Antioch lands Amazon Ring as a customer to validate its hybrid simulation approach for training physical AI systems

Speaker 2: And we have our next guest, Harry from Antioch. He was supposed to be on earlier. We brought him to the end of the show, but we're very excited to be joined by Harry, the cofounder and CEO of Antioch. Welcome to the show. Sorry for the switch up on the scheduling. Thank you for being flexible. How are you doing? What's going on?

Speaker 14: Doing great. Thank you guys so much for having me. I'm excited to to close this out with you both. Yes. Amazing. Excited to have you here. Please,

Speaker 2: we'll we'll get to the news, but I wanna I wanna understand how you got into the business of training robots in simulation. A bunch of questions about sim to real gap and all of this stuff, what's going on in the broader ecosystem. But where have you been focused? Where are you focused now?

Speaker 14: Yeah. A 100%. So our cofounding team at Antioch all met at Stanford working on physical AI, applied AI. Mhmm. I spent some time at the autopilot team at Tesla as well Mhmm. And then kind of went into a bit of a company building mode with many of the same team that we have today. Sure. And so I think, you know, our observation, really, looking at the industries that have kind of moved to the fastest over the last couple of years is that these are the industries that have sort of unlocked recursive self improvement. Right? Like, earlier today, talked about. Yep. Exactly. Math. Navia Stokes. Scott was on. I think, you know, Cognition and Devon have done a fantastic job of this Yep. In the world of software. And I think our observation is that, you know, automating the physical world is really the defining economic opportunity of our time. Right? You look at the GDP that's tied up in that opportunity, but we don't have that recursive self improvement. Mhmm. And so, really, at Antioch, everything that we do is built around this idea

Speaker 2: of how we unlock RSI, how we unlock goal mode, you know, for physical autonomous systems. So, yeah, how do you do that? I mean, I don't know what the status quo is. We saw a bunch of people using Blender. I know you can make an inverse kinematic model in Blender. We have Unreal Engine. You can do some simulation stuff. It I I I can play a video game and watch a robot walk around. It looks like you could learn from that, but clearly, you need to go deeper. So what's missing from just build your robot in Unreal Engine, have it press a bunch of the keys until it learns to walk around?

Speaker 14: Yeah. It's a great question. So I think, you know, broadly, the the the the market today is a spectrum defined by two ends. One end is kind of exactly what you're describing. So it's these classical, you know, like, almost video game like engines with with really high fidelity. And on the other end, I think we're we're seeing a really promising landscape of world models kind of come to the fore. Right? And I think our observation at Antioch is that both of those approaches today experience a reasonably substantial sim to real gap. Right? There's a fidelity issue that means that if you're relying on one of those approaches in a singular sense, you're gonna be missing some of the real stuff about the real world, and you're gonna experience a bit of a rough landing in reality. So our view is that, you know, the reason why that is in the the sort of, like, classical world of video game style simulation is it's just really hard to encode everything about the real world in software. Right? Like, you go out to the real world, you find out, hey. Actually, the wind matters now. Now I need to add wind into my simulation. And it's this long tail of whack a mole. Sure. Whereas on sort of, like, end to end learn side, you actually experience a very similar thing, but it's about a data accumulation strategy. Mhmm. And in the world of physical AI, we don't have infinite data, not even close. And so, you know, we believe that these world models are going to be the right approach, but we just need to kind of play the game on the table right now to actually help, you know, real companies building real things in the present day. And so our approach is a bit of a hybrid, where we use that classical simulation where it works, and we kind of learn the gaps. We learn where it doesn't. And it's helping us sort of move our customers along, you know, that spectrum essentially, eventually towards these world models. You know, we've seen some incredible launches from World Labs and others, and and that's an incredibly exciting future. We think that's where the puck is headed. Mhmm. But we need to kind of, like, help shift real companies in that direction over time as the technology car becomes completely ready.

Speaker 1: Talk about your various robotic timelines Mhmm. On on the autonomous driving side every time I've asked any automotive technology executive.

Speaker 2: Someone someone came on the show and said, like, 2050.

Speaker 1: No. It's more like 2040 for a major supplier of of technology for pretty much every car. I'm not gonna say too much because people will guess it, but he he basically wouldn't give an answer. He was like, may maybe we might You have people on the show that think we're getting Dyson sphere before this guy thinks we're getting full self driving cars.

Speaker 2: That's the range of predictions we're dealing with on this show. Yeah. And so that's a category where, like, we see

Speaker 1: close to full self driving already Yeah. With with, you know, Tesla and Waymo and all these things. So the technology actually exists and you have industry executives saying, like, yeah, it's gonna give give me fifteen years minimum. But I'm curious for you from your lens, I I expect a lot of this stuff to come, you know, real advancements to come from, you know, new companies, and and I'm curious your view. Mhmm.

Speaker 14: Yeah. I mean, I think now it all comes down to the sort of data flywheel. Right? And so I think the advantage that autonomous driving has by the way, don't think it's it's 2050. Right? I mean, I think we've we've seen Tesla and and Waymo and also companies like Wave actually now doing these deployments that work extremely well. Totally. And so the reason why is these companies have built this incredible data accumulation flywheel. Right? So Tesla is particularly brilliant because if you buy one of those cars, you're essentially paying to help them train the system and get kind of more and more data into that engine. And I think now we've kind of got good line of sight on architecturally, what do the models need to look like that sort of unlock that autonomous future? And so bringing that to new industries is really just a function of, are you able to get the data at the scale and also in the kind of right categories, not only when things work well, but also really importantly when things are not working well. Yeah. And so I think that timeline is gonna be purely a question of how how quickly can you build that flywheel. We've obviously got a bit of a chicken and egg kind of scenario in the world of robotics, in the world of, you know, industrial automation and these types of things where deployments are a little bit nascent. And so that's kind of really one of our key bets at Antioch is, like, if you wanna bootstrap that incredible opportunity, you need to figure out a way to be much more sample efficient than we are today. And so, again, that's our hybrid simulation approach. Right? That's like we can take a small amount of data from the real world, use that to train improvements to simulation. That simulation then trains a better version of the robot in the physical world, you can scale your deployments faster, and it becomes this kind of virtuous

Speaker 1: flywheel effectively. I can't wait for robotics companies to be,

Speaker 2: playing the benchmark game. Right? Instead of the Pelican, it'll be like Mona leap Mona leap. Juggle five balls. Yeah. Yeah. Six balls. Juggle seven balls. Juggle eight balls. Mona Lisa bench. And we'll be like, it still can't make me a good sandwich.

Speaker 1: Yeah. 100%. Yeah.

Speaker 2: It put the mustard on the top, and I like it on the bottom.

Speaker 14: It's not here. Possibilities are endless. Right? I mean, we saw this with the with micro duck in the last couple of weeks too. Right? Like What's micro duck? What is this? Ridiculous benchmarks of ducks, you know, balancing balls and and and things like that. We're gonna see a proliferation and explosion of this. How many customers are in your TAM? Because we've had a few humanoid robotics companies. There's, like, a few names.

Speaker 2: Obviously, there's the self driving car companies. There's some industrial stuff that's happening. But, like, are you in if you in the absolute top of your CRM, are we talking about, like, a 100 companies? Are there thousands of these companies? Like, I can imagine building a great business selling to 20

Speaker 14: robotics winners. Right? But, like, how how big is this market, and where is it going? Yeah. It's it's a great question. So I think a couple of responses to that. So so one is, like, even present day, it's massive. Like, we're talking tens of thousands of companies. There's all the ones that you'd think about. Right? So the humanoid type companies self driving all the same kind of stuff that you described. But for example, today, we we announced our partnership with with Amazon, and in particular, the Ring team at Amazon. Right? And so this is a smart security device. And so, you know, essentially, it's any system that has this hardware, software, machine learning component where testing in the real world is really difficult and really expensive, and you need to kinda get that recursive self improvement flywheel. Sure. And so I think it's there are a ton of companies here that you probably wouldn't typically think about as being physical AI, but really are. Yeah. Everything from a robotic vacuum cleaner to, like, robotic drone, camera drone, sports. Like, there's there's just gonna be robotic pieces

Speaker 2: even if it's not a robot, a humanoid in every category. There will be something that benefits from this. Fascinating. Well, a great what a great industry to be in. Very excited. So glad that you have some fresh funding and good luck with the journey. We'll talk to you soon. Yeah. Great to meet you, Harry. Amazing. Come back on soon. Thanks, guys. Have a great rest of day. Appreciate Goodbye. Up next, we have a surprise guest, Rohan, who connected GPT six Astra to a wearable he built for back pain, and it's giving him real time back pain physical therapy. We're gonna bring him in. I saw some people in the chat asking, chanting for Rohan.

Speaker 1: See if he can handle foot pain because I destroyed my foot surfing this weekend. What's going on, Rohan? How are you doing? Hey. How are doing, guys? Nice to meet you guys. How are doing? Yeah. Great to meet you. Glad to have you pop on here. Busy busy day in the in the world of Yeah. Of technology, but you managed to break through. Yes. Huge. Yesterday. Here we are. Little Sunday. Ripper. Yeah.

Speaker 2: Absolutely. Yeah. Breaking us down. What was your process? How long did this take? What was your goal? Set the stage for us. Yeah. Yeah. For sure. So, like,

Speaker 8: actually goes back to those 13, had back pain for like a decade in and out of care. Didn't do a PT, but like, you know, like PT doesn't have the kind of classic modern like measure, like measure again. It's just like kind of looking at and they're like, all right, here's some exercises. You know, I'll see you next. Let's see how it goes.

Speaker 4: I'm back then at uni, decided, this is this is not it. Moved to a childhood bedroom, built something, balance, like, thread on Twitter saying, who's running angel checks for hardware? Just DM'd everyone.

Speaker 8: New from Boomsubersonic actually got back to me, got on the call. First call I ever kind of took. He brought me a check. I was like, oh, wow. Okay. This is something. That's me. Moved to China.

Speaker 1: And basically, he just took that check to China, just slept in the back of Shenzhen and smoked Hey. We met. We've we've met. You I just realized that we we met before. Yeah. Sorry. I I I didn't put it together. We we had a we had a phone call. We had a phone call. Yeah. Will Will was Will introduced me and was like, you gotta meet this guy. Crazy. He's living in China. He's solving something solving back pain. That's amazing. Okay. Very Chinese time in my life. Was Yeah. Yeah. Smoking

Speaker 4: cigarettes, I'm sure. I I highly recommend it. I know.

Speaker 2: With AI, we will invent the smart cigarette that tells you exactly It's not actually I saw a

Speaker 8: saw a LinkedIn cigarette somewhere, which is I think hilarious. Wow.

Speaker 2: Anyway, sidetrack.

Speaker 8: Anyway, sidetrack sidetrack. So, yeah, build this MVP, shared it on Reddit, got like, you know, really, really good reception there, met our first customer, that's me now, there we go. And yeah, basically took that, came to the valley, put together some money and like, was like, okay, like, let me try build this thing. And it was like really kind of it's it's just me, one man team. Like taught myself how to build hardware, moved to China, build all myself, same with the code. And it's like, it's just honestly nice to see the fruits of my labors. I don't know people like, people have been coming at like, it texts me, they'll be like, okay, I've had back pain, I've had ankylospondylitis, other congenital condition, how can that be used like for their conditions? It's just really interesting because I'm like, okay, well, this is great. I'm really just kind of humbled by it and really want to double down and really kind of like bring this out to the world and hopefully help many, many more people in just about that. So quick timeline. You spent a long time actually building this wearable, getting it manufactured.

Speaker 2: So you effectively had a data feed, maybe an API, something spitting out data. And then over the weekend, you visualized it in this particular way and and wired up a three d model to the data that you were already collecting. But the plan is to sell the device that collects the data that then will be enabled by AI and everything else.

Speaker 8: Yeah. Basically, it's yeah. Like, it it very fundamentally, yes. It's like for consumers. It's for people with back pain to understand the road. Because like, you, Johnny, you said you you have foot pain. Hopefully, your PT works out, but it's like, wouldn't be nice that if you go to one or two or three PTs, you have like a ground. Like okay like my foot's getting better or it's not, but this PT is working for me and these two aren't. That's what I want to give people with back pain care, probably in back pain care. Yeah and then the kind of greater take there is really I think that you know with what these models can do now, we can build pretty much whatever we want on the software side and really the data, if the data collection that really rules, because like, okay, what does the AI need to make intelligent decisions about my body? Like we'll have an AI in our pocket, but it needs to understand like what's going on with our body right now. And I'm really excited to like kind of bring out this form factor of patches that you stick to your body, because I think that that can really nicely scale. Like we've already done wrists and fingers if you think about it. And like now heads are kind of coming to get up, coming to play. We can cover the rest of the body and, like, kind of basically collect every biomarker that we might need for our AIBCPs,

Speaker 2: make great decisions about our health day to day. What's the plan to sell a lot of these? I could imagine everything from Facebook ads to working to infomercials and late night That's a great question. Infomercials or Shark Tank? No. On infomercials, smoking Chinese cigarettes. No. I mean, like, there's a lot of different ways to you can get reviewers and sponsored podcasts. Ironic sense. Infomercials would rip, actually. Right? I mean, you're sitting there. You're right. Your back might be hurting. May maybe. And I bet you the inventory is really cheap nowadays.

Speaker 8: I mean, yeah. And then there was a live setting. It's like infomercials before. Sure. Sure. Sure. Yeah. Like candidly, like one of my investors, Justin Mares, wrote his great book, if you know,

Speaker 4: and I've like basically over the, this is actually how this came about. Kind of our moment. It's a broth guy, right? Amarang, sorry. That's broth guy. We

Speaker 8: love Joss. He's a good friend. If anyone knows anything about selling a lot of like a product, every miss a book themselves, right? And basically just been hammering out like a process of like, Hey, let's just see what channel works. Sure. Doing a bit of agency and like just testing out, tried page. I was like, okay, let's now try Twitter. This is actually the results of me trying Twitter. Yeah. Which is kind of like snowboarding and like, we'll just see how far it goes and really make sure that we are where our customers want us to be.

Speaker 2: So people are joining the wait list at yourbackhurts.com. Give me a timeline. When can people actually buy this? Are you gonna take are you gonna take deposits and then ship, or do you wanna go straight to order and then ship? Is this going to be the Tesla Roadster of back pain?

Speaker 8: Good question. I think we're aiming to ship January, the start of January. We'd like to

Speaker 4: make it a lot more affordable than Tesla Roadster.

Speaker 8: And I think we can. We've done like I spent so long building this. Like, I've done with a lot of engineering in the back to actually just shove down our unit economics. Cool. To basically we've I'm trying to I'm trying everything I can to jog less, basically, and still make it work for people. So, yeah, like, aim to ship in in January. Sign up. Your, yourbackhurts.com. And Good domain. We'll basically and we'll open the wait list to our first customers.

Speaker 2: Very cool.

Speaker 1: Well, great to great to see you face to face. Yeah. And come back on. Yeah. Come back on. We'll talk to you soon. Yeah.

Speaker 2: Alright. Take it easy. Have a good one. Thanks, Rohan. We have the first results of Waldo bench. Tyler put them together. Let's see. Let's see how ChatGPT images 2.5 is doing. What'd you make, Tyler? You made OpenAI Dev Day. Wait. Actually, this is sort of hard. I it looks good, but I can't find Waldo. I need to zoom in. Where is Waldo? Is there only one Waldo here? Yeah. Okay. I found him. Yeah. It needs to be a little bit more detailed when I zoom way in. If I zoom way in, faces start getting garbled. But, man, some of the zoomed in text is really, really good. It's pretty high fidelity, I think. We're getting close. I want to see this paired with Astra tiled, a lot more reasoning put into it. Let's finally solve Waldo Bench. I think it's possible with modern AI. What do you think?

Speaker 1: Yeah. I can't find Waldo.

Speaker 2: So You gave up? That seems like a good You gave up? I took a quick look. What about in the second one? The second Honestly, one was getting distracted by the beach one. The beach one? Pull up the beach one. Everyone can take a take a quick gander, try and find Waldo. I think this one's pretty easy. Alright. Close Close it. You only had five seconds. Okay. You fell. No. I think Yeah. This one is dense enough. Yeah. Too easy. It's too easy. This one's too easy. This is about one quarter of a real Waldo. For the real Waldo heads out there, they're not they're not gonna be like, this is not this is not soda. Actually, I guess it is state of the art, but it's not super intelligence for Waldo generation. It's pretty good, though. Looks pretty good. I like it. And then what what is this animation?

Speaker 5: Explain the animation that you shared. Yes. This is like stop motion animation, but this is with the new image model. Okay. And I just had to ask her to make it into a video. Oh, cool. So the it's much more consistent. That's like one of the big Consistency? Big improvements. Very fun.

Speaker 2: Well, I look forward to getting Jordy sending me AI images at 3AM when he's vibe designing the next piece of great furniture or something like that. No. It's a lot of fun. Anyway, there are there are other stories.

Speaker 1: And we're gonna get to them tomorrow. Tomorrow? But I'm glad we cracked the fourth hour. We're in the fourth hour. Yes. It's been a while. In the fourth hour. Been a while.

Speaker 2: We got through summer. We did it. We did it. Summer's famously a little slow news day, slow news season. We're back. It's it's September. We're going the fourth hour. Get ready. Hundred and eight days until Christmas.

Speaker 1: Fifth hour. I got a text from a buddy Yeah? Who saw or sorry, a hundred and seven days. He said he already got his tree up. I can't tell if he's messing with me, but but but I but I'm just gonna pretend that that If you put up your Christmas tree a hundred and seven days early,

Speaker 2: you you have some serious botany to do. Like, you need to keep that thing alive. You gotta be watering that thing regularly. There's a lot going on. Like, I don't know that a Christmas tree is meant to survive a hundred and seven days. It's sort of like a new level of challenge. Just say you haven't cut a hole in your floor and gone down

Speaker 1: to the dirt. True. That's you constantly are growing a tree, so you're cutting it and pruning it, whatever. But it's just Yeah. A living tree and you're happy to say you haven't done that. There are some houses that have little tree areas inside the home. Maybe maybe this is the future Christmas home. Design your entire house around

Speaker 2: around being able to grow grow Christmas tree constantly on a never ending cycle. Anyway, thank you for tuning into TBP, and we will be back tomorrow at 11AM sharp.

Speaker 11: Growing flashback.

Speaker 2: Five stars in Apple Podcast and Spotify.