SPEAKER_02: I've been looking for a crypto project that would solve problems in the real world.

SPEAKER_00: Instead of just saying, here are all the tokens, have at it, everybody starts speculating, it's a store of value. You have to make them by solving this mathematical equation.

SPEAKER_03: It's not an understatement to say that we are up against nation states because this is truly like China versus the United States.

Chamath Palihapitiya: In order for the rest of us to take on OpenAI, we need to come up with a way that we can work together. For some people, mining BitTensor is like the most fun game they've ever played, ever.

SPEAKER_11: I predict I'm going to make tens of millions. That's my goal.

SPEAKER_13: All right, everybody, welcome back to this week in Startups Twist. Man, do we have something special for you today?

SPEAKER_01: I have been tau-pilled. Yeah, I think that's fair. You're tau-maxing. BitTensor-maxing. There you go, yeah. All of that because I, all of that, I've been looking for a crypto project that would solve problems in the real world, Lon. Yes. And I felt like we got it on Bitcoin. I made some purchases when it was under 100 bucks. I got hacked. I lost it all. My wife bought a bunch under 100 and 200. We made millions. Fantastic. Wives got like, if you put my Hall of Fame investments on a leaderboard.

SPEAKER_19: Your Ubers.

SPEAKER_22: Two of my wives are in the top seven. Wow. Look at Jade. Top five. Well, dollar amounts and also on a percentage return.

SPEAKER_24: So when is Jade raising her first fund? I think that's the question. She's got half of this one. She's doing okay.

SPEAKER_27: All right. Well, there you go. Yeah.

SPEAKER_29: And rightfully so, having to deal with me for 23 years. But the second bet I made was on BitTensor. Why did I make it on BitTensor? I just saw these subnets actually solving problems in the real world. And I said, this is what I've been waiting for. Yes. And there's a guy named Const, and he's here with us today to talk about BitTensor. We've been talking about it on this program all year long.

SPEAKER_32: It's one of our themes, Lon. I've learned a whole lot about it this year, let me tell you. Now let's get into it. Let's bring on Jacob Steves. He is also known as Const. C-O-N-S-T. Const. We'll find out why. I have a guess. I'm curious. Oh, here he is.

SPEAKER_35: I'm curious as to your guess.

SPEAKER_37: Do we call you Const, Sir Const? I'm not calling him Sir Const. That's not how. Jacob.

SPEAKER_39: Just Jake. It's fine.

SPEAKER_22: Jake. Okay. Jake it is. Is Const because you're constantly shipping?

SPEAKER_41: Or the protocol is constantly changing and annoying people. Okay. There's that.

SPEAKER_45: That too. Actually, it comes from Constantine, the emperor.

SPEAKER_47: Sure. Got it. Makes sense. You are the emperor of BitTensor.

SPEAKER_01: Let's start off with why did you create BitTensor? What's the history here?

SPEAKER_29: And then I want to get into, you know, all the subnets and the economic model, which I find so fascinating. And we're going to break this down. If you're a neophyte to crypto or you think crypto is a scam, I really want you to pay attention to this one because there's two times in the history of crypto I said, definitely not a scam. Definitely something going on here. It was Bitcoin. Bitcoin, I made millions, and it's Tau. I predict I'm going to make tens of millions. That's my goal. So, Const, tell us, where did this all come from, BitTensor and Tau?

SPEAKER_52: Well, I think there's a project you're probably missing there, which is Ethereum. Sure. And a lot of people have said that themselves. And Ethereum took from Bitcoin the ability to write these immutable contracts. And they were like, oh, wow, let's abstract that quality of Bitcoin where you could do a transaction, but why don't we make the programming language that allows you to make any type of complicated transaction? Let's build MakerDi, which is a very complicated system of collateralized lending pools and stable coins. That's all because of the abstraction of the contractual nature of Bitcoin. Hey, let's go beyond transactions, basically.

SPEAKER_55: And that's what people refer to as smart contracts. Yeah, that's the general category.

SPEAKER_52: Smart contracts. Smart contracts. So, Vitalik saw that and went, there you go. You have Solana and you have Ethereum and all the other L2s and L1s of the world that build

SPEAKER_57: smart contracts. And we do that on BitTensor as well, right? So, we also have smart contracts in the platform.

SPEAKER_52: But Bitcoin was, I think, two very incredible innovations. One was this contractual layer that Ethereum spun out. And then the other side would be proof-of-work mining or the computational side of Bitcoin,

Chamath Palihapitiya: which goes, hey, we can aggregate all of these contributors from across the globe together to solve this one very difficult computational problem, which is to just stuff the blockchain of SHA-256s and bury the transactions in this immutable time chain so that it can never be

SPEAKER_52: taken out by the American government or nobody can do a revision. But the consequence was of being able to build this market for a digital commodity. I would say it's like the first digital commodity, something that you could mine. It's digital. And it was Bitcoin. You mine it for producing hashes. And they created this computational network called Bitcoin, which turned out to be incredibly

SPEAKER_62: large, like insanely large.

SPEAKER_00: And this is a key innovation. Instead of just saying, here are all the tokens, have at it, everybody starts speculating, it's a store of value. They said you have to make them.

SPEAKER_29: And you have to make them by solving this mathematical equation, which requires NVIDIA cards and a computer, a server on the network, or just a desktop computer even. And that meant there was a cost to participate. The cost was compute and electricity. Am I correct in like that framing of the innovation?

SPEAKER_66: Yeah, exactly.

SPEAKER_52: And what makes it digital is the way in which it's defined.

SPEAKER_68: It's computationally defined commodity, right? Oil is not computationally defined. It's physically defined. But a Bitcoin hash is actually computationally defined. It's whether or not you have solved the SHOP2B6 algorithm with the inputs. And how many you can solve is hashing power. And that's actually something that you can now trade, by the way. You can trade Bitcoin hashing power.

SPEAKER_69: You can sell it. People buy it, which is quite incredible. And so Bitcoin birthed, you know, the contractual basis and then also the creation of the first digital commodity. And digital commodities are very interesting because when very well-defined mathematical

SPEAKER_52: computational primitives that you can create anywhere in the world by combining things like hardware and electricity together, you build these hyper-competitive markets.

SPEAKER_69: Bitcoin is probably the most efficient market we've ever seen.

SPEAKER_52: In history, anybody, anywhere can buy and sell it. Well, I think anybody can contribute to it from any place on earth by plugging something into the wall. And those are Bitcoins. So Bitcoin miner machines are. And as a consequence of having this permissionless, hyper-competitive market, you know, the efficiency

SPEAKER_69: of producing this digital commodity has just gone exponential. Bitcoin's chart is like this.

SPEAKER_41: But the PAL, the proof-of-work power of Bitcoin is just purely exponential. It just never goes down.

SPEAKER_29: So the price is variable because of speculation, regulation. A different market like Korea embraces it, bans it, and then re-embraces it.

SPEAKER_13: There's so many outside factors that determine it. But the amount of hashing, the amount of computers and energy in the network has just gone straight

SPEAKER_01: up. And this is important because there's another term of art we should define here, which is permissionless and, you know, peer-to-peer. Something is peer-to-peer and permissionless. Explain that in plain English and why that's so important in the history of Bitcoin and then, you know, how that impacts BitTensor as we get there. And I want to really take our time on this because there's so many people who think they understand Bitcoin because they understand the price and they may own some of it, but maybe don't understand these core fundamental principles.

SPEAKER_68: Well, there's two concepts there. So there's permissionlessness and peer-to-peer. And so peer-to-peer is I can send directly to you without an intermediary. That's actually what Bitcoin wanted to solve. They were like, let's make a monetary system. It's the first sentence of the white paper, right?

SPEAKER_69: You know, sending, I develop a mechanism where I can send transactions. From me to you without an intermediary, without the need for a bank, right? Like that's the whole purpose is because we need intermediaries, then the intermediaries

SPEAKER_52: can censor us, which leads us to the second point, which is permissionlessness. So peer-to-peer is the thing he wanted. Permissionlessness was a quality that needed to get to that.

SPEAKER_68: And so permissionless means that anybody can contribute. It doesn't matter from where or who they are.

SPEAKER_57: Think of it like the ultimate form of non-bias. You know, like people are always trying to build these organizations like, yeah, we don't care about race and gender, et cetera, et cetera. But a permissionless market is purely blind.

SPEAKER_42: You can contribute it anonymously from anywhere in the world. And it doesn't matter if you are a bad actor or a good actor.

SPEAKER_05: It's purely a meritocracy.

SPEAKER_01: We don't care if you're in a communist country. We don't care if you're in a democracy.

SPEAKER_13: We don't care if you're 12 years old or 72 years old. We don't care what computer you're using. As long as you've got compute, you could be taking solar energy and converting it into Bitcoin.

SPEAKER_01: You could be, you know, at some nuclear power plant and you have a closet and you put a couple of computers in there, which people did and or, you know, you got some city street lamp and ran it to your tent and put up, you know, and that was when I knew this thing was truly permissionless. When people started hijacking like New York City public lights and running a cable into their apartment to make Bitcoin. This is what permissionless means. It doesn't mean necessarily breaking the law, but it is a fundamental breakthrough in how the world works.

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SPEAKER_67: And it means that we just measure the output.

Chamath Palihapitiya: And when you just measure the output, it means you can optimize the output. And there's nothing hindering our ability to get the maximal amount of that output because

SPEAKER_52: we don't have these boundaries and there's no permission. There's no entrance gate. And it's very difficult to make permissionless systems because anybody anywhere can try to cheat. And, you know, we don't classically do that. It's essentially very difficult to build a permissionless system because up until really

SPEAKER_68: Bitcoin, everything was manually done by humans and humans have biases. So there's going to be permission involved with any system that's, you know, even like, let's say an immigration policy will try to be permissionless in some sense.

SPEAKER_69: So like, we'll be, you know, blind to certain invariable qualities of humans, like their race, et cetera, et cetera.

SPEAKER_68: But it's still very difficult because there's a human in the loop and the humans are going to be, you know, have their biases. Bitcoin is permissionless still to this day. And as a consequence of having, building this pure market, pure permissionless market, you

SPEAKER_57: have people contributing caption power and computing power from all across the globe. And they couldn't have done it unless there was, you know, there are now in the outer rims, which, you know, is really says something about the fact that like there's excess qualities out there that these permissionless markets can take advantage of. And so anyways, Bitcoin invented that first example, and it still is growing day to day.

SPEAKER_52: And well, where Bitensor started from was understanding that, hey, well, a really powerful computer should be applied to the most important computational problem of the 21st century, which is artificial

SPEAKER_69: intelligence. That's where the idea actually started.

SPEAKER_52: I began as a Bitcoiner, so I was highly interested in Bitcoin, and I was also studying artificial intelligence and thought, well, OK, how do we connect these two things?

SPEAKER_57: How do we connect the most powerful computer in the world to the most important computational problem in the world? And that's the founding raison d'etre.

SPEAKER_101: I'll jump in. We got Mark Jeffrey here in the comments.

SPEAKER_24: He says, Bitcoin showed us a new way to do a company instead of hiring. You impose a coin reward. Miners compete to get the reward. Miners join. Miners leave. Anyone anywhere can compete. Is that part of your vision for where you see this going, that this is going to be eventually a engine for starting a company without, you know, doing all of the build out a small business rigmarole that we think of?

SPEAKER_104: Well, let's let's let's let's, you know, pull a thread between these two concepts, right?

SPEAKER_69: So there was a first, OK, big point mining. All right.

Chamath Palihapitiya: Let's see if we can we can build the same type of computational primitive for artificial intelligence.

SPEAKER_68: What we needed to invent in order to do that was a very, I use this word a lot, abstract

SPEAKER_69: consensus mechanism, which is that we needed a way. Bitcoin measures something very, very, in some sense, it's easy to measure. A hash is just binary. It's just true or false. You don't really need to have any complexity there in a consensus mechanism. And but in order to measure something like artificial intelligence, which is very high dimensional, it's like, let's say you're measuring the informationally informational

SPEAKER_52: significance of a thousand twenty four dimensional vector. Right. OK, how useful is that to a machine learning model?

SPEAKER_67: And that's not as simple as checking in hash. And so we we. So Bitcoin, just to summarize that just concisely, Bitcoin had to solve one problem.

SPEAKER_29: Therefore, they just wrote it into the protocol. Like, did you solve the hash? BitTensor has a much bigger mandate, which we'll get into now. And the one criticism of Bitcoin was, hey, beyond speculation and money store transfer, which are

SPEAKER_81: valid things in the world, this is a giant energy sucking machine that doesn't provide any other value. So what is the point of all this compute? And it was during a time of excess compute. Bitcoin was formed in a time when there was plenty of compute available. In fact, people were, you know, trying to figure out ways to get people to consume more.

SPEAKER_01: And they were just desperate for you to fire up something new on AWS or Google Cloud or whatever it happens to be, Rackspace, all these great cloud providers, DigitalOcean, et cetera.

SPEAKER_29: But you did get into this, like, is this really worth it? We'll put that debate aside. Now we are in a compute constrained environment.

SPEAKER_73: And BitTensor is kind of having its moment. So let's go to that origin of BitTensor and how it paralleled and what you built on top of it.

SPEAKER_52: Yeah, so building this way of creating a proof-of-work network for anything, the original intention

SPEAKER_68: was to train machine learning models, which we do.

Chamath Palihapitiya: But it turned out that also there was a lot of different things that we could apply that primitive to that was not just training machine learning models.

SPEAKER_68: We could inference machine learning models, as an example, which is, you know, when you call them and you get the outputs. And so there's a couple of subnets on BitTensor. I think you've talked about this recently, the NG subnet, where you can talk to the miners, the miners contribute to compute and they run the outputs of the model.

SPEAKER_52: So, you know, that's a broadening of the scope of this primitive, you know, just like how

SPEAKER_68: when they abstracted the contracts from Bitcoin, you know, MakerDai took a couple of years for people to invent MakerDai.

Chamath Palihapitiya: At first, it was just decentralized autonomous organizations and they all failed.

SPEAKER_68: And then we actually got really good at decentralized autonomous organizations and we have everything from Athena, et cetera, and on to the future. At first, we tried to do information mining, which was cool, but not enough to push AGI. And then we got really good at some of the base primitive stuff like doing inferences and aggregating compute. But also, and this is one of the points that I know Mark Jeffrey has made in the way that he frames is very interesting, you know, okay, we can buy building these permissionless markets

SPEAKER_69: that anybody can contribute to or anything can contribute into it. Any type of computer can contribute, you can get storage into it.

SPEAKER_68: You can also get the talent and perhaps, you know, that's something else that needed to be mined permissionlessly that hitherto was not, we weren't able to do that. And when you have something simple like Bitcoin mining, you just plug it in the wall, it's done. But for these higher order commodities, I like to describe them like higher order because they

SPEAKER_118: require like hardware to software and then ingenuity.

SPEAKER_123: They actually require somebody going on and solving a problem.

Chamath Palihapitiya: Let's say that you're creating an algorithm that runs inside of software that runs on top of hardware and okay, the hardware is commoditized. The software is becoming commoditized because through artificial intelligence.

SPEAKER_123: And then you have the, you know, the innovation and the algorithm and the intuition and the creativity, all that stuff.

SPEAKER_126: The application in a way, the network layer. And that requires essentially on BitTensor creating a subnet with a new application.

SPEAKER_29: It's almost like BitTensor is, I'm not sure how many subnets there are. I know that's been a, we'll talk about that, but this is where, I mean, I think maybe explaining what the subnets are and what problems they solve because you're essentially creating what I've called the Y Combinator, the Techstars, the accelerator of AI services built on an open platform.

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SPEAKER_124: Ethereum is a blockchain where you can create lots of smart contracts.

SPEAKER_69: Bitensor is a blockchain where you can produce a different type of mining network. Think of it like a contract, but it mines a particular commodity.

SPEAKER_68: It produces something of value from storage to inference to training machine learning models to scraping the web, to stealing API keys and selling them like GM, or pulling innovation from

SPEAKER_57: people across the globe. So Bitensor is a platform for building these contracts, like Ethereum is for maybe more

SPEAKER_69: classic smart contracts so people understand these mining networks.

SPEAKER_68: And then on into that platform, we actually have those projects mine, they all have their own token, there's some standardization in the way that they're built, and they compete against each other to attract investment from other holders in the network, from people to hold

SPEAKER_69: TAO and to those projects that perform well, we actually mint the inflation of TAO.

SPEAKER_57: So TAO has a 21 million cap, it's just like Bitcoin, there's only 21 million coins ever. There's one produced every 12 seconds, actually, well, it's less, that's half is produced every 12 seconds because we've gone through our first halving event.

SPEAKER_140: And that token, that newly minted token gets distributed to these projects as basically additional liquidity.

SPEAKER_143: So each subnet has a reward system built in, instead of getting a Bitcoin for mining the

SPEAKER_29: hash and building out the Bitcoin network, in TAO, you could work on one of 128 different subnets, you have to stake, you have to put up some TAO to make one of these, and then you can earn TAO, more or less. So explain the subnet concept and maybe highlight the top two or three in terms of actual usage and engagement and what problem they solve. Because that really helps people take this from being an abstraction and a philosophy to being, you know, a startup, providing a service.

SPEAKER_57: Yeah. Well, if you don't mind, let me try to just explain how a core subnet works in the first place. Please.

SPEAKER_68: So, because BitTensor, BitTensor is now a meta subnet. It's actually an abstraction on top of itself. So you need to understand the primitive before you can go to a higher level. So the core, a subnet is basically an open network that you can join. And by join, you burn a little bit of a token to prove that you're willing to play the game. And think of it like paying entrance fee or, you know, competing with other people to join this game, to buy a lottery ticket. But if it's not a lottery ticket, because inside this network, your computer that you registered

Chamath Palihapitiya: with is going to do some sort of work. You know, a good example of that type of work is that you're going to get your computer is going to

SPEAKER_69: get queried by clients like yourself, Jason, who's using cloud code and wants to talk to a machine learning model.

SPEAKER_57: And you're going to be running a machine learning model on your machine. And it's going to answer those requests. And it's going to respond with, yeah, you have the capital of Texas is Austin. And, you know, the next step in this agentic thing is to, you know, LS into your folders

SPEAKER_68: and pull this file. Like all of that is basically talking to an LLM and it's computationally expensive. And so you can join this network by paying this little fee and running this software on your computer, which is a machine learning model, and people will talk to it. And inside of this network, you're going to have another set of participants, what we call validators, that are going to check to see if the computer that you added to the network is doing the job faithfully, right? But not just faithfully, perhaps we're also going to check to see if you're doing it fast and faster than the last guy. And what happens in all of these networks is that people joining, there's a continuous role of people joining into the network and they're measured based on what the network is measuring. In this particular design, an example would be, I just described what's called inference speed, right? How fast you can inference a machine learning model. It's measuring how quickly you can answer these questions from the, from the clients and it's paying you more and more based on how quickly you can answer these questions. And if you're meeting some sort of bar, right?

SPEAKER_52: So think of it like a very well-defined written to code description of how we're going to check to

SPEAKER_69: see if the computer that you added to the network is faithfully following the rules and doing the thing and an axis along which you can perform better, right?

SPEAKER_68: So perhaps you can combine and add more computers to your cluster or you can speed them up or you can

Chamath Palihapitiya: improve the software yourself and make it faster at serving these inferences. And if you can do that, you can, you can serve our requests and over time you can make more money in this network.

SPEAKER_69: And so each of these networks are usually the way that we visualize them is like a curve. And so along the x-axis are all of the different participants. Usually it's about 200, it's 256 of them. There's 256 computers, which is often, which is often more than enough. And I'll explain why if you're interested later. And then there's the amount that they get paid, they're getting paid, which tends to go up or it goes up to the top. And then there's the ones that are being cycled out at the bottom and, and think of it like, um, um, a league. Um, like the is a good example. I love it.

SPEAKER_68: So each of the networks on BitTensor has that, this relegation system, this premier league of the thing that the miners, the contributors to the network is, is contributing. So some of them may be inference. A good, a good example, like, um, a sub sample of those projects would be you bring GPUs to the network that people can use.

Chamath Palihapitiya: You, um, provide computing power that people, you inference machine learning models.

SPEAKER_69: So when people are talking to your endpoint that you, um, you respond, um, uh, or it's an example where you actually train a machine learning model. You produce an AI and contribute the AI itself to the network and get paid if your AI is better than the, the other AIs in the network.

SPEAKER_68: Um, that's what we would call a model competition. So that's a little subset that's, that would be like Liam, NG and Afi with some of the top subjects on BitTent. So that's what, that's what they do.

SPEAKER_69: The whole premise of one of these projects is that, that they're able to use this permissionless hyper-competitive

SPEAKER_68: market to produce this digital commodity, inference, compute, or models faster and better than anyone else in the world. Because we're using the power of Bitcoin.

SPEAKER_69: And if it's, if that's not their premise, it doesn't make much sense to build on, on, on BitTensor. Um, and there then that network has its own token. Um, so it's a, think of it like a sub token to tell, uh, which is like a derivative token. We call it a staking token. The, the name we use is an alpha token, um, which is a, a secondary cryptocurrency, which also has 21 million cap

SPEAKER_68: that only exists with inside of that network that we just described.

SPEAKER_69: You need to pay it to enter, um, you get paid in that by doing well. Um, and it's, if you sell it, you sell it into TAL. That is what a subnet looks like. And that Kababti, we call it the alpha token, that token that represents that, that network.

SPEAKER_68: It only has value if at the end of the day, people are going to want to buy that token to get access to the computers in this network. Right. So, so everyone is evaluating these subnets based on whether or not they actually have some sort of long-term potential for producing value. And if, if it's just, oh, it's a network where you can just join.

SPEAKER_69: And if you just break random numbers, no, one's going to buy your token because there's no, there's no way to thread any, like a relationship between that thing having value.

SPEAKER_47: And startup speak for me, that would be a product with product market fit. Perfect.

SPEAKER_29: So for me using energy or ENGY, um, for me, that was, wait a second. These tokens for Quinn, Kimmy and GLM five, two are half the price of other places. Right. And it's like, well, wait, how come those are half the price? And now I'm looking at it, you know, using open router, Claude, Fable.

SPEAKER_81: And Hey, maybe I just plug in my API key for energy. And I use that in my Hermes agent. Right.

SPEAKER_174: Right. Um, you know, and, and, and behind that product is this liquid swarm of anybody in the world

SPEAKER_69: that can enter and try to, to reduce that price over time. So right now it's half the price, but it doesn't necessarily need to stop there because whenever this excess compute, that person can plug themselves into this permissionless network and, and it becomes quite liquid. Right.

SPEAKER_68: So, so, Hey, I, I was, um, I'm not using these computers anymore. Well, I just found the software and then I just sell the inference to, to ENGY.

Chamath Palihapitiya: And as a consequence, um, you know, we can dramatically reduce the, the, the cost of that commodity.

SPEAKER_29: And this dovetails with your previous statement about permissionless and anybody can join to, uh, make a little bit of extra tau or whatever the subnets token is.

SPEAKER_01: So if I'm sitting there and I was, I don't know, um, providing servers to startups and other folks, and I happen to have, you know, a rack that I haven't provisioned yet. And it's going to be a provision, you know, in a hundred days, I have a client who's coming online in a hundred days. What do I do with that for a hundred days? Well, I could give it to ENGY without asking anybody permission and start making money from it. So the downtime would turn into productive time, would turn into revenue generation time.

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SPEAKER_69: It took quite a long time. It's important to put an asterisk here. It took quite a long time for us to figure out how to build these mechanisms. Like just like how, you know, the first smart contracts on Ethereum, like the DAO, didn't work, right? And you had crypto kitties and that was cute. But now you have production level stuff and all of these side chains.

SPEAKER_182: How long has Tau been around? And how long did it take you to get to what I'll call?

SPEAKER_69: Yeah. People have been building mechanisms on BitTensor for two years. So this is the second year that it's been open for people to build mechanisms on BitTensor, but

SPEAKER_68: actually we're 2021. So the network launched with us learning the art of what we call incentive mechanisms. We call subnets that the core design, how like the structure of what a subnet is in which I can

Chamath Palihapitiya: talk about at length. It's actually quite a, it's quite an interesting field of the study, the economic study of how do you

SPEAKER_69: build a permissionless adversarial game where even the worst person in the world, if they,

SPEAKER_118: if Hitler and the devil were to have a child and then that child were to mine on your subnet, they would just produce value, you know?

SPEAKER_00: So you can take away intent, but you need to have some validator that says, hey, this

SPEAKER_29: Hitler Idi Amin putting servers on here is not doing so to kill people or to do harm in the world for being a little bit, you know, dramatic cheeky here.

SPEAKER_01: Yeah, but they are being judged on a specific criteria, which is, does this server provide inference and provide Kimmy or GLM five, two, and there's something there with these validators that do this. Yeah. In the subnets.

SPEAKER_57: Yeah. So, so we built, we built a mechanism that would allow a distributed set of computers that could

SPEAKER_68: take any code that you wrote, Jason, for, you know, describing whether or not the computer

SPEAKER_41: that joined the network is actually, you know, if Hitler and Satan's, you know, a love child was actually doing inference properly. You wrote code that would check that and these computers, which we call validators,

SPEAKER_68: you can elicit them to all run the code at the same time. And if they do so, they will reach consensus if more than 50% of them are running the right code. It's very similar to the way in which, you know, Bitcoin works, right? If more than 50% of the network is running the correct code of Bitcoin, then it doesn't matter

Chamath Palihapitiya: if 49% of them are cheating. It's irrelevant.

SPEAKER_68: The person, the majority, the honest majority will determine the direction of the incentives in the network. So the core mechanism of BitTensor is that we can build mechanisms like this so that, yes, we can build a network that's permissionless and verifiable and auditable, right? If you know that this distributed set is all running this code and it's going to take more than 50% of them and the decentralization of that set, which is measured in proof of state, it's not like Bitcoin. Bitcoin is proof of work, which means that the weight of a node is based on how much compute they provide.

Chamath Palihapitiya: In Ethereum, it's proof of state, which means that the weight of a node in consensus is based on how much economic value they have. It's the same thing in BitTensor.

SPEAKER_29: In this case, if you were, you know, NGL, I'll go to that one because I actually use it. If somebody got on the network and said, yeah, I'm providing GLM-5-2, you know, this open source model from XAI in China, but they were actually using like some old DeepSeq-3 and they were passing it off as GLM-5-2 to use less compute and they were giving wrong inference, the validator would say,

SPEAKER_01: uh-uh-uh, we're checking that you're actually using the right GLM-5-2 code, not 5-1, not 4-0, not some other hack.

SPEAKER_197: Not quantized. Yeah.

SPEAKER_01: Yeah. And we are going to make sure in a way that there's an SLA, a service level agreement here, which you might have with Google or, uh, Amazon Web Services, in a way you've smart contracted or built into the system an SLA, that the service level is, um, mechanically and architecturally built into the system. Am I understanding it correctly in layman's terms?

SPEAKER_201: Yeah. And instead of, um, a contractual SLA, it's a, um, programmatic SLA, right?

SPEAKER_69: So you, you define the way in which you can check to see if they're not, they're doing the

SPEAKER_174: right thing.

SPEAKER_52: So for an inference subnet, it's actually very difficult, but the technology is there.

SPEAKER_68: You, the way you do it is you, you query the endpoint, the endpoint gets a, gets a result. Um, and they have to pair that result with a very cheap, what's called a ZK proof.

SPEAKER_69: Um, in case of NG 53, it's, um, top of lock, which is, um, um, some, an algorithm created by prime intellect, which is, um, basically hashes the hidden states of the model. Hidden states is like halfway through the machine learning model.

SPEAKER_68: They, they take those basically intermediate representation, uh, and then they, they project

SPEAKER_03: them onto a different mathematical space. And, and then they, you don't need to know too much more about it.

SPEAKER_11: But anyways, that's basically proving it, it is what you say it is, is a way to say it. Yeah. I'm planning it. Precisely. And, and, and, and on BitTensor, these things get like, it's one thing to write a paper.

SPEAKER_69: It's a completely other dimension to, to write an on BitTensor, because we have the smartest people in the world and we have the smartest hackers in the world. And they're just right next to each other.

SPEAKER_68: And they're, they're going to break, they're going to break your system. And, and if you can build a system that on launch doesn't break, you know, it's like,

SPEAKER_69: oh my God, it's unbelievable because.

SPEAKER_29: So this is critically important constant. Um, you know, if it's permissionless, uh, you can have bad actors and then the bad actors can be forced to act well and be good actors by the architecture.

SPEAKER_01: But you're constantly being stress tested. You're constantly being attacked because it's permissionless and global. You have attackers who say, Hey, there's something at stake here. I can get tokens that are worth something.

SPEAKER_13: So there's a value in hacking the system. Therefore the system must be architected properly. And that's a big part of your job. And the BitTensor foundation's job is to make sure the rules and the architecture,

SPEAKER_01: and in a way you're the police officers or something, you're, you're the, you're the Jedi Knights of this, uh, system, making sure there's peace and that it's being done properly.

SPEAKER_68: The, the, the, the, this is the, the role that we play, um, reluctantly while it's still

SPEAKER_69: required, um, you know, as all subnet owners do themselves.

SPEAKER_68: So at the, at the level of the individual subnet, they're, they're, they build a mechanism and it breaks and then they fix it and then it breaks and then it fix it and then they get it right. And then it starts working and then they make a million bucks.

SPEAKER_69: And, um, but in that time, it often requires a lot of massaging and, and realignment, you know, um, to, to, to, to, to fight against effectively, you know, nation state militia shows, uh, you know, there's a level of the people that are trying to destroy your project. And for a lot of people starting to subnet on BitTensor, they, they don't really understand

SPEAKER_57: that that's what they're up against, which is, I think kind of cute. I often have these phone calls with people. They're like, oh, I want to start a subnet. And I'm like, oh, that's so nice. Um, but you're, you're really not coming at it with the, with the right level of intensity

SPEAKER_216: because you, you, the, the, your adversaries on this network are, are very serious. And, um, and, and so they could literally be a nation state.

SPEAKER_29: It could be North Korea, which like North Korea is so desperate for revenue, Lon. I don't know if you remember this story, which we covered North Korea was placing

SPEAKER_13: developers as remote developers in companies, not because they were hacking the companies.

SPEAKER_219: They wanted the six figure jobs. Yeah.

SPEAKER_24: Yeah. No, we talked about how those companies had to develop like whole protocols to

SPEAKER_222: identify when a North Korean was applying for a job, pretending to be a different kind of remote worker. Yeah. I remember that.

SPEAKER_57: Right. Crazy. Building that is difficult, but we have now, I would say like the top 10 to 20,

Chamath Palihapitiya: some of the, some of the, which are, are premier and they're experts at this.

SPEAKER_225: And they, they can do things like, um, uh, you know, 7.51 figured a GPU attestation, um, you know, trusted execution without trusted execution, which is quite incredible.

SPEAKER_68: They don't even have TEEs, which is what, what, you know, Intel does. They, they built the algorithms that could go in SSH into the GPU and checked everything in the world to make sure that that person has that computer and is not cheating. And they built the economic privatives to make that, that makes sense. And so, you know, now those churn, um, but it took a while for us to build those things.

SPEAKER_69: And so this is all just one level. That's just a level of the mechanisms on BitTensor, but it in an absolutely insane.

SPEAKER_68: Um, um, but it turned out well decision, um, by us, we decided to make the actual creation of the mechanisms themselves on the BitTensor blockchain, uh, mechanism itself.

SPEAKER_69: So we went, okay, hey, let's apply ourselves to ourselves. We're really good at building these permissionless mechanisms. Let's build a permissionless mechanism, a mechanism which selects permissionless mechanisms.

SPEAKER_68: And, and so that was what we launched just over a year ago, um, which we call the NanoTau, which is where all of these subnets got their own token.

SPEAKER_69: All those tokens got paired to BitTau and all of them are trying to push the, the metric of success inside our ecosystem, which is increasing their price while staying on basically. And so we are, our thesis at the, like the highest level of BitTensor is that, um, we want to like, hey, this network is going to incentivize and try to optimize people for bringing inferences. Great. Well, we're going to optimize people bringing projects into this ecosystem that

SPEAKER_68: produce a lot of value, which we can measure with price more or less. And, and, um, uh, you know, with a couple of knobs here and obviously it's, it's complicated

Chamath Palihapitiya: and we can get into it, but that is what we think it will pull the most amount of innovation into the

SPEAKER_68: ecosystem and, and also drive the most amount of value in, into BitTensor, which, um, I believe does work. When you look at the way in which like the, you know, the, the hand overhead crawling towards performing well in this ecosystem, the more competitive we make it, like the, the more badass the teams have become. And it's quite impressive. Like, uh, it's truly become, it's really reached a point now where the entrepreneurs in this chain are so much better

SPEAKER_57: than I am. Um, and, uh, and many times smarter. And, and I, I listened to as many of them as I can,

Chamath Palihapitiya: because they all, um, often are mentors and guides for us at the ecosystem. You know,

SPEAKER_64: how can we improve, um, the core incentives of, um, the, the BitTensor blockchain.

SPEAKER_29: So this is a good pausing moment. Again, we'll just stay on, uh, ENGY.AI. We've been talking about the person running that is a guy named, uh, Ning Ren, N-I-N-G-R-E-N. This person came to BitTensor, how and why, what, who are these people that come to you or to, to the BitTensor foundation? And we should understand what that is and how that works. Who are these people who are attracted to creating these projects and what's their goal? Are they, uh, like freedom loving individuals? Are they entrepreneurs? Are they hackers? Are they some combination of these?

SPEAKER_233: A good number of them are scammers.

SPEAKER_201: And, and, um, you, you, you kind of can't avoid that because, um, you know, you have the doors open.

SPEAKER_69: And that's part of what we're doing is where we're saying, Hey, we can, we can like open our eyelids the most, and that's what is going to make us win. Um, but you know, we're staring right at the sun. So you're going to get a lot of stuff. And then, so there's a lot of bullshit and, and, and, but then there's also the best stuff and the highest quality and the most intelligent people that, that if they can get across the stigma, because there's a lot of stigma in crypto and well-earned, honestly. Um, um, and, uh, if they can get through that stigma and they can look at the technology and they can

SPEAKER_68: see what they can build here and they understand technically and, and philosophically, like why this is such a powerful primitive. Um, yeah, those people that see it, they come and, but the, you need it, you need a level of, um, um, openness for sure, because it's, it's, it's not the, the, the most dreaded path. Right. Um, and, uh, but so someone like Ning, like I, I, I, he, he was brought in, um, by, um, by Algod, I believe, um, who built a team and said, he knew this guy who'd worked at, um, Google brain, um, where I'd also worked and he didn't know about it and, but he was told about it and he thought it was super interesting, um, as it is. And, uh, and so he would got a fascinated and obsessed with it, you know, so there's those people, those types of people that, that, that come in and, and then, and then there's, and then there's people that, um, have absolutely no academic experience, but they're just raw entrepreneurs

Chamath Palihapitiya: that, that go, Hey, wow, this is novel. Um, this is the thing that will allow me to build something that can break down that glass ceiling where, you know, can you really break past these fiat funded companies by just doing the same thing that they're doing? And I think that the, the, the compa, the thing that compels a lot of entrepreneurs in our ecosystem, which compels me, um, is that we need to do something different and better and more powerful than what they're doing. Um, otherwise, they'll just beat us with more cash. Right. And, and so, so this is also mind blowing. The fact that

SPEAKER_29: BitTensor exists and it's relatively stable and providing functionality to the world that some

SPEAKER_01: number of consumers and enterprises are dependent on is extraordinary as a technical achievement, but it's also extraordinary as you're pointing out, Hey, there's frontier labs, there's open source projects out there. There's a lot of competition. This is the highest degree. Uh, like what is the chess rating yellow or something? Like if this was chess, you're going and playing with the grandmasters, the grandmasters being, uh, you know, Elon Musk, you know, uh, Claude, Sam Altman, open AI. I mean,

SPEAKER_29: these, uh, Gemini, Sergey Brin and his team, like these are the most elite and you have to beat

SPEAKER_01: them in the offering if it was inference in this case. So it's, it's, it's an extraordinary achievement

SPEAKER_183: on so many levels right now. And, and yet the largest supercomputer in the world is, is Bitcoin.

Chamath Palihapitiya: So like the, the, the, this is the technology that has the example of the only thing that's beat them

SPEAKER_68: in size. So, you know, there's something to be mined here and, and like, it's, it's, um, uh, it takes

SPEAKER_69: time to build the future. And, but now we're really starting to see like, what's so exciting right now

SPEAKER_68: in, in, in, in BitTensor is that these primitives just, they just make perfect sense now. So, you know, um, we just, we turned up the emissions for a lot of supplements. We said, Hey, you got to turn up, um, the amount that you're paying miners. And, you know, some of the mechanisms like, um, like 51, they just, the revenue just scales with the more, the more money you put in because it's

SPEAKER_69: permissionless, but there's the companies don't act like that, right? You, oh, here's a bunch of

SPEAKER_68: money. Oh, it's not necessarily that you can just distribute that immediately and scale efficiently. There's so much inefficiency when you have that, you try to scale a billion dollars through a human

Chamath Palihapitiya: organization. You have to hire people. We were talking about this at the beginning of the call. You have to fire people. You have to give them contracts. You have to get spaces. You have to find all these things, put them all together. It's not easy, but, but a mechanism that's just described by really just, uh, a code base and a permissionless market, you just pump more money through it and it just scales. So, you know, because they, they've, they tripled the amount of, um, money they're paying miners, they've tripled their amount of compute in two months. And, and, and so, um, like these things are really beginning to work and it's really exciting to see how they are, um, coming together and meshing together in an ecosystem.

SPEAKER_52: Um, but our goal is not just to do more compute and more inference. That's fantastic. Um, our goal is actually to, to truly head to head with, with, um, the elite centralized labs at intelligence.

SPEAKER_68: This is, I think the pinnacle commodity that we can, we can measure, um, inference and compute and storage. These are sort of predicates. Um, but the, you know, this is, this is where I think that, uh, well, this is where we're going to go and this is, and this is what we're, we're trying to build right now. And it's much more difficult to measure intelligence. A computer is very difficult and inference is extremely hard, but measuring intelligence is still possible. It's just very, very, very abstract and, and, and hard to get at. So this is what, you know, my personal mission is.

SPEAKER_69: And, and I actually run a subnet on, on the tensor right now is for the call. I was working on it right now. It's called affine. So we're, we're building the, the code, the mechanism that can actually

SPEAKER_68: measure that pinnacle element. Like what does it mean to, to actually grasp in your hand intelligence

SPEAKER_251: as a, as a commodity? Um, so that's, that's the thing that, that we're thinking.

SPEAKER_01: And how would one measure that you, we do have like humanity's last test. We have all kinds of benchmarks from LM arena. Is it as simple as, uh, saying here are intelligence tests.

SPEAKER_254: And I, I want to hear more about affine, uh, A F F I N is affine. A F I N A F F I N A F F I N A F F I N.

SPEAKER_262: Okay. So that's something A F F I N I N E. And that's subnet number. 120.

SPEAKER_01: There's a hundred, there's 128 of them right now. There's 120. There's been talk about 256. What does it take to start a subnet? How does that work? Do you go to a board of subnets? You go to the other ones and say, Hey, I want to do this. How does it work? Well, if we had a board, then we

SPEAKER_266: wouldn't really be permissionless. Um, you, um, and so anyone can, can register one. Um, and the way

SPEAKER_57: that you do that is, is exactly the same fractal like design. So it's, it's, um, the same way that you would register into a network, uh, of a subnet. You basically burn some token, um, to pay an

SPEAKER_68: entrance fee into the, into the, uh, league. In this case, the league is 128 in size. A normal subnet is 256. So we'll probably get to try, we're trying to get to do 256 at that level as well. Um, so you enter into the network and you start building your system and people show up and go,

SPEAKER_271: Hey, what, how many, how do you have to burn or contribute? This is like buying a franchise,

SPEAKER_272: right? Like buying a team, uh, in a league. Yeah. Let's look it up. Oh, okay. And how is that

SPEAKER_225: determined? Yeah. It, it changes, um, every block, um, on, on, on the tensor. And so what we, we use is

SPEAKER_57: a, a bit, it's kind of like a Dutch auction. Um, so the current rate is 608 tau and we're trying to lower that. So it's, it's about, it costs about $121,000 to have one of these 100, 128 slots,

Chamath Palihapitiya: which is a lot of too much. And we want to lower that, um, a lot because, but it's a Dutch auction. So, so the price decreases until somebody is willing to pay for it. And then when somebody, when a subnet registers, we double the price and then we lower it again.

SPEAKER_29: Well, I mean, it's essentially the cost of joining, uh, the money you would get if you joined, uh, an accelerator. It's classically been 125 K so it's not to my mind, like a crazy number. Um, but it's certainly not nothing. So where does that tau go? It just gets burnt and the, it lowers the amount of tau in the network or it gets into the foundation. Where's the tau go? It goes to you. Who does it

SPEAKER_67: go to the other subnets? It actually goes to, um, create liquidity in the initial pooling

SPEAKER_68: between tau and your alpha, what we call alpha tokens. So the subnet token, it creates liquidity.

SPEAKER_69: So all of these subnets in BitTensor Alt 128 have a liquidity pool, um, a V3 automatic market maker. It's, it's basically somewhere you can just buy the token through with a little bit of slippage. Um,

Chamath Palihapitiya: it's a, it's a smart contract itself. Um, and so each one of them has a pairing with tau. Um, and so you have to buy tau in order to buy the, buy those subnet tokens. Um, and this, this is, you know,

SPEAKER_69: one of the ways in which we, we build demand for the underlying token, right? It's like the US dollar, right. It has all of these amazing companies inside of the US dollar in the US system. And

SPEAKER_00: it has a massive network effect. So we're creating people speculating and just buying five of them and sitting on them. If I was like, uh, an investor and a speculator, I just buy four of them for $500,000

SPEAKER_225: and sit on them. You'd be like, I'm a speculator. Um, you know, not financial advice. Yes, you, you

SPEAKER_68: definitely could do that. And people do do that. Right. The, the, we, you know, we, we had this, um,

SPEAKER_69: um, question problem, which was how are we going to, we, we built the platform so people could build

Chamath Palihapitiya: these systems. Um, but we wanted to know, how are we going to incentivize the teams? Okay. We still have, you know, 10 million tau to distribute and okay, well, what we're really good at is, is an

SPEAKER_68: optimization mechanism. So let's build an optimization mechanism that these teams can, can compete in,

Chamath Palihapitiya: right. But they get, they come in and they, they get relegated and they go to the top. Um, and so

SPEAKER_69: we came up with this idea of using the price of a paired token as the thing that we would measure. And, uh, you know, that allowed us to build, allowed us to distribute the inflation of tau itself

Chamath Palihapitiya: into a network with people who are mining by creating subnet. So people create subnets and they mine tau by creating subnets. That's the, that's the, the, the meta system in BitTensor. And, and the idea is, is that, you know, as a whole, this network, if we optimize for all those products

SPEAKER_118: to produce value, which is measured by their price, we can actually elicit the internal machinery of

SPEAKER_69: capitalism, which is thousands of individuals from DGNs to, to, to scientists and the like, to longterm investors. We can elicit all of that swarm intelligence to properly order rank these

Chamath Palihapitiya: projects, right? Like, you know, Hey, if you were to sit down right now, Jason and go, Hey, how can we order all of the companies in the United States? And you didn't have the, the NASDAQ. How would you do that? It would be an impossible job. So, so what we do is in society is we use markets to do that ranking order for us in some way. Um, so we did the same thing inside the internal market of BitTensor. We elicited this, this, um, uh, market mechanism, this, this trading system to,

SPEAKER_69: to, to, to order the subnets and, and push the, the, the, the cream to the top, um, which is, which is what you see. So, you know, when you're entering into the BitTensor ecosystem, I often tell

SPEAKER_118: people, you know, be careful because there's, there's some rotten milk, uh, and there's some cream and, and the cream is more expensive than there's raw milk. And your job as for playing this game is to actually help us contribute to the, the, um, the actual movement of this internal system, right? Like you're actually governing BitTensor. In that way, BitTensor is, is highly decentralized.

Chamath Palihapitiya: They, they're probably one of the most decentralized networks ever produced. Like,

SPEAKER_68: it's not like a 10 nodes. It's not a thousand nodes. It's tens of thousands of nodes, uh, contributing and making, um, making informed bets on the truth. And so, uh, that's how we, we, we built.

Chamath Palihapitiya: Yeah. That's how we built it. And that's, that's, that's holistically, I think what it looks like.

SPEAKER_300: It's extraordinary. Lon, you had a question. Yeah. Uh, we got to talk more about AFI before

SPEAKER_24: there, we were on a full on chat mutiny. They really, the folks want to, want to hear about this. So I'm curious, this is the idea is you're building models and if so, how are you setting up the competition or how are you setting up the landscape to ensure that your miners are producing

SPEAKER_302: the next generation of models, like of the kind of model that you want to train and design?

SPEAKER_03: Last year, we did produce, um, a model that was better than Quen's best model at the 35 billion range. And just before we launched it, they launched another 35 billion model that was better than ours. Um, and so, you know, everybody's in the race now. It's an unbelievable race. And like we, you know, it's, it's, it's not an understatement to say that we, we, we are up against nation states

SPEAKER_68: because it, this is truly like China versus the United States. And there's that level of funding. Um, so it's, it's, it's, it's in no, it's in no, um, ways easy, but we did produce a very good model,

SPEAKER_57: but it didn't take us to, to the level we wanted. Um, I find, um, logo is, is, is mining reasoning.

Chamath Palihapitiya: And reasoning is the way in which a model thinks to itself so that it can answer a question properly.

SPEAKER_69: And, um, so what we have them, the miners on the network do is we have them produce machine learning models that can produce reasoning that they can reason in a way that makes other machine learning

Chamath Palihapitiya: models answer the right question. So it's sort of indirect. It's like, um, imagine if I

SPEAKER_69: were like, I w you would think that I'm smart if whenever you talked to me, you felt a lot smarter yourself, right? Um, like I knew it happens better, right? That's a really good sign. It's like a, it's actually reflective, right? It's like you, you, you feel you're, you're, you know that I'm smart.

SPEAKER_68: If when you talk to me, like things make sense to yourself. And, and so, um, we, we, um, right now, this is, this is what we're, we're honing in on, um, as the, like the core mechanism for, for, for that network as, um, like we, even if, um, it doesn't necessarily produce, um, models that are

SPEAKER_69: like, uh, stylistically perfect, they, they can, um, at the very least, if this is successful, be adapters to other machine learning models. So you could run GLM and instead of GLM thinking,

SPEAKER_68: you would just talk to the smaller model and then be able to go like 30 times faster. Got it. Um, but that's not, that's not the end goal. Actually, the end goal is that we think that, we think that, um,

Chamath Palihapitiya: mining that, that, um, latent space of thought, um, is, is going to be the prerequisite for us

SPEAKER_68: training incredibly good models. And so that's what we're doing right now. If you go to Affine.io, you can, you could participate. Um, I, for people on the call that are interested, like, um, we're in this really interesting period of time where you don't need to be a machine learning engineer and you don't

Chamath Palihapitiya: necessarily need to even be a computer scientist to participate in these networks. Like you could literally just get your open claw and send them the website and he will tell you, or it will tell

SPEAKER_311: you whatever you want, the gender you want. They will tell you, they will tell you, they will tell you.

SPEAKER_313: I don't know what you guys are talking about. He's a he and he is my friend. So he's a he. Okay,

SPEAKER_68: great. Um, he will tell you what is needed to participate in these games. And, and if you, you can just basically send your AIs to, to participate in these networks, which is really, really interesting, um, time to be alive where, um, and this is what I was talking about, um, before

Chamath Palihapitiya: the call started with you, Lon about how, and we work, um, remotely. So I'm, I'm, I'm nowhere near the

SPEAKER_69: rest of my team right now, but, uh, we can all work together through these, these games. We don't

Chamath Palihapitiya: need to be in the same room. We just build these really well-defined computational and innovation games. And then anybody in the world can contribute almost without communicating by just sharing the best work they can, they can. And if you're not good, if you don't come to work, it doesn't matter

SPEAKER_69: because you're just going to get replaced, um, very quickly in these hyper-competitive systems. So, um, yeah, and then the, the, the call that I've always said from the very beginning, like, I don't actually really sell Tau the token. I, I, I, I sell, I sell that you can be part of this. Um, and, and you should, because there's, it's very exciting and it's very fun. It's like, for some people mining BitTensor is like the most fun game they've ever played ever, like most dynamic,

SPEAKER_320: most interesting, most in, in rewarding thing. Along, along those lines, um, one thing I'm really

SPEAKER_24: fascinated about, we've talked to other subnet or we've talked to subnet owners about this. Like when you're first setting up these competitions, how much are you sort of thinking about the 4d chess of it? Like I have to create a competition or a system that's so tight that no bad actor can come in and like game my system. Like how, how much of a part of the project

SPEAKER_302: is that, that sort of adversarial thinking about?

SPEAKER_323: I would say that that is the entirety of the project.

SPEAKER_302: Okay. Fair enough.

SPEAKER_118: Um, the, the, all the other stuff is fluff in some sense. Yeah. Because you're, you're, you have to think about your, you can start with all the marketing.

SPEAKER_69: You can start with a website and that might help you and people will maybe invest in your project because they're like, oh, this guy's got a good sense of sales. Um, but if, if you, if you can't solve the adversarial problem in the network, then the network produces no value. Right.

Chamath Palihapitiya: And so, you know, it's, then it's just lipstick on a pig and the you, you're, you're wasting your time. You're wasting my time. You're wasting everyone's time. And that's the most fun thing. And when you get down to, you know, how do you resist adversaries? You often find that there's this

SPEAKER_69: very like compressed idea at the very core, um, a very simple, compressed idea that, um, you're, you're truly build it. That is, is like a elemental, right? Okay. Oh, interesting. So inference verification is, is actually information. Checking information parent, you're checking that the information produced by this thing is similar to this thing. And so they're,

SPEAKER_68: they're actually, they're producing inferences, but underneath the hood, they're producing information and they're trying to produce the information as fast as they can.

SPEAKER_69: And, and so that, that is actually the commodity of an inference network, which people don't talk about. Um, so anyways, that's like the, you know,

SPEAKER_338: the inside baseball philosophy of the stuff, but I, I, I, I, I love that the most.

SPEAKER_01: There was a, there was an interesting moment when this, uh, language model got created.

SPEAKER_340: Uh, we were on all in and Chamath brought it up with Jensen. You probably saw that clip where

SPEAKER_179: he was like, this is so impressive. Uh, talk a little bit about not only that moment and like

SPEAKER_02: what you took away from it, but then the controversy with that subnet and it imploding. Right. And what we learned from that. Yeah.

SPEAKER_57: The reason why Templar is called Templar is because when we were building this subnet,

SPEAKER_69: um, we, we talked about how one of the holy grails of, in the artificial intelligence field and the

SPEAKER_68: psyche and Neuosphere, whatever you call it, um, is not decentralized inference or decentralized computing, or even what I'm talking about with, with affine, with the model training, it's, um,

Chamath Palihapitiya: decentralized training, which is where you have a computer and I have a computer, lawn has a computer and they are making the same machine learning model at the same time.

SPEAKER_69: Because in order to train a trillion from our machine learning model, you need a hell of a lot

Chamath Palihapitiya: of compute. So in order for, for the, the rest of us to organically, you know, uh, self-organize and to take on open AI, we need to come up with a way that we can work together. Um, and because we only together, we will have enough compute to compete with the big guys that have the, the billion dollars investments in, in, in, in infrastructure. So, so, you know, we need to figure out how we can train together, but in order to do that, we come up with some, come up against some like fundamental limits of physics, which is that the way in which these machine learning models are trained is that

SPEAKER_68: they merge their weights every step, more or less, they merge them. So you do some work and I do some work and then we just merge them together. And if, if one of the parameters in your network is pointing this way and the other one's pointing this way and this one's pointing this way, we find this middle

SPEAKER_69: point, which would be where we're, the model is the most intelligent because it's, we've, you've traded on some data and I've traded on some data, right? Makes sense, right? So, but in order to move

Chamath Palihapitiya: these models across the wire, it's like heavily expensive in terms of bandwidth. We're talking like a terabyte of data more, right? And if we want to do hundreds of thousands of steps, well, it's hundreds of thousands of terabytes that we need to communicate, which is more than any of us have at our home

SPEAKER_69: connections. And, and it's certainly not what an average person has at their home and not on their laptops. And so the, how do we aggregate together in the first place, if we can't even do it with the internet connection we have, and maybe we can do it, but it's going to take 10 years, right? Which then is no longer important, right? So, and then on BitTensor to put it from another perspective, we have all of these compute aggregators like Liam and Targon and Cube, which people actually bring in, they plug in

Chamath Palihapitiya: their GPUs, but these GPUs are all over the world. So for us to use them, we need to come up with a

SPEAKER_69: decentralized training mechanism. So, so in some sense, like training a trillion trillion per hour model is sort of beyond what BitTensor can do until we can come up with an algorithm that stitches

Chamath Palihapitiya: together all of the compute in a way that gets around this bandwidth problem. And it's also the holy grail because, you know, a lot of people are thinking about this problem and people have thought about this problem for a very long time, including myself. So that's why it's called Templar because the Templars were trying to find the holy grail or they were protecting the holy grail. And, and, and so we came up with that name and we started working on the, the way in which to do that,

SPEAKER_68: which was to take advantage of some of BitTensor's primitives. Like we, we have hippias bucket storage and so where all the computers can upload to single places and then download, which is kind of cool.

Chamath Palihapitiya: And so we built this out and then we also built the algorithm where we could, what would we be measuring that the miners are doing in the first place? So as I, as I talked to you about like the,

SPEAKER_69: the core of the, the, the problem is, okay, well, how do I know that you, you did the inference, right?

SPEAKER_68: So in this, it would be, how do we know that you did the training? How did you, how do we know you trained on that particular subset of the data that we needed you to train on in order for you to merge with us? And how do we know that you don't just contribute bullshit that destroys our model while we're training? That's really difficult. And training machine animals is a very fragile thing.

SPEAKER_76: If you have one person there that's talking around, it's just my language, it can destroy the whole thing.

SPEAKER_29: So this is why, like when Yulon talks about, Hey, the next version of Grok's coming out, he has Colossus. It's a, some number of day training run. You throw some, you know, wrench into that machine. You got to start over. And so that's perfect. So what happened when Templar and the rug pull, I know this is like the one thing people use as an adapt, attack vector on BitTensor and the subnets and the architecture, just candidly, what happened? Did just somebody run off with essentially all the

SPEAKER_357: Tau in their subnet and just tell everybody to fuck off. And, you know, it's just part of the system.

SPEAKER_358: I mean, if effectively, you know, in, in, in, in any company, if somebody works in the company,

SPEAKER_68: they CEO starts a company, they can just leave. There's actually no, usually there's no contract.

Chamath Palihapitiya: You invest in me. I just, you invest in my company and I just be like, I don't want to do it.

SPEAKER_68: Right. Um, this happens in early stage startup, because some people look at what it's like to

Chamath Palihapitiya: be an entrepreneur and they go, well, that's a lot of work for me. Yeah. And, and not only is it a lot of work, it's a lot of stress, stress, and, um, I just got some investment into my company and

SPEAKER_68: maybe I think I'm just going to take that. So, so what this person did was they just sold their shares more or less. They sold their shares. And, uh, while they also sold their shares, they wanted to cover up that they were leaving with a reason for leaving. Like it's not that I'm a bad

Chamath Palihapitiya: guy, it's their bad. And so I, I looked to it and I'm going to cover my ass as I do something that's really shitty, which is take a bunch of people's investment and then just walk away, which is a pretty standard thing in, in, in crypto, right? Because it's, you know, one of the things about

SPEAKER_68: permissionless markets and us allowing for early stage startups from non-accredited investors and successes, things like this is, is that basically people get burned and, and, and because, because most of the time, or like a lot of the time, this kind of stuff can happen. Right. And so, yeah.

Chamath Palihapitiya: Um, so they, they wrote an article that basically said, Hey, we're not, we're not bad for leaving. It's, it's the mean network and, and cons for being an evil dictator,

SPEAKER_67: um, because he sold some of our token and that was mean. And, um, um, and then what did they go

SPEAKER_251: do? They did another startup or they want to, no, the project fell apart immediately. Um, and I mean,

SPEAKER_69: of course it does because you, you, you, you, you, you kind of like, you can't burn your reputation like that. You can't take money from, by the way, to, to, to, to follow you.

SPEAKER_29: Yeah. It's a reoccurring issue for a Y Combinator where somebody goes to the Y Combinator program. If you get accepted, not only do they put that 125 K in, they will give you a long $375,000 in an uncapped note. Then you get a bunch of people excited and you might get a bunch of people on demo data. Let's just theoretically say, put in another million. Well, I had, this is, I've had multiple people contact me that say, Jake, you know, you wrote the book angel. What should I do here that I'm not getting updates. The project's not active. They raised 1.5 million and they're just using it to live off of. And we don't see any updates and they're not shipping any product. What do we do? And I say, that's the cost of going to the casino is you could have bad actors. Now you could file a lawsuit. You could do all these kinds of things. The, you put 50 K in to file a lawsuit would be 250 K to then pursue. It would be a million dollars. And then the outcome is maybe

SPEAKER_01: you get back your 50 in the best case scenario. So talking about trust, the startup ecosystem is largely based on trust. We've literally had people take the money from their bank account when we give them these small checks and just YOLO it and go crazy. Um, and it, it, it's like you, it's kind of like, um, credit card, bad credit cards. You know, you get like a 2% or 1% fraud and you're like, okay, whatever 50 basis points of fraud is what it's going to be. I mean, this happens in every

SPEAKER_24: industry. You remember there was that Netflix sci-fi show they were creating and the guy just took the budget that they gave him and just went and bought a bunch of mattresses. That really happened. That really happened. I think he's in jail now where he got, yeah, he got sentenced already,

SPEAKER_29: but yeah, he bought like watches and yeah. Fraud can happen in Hollywood startups and on the BitTensor network, I think is the, but you did tighten up the Tensor network a bit based on that?

SPEAKER_394: Related question from the chat room. Oh, sorry. Go ahead.

SPEAKER_68: Well, I just want to make a point here, right? So it's a double-edged sword to allow

SPEAKER_69: anybody permissionlessly to have access to early stage startups, right? Like this is one thing that crypto does, right? It's like, okay, you can be on the ground floor potentially, um, where you can't for open AI. And so, um, like this, but as a consequence, we've learned that there's a lot of issues. And so crypto has become a lot more prickly and has thorns now because people have woken up to the the realities of this network now. So it's double-edged sword, but the benefit is that people get access. And, and I would say that overall in the BitTensor ecosystem, we've made people a lot more money than they've lost. So, so that's, you know, I hold onto that now, but the reason why it's

Chamath Palihapitiya: particularly bad in crypto is because in early stage startups, um, you would invest in, um, they can't just

SPEAKER_68: go and sell their equity into a market automatically because there's no, there's not, there's no market

SPEAKER_55: for that. There's no, there's no like, um, there's no secondary market of startups, although people have

SPEAKER_68: tried, but even for the nascent ones, there would be no buy side. Exactly. Um, and so, but we, we in

Chamath Palihapitiya: crypto build those, the buy side and we let these things float. And, and as a consequence, it's part of our technology, right? I spoke about how like this internal market is how BitTensor works. It's actually a functioning aspect of the machine is that we have individuals that govern the network through market dynamics. It's not something that we're not, we're not just creating alpha tokens

SPEAKER_69: for nothing. We're using them to move like a computer through space. And, um, but so the reason why it's particularly pernicious in crypto in general is because anyone can just go and sell on market,

Chamath Palihapitiya: hence rug pulls, right? It doesn't exist in startups because you, you can't do that. There's not going to be a second market, secondary market. And if you just go to your investors and go, Hey, quickly,

SPEAKER_69: give me my $10 million. I I'm out. They'll go, actually, I don't know if I want to buy that, um, because you're selling. Um, so what we built into the, into the chain was basically the best that we could do is go, Hey, let's build sort of an ability for, for the, the subnet teams, not enforced,

Chamath Palihapitiya: um, that allow them to, to express their conviction called conviction by locking their tokens effectively.

SPEAKER_69: And in a way that if they want to go and sell them in one big cell, that's a public event. And, and

SPEAKER_68: anybody who invested before them could be like, you know what? I think I want to get out of here because you just did that. And so that's actually led to this. Um, it's been very beautiful actually, to see a lot of the teams, you know, um, go up to the plate and be like, yeah, you know what?

SPEAKER_69: I'm locking this thing perpetually for years. And, um, uh, which by the way, we call that vesting

SPEAKER_29: in startups where you vest your shares over time and then you can't sell them. And if you do want to sell them, there's a board decision, Hey, we're going to do a secondary offering. Hey, as we wrap here, tell us about the foundation and its role. We had a bunch of questions. Maria one,

SPEAKER_409: two, three asked as well. Uh, not Maria, um, six, seven, eight. There's a Maria one, two, three lines.

SPEAKER_411: It's got to keep your Maria's distinct. It's so separate here in the chat, but what's the role of the foundation in all of this? And

SPEAKER_29: then what's your stake in all of this? Like as the creator of this, do you have like a gazillion to how and how do, how do you stay motivated to keep this holding on? Cause my gut tells me it's, you know, Tao isn't at the point yet where it would keep going if cons went away. So how far are you away

SPEAKER_81: from making yourself irrelevant in this equation? And then what does the foundation do?

SPEAKER_225: I think it would definitely continue if I went away. Um, um, most certainly it would be,

Chamath Palihapitiya: I think, I hope that people would miss me. Um, but, but there's a, there's a vibrant community of

SPEAKER_68: people that have a, that want to really participate in this network and that they do, and there's an open source community and those people contribute. And there's 128 different teams that understand

Chamath Palihapitiya: this technology really well and want the system to go along. And, um, there's the foundation in Canada, which, um, is no longer involved with development. Um, and there's now another foundation

SPEAKER_52: which is more shielded, which is purely about upgrading the chain. So we, we push a lot of changes

Chamath Palihapitiya: to, to tweak and improve the mechanism. Um, and that goes through our, you know, internal governance system, um, on the chain, which we call the triumvirate and that, which is this can, this

SPEAKER_52: year are going to be expanded basically because we have a chain that we can build government systems

SPEAKER_69: directly into it. Um, we, we intend to build, uh, all of that this year. Um, so that that's, that's,

SPEAKER_57: that's the, the two foundations you might call it. Um, um, there's the Open Tensive Foundation and there's the RAL Foundation and they, they hold different sides. One's more marketing and outreach, et cetera. And one's more development and, and upgrading the chain. Um, I'm no longer involved

SPEAKER_69: with OpenTensor. I, I work just, just in the RAL Foundation. Um, and I program, that's my language.

SPEAKER_52: Actually, this is very unfamiliar for me. Like I am a programmer, uh, at heart. That's how I speak.

SPEAKER_29: Awesome. Well, listen, continued success with it. Uh, we're going to be monitoring all these subnets. As I told everybody, like, I think buying one Tau, just buy one Tau is what I've been telling folks. Why? Because I think it is a ticket to watch something spec, some, a spectacular experiment occur. So if you think of it long, like going to see a basketball game, just your 200 bucks or

SPEAKER_37: whatever it's trading at. Um, if it's, and I have like the finals, you're, you're $15,000 or whatever. Okay. Sure. Uh, but who's got it. It's, it's, it's a front row seat to like bet and learn,

SPEAKER_29: and it's not financial advice, but it's also like in my, in one way, a vote for me. Like if you were giving a, you know, go fund me, like, I almost see it like it's a vote of confidence that maybe there could be a decentralized AI intelligence platform out there. And that's good for humanity. Yeah. Pardon me. So I, I see it as like, and then, oh, and there's a third,

David Friedberg: what if it is Bitcoin? And what if it goes from 200 to 60,000 or 120,000? Hey, that could be like a great bet. Don't sell too early. That's what I learned about Bitcoin. I had

SPEAKER_423: a few, and then I sold it when it hit like a thousand. Like, I didn't think it was going to go. Dummy. You're a big dummy. Uh, you gotta ride your winners. Anyway, how important is it to get

SPEAKER_29: people promoting this and getting involved in it? Or is that like actually a distraction? The fact that like people like me are speculating on it now and interested in it. And I'm a,

SPEAKER_222: obviously a venture capitalist. It's a very passionate fan base. It's a very talkative, passionate ecosystem. Anytime we talk about. Definitely on X.

SPEAKER_390: Tao. But yeah, the, the video goes crazy. How do you think about the pumping in crypto or,

SPEAKER_29: you know, non builders like myself saying, Hey, I want to invest in this. Cause I think there's something here and I'm fascinated by it. And I like to make a return. And I think this is like risk adjusted for me, like a great, you know, Hey, maybe this thing, you know, I look at and go, Hey, maybe this thing can go a hundred or a thousand X. That's why I make the bet. It's like a real long shot kind of bet in my mind, but it's also fascinating.

SPEAKER_13: So how does speculators like me and then pumping and all that impact these projects

SPEAKER_68: on a practical basis, if at all? Well, I mean, I think that it's inevitable. Um, and we can't really avoid it. It's the nature of markets, but it's not the, the goal. Like the reason why we have Tao in the first place is so that everyone can have some and that everyone can join and that it can be split up. And, and in the first place, they can be split up. It turned

SPEAKER_69: into a whole 21 million of these things. So like, I love it that there's people that are excited and that's amazing. And, and the, the ones that are inauthentically promoting it, they come, they go. Um, there's always people that are going to say shit on both sides. Uh, I can't stop them. Um, I, I, I am way more interested in, in people that, that are, um, that see it the same way that I see it, which is more as a really, really powerful technology. And that are super excited about that

Chamath Palihapitiya: and the potential for us to build something that's novel and unique and, and, and competitive. Um, and the third path or what artificial intelligence can be born out of. So the, the, yeah, but the,

SPEAKER_41: the market dynamics were also really fun. I'm in all the press chats as well.

SPEAKER_357: All right. There you go. We've got over an hour with the man himself constantly shipping. Uh, you can follow him on Twitter. Thanks Jason. Reborn. Uh, great follow. And I think it's great that

SPEAKER_29: you're going out and talking, so I appreciate it. I know you got to get back to coding, but I think it's important for people to understand it. Like just from first principles, he did a great job of

SPEAKER_01: sharing that with our audience today. All right, let's drop. Yeah. Thanks so much for being here. That was great. Here's the thing, Lon, you know, these projects are so, um, they have so much potential and the intention is super important. And when you spend an hour talking to him, it reminds me of what I saw in the, the Bitcoin early, uh, true believers. And I had somebody on, I think in 2011, um, and I saw it then I didn't make a big enough bet. Um, and now I see this and it just, all

SPEAKER_13: the signaling is going off. So I was like, all right, let me put like, I don't know if I put like half a million or seven 50 into this, something like that. Not a lot of money for me, be the equivalent

SPEAKER_01: of like, you know, maybe somebody putting in, I don't know, a couple of thousand dollars, right? Or I don't know, $10,000, whatever, you know, it's, it's a smaller bet for me. It's not out of my funds. It's just a personal, because I think there's something here that's notable. And my signaling goes up just like I had the open clause signaling. You remember when that happened? I was like,

SPEAKER_13: guys, we gotta like pay attention to this because this reminds me of the mobile cloud computing era when like local mobile GPS all started coming together. I got that signaling for Uber and Robin hood. Hey, what would mobile do to trading and getting a car and GPS and all that stuff and being early on a lot of these things, same thing I saw when I bought the 16 Tesla. Now that doesn't mean you're going to be right. Right. Yeah. But you do have to place the bet is what I've

SPEAKER_24: learned. Yeah. I mean, sometimes you are right, but it's just not the right bet at the right moment. I mean, there's no way, you know, there's, there's no way to know which bet is the correct

SPEAKER_29: one, but it, you know, it keeps things interesting. Yes. And you know, like the interesting thing for Tesla was, you know, I bought the two cars for 300,000. I wish I just bought 300,000 in shares and just let it sit forever, you know? Yeah. Uh, and I always go, oh, you know,

SPEAKER_272: I did have shares and whatever, but I, and I did fine. So I'm not complaining.

SPEAKER_222: Insight on this stuff is always 2020. Like if I could go back in time and tell myself not to sell my few Bitcoin when they were in the thousand, I was like, well, I can't believe how much money

SPEAKER_24: I made. What a windfall. I'm going to go get myself, you know, and the mistake was you should,

SPEAKER_00: you could have sold 10% or 20%, but you want to keep it. If something's accelerating, you have to ask yourself, is it going to stop accelerating? Is this just, and so when I look at

SPEAKER_272: this, yeah, I, I, I think I might actually be down right now in my BitTensor bet, which I made

SPEAKER_29: this year. And I like to be vocal and clear about this so that nobody thinks I have some nefarious reason. And that's why I told folks just buy one, because I realized people were starting to take

SPEAKER_13: my tweets online, Lon, and you know, there's all these Pelosi tracker. Now there's a J Cal tracker. So when I bought Figma or I said Uber was at a bottom last week when it was like 67, when people were

SPEAKER_418: panicking, Michael sailoring, they think you're, you're, I'm not YOLO. I'm not trying to influence

SPEAKER_13: anybody. I just like to be honest about it. And when I J trade something, I just take a screenshot now. Like I bought Figma when it was at 20. Cause I was like, you know what? Dylan was like a beast. Um, we should have him on the program. It's like, Dylan's a beast, you know, like he's not going to sit here and lay it down. He understands designers better than anybody. He understands how to make a great tool, build a great brand, get people to pay for it, all that great

SPEAKER_81: stuff. I like to make a bet on him. It's now up to $25 a share, whatever. And now they're doing the

SPEAKER_01: same thing. They're retweeting it. J Cal called the bottom on this. J Cal called the bottom on this. You know, uh, my promise to you as the people who listen to this specific pocket is when I do it, I'm going to try to be transparent about it to the extent I can. I can't do that with private companies because it's not my choice to make the funding announcement, right? That's the founder and the board's choice. So sometimes I will make a private bet. I'm not at liberty to talk about that, but I try to be transparent with the public only because the J trading is all I'm, I'm not day trading. I'm J trading. J trading is hold for a decade. That's the nature of J trading. Now you

SPEAKER_13: may want to quit a stock if you get new information, but I like to find stocks that I'm comfortable

SPEAKER_24: holding for a decade. That's my whole period I'm looking for. Sure. You got to wait. You got in tech, the strategy is always wait for there to be a bad news cycle. The stock dips, you get it in a little discount and then you just hold on to it forever. That's the, that's the play.

SPEAKER_222: That's your this week in startups. We'll see you next time. Bye. Bye. Bye.