This week, WIRED learned that OpenAI is ramping up its efforts in robotics—specifically, by hiring researchers who work on AI systems for humanoid robots. Humanoids, robots built to resemble us and perform daily tasks, were famous for their clumsiness just a few years ago. Senior writer Will Knight tells us about how that’s rapidly changing on today’s episode cohosted by Michael Calore and senior correspondent Kylie Robison.
Mentioned in this episode:
OpenAI Ramps Up Robotics Work in Race Toward AGI by Will Knight
Humanoid Robots Are Coming of Age by Will Knight
2025 Is the Year of the Humanoid Robot Factory Worker by Russell Brandom
You can follow Michael Calore on Bluesky at @snackfight, Will Knight on Bluesky at @willknight, and Kylie Robison on Bluesky at @kylierobison.com. Write to us at uncannyvalley@wired.com.
How to Listen
You can always listen to this week’s podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here’s how:
If you’re on an iPhone or iPad, open the app called Podcasts, or just tap this link. You can also download an app like Overcast or Pocket Casts and search for “uncanny valley.” We’re on Spotify too.
Transcript
Note: This is an automated transcript, which may contain errors.
Michael Calore: Hey, Kylie. How are you doing?
Kylie Robison: Good. How are you?
Michael Calore: Not bad. Good to see you here in the chair-
Kylie Robison: I know.
Michael Calore: … that is normally occupied by Lauren Goode.
Kylie Robison: My Lord and Savior Lauren Goode, I’m so happy to fill in for her, but I miss her dearly.
Michael Calore: We all do. Also on the show this week, we have Will Knight, our AI expert at WIRED. Welcome back to the show, Will. How are you doing?
Will Knight: Hello. Good to be here. I’m doing well, thanks.
Michael Calore: Given the topic of today’s episode, I want to ask you both. Do you have a favorite robot movie?
Kylie Robison: Yeah. Mine is The Iron Giant. I was just looking up when that came out because I swear I watched it on VHS as a kid. Yeah, I loved that movie. It’s got a very sad ending. The robot is no longer. Oh.
Michael Calore: I guess we can probably spoil it, it’s been decades.
Kylie Robison: Yeah, it came out in 1999. I think it’ll be OK.
Michael Calore: Will, what is your favorite robot movie?
Will Knight: OK. I’m going to recommend Blade Runner, which is obviously a very famous movie and very beautiful. When I first saw it, I just thought it was just really cool. Then I later read the book and realized that it’s ambiguous about whether some of the characters are robots or humans. I found it mind-blowing, this idea of blurring what it means to be human and asking whether you can build something that’s human. Which I think we’re a long way from, but it’s an interesting idea.
Michael Calore: That’s great. I’m going to go with RoboCop.
Kylie Robison: Nice.
Michael Calore: Which is the absolute opposite of The Iron Giant. But it’s a great movie and it’s timeless. It’s a wonderful film. It’s Paul Verhoeven going extremely hard in the ’80s mode. And it’s about the privatization of the police force and the corporatization of America’s public services, it’s very prescient.
Kylie Robison: Very classic Michael pick.
Michael Calore: Thanks. This is WIRED’s Uncanny Valley, a show about the people, power, and influence of Silicon Valley. Today we’re talking about how the field of robotics is shaping up to be the next frontier in the race towards artificial general intelligence, or AGI. Earlier this week, we learned that OpenAI is ramping up its efforts in robotics, specifically by hiring researchers who work on AI systems for humanoid robots. Humanoids, robots that were built to resemble us and perform daily tasks, were famous for their clumsiness just a few years ago. But as our colleague Will writes about, advancements in machine learning and hardware have renewed interest in humanoid technology, particularly within the AI industry. We’ll dive into what companies like OpenAI are hoping to gain by investing in robots and what their decision means for how experts are thinking about what is really needed to achieve AGI, and what it might mean for us, the real humans, if that happens. I’m Michael Calore, director of consumer technology and culture.
Kylie Robison: I’m Kylie Robison, senior correspondent covering the business of AI.
Will Knight: And I’m Will Knight, senior writer at WIRED.
Michael Calore: Will, this is an obvious question, but we have to start here. What exactly are we talking about when we talk about humanoid robots? How are they different from other robots?
Will Knight: Yeah, I think it is a good question really. The obvious answer is that they’re shaped like a human, so they have legs, arms, head like we do. But the bigger point really is, and the reason why the industry’s so interested in this, is because they are designed to operate in the world that we live in which is designed for humans. Going upstairs, sitting inside vehicles, anything a human could do that those machines could do. When you’re talking about trying to build something that has human-like intelligence, if that’s how you define AI, then a big element of that is being able to do things in the real world. ChatGPT is getting very, very good at advanced math, but it can’t make you a cup of coffee. It couldn’t come into your home and make a cup. Or maybe it can, given what they’re working on.
Michael Calore: Right.
Will Knight: But that’s the idea.
Michael Calore: As you reported on, Will, and others at WIRED have talked about, humanoid robots do not have the most stellar reputation. They’re seen as gimmicky. We’ve all seen videos of the Boston Dynamics robot that dances. That video on YouTube has over 40 million views. As far as we know, based on what the company’s put out there, that’s about all they’re good at. At their worst, they’re seen as unreliable at performing desired tasks. They fall over, they bump into each other. How is that perception changing right now?
Will Knight: Yeah. I remember going to the DARPA Robotics Challenge, back in I think it’s 2012 when it first happened. This was a really interesting idea. After the Fukushima nuclear accident, they couldn’t bring robots in to work in this really radioactive environment, so the idea was can we develop robots that could go into that human environment. But back then, these humanoids, they moved unbelievably slowly. They just kept falling over, often in really comedic ways. In the years since that, actually a lot of what’s happened is really the hardware has gotten better and better. And some key things, like motors. In order to move like a human, we think of robots as being more capable than us, but you need to be able to move very, very quickly and very explosively in order to do things like balance. That’s what Boston Dynamics pioneered. That kind of hardware has gotten a lot more available, which is why you’re starting to see more humanoids come on the market.
Michael Calore: Now we know that OpenAI is very interested in humanoid robots. What did you recently learn from your reporting about the company?
Will Knight: I’ve been following a lot of different labs that are working on robots. Humanoids is a really big thing because it allows you to explore this human physical element of intelligence. I discovered firstly that they were hiring people to work on humanoids. Then also, if you look at the job listings they have and other signals, you can see this picture where they’re clearly ramping up their robotics work. Which I think makes perfect sense, given that a lot of other labs are doing that. A lot of other companies working on AI seem to be looking towards this physical side of intelligence now.
Kylie Robison: Yeah. When I read the piece, my first thought was Yann LeCun’s position where we’re not going to reach AGI or AI that’s smarter than all of us without that physical intelligence bit, without it learning in the real world, like any other carbon-based life form. It’s interesting that OpenAI is picking up their robotics work once again, after they shuttered in back in 2021. You product that they were doing pretty solid work on robotics, doing things like developing an algorithm capable of solving a Rubik’s cube using a human-like hand. Now it seems like OpenAI is not just returning to robotics, but bringing that AI focus to it, exactly what LeCun wanted. Back in February, OpenAI parted ways with Figure AI, a startup focused on creating humanoid robots. How do you think internal OpenAI initiatives can be competitive in comparison to companies that have been consistently developing humanoid robots? The whole time I’m thinking about the robot on meat hooks. I would be remiss if I did not mention the robot on meat hooks.
Will Knight: What’s the robot on meat hooks?
Kylie Robison: The humanoid robot on meat hooks? I’m so shocked that you haven’t seen this. Have you seen this, Michael?
Michael Calore: No.
Kylie Robison: Oh, my God. Do I just have the most cursed algorithm on planet Earth? I really hope that the listeners know what I’m talking about, and/or should go search it up because it’s the most horrifying thing. It almost feels like a gimmick. Will, do you know what this is? Did you just look it up?
Will Knight: They’ll often have a humanoid on a chain so it doesn’t fall over and it goes berserk.
Kylie Robison: Yeah, yeah. And it loses it.
Will Knight: Is that the one?
Kylie Robison: Yes.
Will Knight: OK, yeah.
Kylie Robison: And then it breaks because there was some problem in the algorithm. Yeah, it goes berserk. It’s horrifying, but probably a good marketing stunt. But I imagine OpenAI’s not going to have humanoids on meat hooks.
Will Knight: If they’re developing the hardware, they probably will have them on something.
Kylie Robison: Incredible.
Will Knight: So they don’t break themselves if they fall over. They are going to be competing with a lot of companies that are already putting videos of robots out there and stuff. The advantages that they have is in the algorithms already. Large language models, one of the remarkable things about them, is they have a surprising amount of understanding of the physical world. But to get to the next step, whether they’re developing hardware or just doing the algorithms, they’re going to have to work on these systems that are better able to really understand how to operate in the real world. So how to move limbs and do things like manipulation, which is one of the really big unsolved challenges.
Michael Calore: Yeah. Some of the big competitors, we should talk about. Kylie, you just mentioned Figure. There’s also Agility Robotics, Apptronik, some household names. Who are the ones that we should think about?
Will Knight: Yeah, there are a slew of companies doing humanoids now. You’ll see these videos in your social media. Agility is one of the better known ones, Apptronik. Boston Dynamics of course has pioneered this. Figure AI is very prominent with some of its demonstrations or videos. Of course, Tesla. Be remiss not to mention that. I think Tesla is going to be, not just because of Elon, but because they are actually very steeped in developing AI for the physical world through their cars, and also through the manufacturing that they do, clearly Elon seems to be really focused on that. Now I think they will be a really prominent competitor. We should also mention Unitree, which is this Chinese company which is now the biggest manufacturer of humanoid robots. I think they just filed for their IPO. I got to see a bunch of their robots. They’re really low cost. They’re not super sophisticated I think, but they’re very low cost and very able to do things like dancing. I went to a conference in China where they had them doing boxing matches, punching each other and doing kung-fu, which was quite impressive. But yeah, there is a growing number of companies. I think going is also doing some stuff with Apptronik, one of the US firms.
Michael Calore: It feels like the end game for the AI companies is to create humanoid robots that can function in place of a human in factory environments, in shipping and receiving, and around the home doing helpful tasks. The robotics companies have the same goals as well. They want to be able to sell a robot that can do this. But specifically with OpenAI, what are their primary motivations here outside of just the race for AGI?
Will Knight: Well, I think one of the big things to mention is GPT-5, the latest model which was meant to be a huge leap forward, kind of flopped. I think they, like others, are looking for the next big thing, the next direction in AI. Developing a better understanding of the physical world would mean building world models, so-called world models, is going to be a very important thing for AI. I think that this could be very important for consumer hardware, like smart glasses or whatever hardware they’re working on, because robots, by navigating the physical world, especially if they have digits like us, will interact with the same world that we do. You do have the potential to build models that are going to have a much better of the understanding of the world you’re walking through. I think there could be a bunch of commercial applications there as well.
Kylie Robison: Yeah. You previously brought up Elon Musk. Something I’d heard in my reporting and I’d reported a portion of this in my newsletter is during a recent all-hands with X and xAI people, which are the companies he also owns, he said that Tesla and the humanoid robot from there, he said that’s their biggest advantage at xAI is having those robotics in the background. Which is not something OpenAI has, they’re building this from the ground up which is notoriously hard. But the hype is very real, just like with the AGI hype. Now we’ve moved on to some sort of humanoid robot hype. Sam Altman, the CEO of OpenAI, recently told Bloomberg that people would soon be walking down the street and see seven robots walk past you doing this, or whatever. It’s going to feel very sci-fi. He said that moment isn’t very far away. You reported in your piece that it’s a $5 billion industry right now and could be worth five-trillion by 2050. What do you think of this hype? Is it warranted?
Will Knight: Yeah, I think it is really hyped. I think one of the problematic things with robots, you can compare to ChatGPT and large language models, is you can do these demo videos that are really stunning and people naturally think, “Oh, the robot can do incredible things,” if you see it making a drink or loading the dishwasher. But the thing is that often in those situations, it might be teleoperated. It might only work once time out of 100. That is the reality right now. Getting these things to be super reliable in any environment I think is an enormous, enormous thing. Just look at self-driving cars. We’re 20 years in and they’re just operating in some situations now, some roads. We’re talking about something that’s supposed to go into completely unstructured human world. I think it’s a lot further off.
Michael Calore: OK, that’s a good place to take a break. We’ll come right back. Welcome back to Uncanny Valley. Today we’re talking about Silicon Valley’s renewed interest in humanoid robots. We just broke down why some AI players, like OpenAI and its competitors, are betting that these robots could be the key to achieving AGI. But human robots have also been billed for years and years as being the future aids that would help us in our daily tasks, and in some cases take over our jobs for us. I want to know if that’s still the promise. If we are creating humanoid robots that can do the tasks of humans, how much further do we have to go for that to become actual reality?
Will Knight: There are a ton of robots that do work that humans do. Through the history of robotics, it’s been the case of expanding what they do from incredibly niche things. Manufacturing robots do these very, very precise things, but have zero flexibility. We started to see in recent decades more robots expand what they can do. You see mobile robots that can navigate very simple environments. In the last few years, we’ve started to see even some humanoids being tested. Amazon has invested in this company, Agility, which has a humanoid. They’ve been testing it in some of their facilities, having it move boxes around. For a company like Amazon, they have a really, really strong history of deploying robots in their ecommerce warehouses in really valuable and useful ways. What they’re doing with these humanoids I think is exploring how they can slot in for humans in some situations. They have a very cyclical need for workers and sometimes code a humanoid, fit it and do just a simple thing like moving boxes from A to B. But always with robots, the case is how much unstructured, unfamiliar stuff can you get those to do? It’s still limited, so you have to narrow it down.
Kylie Robison: Yeah. You already mentioned Amazon. Some of these companies say they want to address labor shortages in industries where more workers are needed. My first thought as we’re talking about robots at Amazon, they don’t have to pee in cups and unionize.
Michael Calore: Yeah.
Kylie Robison: That’s very nice for them I suppose.
Michael Calore: Very good point.
Kylie Robison: They need more workers in manufacturing, shipping, and logistics. The hope is that these humanoids will take over the more dangerous, less desirable jobs, too. Later this year, Boston Dynamics plans to put its all-electric humanoid robot Atlas to work in a Hyundai factory. The company already has a dog-like robot, Spot, and a warehouse robot, Stretch. What a funny name.
Michael Calore: Nope.
Kylie Robison: Which is already deployed in industrial sites. The Hyundai pilot will be the first time their humanoid is used in a commercial manufacturing setting.
Michael Calore: Yeah. If you watch the videos and the promotional materials, go to the websites of these companies, all of them show robots on the factory floor.
Kylie Robison: Yeah.
Michael Calore: And they show them doing things like putting the bumper cover onto the bumper.
Kylie Robison: Yeah.
Michael Calore: Or taking boxes off of a palette and organizing it onto shelf space based on what’s inside the box.
Kylie Robison: Yeah.
Michael Calore: Because they can scan it with their robot eyes. Which sounds creepy, but that’s also how the world works now.
Kylie Robison: Cool band name, Robot Eyes.
Michael Calore: Indeed. The thing that I think a lot of people think about when they think about humanoid robots is the robot butler. You do see that imagery in some of the promotional materials. Like Figure AI, who we talked about, they have a video of their robot folding the laundry, watering the plants, putting the dog food into the dog food bowl. I think about a future like that where we have robots in our home as a lot further off from the manufacturing and the warehouse environments. Will, I think you’ve hinted at this by talking about the unstructured reality of life that we live in.
Will Knight: That’s right. In any of those demos, you have to ask how much programming, how much preparation did they have to do to have it fill the dog food bowl? The thing that humans do is you go into an unfamiliar home, and can you feed the dog? You have to figure out where the bowl is, and you have to figure out how to pick it up, which is something that robots just struggle with. Anything that’s unstructured like that, it’s going to be much more challenging. I think it is really important to bear in mind when you’re seeing those demos that the implication is that it can do the sort of thing that a human does, but it’s a lot more complicated to do that in the real world. This is why things like manufacturing and ecommerce are going to be much more the environment that you’ll see them come into first.
Michael Calore: I think when you talk about how to program a robot, the old way of programming it would be you show it where the bowl is, you show it how to pick up the bowl, you show it where the dog food is, and you show it where the bowl goes after it’s full. But in the world of AI robotic programming, then there all of those little micro-decisions being made about grip and sensing touch, and the angle that you’re supposed to hold the box at so that the dog food can flow out of it. All of those things are variables that the robot is going to have to figure out on its own, which is probably where the machine intelligence aspect of it comes in. It seems to me, when these companies are talking about, “Yeah, we’re hiring these people to build these things out and turn them into these machines that we can use in our homes,” those are the sorts of targets that they’re looking at, right?
Kylie Robison: I am thinking about just about a decade ago, AI was figuring out whether a dog was a dog and a cat was a cat, and now we’re at this point where can it even feed these animals feels strange.
Will Knight: That’s such a good point. The thing that is interesting right now is that, as difficult as doing a task like that is, we’re starting to see these models which are more general. The idea is you’re going to have something that is eventually something like ChatGPT for language with the physical world, it’ll be so general. You are starting to see these models that in very limited situations can take what they’ve learned to do one thing, and given a new unfamiliar task, do it in a slight more reliable way. This is one of the things that’s creating that excitement. That is still a long way from figuring out a completely unfamiliar kitchen. But we’re starting to see the beginnings of that, which is really exciting I think.
Kylie Robison: Yeah. All technologies have their own challenges, especially the nascent ones like large language models and robots. For large language models, there’s hallucinations, sycophancy. I think the optimistic people that I talk to would argue humans do that, too. But sticking with the technology, what are the challenges for humanoids? What are you keeping your eye on?
Michael Calore: Don’t kill people.
Kylie Robison: Right. Number one.
Michael Calore: Do no harm.
Kylie Robison: We could do a whole segment on that.
Will Knight: Yeah. Well, speaking of that, I’ve written some stories about how, if you take something like hallucinations in language models and then extrapolate to a model operating in the physical world, there’s a lot more potential for it to go wrong. I think these things, there are going to be a little bit of security issues, reliability issues. Generally, developing the models that are going to be more general is not a sure thing. They don’t know if you can get enough data to do that in a general way because we don’t have an internet’s worth of all physical actions that humans do in the real world. That is going to be a real challenge. I think also, it’s really important to remember that the hardware is fundamentally different. We certainly don’t have hands that are capable of as fine control as humans. The idea that you’ll be able to do everything … Some physical things you’ll be able to do way better and that’s always been the case. But there are certain things that the hardware can’t do as well. It is interesting to see companies try to innovate there. Tesla’s doing some interesting stuff, as are others.
Kylie Robison: Is it too spicy to talk about robots and warfare? In a recent podcast with Tucker Carlson, Sam Altman said, “I don’t really understand how the military is using our models.” Tucker really pressed. It’s crazy. He’s like, “Well, I don’t have an army of humanoids.” One time I was doing source reach-outs and someone told me, “I can’t talk, but you’re the first on my list if I find out Sam Altman has an army of humanoid robots at the bottom of Chase Center.” Seriously, we are going toward this future, and we don’t even understand how it’s going to be used in the military. It feels far off, but it’s concerning.
Michael Calore: Yeah.
Will Knight: I’ve done some reporting about AI pre-language models as well as current stuff being used in the defense world. I think one of the things is that actually, those models have to be used in very limited ways because they’re so unreliable. The worry I guess is that people want to rush so much and you have this pressure, this hype, that you start to bend what you’re willing to do. By and large, those systems are too unreliable to be used in a lot of contexts really.
Kylie Robison: Thank God.
Michael Calore: Yeah, thank God. All right. Well, thank you both for bringing your best human selves to this conversation. We’re going to take another break and we’ll come right back. Thank you both for a great conversation. It is now time to go to our very human recommendations. Kylie, you’re our guest in the chair in the studio, so you get to go first.
Kylie Robison: Yay.
Michael Calore: What’s your recommendation?
Kylie Robison: I am addicted to a podcast called Armchair Expert with Dax Shepherd. They interview celebrities and sometimes experts in fields. They did one on hoarding, which was really interesting.
Michael Calore: Whoa.
Kylie Robison: One on multidimensional; we live in a simulation.
Michael Calore: Nope.
Kylie Robison: Very, very creepy stuff. They don’t talk about tech regularly. They are doing celebrity interviews. But recently they’ve been talking about AI a lot more. As a reporter who’s listened to this podcast for years, I’m just screaming at my phone. At one point, they were trying to remember the name of Yoshua Bengio. I’m washing my dishes going, “Yoshua Bengio! Yoshua Bengio!” Then this morning when I was listening as I was getting ready, they were talking about mechanistic interpretability, but they didn’t say that. They were saying, “Didn’t you hear that AI models have this own language that we can’t decipher and they can talk to each other?” I’m once again screaming at my phone. I’m like, “Oh, my God, talk about mechanistic interpretability. Who would care about that but me?” I love that podcast and highly recommend listening, after you listen to Uncanny Valley of course.
Michael Calore: Even with all of its flaws, it’s still a good listen?
Kylie Robison: It has many flaws, many flaws. I think sometimes you hate listen to it. I don’t know how to describe you just violently disagree with some of Dax’s points. Otherwise, I don’t think I would hear those points. I’m in San Francisco. I’m in the AI bubble, and it’s an interesting point of view. But yes, with all of its flaws, it’d still recommend.
Michael Calore: Great. Yeah, it sounds like you’re learning a lot, too.
Kylie Robison: Yeah, that’s true. I didn’t know about the simulation theory, how deep it went and who subscribes to it. Sam Altman in Keach Hagey’s book, he says that he sort of believes in it. I was like, “Oh, how nice, the guy creating the simulation believes he’s God.”
Michael Calore: Yeah. That’s a whole other topic.
Kylie Robison: Yes, it is. Will, what do you recommend?
Will Knight: I’m going to recommend what I think is a genuinely useful home robot. I have a cat, Leono. He’s very nice but will bring in various animals, dead or alive, into our house overnight. Often including large rabbits that then run around.
Kylie Robison: Oh my God.
Will Knight: This cat flap has a camera and computer vision, and it can tell if your cat has a rat or something in it’s mouth. It will say, “Contraband detected,” and then block them from coming in, which I think is a good thing.
Michael Calore: When it sends you a notification that contraband is detected, do you get a photo of what your cat has in its mouth?
Will Knight: Yes. I think you get a video feed of it trying to bring in. There’s some on the website, which are great. There’s one that has a very large frog in its mouth.
Kylie Robison: You got to put a wanted poster with these.
Will Knight: Yes.
Kylie Robison: “Wanted: cat with frog in mouth.”
Will Knight: Yes. I should say it’s called OnlyCat. That’s the name of it. I’m actually genuinely curious how she brings these rabbits into the house.
Kylie Robison: Me, too.
Will Knight: I need to put a camera down there, because they’re enormous. I feel like she has to go through, go back and get it, and then pull it. It must be.
Kylie Robison: Wow. Free rabbit.
Michael Calore: She’s really working hard.
Kylie Robison: Paying her rent.
Will Knight: Yeah. Yeah. Over to you, Mike.
Michael Calore: OK. I would like to recommend merino wool T-shirts.
Kylie Robison: Why? I would imagine merino wool is quite sweaty, actually. I don’t know why I think that.
Michael Calore: No. It’s the opposite.
Kylie Robison: I see.
Michael Calore: We’re in mid-September here in San Francisco, which is also known as actual summer.
Kylie Robison: Yeah.
Michael Calore: It’s very hot, so I’ve been thinking a lot about thermal regulation. I’m also doing a lot of activities, like hiking and canoeing and doing things out in the wilderness and getting sweaty. The merino wool T-shirt has been really great. A bunch of different people make them. My colleague Scott Gilbertson, who works on the WIRED reviews team, has been testing a bunch and he’s written about them. He has a buying guide that gets continuously updated once a season with all the best picks. His favorite is the Proof 72-hour merino wool T-shirt. Mine is the one that Smartwool makes. Merino wool is very, very good at wicking moisture away from your body so it actually keeps you cooler. It keeps you from really feeling sweaty, which is nice. Even if you’re not particularly sweating hard, it’ll just make you feel fresher. And also, it has natural antimicrobial properties.
Kylie Robison: Whoa.
Michael Calore: It doesn’t stink after a couple of days, which is really important if you’re camping.
Kylie Robison: Right.
Michael Calore: I’ve been wearing them on runs. I’ve been wearing synthetic blends a lot, and I’m over those, because those are starting to fall apart. I’m investing in merino wool. They’re quite expensive. They can cost 60, 70, $80 for a good shirt. But you’ll only need a couple-
Kylie Robison: Yeah.
Michael Calore: … because you don’t need to wash them as often. Yeah, they’re great. It’s an old-school innovation.
Kylie Robison: Yeah. It reminds me that merino wool socks are recommended for Burning Man. That makes a lot more sense now.
Michael Calore: Is this where you tell us that you went to Burning Man this year?
Kylie Robison: Oh, God. No, no, no, I never. I don’t know what you’re talking about.
Will Knight: I’m actually wearing a merino wool T-shirt right now.
Kylie Robison: Whoa.
Michael Calore: Are you?
Will Knight: I can endorse. I’ve got an Icebreaker one-
Kylie Robison: #notsponsored.
Will Knight: … which is very nice.
Michael Calore: Yeah. Yeah, this is not spon con. We’re living the real life here. All right. Well, thank you both for being here and talking about robots. We appreciate you very much from the bottoms of our cold mechanical hearts powered by sycophantic LLM models. Will, thank you for coming on the show.
Will Knight: Yeah. Thank you for having me. It’s been great.
Michael Calore: Kylie, thanks for being here.
Kylie Robison: Of course, happy to.
Michael Calore: Thank you for listening to Uncanny Valley. If you liked what you heard today, make sure to follow our show and rate it on your podcast app of choice. If you’d like to get in touch with us with any questions, comments, or show suggestions, you can write to us at uncannyvalley@WIRED.com. Today’s show was produced by Adriana Tapia and Mark Lyda. Amar Lal at Macro Sound mixed this episode. Mark Lyda is our San Francisco studio engineer. Kate Osborn is our executive producer. Katie Drummond is WIRED’s global editorial director. Chris Bannon is Condé Nast’s head of global audio.
