Neurosymbolic AI outperforms chatbots and product search | E2327

This Week In Startups is made possible by: Vanta https://www.vanta.com/twist Agree https://agree.com YSecurity https://YSecurity.io/TWIST Today's show: Frontier AI models can ace PhD-level exams, but it's still bad at tracking down the product you want in the style that suits you. Onton's Zach Hudson tell us that the problem is that models are a black box. His solution? A neurosymbolic model, Ontology 1, that learns about your taste and preferred aesthetic over time, then produces product searches tailored specifically to you, rather than just using keywords and relevant tags. How do neurosymbolic models work, and how does Onton understand your prompts and favorite design trends? And why aren't the frontier labs working on neurosymbolic models of their own? Zach joins Jason and Lon to discuss. PLUS, following a record-smashing SpaceX IPO, Ashi Dissanayake of Spacium makes the case that the real bottleneck in space isn't launching rockets off the ground any more. It's refueling in orbit. Guests Zach Hudson on X: ****https://x.com/nosduhz Onton: https://onton.com/ Spacium: https://spaceium.com/ Spacium on X: https://x.com/SpaceiumInc Relevant Links Poolside's journey to AGI: https://poolside.ai/vision/purpose Startup Archive: Sam Altman on the Paul Graham advice that saved OpenAI: https://www.startuparchive.org/p/sam-altman-on-the-paul-graham-advice-that-saved-open-ai-always-make-an-api Kelly Wearstler: https://www.kellywearstler.com/ Ennis House: https://franklloydwright.org/site/ennis-house/ Los Feliz Living: Ennis House profile: https://www.losfelizliving.com/los-feliz-historic-homes/ennis-house-los-feliz-hcm-149 Indiewire: Ennis-inspired "The Studio" offices: https://www.indiewire.com/features/craft/the-studio-production-design-interview-seth-rogen-1235114365/ Monocle Magazine: https://monocle.com/ Spaceium on Y Combinator: https://www.ycombinator.com/companies/spaceium-inc Orbit Fab's RAFTI: https://www.orbitfab.com/rafti/ James Webb Space Telescope: https://science.nasa.gov/mission/webb/ Houzz: https://www.houzz.com/ Timestamps: 0:00 Zach Hudson joins: What is "neurosymbolic search" 4:03 Ontology 1 isn't a black box 6:31 Who is using Onton? 9:36 Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist 11:35 Could neurosymbolic models reach AGI? 13:19 Jason loves Wright's Ennis House 17:03 The shape of AI companies is changing 19:28 Agree.com - Stop chasing invoices and automate your entire contract-to-cash stack. Go to https://agree.com and tell them Jason sent you to get 50% off for life! 22:32 UGC as a data moat 26:03 The Dead Internet Theory 28:13 Ashi Dissanayake of Spacium joins 29:52 YSecurity - The on-demand security team for startups. Need enterprise-grade security without hiring a $400k CISO? YSecurity gives you 40+ expert engineers, matched to exactly what you need, by the hour, with your first six hours completely free. Go to https://YSecurity.io/TWIST 31:50 Storables vs. cryogenics: the zero-boil-off breakthrough 34:11 All kinds of propulsion requires refueling 36:09 Getting more value from LEO to GEO 38:28 Moving at rocket speed 44:54 Why demand is so acute 49:37 The investing climate for space, post-SpaceX 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 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

• 0:00Speaker 00

  LLMs are not good at this. They regress to the mean and they find the most probable thing. And that's often not what you need personally. When you need to remove hallucination and you need trust, you need to be able to see inside the model. That's where neurosymbolic models can really shine.

• 0:13Speaker 01

  You really have built something between Houzz and Pinterest that is more powerful than both of them.

• 0:19Speaker 00

  The performance of it is already 2.5x greater than some of the largest search companies in the world. It learns that new information, so you don't have to go do another training run. It's literally updating in real time. It's 1/1,000 the cost of an average frontier model training run in the US.

• 0:33Speaker 02

  This week in startups is brought to you by Y Security, the on-demand security team for startups. Need enterprise-grade security without hiring a $400,000 CISO? Security gives you 40-plus expert engineers matched to exactly what you need by the hour, with your first six hours completely free. Go to ysecurity.io/twist. Agree.com. Stop chasing invoices and automate your entire contract to CashStack. Go to agree.com and tell them Jason sent you to get 50% off for life. And Vanta. Compliance and security shouldn't be a deal breaker for startups to win new business. Vanta makes it easy for companies to get a SOC 2 report fast. Get $1,000 off for a limited time at www.vanta.com/twist.

• 1:13Speaker 03

  We got Zach Hudson joining us. He's the CEO of Onton. They're a search and discovery engine for e-commerce. It's powered, Jason, by a neurosymbolic AI model. What does that mean? You can see it for yourself at onton.com. We're going to find out right now. Zach, thank you so much for joining us.

• 1:29Speaker 00

  Thanks for having me.

• 1:30Speaker 01

  Yeah, and you can give us a demo, so let's, you know, show don't tell.

• 1:33Speaker 00

  Oh, yeah, yeah, yeah.

• 1:34Speaker 01

  What have you built, yeah?

• 1:35Speaker 00

  We can dive right in.

• 1:36Speaker 03

  Let's do it.

• 1:37Speaker 01

  Oh, and we're investors in the company, just as a full disclosure here, yeah.

• 1:42Speaker 00

  I'll give your audience a chance to go and do some, some basic searches for themselves, but why don't I jump in and do some more of the mind-blowing searches? And a little bit of, a little bit of background about Onton. We have these things called surfaces within our product. If you've ever, have Lon or Jason, have either of you used Notion before? I'm sure you have.

• 2:00Speaker 01

  Plenty of times, more than once.

• 2:02Speaker 00

  So they have these things called blocks there. They let you organize information in different ways. People do this quite often in shopping journeys, too, where they create lists, they create mood boards, these types of things. These are surfaces within Onton. And so what I've prepared for you was I put together some of your favorite things, Jason, or at least some of the hotels that you've mentioned, some of your, the things you mentioned were.

• 2:23Speaker 01

  We sent him some cheat codes, Jason. Monocle Magazine, a few hotels we know you like. We helped Zach build this for you.

• 2:29Speaker 00

  I went back through some of the podcasts, you know, grabbed a few others. But we, we, we put this into a canvas, but what the, what we've realized recently is it wasn't possible to search these without this model. And so I want to use this to demonstrate what Ontology One can do in this, this type, the amount of context these models can take.

• 2:48Speaker 01

  Okay.

• 2:48Speaker 00

  So I'm just going to go search with it. I'm going to use this just to go, hey, try to find some stuff.

• 2:54Speaker 01

  Okay, so you put images on a mood board of hotels I've stayed in and the design of those hotels. So I saw one of them there, looked like the room at the Artisan in Singapore, yeah?

• 3:06Speaker 00

  And I put even more than that. I put actual products in here, I put some text and other context in here, and Ontology is able to suck all of that up. So it's, it is a lot like a mood board, but Ontology One will look at that whole thing and it will try to understand it. The thing that I want to call out here is that it's learning in real time. We don't have any tags for these things. We, we don't have any, this isn't like human labeled. It is going off and learning them, but more importantly, it's storing that understanding in the model. So every next search that's happening with Onton is getting a tiny bit smarter and, and a little bit faster, which is incredible. We don't have to go back and do another training run with this model to get better accuracy.

• 3:47Speaker 00

  So it's learning about things like organic, burlwood, brass, floral, all the things that it thinks goes into this. And in my opinion, these look pretty accurate or look like they could belong in some of these hotels. I can almost one-shot the room that I'm going for, which is a lot more like how e-commerce search actually works, right? You have a, a chair and you want that chair to match the lamp, or you have the lamp and you want it to match the other parts of your room. Ontology can do multi-category, multi-object searches. But more importantly, it can be honest with you. It can tell you exactly why these results came back definitively. I can look down into the model, it's not a black box like a typical LLM, and see why these results came back. So it can be honest and, and explain these things.

• 4:28Speaker 00

  But I could, I can take this and I could throw this product back into this mood board and I can use this to recursively search. So I've plopped it in here, I could see how it looks.

• 4:38Speaker 01

  So you put a Cambridge love seat here in a 1930s Hollywood style. Only 16,000, Lon, so this might be right in your zone for you.

• 4:48Speaker 00

  It's out of my budget, but maybe in y'all's budget.

• 4:52Speaker 00

  But I can recursively search with this. So I've now updated Ontology's understanding, I could go do another search. But just to show you what, what else this could do, I can, I, I've prepared one more for you. I've taken one of your friends, Jason, and.

• 5:06Speaker 01

  She's mopping a sweater.

• 5:07Speaker 00

  Throw, throw, maybe this meme is already dead. I know new cycles are pretty fast these days, but I can take this one and I could also search with it. And let's just drag it into the search bar and let's see what comes back. But actually, let's augment it. Let's say couches that fit this vibe of these AI-generated.

• 5:25Speaker 01

  And the vibe being Chamot's oversized white turtle.

• 5:29Speaker 00

  Oversized, creamy, off-white, oversized, chunky knit. It's, it's learning these concepts in real time, which is incredible. So I think this is what Ontology can, this is what Ontology can do, and it's getting smarter with every search. So it's, it's literally learning these concepts. So this is, we're really excited about it. And the performance of it is already 2.5x greater than some of the largest search companies in the world. We have a smaller index size, so I can only imagine where, or, excuse me, yeah. I can only imagine where it goes once we start adding some new categories.

• 6:03Speaker 01

  Who, who's your customer for this? Obviously designers, consumers, everybody wants to find that perfect object to put in their room, et cetera. And then also people want to build a new product. So I was looking at this thinking, hey, what if I want to get custom furniture made? Or I'm a manufacturer? And I say, here's the Blade Runner apartment that I love. I'm a big fan of Deckard's apartment. So can you get a picture of Deckard's apartment and the Blade Runner set? And then make me a Blade Runner, find me Blade Runner-esque, you know, an aesthetic?

• 6:45Speaker 03

  You can definitely do this, Jason. I'm going to share my screen. I did this just to play around with Onton with Twin Peaks and it's been. So you can see, I just put in a bunch of images from the TV show Twin Peaks. And when you search, it can find anything like rustic, it knows about rustic decor. It knows about, you know, like, oh, you're looking for surreal or weird things. Like, it's actually pretty good. So if I just put like items for a Twin Peaks living room, it'll just.

• 7:14Speaker 01

  Or a bar, you know, you could make like a restaurant that looks like Twin Peaks.

• 7:18Speaker 03

  It's, it's really cool how it sort of understands like, like, it's like vibe search or aesthetic search beyond just like items or, or words you're putting in.

• 7:27Speaker 01

  This is the problem, like taste is so beyond what, and you've talked about this endlessly, Lon, just like can't make jokes, it doesn't have taste. Zach, have you cracked essentially tastier?

• 7:39Speaker 00

  I think a lot of people bundle a shopping journey into two categories or into one thing. And I think it's actually a lot more nuanced than that. I think there are wants and needs. Needs are like toothpaste, deodorant, the things that you order very quickly. They're often commoditized products. But there are a lot of other products, broadly speaking, they're above $50 and they're driven by totally different things. Taste is one of them, self-expression, emotion, imagery. LLMs are not good at this. They, they regress to the mean and they find the most probable thing, and that's often not what you need personally. So what this model is capable of doing is it's capable of learning about you, too. While it's learning from searches today, it can start to update itself to where the search index reflects the things that you really enjoy in a, in a very different way.

• 8:24Speaker 00

  So it's, it's not like there's just one output that is the most, the most likely. It is, it's creating its own separate understanding for, for every search. It's, it's learning that new information.

• 8:36Speaker 01

  I've got a question. Check this out at onton.com, O-N-T-O-N.com. We invested back in 21, 22 during COVID. And this was a search engine, this is pre-ChatGPT. The world caught up with you in some ways in terms of AI and you've now got millions of people doing searches, yeah?

• 8:55Speaker 00

  That's right, yeah. We're on track for hundreds of millions of searches over the next year, and that's only in one category. So as we move into others, I would expect that to multiply pretty quickly.

• 9:03Speaker 01

  What's the revenue model now for the business, five years in here, six years in?

• 9:08Speaker 00

  Yeah, so we do make some money from affiliate commission, but I think our, our largest revenue opportunities are in front of us. We're releasing an API of Ontology to be plugged into all these stores. And I think it, there's, the reason why there's an API instead of, say, a Shopify plugin is because we found this model to be generalizable. It can be applied to use cases beyond just e-commerce, too. But obviously, that's, that's where we're starting, that's our bread and butter. So that's, that's how we're, that's how we're making money, but we have been really focused on, on user growth.

• 9:36Speaker 01

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• 10:19Speaker 01

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• 10:49Speaker 00

  That's right.

• 10:50Speaker 03

  Yeah. Interesting. Another product category coming?

• 10:53Speaker 00

  Apparel is coming next. We see tons of searches for apparel right now that we're not fulfilling. Electronics is probably the next category after that, which you'd be surprised. I think what is fascinating about ontology is that as it learns about one category, it makes the next category smarter. Right? So as we learn what polyester is in, say, home decor and furniture, the search engine gets better at understanding what polyester means for something like apparel. And so it makes it a little bit easier for us to move into categories than what's historically been the case with e-commerce, where you have to set up the whole, you have to set up everything. You have to go through, you have to relabel all the products. You basically have to build your, your understanding of that category before you move into it.

• 11:35Speaker 03

  I was gonna say on this week in AI, this week we had Iso Konton from Poolside, and he was talking about how they're pursuing coding models as the route to AGI. Like teaching, if you make a model perfect at coding, eventually it'll become super intelligent and self-aware. I'm curious, do you feel like neuro-symbolic models could one day get there too? Because you are kind of teaching the model about our world and sensory experience in ways that a coding model is not.

• 12:02Speaker 00

  That's a really great question, Lon. I think there's a lot of use cases for large language models. I'm not saying they're not useless, but they are great at certain categories of things at a certain scale. They're, in my opinion, they're great at verifiable domains. Coding is one of those. Mathematics, you're starting to see them break into mathematics because they have verifiable, you can use things externally to verify the output of those large language models. But when you need, when you, when you need to remove hallucination and you need trust, you need to be able to see inside the model, that's where neuro-symbolic models can really shine. So I'd say the other three benefits are it's less expensive. The cost of this model to outperform those search companies that I mentioned was one,

• 12:43Speaker 00

  it's one one thousandth the cost of an average frontier model training run in the US, which is incredible. It learns that new information, so you don't have to go do another training run. It's literally updating in real time. And then the final thing is probably trust. So I think at a certain scale, this model could replace some of those things. It can do things like, I think at a certain scale, all models can actually be pretty great at a lot of tasks. But the question is, is it the most efficient model for the job? And neuro-symbolic models are quite a lot more efficient at certain tasks like the e-commerce.

• 13:15Speaker 01

  Lon, I just put in like, I got the actual image of the, I think it's the Ennis House in LA. That was a Franklin Lloyd Wright. Yeah, and I almost bought this house, by the way. My wife stopped me. Well, it was only going for like $4 million.

• 13:32Speaker 03

  I feel like if you own a Frank Lloyd Wright house, you have to like let people in it to look around, right? Like you kind of, it's like, you own a piece of art now.

• 13:41Speaker 01

  Well, and it was, so there's that. Then you also, Zach, had to restore it. And all these tiles that he made, you would have to go source. And it turns out, Ron Burkle, also a fan of Blade Runner and Real Estate, the billionaire, bought it. I think he spent like $15 million renovating it.

• 14:04Speaker 01

  And then I don't think he sold it. I think it's now part of like, like some sort of.

• 14:11Speaker 03

  Wait, is it trust? Yeah, I think, I think, I think you're right. I'll look at it.

• 14:15Speaker 01

  Yeah. If I remember the history correctly, so when I was like, starting to look at it, it was like, yeah. You, this is not gonna work, son. You're gonna need to spend $10 million for a two-bedroom house. That is totally the opposite of kid-friendly. It's totally dysfunctional house. But it was, this is like a really powerful solution that you've come to. Is this now a small, like a, a small language model that you own and is proprietary? Or is it like more like a harness? When we think about it as a business and the IP here, what is it that you actually own here?

• 14:49Speaker 00

  Yeah, this is a fundamentally different type of model. So the way that you can think of a large language model, or actually to oversimplify the brain, you have like two halves of the brain, right? Maybe an intuitive side and a logical side. LLMs are kind of like the intuitive side of the brain. But it's when you apply a logical side to the brain that you really start to unlock the intelligence of it. Some people, psychologists, sometimes talk about this as like Systems One versus Systems Two thinking. LLMs are great at Systems One. So the, the, this is literally a completely different model. It's a union of a couple of different things, but we do have a lot of things we built in-house to make this work, including a graph database that stores a lot of this information. And it stores information in a very different way. So yeah, it's, it's, it's a completely new approach.

• 15:34Speaker 00

  I wouldn't even call it a small language model. You could scale this thing up a lot.

• 15:38Speaker 01

  Really super fascinating and it's great. Like it's one of the amazing things, Lon, about when you invest in smart people. You know, especially when you're like, we're a super early stage. I'm, I'm not sure exactly, you went through the accelerator, then we put a little extra money in. I mean, this was years ago and I've been like, wow, I wonder what's going on with Anton. And like just, you guys just grind it and grind it and grind it and figure it out. But you, if, my investment philosophy is passionate founders who are gritty and figure things out and are passionate about that space, so they're not gonna give up. And here we are. This is an incredible product. What's the future of the company, do you think?

• 16:15Speaker 00

  Yeah, I think we're launching that API for a lot of reasons. Like there's this, this great quote from Sam Altman quoting Paul Graham that says, "You should always create an API, no matter what, good things will happen." And, and the, that was, we can largely think the Cambrian explosion of AI products that happened from that. It was around GPT-3 that they launched their API and you see all, all the opportunities that develop. So we see a very similar path for this model for our company. Yes, we do still want to be the starting point of every shopping journey. Actually, let me, let me kind of reframe this differently because I think the, the shape of companies are changing right now. Previously, you would be a product company and you would have a technology that would really,

• 17:00Speaker 00

  you would have a technology that would help you reinforce that strength. I think companies are kind of becoming a little bit different now, especially with the AI labs. You have a model that is good at certain tasks and your product is a reflection of what the model is good at. In our case, we have a neuro-symbolic model that is great at e-commerce and all these trustworthy searches where you need accuracy. All the other AI companies that are using large language models, they're, we could take OpenAI, for example. They have a model that was great at conversation, so they launched ChatGPT. And now they're starting to see code, the ability to do code, so they launched Codex. So I think the, the, the structure of companies is changing and Anton is no, no different.

• 17:40Speaker 01

  Amazing. And just like now, I'm totally distracted, putting in Tyrell Corporation.

• 17:46Speaker 03

  It's fun to play with. Notably, the Ennis House, it is featured in Blade Runner. It's Mayan, Neo Mayan architecture. It's in Los Feliz. It was memorably in the 1959 horror classic House on Haunted Hill, Jason. That's the, that's the, the other most iconic movie. And that's one of the reasons it fell into disrepair. That movie came out in the late 50s, it was a sensation. And kids would then go find that house and like scream and carry on outside because it was in this big horror movie. And so people, nobody wanted to live there. And so in the 60s, it fell into abandoned disrepair. Isn't that interesting?

• 18:18Speaker 01

  Well, and to show you the, to show you the depth of this and how crazy it is, I was watching this incredible show on, I don't know if it's HBO or if it was Apple TV, but I'm watching Seth Rogen's show, The Studio.

• 18:34Speaker 03

  It's Apple TV.

• 18:36Speaker 01

  Apple TV, thank you. And it turns out, and I was like, I said to my office, is that the Ennis House that we wanted to buy? And the production team behind the studio looked to Wright's Mayan Revival Designs for the.

• 18:47Speaker 03

  There it is.

• 18:48Speaker 01

  Company's headquarters. And so look at this. In this image, you see the Mayan tiles. They're not exactly the same, but you're, you're like, why is this so iconic? And there it is. That's the Ennis House. That's the studio, conference room. And it's just super fascinating to me. And this is the interior of the Ennis House. Like that's the walkway.

• 19:14Speaker 01

  And here is, you know, this like famous scene with Ron Howard and Seth Rogen. And they, they did this. Now, if they had Anton, my lord, they could have gone even deeper on this. So I think for, Agree is the number one fastest way to go from contract to cash. And right now, they're offering an exclusive deal just for Twist listeners. Go right now to agree.com, sign up for their all-in-one contract to cash workflow. And Agree is going to give you 50% off for life. That's right. 50% off for life. If your company makes money through contracts and invoices, you already know it can be painful, time-wasting with all these steps.

• 19:55Speaker 01

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• 20:39Speaker 00

  That's easier.

• 20:40Speaker 01

  The canvases, the mood board canvases, like this feels like it could have a Pinterest-like workflow where you could get designers, Kelly Wearstler, a friend of mine, or any of these folks to start making canvases here. In fact, Lon, can you make sure we get this to Kelly?

• 20:57Speaker 03

  Sure.

• 20:58Speaker 01

  And do an intro with Zach and Kelly because she's gone crazy on AI.

• 21:02Speaker 03

  Yeah.

• 21:02Speaker 01

  You know Kelly Wearstler, I'm sure.

• 21:03Speaker 03

  I, I know someone who knows her very well. Let me put it that way.

• 21:07Speaker 01

  That's how you know that person is good. I know Kelly. Anyway, you know, she has her own studio and, you know, like this whole warehouse and everything. And she's just obsessed with AI. I think you should get some of these like iconic folks as advisors to the company. Have them start making their own mood boards. And I think. Taking the next step, which is I want to make this product in the world. That's like another interesting angle, like what, what can't I find that I could manufacture? So there are all these interesting people who like to manufacture products. So this, what this could do for furniture, for decor is just unlimited.

• 21:50Speaker 01

  I, you, you really have built something between Houzz and Pinterest that is more powerful than both of them. And that would create a sharing, like if I could share, can you share the canvases? So I could share one with Kelly.

• 22:03Speaker 00

  They're, they're starting to be public. We, we, uh, we're, we're going down that exact same line of thinking.

• 22:10Speaker 01

  What I like about that one, Zach, by the way, is it creates a viral loop. And you know, this is when you go to market law and one of the tactical, practical things that founders sometimes forget, which is what if one person who uses my service introduces it to 1.5 people or 2.6 people and then gets in a search engine? Oh my Lord, then the site starts growing while you're.

• 22:34Speaker 00

  I, I think one other thing I would say about that though, is while it does help with a viral loop, I, the L L M's and these, these AI companies are cannibalizing the data that they rely on for, for training. Like, uh, you see, think about how many bloggers, wire cutters, these types of posts that used to exist in the world are disappearing because there's no incentive to write them anymore. They're just going to get sucked up immediately. If you don't build content into your product, if you don't build user generated content into your product, you have nothing to train on or give these recommendations. Super important for e-commerce. That's why we started doing it now, but it's also super important to think about for a lot of other categories of searches.

• 23:14Speaker 01

  Yeah, I, I just, I'm thinking about the number of pages that Google has for Anton that are, um, yeah, I wonder like how many pages you actually have in there. And this is super interesting. Do you get a lot of search traffic anymore or is that game over?

• 23:33Speaker 00

  You know, I don't think that, I know people are talking about A E O a lot. Uh, we invested a lot into S E O and it still continues to drive, uh, users over to Anton.

• 23:43Speaker 01

  Wait, what was the term you used?

• 23:45Speaker 00

  A E O, answer engine optimization. Other people call this G E O.

• 23:48Speaker 01

  G E O is the other one, generative engine optimization. People go back.

• 23:51Speaker 00

  In my opinion, it's largely the same thing. They're, they're using existing search indexes and they're putting L L M's on top of it. So the things that worked for S E O kind of still work for A E O and G E O. It's almost more of a wild, it's almost more of a wild west than it used to be. But, um, you do have to think about how people query these things differently. Like the queries aren't just keywords anymore. They're, they're much more long form. So that's the only difference between S E O and A E O, in my opinion, on, on how you're creating content.

• 24:18Speaker 01

  It's still all about Wikipedia and Reddit, it turns out. If you, if you rank well in those sites, you do well no matter what.

• 24:25Speaker 00

  Exactly.

• 24:25Speaker 01

  Um, it does create some signal. I did see somebody create a fake, some of my social media feed, somebody created a fake brand, a fake deodorant brand, and then they got the L L M's to think it was real. And if, within like three weeks, they had chat G P T, uh, singing the praises of a fake deodorant brand. So AI slop and what, what is that term they use Lon for? Like the moment the web went from human to.

• 24:57Speaker 03

  In shittification.

• 24:59Speaker 01

  In shittification is one, right? That, that's like with products deprecating, but there's another one, like the human created web then giving way to this non-human web, um, where there's a, there's a major problem on Reddit right now with AI slop going in there. And this is where we're going to just have to make a decision of the human web versus AI slop. What do you have thoughts on this, Zach? And what, what we need to do?

• 25:25Speaker 00

  This is why we built ontology one, like, uh, we were, we put L L M's on top of e-commerce data and it would hallucinate all the time and come back with products that were completely irrelevant. Uh, e-commerce data is so bad because it's been gamed for so long. And, uh, just imagine how that's going to compound. So that's, that's why we built ontology. I, I really don't envy the position of a lot of the social companies right now. I know Reddit is dealing with bot attacks constantly. We're, we're dealing with bot attacks and AI agents that are going after Anton regularly. It's, it's, it's a, it's a big pain point.

• 25:58Speaker 03

  Jason, I think you may be thinking of the dead internet theory, the idea that over time bot, bots will take over and there won't be any human created content left to find.

• 26:09Speaker 01

  I think we already hit it, but it's the, um, yeah, when automated accounts and AI slop outnumber the traffic and or content.

• 26:20Speaker 03

  I think you, I think you already see this most prominently in comment threads and replies, where once there used to be robust conversations, after almost every piece of content you could find, and now it's almost always a bunch of bots talking to each other, overwhelming human participation.

• 26:35Speaker 01

  This is why I charge $1 a month to reply to most of my tweets. I have 2,200 people out of my 1.5 million followers. So 1% would have been.

• 26:45Speaker 03

  I'm one of them.

• 26:45Speaker 01

  15,000. So I think I have 10 basis points on my follower count who pay a dollar a month. I give it to charity and I'm, I'm increasingly think I'm just going to give it back to people randomly. Like I'll just pick one of the, I may just pick one of the followers, 10 of the followers.

• 27:01Speaker 03

  X money, you could just easily do that. That's right.

• 27:05Speaker 01

  And then it's just like, okay, you're, I mean, I might get then maybe a, uh, one of these organizations is going to come to me and say, I'm, I'm creating a gambling, multi-loving marketing scheme. Because if you put a dollar in, you have a chance at 2,200 every month.

• 27:20Speaker 03

  You're going to be the Bernie Madoff of X money. Yeah.

• 27:24Speaker 00

  Of the people that pay, are there still bots?

• 27:26Speaker 01

  No, no, there are MAGA lunatics who think I'm a libtard. And there are libtards who think I'm a MAGA lunatic. And I told them, you can insult me, you can flame me. If you give the dollar to charity, I'm all with it. It's totally fine. Uh, Zach, continued success, everybody. Check out onton.com.

• 27:51Speaker 00

  Thank you.

• 27:52Speaker 01

  Very cool product.

• 27:53Speaker 04

  All right. Today we're going back to space, my absolute favorite place to build. There are so many startups that are doing interesting things in our local orbit and in our solar system. I love talking to all of them, especially right now in the wake of the SpaceX IPO. I feel like a lot more people are paying attention to space. So it's a great time to sit down with the founder, building something I think we're really going to need and not many people know about. So please join me in welcoming Ashi Dissanayake to the show. She's the CEO and co-founder over at Spacium. Ashi, how you doing?

• 28:20Speaker 05

  I'm really good. Thank you so much for having me here.

• 28:23Speaker 04

  I'm so glad because we get to talk about a pet topic of mine that I really love, which is in orbit or in space refueling, which I think is really cool and really matters. But I think a lot of folks don't know, uh, what it is and, and why it does matter. So could you start by just giving people kind of the, the, the basics as to why this is a problem that needs to be solved now?

• 28:42Speaker 05

  Yeah. Okay. So space industry is growing rapidly and we see so many space companies coming up with like OTVs, uh, moon missions, launch vehicles, and all these missions and all these spacecrafts are currently limited by the amount of fuel they can carry. So which compromises on the payload, uh, how much payload they can carry, how far, how much further they can go in space. Uh, they can't do crazy manuals, they can't change orbits. So that's what we are solving because it's like, just think of us like a service station here on earth, but in space. Because when the highways were built and like on earth, you need fuel to go from one corner to the other side of the city, right? Like, oh, from LA to New York, you need, you need fuel.

• 29:27Speaker 05

  But that's exactly what we need in space as well. So to go from place to place, you need fuel. And right now all these spacecrafts are launched by the exact amount of fuel they need for the mission. And to replace with fuel, you need to compromise on the payload or you need to compromise on the range they can travel. So that's where we come in and we basically give them more options. So now they can travel further, carry more payload.

• 29:52Speaker 01

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• 30:55Speaker 04

  The flexibility point is very interesting. I hadn't thought about how the fuel load would be exactly tied to the target mission. But if something goes wrong and they need more fuel, what do spacecraft do today to compensate for that crisis?

• 31:07Speaker 05

  Right now they just have to scrap that whole mission and send a new one. So yeah, it's and it's not just about missions going wrong, even the missions that do go well, they're currently being compromised.

• 31:19Speaker 04

  So what types of fuels do we need to have in these future orbital gas stations that we're going to have? Because I know there's a variety of propellants used in space. And I'm curious which ones you think are the most important and the ones you're going to target first.

• 31:31Speaker 05

  That's a really good question, so I'm going to give a bit of background. So we are doing two types of fuel. So we are doing storables as well as cryogenic fuel. So storables are xenon, prepped on hydrogen. And cryogenics are the ones that have to be stored at really cold temperatures, like minus one hundred and ninety Celsius or minus three hundred and ten Fahrenheit. So that's, that's really cold. So that's liquid oxygen. Yeah, that's for an example, liquid oxygen. So if you don't store these fuels at these really cold temperatures, what happens is the temperature increases and you basically have to boil off, like which means it's like water, like it boils off and then you had to wind it out. Otherwise, we are going to see an explosion.

• 32:10Speaker 04

  So you paid all that money to get it up there? And then you have to slowly release it if you can't keep it at the proper temperatures? That's terrible.

• 32:15Speaker 05

  Exactly, exactly. So that's something we practiced during Y C. We went through summer of twenty twenty four, we went through Y C. So me and my co-founder, we cracked that problem. We built zero boil-off technology, fuel tanks. So our first idea was, before we even got more customer demand, we were solving for crashing. We were solving to store and transfer cryogenic fuel. Because that's what the initial demand we got from our customers. So that's why we solved the zero boil-off technology. And then we got reached out by other customers who wanting like water or xenon. And so then we decided, okay, now we had to be more fuel agnostic. So we are going to do storables as well as cryogenic fuel. So our initial customers or initial stations will have xenon.

• 33:00Speaker 05

  And then those customers are also moving into cryogenics in the long term. So, so yeah, we are fuel agnostic.

• 33:05Speaker 04

  So you're going kind of both ways then, both the fuel you have to keep cold and the fuel you don't, simply because customers want both. Do cryogenic, do fuels that need to be kept cold have a different use than fuels that are less temperature sensitive? Are they used for longer flights or shorter bursts? How does that break down?

• 33:22Speaker 05

  Yeah, so it also depends on what our customers, the other spacecraft, their mission is, right? So with cryogenic fuels, you get more energy. So that's the basic idea. So for an example, if you want to go to the moon, they mostly use cryogenic fuel. So we have customers who's asking for like ten metric tons of project fuel because of their moon mission.

• 33:43Speaker 04

  Tons?

• 33:44Speaker 05

  Yeah, so because of their moon missions.

• 33:47Speaker 04

  Just to be clear, Ashi, we're talking about bringing all this up from the planet's surface to orbit, and then you're going to provide it, but we're not manufacturing anything in, in space yet at that quantity.

• 33:56Speaker 05

  No, no.

• 33:57Speaker 04

  Wow, that's a lot of weight.

• 33:59Speaker 05

  That is a lot of weight. That is a lot of weight, yes.

• 34:02Speaker 04

  Okay, before we get too far into the weight point, I want to ask about electronic propulsion. Because I think probably folks listening to this have heard about the use of electronic propulsion to do some in-orbit maneuvers. So why does that fail to meet the needs of customers that are coming to you for cooled or just kind of gaseous liquids?

• 34:18Speaker 05

  Yeah, so electric propulsion, like, we think electric propulsion can also really benefit from refilling. So it's not like, oh, electric propulsion is not going to work. It definitely works, but they will benefit from refilling. So electric, chemical, even nuclear propulsion will benefit from refilling. Because NASA is building nuclear propulsion and he has spoken to some, like some companies and who thinks they will need refilling even for their nuclear propulsion systems as well. So it's not just one type of propulsion system won't work. It's just they can get more benefit from being able to refill.

• 34:52Speaker 04

  Let's talk about launch costs, because I was talking to the folks over at Star Cloud, a very cool company. They want to build in-orbit data centers. And the CEO was like, look, our entire company is a bet on launch costs coming down. If Starship and New Glenn don't work, then we don't work because the economics don't pencil out. I'm curious about what needs to happen to make it feasible to bring up those ten metric tons that you mentioned earlier of fuel? Because I think right now, if we did it all with Falcon nines, it would take a while just to get that one set of material up to orbit. So do you guys also depend as much as other companies on heavier launch vehicles coming to market?

• 35:29Speaker 05

  No, not really, because our initial customers are asking for like two metric tons, five metric tons. So we have that capability currently to actually send the amount of fuel we need. But yes, when we have, when we have to like scale up more, like ten to thirty metric tons of fuel, yes, then we do have to depend on heavier launch vehicles where we have to, where we can send more payload to space. But we are not depending on Starship or New Glenn for our business plan to work. Because right now, everybody launches. And where we actually give value to our customers, how much they can do once they go to space, once they know. So that's when we actually give them value. They can go from Leo to Gio.

• 36:12Speaker 05

  I, I, I watched your session with Tom Mular, where he mentioned like for their helios, they could, if there was a refueling station, they could get more value out of their helios from Leo to Gio. That's exactly what we are providing. So we don't really depend on the launch vehicles, like launch vehicle cost.

• 36:31Speaker 04

  Well, but it does improve your economics. I presume that if we had lower launch costs, then it's a tailwind, not a, not a glass wall.

• 36:38Speaker 05

  Exactly, because at the moment, like we all pay the same amount for the launch. And, and also our technology allows us to store for longer periods of, to store fuel in, in space for long periods of time. So that actually benefits.

• 36:54Speaker 04

  Zero boil-off thing that you invented during Y C, yeah.

• 36:57Speaker 05

  Exactly, as also we can store, like for xenon as well, we can store for longer periods as well.

• 37:02Speaker 04

  That's fantastic. Okay, so quickly, before we get into what you've built and how it's performing up in space, to me, this sounds like a, a piece of work that will apply equally to both commercial and defense applications. One, is that correct? And two, where are you seeing the most early demand? You mentioned customers earlier. I'm curious where they're going to fall on the commercial defense continuum.

• 37:25Speaker 05

  Yeah, so we see demand from both sides. So we don't just see the demand from the defense side. We have, like, all our customers right now are commercial customers.

• 37:37Speaker 04

  Oh, interesting.

• 37:38Speaker 05

  Yeah, because like Y C says, build something people want. So we actually wanted to make sure we have people who's going to buy our product. So we iterated so much with what our customers want. So we would build something and then we would always get feedback. And then if they don't like it, we would change it. So, so we got like, over the last two years, we got so much feedback in terms of hardware, in terms of type of fuel, in terms of how our hardware should dock their spacecraft or how, like, how much risk they're willing to take. So.

• 38:10Speaker 04

  I, I'm blown away by how fast your company has moved. You went through Y C and, oh, I forget, was it two thousand twenty four?

• 38:16Speaker 05

  Yeah, summer.

• 38:17Speaker 04

  Yeah, so summer, summer of oh four. And you've already put some media into space, you've already done iterations, invented technology. But when I was growing up, space was slow and hard and it took forever to do anything. And you're thinking about, you know, like multi-decade NASA contracts and, you know, cool stuff. Like, everyone loves the James Webb Space Telescope, but like, oh, God, did that take a while? So what's changed that allows, and I say this with respect, a scrappy startup like Spacium to do so much so quickly?

• 38:43Speaker 05

  Yeah, so, so me and my co-founder, we, we love space. We are like, we, that's what we leave for, to, for Spacium, to send pillows to space and to build this refueling station. So we built our first payload in five months, just the two of us, in five months. And it was the most accurate robotic actuator ever tested in space. The way we did that was obviously obsession was a big part of it, like, because we never felt like we are working. It was just every day doing something we love. But, and also we brought all the testing in-house. So our iteration cycle was really quick compared to how space industry usually works.

• 39:26Speaker 05

  So we brought all the testing in-house, we do ninety-eight percent of the testing in-house. So, so we build something, we test, and if we test it until it breaks, and then we improve on it. So that's why we were able to like do so much with so little time.

• 39:40Speaker 04

  When it comes to, you know, the fast iteration of building something, testing it, improving it, building it again, we're going to talk about your robotic actuator in a second, but this is a piece of, of physical technology. So do you guys also have like in-house milling machines and all the necessary tools to quickly prototype and iterate?

• 39:58Speaker 05

  Yeah, we do. We have T-Racks, we have vibration bins, we have like.

• 40:02Speaker 04

  And you can do all that with, with two people?

• 40:04Speaker 05

  We did that with two people for the first mission, but then for our next missions coming up, we, we are growing. Now we are a growing team, so it's not two people anymore.

• 40:13Speaker 04

  I was going to say, like, I mean, someone has to like into the trash cans, do the payroll. Like, I mean, there's a lot of work to do just to keep the business from falling off a cliff.

• 40:19Speaker 05

  Yeah, yeah.

• 40:20Speaker 04

  Let alone do it all.

• 40:21Speaker 05

  Yeah, it's not anymore. So we, it, it does, we had like, we have so many fun stories about what happened during our first mission, because it was just the two of us and it was like crazy. We have one of our advisors, he was one of the early employees at SpaceX. And at one point, because we went quiet, like we did not reply to him. And then he was so worried something happened to us because it was the two of us building pillows. And he was like, where's Ashi? And we're like, we don't know where you guys are. And then, but we were fine, obviously. And.

• 40:53Speaker 05

  We are fine, but and then after the first mission, we started hiring. Now we have amazing engineers who's also equally obsessed.

• 41:01Speaker 04

  How big is the team now?

• 41:02Speaker 05

  We are, we are seven people. We also like to keep the team lean. Uh, because it, it helps us move faster because then we don't have a lot of bureaucracy and we don't have like, everybody owns their piece in the fossil and then moves forward.

• 41:16Speaker 04

  Yeah, no, I, I've heard that from software entrepreneurs, robotics entrepreneurs, space entrepreneurs. It's like every single person is like, we want to have the, the minimum viable team accelerated with as much technology as we can. So that makes good sense. Let's talk about your mission though. So, uh, you were on a rocket last December. And then this February, you announced that you quote flew the most precise robotic actuator ever tested in orbit.

• 41:39Speaker 05

  Correct.

• 41:40Speaker 04

  So what does that mean? And why does it matter for in space refueling?

• 41:44Speaker 05

  So robotic actuator is what goes into our robotic arm. So with that precision of the actuator and exactly end of the robotic arm will be, we'll have a precision within zero point five millimeters. So with that type of precision, we can dock to the incoming spacecraft so precisely, and it reduces the risk for all our customers. And then we start the refueling process. So that's why that type of precision is so important because if you can't dock to the incoming spacecraft precisely,

• 42:16Speaker 05

  it's going to be a huge disaster basically.

• 42:18Speaker 04

  Are the tolerances that fine for docking onto a spacecraft? Okay.

• 42:22Speaker 05

  Oh, yes.

• 42:23Speaker 04

  Okay, why? And, and this is just pure ignorance leaking through here, but I, I, it doesn't make sense to me that it has to be that precise, given that we're talking about a thing and then, you know, like, why can't it just like, like fit once it's like attached?

• 42:36Speaker 05

  What do you mean? Why can't it fit?

• 42:38Speaker 04

  Well, you know, like, um, go back to Apollo thirteen, the movie, right? When they're trying to like, put the thing into the, the connector port, like it kind of like missed and kind of like hit and it kind of slid in. Why, why isn't there a little more tolerance to the linking of your future space station refueling facility and the spacecraft? Like what drives that lack of tolerance?

• 42:58Speaker 05

  Yeah. So if you can't dock precisely, it really increases the risk for our customer. And that's not a risk our customers are willing to take. So this is another feedback that we got from our customers. So.

• 43:10Speaker 04

  Are, are spacecraft just less tough and durable that I'm imagining? And therefore you have to treat them very, very carefully and therefore the tolerance is also fine?

• 43:21Speaker 05

  That's also a funny part because, uh, we build cars to like be really robust, right? But we build spacecrafts like they're like little babies that we can't even like do anything. Like if something goes wrong. And also in, in, in space, we, we don't have humans on board. So if something goes wrong, we don't have, usually we don't get a second chance to fix that. And, uh, so that's why we want to make sure everything goes smoothly. The first time, very less risk. Because with risk, like it's like a chain of events, like more things can go wrong.

• 43:53Speaker 04

  Okay. So you got your actuator up in space. You showed that it works. What is next for the company as you work towards building your first actual facility that can accept fuel and disperse it?

• 44:05Speaker 05

  So we have a, a Xenon fuel transfer. This is the first time I'm saying this out loud. So yeah, you guys are the first to know. So, uh,

• 44:14Speaker 04

  Thank you. We appreciate that.

• 44:15Speaker 05

  Yeah, of course. We have a, a mission coming up really soon within the next year also. So we'll be transferring Xenon in orbit. And then we have another mission coming up within the next one and a half to two years where we, we have a free flying spacecraft and we will be docking into another spacecraft and we also transing Xenon in orbit. So those two missions are coming up and we are fully funded for those as, as of right now.

• 44:39Speaker 04

  Fantastic. Now, uh, demand for in space refueling is not a hypothetical. It sounds like. It sounds like this is a, a very acute need now, essentially. Okay, cool. Cause sometimes you build ahead of the market. Sometimes you chase, you know, what's needed, but it sounds like the moment you can get this up and running, you're going to have business.

• 44:58Speaker 05

  Exactly. The, I have heard from like bigger companies. They're waiting until someone actually put their stations up there. And then they will start building their technology around refueling. But so it's just kind of like chicken and egg problem. And also we do have customers who spring and take that risk and actually, uh, get, you know.

• 45:20Speaker 04

  I, I'm really excited. Once we have this technology, I think it's going to make everything so much easier and less, uh, unfault tolerant. And then hopefully you can build more robust things and do more stuff and get back to the moon. And then I can go to space too. It's going to be fantastic. So I did talk to a company called Orbit Fab a while back and they were talking about their kind of like standardized enterprise refueling interface, which I think is kind of the connector port. So I'm curious, what's the spacium approach to connectivity to other spacecraft? Is that a difficult thing? Or am I over indexing on a bit of news that I just happened to remember before we jumped on the chat?

• 45:53Speaker 05

  No, that's, that's a really good question. So that's something I would love to talk, but there will be some really groundbreaking news coming up about standardization really soon. So let's keep an eye out and, uh, yeah.

• 46:07Speaker 04

  Can you, uh, give, give me a hint about really soon? Like should I have my eyes peeled in the next couple of weeks or is this like a Q4 thing?

• 46:14Speaker 05

  Oh, no, like within the next month or two.

• 46:16Speaker 04

  Okay, okay, cool. But we'll have to have you back on to talk about that because I'm really curious. Cause I, I mean, like, um, I hope that there's eventually a USBC equivalent for in space refueling. So that way it's super simple, super quick. And everyone can use the same thing to avoid, I mean, just an unnecessary plethora of dongles in space to do the connections that we need. I just, it seems simpler to have one standard. So I'm, I'm hopeful, uh, on that front.

• 46:40Speaker 05

  Yeah. Standardization is really important. And also it's not just about standardization. It's about how that standardization works and adapts to the current and current spacecrafts or future spacecrafts. Right? So, so yeah. Keep, keep eyes out for it.

• 46:55Speaker 04

  Uh, we were talking at the top of the show about how, once we have the ability to refill spacecraft, they can do more. They, they can, you know, make more adjustments to their course or change their mission or just be more flexible. And right now, if, if you have to send up a spacecraft with all the fuel it's ever going to have, that impacts design choices. Once you take away that requirement, how quickly will we see spacecraft design change to take advantage of what you guys are building? And also just to have more, I don't know, longevity per spacecraft?

• 47:25Speaker 05

  Oh yeah. We will see that change coming in the, within the next three to five years. Because when we started a space and we had to tell space companies, okay, if you refill, this is what you get. But now I don't have to explain to them anymore. Like they already know and people are already trying to build their spacecrafts around refueling, thinking that, okay, there will be refueling in the future. Okay. If like, because it's such a constraint and with all that mass you want to put into space and with all these, uh,

• 47:59Speaker 05

  things that we want to do in space without refueling is such a bottleneck. So, so people have actually started developing their space missions around refueling.

• 48:07Speaker 04

  Okay. Now you mentioned that your next couple of missions are already fully funded. I know you guys raised, I think it was a 6.3 million dollar round last year in quote, four weeks. That's impressive.

• 48:18Speaker 04

  Are you guys going to need to, to access more capital to finish those missions? And have you already kind of apportioned that? Or does that initial round take you all the way through your next few missions?

• 48:26Speaker 05

  Yeah, we actually raised more money after that seed round. It was, so we have raised close like 12, uh, 30 million dollars so far. So that covers all our three missions, the one that which we already flew and this next two missions as well. I'll bet we are planning to like, um, come into like, uh, more profitability soon because we do have commercial contracts, uh, close to a hundred million dollars and we have two billion, two, two plus billion dollars in LOI. So all those ones, all those commercial contracts convert, we'll be able to, uh, be profitable.

• 48:59Speaker 04

  Yeah, it's kind of rare that a, that a company comes and says, yeah, we have $2 billion in LOIs for our technology that we're still building. I think that is the definition of building something people want. Now, thinking about the broader, uh, venture ecosystem, if, if you look around technology today, it's kind of all AI and that's absorbing a whole bunch of capital. Is there still enough investors out there focused on space to keep the space startup industry healthy in the near term?

• 49:27Speaker 05

  Yeah, I think so because space is really hard right now. I have been reached out by multiple investors wanting to talk and better understand our, uh, our technology. So I think investors are really still interested in space industry as a whole.

• 49:43Speaker 04

  And then last question is just, you know, pretty simple. SpaceX went public recently, mentioned this at the top. I'm curious if that has changed the, the level of excitement and enthusiasm around investing in space focused companies? Because I think a lot of people that weren't paying much attention and kind of forgotten, you know, the fast launch cadence of Falcon 9s and all that. Uh, are now once again paying more attention and I wonder if that's, uh, creating positive momentum for the space industry?

• 50:08Speaker 05

  It definitely does. Yes, I would say that. Yeah.

• 50:12Speaker 04

  And how does that, how does that manifest?

• 50:13Speaker 05

  Because I think a lot of people started giving more attention to the space industry after SpaceX IPO. They're like, oh, space industry. And then they became so interested. Okay. There's actually, there's an industry that we should pay more attention to. Because, uh, it wasn't, space industry was there, but people weren't really paying attention until SpaceX IPO. And now people are like, okay, space industry is something we should actually talk about. There's so many cool things happening. There's all these new technologies coming up. And end of the day, space industry will benefit life on earth. So once people understand that, people will pay more attention. But I have, I have been reached out by a lot of people wanting to talk about space industry after SpaceX IPO.

• 50:57Speaker 04

  That makes me so happy to hear. I, I feel like, uh, it's always good when an IPO can kind of shine light on an entire industry and help everyone at once. Oh, actually, Ashi, before I let you go, uh, you are building this company in San Francisco and I tend to hear about companies building things that fly and zip around, building in Southern California. So can you just tell me why SF or the Bay Area kind of writ large is the right place to build spacium?

• 51:22Speaker 05

  It's because after YC, we realized SF is maybe want to be, uh, because the energy is crazy. Everybody's, everybody wants to build something huge. And that's the, and as space and we are.

• 51:38Speaker 05

  We are weird. So we want to be in a place where there's a lot of people who wants to be like really crazy things. And that really, and so, so we really felt like SF is where we belong. So, so now we made that decision. We want to be in SF.

• 51:56Speaker 04

  I, I love San Francisco. I miss my city. But what about, what about you guys is, is quote, quote weird? Because I feel like everything we've talked about is within the, the normal envelope of like technology. So are you weird in a space startup sense? Or are you guys a different type of company in startups as a whole?

• 52:13Speaker 05

  Uh, I think it's a little bit of both. It's just how we test things, how, how fast we move. And even like when we intervene, uh, people, we are not, we are never looking for, uh, technical knowledge. We are looking for some type of technical knowledge, but we're always looking for that mindset who wants to, who are obsessed, who wants to just work from 5:00 AM to like 8:00 PM. So that's when we actually start working, like every morning we start at like 5:00 to 6:00 AM and then, and then everybody just grinding. But it's such a fun working environment. That, that's, that's the cool part. Like, sometimes I had to tell people like, please go home. They're like, oh no, I want to finish this. I'll go home after. I'm like, no, please go home.

• 52:59Speaker 05

  So, so it's not like I'm forcing anybody to stay up late. Everybody wants to stay late. So, so weird in the sense it's like,

• 53:07Speaker 05

  how every, everything we build is, we, we always find a way to, we always find a hack to make it cheaper or how to move faster.

• 53:18Speaker 04

  No, I, I really appreciate that. I think the, the hours you work sound incredible. I just, I, I, I have tiny babies and so I, I cannot do that. But I'm always very jealous of people that can devote that much of their time to their core projects. I, I feel like I get 60% as much done each day as I need to, and it compounds over time. So now I'm about eight years behind. Anyways, Ashi, uh, thank you so much for coming on. What's the URL? Uh, so people can go find out about the company? And are there any roles you're hiring for that you would love to shout out to, uh, our audience?

• 53:50Speaker 05

  Yeah, we are looking for software engineers. So if anybody who's obsessed and want to build really cool stuff that, that can send to space, yes, please reach out.

• 54:00Speaker 04

  And it's, uh, spaceium.com, S P, S P A C E I U M dot com.

• 54:05Speaker 05

  Yes.

• 54:06Speaker 04

  So check it out, everybody. All right. And when you announce the, your next mission or two, we'd love to have you back on, Ashi. But in the meantime, go for it, kick ass, and, uh, let's go to the stars.

• 54:14Speaker 05

  All right. Thank you so much, Alex. It was really nice talking to you. Thank you so much for having me.