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Unknown A
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.
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Unknown B
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Unknown A
Up, right as we were going to record today, the Supreme Court of the United States upheld a law that would force a divestiture or ban of TikTok in the US on January 19th. This is enormous news. I've only had a chance to skim what SCOTUS wrote, but the gist is that if you were hoping that TikTok was going to be saved at the very last moment, 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 and a half billion. What are we talking about?
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Unknown A
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, Techwitch 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 techwrench for getting even more on the bench. Saga Number number two, $150 million. That's how much money Crypto wallet Phantom raised this week and it raised that one 50 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 then wallets from Metamask and Coinbase Wallet combined back in November and December.
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Unknown A
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 and a half billion dollars. 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.
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Unknown A
To put the 12 and a half billion dollars into context, I just want to say that we have been seeing a 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 rich are getting richer and the emerging managers are struggling. That's why when I see a 12 and a half billion dollar 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 Weaviate and 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.
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Unknown A
But also because I think they're incredibly interesting companies that highlight where we are seeing a lot of innovation in the market today. So first up, co founder and CEO of weaviate, it's Bob Van Lout. Weaviate is a startup that's a bit in the weeds of the AI revolution, but I really do think it could 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 vespa, Pinecone and Weaviate.
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Unknown A
Now, I've known about Weaviate for some time, including back before ChatGPT changed everything. And given how critical vector databases are to AI apps today, and how early weaviate was, along with strong factors and an innovative open source model, those are the reasons why I added the company to Archvist 500. Now today, to walk us through why vectors matter, I have Bob Van Lout, the CEO and co founder of weaviate. Bob, welcome to the show.
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Unknown C
Well, thanks for having me. Alex, great to. Great to see you again. It was quite some time ago, so this is. This is awesome.
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Unknown A
I know. So for people who don't know, way back in the day, I think it was like 2019, 2020. So way before ChatGPT, you and I sat down and you were incredibly patient and explained 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, towards weaviate 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 weaviate and the future, but I think a little background is going to help.
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Unknown C
This sounds wonderful and I look forward to it. So, all right, let's go.
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Unknown A
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.
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Unknown C
What direction, that's correct.
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Unknown A
One out of one. Nailing it. All right. Now, vector embeddings are essentially assigning vector values to words and or sentences. So essentially it's taking data and then assigning those vectors to those individual pieces of unstructured or structured data.
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Unknown C
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're probably going to double click on what that means. But that's why they've become so valuable to work with unstructured data.
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Unknown A
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.
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Unknown C
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. We'll get to that. Yeah, 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?
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Unknown D
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Unknown D
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Unknown C
And so, for example, if you think about cities, right? If you go from New York to Boston and you want to say something about the relation between New York, Boston and let's say, San Francisco, you could say, well, Boston is closer just in miles to New York than it is to San Francisco. 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.
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Unknown A
Sure, yeah.
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Unknown C
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, an image is 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, 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.
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Unknown A
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 data set 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.
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Unknown C
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. 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. We have 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, and so on and so forth. If we want to do it for every city in the world, we get a pretty big Excel file. Yeah, right, A pretty big Excel file. So, and now if you say now, let's have a machine calculate any random distance between cities.
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Unknown C
It takes quite some time to do that.
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Unknown A
Yeah.
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Unknown C
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 do it the time it takes to go through that huge Excel file that you created. It just, it doesn't work. It's. 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, 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.
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Unknown C
And that worked. And people were 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.
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Unknown A
Okay.
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Unknown C
Trained it on Wikipedia. And 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.
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Unknown A
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 sheet 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 that 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.
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Unknown C
So if you take a sentence like, well, the Eiffel Towers in Paris, right? That's a good example. Let's just, let's 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. In a sentence. And then back in the day, you had to calculate all the corpus of Wikipedia. How often does it happen?
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Unknown A
Right.
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Unknown C
But now we train them out. Model, what do you think the distance is between Eiffel and Tower? And we call that co occurrence. Okay, 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 often the distance between iPhone Tau1, we now had a model that could predict this way faster than brute force, calculating that by going through a whole Excel file.
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Unknown A
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 and 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 eva, because you guys have made an open source vector database.
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Unknown C
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 can do stuff with them. But then what do you do with stuff that is in a space? You calculate distances you want to make. 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 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.
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Unknown A
Yeah.
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Unknown C
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.
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Unknown A
Mm.
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Unknown C
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, 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 like, is it a large. It's a very big industry.
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Unknown A
Oracle's a big company, I hear.
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Unknown C
Yeah, yes, yes. But look at in the Nasdaq. You see a lot of database companies are on the Nasdaq and including Oracle. Right?
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Unknown A
Yeah.
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Unknown C
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 we can.
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Unknown A
Right.
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Unknown C
So that's kind of how you jump into the, that opportunity. Because that new data type immersed what we didn't know back then that ML would turn into what we now call AI and how big it would become. We get. I, I could claim I would, that I foresaw that, but of course I didn't know.
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Unknown A
Sometimes you're early and then suddenly the tree drops a lot of, you know, fruit down upon you. And that's called product market fit by both preparation and luck.
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Unknown C
Yes. There's a famous quote from, I think it's from Mark Andreessen where he says, you know, you have market fit if, and I might be paraphrasing where 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?
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Unknown E
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Unknown E
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Unknown A
That's really funny. I. The way that I heard that was you have product market fit when customers 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.
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Unknown C
Okay, yes.
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Unknown A
But let's talk about why the market cares about what you've built. So the way that I understand it is vector databases unlock several things that make AI applications essentially useful, one of which is RAG or Retrieval Augmented Generation. And for folks 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.
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Unknown C
Yes. So there's just one piece of history, I think, that's relevant here, please. 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 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 factor embeddings, and then said, what's the center of this paragraph? And that's how it was placed in vector space.
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Unknown F
Yes.
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Unknown C
The challenge with that was that the more words you're adding, the bigger your paragraph became, the more centered in the central vector space, losing its meaning.
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Unknown A
There was essentially it became too generic.
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Unknown C
Yes. So and the reason for that was that the individual vector embeddings don't know anything about the next vector embedding in your paragraph. Right. And now there was a paper release that was called Attention youl Need the famous Transformers paper who solved that problem. So rather than looking at that co occurrence, does Eiffel sit next to Tower, the model? 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. And now all of a sudden the model kept the context. Or it could predict the next token in that string of vector embeddings.
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Unknown A
And that is generative AI.
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Unknown C
Yes, exactly. So what happens is if you have a sentence, so the Eiffel Tower is in. Takes all these individual words that is translated into a so called token. Token is just an id. Just as just an id. So it turns into 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. We say what is token 512?
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Unknown A
Well, Paris, that's Eiffel Tower in data that. Paris.
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Unknown C
Paris, yes. So that's how it does it. And so that existed by the way, these model. So we had something in Inside weaviate, we call it Generative Search, where we did that.
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Unknown A
Yeah.
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Unknown C
But it was not really adopted. And then there was this company, you might have heard of them, they're called OpenAI.
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Unknown A
Oh yeah, yeah, it rings a bell. Yeah, yeah, yeah.
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Unknown C
And they created, they like, how are we going to show this to the world? How are we going to do this? Because I have friends who work at Open Air work and they were asking we, all of us, we 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?
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Unknown A
Yes.
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Unknown C
And we were like, hello, good news.
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Unknown A
Well, so I mean you were there with the technology to help people take their structured and unstructured data and get it into a vector database so that it could be used in an AI context. But did, did we v8 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. Yeah, off the shelf, if you will.
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Unknown C
Okay, so the, the answer is no. So if you look at the, 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 but. And with zero knowledge. But then it knows that if I need that knowledge. I need to augment what I generate by retrieving something. Right. So retrieval, augmented generation. Yeah and that was, that was the idea. So the first we call it internally we call this primitive rack. So that was just use the vector database to retrieve the unstructured data, pipe that over to the, to the model and generate the output. That already created a lot of value for people. But now there are two interesting things happening.
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Unknown C
So one is like there's a lot of work happening to more intertwine the models and the database together. Okay, funny side note, that's where the name weaviate 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 general answer. That's a one way street. What if we just pipe that back into the database, we call that an agent.
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Unknown A
I was go, 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 Weavier 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.
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Unknown C
Yes, that is correct. But there's another way, another perspective to. Of course that's how we tell the world what you can do with We've yet but I'm assuming that the people, I mean I'm 99.9% sure that people listening to this are tech enthusiasts. Right. So what is interesting is that of course it's a 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 functionalities, indeed the hybrid search and the offloading and that kind of stuff was. What's more exciting is that you can now build this in like five lines of Python code combining the model and weaviate. And that is of course the new thing in this new paradigm of AI infrastructure.
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Unknown A
So essentially weaviate'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. Non cto.
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Unknown C
Exactly. And that is the. So the. What's happening now, a lot of work that's happening now is that the barrier to entry for developer is going down.
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Unknown A
Fast, which means that the aperture for what can be built is getting wider.
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Unknown C
Yes, exactly. And I always like to say that because we often talk about this technology of like, how does it work under 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're just, you know, out of college and you work for a company and you want to build a Rack application. Yeah, this stuff's not easy. So we're doing a lot of work to help people just to get started with five lines of code.
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Unknown F
Yeah.
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Unknown C
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 kinds of applications.
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Unknown A
Great segue. We're going to talk business model, then we're going to get to agents really quick. So the thing about weaviate, 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 weaviate 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 weaviate?
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Unknown C
Those are the main. We also have something called byoc, which stands for bring your own 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.
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Unknown A
Yeah.
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Unknown D
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Unknown D
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Unknown A
Thinking about the revenue mix at weaviate, you have stuff aimed at smaller developers and stuff aimed at larger corporate customers. What's driving the majority of revenue growth for we've today?
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Unknown C
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. That's very. There's nothing new under the sun there.
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Unknown A
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 we've. It'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?
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Unknown C
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.
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Unknown A
For sure.
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Unknown C
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, they're relatively smaller but they, they work, you know, they, they just want to build these new kind of things.
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Unknown A
They adopt quickly.
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Unknown C
Yes. But when we released that the when we got to our first million dollars 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.
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Unknown A
Oh no, not procurement.
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Unknown C
This guy sends me an email and he says like can you come on a call? So I joined him on a call and he held up like the procurement. He said, what's this sla? He 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.
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Unknown F
Right.
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Unknown C
So and that was because of the enterprises going live as well. And that in. And that was also related to the fact that a lot of these applications went to production. So that is like that's was what happened basically. And so that gone well, it's, it's, it's the good old, old fashioned, old fashioned story of open source infrastructure. Project gets adoption, wants to scale up, starts bottom up. Enterprise sellers come in. It's, it's, that's again a very classical story but it's excited to see this, to see this happening. And it's like a, there's, there are even database companies and I will not mention them but on their earnings calls we are mentioned as 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 crossed 100 people.
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Unknown C
So it's fantastic.
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Unknown A
Yeah. So let's talk about the future then. Because you mentioned 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 weavy perspective, what is an agent and is it a real thing or a marketing term?
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Unknown C
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 called 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 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.
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Unknown C
Yeah. We somehow need to explain to the world what's, what's happening. Right. And then the market kind of consolidates on certain language and that kind of stuff.
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Unknown A
Yeah.
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Unknown C
But it's definitely a thing. So the steps that we've been taking was like from vector search to Rack, which was that one way street to the agents where you create these, these feedback loops. So what we see is that the majority of customers started with vector search. So if a lot of these E commerce kind of things, we now have like a lot of ask AI features that we're, that we're powering. Let me come up with an example. So the search in the, so the Ask AI feature in Supplements, for example, that's a great example of that sort of runs on, we've gate with a model, then that's Rack. And now we see that people start to build the new things with these agents. The reason why that's so interesting was that if you go back in time to vector search, then the devil advocates argument for what we're doing was like, yeah, this is all great, but isn't this just search?
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Unknown A
Right.
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Unknown C
And that was kind of. Yeah, yeah, it was kind of true. Right. So, and, but then all of a sudden this new unique use case emerged in racing and now this new use, these set of use cases because of what we like to call these agentic architectures are Emerging and all of a sudden validating more and more and more and more the existence of what we call these, 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 a technology perspective, but from a business perspective that we bring to the world.
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Unknown A
So, I mean, just to be clear, without vector databases there is no agentic AI, period. Full stop.
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Unknown C
Well, okay, so you know, if you want to, if you want to open a bottle of beer, you can also use a spoon to do that. Right. So it's like if you want it open, you can always open it, but ideally you just use beer opener.
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Unknown A
Right.
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Unknown C
So it can open.
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Unknown A
Yeah.
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Unknown C
And that's what Defector Database does. 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 angry. It's like, I can do everything in the world with my old database. Sure.
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Unknown F
Yes.
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Unknown C
But turns out that the majority of developers just want to use the tooling that is built for those specific use cases.
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Unknown A
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 it's a $10 billion company.
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Unknown F
Yeah.
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Unknown C
But if I may say something about that, we use that, that one. We use that often internally at weaviate. Exactly that, that thread on Hacker News. Because what sometimes people like hardcore developers like the best, the creme de la creme of developers sometimes forget is that not everybody is like them. And that is a large group of developers that just want to build great software for their own business, for the company they work for.
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Unknown A
Sure.
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Unknown C
And they need the help. You may need the 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.
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Unknown A
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 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 see happening this year that we should be looking forward to so What I really.
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Unknown C
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 in, 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 say otherwise, you have to cut this out.
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Unknown A
But we have a, we have a beep, we'll just put that in.
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Unknown C
Yeah, so, but this is, 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 that arise 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.
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Unknown A
Why does fixing that crap in, crap out, paradigm change the world? Because to me, we've gotten this far with that chicken, chicken salad. Why do we need this? And what will that, what will that change for the industry?
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Unknown C
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 so 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 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 been running those startups as well.
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Unknown C
But the, that is the, the what I'm so excited about is going to solve that age old 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.
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Unknown A
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 and do. That's an enormous possible new opening. Yes.
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Unknown C
And also this is the time. So do you remember those days that people like there was like, you can build these mobile apps or these. Do you remember that people were like playing around with this thought Vivid. This is it for AI now. Start now. Not next year, now. It's. That's how excited am I am about this because this is the start of the new paradigm. So this is the time to build.
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Unknown A
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.
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Unknown C
Bob, thanks so much for having me.
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Unknown A
All right, Bob, thank you so much.
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Unknown C
Thank you.
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Unknown A
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 2024, 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, for a reason. We are 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.
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Unknown A
So if we take a look at this chart right here, this is a map of the US 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 Altman think that that is going to be a near term solution.
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Unknown A
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 Lumen Orbit. Philip, how are you?
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Unknown F
Hello Alex. Thanks so much for having me. And a huge honor to be on the twist 500. So I appreciate it.
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Unknown A
Thanks man. It's a Fun project on twist500.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 and 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.
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Unknown F
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 cool in 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 the 70s or 60s, but I think in 20 years time the forecasts 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 sent into space, instead of having this 95% efficiency loss using microwaves to transfer the energy down, we can just use all that data into all of that energy in space.
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Unknown F
And that's really, really where the idea came from.
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Unknown A
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.
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Unknown F
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.
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Unknown A
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?
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Unknown F
It would be great if we have some competition to Starship in terms of the launch cost.
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Unknown A
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.
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Unknown F
Yeah, 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, there's nothing against the laws of physics to stop us doing this. It just requires a very large, they call it black body radiation to radiate in infrared into deep space. We just have to keep this black panel at around 20 degrees Celsius or higher, and that will radiate a lot of heating space. 800 watts per square meter.
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Unknown A
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? Is that not much?
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Unknown F
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 1 square meter of solar panel in space generates around 200 watts, and 1 square meter of radiator disarray dissipates around 800, 800 watts. So you need about a quarter the size, the surface area of the solar panel on the radiator side.
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Unknown A
So we're going to need a 4 kilometer by 4 kilometer square of solar panels to power. I believe it's a 5 gigawatt data center. And then we need a 1km by 1km square of radiator to, yeah, 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 safe from little objects that are zipping around so fast?
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Unknown F
To solve the problem of orbital debris, either you fly very low or you fly very high. So in the first two missions, we're flying what's called Velio, very low Earth orbits around anything below 400 km is very clean orbit. You don't really get hit by anything there because there's low levels of upper atmosphere. So stuff deorbits naturally within a few months anyway. It's where the ISS flies, for example. The problem with that is you need propulsion. So as the satellite gets 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 1200 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.
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Unknown F
There's hardly any orbital debris up there because most people are flying in this leo band of 400-800km. So that's really how you go about it.
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Unknown A
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.
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Unknown F
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 far, it's fine to fly higher. So for these small puny ones that we're doing in the demonstration, we're flying very low. Once we scale up, it's okay to fly high.
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Unknown A
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?
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Unknown F
Yes, it's about a 1kW 50kW satellite. It's about 100 times more powerful GPU compute than has ever been flown in space. We'll have the state of the art terrestrial Nvidia 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 Nvidia, they're at least 100 times less performant than the state of the art AI training chips that they have.
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Unknown A
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 here 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.
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Unknown F
Yes. On the first demonstrator we have three ports of connectivity. So we have a small terminal to connect into the Iridium network. That's another constellation that we've been data through. We have an antenna to connect to customer satellites and then we have also an antenna to connect ground stations. It's not great connecting to ground stations because you have to wait till you pass over one. And it's very. It's fairly slow bandwidth on the second satellite, Lumen 2 which we've got booked for launch in mid-2026. 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 directly into the Starlink network. Ideally no contracts signed with that yet, but they announced the product called Blazer earlier this year.
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Unknown F
Stands for Plug and Play laser which enables satellite customers to connect directly into Starlink.
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Unknown A
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. 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.
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Unknown F
There very much is, yeah. There's a huge lack of and demand for compute in orbit right now. People are just not solved the problem of putting the high performance terrestrial GPUs in space and the initial customers will be, you know, military satellites and other types of Earth observation constellations. And then that enables us to build out the expertise and the capabilities. And 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 cost comes down, this service that can move almost all data centers to space from Earth.
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Unknown A
I'm curious about about the venture thesis here because this is going to be hard. This is going to be super duper duper 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 SaaS. 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?
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Unknown F
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 timeframe 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 business, basically.
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Unknown A
Oh, no, there's. If you can make this work, the TAM is infinite.
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Unknown F
Yeah, yeah, exactly.
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Unknown A
Yeah, no, 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 H1 hundreds 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 a 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.
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Unknown F
In orbit, they have a longer lifetime. And the reason is so. So you have exactly the same problem terrestrially. So we're expecting four year life of the chips, but terrestrially. And that's roughly the same as it is as terrestrially. The problem with on Earth is if you're paying $0.05 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 $0.05 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.
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Unknown A
So there's no real downside. Actually, we have a, 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.
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Unknown F
Yeah, so maybe I'll talk instead of a ten year timeframe, I'll talk about the four year time frame, which is just the life of the chips. Because then the economics of it are very clear. Let's say you run a 40 megawatt data center, which is what you can fit in one full starship payload base. So it's about 100 tons worth of compute, solar and radiators and satellite structure. 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 the data center is paying, that's $140 million just in electricity cost alone versus you can launch it. Yeah, it's crazy. Yeah, you can launch it, depending who you believe. Elon's saying it's going to be $5 million for the launch cost. But you know, Even assuming it's $10 million or more, your solar panels are another $5 million.
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Unknown F
And then everything else nets out. So the cost of the chips are the same, cost of the radiators and coolant loops are the same. So instead of $140 million for electricity for four years threshold, you've got $10 million for launching solar in 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 megawatt data center terrestrially, there's not many places in North America where you can draw that amount of power or.
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Unknown A
Absolutely.
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Unknown F
It's like two decade lead time to build that type of energy project. Whereas you can just launch five of our modules, put them, locate them physically in the same place in space, and you're running. In a month you can be up and running a 200 megawatt 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.
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Unknown A
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, like do four of those and then have eight by eight and then you could do four of those and have 16 by 16. I mean, this is modular.
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Unknown F
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 radiators coming out each side so you can just keep attaching modules.
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Unknown A
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 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?
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Unknown F
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 that problem is relatively solved. In fact, my co founder CTO was previously designing NASA's lunar path on the mission and he was responsible for deploying very large solar panels and radiators.
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Unknown A
Well, very, very large for current standards.
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Unknown F
Nothing compared to what's coming.
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Unknown A
Yeah, yeah, because I've seen those unfurl like wings and it's, it's like, I don't know, several hundred square feet, not square kilometers.
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Unknown F
I mean, essentially what we're going to be doing is unrolling them because it's going to be so large you can roll it. Yeah, you have these very thin, flexible solar cells. So that's the longer term way that we'll be deploying these. Enon Living 2 will 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 anything.
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Unknown A
It doesn't sound science fiction at all actually, because it's much easier to have humanoid robots in space than it is to have humans in space. Humans are so fragile.
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Unknown F
Yes.
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Unknown A
Like we are just little bags of meat that's shocked by small amounts of electricity. And it's water. Just talking like it's not. It's not good robots. We already have pretty good effectors, effectuators, whatever they're called. And they're not heavy and if we can just charge them, we can have.
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Unknown F
Yeah.
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Unknown A
Oh man, I'm so excited. This is going to be so cool.
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Unknown F
Yeah, yeah. I can't remember I saw this tweet. Maybe it was from Mark Andreessen. He's 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, humanoid robots going to come true. Everything's going to come true. And it is soon. It is.
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Unknown A
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.
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Unknown F
It's all dependent on getting starship flying frequently. Starship and New Glenn and who any other of these types of rockets. If there were to be some, you know, for example, if, 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 reentered. 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 now is going to happen.
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Unknown A
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 load 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?
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Unknown F
I mean firstly I do think there'll be a couple of. 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 hyperscalers 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 we're super far ahead now. I think nobody's even close to what we're doing and then the final mode is quite capital intensive and I think we certainly in the startup world, we're ahead of everybody in that, that game.
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Unknown A
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, it's tricky. It's tricky up there. It's funny how fast something goes from that will never happen to. Oh really? To.
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Unknown F
Of course, yeah, you wouldn't. So when we put the white paper out, we had quite a few folks being like, these guys are crazy. This is never going to happen. And in the last like three months, it's, everything seems to have changed and now it's like an inevitability almost. So yeah, it's true.
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Unknown A
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'd 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. Phillip. So good luck and thank you for coming on.
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Unknown F
Awesome. Thank you so much for having me.
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Unknown A
All right, friends, that is Twist for the Spine Friday. We are going to have a packed week next week. There's a couple 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 Alex. Jason is X.com Jason. You are X.com my favorite person and I will see you on Monday.
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Unknown C
Bye.