you know, interestingly, most people in the industry hate it too, but it is still a term that is all over the place.
I just need to be on record about that.
You know, we're going from a time where we had, you know, for example, your turn signal used to be controlled by something called a relay, which is a physical device that would kind of click back and forth to turn the bulb on and off. Now, how long it takes for that bulb to go on and off is defined in software. And so you can go in and change code and change, you know, how fast your blinker turns, for example. Horns, active safety, everything else used to be a bunch of discrete hardware components that are all kind of being pulled together into these more powerful systems-on-a-chip from companies like Qualcomm. And so at that point, you know, we're pulling away from the old integrated development ways where it was kind of dated hardware and, you know, kind of arcane ways of doing things into more standardized software development procedures to write software for a new car.
That means that we've got, you know, masses of software that we never had before. Huge integration efforts, and we also have more regulations internationally for cybersecurity in cars that we never had to worry about before. It's creating this snowball effect where software in cars is getting bigger and bigger and bigger and bigger. And most automotive developers at this point really aren't prepared for that because they've never had to deal with this before.
And so there's definitely an opportunity here for AI to come in and help when it comes to things like creating documentation, automated unit testing, and things like that. You know, chores which are, as a former software developer, I can say are not exactly the things that you look forward to doing in the morning, but they are very important things to do nevertheless. And those are great opportunities for AI to come in. But again, we're talking about those are tasks that you would throw at a new software developer who was just fresh out of school. And we're again kind of raising the bar there. But again, that has the opportunity to make code more reliable, develop more quickly, and ultimately again help to move that pipeline in because we've got years' worth of software development to make these cars ready to roll. If we can pull that in too, we can again get these cars on the road more quickly.
What's your sense of how all of this is going to run into regulatory issues? I think there are so many open questions about how we're supposed to regulate AI, but we already regulate cars. So it feels to me like we're going to approach AI in the automotive industry in sort of the opposite, non-blue-sky-solutionering way that we are in a lot of the rest of the tech industry. What is going to happen here? What's your sense?
When it comes to regulations for cars on the road, you know, things like crash testing, emissions testing, that's all real-world stuff that has to be passed. So whether it's happening by AI or not, that's not changing.
The proof is in the pudding on that one.
Exactly. Yeah. But there is a whole new era of regulation when it comes to software, making sure that your vehicle is updated regularly, any cybersecurity issues are addressed quickly, and that those updates continue on for the life of the vehicle, probably about 10 years or so. And that's an area where AI can be a huge help for these companies, both in terms of tracking issues, in terms of updating patches, in terms of making sure that all that software is up to date. That's something that again most organizations are really struggling to build into and build a solid software house around. So AI I think can be a big help there, both in terms of testing and also in terms of deploying these updates. So I think that there will be help on that side of things, but ultimately, you know, it's not going to help you pass your MPG requirements or your crash testing if you don't have that stuff nailed down in the beginning.
There, which I would argue is a pretty good regulatory scheme. At some point you have to put the damn thing on the road and see how it goes.
And drive it into a wall.
Drive it into a wall.
We should make AI drive into walls more often, I think, and that's a good metaphor for a lot of things right now. We're trying to make cars not drive into walls more often using AI, but for sure you should make sure that it still can survive if it does. So, exactly. So, cast this out for me a little bit. And I think the sense I got from a lot of the folks, you talked to GM a bunch in particular, and the sense I get from reading your piece is that this is integrated into a lot of processes but is not yet sort of the driving force of everything. But let's just say hypothetically this stuff keeps getting better over the next couple of years, keeps getting worked into a lot of these things, and we actually do successfully shrink the process of making a car from five or six years to let's say three years, like you said, if that's sort of the north star.
How does that change cars, do you think? Like what does the world of cars look like at that turnaround speed?
It could make cars more affordable for one thing. You know, if you can pull a lot of that development effort out, you can drop the R&D cost down quite a bit. And you know, we're talking about buying a lot of metal, a lot of circuitry, a lot of components when you're buying cars, but you're also paying off that R&D effort which went into the development of that. So theoretically, we could see reduced cost, which is great. But I think ultimately the big thing is we'll see manufacturers able to keep up with the incredibly fast-paced change of business right now globally. You know, we're seeing changes in tariff structures every day practically at this point. We're seeing global strife that's causing a lot of issues when it comes to production management, supply chain, and that kind of thing. Things are a lot more complicated than they were five years ago.
So ultimately, I think we'll see manufacturers being able to bring cars to market that are the cars that people actually want to buy much more quickly. They'll be closer to the trends in the market, which are swinging up and down much more rapidly than before. And so we'll see cars that are what people want, which I think is great. But we'll also, I think, see fewer Hail Mary pitches. You know, we've seen a lot of cars over the years that were not what people really wanted, that kind of went in the face of the trends of the industry, but yet were fantastic cars. And so those are kind of icons. And I hope that we don't see manufacturers just kind of chasing their tails, just chasing the industry, just chasing trends. We see manufacturers still make these big bets, these big purchases, these big efforts into cars that are going to define what the brand is much more so than trying to chase some trend.
You know, we see so many movies on Netflix now that I just assume were kind of algorithmically generated of, you know, holiday movie plus action movie equals, you know, Dwayne Johnson is suddenly an elf. You know, I hope that we don't see cars like that where it's like, okay, we need a new four-passenger crossover convertible, and so therefore that car now exists. I hope we don't fall into that trap.
Totally. First of all, the movie you just described is called Red Notice, and it's terrible. And no one should see it. But it sure exists. I do wonder about that because you also describe there's a bit of the GM process now that involves kind of mood boarding, right? Like you said, the goal is not feed a bunch of prompts into ChatGPT and have it spit out a car. At least that's not the goal yet.
But I do wonder at some point what that's going to start to look like because someone will start to do that, right? Like that is just where a lot of this stuff is headed. And I think back to five or six years ago and every car looked like the future, right? Like this was the thing everybody was doing was like, what if everything was sort of swoopy and spacey and looked a little bit like a car you might have seen in Blade Runner. That was the thing. And I could absolutely 100% see that being essentially what comes out of a sort of generative AI process, right? You either feed it decades of cars and it spits out more cars, like you said, and we get this sort of homogenization of everything, or you say, how do I make it cool, and it's referencing a bunch of sci-fi movies and a bunch of old stuff and it's spitting out the future in the most sort of desensitized, lowest-common-denominator way.
It just strikes me as very difficult to imagine a world in which these tools are actually able to give us big new cool ideas about cars. But maybe I'm underselling it. Are you hearing anything that suggests this stuff can actually sort of help us move design forward?
I definitely think that there is opportunities for this to help. And again, the designers are still the ones who are in control of what these cars look like. You know, these are still concepts coming from the minds of human beings. And so, you know, what these tools are doing is helping them visualize what those cars look like. So, one of the tasks that GM is automating now is the ability to take those sketches, turn them into 3D models, but then plug those 3D models into basically quick rolling videos to show them what those cars look like in the real world. And so, that lets them tell very early on, is this actually going to work on the highways? Is it going to look really good when it's driving down to work in the morning? That kind of thing. And so, that they can make those decisions earlier and pivot more quickly.
And so, that might actually help them be more edgy in those designs. It might help them to create designs that are a little bit more provocative, but make sure that they still work in the real world. And I hope that that's what we see, that it doesn't result in them having to be conservative at every step, that they can take those big leaps and basically check them out early and make sure that they work both from a practical standpoint but also from a design standpoint. But again, the human beings are still in control. They're just having their tasks sped up right now. And I hope that that results in better designs. But we're still going to have to wait a couple years to find out, even at the most accelerated of timelines.
That's fair. All right. So, before I let you go here, let's talk about the car that is the most opposite of all of that stuff. No ChatGPT model would ever invent the Slate Truck. There's just no chance. Real quick, catch me up on what's going on with the Slate Truck. You were on the show what, a year or so ago now?
I think so. Yeah. And so, the lack of federal funding now, you know, $3,500 rebate is gone. That was a really big push for the Slate Truck, a mid-$25,000 truck. Though it was going to be really effectively an $18,000-$19,000 truck, which was a hugely attractive proposition.
And was super minimalism, right? It was going to be, it had no features, no radio. Basically, everything was an add-on, but by default, it was going to be super duper cheap and super super simple. And this really struck for people, right?
And for an $18,000 or $19,000 truck, that makes a lot of sense. For a mid-$20,000 truck, that's a little bit of a harder sell. You know, the cheapest truck on the market right now is the Ford Maverick XL, starts a little over $28,000. So Slate is still hoping to undercut that by a fair few thousand, but on the Maverick, you do get things like power windows, you get a stereo, you get paint. You don't get those things generally speaking.
Yeah. Yeah. You don't get any of that with the Slate.
And so really what Slate's hoping to do is really attract people who are going to be doing DIY, 3D printing accessories in their car, wrapping them in vinyl themselves, that kind of thing. There have been some changes. A couple weeks ago, Slate got $650 million in funding, which they say will be enough for them to get it into production sometime later this year. So, things are looking all good on the finance side. That's good news.
They did get a new CEO though, which was definitely a big surprise to all of us. Peter Ferris, who actually is a former Amazon executive, came on board to take over the company and basically bring them forward to production. You know, when I visited Slate before they announced things, they were very not really excited about talking about the Amazon connection, kind of downplaying that as Bezos being an investor, but this not being an Amazon company. Now, with Ferris taking over as CEO, it's kind of hard to say that there's no connection between Amazon and Slate, but we'll see how that shakes out.
Chris Barman, who was the CEO, she is still at the company, but now she's basically overseeing vehicle development, design, and production, which is a role that she was pretty close to what she had back at FCA before. So, things are still looking good for Slate. Really the question mark is are people going to be willing to spend that extra money to get it versus what they were going to be spending effectively before.
Yeah. Do you think this idea is still as compelling as it was a year ago under very different, you know, understandings about a, the world and b, the price of cars? Because I think that there is something to people's relationships with their trucks in particular that suggests giving them this kind of DIY stuff is cool and exciting. But I do wonder if, like you said, the world of cars and the world of the world has changed a lot in 12 months.
It definitely has. There's a lot less enthusiasm around EVs than there was 12 months ago. The lack of federal incentives is a huge blow to the company. But ultimately there's still a need for, I think, a cheap small truck. I just fear that it's maybe not quite cheap enough, and I think that a lot of the people who would be interested in this thing are maybe not super excited about something that doesn't burn gasoline.
But the idea of the personalization, the customization that's built into the product, I think is really interesting, and I'm still excited to see it come to the road, and I'm excited to spend more time with one to see how people, you know, customize their trucks. I think there's going to be a huge DIY community around it, and I'm excited to see that. But as far as a mass-market success, I don't think it's probably shaping up to be that right now at that higher cost, especially with Ford promising to bring a sub-$20,000 electric truck to market as well, with, you know, paint and stereo and other things. But that's probably at least another year off. We'll have to see what that looks like.
Yeah. Again, all of these cycles longer than anyone ever thinks.
But these things are coming just slowly.
Yeah. And Slate is in, again, they're in a good place financially. The truck should go into production in Indiana before the end of the year. And I'm very much excited to see them on the road.
Me too. All right, Tim Stevens, thank you as always. Good to see you.
All right, we got to take a break and then we're going to come back and we're going to talk about more AI. We'll be right back.
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All right, we're back. The Verge's senior AI reporter Hayden Field is here. Hi, Hayden.
Super chill times in your world as always. Just not a lot going on.
Never a lot going on. You know, every week is just a snooze.
