Josh Wolfe: The ChatGPT of Robotics is Coming

24:21They can't tell a narrative. They can't convince people to move across the country and join them. They can't raise capital. And therefore, they're not going to be a great entrepreneur and they're probably better off as a scientist. But when we're making evaluations, it's how much money is going to accomplish what in what period of time and who's going to care. It's like if you were playing poker, you're looking at your hand, you're figuring out how much money do you have to ante up for the next round? And then what's the exogenous? What's the outside view? What's the market going to say? And will they care?

24:45That's why I'm, you know, we talked a little bit about this, but very skeptical about other fields that people are funding right now in fusion or in quantum computing. I'm so skeptical about them in part from hardened cynicism of 20 years of people pitching us things that are always about unbreakable cryptography and femtosecond annealing of this quantum. And I'm like, so what? Most of the things that people promised about unbreakable cryptography or molecular modeling, people are doing. They're just not using quantum computers. They're using GPUs. They're using NVIDIA chips. They're using new algorithms.

25:17So I've been very skeptical about that. I've also been very skeptical about Fusion for the same sort of reason that, to your point, there's like this ignorance arbitrage that people take advantage of. They take advantage of that investors don't fully understand something. It's hot. It's on the front page of a newspaper or magazine or whatever. It's a buzz, and they want to play in it, and so they invest in it. And that's how you get frauds. So we're always looking and basically trying to say, is this academic practitioner commercially minded? They're probably not going to leave their job, so we're only getting them 20 % of their time.

25:46Is the intellectual property real? And then oftentimes, to your point about the papers, there is a high referenceability in papers. A paper that is cited enormously or immensely has a lot more credibility because you have the vainglorious error detection and correction of other scientists who are trying to shoot that person down who has all the fame for doing it, trying to seize that mantle. And so scientists are not a benevolent bunch. They're just as competitive as an investigative journalist trying to break the story or investor or an A &R rep for a music band trying to get there before everybody else.

26:18And we're no different. They're no different. But that's how all this stuff progresses. Talk to us about the robotic arm. I'm going to take the bait. Yeah. The first point I would make is I don't believe in these humanoid robot arms with like fingers and, you know, high dexterity. I think it's a cool parlor trick, but I just, I think we should have robotic arms that look more like Swiss army knives that are swapping and moving stuff. And you can see a bunch of these things online, you know, different tools for different tasks and be able to do them instantaneously. When you zoom out to the industry structure, you've got FANUC, which is a major Japanese player.

26:50They started with industrial robots. They do factory automation, but they've been a key player, probably a$25,$30 billion enterprise value company. You've got ABB, which is Swiss, Swedish, multinational industrial robot arms. Those are most of the things that you would see in like even a Tesla gigafactory or something where Elon's talking about all their automated things. And they've got some of these ABB arms or the other one is KUKA, K-U-K-A, which is a German company. And they were one of the great leaders. They got bought by a Chinese company, I want to say 2016 or 2017. And China's made a bunch of, I think, very smart investments acquiring technologies that were a little bit before their time.

27:28And I think it presents some geopolitical things that probably in five or 10 years, we'll be looking at and saying, my gosh, how is the dominant robot arm supplier or robot body supplier akin to being dependent on TSMC, a Chinese company? So I think there's going to be national robot companies that form in the same way you're starting to see national AI companies form. How does a company like physical intelligence, how are they solving the data problem? Because like you said, there isn't just the equivalent of a robot internet where they can watch millions of hours of a robot arm trying to do something or humans doing something or whatever.

28:08What is the approach by which the token problem is being solved? I would call it the easy way to do it, which is actually quite trivial and hard is doing what people originally did with robotic surgery. So we had a company called Oris Surgical Robotics. We sold it to J &J for$6 billion. It started with surgeons operating these things in like a telerobot. So they had little pinchers on their fingers and, you know, from five feet away or in a totally clean operating room, they were operating, but it was their hands being teletransmitted to the device. And so that is the first way, which is come up with a hundred different tasks, Maybe the highest frequency things like washing dishes, folding clothes, again, unstructured environments, being able to do them in multiple different houses, multiple different heights, multiple different, you know, wet clothes, dry clothes, being able to pour coffee, being able to have the dexterity to open up a K-cup.

29:00I actually don't drink those. I think they're disgusting, but, you know, put it into a coffee machine. I'll drink any of that slop. And there it's an engineer that is operating them. and the movement of compensating for gravity, how much force, how much tension, how much pressure, that's all information. It's information that historically has not been captured. And some of that is then extensible. And this is a really cool thing. You can see some of these robots. You might have five different robots, but they have this amazing thing called transfer learning. You teach one robot a thing and suddenly the other robot, which is disconnected, or it's connected through the internet through it, but it can actually learn what that robot just learned.

29:39perform the task. So that's actually pretty eerie and pretty cool. It's the same sort of thing. Like if I had one robot that saw where I tossed a ball in a room, but three other robots didn't know, they would instantly know because they have the eyes of the first robot. So there's all kinds of training like that. Then there's schematics and drawings. So I was alluding to this before of like Ikea drawings, but once you can take diagrams and schematics and actually use visual language models is something that opening eye is a partner here with physical intelligence and they have pioneered some of that, that's going to be wild too, where you can literally just show an Ikea drawing and the robot goes with a constrained set of limited pieces that they have to put together, a set of screws and wrenches and whatnot, and they can completely assemble whatever it is, a nursery thing or furniture or desk, which I think is going to be pretty wild for people to see too.

30:29That would actually be a major quality of life improvement, not having to put together Ikea furniture. That'd be amazing. Could you talk a little bit more about how robots are learning and I guess like what the different types of learning are or different patterns of learning and what you've seen that's been most promising so far? You have two main categories, or maybe three. You've got supervised learning. So there you've got input data. The robot is learning. It's being corrected. It's being told in some cases, like I described before, whether it's a voice or a gesture or a nudge. Then you have unsupervised learning where the robots are basically training on unstructured data.

31:11They get to discover patterns. They encounter the world. They encounter boundaries, gravity, those kinds of things. and it might be slower in that case, but they're reducing the dimensions for error. There's something that I coined this term, I call it MBTFU, which is mean time between F-ups. But you want that to be as long as possible. If you go back to like the early Roombas, you know, a Roomba would not know if it was cleaning up a spill of chocolate milk or if your dog made a mess and it smears it like Nutella all over your floor, right? You want to increase as long as possible the meantime between, I mean, you know, basically error reduction.

31:48Then you've got reinforcement learning, imitation learning, where you might be controlling the robot or having it mimic you. There's this idea of transfer learning where a single robot learns something, but it can transfer it to different robots or from a different domain. So people are trying lots of different approaches and the more different mechanisms you have, people are then going to figure out, okay, which is the least data intensive or which has the lowest latency and is the quickest or which is the best system for training a robot that, um, you put it in totally unstructured environment and without any training, sort of what they call zero shot learning.

32:23It's able to figure out from prior knowledge. Um, I know that I can't go through that chair. Um, I know that it swivels. I have to turn it this way. I know how much force I need to pick up a general Coke can, uh, and, and those kinds of things. And, and, uh, I think again, it's going to be enlightening of how much we, as we navigate any given minute in our life, take for granted all this intuitive, tacit knowledge that we have about the physical world. And there really is this intuitive physics of how we move around. Robots are going to learn that. Just really simple question. In the next five years, is it plausible that I'll have a robot in my house where I can take all the clothes out of the dryer, drop it into something and have it turned into folded clothes?

33:10And, or if not that, what could be that chat GPT of robots that's right around the corner? Well, one thing which I posited to the team, because I was thinking about exactly that, you know, what would be a really cool thing? I lose stuff in our apartment all the time. We have a bunch of different rooms. I would love to basically say, has anybody seen, which is often what I do to my wife and three kids, has anybody seen my wallet? Has anybody see my glasses. Okay. Just announcing that you can see that a robot with a series of things in your home that would have visual identifiers and visual learning, machine learning to be able to spot objects in a video frame could say, yes, Josh, I know exactly where they are and go and retrieve them.

33:52So fetch and retrieving objects in the home to me would be a pretty cool thing. Where did I put that remote or whatever? And the robot basically knows because they can go through the DVR of the home and they basically know where it is and they can fetch and retrieve it with the right physics. Folding laundry, I don't know how to handicap that. We can do that now pretty crudely. That's the one I want because I have a small New York City apartment. I don't lose things like too much basically. I need that folding robot. What are you going to pay for that though? I don't know. Would you pay five grand, 10 grand for a robot that folded your clothes?

34:25Probably not. So it's got to have a high, that's why most of these things have found their way into industrial uses first. And over time, they'll get cheaper and cheaper, they'll get better and better. But look, I'm one of the few people that have an Amazon Astro. It's a robot that rolls around the house and you can tell it to go into a room. You can put something in the back of it and it can do facial identification for my family. So I can say, where's Quinn or Bodie? And they'll find my younger kids and I can send a message. It annoys the hell out of my wife, but I think it's sort of cool. And every robot that comes out, I'll be an early adopter.

34:59It's really cute. And yeah, we like funding these things.

35:18The Chase Inc. Business Premier Card is made for business owners who make things happen. Designed for high spend and limitless cash back, Inc. Business Premier is a painful card with built-in flexibility. Get the buying power you need to make large purchases, cover unexpected expenses, and help your business grow. Earn a total of 2.5 % cash back on every purchase of$5 ,000 or more. Plus, earn unlimited 2 % cash back on every other purchase, giving you unlimited earned potential to invest cash back into your business, from innovation and technology to everyday expenses. Inc. Business Premier is the only business credit card with 2.5 % cash back on every purchase of$5 ,000 or more.

35:58And it's part of a suite of credit cards from Chase for Business, designed to meet your needs every step along the way. Learn more at chase.com forward slash business card. Chase for Business. Make more of what's yours. Accounts subject to credit approval. Restrictions and limitations apply. Cards are issued by JPMorgan Chase Bank and a member FDIC. Support for the show comes from Public.com. You're thoughtful about where your money goes. You've got your core holdings, some recurring crypto buys, maybe even a few strategic option plays on the side. The point is you're engaged with your investments and public gets that.

36:32That's why they built an investing platform for those who take it seriously. On public, you can put together a multi-asset portfolio for the long haul. Stocks, bonds, options, crypto, it's all there. Plus an industry leading 3.6 % APY, high yield cash account. Switch to the platform built for those who take investing seriously. Go to public.com slash market and earn an uncapped 1 % bonus when you transfer your portfolio. That's public.com slash market. Paid for by Public Investing. All investing involves the risk of loss, including loss of principal. Brokered services for U.S.-listed registered securities, options, and bonds in a self-directed account are offered by Public Investing, Inc., member FINRA, and SIPC.

37:11Crypto trading provided by XeroHash. Complete disclosures available at public.com slash disclosures. So one of the interesting things about the current tech cycle and all this enthusiasm for AI is that so far it's been the big incumbents who seem to be winning here. And part of that is because the capital investment needed is so large. The amount of data needed is so large. When it comes to robotics, would you expect to see a similar thing and then added on to that? Could you have a situation where, you know, if you are a large manufacturer or perhaps you are a company that has a lot of proprietary data, like an insurance company or a financial company or something like that, could you develop your own robotics?

37:58Like, would that be your edge here? And could you potentially just do it yourself? Adam Werner On the first case, if I look at the current landscape, the one company in sort of the big magnificent seven would be Amazon, just because they have already been investing in this for a long time. Jeff Bezos is very passionate about robots. So I think there's a DNA there where doing things that enable them to do the three things that Jeff loves to do historically, which is increased choice and availability, increased convenience for customers and lower prices and factory automation warehouses delivery.

38:34Even when they bought our company for a little over a billion dollars called Zooks that was doing autonomous driving, they have a long-term intention to be able to do 24-7 right-hand turn lanes navigating around the city with a human that's basically delivering last miles kind of stuff. And so I could see Amazon doing that. Microsoft, I don't really see them getting into robotics in a significant way. They have some R &D efforts. Google, and we talked a little bit about this phenomenon, but they are the third or fourth group where we have taken a team out of a big tech. We did it with Google with a company called Osmo to create basically Shazam for smell.

39:08We did it with a bio AI company called Evolutionary Scale out of Meta that's going to be announced more publicly soon. And then we did it with this team out of Google that became physical intelligence between Google, DeepMind, and OpenAI and Stanford and Berkeley. So I think that this is going to be more the startups. The beneficiaries of the money will still be NVIDIA and some of the chip players, some of the hardware providers, because we need the hardware to be able to train the robots. But I really think that this is an open field. And again, just going back to that stat of how many large language models there are for chatbots and how few there are for robots, I think it's a big opportunity.

39:43Now, five years from now, we might have a bubble in robots, but today I think it's a really exciting field. Just on existing AI and competitive advantage. So when I first in the year 2000, I think, encountered Google, I used it. I was like, this is way better than Yahoo or anything else. I never stopped after that again. With chatbots, just going back, not even talking about robotics here, it's like, I got a chat GPT Pro account or whatever early on, and I thought it was pretty cool. And I used it for some stuff. And then the new version of Claude came out from Anthropic, And I was like, oh, this is actually kind of cool.

40:20And I kind of like it more. I don't really know why I like it more, but for some reason I like it more. And I had no problem switching over. Could it be possible that some of these core models do not prove to be as sticky or have as deep moats as people expect? Totally. Inflection, which launched there is called Pi, just wasn't that good. They've now gone to Microsoft. Anthropic, at first, was a little bit behind. They are the most performative model. They're the best performing one. They're the one that I also, like you, use the most. why it's fastest. It has a little bit more. It seems to talk a little better.

40:55Yeah. But here's an interesting thing to your point about, you know, sort of moats and Google early on. Most searches for Google, and remember Google's$280 billion ad revenue generated, you know, enormous. Google, most of those are like five to 10 word searches, right? So you put something in your restaurant in West Village or whatever. Perplexity, which I don't know if you've used. We met the founder early on and we ended up not investing and it was probably a miss for us, but the founder there focused, you know what, I'm going to do the 0.2 % of searches or the 1 % of searches that are longer form where people really want to ask a full question.

41:34I see a lot of people that are using it and it's not coming up with some woke answer or some pithy grok-like response. It's actually a well-researched, footnoted, sources cited response. So to me, the two things that I use most on the chatbot side right now are Claude from Anthropic and perplexity. But in six months, there might be something totally different. And the sheer number of models that are available on open source, who knows what Apple ends up doing here and that being integrated. Siri sucked, but so did Alexa. Amazon's making moves. Apple will too. So yeah. But you zoom out for a second.

42:13All of this stuff in venture, venture is going through this downturn. The one area where there are very high valuations and a lot of money flowing and a lot of talent has been in AI. Therefore, the future returns on these things are going to be lower. That's why we at Lux decided we're going to focus on AI in the physical world, having done all the stuff over the past five years. I would say there's this five-year psychological bias. Everybody wants to be invested today where you should have been five years ago. So Hugging Face and Mosaic, which we sold the Databricks, we were in that five years ago.

42:44Today, we're really interested in biology and robotics and AI's use in those. And then I have a really weird theme, which is so sexy because it's unsexy to me and to others. You understand accounting, vast majority of venture investors and many startup people don't, but you take CapEx. CapEx is made up of two pieces, growth and maintenance. And everybody's been funding growth, growth, growth, growth, growth, investor, growth. So I got interested in maintenance. Why? Because you have trillions of dollars of assets, infrastructure, hospital systems, energy systems, buildings that need to be maintained.

43:19And every generation and every new startup and every new investor always wants to do the new, new thing. It's why we get new music and we get new food and we get new fashion, but there's all these neglected assets. And I think you can apply new technology to maintaining these systems. And so I've become obsessed with this unsexy theme of maintenance, which I think is going to become a hot area over the next few years. Wait, you mean maintenance of physical infrastructure? So the idea that you could have, I don't know, a little robot that goes around your factory or a bunch of highways and sort of surveys it for cracks or things that it thinks needs to be fixed.

43:53Totally. Could be infrastructure for transportation. It could be inside of hospitals where there are road routine things. And it's also oddly, and I know you guys have covered this, but the idea that AI is really coming for the white collar workers. You know, you joke that you could talk about AI and generate a script, you know, based on AI. Oh, no, it wasn't a joke. I'm very serious. They always thought that they were relatively insulated and it was the blue collar workers. But let me tell you, the guy that put me in business, Bill Conway, the founder of the Carlisle Group, he's spending all his philanthropic money or a significant portion of it funding nursing schools.

44:28Why? Because they identified a very high magnitude impact because we have such a shortage of nurses in this country. That's an opportunity for maintenance where robots and technology can play a role. How do you augment and help help nurses? Plumbers, we have a massive shortage of plumbers in this country. And so I actually think that the blue collar workers empowered by technology and maintaining all of these systems around us are actually going to be a winning combination. I want to talk about another aspect of, I guess, AI investing, which is that in the sort of the SaaS wave, the 2010s decade, like compute was very cheap, right?

45:05And so basically like that part, you like plug into AWS and it's sort of, yeah, I know it probably costs them money, but it's not a big lineup or a line item ultimately for a lot of these companies. How does that change in 2024 when you're dealing with an AI company and electricity bills exist or hardware accumulation, depending on where they are in the stack. How as an investor do you think about like the changing? I guess like people talk about like, you know, shifting, you know, having to spend more on CapEx versus OpEx relative to the prior generation of tech startups. How does that play out in the investments you choose?

45:40It's a great question. In the AI world, and then I'll give you the biology world. On the AI side, you know, take open AI. These are all rumored numbers, nothing's fully confirmed, but 2 billion, maybe 3 billion of revenue. I think about 10 million people paying 20 bucks a month or thereabout, and probably 100 million users. I don't know how many of those are unique, but they're not making money on that. They're losing several billion dollars today because you have these upfront costs, big capex, a lot of training. And then you try to maybe do some big enterprise deals. A company like Hugging Face is profitable because they're just hosting it and letting people run inference and then charging and making margin on that kind of stuff.

46:16So that to me is interesting of the people that spent a ton of money and they've got to earn it back. And can you get pricing power by going from 20 bucks a month to 30 bucks a month? And maybe you get that because now you have OpenAI Premium where you have access to, say, Sora for video generation or something like that. So that's going to be a big question on are these profitable investments? Not are they cool, not are they world changing? Absolutely. But are they profitable investments? And look, the market may not care if they're profitable. Market funds all kinds of unprofitable things that they believe in the narrative and the story.

46:46But thinking about fundamental businesses and the economic changes between CapEx and OpEx, I think in AI, it's very hard if you are building out your data centers, trying to do your own training, your own inference, hosting these models. It's very hard. Biology. We will see an AWS moment where instead of you having to be a biotech firm that opens your own wet lab or moves into Alexandria real estate, which specializes in hosting biotech companies in all these different regions approximate to academic research centers, you will be able to just take your experiment and upload it to the cloud where there are cloud-based robotic labs.

47:23We funded some of these. There's one company called Stratios. There's a ton that are going to come on Wave. And this is exciting because you can be a scientist on the beach in the Bahamas, pull up your iPad, run an experiment. The robots are performing 90 % of the activity of pouring something from a beaker into another, running a centrifuge. And then the data that comes off of that. And this is the really cool part. Then the robot and the machines will actually say to you, hey, do you want to run this experiment but change these four parameters or these variables? And you just click a button, yes, as though it's reverse prompting you.

47:55And then you run another experiment. So the implication here is that the boost in productivity for science, for generation of truth, of new information, of new knowledge, that to me is the most exciting thing. And the companies that capture that, forget about the societal dividend, I think are going to make a lot of money. Yeah, this actually reminds me of the conversation that we had regarding snack food innovation and this idea that you can use a sort of factorial like simulation just to run new processes through your factory and see how they would actually work out and what the supply chain might look like.

48:30But not to give in to my five-year bias too much and overly focus on chat GPT, but where are we in terms of context window expansion? Because this is something we spoke about with you last year. And I think for a lot of people, it's probably one of the overriding annoyances with something like chat GPT, the fact that you can't actually copy and paste that much text into it and that you are limited in terms of the output that it actually gives you. Have there been major advancements since we last spoke to you? Well, the Claw 3 is one of the largest. And then you've got all kinds of interesting collaborations.

49:06You've got NVIDIA and Microsoft did one with a huge number of tokens. You've got A121 Labs that has this thing called Jurassic. Again, a lot of people are making headway here. But we are, I think, a year away from you being able to upload hundreds of PDFs, thousands of books, if they're not already immediately referenceable, and be able to detect pattern change amongst documents, summarize and unearth the entirety of key concepts. And then I think the most valuable thing will be it prompting you to say, here's a question you didn't ask about all these documents that you just uploaded. So yeah, I think we're just keep increasing the context window.

49:50But that said, most of the history of innovation is just like, keep increasing this factor. And then somebody else comes along and invent something. It's like that factor doesn't matter anymore. You know, my favorite example of this is like sailboats. You find these sailing ships back in the day, they just kept adding more and more sales. Like these things started to look ridiculous, you know, and then somebody invents the electric motor and you have a motorboat. So I think we'll have the same sort of thing. And then people will figure out, hey, there's a better architecture here than just constantly increasing the context window.

50:23And some of that might be with memory retrieval, being able to reference other models and just go into the archive of what they have. So yeah, that's going to keep expanding. Josh Wolf, Lux Capital, thank you so much for coming back on OddLots. Always great to get an update on what you're interested in. It was great to be with you guys. Yeah. Good. You're preparing already to ingratiate yourself and to blend in with the humanoid. Thank you so much. That was fantastic.

51:06First of all, Tracy, I really like talking to Josh and always like getting an update. I I really do want the folding, the clothes folding robot though. Like I actually think that's a really big deal and would make almost everyone's life better if they didn't have to worry about folding clothes. I agree. It would be far more useful to have something doing physical tasks like folding laundry versus telling you where your lost room is in your house. That's what I want. I need that folding robot. I mean, I will say I know everyone likes to make fun of Alexa as well. Yeah. But our house, we've kitted out all the lights on.

51:41They're all those smart bulbs because we don't have any overhead wiring. So like everything has to be lamps. So if you didn't have a robot that was able to turn on all of your appliances at once in a room, it would be incredibly annoying because you would be going from lamp to lamp to lamp. So it does make a difference in my daily life, at least. I mean, there's so much to pull out from that. So one thing that I thought was interesting from an industrial policy perspective was Josh's discussion of some of the robotic capabilities being developed in places like China and the idea that we might have another chips semiconductor like situation on our hands where we wake up in 10 years and realize that a primary component of robotics is being built much more efficiently and cheaply elsewhere outside of the US or the West.

52:32And then the other thing I thought was interesting was the idea of leapfrogging, right? So I think a lot of people, myself included, when we think of technological advances, it's like, can this do this thing slightly faster? Can it do it on a slightly larger scale to the point about the context window and expansion there? But you can leapfrog in technology, as Josh was saying, and you can go from the sailboat to the motorboat or you could bypass human evolution, for instance. And instead of having humanoid robots, you could have a Edward Scissorhands like thing with a Swiss Army knife on the end of his arm.

53:11Yeah, that made a ton of sense to me, which is like if you're like starting from scratch, like it's not obvious that the human form that was, you know, developed over millions of years through evolution is necessarily the thing you want to create or recreate to do various tasks that you need. There was a lot in there that I like. The thing that he would talk about at the end, it sort of sounded like cloud kitchens, but for biology labs. So if you just have all the robots do it and then they can prompt you for other ideas, that's interesting. It does seem exciting, the idea of ways to accumulate training data for these sort of like, you know, that you could maybe solve the mechanical engineering.

53:50But without, you know, there's no equivalent of like all of the text on Reddit or Wikipedia or whatever that change or, you know, Google Books or YouTube. So like having to recreate that as a bottleneck for building robots was really interesting. I love the term he used. I think it was ignorance arbitrage. Yeah. Which is a really great term. So it's like, yeah, like when a lot of like pure science spaces, you're going to get investors who are willing to throw money at someone who just like has a really good idea on paper because that person is smart. Well, I think this is also the really unusual thing about this particular cycle, which is the dominance of the incumbents and the fact that on the one hand, you do have a bunch of open source software.

54:31And to some extent, you can take something off of a repository and you can pitch it to investors and say this is the next big thing. And they might not have the technological expertise to actually evaluate that. But when it comes to making actual advancements in something like robotics, it does feel like you have to have an edge in one respect or another. You either have to have the capital to deploy or you have to have access to that data. So I don't know. I guess we'll see how it shakes out. I guess we'll have Josh back next year. Yeah, next year. Next spring or summer to see what the next big thing is then.

55:06Hopefully he can bring a robot with him of some sort. A folding robot. Yeah. All right. Shall we leave it there? Let's leave it there. This has been another episode of the Oddbots podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Jill Weisenthal. You can follow me at The Stalwart. Follow our guest, Josh Wolf. He's at Wolf Josh. Follow our producers, Carmen Rodriguez at Carmen Ehrman, Dashiell Bennett at Dashbot, and Kale Brooks at Kale Brooks. Thank you to our producer, Moses Andam. For more Oddbots content, go to bloomberg.com slash oddbots, where we have transcripts, a blog, and a newsletter.

55:39And if you want to chat about all of these topics, including AI and robotics. There's a room in that in the OddLots Discord chat room, discord.gg slash oddlots. Go check it out. And if you enjoy OddLots, if you want us to crowdsource buying a Unitree humanoid robot or something similar. On AliExpress. That's right. Then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is connect your Bloomberg subscription with Apple Podcasts. Thanks for listening.

56:46We buy insurance for peace of mind, but every year millions of claims are denied. Not because people did anything wrong, but because their policies quietly excluded what happened. Insurers know every detail. Policyholders rarely do. That's why My Policy Advocate exists. For just 27 cents a day, their platform reads your policies and explains where you are vulnerable. They don't sell insurance. They deliver transparency. Before you trust your policy to protect you, let My Policy Advocate tell you what it really says. Go to MyPolicyAdvocate.com. In business, a thoughtful gift does more than say thank you.

57:20It recognizes achievement, builds loyalty, and shows someone they are genuinely valued. With 4imprint, you can choose from thousands of high-quality products, apparel, drinkware, tech, and more, designed to leave a lasting impression. And with expert support, dependable service, and their 360-degree guarantee, your gift will arrive exactly as intended, on time and on brand. Explore gifting with purpose at 4imprint.com. 4imprint, for certain.