This 22-Year-Old Built TikTok for Mobile Games, and It's Growing Fast | E2276

This Week In Startups is made possible by: LinkedIn Jobs - LinkedIn.com/twist Every.io - every.io Render - render.com/twist Today's show: *Imagine scrolling through mobile games the way your flip through your TikTok feed. What if you could build your own game in 90 seconds and share it with the world? That's what over 100,000 users are enjoying right now on Nanogram. On TWiST, we meet the co-founders: 22-year-old CEO Albert Brotherton and 24-year-old CTO Boris Radilov. They demo the app for us on the air, and Jason immediately spies the potential. Power users are playing 25+ games per session already. We're digging into the co-founders future plans, including the potential for ads in your game feed, in this TWiST exclusive. PLUS domestic helper bots are here. NEO from 1X Technologies can do the laundry, open the door for guests, and even read and understand Post-It Note messages. Alex talks with CEO Bernt Børnich about building the safety-first 66-lb humanoid, why world models are so crucial for training robots in particular, and why homes are even tougher places for robots to navigate than factory floors. Timestamps:0:59 Nanogram co-creators Albert and Boris join the show 3:42 Nanogram is TikTok for casual AI-generated games 4:27 Building Nanogram with Google Gemini 5:49 Draper and Associates: https://www.draper.vc 6:11 Integrating ads into the game feed 7:36 Roblox: https://www.roblox.com World of Warcraft: https://worldofwarcraft.blizzard.com 8:25 Building in New York 9:07 Jason is obsessed with the Staples Baddie https://www.tiktok.com/@blivxx 10:12 LinkedIn Jobs - Hire right, the first time. Post your first job and get $100 off towards your job post at https://LinkedIn.com/twist 20:36 Every.io - For all of your incorporation, banking, payroll, benefits, accounting, taxes or other back-office administration needs, visit https://every.io 21:40 1X CEO and founder Bernt Børnich joins the show https://x.com/BerntBornich 22:31 Designing robots to actually live around people 26:30 Teaching NEO about movement in the physical world 29:00 TechCrunch coverage: https://techcrunch.com/2026/01/13/neo-humanoid-maker-1x-releases-world-model-to-help-bots-learn-what-they-see/ 29:58 Render: Find out why 5 million developers are already using the all-in-one cloud platform, Render. Go to https://render.com/twist and apply for the Render Startup Program to get $500-$100,000 in free credits, depending on your stage and backers. 31:04 Moving beyond training data to true general intelligence 33:17 Ensuring NEO pursues the safest possible path 34:19 Why are world models so important? 41:51 1X World Model Challenge on GitHub: https://github.com/1x-technologies/1xgpt 45:14 Robots need to train on a lot of data… Where does it all come from? 50:14 Manufacturing NEO from raw materials in San Carlos 56:32 Robots building robots Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Check out all our partner offers: https://partners.launch.co/ Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Welcome back to This Week in Startup. With me, Lon Harris. I'm Jason Calacanis.

• 0:03Speaker 01

  This Week in Startups is brought to you by Render. Find out why 5 million developers are already using the all-in-one cloud platform, Render. Go to render.com/twist and apply for the Render Startup Program to get $500 to $100,000 in free credits, depending on your stage and backers. Every.io for all your incorporation, banking, payroll, benefits, accounting, taxes, or other back-office administration needs, visit every.io. And LinkedIn Jobs. Hire right the first time. Post your first job and get $100 off towards your job post at LinkedIn.com/twist.

• 0:42Speaker 00

  We've got Albert Brotherton and Boris Radulov. Jason, they're the co-creators of Nanogram. I really like this. This is a cool thing. It's basically TikTok for mobile video games. It's available right now. It's in the Google Play Store. It's in the iOS Store. Guys, thanks so much for being here.

• 0:58Speaker 02

  Thanks for having us, guys. So tell us what you're working on. Yeah, Boris, do you want to kind of bring up the demo and walk them through it?

• 1:03Speaker 03

  Yes, yes. Just give me one second to get my screen sharing on.

• 1:07Speaker 00

  I have it on my phone, Jason. It's available now. It's basically work. It's like, think TikTok, but people can create their own video games very quickly, like casual mobile games. And then you just scroll. When you're tired of playing one, you just scroll up and you go to the next game. It's pretty remarkable.

• 1:23Speaker 03

  Typical TikTok feed, this is the MVP we put out a month and a half ago. You're playing a game, you get bored, and you're going to the next game. You play this game, you get bored, you're going to the next one. It's a very simple concept, proving to be quite interesting. But the whole brain-rob maxing of playing multiple games in a row is just part of the flywheel. The second half of the flywheel is the creation aspect.

• 1:44Speaker 00

  When you're looking for a new game to play, it can be that complete tyranny of choice, like choice paralysis. Like, there's hundreds of games. I don't have time to research every single video game. This is just like, open your phone, start playing a game.

• 1:56Speaker 03

  The creation is even cooler. If you go on your Create tab, you put Create with AI, and you have all these templates. You could go off, or you could do a custom template. I'm just going to quickly demo something real quick for you. So when I was like 14, I started programming because I wanted to make video games. And you know, now with AI and agents, something that would have taken me a week back then now takes 90 seconds. So for example, if I want to make like a 3D version of Flappy Bird, all I have to do is go on 3D. Right Flappy Bird, hit generate and now it's gonna cook up the game for me. And what's really cool is while you wait for the agent, you get to play other games that other people have made for inspiration. Uh, and usually on the first prompt, it takes about 60 to 90 seconds to like one shot a full game.

• 2:42Speaker 03

  Uh, and after that, the cool part is that you're absolutely free to remix it. I'm having a hard time playing and talking here at the same time. I have to forgive my poor performance. But uh, the idea, yeah, the idea is uh, after that, you can remix the game as many times as you want. Uh, and we'll just probably get that shortly. Uh, additionally, you can remix other people's games. So for example, if I make like a really cool game, I send it to Jason here. And he can remix it, change it in a way that he likes. So here is the 3D Flappy Bird that I made for us. Uh, and this is very basic. This is like a starting point for us and I get to play this. And as a little kid, uh, I, I, this would have taken me a few days to figure out before the age of AI.

• 3:27Speaker 03

  But interesting enough, I can now do this in like 90 seconds and I can go prompt it and I can say, "Hey, can you please add guns to this?"

• 3:40Speaker 00

  I like that you said please, very polite.

• 3:42Jason Calacanis?

  So this is all just AI prompting to make games. What's the engine behind it? We use this Claude or something, or is this like some bespoke AI engine to make games?

• 3:53Speaker 03

  What we're doing is we've created a custom game engine and then we're using Gemini. Uh, and a bunch of agentic harnesses and like tool calling to give it the ability to create 3D assets, create 2D assets out of like pixel art. And, and things like this. Basically what we've created here is we're trying to rely on the native platforms as much as possible. So we're really relying on Gemini to do the tool calling for us. And then we're putting a bunch of tools such as like generate 3D mesh, generate uh, 2D pixel art, generate sound, things like this. And then it figures out how to code these, put it in the game and, and do all sort of stuff for the user.

• 4:27Jason Calacanis?

  Genius. Can I take Lon's game and build off of it and fork it?

• 4:32Speaker 03

  Yep, yep, yep.

• 4:33Speaker 02

  So you can take, like Lon can post like, you know, uh, a Flappy Bird and you can say, "I want to add guns to this." You could send a picture. Well, I mean, at the moment we don't have.

• 4:41Jason Calacanis?

  Okay. So that's inherent in it is you just fork stuff.

• 4:44Speaker 00

  It's like the remixing.

• 4:45Jason Calacanis?

  It's remixing, yeah.

• 4:46Speaker 00

  Yeah, it's like the remixing on Sora where I could see your video and I could say, "Make them say this instead." And it's just like everybody's idea builds on everybody else's idea. Yeah.

• 4:53Jason Calacanis?

  What's the business model here and who are you two dudes?

• 4:58Speaker 02

  So I'm Albert, this is Boris. I'm the CEO, he's CTO. And yeah, I mean, the business model is, I mean, look, at the end of the day, we're like a feed of interactive content. Right? That's, that's like, I'm not even saying games because that's the direction we're moving in, right? Interactive content. And, you know, we have a lot of rep experiments we want to do, right? Because this is a very new field. We can't know exactly what it's going to be, right? But I think it's a very interesting space where, you know, there is no accessible interactive ad space that's really easily distributed right now. Right? So I'll use an example here. So let's take like a big advertising provider like Domino's, right? And they wanna, you know, do some easy ad space stuff. They're gonna go on short form content, right? And they'll have a marketing team that's great at short form content.

• 5:42Speaker 02

  They'll post on Instagram, they'll post on TikTok, right? But, you know, at the end of the day, interaction is much higher converting, right?

• 5:49Jason Calacanis?

  Yeah, and it's engagement, so they're gonna spend more time on it. So Domino's can come in and say, I want to make an Asteroids-like game where the boulders and the asteroids are dominos. And when you break them apart, they turn into the pretzel bites. And then I want to have a spaceship go by that's the brownie, whatever it is. Genius.

• 6:07Speaker 02

  And make the pizza. And the call to action can be order the exact pizza you've made in the game, for example. Yeah, very cool.

• 6:12Jason Calacanis?

  Albert and Boris, you guys went to school together? You're, you're in high school right now? What's the story?

• 6:17Speaker 02

  No, no. So I'm, I'm 22, Boris is 24. We've worked together.

• 6:21Speaker 03

  I'm glad I look so young, Jason.

• 6:23Jason Calacanis?

  I was referring to Albert, but okay. No, joking. You're both pretty young. But so you're, you're out of college. Did you skip college? You went to college?

• 6:31Speaker 02

  I, I skipped college personally.

• 6:33Jason Calacanis?

  Well done.

• 6:34Speaker 03

  I went to college, unfortunately, and I went.

• 6:36Jason Calacanis?

  Where'd you go? What'd you study?

• 6:37Speaker 03

  University of Leeds. I studied computer science. And yeah, unfortunately, it was right during COVID. So for the two of the three years, I literally just didn't go to classes. So I guess.

• 6:47Jason Calacanis?

  So you paid a fortune and got nothing, got it?

• 6:49Speaker 03

  Yeah, yeah.

• 6:51Jason Calacanis?

  So how much debt you got, Boris? How much of the debt?

• 6:53Speaker 02

  Not much. It's like 40, 50K. In the UK, it's quite cheap.

• 6:57Jason Calacanis?

  Not much. $50,000. Okay, we gotta make this startup work.

• 7:00Speaker 02

  I mean, compared to some of the people that like in the US where they're getting on, yeah.

• 7:04Jason Calacanis?

  All right, Albert, what's your story? You're the business guy, you're a developer, who are you?

• 7:09Speaker 02

  I'm the business guy. I'm the business guy, I'm the hustler. Yeah, I mean, I've kind of, I've been a gamer since I was six, by the way. I'm like one of the weird people in Gen Z that plays a lot of World of Warcraft, which is kind of like more of a millennial thing.

• 7:20Jason Calacanis?

  Wow, very old school.

• 7:22Speaker 02

  Boris is two, by the way, we play together, but I've played since I was six.

• 7:25Jason Calacanis?

  Is that how you met? Did you meet in World of Warcraft?

• 7:27Speaker 02

  No, we met through a childhood friend that kind of grew up with, you know, me and grew up with Boris, and he just intro'd us about three years ago, and we kind of kicked it off and became best friends.

• 7:36Jason Calacanis?

  Got it. And now you're sitting in a gamer chair, right? That's a gamer chair, Albert?

• 7:40Speaker 02

  Yeah, the razor, yeah.

• 7:41Jason Calacanis?

  It's a razor one. See, now, this is what I, I'm like the Columbo of investors, like, I look for little clues, and I'm like, hey, you, hey, Lon, I noticed Albert is sitting in a chair. The chair is a gamer chair, and it's the brand Razor. And I had a Razor, I brother-in-law had a Razor mouse, and it was used for playing games like World of Warcraft. So you're very much into gaming, are you not, Albert? So you guys vibe-coded all this, you did it. You have, there you go, Razor, and it has weights in it, too. You can put different weights into it, depending on the game you're playing. What's your story? You guys are gonna raise money for this? You want to come to an incubator? You have raised money? You did go to an incubator? Have you incorporated? What's the story here, because this is a great idea?

• 8:20Speaker 02

  Yeah, yeah, so we have raised money. I mean, it's a really exciting time for us. We've raised from drive-

• 8:25Jason Calacanis?

  Awesome, fantastic. Well, and where are you based?

• 8:28Speaker 02

  So we're based out of London and Sophia at the moment, but with plans to go towards New York, yeah.

• 8:33Jason Calacanis?

  Okay, great, my hometown. It's great, just, when you go there, please don't join ISIS, okay? So when you get to Ellis Island, they're gonna be like, would you like to go to the ISIS training camp? Or you'd like to go to Brooklyn? Or whatever it is, no ISIS.

• 8:45Speaker 02

  Well, it's a bit like that in London, to be honest, too, so don't worry, you know.

• 8:48Jason Calacanis?

  Yeah, you just have to defend yourself as young people against these ideologies. Don't blow anything up. Fantastic, New York's a great place for you, because there's marketing in New York, and people experiment, and this could be permissionless. So if you just tell brands, hey, you can just build whatever you want as a brand, some young person at a brand, I don't know if you're watching the Staples Baddie? Do you know about Staples Baddie?

• 9:13Speaker 02

  No.

• 9:13Jason Calacanis?

  Lon, do you know about Staples Baddie on TikTok? Okay, so one of our team, Jaga, will very quickly-

• 9:18Speaker 00

  I found it, I found it.

• 9:19Jason Calacanis?

  And Staples Baddie, she basically is passionate about Staples, and she works at Staples. She is like a baddie in the internet slang, and she just talks about, like, here's the best pen. And she was talking about pens the other day and I was looking for a new pen and I don't want to spend a dollar on a pen, but I don't want to spend a hundred dollars on a pen. And I was searching for a pen and then she came up and one of the pens that she liked was the Zebra G750 that I literally bought it. Now, I didn't happen to buy it from Staples, but I did follow her. So now I'm giving credit to Staples for it. Anyway, my point is she's getting more views and Lon probably has done a bunch of research on her right now. She's

• 10:02Jason Calacanis?

  gotten more views and has done more for the brand online than anybody in corporate who spent $10 million or $100 million ever had because she connected with people authentically. Hiring can be its own full-time job and hey, guess what? I already have a full-time job. I make podcasts and I invest. But when you're running a small company, we both know every hire matters. You don't want to waste any of the seats you have at your company. And the best partner you can have is LinkedIn Hiring Pro. Why? There's a billion people using LinkedIn. All the great talent are there. If you're proud of your work, you build a LinkedIn page and you update it. LinkedIn Hiring Pro is going to streamline and simplify the entire process for you. Nearly 60% of companies using LinkedIn Hiring Pro. You're going to get an incredible candidate to interview in the first week. And, you know,

• 10:47Jason Calacanis?

  we're looking for a new producer for the pod. We did shout outs here on the show. We posted it on my social media. We asked friends. You know where we found our next great hire? LinkedIn. And it was competitive. We had like three or four really good choices. So hire right the first time. Post your first job and get a hundred dollars off towards your post at LinkedIn.com/hiringprooffer. That's LinkedIn.com/hiringprooffer. Terms and conditions apply. She went through journals. She went through pens. Now she's getting picked up or whatever. Now, other people, Office Depot is trying to find their baddie. It's become like a whole thing.

• 11:20Speaker 00

  It's like the, the fast food burger thing that you were following that last week? It's the same, it's the same concept. The McDonald's CEO did a TikTok where he ate-

• 11:28Jason Calacanis?

  Oh, yes. Explain to the audience.

• 11:29Speaker 03

  That was, that was really bad. That was really bad.

• 11:32Speaker 00

  He ate a burger, but it, he took a really small bite and it looks like he's disgusted by his own burgers. And so now every other fast food CEO has done their own version where they love their burgers and they're eating them, stuffing their face with them. And it really became like every single fast food CEO had to do one to get the momentum from this viral McDonald's video.

• 11:55Jason Calacanis?

  I think the problem with his video, because I did see it.

• 11:58Speaker 00

  Yeah.

• 11:59Jason Calacanis?

  And the problem was- He's very robotic.

• 12:02Speaker 00

  Yes.

• 12:03Jason Calacanis?

  He's almost like an MBA who's a bit on the spectrum. I don't want to diagnose people, but when I say like, he's robotic, you get the idea. It's not meant to like.

• 12:11Speaker 00

  He's awkward and it just, it's.

• 12:13Jason Calacanis?

  He's awkward.

• 12:14Speaker 00

  He's not a good enough actor to pull off like, I really am enjoying this and I want to eat this burger.

• 12:19Jason Calacanis?

  He's not even able to pull off, he's a human.

• 12:21Speaker 00

  No, at one point he goes, and this, and this is gonna be my lunch later. And that's not a hard thing to convince me that you're gonna eat a burger for lunch, but I don't believe him. I don't, in that moment, I don't believe him.

• 12:32Jason Calacanis?

  He referred to the burger as product, a product. It was like very awkward and he takes a very tiny bite.

• 12:37Speaker 00

  He does not take a big bite. I thought the same thing about the Burger King guy. Honestly, the Burger King guy did one and I was like, he also doesn't really take like a, a big, like a man-sized bite, like he loves burgers.

• 12:49Speaker 03

  The question is, who let them post this? Like, there was probably a whole marketing department there.

• 12:53Speaker 00

  Yeah, like, right, there wasn't a social media person there being like, can we, can you take a bigger bite? Like, you like your burger? Cause that looks so dainty. It's like, it's, yeah, he even shows it. It's like, it doesn't look like bit into.

• 13:07Jason Calacanis?

  It's so clear, Albert, when you see this, that this person does not like their own product.

• 13:14Speaker 00

  It couldn't be more obvious.

• 13:16Jason Calacanis?

  There is literally a salad that has been made to his specification by his chef off camera. And he is going to spit out whatever he ate, swish his mouth with some, Pellegrino at the exact temperature he wants. And then he's gonna eat that frickin salad that his chef made with, that literally each item was weighed and put into his, you know, spreadsheet where he keeps his calorie count. What you need is somebody who actually is sitting there with the chef saying, put more onions on and like, you know, the mustard ratio is off, which is what the, crock, which, which is what the McDonald's brothers were doing. Albert, what's the vision here for, oh, so anyway, back to the permission list, are we talking about brands?

• 13:59Jason Calacanis?

  I think you just let the brands do whatever they want on it and then you give them the ability to promote them. Yeah. Which would be just what brands are doing now. Brands just make content. There's no permission to do that. And if they get views and they do a good job, it gets views organically. But they can boost it. Um, and they can pay to boost. I think just paying to boost, such a good idea. Then what about monetization from I'm the creator? Could I put a game up there and say, I, if you want more levels, you can buy coins? Or is that annoying to customers and against your philosophy, Albert?

• 14:32Speaker 02

  So again, this is a big rev experiment we want to do. Like, look, there's two, there's two ways I look at it. You can look at someone like Roblox, right? That's done incredible creator currency, right? However, their games are a hundred percent more long form, right? Where you're not going to be playing like, you know, a hundred games an hour, right? Or more, right? So, I mean, it's very normal to have microtransactions and, you know, a creator currency where you as a creator can say, "Hey, look, I want to add an extra level, you know, clear, it's clear. I got a million plays on my game. Let's add an extra level for my big supporters that want to do it, right?" Um, this is something we want to experiment with. So yes, we're, we're, we're looking at experimenting with a creator currency, you know? Because I think there's a big incentive there for creators, right? So let's say you, you put a game up, right? Short form, you made it in two minutes, right? You did like three prompts and you get a million plays, right?

• 15:17Speaker 02

  And just so we have a game in, in our minds that we all know, let's just say it's like Candy Crush and Candy Crush doesn't exist, right? And you pioneered the first level. I want there to be a big incentive for you to kind of, this is something you don't see in socials, you know? Like iterate on that already posted thing, right? Unit of content. And this is something you don't do in like Instagram and TikTok. You know, the moment you like press post, that's it, you know? The ship sailed. So, I mean, there, there is an aspect that we want to test on this, but to be totally, to be totally frank, we're too early, I think, to really know how that will go. And, you know, we are not the ones that can decide if it will work or not on that.

• 15:50Jason Calacanis?

  I think you spend the first literal two years just trying to make the tool so good and so addictive that people get into it.

• 16:00Speaker 02

  I mean, it's already kind of getting there. I mean, have, do you know anything about like our attention and like kind of our engagement?

• 16:05Jason Calacanis?

  Tell us a little bit about it.

• 16:07Speaker 02

  Quite cool. So we launched like mid-January and we've got about 100k users so far. And 20% of them are what we call power users, right? And these guys play more than 25 games per session. And the average session time is around 21 minutes. And they do two sessions a day. So they're playing, you know, just under an hour a day and going through over 50 games. And I think this is really cool because, I mean, you saw like a brief glimpse of the feed. We don't have an algorithm, A, which is what drives most social engagement on platforms like Instagram and TikTok. Again, a point on algorithm, you know, I won't linger too long, is never been introduced to gaming before. Right? Drives most short-form social engagement, but never been done in gaming, right? So we have really high session times for, you know, basically giving you slop, right?

• 16:50Speaker 02

  That's what it is, you know, until it gets better. And, you know, what we're seeing is kind of people are already really excited about it.

• 16:58Jason Calacanis?

  Yeah, I think this is going to be a breakout hit. And how do you get the first couple of casual users? Where do you find users for the product?

• 17:09Speaker 02

  We started this with Discord, to be honest. You know, we started a Discord community. Like, we had the idea in November. We started a Discord, got it to like 10k people, but as Discord is, not all 10k people were kind of super active. Kind of like 2k of them were. And these were like our test users. And we had like a running beta of 200 people up until we launched in January. And these guys loved it. Like, everyone that made games back then are still making games today. So, you know, insane, you know, commitment to it. And at the time, we didn't have like game generation. They were like doing it on their own and, you know, uploading it. But then, you know, a huge way, you know, and I won't really touch on ads because, I mean, ads are a quite obvious way to get users. But, you know, another really interesting thing is sharing games, right? Like, I don't know, you mentioned a lot about socials and sharing stuff. Have you shared a Twitter, a tweet, you know?

• 17:53Jason Calacanis?

  Of course. Yeah, I mean, in the group chat, etc. So that's going to be in high scores and sharing. And then, not to mention, you have games that are... A duel, so like Lon and I could play versus each other, or we could invite each other to beat a score. So that's where like the loop could happen, right?

• 18:09Speaker 02

  A hundred percent.

• 18:10Jason Calacanis?

  A wordle type game, and then I send it to Lon and say, "Look, I solved this in three out of five."

• 18:15Speaker 02

  Yeah.

• 18:15Jason Calacanis?

  Can you try and beat it?

• 18:17Speaker 02

  Well, yeah, I think it's really interesting because like, look, I think the big point I'm trying to make with this is it's not in our culture to share games. Right? Like you say, you share, you know, reels.

• 18:25Jason Calacanis?

  That's true.

• 18:26Speaker 02

  But when was the last time you were playing a game?

• 18:28Jason Calacanis?

  Well, I will say there is one. It's, um, people will share clips of like specific kills in Call of Duty on YouTube, whatever. But that's like deep gamer culture. It's not casual.

• 18:39Speaker 02

  That's still content. That's still content. And that's still super niche. At the end of the day, like, you know, you will, if you are an avid Instagram user, you will daily send reels. I mean, this is a way of keeping in contact. Like, I'm a middle child. I have two brothers. I send them reels every day and they send back, right?

• 18:53Jason Calacanis?

  Yes.

• 18:53Speaker 02

  But games aren't something people share. They don't. So this is like one of two big questions we had before doing this of, and by the way, we have the answers. I think it's really interesting. But these are one of two big questions we had before doing this was like, A, do people want to share games? Because they don't right now, right? And B, will this shift look the same as the long form content YouTube shift to short form content Instagram, TikTok, right? So the answer for the first one is yes. Per hundred likes on the app right now, we're getting, because we have likes and shares that you didn't see on Boris's beta version, because it's a beta version that we're running right now. But per hundred likes, we're getting between 30 and 50 shares at the moment. So huge amounts, nearly one in two.

• 19:29Speaker 02

  And then when it comes to the long form, short form shift, I've already told you, you know, the stats of we have 20% of our user base going through 25 games per session in less than 25 minutes, right? Around 21 minutes. So I mean, the big thing here that was like a huge sign for us was, look, when you are talking about long form content and before TikTok and Instagram, there is a use case to spend three seconds on a video. It's your camera roll. Genius. The camera roll from iPhone, whatever Android has as well, right? You can go watch three second clips that you've taken yourself or a friend to send you and scroll through them. But games are very different, right?

• 20:03Jason Calacanis?

  Yes.

• 20:03Speaker 02

  Because with a game, you go on Flappy Bird, what's the session time? It's like eight minutes, you know? You go Subway Sub, something like this. I mean, don't quote me on those stats. I don't know exactly what Subway Sub's session time is in, but it's not five seconds, right? But I mean, so this was our big question. Will people basically do this hyper casual doom scrolling, but for gaming? And the will be answers, yes.

• 20:21Jason Calacanis?

  Awesome. Great idea. Can't wait to get working with you and we will have you back on the program in six months and see what your progress has been. Really excited. Albert and Boris, thanks for coming on the pod. Also, John, it's been a pleasure. I look at 10,000 startups every year. What is the essential nature of Uber or Robinhood, these incredible companies I was lucky enough to invest in? They focused on their customers. They focused on their product. And then they had support for chores. Let's call it what it is. Your incorporation, your banking, your payroll, your benefits, your taxes, all of that back office. You can drown in that, or you can find a partner who loves doing chores in every.io wants to do this detailed work for you. They're going to handle your Delaware

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• 21:40Speaker 05

  Welcome back to Twist. My name is Alex and today we're going to solve one of the largest pain points in my life, which is picking up after my children. I have a great many children. They leave things all over the house, on top of things, underneath things. It's a mess. It's a constant struggle to keep my house up to a reasonable standard of care, which means that I'm constantly bending over and making bottles and changing diapers. Now, I love my kids, but oh my gosh, would I not appreciate a little bit more help. The good news for parents like myself out there is that there are companies building humanoid robots that are going to make our lives easier in time. And even more importantly, there's one company called One X that's making a robot called Neo that's designed just for your home. It's my absolute dream. So to tell us more about it, how it's going to

• 22:23Speaker 05

  come to market and when, please join me in welcoming to the show. It's Bernd Bernik. How you doing? Welcome to the show.

• 22:28Speaker 06

  Hey, great to be here. Looking forward to this one.

• 22:31Speaker 05

  So I've known about OneX for a while, you know, keeping track of your progress and so forth. But one thing I didn't know was that you guys actually got started building robots not for the home, but for industrial settings. And you had a robot called the Eve Industrial on Wheels years back, but made a pivot towards the home. But take us back in time to the robot that you guys actually put into, you know, industrial applications years back.

• 22:55Speaker 06

  When I started the company, it's been 11 years. It's been a while. The first things that I really wrote down, they still stand today. It's like, we want to make robots that are safe, so they can live and learn among people. They need to be capable, like they need to have the dexterity, the strength, the agility that we have, or they're just toys. And then, of course, it needs to be scalable and affordable so that it can have an impact. And when we designed Eve, it was very early and we didn't have the power density and the technology yet to make this as general as we would wish, but we can make it very general for its time. And it was an amazing robot. And what people might not know is that I actually had Eve at home for multiple years.

• 23:37Speaker 05

  Oh, really? So you had the robot that was designed for what appeared to be...

• 23:40Speaker 06

  Well, it wasn't designed for industrial use cases, but it was designed for general labor. That's the thing about humanoid, right? So to me, humanoid is a play and like, you want to create general labor. Because at scale, if you look at the system at the limit, what is going to be the most reliable, the most affordable, the most intelligent and the most helpful is going to be whatever has the largest scale. And all technology goes through these cycles. And maybe the simplest one to talk about is computers, right? Started with mainframes, and at some point, consumer PCs came around. And it got scaled to this incredible number and the ecosystem and the reliability and the costs

• 24:22Speaker 06

  and everything that comes with it, right? So now it's just this one tool. It's like the hammer that solves everything. And whether you're like typing something up or you're recording a podcast, we're all doing it on the same computer, right? There's no specialize. In the end, it actually goes full circle. And the market is so big that you get specialization again. It's happening in computers now. You have like specific computes for inference, for training, for simulation workloads, for all kinds of things, right? Because each of these markets are gigantic at this point. This is happening with robotics or physical AI. It started with industrial robots. That's kind of like our mainframe.

• 25:03Speaker 06

  Now we're going through the general labor phase where you just want to create the machine that is as general as possible. That can really unlock humanity from kind of being bottlenecked by access to productivity.

• 25:18Speaker 05

  Yeah.

• 25:19Speaker 06

  In the end, it'll probably be all Star Wars and there will be different droids for all kinds. Anyway, Eve, that's a long way and wide way of saying like Eve was our best shot at like making something that's as general as possible to get started on this multi-decade journey of how do we create something that can

• 25:37Speaker 06

  completely and fully kind of empower us as humans to work on the things that matter. And I had it at home because it was very important for me to make sure that like, hey, this is going to be in people's homes at some point.

• 25:51Speaker 05

  Right.

• 25:51Speaker 06

  We need to start early, but it wasn't safe enough. That that's actually the thing like this extremely safe robot for what it is. And I would argue it's safer than most of the older robots out there today, but it was a bit too heavy.

• 26:02Speaker 05

  Okay.

• 26:02Speaker 06

  And not quite general enough. And that was really what led us to use this for industrial markets because I I've been working all my life on robotics and we've built a lot of beautiful robots up through the years, but like they didn't have any real world impact. And it was so important to me when I started One X, we want to do something that has real world impact. So it can't be on YouTube, it can't be on the lab, it has to actually be out there doing things. And those industrial use cases were great early applications.

• 26:30Speaker 05

  But if you want to make something that is generally useful to humans, you're going to need a couple of things to make that happen. Going off of the original Eve idea into Neo, which is legs. And I would presume modern AI techniques that allow the robot to become more generally useful by learning new things on the go. So it sounds like you had a good proof of concept and then you refined it and then imbued it with the latest in kind of modern AI. And as a combination, you've come up with Neo, which we're going to put on the screen right now. And and so everyone can see it. And the result is essentially a softer, lighter, more intelligent and more frankly, human robot is my read of Neo. Is that a fair encapsulation?

• 27:10Speaker 06

  There are many reasons for this. Let me, let me start with a couple of like very important ones that I think is quite unique to One X. We've been very all in on, we have to create something. That moves and interacts with the world exactly like a human does. All the way down to like the smallest interaction of like, how, what's the stiffness of like the tissue and your skin? And how does that interact with the world, right? If you get that right and the sensing and everything. Then you can take all of humanity's knowledge, mostly encoded in video and other things that exists out there. And these priors hold and you can use them to get intelligence of like off the ground.

• 27:52Speaker 06

  And this is so important because the alternative is go out and create an internet size. Data, I'm not saying these things aren't useful. We also use teleop.

• 28:01Speaker 05

  Yeah.

• 28:02Speaker 06

  It's not the solution to bootstrap your way to having billions of tokens that you can train your models on. So you kind of have to find some way to bootstrap. And I think that's what makes me the most excited about Neo these days. It's that we're getting to where we're clearly seeing that that works, like the world model work that we've been putting out. And there's some amazing stuff coming right around the corner, actually, on this. That's a continuation of that. Really starts proving out that this bet is a bet that holds and we can get true general intelligence. Most of what you see robots do today on YouTube.

• 28:38Speaker 06

  Are kind of like fine tuned policies, as we call them, meaning like it's not necessarily one task, but like you take a set of tasks and you gather a lot of data on specifically these tasks.

• 28:47Speaker 05

  Yeah.

• 28:47Speaker 06

  You can have the robot do these tasks, maybe in another environment, but the tasks are the same tasks and they're very similar. What we're building here is actually true general intelligence where you can ask the robot to essentially do anything and it will do a pretty good job at it.

• 29:00Speaker 05

  Let's go back. We're going to get into world models in a second, but the point you made about Neo, the robot for your home designed for that. Uh, having similar physical attributes to humans. You mentioned like finger skin tension, for example. That allows you to map how humans interact to the world to the robot itself. So that way it can learn more cleanly, accurately, quickly from how the world operates. So essentially, as you make it more human, it learns faster.

• 29:31Speaker 06

  It does, right? It's just like, if, if, if I pick something up, I do that in a specific manner with my fingers. Like I rotate, if like, if I rotate that one, like you just rotated that in your hand, right? The way you use your fingers to do that. It's very hard to transfer that to like a three finger gripper or a claw or whatever, right? It doesn't transfer. So like, yeah, then you would need to go out and gather all that data. So that's one part of it that's just so important. It learns faster.

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• 30:41Jason Calacanis?

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• 31:01Speaker 06

  Now, the second one, of course, is that the world is made for us.

• 31:04Jason Calacanis?

  Yeah.

• 31:05Speaker 06

  So like, if you want to get around the home and like do all these things that you don't want to do yourself, you kind of have to be very close to human. And I also think there's something quite magical about creating something that the embodiment kind of connects to us emotionally. And the companion part of the product is also so big part of the product that doesn't get talked enough about. It's not just about doing the labor. It's about doing the labor and being your companion through our life and how this can kind of like help you interact better with technology. And

• 31:38Speaker 06

  I think we're very quickly trending, at least for me now that's using this every day, in the direction of like, I don't talk to my computer, I talk to my robot.

• 31:47Speaker 05

  Yeah.

• 31:47Speaker 06

  It's you away from screens and it gets you more present in like your everyday life. And I think there's a beautiful story to be told there someday about how this can get us away from screens.

• 31:56Speaker 05

  Well, I, I, I think also there's an enormous elder care component to this. I mean, we, we've all heard about how society is graying, how our elderly, you know, parents and friends are lonely. And so to me, like, sure, we have pets, that's great, but pets can't help you with dishes. And you know what? What if you're older and need help and you're a little bit lonely? Well, we can kind of fulfill a lot of that with one device in the case of Neo. And I, I don't view that as dystopian at all. I view it as a way that humans are taking care of humans via technology. And to me, that's a, a good future.

• 32:25Speaker 06

  100% agree. And this is actually one of the main things that gets me up in the morning. I think we, we are at a point in history where this is not optional. Like, we, we have to apply technology to solve this because we have to be able to give everyone,

• 32:40Speaker 06

  essentially like dignity in, in the way you get treated as you age. And

• 32:46Speaker 06

  people sometimes mistake this for being like a replacement. It is not. And I like to say, as someone who has one at home, like, it doesn't, it doesn't replace my dog. It doesn't replace my kids. It doesn't replace my wife. It is something new, right? Uh, and just like a dog is also something new that you add to your family.

• 33:05Speaker 05

  Yeah.

• 33:06Speaker 06

  This beautiful companion through our life that like is always on your side. It's helping you with everything. It remembers what's going on. And I kind of almost see it more like my, my Hobbs in Calvin and Hobbes.

• 33:16Speaker 05

  Uh huh.

• 33:17Speaker 06

  Yeah.

• 33:18Speaker 05

  Yeah, yeah, yeah. Well, if you don't know that reference, Covenant Hobbs is an American, uh, child-focused comic strip. And a young boy has a stuffed tiger that he thinks is real and they play games and such. So that's, that's the context of it. So essentially it becomes-

• 33:32Speaker 06

  If you don't get that context, go read it.

• 33:34Speaker 05

  Yeah, go Bill Waterston.

• 33:37Speaker 06

  It's amazing.

• 33:39Speaker 05

  It's a great, I grew up on it. So that's, that's very near and dear to my heart. So here's the thing, though, you're talking about, you know, YouTube demos and we're not going to name out any names. We're not going to say figure, we're not going to name any of the companies out there who are famous for doing a lot of demos online. Versus the ability to make something that works, you know, in your home. So you have Neo at home right now, and I know pre-orders are up. It's going to start shipping later this year. We'll get to that in a second, 20K or 500 bucks a month. So how good now is Neo at general household tasks? And how quickly is it improving against that problem set? And the context here, Bernd, is that I'm curious about the 1X world model.

• 34:19Speaker 05

  And I'm also curious about how teleop plays into this because I love the idea of being able to call an expert into my Neo to help it learn a new task. But at the same time, to me, with my unlettered perspective here is that certainly after enough usage, we'll have closed those gaps. Because enough people will have needed that bit of help. So I'm curious about compared to YouTube demos, how we're doing today and how quickly we're improving Neo.

• 34:42Speaker 06

  I want to back out one step. I'll answer that from like a bird's eye view. So first of all, you asked a question earlier, which I kind of covered, but not all of it, which is why the home first, right? Because this is about general labor. So it's not just the home. But it has to start in a home because we want our machines to live and learn among people. And we wanted to like hold the door open for grandma and we wanted to understand the social context of work. And this enormous diversity is actually what gives intelligence. So if you're in this narrow environment where you're seeing the same thing every day, you don't actually learn. It's the same for people, like we to learn. So that's why kind of the home has to happen first. And that's also why I think like this, let's call it the early adopter program, like what, what we're shipping here.

• 35:27Speaker 06

  The 20K robots literally was called the early adopter program. And it's gonna be rough. I'm just gonna like tell everyone upfront, it's gonna be rough because it's the first time in history anyone does this. But it's gonna be one heck of a journey. And right now, I would say. In my home, the robot is doing a reasonably good job. It's not doing everything, but it's like it's doing my laundry. It's doing a lot of the tidying and cleaning. It's it's doing a lot of fun stuff just around like companionship and just being around. Uh, like opening the door if like a guest's coming over or all these things. That's a whole party trick. Opening a door when the door when the door dash guy comes over is actually a lot of fun. Um, just see with your package.

• 36:09Speaker 06

  So, um, now the way this works right now is there are two modes. And we're gonna listen to customer feedback also. So like, no, this is locked in stone. But like, the way it works is there's two modes. There's best effort autonomy mode, where you're running the world model, and it's doing like whatever I ask it to do, and it'll do best effort. And you can ask it to do things with just speech. You can talk to it. You just talk to it. And it's extremely general, right? The thing that blew my mind the most this Saturday was like, I was just asked the robot like, hey, can you pick the posted note on the board over there and read it to me? And the robot could do that. That's not the training data. Like, that's pretty magical to me, right? That's actual true general intelligence.

• 36:49Speaker 06

  Now, next time I ask it about the same thing, because it was so fun, hey, look at this, like, Neo, go read the posted note. It didn't get the posted note. So it doesn't always work. That's my problem. Problemistic AI decked. And this is actually the magic of the world model. Because what it gives you is it gives you this incredible general base layer of intelligence. Where you can have the robots sensibly, just based on voice, it'll have a sensible approach to essentially any task. Now, all you need to do is to have it try it, and then it learns. And that's a lot of fun. Um, there are tasks where this is extremely fun and just works. Like, it's very good at laundry, because if it screws up folding my shirt, I just ask it to do it again. Right. Not a lot of risk.

• 37:34Speaker 06

  If it's taking grandma's like ancient vase or the cabinet to put it on the table, I would probably not do it that way. Um, so there are tasks where easily repeatable and where like failure is not a problem. Then the robot is actually very good. Opening a door is not a good example, because we make the robots just like us, right? To be soft, compliant, low energy, lightweight. So like, it won't hurt itself. It won't hurt the door. It can just try it. It gets it.

• 37:59Speaker 06

  So for these tasks, it's starting to work extremely well. For tasks where you don't want it to fail, then we're still relying largely on scaling our fleet also internally to work on these tasks, to get them to where there's an extremely high probability of success. And also that you have a very good way of ensuring that if the task is going to fail, you identify it early and you stop.

• 38:22Speaker 06

  This is another thing that's incredibly cool about the world model. Because it works very similar to how we do when we're thinking, right? So you said you have kids. So if they're gonna go around like pick up the coffee cup with hot coffee, then you will immediately kind of like simulate forward what can go wrong. Like through your mind, immediately shows up like you pouring coffee on the kid or like all these things. Yeah, yeah, yeah, yeah. And then you select the safest kind of like trajectory to achieve your task based on all these constraints. And this is literally what the world model is doing. It is looking forward, simulating like if I take these actions, what will happen? And that's kind of like a search and then like, hey, here's the best way to do this. Yeah. And we're working very heavily on the safety side of that current.

• 39:05Speaker 06

  And like, how do you ensure that the robot always takes the safest possible paths to do things? And how do you ensure that if there's unduly risk that the robot does not do the task? Now, this is a work in progress, but it's incredibly important progress. Because safety has two aspects. It's the aspect where like the robot just is physically not able to harm you because it's light enough and low energy and soft and all these things that we've been working on for a decade. And that's kind of like, that's, that's a separate thing because that's just, you prove out that this is safe. Standard certifications. And then you have the AI part of things where right now for customers, we limit to some extent what the robot is allowed to do because we don't.

• 39:50Speaker 06

  It's not allowed to cook, for example. It's not allowed to cook, et cetera, right? No, no, no hot liquids, no like dangerous items, these kind of things. But of course, long term, we want to be able to do this with a very good safety profile. And that's really like, I think now the bleeding edge of like what's getting worked on on the AI side to ensure these things can be done safely. Can you? So in my. Oh, sorry, please. I was just gonna say like, so in my house, that best effort AI mode is what I use when I'm home. And it's a lot of fun. When I leave for work, I just take off my Neo app. And I say that I'm leaving, do all the daily chores. When I come home, it's all done. And there is sometimes tell up involved in that. To make sure that everything gets done super well.

• 40:32Speaker 05

  Yeah.

• 40:33Speaker 06

  And I don't care. I'm not there.

• 40:34Speaker 05

  I don't care either. I just, I just want the laundry to be done. I want there to be clean socks for every child when they're needed. You know, like, I mean, that's, I, that's like, people are like, oh, 500 bucks a month. I'm like, yeah, but do you know how much this would save my life? Like, it's, it gets worth it pretty quickly. Now, I want to talk about the world model for a second. You guys have written a lot about how VLMs are not a sufficient answer to general intelligence for physical AI, aka robots. For folks out there who are listening, who are not as deep into AI as you are, can you explain what a world model is? And what it took to build the one you guys have released and apparently are going to be updating very soon?

• 41:14Speaker 06

  VLMs are essentially take a language model.

• 41:19Speaker 06

  And then both done some actions that a robot can do. So, and how does this work then in practice, right? So, okay, you take a screenshot essentially of the world.

• 41:30Speaker 06

  So, yeah, and you have some text of what you want to achieve, and you have a picture of how the world works. And then you make a plan. And then you start executing this plan. And you take a new screenshot of the world and do this. And it doesn't really capture the dynamics of the world. And the beautiful thing about human intelligence is that we understand how the world works. Down to like, we can visualize what will happen when we do things. And to do this, you need to capture like. The spatial and temporal dynamics, essentially. That's a complicated word, but what it essentially just means is we live in 3D and we care about time. We see how the world evolves in 3D over time.

• 42:13Speaker 06

  Language models is like a 2D screenshot. It doesn't have time. It doesn't have 3D.

• 42:21Speaker 06

  And it works incredibly well, by the way. Like, language models are incredible.

• 42:25Speaker 05

  I fucking love them.

• 42:26Speaker 06

  Yeah, yeah, yeah. It's just, it's not the full solution for general intelligence. It's a very narrow type intelligence that works really well on a subset of problems. And it's very exciting to see that when you actually train these world models, which then essentially what we're training them on is, can you predict what will happen?

• 42:45Speaker 06

  So very canonical example would be if I take this and I drop it, what happens? That doesn't sound too complicated. You can do that. And that's like, it's not magical. Like it learns how physics works essentially. Mm hmm. But when you start doing this at scale, there's a lot of magical things that happen. So for example, back to why the home and why among people? To navigate a social situation, to get your coke in the fridge.

• 43:11Speaker 06

  The robot needs to understand how people will behave. Oh, in the world model. We can see that we can see now in the world model that like people appear in the world model. Right? Because the robot is thinking about what people, how they will look, what they will do, etc. Right. People appear in the world model and behave like people. This is kind of like a inception type argument here, but like it's kind of like an AGI complete problem. Because to be able to fully understand how to interact with the world, you need to be able to fully simulate how people work. Yeah. Um, and it is just, in my opinion, it's the, it's the natural next step in intelligence, right? So it's not that we're not using language. Clearly, language has, is a part of our intelligence. But language is not the backbone of our intelligence.

• 43:55Speaker 06

  That is our sensory visual body and how we interact with the world. And you can see this with kids, right? They, they learn how the world works and then they start to express this through language. Slowly. Yeah. So it's, uh, it's, it's really exciting and we are starting to see, like I said, like the first kind of breadcrumbs of like things. With robot data like this, working better than the pure digital data. Getting us increasingly confident that like the future of AI will be models that actually train on embodiment, not just the web. Now, we still train on the web. It's not like instead of. It's in addition to.

• 44:39Speaker 05

  Yeah.

• 44:39Speaker 06

  But it's currently a missing component that I'm very excited about.

• 44:41Speaker 05

  So when you get the early adopter Neos out into the market this year, and I do want to ask you about how many, when, and so forth in a second, but putting that aside for now. As you get more of them out there, that increases essentially the Neo footprint in the real world to test its vision model against reality. And I presume greatly increase the flywheel of learning that you guys can then bring to bear on future iterations to its intelligence, right?

• 45:07Speaker 06

  Hundred percent. Like, this is one of the most important things we do, right? And why we need to get it out there as early as possible.

• 45:14Speaker 05

  That's why I'm excited, actually, that you're saying it's going to be a little rough for the early adopter, because that means you're getting that out probably as soon as you can to get the learning started to make it better. So how many Neos do you need in homes to have the right influx of data? To learn and improve the world model and embodied intelligence as quickly as you like? Is it 100 Neos? Is it 10,000? I don't have a good sense of scale.

• 45:42Speaker 06

  The honest answer here is that no one knows, because it's not been done before.

• 45:46Speaker 05

  True.

• 45:46Speaker 06

  I can give you some first principle numbers.

• 45:48Speaker 05

  Yeah.

• 45:49Speaker 06

  We know that pre-training on a data set roughly the size of YouTube gets you very far with respect to general intelligence. Now, of course, that data doesn't actually have what we call agentic behavior, and it doesn't have physical interactions with like forces. The agentic behavior is actually extremely important, so let me spend 30 seconds on it. When we train on video, you only know what's going to happen next. You don't know what was the action that the agent took. So like, you know, if I'm going to pick up my phone over there.

• 46:28Speaker 06

  I think, first of all, I have a goal I want to achieve. I want to pick up my phone. So I know the goal. And then I decide to take an action to go do it. And then you see the result. So these are like the three different types of data. Video only has the last one. Robot data has all three. It has the internal state of a robot, of what was it thinking, what was it trying to achieve? What was the actions it attempt, decided to attempt? Here you see the result. So this data is way richer. So we hope we can get away with way less data. But if you look at YouTube as an example, about ten thousand robots, you will be having about the same influx of data as YouTube has. So that's a good baseline.

• 47:05Speaker 05

  That's not a small number, but not an insurmountably large number, because you could, you could get half of YouTube with five thousand, I presume, it kind of scales up and down. And the YouTube corpus is so big. Like, that's actually a pretty impressive amount of data inflow from just ten thousand robots compared to all of YouTube.

• 47:24Speaker 06

  And just to, to, to, to, to the little story, it's not all of existing YouTube, but the upload rate to YouTube, which is growing, is about the same as the influx from ten thousand robots.

• 47:34Speaker 05

  Oh, okay, I see, I see, I see, I see. Okay. Well, that's, that's still doable. That, that's

• 47:39Speaker 06

  It's very doable, but it doesn't stop there, of course. Like, the goal here is to create something that is so intelligent, that it can really accelerate the progress of humanity, right? You will have robots helping us build out all our infrastructure. Make sure we have enough manufacturing, enough compute, data centers, power infrastructure. Everything is done sustainably, because we don't have to cut corners on cost and labor. Why not do everything sustainably? Progress science, like, these AI models will not really help us solve all the outstanding problems in science, or especially medicine and Without doing lab work, right?

• 48:21Speaker 06

  Your AI has to design the experiments, run the experiments, qualify and make sure the data was done correctly. Iterate on this, like that, that, that, that's how you do research. And today it's extremely ball like in that models are getting good at coming up with suggestions, but they can't close the loop and check whether it worked.

• 48:38Speaker 05

  Well, we have to go into the real world for that. And if we're going to do that, we're going to ape human institutions like laboratories and so forth. So it makes sense to have a humanoid robot doing that. It's just, it's funny because when we're talking about Neo today, I'm thinking about having a helper in the house. Cause that's what I need and I, I'm, I'm fixated on that because it's what I want. But in your example of improving the intelligence of these general purpose robots over time to do more and more things. To me, they're moving in some sense away from the home and into a dark factory or a dark lab where there's not humans per se. It's just robots doing what we need them to do. So it's interesting that we end up training them at home to be more generally useful out on their own. It's almost like we're raising children as a, as a, like a class of, of robots and then sending them out into the world.

• 49:25Speaker 05

  What's the time gap between, you know, Neo getting good enough at the home stuff that you're like, this is ready for everyone to buy one. And when we can have humanoid robots helping us automate laboratory work to accelerate science. Is that a short time period or is that relatively long?

• 49:41Speaker 06

  It's quite short.

• 49:44Speaker 06

  The home is actually the most complex.

• 49:46Speaker 05

  Really?

• 49:47Speaker 06

  Why it's, it's such a good place to start because you have to solve. Then you're just like, you jump into the water and now you need to learn to swim, right? So like, um, I do want to make sure that we really strive to satisfy all of our customers and the demand. And that's kind of like what's constraining it now. So exactly when we roll out in some of these other markets, really depends on how quickly we can scale manufacturing.

• 50:14Speaker 05

  Okay. So let's talk about that. Um, before we jumped on, you guys just opened up a new facility in San Carlos. You're going to have, I think you said, design and even manufacturing under one roof?

• 50:24Speaker 06

  Absolutely. Everything on a one roof here. So research, um, development, AI, production, service. And in production, we also mean manufacturing, development and R and D, right? We build the machines that build the machine, um, everything on a one roof and. This, you know, actually, this is one of the things that I'm very proud of with VonX and it's quite unique. We literally go raw materials, even further than raw materials, we develop new alloys and materials. All the way up to the foundation models on the AI side on a one roof. And for zero to one technology. This is incredibly important.

• 51:06Speaker 06

  Because the development speed is just going to be a function of how quickly you can iterate across the entire stack. And if you're reliant on suppliers, it's going to be extremely hard. But even more importantly. Almost all of the great discoveries that we have made on how to make robots safer, more affordable, and just in general, better. They are between the lines of these disciplines of science, right? So it's when someone on the hardware side says like, hey, I see you're struggling with that. If I do this, I think a robot would learn better. Uh, or like, oh, you need these tolerances in assembly. That's really hard. You don't actually need that. I can just use a neural net and we can learn how to calibrate this. And you can like lose all your tolerances and we can produce cheaper. And it's like this incredible kind of almost like Bell Labs type thing.

• 51:50Speaker 05

  Yeah.

• 51:51Speaker 06

  Of science. And I think it's the most exciting place in the world to work, actually.

• 51:57Speaker 05

  It's one of the few, I think, really good examples of when in-person work is not just better, it's much better. And I, I liked it that you're having that effect, but at this new facility in San Carlos, when you're at early stages of productivity, how many Neos can you make per quarter, per year? Is it five, fifty, five hundred?

• 52:19Speaker 06

  The factory that we have in Hayward, that is now up and running fully. Uh, and that will be the factory that delivers home robots this year.

• 52:27Speaker 05

  Okay.

• 52:27Speaker 06

  That can do tens of thousands per year.

• 52:32Speaker 06

  The one that we're building in Hayward now can do hundreds of thousands per year.

• 52:35Speaker 05

  Tens of thousands per year is a lot, given our earlier point about YouTube, but I'm curious, is there enough early commercial demand from early adopters to keep your factory running at that pace? Or are you guys more looking to sell like a few thousand, uh, to get them out there to start the, the faster learning process?

• 52:53Speaker 06

  Uh, the, there's demand, but of course the product quality needs to be there.

• 52:57Speaker 05

  Yeah.

• 52:57Speaker 06

  So right now we're being very cautious and we're essentially doing very fast ramp of like a batch. Then we evaluate in the market because we do the point. We're not at end customers yet, but are in homes, right? And things are under NDA and stuff, but like we, we, of course, we're testing this, so. It goes into, goes, it goes into homes. Uh, and some older industrial applications. And then we get data, we figure out how well this works, we figure out what's not, what's not optimal.

• 53:31Speaker 06

  We rev the design, we do a quick batch again, so we're kind of like running our factory at like full steam, retool, full steam, retool. And we're doing this until the product is rock stable. You only get to do this once, like this is the first time in history that anyone does this and ships robots like this. And we want to make it right.

• 53:47Speaker 05

  Yeah.

• 53:48Speaker 06

  That's also why we haven't really given a specific date on shipment. We've said, we're, we're shipping. Twenty twenty six, and we will, we're good on track for that. But it is when it's ready, because you only do this once.

• 53:59Speaker 05

  No, no, I, I, I, that makes perfect sense to me. I can wait six months. It's fine. I will not wait six years, but I will wait six months.

• 54:05Speaker 06

  Yeah.

• 54:05Speaker 05

  Um, I'm a financial nerd, Brent, so I'm, I'm really curious about the twenty thousand dollar price point. And, um, everyone knows economies of scale, bring down the price of things and so forth. You're doing smaller batches, but does twenty thousand dollars cover the bill of materials? Does it cover bill of materials plus labor? Is it profitable? Is it super unprofitable? I just don't know where to like, where to peg that number in terms of economics for humanoid robots.

• 54:31Speaker 06

  So I have to take you back all the way to the beginning then. Because if you don't think about. The scale and affordability from day one, how are you going to manufacture this at scale? Then you're not going to get there. Because you essentially get locked into a corner because you're making specific decisions about the design. And actually even the technical direction. So if I'm going to make a parallel, I'd say, do you want to make an electric car or gasoline car? You're not going to make a gasoline car in small volume. And then when you're like, now we're going to ramp, let's make an electric car. No, like you've spent the last decade making the wrong technology. It doesn't work. So. We really thought about this from day one. There's going to be billions of humanoids on the planet. Meaning you need to think about.

• 55:16Speaker 06

  Is there any special raw materials in there that are rare? Even further, like, how much raw material is in there? Neo just weighs sixty-six pounds, right, or thirty kilos? Like, almost a third of most of our competitors. Well, that's a third of the raw materials. That really matters when you're gonna make a billion.

• 55:35Speaker 05

  Yeah, because it dramatically reduces the number of ships that have to bring gallium or whatever, you know.

• 55:40Speaker 06

  And mining refinement, like, at a billion, you're starting to even think about, like, oh, that's a significant percentage of the world's aluminium and magnesium. Like, how are you gonna refine that, right?

• 55:49Speaker 05

  So my first thought was, when you said billions, was, do we have enough metal available to us? Because, and then I think about how many cars we make per year, but it's not a, it's not an insubstantial demand on, you know, raw materials, resources, and commodities. Like, that's a lot of new product to bring to market.

• 56:07Speaker 06

  Oh, clearly it is. So, yeah, but you have to think about that. And then you have to think about, like, tolerances, right? How accurate does this need to be? Because it sets your, um, the cost of, like, refining, essentially, at these volumes, you take raw material cost and you refine it into finished product. And that's refinement step can actually be very cheap if you have the right output, right? If, if what you need is something that doesn't need to be that accurate. So that has to be core part of your design. And what we're doing here with these unique motors that we have developed, that allows us to pull on these tendons instead of the classical gears.

• 56:45Speaker 05

  One of the earliest discoveries of the company, even before Eve went to the industrial applications, yeah.

• 56:50Speaker 06

  Yeah, yeah, this is like Neo is just the nth generation of that technology, right? That is extremely lenient with respect to manufacturing. Like, we can get away with very, very few parts, uh, very loose tolerances, no special materials. And if you then do all this and you start also working really hard on how to automate assembly. And of course, we are using robots in our factory, robots building robots, Neos are building Neos.

• 57:17Speaker 05

  Text me a picture of that when we're done, thanks.

• 57:19Speaker 06

  Then we, we can get very low on cost. That being said, it's an incredibly complex system. It's probably one of the most complex systems on the planet. Uh, so, um, it's gonna take us some volume to get the cost down to where we would make money on that and have a margin. But it is a sustainable business, which is the most important to me.

• 57:39Speaker 05

  I'm, I'm just really glad to hear that because. Twenty thousand dollars is the point at which I can probably just buy one, you know, without, without having to like do math. But if it was fifty, then I'm gonna say, I'd rather have, you know, a year of my kid's college paid or something. So if it works at twenty, hot damn. That I can see, you know, the, the, the real volume coming out and just being, being tremendous. I'm, I'm really excited about this. And I take it, given the work you've done on supply and components and stuff, you're relatively de-risked from China in terms of supply chain issues?

• 58:15Speaker 06

  There are certain key components that is very hard to get. Magnets being the obvious one that everyone talks about.

• 58:21Speaker 05

  Yep.

• 58:22Speaker 06

  Now, we're deep into magnets, right? Because like even the first year of OneX, what I was working on was like designing new processes for magnets, because the way we do our own motors. And we have some great companies there that we've been working with in, in China on like co-developing these specific new ways of doing magnetics. We're working towards also ensuring that this is something we can do in the US.

• 58:49Speaker 05

  Yeah.

• 58:50Speaker 06

  But, and there are amazing programs around this that are getting spun up. So I'm very hopeful that this will be something that we can handle in the future. But it's not there yet. It's, it's, it's one of the many things that need to happen. But I mean, outside of that, most of it is raw materials that is generally accessible in the open market, right? So copper, aluminium, et cetera, steel. It's cheaper in some of the countries in Asia, but it's also available here.

• 59:16Speaker 05

  And you don't have to worry about boats, shipping, times, tariffs and other things. All right. Last question before I let you go, Ben. You guys initially raised from, as far as I can tell, like some Norwegian investors, you went ahead and have expanded your capital base with investors from around the world. How much capital do you need to get Neo through its launch this year and then into its next generation? Is that another couple hundred million? Is that billions of dollars? I'm, I'm curious.

• 59:45Speaker 06

  The short answer is we have enough money to ship Neo.

• 59:48Speaker 05

  Okay.

• 59:49Speaker 06

  The longer answer is I think the impact that this is going to have on humanity is not yet fully understood. It's probably going to be one of the most impactful technologies ever. Everything we consume is physical in the end. And intelligence becoming general and physical.

• 1:00:10Speaker 06

  Is going to unblock essentially a new way of life, right?

• 1:00:19Speaker 06

  I am very excited to push that to happen as fast as possible. Because I think it can build a very, very beautiful world. Where we all have what we need. And where we can all focus on what makes us human instead of

• 1:00:34Speaker 06

  doing physical labor so that we can get paid. And

• 1:00:41Speaker 06

  that's a long way of saying we will probably raise money. Because we can accelerate the path.

• 1:00:48Speaker 05

  Yeah.

• 1:00:49Speaker 06

  But we don't, we don't need it to ship to consumers. That's the risk.

• 1:00:53Speaker 05

  I'm just trying to figure out how much you need. Like, uh, cause if you were a SaaS company, I could literally just like sit down, think about your number of employees and your current CAC and like, I can do the math. Robots are so different. So.

• 1:01:05Speaker 06

  Well, let me say it like this, like if you want to go max max here. It's, we need as much compute as everyone else, right? We're going to train the best models. Now we're going to train way better models than everyone else because we're going to have we're going to have better data. I'm also saying we're going to be smarter than them. There's a lot of smart people there, but we're just going to have better data. And then we need to also build out all the manufacturing. So it's going to be a very capital intensive race. That's the, that's the short way of saying that. Uh, we do have a very potentially unfair advantage here though. Which is that we can labor overcharge our own scale.

• 1:01:43Speaker 05

  Cause you're making your own general purpose robots. No, I, I mean, have you thought about just calling up Jensen and saying, look, I want to, I'm in a hurry. I'm going to, I'm going to buy so much compute. You should definitely give me another pile of money. So I can just go faster now. Like I, I guess I'm, I'm happily impatient, if that makes sense. I would like this future to become even faster. So I, I just hope that the investors out there get it. And are like knocking on your door constantly trying to give you more money because I don't think we can get to this feature fast enough. I'm just, I'm just excited.

• 1:02:15Speaker 06

  I love the positivity. It's going to echo a future.

• 1:02:18Speaker 05

  So, uh, come back on the show when Neo starts to go out to, uh, non NDA people. Cause I'm, I'm really curious to see how the reviews come and, um, I'm gonna start asking my spouse if I can, if I can throw my name down, but I think she's gonna have something more practical in mind. But Baron, thank you so much for coming on and explaining this to me and walking me through it. If people want to learn more, what's the website? Where should they go? OneX.Tech. Clean and simple. Clean and simple. This is a lot of fun. A lot of fun. Thank you for the show. I appreciate you.