This Week in Startups - TikTok Ban, Data Centers in Space… and What's a Vector? | E2073

Episode Date: January 17, 2025

Today’s show: Alex sits down with two CEOs from companies freshly added to the TWIST500. We get a masterclass from Bob van Luijt of Weaviate, on vector databases helping AI and LLMs do what they do. An then Philip Johnston from Lumen Orbit explains his vision of building data centers in space! * Timestamps: (0:00) Alex kicks off the show. (0:58) Supreme Court ruling on TikTok and key market numbers (3:49) Interview introduction with Weaviate and Lumen Orbit CEOs (4:06) Deep dive into vector databases with Weaviate's Bob van Luijt. (8:23) Lemon. TWiST listeners get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (11:35) Exploring deep learning, vector indexing, and Weaviate's open-source significance (18:03) Northwest Registered Agent. For just $39 plus state fees, Northwest will handle your complete business identity. Visit https://www.northwestregisteredagent.com/twist ⁠today. (19:32) Discussion on vector databases use cases and retrieval augmented generation (RAG) (27:17) Weaviate's business model, revenue streams, and growth (28:12) Vanta. TWiST listeners automate your SOC2 and get $1,000 off at http://www.vanta.com/twist (32:52) The future of AI, agentic architectures, and enterprise adoption (40:50) Lumen Orbit's vision for space-based data centers (42:54) Philip Johnston on the technical aspects of Lumen Orbit (49:12) Satellite demonstrators, technology challenges, and VC interest (53:26) Addressing chip obsolescence and cost advantages in space (56:21) Deployment strategies and competition in the space data center market (1:00:12) Strategies for staying ahead of competition and future plans for Lumen Orbit * 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 Check out Weaviate: https://weaviate.io/ Check out Lumen Orbit: https://www.lumenorbit.com/ * Follow Bob: X: https://x.com/bobvanluijt LinkedIn: https://www.linkedin.com/in/bobvanluijt * Follow Philip: X: https://x.com/johnstonphil LinkedIn: https://www.linkedin.com/in/johnstonphilip/ * 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 * Thank you to our partners: (8:23) Lemon. TWiST listeners get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (18:03) Northwest Registered Agent. For just $39 plus state fees, Northwest will handle your complete business identity. Visit https://www.northwestregisteredagent.com/twist (28:12) Vanta. TWiST listeners automate your SOC2 and get $1,000 off at http://www.vanta.com/twist * 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 * Subscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

Hey, everybody. Welcome back to Twist. This is Alex. Today is Friday. We have a pretty special show for you today. I'm talking to two founders of two of the most interesting private market companies in the world. But before we jump into that, I have a couple of news items because I don't want you to go into the weekend behind on all things that matter. This weekend startups is brought to you by Limmon.io, hire pre-vetted remote developers. Get 15% off your first four weeks of developer time at lemon.i.o slash twist. Northwest registered agent. Starting your business should be simple. With Northwest Registered Agent, you can form your entire business identity in just 10 clicks and 10 minutes. From LLC to trademarks, domains to custom websites, they've got you covered. Get more privacy, more options, and more done. Visit Northwest Registeredagent.com slash Twist today. 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 SOC2 report fast. Twist listeners can get $1,000 off for a limited time at vanta.com slash twist. First up, right as we were going to record today, the Supreme Court of the United States upheld a law that would force a divesture or ban of TikTok in the U.S. on January 19th. This is enormous news. I've only had a chance to skim what Scotis wrote, but the gist is that if you were hoping that TikTok was going to be saved at the very last moment, you probably not. We're going to see some chaos next week. It's going to be a big story.

Also, don't forget, of course, next week is the inauguration. so it's going to be packed, stick close to twist. Now, on the news front, a couple of things for you. I have three numbers that you need to know. The first one is $65.4 million. The second one is $150 million, and then the third is $12.5 billion. What are we talking about? Well, the first one, $65.4 million.

That is how much money former accounting startup bench had in liabilities when it failed. A question in the market when bench went from alive to dead seemingly overnight. is, why did that happen? Well, I don't think people understood the liabilities the company had racked up. Now, TechCrunch reports that much of the money is actually owed to the National Bank of Canada, so figure that out as you want to Canada, but also employees, investors, and executives are also owed money. This is a mess. It's still being unraveled, but shout out TechBunch for getting even more on the bench saga. Number two, $150 million. That's how much money, Crypto Wallet Phantom raised this week. And it raised that $150 at a $3 billion valuation, so a multi-unicorn price tag.

The crypto company claims 15 million monthly active users and more active traders and trading revenue than wallets from Metamask and Coinbase wallet combined back in November and December. All that's to say that people are really using it. What's going on? Well, after the most recent election and Bitcoin reaching $100,000 per token, people are once again bullish on all things crypto. There's been a vibe shift. as we say. And when that happens, well, consumers get more interested in crypto, crypto on ramps,

crypto exchanges, crypto wallets, and then companies like Phantom see a nice bump in their usage, and then the investors show back up. Crypto loves to go up, crypto loves to go down. Right now, we are on one heck of an upswing. And our final number for today is $12.5 billion. That's how much money insight partners just raised for a number of new funds. This includes, as you might expect, a flagship fund, and also money for, quote, a dedicated buyout co-invest fund. So expect a multi-strategy approach from Insight with this new amount of money. To put the $12.5 billion into context, I just want to say that we have been seeing venture concentration lately. The number of funds that are raising money seems to be going down,

and the larger funds seem to be doing the best. So the richer getting richer and the emerging managers are struggling. That's why when I see a $12.5 billion raise from Insight, I'm like, yeah, I can see that. That fits actually pretty well with the news cycle we have seen. All right, now that we are caught up on the news, I want to do two interviews. One with the CEO and co-founder of Wii V8 and then with the co-founder and CEO of Lumen Orbit. Why these two companies, well, they are the newest additions to the Twist 500, our ever-growing list of the 500 most important private market companies in the world, but also because I think they're incredibly interesting companies that highlight where we are seeing a lot of innovation in the market.

So, first up, co-founder and CEO of Wevi-8, it's Bob Van Lout. Wevi-8 is a startup that's a bit in the weeds of the AI revolution, but I really do think it can become an absolute household name in the world of technology in short order. We'll talk vectors with Bob, and then we're going to jump into an interview with Lumen, which is all about space. Let's go. So by now, you know all about LLMs and GPUs, but to understand how many AI apps are actually built today, you're going to need to understand vectors and vector

databases. They are a critical enough part of the modern AI stack that there are a number of startups working on them, including VAspa, Pinecone, and WeV8. Now, I've known about WeV8 for some time, including back before ChatGPT, changed everything. And given how critical vector databases are to AI apps today and how early WeV8 was, along with strong factors and an innovative open source model, those are the reasons why I added the company to ArchWist 500. Now, today, to walk us through Why Vectors Matter, I have Bob Van Lout, the CEO and co-founder of WeV8. Bob, welcome to the show. Thanks for having me, Alex.

It's great to see you again. It was quite some time ago. So this is awesome. I know. So for people who don't know, way back in the day, I think it was like 2019, 2020. So way before chat cheap, T, you and I sat down and you were incredibly patient and explained a vector databases to me, which I retained for probably about two weeks and then it flew out of my head because I don't do what you do, but it really does seem like the market has come

towards WeV8 and you've become a company name that I feel like I see quite often when I'm doing AI research. So, Bob, I was thinking today, you and I could do a little bit of a class, if you will, and start by walking people through vectors and why they matter. And then I think we'll talk about Weviate and the future, but I think a little background's going to help. This sounds wonderful and I look forward to it. So, uh, all right. Let's go. What I'm going to do is I'm going to try to explain a series of concepts as they build on one another, and then I'm going to lean on you for some confirmation.

So, first of all, a vector. It's a mathematical thing. It's a numerical representation of data that provides both magnitude and direction. So essentially, how far away and in what direction? That's correct. One out of one. Nailing it. All right.

Now, vector embeddings are essentially assigning vector values to words and or, or, you know, sentences. So essentially it's taking data and then assigning those vectors to those individual pieces of unstructured or structured data. That is correct as well. And to add to that, the reason why that's so interesting is because if we ask ourselves the questions, how can we make sense of any type of data that's unstructured, be it language, be it images, be it audio, can be anything? What method can we use to do something valuable with them? And the answer to that question is if we organize them in space and we do that by assigning vector embeddings, we can work with it and we do that by distance calculations.

And we'll probably go to double click on what that means, but that's why they've become so valuable to work with unstructured data. And when we talk about things in space, Bob, I pulled up a graphic here that I think shows people a little bit of what we're talking about here. This shows the proximity of several different data points. And essentially my understanding here is that because we would have vector embeddings for these different concepts, wolf, dog, cat. We can see the distance between them and see that they are relatively close to one another, which means that they are related. That is correct. And

the way we do that, and that's why it gets so exciting, is that the researchers who put in the work, and by the way, we're not talking about recent work. This is done way back. The only issue was that we didn't have machine learning to train. We'll get to that. But that was that these researchers ask themselves the questions, how can we somehow say something about language in any way, shape, or form that it relates to each other? All right, founders, let's be real. Finding great developers is tough, especially when you're trying to run and scale your startup and raise money. All of this leads to you having slow product velocity. But here's the good news. I've got a tip that's going to save your time. It's going to save your money and a ton of headaches. You need to check out lemon.io. Lemon.io has thousands of on-demand developers who can help you. They've done the work to find and vet developers who are experienced, who are results-oriented, and who charge competitive rates. Great developers can be hard to find and integrate into your team. Lemon.io handles all of that for you. Startups choose Lemon.i.o because they only offer handpicked developers with at least three years of experience and who are the best of the best. Just one percent of their candidates are accepted. And if something goes wrong, lemon.io will find you a replacement developer, ASAP.

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That's a piece of information. So what these researchers came up with for language, and this is super exciting, they said, what we can do is we can count words in a sentence. So we can basically say, if we, for example, take the word group Eiffel and Tower, so the Eiffel Tower, probably somewhere in that sentence, we're going to find Paris and not Madrid to make something

that. That is what it does. So that is what they came up with. We can make distances by counting the distances of words in sentences. But for example, when we deal with images, same question. So how can we say something about images? Wait, and images based on pixels. And pixels have a color.

So if we have a Granny Smith, Apple, we're probably going to see a lot of green in certain situations in that image. Yes. That's how it's done. So it doesn't matter what the modality is. It can be anything, but the researchers think, how are we going to say something about distance? And that's what they compress and capture in that embedding. And this is a very cool toy, unless you have the ability to use machine learning to do what's

called vector indexing, which is going through a large dataset, and then, applying vector embeddings, the numbers, essentially distances, to those data points. And Bob, this is when it starts to feel to me less like technology and more like magic, because I get everything you're saying, but to me, having a machine learning model know how to assign the vector numbers to the discrete data points is where I struggle a little bit. So I was hoping you could just kind of double click on that so we can all understand better. Yes. So let's go back to the first example of the cities, right? And I assume that the audience can picture an Excel file in there. So picture an Excel file. So we want to say something about, let's start with three cities. So we start

with San Francisco, New York and Boston. So we have in every column, we start with the headers, San Francisco, we have New York, we have Boston. And we do that in the first row as well. And then we're basically going to say the distance from Boston to Boston is zero. The distance from Boston to New York is X and the Boston from San Francisco one and so forth. If we want to do this for every city in the world, you get a pretty big Excel file. Yeah. Right? It pretty big Excel file.

And now if you say, well, let's have a machine, calculate any random distance between cities, it takes quite some time to do that. Yeah. But now imagine that you do this with words and that you create this Excel file for every single word that you can find on the web. Long story short, you can't do it. The time it takes to go through that huge Excel file that you created, it just doesn't work. So the academic research and the philosophy behind this is like, I believe, almost 100 years old.

We just couldn't do it because it was just, we still today, we do not have the computing power because it gets slower in a linear fashion. Every word that we add gets a little bit slower. So now the idea came, and this was in the wake of deep learning, 10, 15 years ago, is like, what? if we make that Excel file, but rather than just calculating everything brute force, we're going to train a model to predict what that distance is. And that worked. And people are like, whoa, this actually works. And that was the unique thing that happened because now all of a sudden we can, so I started

my career with a thing from Stanford called Glove, trained it on Wikipedia. And now all of a sudden, we could just do that. I could just do that on my laptop. And that was, there was a breakthrough. Where I lose you slightly here is the way that I understood vector indexing was that these are essentially the pre-calculated distances between different vectors. But you just said that in the Excel Street model, it becomes very difficult to do because it scales linearly in terms of complexity. The prediction element that you just said, or the probability element you just said, can you can you double click on that and how deep learning allows us to predict or project distances and be more efficient? So if you take a sentence like, well, the Eiffel Tower is in Paris, right?

That's a group. Let's just make it even easier. Like, Eiffel and Tower, just those two words. So what you do is like you encounter that word group in a sentence. And then back in the day, you had to calculate all the corpus of Wikipedia. How often does that happen? Right. But now we train the model.

I say, model, what do you think the distance is between Eiffel and Tower? And we call that co-occurrence. So co-occurrence in a sentence. And then the model started to predict, well, I predict that the distance between the word Eiffel and the word tower is one. And that is where it became really good at. So the more we train it, the better it got at it. There's some caveats there, but sure, just for the sake of argument.

And now all of a sudden, rather than going to the whole data corpus of Wikipedia that we had in our mental Wikipedia page, to say, like, is actually the office? the distance between iPhone Tower 1, we now had a model that could predict this way faster than brute force calculating that bygrowing through our whole Excel file. So it took something that was essentially impossible, going through the whole Excel file of everything, and made it not only faster but possible, so it was kind of a double win, and that unlocked quite a lot. Now, we start with vectors, which is numerical distance and magnitude direction.

We have vector embeddings, which are numerical values associated with individual bits of data, vector indexing, essentially figuring out how far apart they are. All of that is stored inside of a vector database. And this brings us to EV8 because you guys have made an open source vector database. Yes. So because now when we had, so 10 years ago, when we had these embeddings and we started to work with them, we were like, okay, this is great. We now have these embeddings. We couldn't do stuff with them.

But then the thought of what do you do with stuff that is in a space, you calculate distances. You want to know that there's a relation between Eiffel Tower and Paris and those kind of things. Or if you have documents and you want to store the documents or the images, those kind of things. And then the thing was, hey, wait a second. The vector embedding was a very, very obscure data type. All of a sudden it comes into prominence because of this machine learning thing where even now, if you take the modern models today, like from Hugging Face and you would open them up, it's vector embeddings all the way down.

And you need to store them somewhere and you need to have them somewhere. And we were like, hey, wait a second, there's no database purpose built to deal with this new data type. And just not to nerd out too much on databases, but because the thing is that there tends to be this thing happens in the database industry, which is a rather large industry for people listening. It's a large, it's a very big industry. Oracle's a big company, I hear. Yeah. Yes, yes. But look at in the NASDAQ, right?

So you see a lot of database companies are on the Nessig and are including Oracle. Yeah. And so what happens is like if a new data type comes into prominence, so, for example, think back in the NoSQL day with documents, graph databases, and so on and so forth, that tends to be like a new wave of database companies. And we're part of that. So when I started this, I was not aware of this market dynamic. I learned that by doing it.

But that's why we saw that, hey, new data type, who does this? Nobody. Ah, we can. Right. So that's kind of how you jump into that opportunity, because that new data type immerse, what we didn't know back then, that ML would turn into what we now call AI and how big it would become. I could claim that I foresaw that, but of course I didn't.

No, sometimes you're early, and then suddenly the tree drops a lot of fruit down upon you. And that's called product market fit by both preparation and luck. Yes, there's a famous quote from, I think it's from Mark. And recently where he says, you know you have market fit if, and I might be paraphrasing, but he says like, if the market puts two fingers off your nose and pulls them towards you, that's what happened. What AI did for us, right? You were like, whoa.

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are ripping the product out of your hands. But that same idea. It's when the market is coming to you and going more, more, more. Okay. But let's talk about why the market carries about what you've built. So the way that I understand it is vector databases unlock several things that make AI applications are essentially useful, one of which is RAG or retrieval augmented generation. And for folks who are not familiar, the way that I understand RAG is you take an LLM, you have your own data, and it allows you to have your own data interplay with the LLM without needing to retrain the original model. Yes.

So there's one piece of history, I think, that's relevant here. And that is one of the issues we had. So what we did was if we stored data in the database with these vector embeddings, we looked at the words, for example, in a paragraph of data. So you might remember that I built you a little prototype. I do. And how we did that was so we looked at the individual words, I mean, of the database, looked at the individual word,

assigned all these vector embeddings and then said, what's the center of this paragraph? And that's how it was placed in vector space. Yes. The challenge with that was that the more words you had, and the bigger your paragraph became, the more it centered in the center of vector space, losing its meaning.

Essentially, it became too generic. Yes. And the reason for that was that the individual vector embeddings don't know anything about the next vector embedding in your paragraph. And now there was a paper release that was called Attentional Unite, the famous Transformers paper, who solved that problem.

So rather than looking at that co-occurrence, does Eiffel sit next to Tower, the model played, and I'm, you know, I'm doing air quotes here, it played a game of telephone. So it's like, hey, next to Eiffel, we have tower, and tower we have in, and next to in we have Paris. And that's how it starts this. And now all of a sudden, the model kept the context, or it could predict the next token in that, in that string of vector embeddings.

And that is generative AI. Yes, exactly. So what happens is if you have a sentence. So the Eiffel Tower is in, dot, dot, it takes all these individual words that is translated into a so-called token. It's just an ID. It's just an ID. So it turns it in them. It requests from the model the vector embedding for all these individual IDs, creates a string of all these vector embeddings.

And then it says, model, please, based on these vector embeddings, predict the next one. It returns a vector embedding. We do similarity search on it. It says it's token 512. And we say, what is Stokin 50012? Well, Paris. Eiffel Tower in, dot-da-da-da-da-Paris. Paris. Yes.

So that's how it does it. And so that existed, by the way, this model. So we had something inside Weviate. We call it Generative Search, where we did that. Yeah. But it was not really adopted. And then there was this company. You might have heard of them.

They're called Open AI. Oh, yeah, yeah. It brings a bell. Yeah, yeah, yeah. And they created, they're like, how are we going to show this to the world? We're going to do this. Because I've friends.

who were at Open Air Work, and they were asking, all of us were like, how are we going to make people aware of this? And they just did this smart thing with the chat interface and good for them because now all of a sudden, people were like,

whoa, this is amazing. And the immediate follow-up question was, how do I do that with my data? Yes. And we were like, hello. Good news. Well, so, I mean, you were there with the technology to help people take their structured

and non-structured data and get it into a vector database so that it could be used in an AI context. But did we need to do any work on the models themselves that actually did the vector embeddings and assigned all these values? Or was that technology already in the market and off the shelf, if you will? Okay, so the answers, no.

So if you look at the original research paper, the retrieval augmented generation research paper, it was more sophisticated to what we do today. So what that paper argued was, you know, you can make these big models with information. What if you can somehow make them smaller? So think about like a understanding the language with zero knowledge,

but that it knows that if I need that knowledge, I need to augment what I generate by retrieving something, right? So retrieval augmented generation. And that was the idea. So the first, we call it internally, we call this primitive rank. So that was just used the vector database to retrieve the unstructured data, pipe that over to the model and generate the output that already create a lot of value for people. But now there are two interesting things happening.

So one is like there's a lot of work happening to more intertwine the models and the database together. On his side note, that's where the name Weeviate comes from, from weaving the model and the database together. But the second thing that's happening, that people are like, hey, wait a second, if we do this rack stuff, so we have a query, run to the model, get us from the database. to the gentleman and answer, that's a one-way street. What, if we just pipe that back into the database? We call that an agent. I was going, aha.

This is where I was going with this. So actually, I'm going to stop you there and say, let's just make sure we understand what vector databases are used for today. And I'm literally pulling from your sales material on the Wii VA website. But similarity search, hybrid search, and enabling rag are what I might consider the key kind of corporate use cases for vector databases today? Yes, that is correct. But there's another way, another perspective to, of course, that's how we, you know,

tell the world what you can do with we've yet, but I'm assuming that the people, I mean, I'm 99.9% certain that people listening to this are tech antishes, right? So what is interesting is that of course is the technological innovation. Of course. But what cannot be underestimated is the developer experience. So one of the things that happened there as well is that the way that you can build these kind of applications that you just mentioned where the functionality is indeed the hybrid search and the offloading and that kind of stuff. But it's more exciting. You can now build this in like five lines of Python code combining the model and with it.

And that is, of course, the new thing in this new paradigm of AI infrastructure. So essentially, WeV8's vector database handles the tricky bits of vectors for you. the major models handle all the tricky bits of making a large language model, and then me, the developer with a bucket of data and a OpenAI API key, just to pick one provider, maybe an anthropic API key, I can very quickly go, Bing, Bing, connect them together, weave them, perhaps, and then out comes an application, and I look like a genius internally, and I get a raise, and now I'm the CTO. Exactly.

And that is the, so the, what's happening now, a lot of work that's happening now is that the, the barrier to entry for developer is going down fast. Yes, which means that the aperture for what can be built is getting wider. Yes, exactly. And I always like to say that because we often talk about this technology of like, how does it work on the hood? What is the functionality? And I appreciate all that.

But I also want to say how important it is to help the developer. Because, you know, there's like a lot of genius developers walking around, you know, who know how to do these things. But there's also a lot of people. Just if you just, you know, out of color. and you work for a company and you want to build a rack application.

That stuff's not easy. So we're doing a lot of work to help people just to get started with five lines of code. And that cannot be underestimated. There's a lot of work happening there in these new AI infrastructure companies to help not some developers,

but all developers to build these kind of applications. Great segue. We're going to talk business model, then we're going to get to agents really quick. So the thing about WeeV8, one element that I like about the company is that it's an open source piece of software, with services attached to it.

And these seem to come in two varieties. One is essentially a serverless managed instance. And then you also have what I would call enterprise partnerships with major cloud providers. I think on the website you have AWS GCP and also Azure, so the big three, which means that I can go essentially if I'm an Azure customer and I can spin up we V8 vector databases on my existing cloud infrastructure. Apart from that, is there another element to the business model that founders listening

should understand, or are those the two main planks today for WeV8? Those are the amazing. We also have something called a BIOC, which stands for bringing on cloud. But the nice thing is, and that's your point, is that thanks to the open source model, there's a wide variety of deployment options. So that's correct, yeah. Yeah. Nailing product market fit is every founder's top priority.

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What's driving the majority of revenue growth for Wii V-A-t today? Nothing has changed there when it comes to traditional infrastructure companies. Enterprises, they pay the most from a qualitative perspective, startups are more quantitative, right? So lots of startups, but they have smaller bills, enterprises, they pay more, right? So it's very, there's nothing new on the sun there. Totally. That's what I figured, but always worth making sure that something unexpected isn't happening. You know, we were joking earlier about the market coming to you and being a little bit early and ahead of a major wave. I presume weviate's growing very quickly. What can you tell us, Bob,

about the company's growth last year and what you're shooting for this year? So indeed, we have an open source model. So what's important to bear in mind if you build infrastructure, that takes time and investment to actually build. So we started to monetize halfway through 2023, and we started doing that with our serverless offering, which is just shared resource and those kind of things. That went very fast. And then we started to also get the, yeah, because it's the startups, right? So they're relatively smaller, but they work, you know, they just want to build these new kind of things. They adopt quickly.

Yes. But when we released that, when we got to our first million dollars, it was like, that was just in an instant with just all these kind of developers. But then something interesting happened because we also got the first enterprise requests in. But the problem was I didn't have any enterprise sellers. I mean, I now have like built last year a whole team. And I remember. So the anecdote that I have there, that was like the first ever enterprise contract,

they asked for an SLA. So I was like, how do I get? to an SLA. So I just downloaded one from the web. So these developers, they already built, they were ready to go live. And I was talking to procurement. Oh, no. Not procurement. This guy sends me an email and he says like, hey, come on a call. So I joined him on a call. And he held up like the procurement. He said, like, what's this? SLA? I said, what's this? I said, that's an SLA. He said, you have to promise me one thing. He said, I'm going to sign this, but never, ever show this document to anybody else. And then I was like, now it's the time.

to spin up our enterprise skills. And now we have an amazing team with enterprise sellers. And so we saw a lot of growth last year. And that was because of the enterprises going live as well. And that was also related to the fact that a lot of these applications went to production. So that is like, that's what happened, basically. And so that's all gone well. It's the good old old fashioned story of open source infrastructure project.

gets adoption, wants to scale up, starts bottom up, enterprise sellers come in. That's, again, a very classical story, but it's excited to see this happening. And it's like, there are even database companies, and I will not mention them. But on their earnings calls, we are mentioned. That's like, what do you think about these new kind of players? So it's amazing what's happening right now. And it's a, I couldn't be, you know, prouder than I am today. So it's like, we're like, we just cross 100 people.

So it's fantastic. Yeah. So let's talk about the future thing. Because you mention agents. And this is the thing that everyone's been, been talking about. I think if I read the phrase agentic AI one more time, I'm going to lose it. I think that what we've ended up with is the word agent meaning too many things at once and therefore meaning nothing.

Now, earlier, you set up a very specific mental model of how agents work, which was, if I may, feeding information back into a model after a rag process. but I may have gotten that wrong. So, Bob, from the WiiVA perspective, what is an agent? And is it a real thing or a marketing term? So what an agent is, an agent does something with your data, right? So rather than just presenting it, you give it a prompt to do something with it. So let me give you a simple example.

We have this concept of generative feedback loops. So when data goes into the database, there's an agent that looks at it. And you could give the prompt. every data object that comes in needs to be written in American English. So if you send in a million in American English and one in that's in Spanish, it will store it as American English, right? So that's an example of a very tiny, simple agent. So is it a real thing or is it a marketing term?

The answer to this question is yes. So it's absolutely a real thing. But of course, it's also a marketing thing. And I don't mind that too much because it's like a, yeah, we somehow need to explain to the world what's happening, right? And then the market kind of consolidates on certain language and that kind of stuff. Yeah. But it's definitely a thing.

So the steps that we've been taking was like from Vector Search to Rack, which was at one-way street, to the agents where you create these feedback loops. So what we see is that the majority of customers started with Vector Search. So we have a lot of these e-commerce kind of things. We now have like a lot of ASK-K-I features. that we're powering. Let me come up with an example. So the search in the sort of ASGII feature in superhuman, for example.

That's a great example of that sort of runs on VEviate with a model. Then that's WRAC. And now we see that people start to build the new things with these agents. The reason why that's so interesting was it if you go back in time to affect the search, then the devil advocate's argument for what we're doing was like, yeah, this is all great, but isn't this just search? Right. And it was kind of true, right?

But then all of a sudden, this new unique use case emerged in REC. And now this new use, these set of use cases because of what we like to call these ergentic architectures are emerging, and all of a sudden validating more and more and more and more the existence of what we call these vector databases. So that's why it's so important to us and that's why we talk about it so much, because we believe that that's a unique value that not from technology perspective, but from a business perspective that we bring to the world. So, I mean, just to be clear, without vector databases, there is no agentic AI, period,

full stop. Well, okay. So, you know, if you want to open a bottle of beer, you can also use a spoon to do that, right? So it's like a, if you want it open, you can always open it. But ideally, you just use beer opener, right? So it can open. And that's what the fact that database does. It just, it just makes it easier to do that.

It's just, it's built for it. That's why people adopt it and use it. That goes back in time. You mentioned like the famous old school database in existence is Oracle. There's probably somebody listening to this podcast sitting there. And he's like, I can do everything in the world with my old database. Sure. Yes.

But it turns out that the majority of developers just want to use the tooling that is built for those specific use. Exactly. And this is the old famous comment on Hacker News when someone announced Dropbox and they're like, oh, that's just a quick data pull and I can build that myself. No one's going to use it. Well, it turns out to $10 billion company. Yeah, but if I may say something about that. We use that one.

We use that often internally at V8, exactly that threat on Hatternews because, but sometimes people like hardcore developers, like the best, the crown or the cram of developers, sometimes forget, is that not everybody is like them. Ah. And that is a large group of developers.

They just want to build great software for their own business, for the company they work for. Sure. And they need to help, they need to tooling to do that. And that's where the developer experience plays such an important role. And that can, in my opinion, cannot be underestimated. Bob, can you leave us with just your perspective on where the AI industry is going this year from the kind of like enterprise app perspective?

You talk to a lot more people than I do who are probably willing to share more than they're willing to share with me. So in conversations with customers, partners, rivals, and so forth, what do you do? see happening this year that we should be looking forward to? So what I really hope this year that we will see is the paradigm shift that we're expecting from AI. And let me explain what I mean with that, is that the biggest problem in data today, since day one we're storing data is master data management.

If I, excuse my French, the shit and shit out paradigm has not changed. But thanks to the agentic architectures, for the first time we can have these models, they can have an quote-unquote opinion on your data. And for the first time, as I always like to say, we can turn chicken shit into chicken salad based on the data. That is the biggest issue we've seen. I hope you don't mind me. I'll have to cut this out. But we have a, we have a beep. We'll just put that in. No, but this is the first time where we're seeing that the biggest problem in data management, in general, master data management, that there's a solution at the rise. And that's enabled through these agentic architectures. And that's why I'm

so excited about. Am I excited about all that other stuff? Of course. But this is not, this is the paradigm shift. Why does fixing that crap in, crap out paradigm change the world? Because to me, we've gotten this far with that chicken shit to chicken salad. Why do we need this? And what will that change for the industry? The issue is like, yes, we've gotten very far, but we're not where we want to be. So I recently spoke to a CTO of a large company and he said, if my leadership asks me, like, how many products did we sell globally last year? I cannot answer the question. I have the data, but it's such a mess, and I cannot have humans try to fix it.

Data is coming in faster than the people can fix it. I cannot solve this problem. So this is a huge problem, but let's not forget that is solving an existing problem, very important, but it will also open the door to new businesses and new ways for people to new products, new startups, new ideas. based on this new paradigm. And what that is, I don't know, because if I would have known, I probably would have to be running those startups as well.

But that is what I'm so excited about. It's going to solve that ageful problem of bad data, and it's going to open the door to new products, new solutions, be it in the enterprise, be it in startups, like everywhere. Well, that is a great note to end on because you just told people that might be looking for an idea, what to build next, what to go forth and do. that's an enormous possible new opening.

Yes, and also, this is the time. So do you remember those days that people were like, you can build these mobile apps or these mobile? Do you remember that people were like playing around with this thought? Vividly. This is it for AI. Now, start now, not next year. Now, that's how excited I am about this because this is the start of the new paradigm.

So this is the time to build. I am so excited for 2025. I think it's going to be busy. as heck. And I'm also really glad that by having you back on, I got to go back through all my notes about what is a vector and had to. I don't live in your world every day, Bob. Thanks so much for having me. All right. Bob, thank you so much. Thank you. And next up, co-founder and CEO of Lumen Orbit, it's Philip Johnston. Lumen Orbit is a bet that in the future we are going to put our digital brains in space instead of inside on the ground floor of the local gravity well. If you care about how big a rocket we can shoot up into space and how much it can carry,

well, you're going to absolutely love what Lumen Orbit wants to do with lower launch costs and greater launch capacity. Let's talk. If there's one thing we talked about a lot in 2020, it was the need for more compute to help power our AI future. Be it new chips from Nvidia, new data centers around the world, it was a recurring topic for a reason. We're doing a lot with AI, and it's very compute intensive.

But when we think about what we are doing to power that commute today, it's worth keeping in mind how much energy goes into the process. So if we take a look at this chart right here, this is a map of the U.S., breaking down each state by how much of their total power consumption, data centers use today. Now, there's a handful of states in the 10 to 15% range. There's one state that's 26%, but most are between 1, 4, 5.

However, things are going to change. Here's some data from Bain showing how much the anticipated gain is in energy demands for data centers over the next couple of years. It's going to be exponential, probably, and therefore, we're going to need new solutions to help make sure that we can power all the compute we need to build the AI that we want. So what are we going to do to find all of this power? Well, we could turn to fusion. Some people like Sam Malman think that that is going to be a near-term solution. Some people want to get back to building more nuclear reactors. But there is this thing called the sun that we can also

use to pull out energy. Now, I'm sure you're thinking about solar panels and great farms here on Earth, but what about solar panels up in space? Powering data centers that are up in the sky. Well, that's what one startup is doing. So I want you to please welcome the show, Philip Johnston, the co-founder and CEO of Lumenorbit. Philip, how are you? Hello, Alex. Thanks so much for having me. A huge honor to be on Twist 500, so I appreciate it.

Thanks, Ben. It's a fun project on twist 500.com. If anyone wants to go take a look, we're trying to find the, basically the startups are going to have the biggest impact on the world, both in terms of changing how we work and also making a lot of money. And I think that Lumen Orbit could be one of them just because of the sheer scale of your vision.

And that's where I want to start, Philip. So when I think about data centers, I think about large, nondescript buildings that I drive by it, I think, is that a warehousing facility or a data center? Hard to tell from the outside. You clearly have a different vision. So let's start with where does this idea come from? And then I want to talk about progress made so far. Yeah.

So we are building, as you mentioned, very large data centers in space in order to be able to take advantage of the abundant energy, the ability to rapidly, to passively cooling space and the ability to scale. So the idea came from, we initially were looking at space-based solar. and when you have very low launch cost, there's an argument to be made that that starts to make sense, even with this huge efficiency loss transferring energy from space to Earth. It's not a new concept that's been around since like the 70s or 60s. But, you know, I think in 20-year-time, the forecast are that half of all terrestrial electricity consumption

will go into data processing. So if we can find a cheap way of getting the data centers to space, instead of having this 95% efficiency loss using microwaves to transfer the energy down, we can just use all that data, in all of that energy in space. Now that's really where the idea came from. So the video we just showed, I think, details the scale of what you're thinking about because we're not talking about a little bit of compute power in space and we're not

talking about a couple of solar panels. We're talking about a, I think it's a four kilometer per side square block of solar panels, which I presume is thousands and thousands and thousands of cells. To me, it feels a little science fiction to say that we're going to do this. So I'm curious, are we at the point? in which we've sorted out the technology here, and it's more a question of, can we execute this economically?

Or is there still some technology risk to what you're doing? That means we still have a lot of things to sort out. The only risk remaining is that the launch cost needs to come down by a lot. So I will say this to investors. If you don't believe that the launch cost is going to come down by 10x in the next five years, we are not a good investment. If you think it's going to come down by 100x, we're an extremely good investment. And if like Starship PR, you believe it's going to come down by a thousand X, then we'll be the world's most profitable company.

So may it be a thousand X. I mean, honestly, I mean, I'm here for it. Just because I'm curious about this, have you been watching the Blue Origin new Glenn launch delays? That doesn't mean that we're not going to get competing heavy launch vehicles, I hope, right? It would be great if we have some competition to Starship in terms of the launch cost. Okay. So passive cooling in space. When I think about space, it's cold.

Humans can't live up there for clear reasons. But is it easy to actually get rid of heat when you're up in orbit? I'm not actually sure about the physics of that because one thing we know is that data centers do consume a lot of power and make tons of heat. Yeah. No, it's a great point. And the core part of the technology that we're developing is a very large, low-cost, low-mass, deployable radiator.

So, I mean, the quick answer is, no, it's not super easy. And the reason is if you don't have an atmosphere, you don't have convection or conduction. There's nothing against the laws of physics that stop us doing this. It just requires a very large, they call it blackbody radiation to radiate in infrared into deep space. We just have to keep this black panel at around 20 degrees C or higher, and that will radiate a lot of heat in space, 800 watts per squameter. For those folks like myself who look at circuits and outlets and with terror and trepidation, how much is 800 watts? I don't have a good feel for how much energy dissipation that is. Is that a lot?

I mean, a typical household light is around, you know, 20 watts, depending on if you have LEDs. Another way to look at it is in proportion of the solar panels generation versus the radiator. So one square meter of solar panel in space generates around 200 watts, and one square meter of radiated dissipates around 800 watts. So you need about a quarter to the size, the surface area of the solar panel on the radiator side. So we're going to need a four kilometer by four kilometer square of solar panels to power, I believe it's a five gigawatt data center. And then we need a one kilometer by one kilometer square of radiator to dissipate that. Okay. Yeah.

That to me is ambitious. But I absolutely love the idea that if launch costs get down low enough, we can at least get everything up there. Once it's up there, is there a risk that the solar panels are going to get dinged by space? debris, micro asteroids. I know a little bit about space shielding, but you're building an enormous target. It feels like up in space. So how do you, how do you keep a say from little objects that are zipping around so fast? To solve the problem of orbital debris, either you fly very low or you fly very high. So in the first two missions of flying, it's called Vialo, very low with orbits around anything below 400 kilometers is very clean orbit. You don't really get hit by anything there because

there's a low levels of upper atmosphere. So stuff de-orbits naturally within a few months anyway. It's where the ISS flies, for example. The problem with that is you need propulsion. So as a satellite is bigger, you need more energy going into propulsion so you don't slow down. So then once the satellite gets large enough, then you can fly very high around 1,200 kilometers. That also means you're always in the sun, which is great. The problem with that is then you're into the Van Allen radiation belt, and so you need more shielding from radiation.

That's also very clean. There's hardly any orbital debris up there because most people are flying in this Leo band of 400 to 800 kilometers. So that's really how you go about it. But there's a trade-off. then between orbital debris and radiation. And it sounds like it's a better solution, a better problem to have in much higher orbits down the road.

The amount of compute you can have scales with the volume of the satellite and the shielding scales with the surface area. So as the satellite gets bigger, the total amount of shielding you need as a percentage of the mass of the satellite goes to zero. Essentially, if we have a larger satellite, it's flying fire. It's fine to fly higher. So for these small puny ones that we're doing with the demonstration, we're flying very low.

Once we scale up, it's okay to fly it far. Hi, hi. And the demonstrators, I believe, start to fly as early as May of this year. So quickly, tell us what the first demonstrator will be capacity. What are you going to be able to show this year once you get it up into space? Yes, there's about a one kilowatt, 50 kilogram satellite. It's about 100 times more powerful GPU computer than has ever been flown in space. We'll have the state-of-the-art terrestrial invidia chips.

And that's really the big difference of the first one with what everyone else is doing. So normally you would fly radiation-hardened these jets and chips from an invidia. They're at least 100 times less performant than the state of the art AI training chips that they have. So you're going to put that up into VLEO. And then we get to have the fun conversation of how do you get the data up and down? Because just thinking about what you want to do, I get the idea of having lots of constantly on solar power. I understand the cooling effects you can probably take advantage of. There's lots of space in space.

There's many things that make sense to me. But you have to get data up and down, which to me sounds very tricky to do at a high speed to allow this to have the kind of throughput, I presume, you need. So talk to me about how you get information from down here to up there. Yes. On the first demonstrated, we have three ports of connectivity. So we have a small terminal to connect into the Eridium network. That's another constellation that we've been data through. We have an antenna to connect customer satellites, and then we have also an antenna to connect to ground stations.

it's not great connecting to ground station because you have to wait until you pass over one but it's fairly slow bandwidth. On the second satellite, Lumen 2, which we've got booked for launch in mid-20206, it's going to be the first commercial offering, have about 100 times more powerful GPU compute again. That one will have an optical terminal,

possibly two optical terminals on it, which will allow us to connect both to customer satellites and also to directly into the Starlink network, ideally. No contract signed with that yet, but they announced the productical plaza earlier this year, stands for plug and play laser, which enables satellite customers to connect directly into Starling. I was going to ask by optical, you meant laser.

But I want to double click on something that you said that I didn't know about. You mentioned customer satellites. And that implies to me data from them to your either satellite demonstrator or cluster or data center in space and the ability or demand for compute between in space objects. I was thinking about this entirely as terrestrial to space to back again. It sounds like there could be a space-based demand as well in terms of your ability to collect data, crunch it, and send stuff back without it ever needing to go all the way down. There's a huge lack of and demand for compute in orbit right now. People have just not solved the problem of putting high-performance terrestrial GPUs in space.

And the initial customers will be military satellites and other types of Earth observation constellations. And then that enables us to build out the expertise and the capabilities. season as the launch cost then comes down over the next five years. We have a commercial service that produces more cash than it costs to build for the next few years, and that transitions into as launch costs comes down, this service that can move almost all data centers to space from Earth. I'm curious about the venture thesis here, because this is going to be hard. This is going to be super, super, super, super, super hard. It's going to be capital intensive. You're going to have to deal with everything from not only building your own hardware and

dealing with contracts with a lot of very large companies and governments, but also, you know, launch schedules and getting capacity and it's the opposite of like enterprise sass. And so I'm curious when you're out there pitching VCs, what element of this is resonating the most with them to engender such an amazing reaction? We're at the intersection of three trends, which I think to some people, some people view as obvious. The first one is huge demand for energy. The second one is huge demand for commute. And the third one is the launch cost about to come down by 100x. These three trends intersecting, there's an inevitability to what we're doing. It's really just a matter of the time frame. And when what we're doing works, it's going to be, you don't have

to explain the TAM to anybody. It's like, well, it's a 10 trillion dollar business, basically. Oh, no, there's, if you can make this work, the TAM is infinite. Yeah, yeah, exactly. Yeah. Yeah. I know, I'm with you on that. But one thing we have seen, and we just came out of CES, so we heard a lot of really great announcements from a lot of companies, is that generations of chips improve. I don't know exactly who you're working with, so I'm only speaking for myself here, but Nvidia has raved about demand for their upcoming Blackwell line, replacing the kind of venerable H-100s that are out there. And to me, if I had spent all the money to send up my data center into orbit,

and then Nvidia came out with the chip that was picking a random number here, three times as good. I'm going to be pretty mad. So how do you handle essentially just like chips losing their in-market primacy when they're in-space? Actually, in orbit, they have a longer lifetime. And the reason is, so you have exactly the same problem terrestrily. So we're expecting four-year life of the chips. But terrestrial, I mean, that's roughly the same as it is terrestrial.

The problem with on Earth is if you're paying five cents per kilowatt hour for your marginal increase in electricity consumption, essentially in space, our marginal electricity cost is zero. Once we get it up there, and that means that running the chips for longer, five or six years, is more economical than on Earth because on Earth, there comes a point. where you don't want to pay the five cents kilowatt hour because they're not giving you enough value back. But in space, they'll always be giving you some value. Because the power is free. There's no real downside. Actually, we have a table here from the Lumen white paper that I was reading before we jumped on that I think kind of details the economics of this. Because I'm sure that some people watching are still thinking, I'd rather just plug an Ethernet cable into an AWS data center.

Why would I do all this work? Well, as you point out here, the cost of electricity is enormous when we think about the overall cost footprint over, say, a 10-year data center lifespan. So walk us through the economics of how actually getting up to space can save lots of money. Yeah. So maybe I'll talk, instead of our 10-year time frame, I'll talk about the four-year timeframe, which is just the life of the chips because then the economic is very clear. Let's say you run a 40-megawatt data center, which is what you can fit in one for starship

payload base. So it's about 100 tons worth of compute solar and radiators and satellite structure. So if you run that for four years on Earth and you're paying, let's say, 10 cents per kilowatt hour, which is the average is the data center is paying. That's $140 million just in electricity cost alone versus you can launch it. Yeah, it's crazy. You can launch it, depending on you believe. Elon's saying it's going to be $5 million for the launch cost, but even assuming it's $10 million or more, your solar panels are another $5 million. And then everything else nets out.

So the cost of the chips is the same, cost of the radiators and cooling loops are the same. So instead of $140 million for electricity for four years, especially, you've got $10 million for launch. solar and space, and then, you know, that's the trade-off we're doing. And the real beauty of space is you can scale it. So if you want to build a 200-mugatt data center terrestrily, there's not many places in North America where you can draw that amount of power. Absolutely. It's like two-decade lead time to build that type of energy project, whereas you can

just launch five of our modules, locate them physically in the same place in space, and you're running, in a month, you can be up and running a 200-magoddata data center. And you don't have to stop there. You can go up to multiple gigawatts, and that was the point of the video. Can you make the 4-kilometer by 4-kilometer data center that we showed earlier, just connect to another one and then make it like do four of those and then have it by 8 by 8, and then you could do four of those and have 16 by 16? I mean, this is modular, I presume.

We're looking at a design actually now where instead of having all of that compute in one spot in the middle, we'd have it running along a spine, and then you can just attach modules to the spine where the solar panels and radius is coming out each side. So you can just keep attaching modules, yeah. That's the coolest thing ever. One thing I am curious about, though, is once you get out of a payload bay, you go up to space, you come out, to me, this sounds like a tent in a bag. You have to like take it all apart and put it all together. And I'm not going to lie.

I have no idea how that works in space. So how hard is it to unpack the package you send up and turn it into these functioning data centers? And how often will that go wrong and cause a problem? I mean, right now that's how all satellites function. even ones with large solar panels, you know, obviously on the International Space Station have very large solar panels. Yeah. So that problem is relatively solved.

In fact, my co-founder, RCTO, was previously designing NASA's lunar pathfinder mission, and he was responsible for deploying very large solar panels and radiators. Well, very, very large for current standards. Nothing compared to what's coming, yeah. Yeah, because I've seen those unfurrow, like, wings, and it's like, I don't know, several hundred square feet, not square kilometers. I mean, essentially what we're going to be doing is unrolling them because it's going to be so large. You can roll that, yeah, you have these very thin, flexible sort of cells.

So that's the longer term way that we'll be deploying these. Even on Limon 2, we'll do that. But I would just say also robotics in space and for space construction is coming very soon. Like it will be probably five years before data centers are being managed by humanoid robots. And it doesn't take much a big stretch of the imagination to imagine humanoid robots constructing stuff in space. that does sound a bit sci-fi, I agree, but we don't need that yet. We can do everything. It doesn't sound science fiction at all, actually, because it's much easier to have

humanoid robots in space, and it has to have humans in space. Humans are so fragile. Yes. Like, we are just little bags of meat that's shocked by small months of electricity, and it's water just talking. Like, it's not good. Robots, we already have pretty good effectuators, whatever they're called.

And they're not heavy. And if we can just charge them, we can have... Oh, man. I'm so excited. This is going to be so cool. Yeah, yeah. I can't remember. I saw this tweet.

Maybe it was from Mark Andreessen. He was like, I was sitting in the shower the other day. I was just realizing everything is going to come true. Like, the space column is going to come true. Like, humanoid robots are going to come true. So like, everything's going to come true. And it is.

Soon it is. I'm just so excited about this. And we're going to need computing space. We're going to need, you know, VARDA is working on manufacturing. We talked a little bit about launch systems going up. It seems that everything's pointing in one very clear direction, Philip, and that does worry me a little bit. Are there any stumbling blocks that you can see that could dramatically slow down humanity's industrialization of, let's just say, low to higher orbits?

It's all dependent on getting Starship flying frequently, Starship and New Glen and any other of these types of rockets. If there were to be some, you know, for example, if a large scale war breaks out with China or somebody, that would be a very bad situation, which is why I think Elon's so keen on doing it quickly because we're at a very, you know, tight window of time now where we can actually do this. But I mean, all the physics is,

is proven now, like Starship re-entered. People, even until very recently, people thought Starship wouldn't, the math didn't math, and it wasn't even possible physically. But no, we know that everything in now is going to happen. Well, I guess then the question is, how are you going to make sure you stay ahead of your competition then? because the thesis of low launch costs,

you know, solar panel in space, and a lower total bill of ownership is going to resonate with a lot of folks because everyone's building and buying data centers. I mean, I can't go a day without reading. Amazon pledges $5 billion to Tennessee or whatever. How are you going to make sure that you guys stay ahead of,

potentially state-backed, or maybe just like MAG7-backed competitors? I mean, firstly, I do think there will be a couple of winners. If all data centers are going to space, it's not like just one company is going to be doing that. And it wouldn't surprise me if we see Starlink and Azure, or Hyper doing this at some point. All of the big hype scalers are going to need this capability, Microsoft, meta, Google. They don't have Oracle. They don't have space arms themselves. So they'll need to partner

with somebody like us. But I would say the way that any startup stays ahead is we have a moat in our team. We have the most absolutely kick-ass team from SpaceX and all these MIT grads and all these very smart people working on this. It's very hard to pull together this team immediately. And, you know, We're super far ahead now. I think nobody's even even close to what we're doing. And then the final mode is, you know, it is quite capital intensive. And I think we, certainly from the startup, in the startup world, we're ahead of everybody in that game. All right. So I'm going to have you back on the show once the first one goes up later this year so we can talk about it. And may that launch go well and may everything turn on and beep and boop as it should. It's tricky.

It's tricky up there. It's funny how fast something goes from. That will never happen to, oh, really to, of course. Yeah, you wouldn't. So when we put the white paper out, we had quite a few folks that were being like, these guys are crazy, this is never going to happen. And in the last like three months, it's everything seems to change. And now it's like an inevitability almost. So, yeah, it's true.

Well, I'm really excited about it. I can't wait to watch the launch. Please make a lot of noise about it because I'm hoping that it goes well. And you prove that this is possible because there would be nothing more gosh darn science fiction awesome than several square kilometers of solar powered data center in space. That just makes my inner nerd sing phil up, so good luck. And thank you for coming on. Awesome. Thank you so much for having me. All right, friends, that is Twist for this fine Friday. We are going to have a packed week next week.

There's a couple of things going on at the national level that impact the world of startups. So expect us to be on the move and on the mic. Stick close to Twist. We're on all podcasting platforms. We go live on YouTube and all other digital and social places. My name is Alex.x.com slash Alex. Jason is X.com slash Jason. You are X.com. slash my favorite person, and I will see you on Monday. Bye.