Bittensor creator Const on Affine, dTAO, "mining reasoning," and more | E2326

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

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

I predict I'm gonna make tens of millions. That's my goal.

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All right, everybody, welcome back to This Week in Startups Twist. Man, do we have something special for you today. I have been, uh, 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, Mom.

Yes.

And I felt like we got it on Bitcoin. Uh, I made some purchases when it was under $100. I got hacked, I lost it all. My wife bought a bunch under $100 and $200. We made millions. Fantastic. Wife's got like— if you put my Hall of Fame investments on a leaderboard, you're, you're uber— 2 of my wives are in the top 7.

Wow. Look at Jade.

Top 5. Well, dollar amounts and also on a percentage return.

So when is Jade raising her first fund? I think that's the question. Exactly.

Well, Jade's— She's got half of this one.

She's doing okay. Oh, all right.

Well, there you go. Yeah. 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 Konst. And he's here with us today to talk about BitTensor. We've been talking about it on this program all year long. 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 Steeves. 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.

I'm curious as to your guess.

Do we call you Const, Sir Const, Master Const?

I'm not calling him Sir Const. That's not happening.

Jacob.

Just Jake. It's fine.

Jake. Okay.

Jake it is. Is cons because you're constantly shipping? Ooh.

Or the protocol is constantly changing and annoying people.

Okay.

There's that. That too. Actually, it comes from Constantine.

Oh, okay.

The emperor.

Sure.

Got it. Makes sense. You are the emperor of BitTensor. Let's start off with why did you create Bittensor? What's the history here? And then I want to get into, um, 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, 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 2 times in the history of crypto I said definitely not a scam, definitely something going on here. It was Bitcoin I made millions and it's TAO. I predict I'm going to make tens of millions. That's my goal. So, Konst, tell us, where did this all come from? Bittensor and TAO?

Well, I think there's, there's a project you're probably missing there, which is Ethereum. Sure. And, 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 MakerDAO, which is a very complicated system of, you know, collateralized lending pools and, and stablecoins. That's all because of the abstraction of the, the contractual nature of Bitcoin. Hey, let's go beyond transactions basically.

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

Yeah, 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 smart contracts. And we do that on Bittensor as well, right? So we also have smart contract platform. But Bitcoin was, I think, 2 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, 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 full of SHA-256s and bury the transactions in this immutable time chain so that it can never be 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. It's— you mine it through producing hashes. They created this computational network called Bitcoin, which turned out to be incredibly large, like insanely large.

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, 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?

Yeah. Exactly. And what, what makes it digital is the way in which it's defined. It's, it's, it's, it's computationally defined commodity, right? Oil is not computationally defined. It's, it's physically defined. But a Bitcoin hash is, is actually computationally defined. It's whether or not you have solved the SHA-256 algorithm with the inputs and, 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. You can sell it. people buy it, which is quite incredible. And, and so the, the 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 computational primitives that you can create anywhere in the world by combining things like hardware and electricity together, you build these hypercompetitive markets. Bitcoin is probably the most efficient market we've ever seen in history. Anybody anywhere can buy and sell it. Well, anybody can contribute to it from any place on Earth by plugging something into the wall. And those are Bitcoins. That's what Bitcoin miner machines are. And as a consequence of having this permissionless, hypercompetitive market, the efficiency of producing this digital commodity is just gone exponential. Bitcoin's chart is, is like this, but the POW, the, the proof-of-work power of Bitcoin is, is just purely exponential. It just is never—

So the price is variable because of speculation, uh, regulation. A different market like Korea embraces it, bans it, and then re-embraces it. 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 up. And this is important because there's another term of art we should define here, which is permissionless.

Yeah.

And, you know, uh, 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 but maybe don't understand these core fundamental principles?

Well, there's 2 concepts there. So there's permissionlessness and, and peer-to-peer. And, 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, a monetary system. It's the first sentence of the white paper, right? You know, sending— I, I develop a mechanism where I can send a transaction 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 can, 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, he needed to, to get to that. And so permissionless means that anybody can contribute. It doesn't matter from where or who they are. Think of it like the, the, the ultimate form of non-bias. You know, like people are always trying to build these organizations, oh, like, yeah, we don't care about race and, and, and gender, et cetera, et cetera. Well, a permissionless market is, is, It's purely blind. Um, you can contribute anonymously, uh, from anywhere in the world, and it doesn't matter if you are a bad actor or a good actor.

It's purely a meritocracy. We don't care if you're in a common communist country. We don't care if you're in a democracy. 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. 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 a, you know, 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. Yeah?

Mm-hmm, totally.

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And it means that we just measure the output. And when you just measure the output, it means you opt— you can optimize the output. Um, and there's nothing, there's nothing hindering, um, our ability to get the maximal amount of that output because we don't have these boundaries and there's no permission. There's no way. 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, it's actually very difficult to build a permissionless system because up until really Bitcoin, everything was manually done by humans and humans have biases. So, 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. So like it will be, you know, blind to certain invariable qualities of humans, like their race, et cetera, et cetera. 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 have people contributing hashing power and computing power from all across the globe. And they couldn't have done it unless there was, you know, they're now in the outer rims, which, you know, really says something about the fact that, like, there's excess qualities out there.

Yes.

That these permissionless markets can take advantage of. And so anyways, Bitcoin invented that first example, and it still is growing day to day. And, well, where BitTensor 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 intelligence. That's where the idea truly started. I began as a Bitcoiner, so I was highly interested in Bitcoin, and I was also studying artificial intelligence and thought, well, okay, how do we connect these 2 things? How do we connect the most powerful computer in the world, um, to the most important computational problem in the world? And there, that's the founding raison d'être.

I'll jump in. We got, uh, Mark Jeffrey here in the, uh, in the comments. He says Bitcoin showed us a new way to do a company. Instead of hiring, you post 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 an engine for starting a company without, you know, doing all of the build out a small business rigmarole that we think of?

Well, let's, let's, let's, um, you know, pull a thread between these 2 concepts, right? So there was the first, okay, Bitcoin mining. All right, let's see if we can, we can build the same type of computational primitive for artificial intelligence. What we needed to invent in order to do that was a very, I use this word a lot, abstract consensus mechanism, which is that we needed a way— Bitcoin measures something very, very, in a sense, 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 significance of a 1,024-dimensional vector, right? Okay, how useful is that to a machine learning model? And that's not as simple as checking a hash. And so we—

So Bitcoin, just to summarize that just concisely, Bitcoin had to solve one problem, therefore they just wrote it into the protocol, like, right, did you solve the hash? Bittensor has a much bigger mandate, which we'll get into now. Um, and the one criticism of Bitcoin was hey, beyond speculation and money store transfer, which are valid, uh, 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, 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, etc. 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, and Bittensor is kind of having its moment.

Yeah.

So let's go to that origin of Bittensor and how it Paralleled and, and what you built on top of it. Yeah.

Yeah. So building this way of, of, of creating a proof-of-work network for anything was originally, the original intention was to train machine learning models, which we do. But it turned out that, that also there was a lot of different things that we could apply that primitive to that was not just training machine learning models. We could inference machine learning models. as example, which is, you know, when you call them and you get the outputs. And so there's a couple subnets on Bittensor. I think you talked about this recently, the NG subnet where you can, you can talk to the, the miners, the miners contribute the compute and they, and they run the outputs of the model. So, you know, that's a broadening of the scope of this primitive, you know, just like how when they abstracted the, the contracts from, from, from Bitcoin, You know, MakerDAO took a couple years for people to invent MakerDAO. At first it was just decentralized autonomous organizations and they all failed. And, 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, and then we got really good at some of the base primitive stuff like, like doing inferences. and aggregating compute, but also, and this is one of the points that I know Marc Jeffrey has made, and the way that he frames this is very interesting. You know, okay, we can, by building these permissionless markets that anybody can contribute to or anything can contribute into it, any type of computer can contribute, you can get storage into it, you can also get latent 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 require hardware to software and then ingenuity. They actually require somebody going out and solving a problem. 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. And then you have the, you know, the innovation and the algorithm and the intuition and the creativity on the outside.

And the application in a way, the network layer. And that requires essentially on Bittensor creating a subnet with a new application. 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.

Precisely.

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Ethereum is a blockchain where you can create lots of smart contracts. BitTensor 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. commodity. It produces something of, of value from storage to inference to training machine learning models, to scraping the web, to, to stealing API keys and, and, and selling them like GM, or, or pulling innovation from people across the globe. So, so Bittensor is, is a, a platform for building these contracts like Ethereum is for, for maybe more more classic smart contracts that people understand.

Great.

These mining networks. 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, from, from people that hold TAO. And to those projects that perform well, we actually mint the inflation of TAO. 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 than that. Half is produced every 12 seconds because we've gone through our first halving event. And that, that token, that newly minted token gets distributed to these projects as basically additional liquidity.

Now, so each subnet— Has a reward system built in. Instead of getting a Bitcoin for mining the 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, the top 2 or 3 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?

Well, I, if you don't mind, let me try to just explain how a core subnet works in the first place. So, um, because, because BitTensor, BitTensor is now, um, um, a meta subnet. It's, 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, the, a subnet is basically an, an open network that you can join. And by join, you, you, you burn a little bit of a token to prove that you're willing to play the game. And think of it like paying an entrance fee or, you know, competing with other people to join this game, to, to put your, put, to buy a lottery ticket. But it, if it's not a lottery ticket, because inside this network, your computer that you registered with is going to do some sort of work. Now, a good example of that type of work is that you're going to get— your computer is going to get queried by clients like yourself, Jason, who's using Claude Code and wants to talk to a machine learning model. 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 know, the capital of Texas is Austin. And, you know, the next step in this agentic thing is to, you know, ls into your into your folders and pull this file. Like, all of that is basically talking to an LLM, and it's computationally expensive. And so you can, 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, 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 there's 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, it's, 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? So think of it like a very well-defined, written-to-code description of how we're going to check to 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? So perhaps you can combine and add more computers to your cluster, or you can speed them up, or you can improve the software yourself and, and make it faster at serving these inferences. And if you can do that, you can, you can serve more requests, and over time you can make more money in this network. And so each of these networks are usually the, the way that we visualize them is, 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 at the, well, it goes up at the, to the top. And then there's the ones that are being cycled out at the bottom. And, and think of it like a league, like the Premier League is a good example.

Love it.

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 contributing. So some of them may be inference. Okay. A good, a good example, like a subsample of those projects would be You bring GPUs to the network that people can use. You provide computing power that people— you inference machine learning models. So when people are talking to your endpoint that you, you respond. 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. That's what we would call a model competition. So that's a, a little subset. That's, that would be like Liam, Engie, and AFI. with some of the top subnets on Bittensor. So that's what, that's what they do. The whole premise of one of these projects is that, that they're able to use this permissionless, hypercompetitive 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. And if it's, if that's not their premise, it doesn't make much sense to build on, on, on Bittensor. And there then that network has its own token. So it's a— think of it like a subtoken to TAO, which is like a derivative token. We call it a staking token. The, the name we use is an alpha token, which is a secondary cryptocurrency which also has $21 million cap that only exists inside of that network that we just described. You need to pay it to enter. You get paid in that by doing well. And it's, if you sell it, you sell it into TAO. That is what a subnet looks like. And that commodity, what we would call the alpha token, that token that represents that, that network, 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 on 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. If, if it's just, oh, it's a network where you can just join and if you just rake 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 and—

Startup speak, for me, that would be a product with product-market fit. So for me, using Energy or ENGY, um, for me that was, wait a second, these tokens for Quen, Kimi, and GLM-52 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 OpenRouter, Claude, Fable, and hey, maybe I just plug in my API key for energy and I use that in my Hermes agent.

Right, right. Um, you know, and, and, and behind that product is this liquid swarm of anybody in the world 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 there's excess compute, that person can plug themselves into this permissionless network and, and it becomes quite liquid, right? So, so, hey, I, I was, I'm not using these computers anymore. Well, I just run the software and then I just sell the inference to, to Engie. And as a consequence, you know, we can dramatically reduce the, the, the cost of that commodity.

Now, and this dovetails with your previous statement about permissionless and anybody can join to make a little bit of extra TAO or whatever the subnet's token is. So if I'm sitting there and I was, I don't know, 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 gonna be provisioned, you know, in 100 days, I have a client who's coming online in 100 days, what do I do with that for 100 days? Well, I could give it to NG without asking anybody permission and start making money from it. So the downtime would turn into productive time, would turn into revenue generation time.

Precisely.

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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 CryptoKitties and that was cute, but now you have production level stuff and, and all of these sidechains.

How long has Tao been around and, and how long did it take you to get to what I'll call— Yeah.

Yeah. People have been building mechanisms on Bittensor since, for 2 years. So this is the second year that it's been open for people to build mechanisms on Bittensor, but actually we're 2021. So the network launched with us learning the art of what we call incentive mechanisms. We call subnets the, the core design, how like the structure of what a subnet is, which I can talk about at length. It's actually quite an interesting field of the study, the economic study of how do you build a permissionless adversarial game where even the worst person in the world, if Hitler and the devil were to have a child, when that child were to mine on your subnet, they would just produce value.

So you can take away intent, but you need to have some validator that says, hey, this Hitler, Idi Amin, putting servers on here is not doing so to kill people or to do harm in the world. We're being a little bit, you know—

Dramatic.

Cheeky here. Yeah, but they are being judged on a specific criteria, which is, does this server provide inference and provide KIMI or GLM-5-2? And, and there's something there with these validators that do this. Yeah. In the subnets.

Exactly.

Yeah. So, so we built, we built a mechanism that would allow a distributed set of computers that could take any code that you wrote, Jason, for, you know, describing whether or not the computer that joined the network is actually, you know, if, um, uh, Hitler and Satan's, you know, love child was actually doing inference properly. Um, You wrote code that would check that. And these computers, which we call validators, 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 Bitcoin works, right? If more than 50% of the network is running the correct code of Bitcoin, then it doesn't matter if 49% of them are cheating.

Right.

it's irrelevant. The, the person, the, the, the, the majority, the honest majority will determine the, just the direction of the incentives in the network. So the, the core mechanism of Bittensor is that we can build mechanisms like this so that, so that yes, we can build a network that's permissionless and, and verifiable and auditable, right? If you know that this distributed set is, is, is all running this code and it's going to take more than 50% of them and, and the decentralization of that set, which is measured in, in proof of stake, 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. In Ethereum, it's proof of stake, 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—

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 DeepSeek 3 and they were passing it off as GLM-52 to use less compute and they were giving wrong inference, the validator would say, uh-uh-uh, we're checking that you're actually using the right GLM-52 code, not 51, not 40, not some other hack.

Not quantized.

Yeah.

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 Amazon Web Services. In a way, you've smart contracted or built into the system an SLA. The service level is mechanically and architecturally built into the system. Am I understanding it correctly in layman's terms?

Yeah. And instead of a contractual SLA, it's a programmatic SLA. Right? So you, you define the way in which you can check to see if they're not, they're doing the right thing.

Mm-hmm.

So for an inference subnet, it's actually very difficult, but the, the technology is there. You, the way you do it is you, you query the endpoint, the endpoint gets a, gets a result, and they have to pair that result with a very cheap, what's called a, a ZK proof. In case of NG53, it's Topolock, which is some, an algorithm created by Prime Intellect, which basically hashes the hidden states of the model. Hidden states is halfway through the machine learning model. They take those intermediate representations and then they project them onto a different mathematical space. You don't need to know too much more about it. But anyways, that's what it's basically proving.

It is what you say it is, is a way to say it. Yeah, I'm planning.

Precisely. And on BitTensor, These things get like, it's one thing to write a paper. It's a completely other dimension to, to write it 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 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, oh my God, it's unbelievable because—

So this is critically important Const— um, you know, if it's permissionless, you can have bad actors, and then the bad actors can be forced to act well and be good actors by the architecture. 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, 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— and in a way, you're the police officers or something. You're the Jedi Knights of this system, making sure there's peace and that it's being done properly.

This is the role that we play reluctantly while it's Still required.

Yes.

You know, as all subnet owners do themselves. So at the, at the level of the individual subnet, they, they, they build the mechanism and it breaks and then they fix it and then it breaks and then they fix it and then they get it right and then it starts working and then they make a million bucks. And, but in that time, it often requires a lot of massaging and, and realignment, you know, to, to, to, to, to fight against effectively, you know, nation-state militias. You know, that is the level of the people that are trying to destroy your project. And for a lot of people starting a subnet on Bittensor, they, they don't really understand 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 wanna start a subnet. And I'm like, oh, that's so nice. But you're, you're really not coming at it with the, the right level of intensity because you, you know, the, the, your adversaries on this network are, are very serious. And, um, and, and so they could literally be a nation-state.

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 developers—

Sure.

As remote developers in companies, not because they were hacking the companies. They wanted the 6-figure jobs.

Yeah, yeah. No, we talked about—

they wanted the salaries.

How those companies had to develop, like, whole protocols to identify when a North Korean was applying for a job. Pretending to be a different kind of remote worker.

Yeah.

I remember.

Yes.

Right.

Crazy.

Building that is difficult, but we have now, I would say like the top 10 to 20 subnet, subnet, which are, are premier and they're experts at this. And they, they can do things like, you know, subnet 51 figured out GPU attestation, you know, trusted execution without trusted execution, which is quite incredible. 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 check everything in the world to make sure that that person has that computer and is not cheating. And they built the economic primitives to make that, that make sense. And so, you know, now those churn, but it took a while for us to build those things. And so this is all just one level. That's just a level of the mechanisms on Bittensor, but it, in an absolutely insane But it turned out well, decision by us. We decided to make the actual creation of the mechanisms themselves on the Bittensor blockchain mechanism itself. 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 which selects permissionless mechanisms. And, and so that was what we launched just over a year ago, which we call Dynamic TAO, which is where all of these subnets got their own token. All those tokens got paired to the TAO, and all of them are trying to push the metric of success inside our ecosystem, which is increasing their price while staying on, basically. And so we, our thesis at the, like the, the highest level of, of Bittensor is that 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 produce a lot of value, which we can measure with price more or less. And, and, you know, with a couple knobs here, and, and obviously it's, it's complicated and, and we can get into it, but that is what we think it will pull the most amount of innovation into the ecosystem and, and also drive the most amount of value in, to Bittensor, which I believe does work. When you look at the way in which the hand overhead crawling towards performing well in this ecosystem, the more competitive we make it, the more badass the teams have become. And it's quite impressive. It's really reached a point now where the entrepreneurs in this chain are so much better than I am and many times smarter. And I listen to as many of them as I can because They all often are mentors and guides for us at the ecosystem. You know, how can we improve the core incentives of the Bittensor blockchain?

So this is a good pausing moment. Again, we'll just stay on engy.ai we've been talking about. The person running that is a guy named 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?

A good number of them are scammers, and, and, um, you, you, you kind of can't avoid that because you have the doors open. And that's part of what we're doing is where we're saying, hey, we can open our eyelids the most and that's what is going to make us win. But we're staring right at the sun, so you're going to get a lot of stuff.

Yeah.

And so, there's a lot of bullshit, but then there's also the best stuff and the highest quality and the most intelligent people that if they can get across the stigma Because there's a lot of stigma in crypto, well earned, honestly. And if they can get through that stigma and they can look at the technology and they can see what they can build here and they understand technically and philosophically why this is such a powerful primitive. Yeah, those people that see it, they come. But you need a level of openness for sure, because it's, it's, it's not the, the most treaded path, right?

Mm-hmm.

And, but so someone like Ning, like, I, I, I, he, he was brought in by, by Alcott, I believe, who built a team and said he knew this guy who'd worked at Google Brain where I'd also worked. And He didn't know about it, but he was told about it and he thought it was super interesting as it is. And so he got fascinated and obsessed with it. So there's those types of people that come in, and then there's people that have absolutely no academic experience, but they're just raw entrepreneurs that go, hey, wow, this is novel. 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 thing that compels a lot of entrepreneurs in our ecosystem, which compels me, 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 Bittensor exists and it's relatively stable and providing functionality to the world that some 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, ELO 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, OpenAI. I mean, these— Gemini, Sergey Brin and his team, like these are the most elite and you have to beat them in the offering, if it was inference in, in this case. So it's, it's, it's an extraordinary achievement on so many levels right now.

And, and yet the largest supercomputer in the world is, is Bitcoin. So like the, the, the, this is the technology that has the example of the only thing that's beat them in size.

So, right.

You know, there's something to be mined here. And like, it takes time to build the future. And, but now we're really starting to see like, what's so exciting right now in BitTensor is that these primitives just, they just make perfect sense now. So, you know, we just, we turned up the emissions for a lot of subnets. We said, hey, you gotta turn up the amount that you're paying miners. And, you know, some of the mechanisms like 51, they just, the revenue just scales with the more money you put in. Because it's permissionless. But there's like, companies don't act like that, right? You, oh, here's a bunch of money. Oh, it's not necessarily that you can just distribute that immediately and, and scale efficiently. There's so much inefficiency when you have that. You try to scale a billion dollars through a human 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, just described by really just a code base and a permissionless market, you just pump more money through it and it just scales. So, you know, because they've, they've tripled the amount of money they're paying miners, they've tripled their amount of compute in 2 months. And, and, and so like these things are really beginning to work and it's really exciting to see how they are coming together and meshing together in an ecosystem. But our goal is not just to do more compute and more inference. That's fantastic. Our goal is actually to, to truly head-to-head with, with the elite centralized labs at intelligence. This is, I think, the pinnacle commodity that we can, we can measure. Inference and compute and storage, these are sort of predicates. But, you know, this is, this is where I think that Well, this is where we're gonna 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. And, and I actually run a subnet on, on Vitensor right now. It's, before 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 measure that pinnacle element? Like, what does it mean to actually grasp in your hand intelligence as a commodity? So that's the thing that we're thinking about.

And how would one measure that? We do have, like, Humanity's Last Test. We have all kinds of benchmarks from LM Arena. Is it as simple as saying, here are intelligence tests? And I want to hear more about Affine. A-F-F-I-N-E?

Affine.

Affine. A F I N, A F F I N.

A F F I N.

A F F I N. Okay.

I N E. I N E. A F F I N I N E.

And that's subnet number 120. 120.

There's 128 of them right now.

There's 128. 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? Do 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 wouldn't really be permissionless. You— and so anyone can, can register one. And the way that you do that is, is exactly the same fractal-like design. So it's, it's the same way that you would register into a network of a subnet. You basically burn some token to pay an entrance fee into the, into the League. In this case, the league is 128 in size. A normal settlement is 256. So we'll probably get to try— we're trying to get to 256 at that level as well. So you enter into the network and you start building your system and people show up and go, hey, what can I do?

How many Tao do you have to burn or contribute? This is like buying a franchise, right? Like buying a team in a league. Yeah.

Let's look it up.

Oh, okay. And how is that determined?

Yeah, it, it changes every block on, on, on BitTensor. And, and so what we, we use is a, a bit, it's kind of like a Dutch auction. So the current rate is 688 TAO, and we are trying to lower that. So it's, it's about, it costs about $121,000 to have one of these 128 slots, which is a lot of too much, and we want to lower that. A lot because, but it's a Dutch auction. So, so the price decreases until somebody's willing to pay for it. And then when somebody, when a subnet registers, we double the price and then we lower it again.

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

It actually goes to, um, create liquidity in the initial pooling between TAO and your alpha, what we call alpha tokens. So the subnet token, it creates liquidity. So all of these subnets in Bittensor, all 128, have a liquidity pool, um, a, a V3 automatic market maker. It's, it's basically, uh, somewhere you can just buy the token through with a little bit of slippage. It's a smart contract itself. And so each one of them has a pairing with Tao. And so you have to buy Tao in order to buy those subnets tokens. And this is one of the ways in which 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 system, and it has a massive network effect. So we're creating—

Are people speculating and just buying 5 of them and sitting on them? If I was like an investor and a speculator, can I just buy 4 of them for $500,000 and sit on them? You'd be like, I'm a speculator.

You know, not financial advice. Yes, you definitely could do that. And people do do that, right? The, the, you know, we, we had this question problem, which was, how are we going to— we built the platform so people could build these systems. we wanted to know how are we going to incentivize the teams? Okay, we still have 10 million TAO to distribute and okay, well, what we're really good at is optimization mechanisms. So let's build an optimization mechanism that these teams can compete in, right? Where they come in, they get relegated or they go to the top. And so we came up with this idea of using the price of a paired token as the thing that we would measure And, you know, that allowed us to build, allowed us to distribute the inflation of Tau itself into a network which people were mining by creating subnets. So people create subnets and they mine Tau by creating subnets. That's the, that's 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 projects to produce value, which is measured by their price, we can actually elicit the internal machinery of capitalism, which is thousands of individuals from degens to scientists and the like, to long-term investors. We can elicit all of that swarm intelligence to properly order rank these projects, right? Like, 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 societies is we use markets to do that ranking order for us in some way. So we did the same thing inside the internal market of Bittensor. We elicited this, this market mechanism, this, this trading system to, to, to order the subnets and, and push the, the, the, the cream to the top, which is, which is what you see. So, you know, when you're entering into the Bittensor ecosystem, I often tell people, like, be, you know, be careful because there's, there's some rotten milk.

Mm-hmm.

And there's some cream and, and the cream is more expensive than this raw milk. And your job as for playing this game is to actually help us contribute to the, the, the actual movement of this internal system, right? Like, yeah, you're actually governing Bittensor in that way. Bittensor is, is highly decentralized. They, they're probably one of the most decentralized networks ever produced. Like, it's not like 10 nodes, it's not 1,000 nodes, it's tens of thousands of nodes. contributing and making informed bets on the truth. And so that's how we built it. And that's holistically, I think, what it looks like.

It's extraordinary. Lon, you had a question?

Yeah, we got to talk more about A5 before we run a full-on chat mutiny. The folks 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 the next generation of models, like of the kind of model that you want to train and design?

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 it's not an understatement to say that we are up against nation states because this is truly like China versus the United States. And there's that level of funding. So it's in no ways easy. But we did produce a very good model, but it didn't take us to the level we wanted. Affine's logo is is mining reasoning. And reasoning is the way in which a model thinks to itself so that it can answer a question properly.

Right.

And so what we have the, the miners on the network do is we, we have them produce machine learning models that can produce reasoning, that they can reason in a way that makes other machine learning models, answer the right question. So it's sort of indirect. It's like, imagine if I were like, you would think that I'm smart if whenever you talked to me, you felt a lot smarter yourself, right? Like, I know you better, right? That's a really good sign. It's actually reflective, right? It's like, you know that I'm smart if when you talk to me, 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, uh, 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.

Yeah.

And instead of GLM thinking, you would just talk to the smaller model and then be able to go like 30 times faster.

Got it.

But that's not the end goal, actually. The end goal is that we think that mining that latent space of thought is going to be the prerequisite for us training incredibly good models. And so that's what we're doing right now. If you go to affine.io, you can participate. For people on the call that are interested, we're in this really interesting period of time where You don't need to be a machine learning engineer, and you don't necessarily need to even be a computer scientist to participate in these networks. Like, you could literally just get your OpenCLaw and send him the website, and he will tell you, or it will tell you, whatever you want, gender you want. They will tell you. They will tell you. They then, they then, they then will—

GAF is a he. 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, great.

He will tell you what is needed to participate in these games. And if you can just basically send your AIs to participate in these networks, which is really interesting time to be alive where— and this is what I was talking about before the call started with you, Lon, about how we work remotely. So I'm nowhere near the rest of my team right now, but we can all work together through these these games. We don't 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. And if you're not good, if you don't come to work, it doesn't matter because you're just going to get replaced very quickly in these hypercompetitive systems. So, yeah, the callout I've always said from the very beginning, I don't actually really sell TAO, 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 the most dynamic, most interesting, most rewarding thing they've ever done.

Along, along those lines, um, one thing I'm really 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 much a part of the project is that, that sort of adversarial thinking about it?

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

Okay, fair enough.

Um, the, the, all the other stuff is fluff. In some sense.

Yeah.

Because you have to think about your— you can start with all the marketing, 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. But if you can't solve the adversarial problem in the network, then the network produces no value.

Right.

And so then it's just lipstick on a pig. And 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 how do you resist adversaries, you often find that there's this very compressed idea at the very core, a very simple compressed idea that you're truly building that is like an elemental, right? Okay. Oh, interesting. So inference verification is, is actually information checking, information pairing. You're checking that the information produced by this thing is similar to this thing. And so they're, 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. 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, the inside baseball philosophy of the stuff. But I, I, I, I love that the most.

There was a— there was an interesting moment when this, uh, language model got created. Uh, we were on All In and Chamath brought it up with Jensen. You probably saw that clip where he was like, this is so impressive. Uh, talk a little bit about not only that moment and What you took away from it, but then the controversy with that subnet and it imploding, right? And what we learned from that.

Yeah.

The reason why Templar is called Templar is because when we were building this subnet, um, we, we talked about how one of the holy grails of— in the artificial intelligence field and the psyche and noosphere, whatever you call call it, is not decentralized inference or decentralized computing, or even what I'm talking about with, with Affine, with the model training. It's decentralized training, which is where you have a computer and I have a computer, Lon has a computer, and they are making the same machine learning model at the same time. Because in order to train a trillion parameter machine learning model, you need a hell of a lot of compute. So in order for, for the, the rest of us to organically, you know, self-organize and to take on OpenAI, we need to come up with a way that we can work together. And because we only together, we will have enough compute to compete with the big guys that have the, the billion-dollar 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 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 you, 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 point, which would— be where we're— the model is the most intelligent because it's— we've— you've trained on some data and I've trained on some data, right? Makes sense, right? So, but in order to move 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 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 connections we have? And, and maybe we can do it, but it's gonna take 10 years, right? Which then is no longer important, right? So, and then on Bittensor, to put it another, from another perspective, we have all of these compute aggregators like Liam and Targon and Qube. which people actually bring in, they plug in their GPUs, but these GPUs are all over the world. So for us to use them, we need to come up with a decentralized training mechanism. So, so in some sense, like training a trillion, trillion parameter model is sort of beyond what Bittensor can do until we can come up with an algorithm that stitches 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. Many 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 so we came up with that name, and we started working on the way in which to do that, which was to take advantage of some of the Tensor's primitives. Like, we have Hippias bucket storage, and so where all the computers can upload to single places and then download, which is kind of cool. 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, the core of the, the, the problem is, okay, well, how do I know that you, you did the inference, right? 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 learning models is a very fragile thing. If you have one person there that's fucking around— excuse my language— it can destroy the whole thing.

So this is why, like, when Elon 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 it has to be perfect. So what happened with Templar and the rug pull? I know this is like the one thing people use as an attack vector on Bittensor and the subnet and the architecture. Just candidly, what happened? Did just somebody run off with essentially all the TAO in their subnet and just tell everybody to fuck off? And, you know, it's just part of the, the system.

I mean, Effectively, you know, in any company, if somebody works in the company, if a CEO starts a company, they can just leave. It's actually, usually there's no contract. You invest in me, you invest in my company and just be like, I don't want to do it anymore.

Right.

This happens in early stage startup because Some people look at what it's like to be an entrepreneur and they go, well, shit, that's a lot of work.

Yeah, not for me.

Yeah. And not only is it a lot of work, it's a lot of stress. And I just got some investment into my company and maybe I think I'm just going to take that. So what this person did was they just sold their shares, more or less. They sold their shares and 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 guy, it's they're bad. And so I look good 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 crypto, right? Because it's one of the things about permissionless markets and us allowing for early stage startups from non-accredited investors and took risks, things like this, is that basically people get burned because most of the time, or a lot of the time, this kind of stuff can happen, right?

Yeah.

So they wrote an article that basically said, hey, we're not bad for leaving. It's the mean network and Cons for being an evil dictator because he sold some of our token and that was mean.

And then what did they go do? They did another startup or they went to work for Tencent?

No, the project fell apart immediately. And I mean, of course it does because you kind of like, you can't burn your reputation like that. You can't take money from someone and expect people to follow you.

This is a recurring issue for Y Combinator where somebody goes to the Y Combinator program. If you get accepted, Not only do they put that $125K in, they will give you, Elon, $375,000 in an uncapped note. Then you get a bunch of people excited and you might get a bunch of people on Demo Day to, let's just theoretically say, put in another million. Well, I had— this is— I've had multiple people contact me that say, J.Cow, you know, you wrote the book Angel. What should I do here? 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 kind of things. The— you put $50K in, to file a lawsuit would be $250K, to then pursue it would be $1 million.

Yeah.

And then the outcome is maybe you get back your $50, right, 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's like you— it's kind of like, um, credit card, bad credit cards, you know, you get like a 2% or 1% fraud.

Yeah.

And you're like, okay, whatever, 50 basis points of fraud is what it's going to be.

I mean, this happens in every 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?

Did that really happen?

That really happened. I think he's in jail now where he got— yeah, he got sentenced already.

But I remember this.

Yeah, he bought like watches and— yeah.

So anyway, fraud can happen in Hollywood, startups, and on the Bittensor Network. I think is the— and, but you did tighten up the Tensor Network a bit based on that.

Related question from the chat room. Oh, sorry, go ahead.

Well, I just want to make a point here, right? So it's a double-edged sword to allow 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 OpenAI. And so 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 realities of this network now. So it's a double-edged sword, but the benefit is that people get access. And I would say that overall in the Bitensor ecosystem, we've made people a lot more money than they've lost. So that's, you know, I hold onto that. No, but the reason why it's particularly bad in crypto is because in early-stage startups that you would invest in, they can't just go and sell their equity into a market automatically because there's no, there's not, there's no market for that. There's no, there's no like—

There's no secondary market of startups, although people have tried. But even for the nascent ones, there would be no buy side.

Exactly. And so, but we, we in crypto build those, the buy side, and we've 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 for nothing. We're using them to move a com— like a computer through space. And But so the reason why it's particularly pernicious in crypto in general is because anyone can just go and sell on market, hence rug pulls, right? It doesn't exist in startups because 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, give me my $10 million, I'm out, they'll go, actually, I don't know if I want to buy that because you're selling. So what we built into the chain was Basically, the best that we could do is go, hey, let's build sort of an ability for the subnet teams, not enforced, that allow them to express their conviction, call it conviction, by locking their tokens effectively and in a way that if they want to go and sell them in one big sell, that's a public event. And 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. It's been very beautiful actually to see a lot of the teams, you know, go up to the plate and be like, yeah, you know what? I'm locking this thing perpetually for years. And—

Which, by the way, we call that vesting 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, Yeah, tell us about the foundation and its role. We had a bunch of questions Maria123 asked as well, uh, not Maria, um, 678. There's a Maria123 line.

There's always got to keep your Marias distinct.

So separate here in the chat. But what's the role of the foundation in all of this? And then what's your stake in all of this? Like, as the creator of this, do you have like a gazillion DAO? And how do you stay motivated to keep this whole thing going? Because my Gut tells me it's, you know, TAO isn't at the point yet where it would keep going if Konst went away. So how far are you away from making yourself irrelevant in this equation? And then what does the foundation do?

I think it would definitely continue if I went away. Most certainly. It would be, I think, I hope that people would miss me. But there's a vibrant community of 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 this technology really well and want the system to go along. And there's the foundation in Canada, which is no longer involved with development. And there's now another foundation which is more shielded, which is purely about upgrading the chain. So we push a lot of changes to tweak and improve the mechanism. And that goes through our internal governance system on the chain, which we call the Triumvirate, and that which is this year going to be expanded basically because we have a chain that we can build government systems directly into it. We intend to build all of that this year. So that's the 2 foundations, you might call it. There's the OpenTensor Foundation and there's the RAO Foundation, and they hold different sides. One's more marketing and outreach, etc., and one's more development and upgrading the chain. I'm no longer involved with OpenTensor. I work just in the RAO Foundation, and I program. That's my language. Actually, this is very unfamiliar for me. Like, I'm a programmer at heart. That's how I speak.

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

But who's counting? It's a front row seat to bet and learn, and it's not financial advice, but it's also, in one way, a vote for me. If you were giving a GoFundMe, I almost see it as 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. I see it as like— and then, oh, and there's a third. 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 a few and then I sold it when it hit like $1,000. Like, I didn't think it was going to go up.

Dummy. You big dummy. You got to ride your winners. Anyway, how important is it to get 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 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. Tao.

But yeah, the video goes crazy.

How do you think about the pumping in crypto or, you know, non-builders like myself saying, hey, I want to invest in this because 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 100 or 1,000x. 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. So how does speculators like me and then pumping and all that impact these projects 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 everyone can have some and that everyone can join and that it can be split up. And in the first place, it can be split up. It turned into a whole 21 million of these things.

Hmm.

So, like, I love it that there's people that are excited and that's amazing. And the ones that are inauthentically promoting it, they come, they go. There's always people that are going to say shit on both sides. I can't stop them. I, I, I'm way more interested in, in people that, that are, are, 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 and the potential for us to build something that's novel and unique and, and, and competitive and the third path for what artificial intelligence can be born out of.

Mm-hmm.

So the, the, yeah, but the, the market dynamics were also really fun. I'm in all the price chats as well.

All right, there you go. We got over an hour with the man himself, Constantly Shipping. You can follow him on Twitter, @ConstReborn. Great follow. And I think it's great that you're going out and talking, so I appreciate it. I know you gotta get back to coding, but I think it's important for people to understand it, like, just from first principles. And you did a great job of sharing that with our audience today. All right, let's drop off Const.

Thanks so much for being here, Const. 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.

Mm-hmm.

Um, and I saw it then. I didn't make a big enough bet. And now I see this and it Just all 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 $750,000 into this, something like that. Not a lot of money for me, be the equivalent 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.

Yeah, $10,000.

Whatever. You know, it's a smaller bet for me. It's not out of my funds. It's just 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?

Sure.

I was like, guys, we gotta like pay attention to this.

I remember.

Because this reminds me of the mobile cloud computing era when like local mobile GPS all started coming together. Got that signaling for Uber and Robinhood. 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 gonna be right.

Right. Yeah.

But you do have to place the bet is what I've 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 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 2 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, I did have shares in it or whatever, but— and I did fine, so I'm not complaining.

I mean, hindsight 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 thousands, I was like, well, I can't believe how much money I made. What a windfall. I'm gonna go get myself, you know—

And the mistake was you should— 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 this, yeah, I, I, I think I might actually be down right now in my BitTensor bet, which I made 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 my tweets online, Lon. And, you know, there's all these Pelosi trackers. Now there's a Jay Cowell tracker. So when I bought Figma or I said Uber was at a bottom last week when it was like $67, when it— yeah, they think you're panicking.

They think you're Michael Saylor-ing. They think you're— you're—

I'm not YOLO-ing. I'm not trying to influence 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 because I was like, you know what, Dylan's like a beast. Um, we should have him on the program. I was 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 stuff. I like to make a bet on him. It's now up to $25 a share, whatever. And now they're doing the same thing. They're retweeting it. Jay Cowell called the bottom on this. Jay Cole called the bottom on this. You know, my promise to you as the people who listen to this specific podcast, 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.

Yeah.

But I try to be transparent with the public ones.

Yeah.

The Jay trades. And 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 may want to quit a stock if you get new information, but I like to find stocks that I'm comfortable holding for a decade. That's my hold period I'm looking for.

Sure, you gotta wait. You got— you— the— in tech, the, the strategy is always wait for there to be a bad news cycle, the stock dips, you get it at a little discount, and then you just hold on to it forever. That's the, that's the play.

That's your This Week in Startups. We'll see you next time. Bye-bye.

Bye-bye.