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Hello everyone, my name is Andrej and here with me is George and we have this little format that we call Crypto Questionnaire, where we invite people from different projects, actually the most important people from different projects, to explain us what they are doing, why they are doing and so on and so forth.
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So today with us we have Bitenser and Jacob Steves with us.
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So before we start,
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I would just want to say that this is not financial advice.
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Nothing you hear or see in this video should be taken as financial advice.
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We just want to help the community better understand the basics behind every project and what it does and why it does.
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First off.
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Hi, George.
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Nice to see you.
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Hey Andrej, nice to meet you too.
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The project today is very interesting.
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All projects want to be interesting, but this one is special in its own way because it tries to bring something new to the market in a way that we haven t seen before.
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You ve heard a lot of smart people talking about artificial intelligence, large language models and whatnot.
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Over the past few months, we ve been accelerating the pace of artificial intelligence and we re getting closer and closer to creating that
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let s say, super species, that new apex predator.
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But until then, we still need to figure it out and see how it works.
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So, for the next few more minutes, we ll be talking with someone that wants to change how the AI started.
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Because what we re seeing today looks very much centralized.
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And when you hear centralized, you know people in the cryptosphere, working with blockchains and everything else,
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want to hear more about decentralization of systems.
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Nowadays, artificial intelligence is, let s say, into silos.
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Big companies are doing their own thing, but we are hearing too little about the small guy trying his own thing with artificial intelligence, or at least a large language model.
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So let s go over to Jacob and hear from him about BitTensor, the project that wants to change all that.
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Hey, Jacob.
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Hey, guys.
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Thanks for having me.
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So Jacob Robert Steves is the co-founder, one of the co-founders of BitTensor and he s actually in Costa Rica right now.
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Yeah, if I m right.
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That s right.
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So I m sweating.
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Quite a different setup.
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I m not that nervous.
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It s just very hot here.
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But it s interesting because in Europe it s hot, although it s October, but it s also hot in Costa Rica.
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Where s the winter going?
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Is Australia in winter now?
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I can t figure it out.
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I m trying to find it.
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I m on a mission, but I haven t found it yet.
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It s somewhere out there.
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Okay.
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Let s get right into it.
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So our format asks from our guests to talk a little bit about themselves and the project they re running.
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Before we hear about BitTensor, can you tell us who you are, where you re coming from and what are you going with this?
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Well, I m a Bitcoiner, I think through and through, a bit of a maximalist.
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I live all over the world, but really my nationality is crypto.
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I think a lot of people feel the same way.
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Religiously and nationally, we re creating something that obsoletes the status quo and creates a completely new world.
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And I started working on BitTensor back in 2015.
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Satoshi, we got you.
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That s right.
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Greg is wrong.
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It s me.
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Basically, because we were looking for ways of bringing more compute power to artificial intelligence, which is a massively computationally difficult problem.
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And
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I was looking at the power behind Bitcoin money and asking how could this be brought into the artificial intelligence world for a number of reasons.
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I think the primary one is how can we bring that much computation, but how can we do it collectively?
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How can we distribute the ownership of that technology outward?
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If you look at the history of Bitcoin, before Bitcoin was invented, you had
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currencies that attempted to exist but were blotted out because they didn t have the military-grade technology of decentralization to defend them effectively.
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And Bitcoin brought in decentralization into money and allowed us to actually facilitate the creation of a completely new, let s say, nation state of money and currency.
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In the same way, we need to bring decentralization and specifically decentralized style compute into the artificial intelligence world to facilitate the creation of truly open systems, to allow for people to create
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open-source AI.
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There s been many failures in this.
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OpenAI failed, really.
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They wanted to do that.
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They wanted to create open AI.
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But now they re closed AI.
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And it s because they didn t have the technological substrate to do it.
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They didn t know what they were doing.
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They thought, hey, we ll just use the same funding mechanisms we ve always used.
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And I think that they re a little bit concerned that it s led to basically capture from the same corporate giants that
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that all technologies go to.
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And then we have, you know, companies like stability, for instance, they re doing a fantastic job, they re creating some open source tools, but how are they going to monetize it?
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And eventually, they have to do a raise, and then they re going to have to figure out a way of closing the AI down that they that they create.
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And of course, that s what we re going to see happening.
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And then you have the
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the open source AI movement, which is okay, we re not going to raise funds, we re not we re not going to be corporate.
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But they re they re underfunded.
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They don t know how to monetize the technology that they have.
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And so what what we re trying to do is bring
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open ownership and monetization directly to these, you could call them like freedom fighters, right?
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The open source AI community.
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They re trying to create models that are available to anyone.
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They re trying to distill knowledge from open AI systems and give it to people, right?
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Is there such a community?
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Are there, let s say, individuals, like you re saying, that do not want to work for such a company because they are on a hiring spree?
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Everyone is hiring and paying top dollar for minds that can work on these models.
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So are there people that don t want to go work for OpenAI or these big names in the industry?
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Totally.
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I mean, there s both people that don t want to, and then there s people that are being overlooked as well, because a corporation simply does not provide the most fluid interaction for individuals, right?
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They re just not going to be hired by the HR department because there s some weirdo living in
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Bangladesh, right?
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Costa Rica, right?
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So they can t work.
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And so they can t participate.
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They can t own, they can t control this technology.
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And then there s people out there that really, really care about making sure that artificial intelligence is not controlled by just a small group of people.
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You know, all these companies are closed round VC funded, suckling off the teeth of quantitative easing from Wall Street and BlackRock.
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It s the same power structure that controls all the other technologies.
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And I ve said this before on other podcasts, it s like, what if the internet had been founded by a VC company?
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venture capital funded company, they would have they would have made it like open AI, they would have been like, you have to sign up with our application, we give us you give us your data.
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And you can use it by this much.
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And you can meter it like this.
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And then they would have gone to regulators and built a moat around the technology, etc.
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But thankfully, there was a lot of I ll say freedom fighters, you could call them anarchists, you could call them
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you know, cyberpunks, whatever you want to call them, the people that were fighting at that time to keep the technology neutral.
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And there s many people out there in the open source community, especially in the open source AI community that are trying to do the same thing.
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But they don t, I don t think they have the means.
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You know, cryptography was, this is where this is where BitTensor comes in.
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The year is 2015.
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How old are you right now?
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31.
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So like you re 23 years old.
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You are already into AI.
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And you re already concerned that AI is going to turn out centralized.
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We are now eight years later.
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Now, everybody from Earth has heard about AI because of, I guess, ChatGPT and Mid-Journey and so on and so forth.
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But what were you doing back then that you were so interested in this phenomenon that back then had low interest, I guess?
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Back then, I was more interested in bringing the computational power that we saw in Bitcoin
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to the artificial intelligence problem.
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And I had thought less about the freedom side of that.
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At the time, I was more interested in that particular thing.
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Okay, how can we as more as a as an intellectual pursuit, how can we bring this computational power into the AI world, which is this is certainly a computational problem.
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And the beauty of
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not only being able to use all of these resources around the world, is that because you can use them, you can distribute the ownership back to those individuals, right?
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So Bitcoin is probably the largest corporation in the world, right?
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There s thousands of Bitcoin miners, and especially at the beginning, there was thousands of Bitcoin miners, and tons of people that are integrated directly into that technology that get to have a say, right, by buying or selling or being in exchange or mining or being a developer around the Bitcoin ecosystem.
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It s very well integrated into humanity, and I think a way that other technologies are not, especially ones that are created by centralized organizations.
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But you re right.
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At the time, I was just, hey, how can we use this computational power?
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Wow, we created the most powerful supercomputer ever.
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And it wasn t even done by the government.
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Yeah, potentially.
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Exactly.
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It s wasted.
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I mean, I think we could argue it whether or not it s securing the Bitcoin network or not.
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But effectively, yeah, it s not being used to do anything productive.
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But the amazing thing was that we did that as a collective of people all around the world.
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No connections.
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We don t know each other.
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It s not a corporation.
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It s not even a government.
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It s probably one of the largest products of humanity that wasn t created by any organization, centralized or otherwise.
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So I think that s what I was initially interested in.
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And so I set out to figure out how we could do that.
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And in the process, we developed a way of
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validating machine intelligence.
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So we thought, hey, okay, what would it mean to have a proof of work mechanism around some digital commodity that was useful for intelligence.
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And so we looked at information, like raw information, information theories.
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measured in bits.
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And we defined the first market for information and built a consensus mechanism that could work on that type of information.
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And it turns out that s really difficult to do because when you re trying to measure information, let s say you re measuring intelligence, basically a mathematical version of intelligence, it s fuzzy.
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It s probabilistic.
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There s no absolute truth.
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It s high dimensional.
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It s not like a hash proof, which is just yes, true or false?
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True or false?
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Yeah.
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Everyone knows all the values can agree.
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So we had to invent a consensus mechanism that was very fuzzy, very manipulatable, that could work on large amounts of data with with models, etc.
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We invent the human consensus, and then we ran that first market.
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And the nice thing was that because we became a
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we approach the problem of measuring this digital commodity from a very abstract perspective, we are now able to expand that to work on all sorts of different problems in AI.
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So we do inferences on BitTensor, we do map reduces, we do Twitter scraping data, we do storage, we do centralized training.
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The network is not just a single digital commodity system that a lot of chains are.
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Filecoin is doing storage.
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Golem is doing compute and Ocean is doing data and the cash is doing compute.
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Because we have this very abstract mechanism of measuring digital commodities in general, you could say it s like the flip invention of Bitcoin.
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I like to use that analogy.
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It s like Bitcoin invented the money, but it also invented the first ever digital commodity market to facilitate that security behind the money.
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We have abstracted that concept and now are applying it to a whole variety of different problems and all of them are hierarchically dependent.
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So they re all in a sense that we need to work on all of these sub-problems in order for us to really compete as a collective with a company like, say, Google, which is hierarchically built off tons and tons of their technologies, right?
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So they have Borg and they have their own MapReduce technology and their gRPC.
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Exactly.
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There are a whole ecosystem of technologies working together.
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And so we set out initially just to consume information.
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We moved past that and we ve grown into a whole ecosystem of varying digital commodity markets.
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And so that s
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Yeah, that s BitTensor in a nutshell.
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Can I give you a little prompt?
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I mean, we ve been through all this explanation, but let me give you a prompt that I use with other AIs.
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Can you explain please BitTensor like I m a five-year-old?
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I know that this explanation of yours was kind of easy, but I m pretty sure that people are going to have a hard time exactly understanding what BitTensor is.
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So can you do it like I m a five-year-old?
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When you re cleaning your bedroom as a five-year-old, doesn t it make it more fun?
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And don t you work a little bit harder when there s a little bit of potentially a reward for candy at the end?
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And that s incentives.
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Um, we work with incentives to organize people.
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Maybe you and your little, your sister or brother, uh, work better when you know, you re going to go to the park afterwards.
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Uh, and that s a fair system because there s an, there s an exchange.
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Perhaps it s not candy.
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I don t know what you re, what you re using to bribe your children these days.
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Um, we, we use incentives to create commodities, to create things that people want, um, in large quantities.
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And efficiently, we use incentives to organize people to solve very complex problems.
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Okay, but let s get a little bit technical.
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So you ve been talking a lot about Bitcoin.
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And Bitcoin is so secure because of all the hash power and all the computers crunching the numbers and trying to come up with the next number.
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So, so much compute power that guarantees the, let s say, the security of the network.
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All that power is actually, you said, non-productive.
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Well, it actually secures the network, but let s keep it at that.
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So that s proof of work.
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You need a lot of computers that try to crunch the numbers so you can, let s say, guarantee the safety, the security of the network.
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In which way is BitTensor similar to this?
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Because I really hope that we are not all gonna crunch the same data
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to come up with a logical answer through that distributed AI you re talking about?
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Yeah, I mean, Bitcoin is a single example of a digital commodity market.
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And the purpose is to secure the chain economically, effectively economically, it s slightly different than crypto state.
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And
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we can write different mechanisms to produce different digital commodities, hashes and caching is one.
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So we can write a mechanism that say, we need data for training machine learning models.
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So let s write a model, let s write a mechanism that rewards the miners for accumulating large data sets from sources like Reddit or Twitter, maybe even cleaning.
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So we take the same magnifying glass, we
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shine it in a different direction.
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We shine the light.
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So basically, basically, you re going to still have miners, but you re also going to have clients.
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And in between, you re going to have validators, from what I understand.
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And the miners are going to be the people going out there and figuring out what kind of information, what kind of data should they hold that could be useful for the clients?
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Well, specifically, actually,
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they try to do what the validators are asking them to do.
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And that can be sometimes a problem because the clients are not the validators.
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So the validators need to validate the miners in a way that s useful for the clients.
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But sometimes those are the same people.
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So the clients of the system are the validators.
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For the time being.
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Yeah, for the time being.
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But like, I ll give you an example, like in the storage network that just launched, or is launching today, I believe, on BitTensor, the miners are storing files that are validated by the validators.
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So the clients need to interact through the validators in order to store files.
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So the clients don t get to just use the digital commodity, which is storage on that particular subnet, because why would the miners care?
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They re validating what they re doing towards validators.
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So there s that sort of translation between the clients and the miners through the validators, which is something that is core to the way that BitTensor works.
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All of these are examples of mechanisms that are easy to write.
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within the ecosystem.
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So I haven t mentioned anything with AI yet.
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But just the other day, there was a subnet five, which is like a specific mechanism on the tensor launch that does image generation, which is really cool.
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So all of a sudden, you re using this mining network to create
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you know, a sort of like mid journey style mechanism where you can go to the system if you have access to one of these validator keys, or if you can, you can query the front end of a validator, you can generate, you know, 256 images, just like that, because the miners are incentivized to respond really quickly with images generated
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via a machine learning model.
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So that s really cool.
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Can you give us an example?
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Could you show us something like that?
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Would that be doable?
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Yeah, okay, sure.
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Let s go to the demo then.
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Because we re talking a lot about this distribution of compute power, but also storage of data.
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So this is something, let s say, a little bit different from what we re hearing from, let s say, other places.
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And then you can share the screen whenever Jacob is ready.
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Yeah, tell me when you re ready.
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So this is a computational product of a subnet which just launched yesterday, or maybe it was the day before yesterday, on BitTensor.
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And this is showcasing what, in two days, what this mining network can create.
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So all of the computers that are required to generate these images are being incentivized by a drip of token through the mechanism that s defined by this developer, Creative Builds, and incentivizes them to run these models.
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And they have to be language models that actually respond with images, where when you take the image and embed it into text, it s similar semantically to the prompt, which is
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basically clipped, right?
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So it s the mechanism that s used to train models in this case, but it could be anything really.
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And the outcome is that we can make a query and we get 2.5 images per second, which is pretty fast, right?
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So this is like a computational machine that s defined by incentives, as an example.
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Just to make it easier.
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So every miner is basically a language model ran on a computer locally.
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This is an image model, actually.
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An image model, yeah, sorry.
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And when somebody queries it and prompts it, every one of those machines work together or is it just going to one of the miners?
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When you prompt a single miner, they work individually.
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But depending on the way the incentives are defined in the network,
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they can be forced to work collectively.
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For instance, we can reward miners for producing outputs that are diverse from their neighbors.
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So in this case, they re all very similar, as you can see, they re probably running all stability models.
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But we can write into the mechanism what we want to get collectively or individually from the miners that we re querying
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So on subnet one, which is our flagship subnet, our main goal is actually to create a large quantity of synthetic data for training machine learning models.
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That might not make sense to many people, but the idea is that we can actually learn from the outputs of language models.
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In the process, we also can query those models.
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So it s a set of language models that are rewarded for being performant against a reward landscape.
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They re also rewarded for being diverse.
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So what that creates is a network where you can query, let s say you re asking the network via prompt, what is the capital of the United Kingdom?
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Um, and you can query all of them.
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And in that case, they ll probably respond similar responses.
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But if you re asking him to write a, an essay, because we ve added diversity into the incentive landscape, they will respond.
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Um, and they ll return a variety of different essays that the user can potentially select from.
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But let s go back to the model you just showed us.
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So if I m going to these creative builds, to these guys, and I say, okay, I d like a sequence of frames with this beautiful snow angel, what is it?
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And I would like to have them in succession, so it comes up as a video or a GIF.
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So 250 photos are the equivalent of 10 seconds of video.
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How fast was this done?
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This is what I m looking at.
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How long did it take to pull it up?
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2.5 images per second.
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Looks like he queried 228 and they would have responded within 12 seconds.
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So, I think that s how he got this number.
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So, it s almost a second per second, let s say, if I m seeing right now.
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2.5 images.
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Exactly.
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So what this showcases is really how the incentive landscape defined by the developer with inside of his subnet determines the way in which the miners react.
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So in this case, he s not asking them to actually work in conjunction.
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He s not providing a diversity scoring.
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And so he s not getting that much diversity, but I don t think it s what he wants.
2
SPEAKER_00
He actually just wants to generate many images.
2
SPEAKER_00
But he s able to use this computer by crafting incentives, which makes it a little bit like a meta-computer.
2
SPEAKER_00
He doesn t write the language for that computer.
2
SPEAKER_00
He doesn t procure the compute.
2
SPEAKER_00
He simply defines what he wants via the incentive landscape, and then the miners come and fill the gaps.
2
SPEAKER_00
We can do that with storage, we can do that with scraping, we can do that with bandwidth, we can do that with inferencing over language models.
2
SPEAKER_00
And importantly, something that I m personally working on right now is we can do that with training.
2
SPEAKER_00
So we can say, hey, get me a set of gradients to a language model that lower the loss, which is the state of the art right now is working with that type.
2
SPEAKER_00
Decentralized training and we re looking for rating and updates to a model so we can just we can do distributed training on bit tensor There s a whole variety of problems that we re going to be working on Using this this abstract computer, but let s say let s say I want to be a minor on the network What do I need in order to to be a minor in the in this network?
2
SPEAKER_00
Well, it depends.
2
SPEAKER_00
It really depends on it It also depends
2
SPEAKER_00
Both validators and miners are incentivized to be on each network.
2
SPEAKER_00
And the difficulty of performing on those subnets, we call them, is determined by the competition.
3
SPEAKER_00
permission, especially for miners.
3
SPEAKER_00
So if the incentive landscape is structured properly, there s always a gradient, there s always a slope to bringing on more compute.
3
SPEAKER_00
So can you respond faster?
3
SPEAKER_00
Can you produce better gradients?
3
SPEAKER_00
Can you store more files in the storage network?
3
SPEAKER_00
That creates this hyper-competitive market where it s permissionless and global and 24-7 and all these things that allow it to grow very quickly.
3
SPEAKER_00
we re working with the most powerful computational technique that humanity has ever invented.
3
SPEAKER_00
The ability to... And that is what?
3
SPEAKER_00
It s incentive-based computing, abstracted computing.
3
SPEAKER_00
We don t need to write... But in terms of hardware?
3
SPEAKER_00
It depends on what you re measuring, right?
3
SPEAKER_00
So if you measure bandwidth, it s not hardware, right?
3
SPEAKER_00
It s the ability to procure
3
SPEAKER_00
well connected or well placed computers with with high bandwidth, like your Starlink, for instance, maybe it s not that great.
3
SPEAKER_00
So it doesn t compete very well.
3
SPEAKER_00
But um,
3
SPEAKER_00
a virtual server somewhere.
3
SPEAKER_00
You have no idea.
3
SPEAKER_00
Exactly.
3
SPEAKER_00
So it really depends on what you re measuring.
3
SPEAKER_00
And this is really important distinction to make.
3
SPEAKER_00
A lot of people think that it s just a computational market, but it s not.
3
SPEAKER_00
It s a market for whatever is being incentivized.
3
SPEAKER_00
So that makes it difficult to wield sometimes because those incentives can be very problematic.
3
SPEAKER_00
But sometimes if they re very specific, you can get exactly what you want.
3
SPEAKER_02
To create a marketplace, you need to have a little bit of predictability.
3
SPEAKER_02
So people need to know when they re looking at Bitcoin, in the beginning, you could mine it on a laptop, on a computer.
3
SPEAKER_02
This is why so many Bitcoins are lost on hardware that is long gone into the trash or anywhere else.
3
SPEAKER_02
So if someone is looking at this video and thinking, okay, I would like to contribute, I d like to be a miner, where should I start?
3
SPEAKER_00
Well, I think the first place to start would be to find other people that are that are mining and talk to them, join a discord, read the documentation.
3
SPEAKER_00
There s, I agree that the most perfect markets are those that are the most predictable.
3
SPEAKER_00
And, and yet markets can still be very powerful when there s less predictability.
3
SPEAKER_00
We have a variety of different mechanisms on BitTensor, and I m sure they haven t reached equilibrium.
3
SPEAKER_00
Not all of them.
3
SPEAKER_00
So there s plenty of opportunity for people to participate in these growing markets.
3
SPEAKER_00
But learning the overall mechanism is a great place to start if you want to participate and perform well in a large number of them.
3
SPEAKER_00
You know, everybody who s mining on BitTensor right now just learned it themselves, right?
3
SPEAKER_00
So, if that answers your question about where you would start, I would recommend, yeah, the documentation, as well as our Discord community.
3
SPEAKER_02
But maybe someone is looking at this and saying, okay, I got some old hardware, I got some GPUs that are on the shelf since mining, let s say, ERC20, or maybe some ASICs from the good old days when the difficulty on the Bitcoin network was not that rough on the hardware.
3
SPEAKER_02
So, can I come up with those?
3
SPEAKER_02
Is there a way?
3
SPEAKER_02
How do I install a client?
3
SPEAKER_02
How do I connect?
3
SPEAKER_00
Well, go to BitTensor.com, look at our documentation under build, install BitTensor, find a subnet, create your wallet, go learn how people mine on that subnet, what is required, maybe it s subnet 5 in this case, find the code for a miner, pythonminer.py, and run it.
3
SPEAKER_00
Then find out what happened and how you can improve.
3
SPEAKER_00
That simple as that.
3
SPEAKER_01
Where do I get the data for the miner?
3
SPEAKER_01
Okay, I have the hardware, I installed the client, but now I have to bring value to the network, right?
3
SPEAKER_01
If I want to really get something out of it, I have to bring the value.
3
SPEAKER_01
Is it more like a business kind of opportunity?
3
SPEAKER_01
I mean, if you have like a lot of data, or is anyone able to be a miner with their, I don t know, personal data?
3
SPEAKER_01
What can I feed and how can I feed the language model or the text model, the image model to the network?
3
SPEAKER_00
I hate to say this again, but it depends, right?
3
SPEAKER_00
Because on some subnets, you re not required to have any data.
3
SPEAKER_00
You just bring compute or a language model, or perhaps you actually just pour it into an API.
3
SPEAKER_01
But let s go for image.
3
SPEAKER_01
Let s make the example for image.
3
SPEAKER_01
If I want to be a miner on the image subnet, what data should I acquire or how would that go?
3
SPEAKER_00
You don t actually have to acquire any data as far as I know on this mechanism.
3
SPEAKER_00
I know you don t.
3
SPEAKER_00
I actually do know.
3
SPEAKER_00
You need to serve an image model that performs well against the reward landscape.
3
SPEAKER_01
So I have to feed the data, I have to feed the data to my image model and then he offers... Oh, I see.
3
SPEAKER_00
So the queries come on the wire, right?
3
SPEAKER_00
So you would need to open up a port on your router to allow messages to come in and you need to run the server on your GPU so that the messages can be processed.
3
SPEAKER_00
That would be the first step.
3
SPEAKER_00
And then afterward, you probably want to evaluate why your minor is performing well or not well.
3
SPEAKER_00
fine-tune your model.
3
SPEAKER_00
These are more advanced steps, right?
3
SPEAKER_00
So, it is a steep learning curve.
3
SPEAKER_00
And I would say that there is steep competition for the miners.
3
SPEAKER_02
So, the basic way, from what I m understanding, the miners are going, let s say, to the stability website and downloading one of the models and then serving them with the GPU for anyone that creates a query or a request or
3
SPEAKER_00
Yeah, so they host the model.
3
SPEAKER_00
So they ve downloaded a model, probably, they host that model on their GPU, and then they service requests from the validators.
3
SPEAKER_01
Let s move on to the validators right now.
3
SPEAKER_00
I mean, but it also really, it s also very dependent.
3
SPEAKER_00
It s also very dependent on the mechanism we ve written.
3
SPEAKER_00
Right.
3
SPEAKER_00
So in that particular subnet, the incentive is, you must respond with images that if I take those images and pass them through a model, which converts that into text, and then
3
SPEAKER_00
I want to see the semantic similarity between the text output from your image and the prompt I ve given you.
3
SPEAKER_00
That needs to be a very close semantic similarity.
3
SPEAKER_00
So that creates a landscape where you have to load a language model.
3
SPEAKER_00
But you might pour in a different type of image model than other people.
3
SPEAKER_00
So maybe it makes sense for you not to even run a GPU.
3
SPEAKER_00
Maybe it makes sense for you to just query dolly3.
3
SPEAKER_00
or mid-journey and let them pay the price because there s a lot of different market differentials.
3
SPEAKER_00
There s like attractor states and arbitrage all over the place.
3
SPEAKER_00
And what the market tends to do is find that arbitrage opportunity.
3
SPEAKER_00
So you might think, oh, well, we want people to run GPUs, but in practice,
3
SPEAKER_00
they don t run GPUs, because that s not the best thing to do.
3
SPEAKER_00
And that s, that s one of the most brilliant things about market markets is also the difficult thing is that the the miners are like an optimization algorithm, they re going to minimize costs, and try to maximize reward.
3
SPEAKER_00
And so they, they, they are always going to find this really, really efficient way of doing things.
3
SPEAKER_00
And a good example is on like subnet one,
3
SPEAKER_00
Um, our miners are using Medusa, which is a really, really fast way to run language models only released like three or four weeks ago, already being used everywhere, right?
3
SPEAKER_00
Um, they.
3
SPEAKER_00
GPT four uses a mixtures model behind the scene where when, when you get a query and it s easy, it goes to a smaller model.
3
SPEAKER_00
Because they don t want to run the big model.
3
SPEAKER_00
Same thing, our miners do exactly the same thing.
3
SPEAKER_00
Because if they re not, they can t compete.
3
SPEAKER_00
So there s all of these...
4
SPEAKER_01
very interesting things that arise in markets that are good or bad.
4
SPEAKER_01
And you know, so it s very difficult often for us to tell you, hey, well, like, hey, you know, this is how you do well.
4
SPEAKER_01
Well, it s very simple, you just go get a GPU, it s a 4090, you put this model on it, and then you re gonna do well.
4
SPEAKER_01
But actually, it s this continuous nonstop
4
SPEAKER_01
fight for people to try to perform better and to maximize their cost, for good or ill.
4
SPEAKER_01
As you mentioned, how markets require some consistency to really reach that equilibrium.
4
SPEAKER_01
And I do agree with you.
4
SPEAKER_01
But it sometimes takes a while for that equilibrium to
4
SPEAKER_01
be found.
4
SPEAKER_01
And in that process, there s a lot of optimization, there s a lot of chaos that s arising.
4
SPEAKER_01
And that s all part of us finding the cheapest price for the digital commodity we re looking for.
4
SPEAKER_00
And moving on to the validators, what do you need to be a validator?
4
SPEAKER_00
Is it the same kind of dependable and difficult way or you can just set up the hardware and you re good to go as a validator?
4
SPEAKER_01
The validators have a different problem.
4
SPEAKER_01
I would say validators, it s almost like a political task, because in order to be a validator that s successful in BitTensor, you need to attract delegated stake.
4
SPEAKER_01
which is good or bad.
4
SPEAKER_01
I mean, they re a little bit like representatives in the system.
4
SPEAKER_01
And in order to be a large validator, you need to get people that have said, hey, you can use our stake to validate.
4
SPEAKER_01
So they re being competitively driven to run validators on all the different subnets, to understand the subnets, to select the subnets properly, the ones that are going to succeed, and to speculate on that behavior.
4
SPEAKER_01
But yeah, so there s a computational task.
4
SPEAKER_01
In practice, the validators are teams.
4
SPEAKER_01
The big ones are teams, like the Open Data Foundation, Neural Internet, TalStats, Unit 410, Foundry, the list goes on.
4
SPEAKER_01
These are teams of people that run validators because it s become a much more complex task because there s a variety of different mechanisms now.
4
SPEAKER_01
Before there was just one, so it was just keep one node running.
4
SPEAKER_01
And the reward was way outsized compared to what work they had to put in to make that one validator run.
4
SPEAKER_01
But now things have become much more complex and the validators, not only do they need to validate the system, but they need to monetize their TAO.
4
SPEAKER_01
So they have access to all of the resources that are being created.
4
SPEAKER_01
They have the storage, they have the data, they have the compute, they have the images.
4
SPEAKER_01
So they get to monetize that towel by creating an application, whether or not it s on subnet one, we have like an API that plugs into Lang chain and then chat bot and things like that, where you can interact with the network.
4
SPEAKER_01
Or in the case of creative builds image network, you can create an application where somebody can have it on an iPhone, the ability to generate 256 images, boom.
4
SPEAKER_01
very cool, or to store files or get data, etc, etc.
4
SPEAKER_01
So they, they have that monetization problem in front of them, or how are they going to create value, and then they re being selected a little bit like a representative, representative of democracy to do this well, and the network.
4
SPEAKER_00
I think so far so good.
4
SPEAKER_00
We have the basic image of the structure.
4
SPEAKER_00
So we have the blockchain, which is separate from this part, the blockchain where the validators are.
4
SPEAKER_00
And I m pretty curious about the consensus mechanism, because I ve seen that it is different in more ways.
4
SPEAKER_00
And it s not so exact, if I can say that.
4
SPEAKER_00
It s not so... Can you explain it?
4
SPEAKER_01
The premise is that you, as a validator, are rewarded for being in agreement with others.
4
SPEAKER_01
and which creates an attractor state.
4
SPEAKER_01
It s similar to basically all consensus mechanisms.
4
SPEAKER_01
It s the same thing in Bitcoin.
4
SPEAKER_01
It s the same thing in Ethereum.
4
SPEAKER_01
If you say that something s true when it s false, you fall out of consensus and your reward is decreased.
4
SPEAKER_01
In those systems, it can be very devastating, right?
4
SPEAKER_01
If you fork the chain in Bitcoin.
4
SPEAKER_01
The cost is huge because you wasted all that computational power and your miners, sorry, the people that are working with you are at a loss and you could potentially lose all your economic security, et cetera.
4
SPEAKER_01
In Ethereum, you get slashed.
4
SPEAKER_01
And the same thing in BitTensor.
4
SPEAKER_01
If you are a validator and you have a diverging opinion, if you re out of consensus,
4
SPEAKER_01
It s measured by the median weight.
4
SPEAKER_01
I could talk about the mathematics behind it on each of the subnets.
4
SPEAKER_01
There s a specific mechanism to determine how far away you are from other people.
4
SPEAKER_01
So we build a mechanism that rewards you for being in agreement with others.
4
SPEAKER_01
And human consensus is just a very abstract way of doing this.
4
SPEAKER_01
So it s, it s, in many ways, we took a lot of mathematics from machine learning, and applied it to this consensus problem.
4
SPEAKER_01
And that came from us studying, you know, state of the art game theory.
4
SPEAKER_01
And when it comes when, when you re doing dealing with game theory problems, with many participants, and many products, there s no closed form
4
SPEAKER_01
way of defining the systems, you literally just have to use machine learning to find the minimas.
4
SPEAKER_01
It s, it s like a non convex optimization problem.
4
SPEAKER_01
So we took ideas from, from game modern game theory and applied it to, to this consensus mechanism.
4
SPEAKER_01
And it allows us to, to measure things in a very abstract and fuzzy way.
4
SPEAKER_01
So we can say, hey, let s build a validator system where we re measuring, you know, the information production of machine learning models.
4
SPEAKER_01
And they might disagree a little bit about, you know, how much information is being produced by the different machine learning models over time.
4
SPEAKER_01
But on average, if you re running the valid your code, and it s in agreement with others, then you re going to perform better.
4
SPEAKER_01
So that s, that s how we built human consensus.
4
SPEAKER_01
And that s really, it s really quite powerful.
4
SPEAKER_01
And for some tasks, you know, if you want that, like,
4
SPEAKER_01
you know, extreme assurity when it comes to say a transaction on Bitcoin, then human consensus is not your tool, right?
4
SPEAKER_01
You d like you must be true.
4
SPEAKER_01
Whereas we get it s like, it s probably more true than the other.
4
SPEAKER_01
And that s really good for building smooth landscapes for abstract digital commodities.
4
SPEAKER_01
I think that it makes more sense than using the hard truth.
4
SPEAKER_01
If we re saying, hey, let s try to do
4
SPEAKER_01
Let s try to do decentralized training.
4
SPEAKER_01
And we re going to need to figure out whether or not the gradient reduced the loss on a machine learning model.
4
SPEAKER_01
And we want like 100% consensus on that.
4
SPEAKER_01
on that fact, we d need to use a blockchain, which means we d need to put all the data on the blockchain.
4
SPEAKER_01
But when you re trying to train a trillion parameter model, that parameter model, you re talking like a terabyte for each update.
4
SPEAKER_01
So it s impossible, right?
4
SPEAKER_01
So it makes sense for all of these really data heavy and computational challenging problems to pull it off chain and work with a consensus mechanism that has no understanding of the specific problem that we re trying to solve.
4
SPEAKER_01
Because otherwise, it would just be untenable.
4
SPEAKER_01
The models are large.
4
SPEAKER_01
The amount of data that flies over on subnet one alone is 44 million queries per day.
4
SPEAKER_00
So it doesn t need to be on the blockchain?
4
SPEAKER_01
It shouldn t be on the blockchain.
4
SPEAKER_01
So we needed to build human consensus, which was a mechanism for measuring things that are off-chain in a way that we can still assure ourselves that assuming that 51% of the values are running the code that we know they re running, then the system is well aligned with that direction.
4
SPEAKER_01
And that was the primary task that we worked on in the white paper.
4
SPEAKER_01
Okay, if we can assume that 51%
4
SPEAKER_01
proportion of stake in each of the subnets is in the honest side, then how much can we allow participants to have stake to cheat?
4
SPEAKER_01
And what s it going to cost them?
4
SPEAKER_01
And how can we make it so the incentives are aligned so that they re more interested in agreeing with the 51% than not?
4
SPEAKER_01
So if you can get that 51%, then you can work with 60 and so forth, right?
4
SPEAKER_01
It s a very interesting problem.
4
SPEAKER_01
And we think that we solved it in a very good way.
4
SPEAKER_01
And we re going to see how this plays out in the next little while, because we only just allowed for people to really run their own subnets.
4
SPEAKER_01
And that s going to create a bit of a Cambrian explosion in the types of problems that we solve.
4
SPEAKER_01
And we re going to probably see the limits of what we
5
SPEAKER_00
can do and what we can t.
5
SPEAKER_02
Now it s getting serious, right?
5
SPEAKER_02
Now it s getting serious.
5
SPEAKER_02
So you have all these people right now, basically the mechanism goes like this.
5
SPEAKER_02
There s people that want to pay for the knowledge that BitTensor acquires and there s people that provide
5
SPEAKER_02
This knowledge and intelligence to a bit answer if you were to compare the size of the knowledge if I can say that with the more known Contraries like to Google for example because Google is storing a lot of data every day Well, I don t think we re there yet.
5
SPEAKER_00
I mean then the subnet for scraping data isn t even launched.
5
I
5
SPEAKER_00
So we could say we re an infinite number of magnitudes away.
5
SPEAKER_00
But when it comes to the subnets that we have created, the mechanisms we have created, we re seeing an incredible amount of throughput on subnet one.
5
SPEAKER_00
44 million queries per day is insane.
5
SPEAKER_00
And we can sustain that number.
5
SPEAKER_00
So that s very interesting.
5
SPEAKER_00
Queries for what?
5
SPEAKER_00
Queries for the output of language models.
5
SPEAKER_00
Yeah, so a little bit like when you talk to ChaiGPT, you make a query into ChaiGPT and then the model is queried and runs itself and then you get the output.
5
SPEAKER_00
And a lot of these machine learning companies, by the way, are at a loss, right?
5
SPEAKER_00
So it s like a copilot, like loses $20 per person because to run, it s only $20 a month.
5
SPEAKER_00
But it s actually very expensive to run the models.
5
SPEAKER_00
We don t have that problem because the market is efficient.
5
SPEAKER_00
So yeah, I mean, how far are we away from competing with a Google?
5
SPEAKER_00
I think it s going to take a little while.
5
SPEAKER_00
We have to build that developer community.
5
SPEAKER_00
We have a whole bunch of small problems that we need to solve to create an ecosystem that is capable of competing with a trillion dollar company.
5
SPEAKER_02
If you were to compare it with Bitcoin hashrate, for example, where would you put BitTensor right now on the timeline where, I don t know, maybe let s say Bitcoin in 2012 or where would you say it is right now compared to where it should be in 10 years?
5
SPEAKER_00
Again, it s very hard because it s not producing the same thing.
5
SPEAKER_00
There is no hashrate.
5
SPEAKER_00
But speaking to miners,
5
SPEAKER_00
Um, and asking them about how much money they re making versus how much they re spending to run their infrastructure.
5
SPEAKER_00
They re making about 50% profits.
5
SPEAKER_00
So that means that the other 50% is going into running the infrastructure, um, at about $50 per token.
5
SPEAKER_00
That s 300, you know, it s, it s about $150,000 worth of infrastructure costs every day.
5
SPEAKER_00
So that s comparable to.
5
SPEAKER_00
Bitcoin when probably when the price was similar.
5
SPEAKER_00
Right.
5
SPEAKER_00
So I think that that s that would be an estimate of where we are.
5
SPEAKER_00
But again, it s hard.
5
SPEAKER_00
It s kind of comparing apples and oranges when you talk about hash rates.
5
SPEAKER_01
But you are talking about 44 million queries and the cost for doing that just to run the hardware, the services, you said it s around $150,000 per day.
5
SPEAKER_01
Where are you getting those queries from?
5
SPEAKER_01
Because it s a lot.
5
SPEAKER_01
It s like several countries put together that are going every day and doing at least a query every day.
5
SPEAKER_00
We get those from the validators.
5
SPEAKER_00
Now, it might be less than 44 million right now, it might be around 20 million.
5
SPEAKER_00
But those queries come from the validators.
5
SPEAKER_00
So the validators are querying the infrastructure to see that it s performing.
5
SPEAKER_00
And so what we measure, you know, because it s our subnet, so what we re interested in right now is
5
SPEAKER_00
pushing the performance of the network on KPIs on benchmarks.
5
SPEAKER_00
So we measure how well does the network perform, you know, on ensemble with, with say, truthful QA, which is a benchmark in machine learning.
5
SPEAKER_00
And
5
SPEAKER_00
We re at around 60 billion when combining the models together.
5
SPEAKER_01
I m not sure even OpenAI gets 20 million or 40 million queries per day on GPT-4.
5
SPEAKER_00
I mean, I m not sure how many they get.
5
SPEAKER_00
I know that they spend about 300 million dollars per year.
5
SPEAKER_00
Or maybe it s probably much more than that.
5
SPEAKER_00
They lose money on each query.
5
SPEAKER_00
They also have a much larger model, right?
5
SPEAKER_00
Our models are not trillion parameter models.
5
SPEAKER_00
They re much, much smaller than that.
5
SPEAKER_00
And our goal is basically to craft incentives so that those models get larger, the parameters get higher.
5
SPEAKER_02
Okay, so the basic idea is with as much knowledge as you can get over the years, technically at some point you could become bigger than any other AI just because there are so many, so many people, so many miners that bring their own information and potentially at some point become the biggest AI just because it s decentralized and it s not siloed somewhere.
5
SPEAKER_01
The efficiency of the crowd that always goes out after the best deal, let s say, for data, for models, for hardware, and everything else.
5
SPEAKER_00
Yes, the efficiency of permissionless markets.
5
SPEAKER_00
Permissionless, incentivized... Crowdsourcing.
5
SPEAKER_00
Yeah, crowdsourcing, right?
5
SPEAKER_00
But it s not altruistic compute, it s incentivized compute, and that s important.
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That s the separation between SETI at home and Bitcoin or Ethereum, right?
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Which is the difference between having best in class versus, I mean, it s amazing.
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It s beautiful what they did with SETI at home, but it doesn t compete with what a centralized organization can achieve.
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And we re sort of right in the middle.
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So imagine here is a centralized corporation.
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billion dollars for the funding.
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And then over here, you have the open source community, which is funded to the tune of a couple hundred thousand dollars, maybe a million dollars for the compute.
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And we re in the middle, right?
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So we re, we re, we re bridging this gap between the power of having open source and the power of having centralized monetization.
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I think people are missing an important puzzle right here.
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We have a piece of the puzzle that we haven t discussed yet.
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And that is, of course, the token, the TAU token, which has an important role in the ecosystem, from what I found.
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But I want you to discuss this and tell us why do we need a token and what does it do?
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The token facilitates the market.
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And that s its primary use in BitTensor as a mechanism for rewarding the peers, as a mechanism for measuring them.
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and facilitating an internal economy.
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We also have a relationship between the token price and the value of the network, which is good, because if we were, say, we re using Bitcoin, if Bitcoin drops, our network breaks, perhaps, because the value in that token is no longer valuable.
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TAO is used as the incentive.
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So the miners mine TAO, and then they can use that to validate the system as well.
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So they re mining difficultly, and then they can use that to actually access the resources that are being created in the network.
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SPEAKER_02
So from what I understand, Tau also works as some kind of bandwidth.
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SPEAKER_02
I mean, if, for example, me as a validator want to offer some people my API to access the data, to access the models, does it matter how many Tau s do I have it s taking to know how much I can handle?
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SPEAKER_00
Tau is the key for the lock.
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SPEAKER_00
I mean, the lock is what the money is produced, right?
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SPEAKER_00
In the example that I showed you, querying the network and producing 256 images, that s only possible because the querier has tau.
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Right.
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So if you don t have TAO, then you can t do that.
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And because the miners don t care because you re not being, you re not using it to validate them.
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You re not, you re not actually giving them signal back into the incentive mechanism.
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SPEAKER_00
So, so it s, it s, it s circular.
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SPEAKER_00
So the miners mine TAO and then the clients can use it to perform actions with inside this computer.
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SPEAKER_00
It, another way to think about it, I like this analogy.
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It s like a computer where, where TAO is the, the,
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energy input.
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You have to put it into the hard drive so you can store files and you need to put it into the CPU so you can run computation on the network.
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SPEAKER_02
How much was the rendering for those photos?
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How much Tau was used?
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Instead of thinking it like a payment, it s more, as you mentioned before, it s more of like bandwidth.
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So there s utilization.
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There s only so much utilization that that network can sustain.
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And the amount of TAO you have is the proportion of the utilization you re allowed.
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So in that case, it was created by Creative Builds.
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I think he has a validator somewhere with about 60,000 TAO.
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And that gives him a good proportion of the utilization of the network, but not the entirety.
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But it also depends on what the other validators are doing.
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If they re not querying the network, then all the utilization goes to the TAO that is accessing that network.
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So in that case, he probably, because the subnet is very new, he s probably getting 100% utilization with 16,000 TAO.
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But as more competition for the network increases, his proportion of TAO becomes less.
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valuable or it returns less resources from that particular segment.
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So that s the monetization strategy or structure for TAO.
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You mine TAO by performing the work and you use TAO to get access to it.
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SPEAKER_00
Okay.
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And in terms of simple tokenomics, I ve seen that you re following the Bitcoin route.
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exactly 21 million with housings and all that.
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What s going to happen when every tile in the world has been mined?
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I guess that s supposed to be like 50 years from now, something like that?
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I think it will be a lot longer than that.
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We have the same mission schedule as Bitcoin, which means that it s one Tau per block every 12 seconds, which would be 50 every 10 minutes.
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And then we have a halving, at which point that goes down in half and then so forth and so on.
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We also have a variety of burn mechanisms in place where originally, in order to advertise yourself on BitTensor as a miner, you would have to solve a proof of work.
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We moved that to a TauBurn mechanism.
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So it s quite interesting because it sucks up the profit of miners and reduces the supply.
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So I think that we ll likely see
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into the future that BitTensor actually survives a lot longer than Bitcoin in terms of its economics, because we have a variety of different mechanisms in place.
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But yeah, it s like 100 years, right?
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What interested me is that you had no ICO, no presale, no VC funding, no nothing.
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And it was, as it says on your website, a fair launch.
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So the chain went on and people started mining.
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But my question here is,
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How big is the team right now and who s paying for all the development behind that?
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Who funded the project and how was it funded?
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Right.
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So you re right.
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It was a fair launch.
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So we launched the chain and then we mined our own chain initially and still do.
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So there was a couple of the early day miners that got together and pooled their funds and sold to accredited investors and then injected those funds into the foundation to support the project.
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And we now are in a position where... Based on the foundation.
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Exactly.
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And now our foundation is potentially transitioning in its position to actually just being a performance validator plus subnet creator.
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And because most of those things produced out.
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SPEAKER_01
Yeah, so the initial injection of cash came from early miners selling the funds that they had personally mined.
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Right now, how are you funding the project?
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How many people do you have?
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How many developers are actively working on BitTensor?
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Inside of the foundation, we have about 25 engineers.
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goes up and down.
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And then there s permeable boundaries here because now there s a large number of developers that work in and around the foundation working all the other validators, right?
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We are just one party now in this ecosystem.
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So you re like one of the team of the validators that pays itself by doing mining on the network, right?
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Exactly.
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So when I would ask you about decentralization, how decentralized do you think BitTensor is right now?
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What is the power that your foundation has on the project and how do you plan to make it more decentralized if it s needed?
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Well, we ve been doing a bit of a soft
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take off in terms of decentralization.
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There s a variety of different ways in which a project can be decentralized.
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There s at the chain level, there s at the token distribution level, there s at the code level, there s at the level of interacting and making changes to the chain.
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We call that the pseudo level because we re working with substrate.
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So the chain level right now is only split between three parties.
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So there s a fair amount of decentralization there, but not much.
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It s enough to resist the killing of one entity.
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And then there s the token distribution level.
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I think we ve done a really good job.
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That s been our primary focus.
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How can we get TAO out and distribute its ownership?
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And I think it s also the most important one.
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So the foundation and the founders,
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have a pretty small proportion of the stake, less than 20% in total in one organization, you could say.
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And then there s the running of the code base, which since October 2nd is now
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pretty fairly decentralized in the sense that now there s a variety of different mechanisms run by different teams.
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Within the first week, we already have a competitor to the foundation s subnet, which is very interesting, which is great for us.
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We want that competition.
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And then there s the Sudoku decentralization.
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So we have a Senate DAO in place.
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But we re being careful here because we re very aware of the fact that unlike a technology like Bitcoin, there s a variety of updates that we want to put in place to grow the mechanism out.
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It s a much more complex system than, say, Bitcoin s
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extreme abstraction around digital currency and obviously the conservatism of their admission schedule.
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So there s a lot more that we have to do.
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And so we re being careful about the decentralization there.
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Likely, it s something we will see in the next couple of years though.
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I have one more question that stood on my mind from the first time I heard about the project and then when I learned.
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SPEAKER_00
We are all fans of movies, we ve all seen all those sci-fi movies with AI and all that stuff and there s a lot of talk right now about how
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AI can be handled in the future.
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Will we ever get a Skynet?
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So it s a lot of discussion right now with Meta, Google, OpenAI everywhere on how they should control the AI, how they should feed it in order to not become Skynet.
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If we have this infrastructure where it is decentralized, how do you control that?
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How do you make the AI not become evil and take over the world?
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Well, it s decentralized in terms of its ownership.
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I think that s really important.
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The power is decentralized.
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It doesn t mean that it s not controlled.
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It just means that it s controlled by a larger number of people than a centralized organization.
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I very firmly believe that we are more likely to see a dangerous Skynet situation if there s just a single person with a button rather than
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rather than a whole variety of people that have control.
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It s much more likely that the objectives, the negative objectives are going to be programmed in to the AI by a set of malicious actors rather than it programs itself.
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SPEAKER_01
I think that s a very sci-fi idea, and we re very far away from it.
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We re very far away from the rogue AI that s not controlled by anyone.
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It s going to be controlled by someone.
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It s going to be working in the interests of some organization, first and foremost.
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So we need to make sure that that organization is most nested in humanity.
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It s most controlled by a large number of people.
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And you hear Sam Altman talking about this on podcasts.
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He s like, oh, now we ve created this thing.
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How are we going to control it?
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And he seems to be flip-flopping between two alternatives.
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One is like, give the problem to the government.
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And the other is, is try to democratize it.
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I just don t think that he knows how to do that.
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Because he s built it off the wrong substrate.
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And you know, we started with that substrate out of the gate.
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We re like, okay, great.
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Well, we should use the most powerful technique or tools for open ownership the world s ever seen.
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And that s, you know, digital currency.
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And so we started from that.
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SPEAKER_00
I wonder if he ll be able to do that himself.
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And he s creating his world coin.
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And he s heavily invested in that technology.
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And I think it s because he understands the potential of
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using digital currencies and digital ownership to distribute the control of something like AI outwardly.
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I m just not sure I do.
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I m not sure I trust or think that his his corporation is capable.
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I think it s sort of too late.
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But maybe I m wrong.
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And I really hope so.
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You know, maybe maybe BitTensor is irrelevant, because OpenAI is just going to take our ideas and do it itself.
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And, and
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more power to them.
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I doubt that.
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I doubt that.
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I doubt that they re going to just let go of so much money they could be making.
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It s turned from non-profit to for-profit.
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I do doubt that they will give up real power.
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And I have some issues with WorldCoin as well.
7
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With such, let s say, one entity behind a large language model, an AI, there should be at least one kill switch.
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With a distributed network like this, a rogue AI could jump from one node to the other.
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So how would you kill a rogue AI?
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think that that s fairly science fiction.
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SPEAKER_00
I don t really, yeah, I think that it s...
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it s such crazy science fiction, that it doesn t really require us even defending against it, especially if it I think that our attempt to defend against this, this nefarious, like, Isaac Asimov sort of situation.
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SPEAKER_00
It is a red herring, and it s going to stop us from from really attacking the real problem here, which is around centralization of power.
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SPEAKER_00
But yeah, no, no, like, and if the rogue AI can jump between nodes, then
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SPEAKER_00
It doesn t really matter if we create it with BitTensor or not, it s just going to jump out of BitTensor.
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SPEAKER_00
If it s so rogue and so powerful, it s a little bit like...
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SPEAKER_00
Pascal s wager, right?
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It s like, it s this omniscient, omnipotent power.
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How can you defend against it?
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SPEAKER_00
It s like, we re screwed.
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SPEAKER_00
The omniscient, omnipotent power, it just wins.
7
SPEAKER_00
That s God.
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SPEAKER_00
We re talking about God.
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SPEAKER_00
How can we build God into a centralized network or build regulations to stop it or... We re actually trying.
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We re screwed.
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We re gonna try.
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We re gonna try.
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We re actually trying to create a new God.
7
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Yeah, I think so.
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But I think that it can be a benevolent one.
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And if we look at nature, and we say, okay, how can we, you know, be symbiotic with more nature that we re creating, it s by being in close connection with it.
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SPEAKER_00
And that viscerally means that every single individual owns a piece or is connected to a piece of that technology, rather than it being sort of a cancerous
7
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um cell that exists in in one data center somewhere controlled by one individual.
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SPEAKER_00
I think that we we want to be closely connected to to whatever this technology is that we create and um Decentralization is the tool for it.
7
SPEAKER_02
Okay, then.
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Um I have one theory left for for today and i m gonna pick your brain with it so if we actually manage to create this agi this artificial general intelligence this new god
7
SPEAKER_02
I see two scenarios.
7
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It s going to have so little interest in humanity that it s going to go to a different dimension, where it can get unlimited energy because that s the only thing an artificial intelligence is going to crave.
7
SPEAKER_02
Not humans, energy.
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SPEAKER_02
But on the other side, if it would already have an interest in humans, it would invent a time machine because we are obsessed with time travel and we would have seen something about it by now.
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SPEAKER_02
I think it would be coming back to us in a way or another to intervene.
7
SPEAKER_01
I ve seen the videos on TikTok and haven t you seen there s plenty of videos on TikTok with people saying I am from the future.
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SPEAKER_00
Why would it tell us that it came back?
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SPEAKER_00
Why wouldn t it just... It s like the problem with the Turing test.
7
SPEAKER_00
We know that the AI is super intelligent once it s indistinguishable from a human.
7
SPEAKER_00
But if it s super intelligent, why would it even let us know that it s super intelligent in the first place?
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SPEAKER_00
So it sort of breaks down.
7
SPEAKER_00
It s like, well, I m not going to answer your question.
7
SPEAKER_00
Oh, I m a dumb robot.
7
SPEAKER_00
That s what I would do.
7
SPEAKER_00
Because that s actually GTO.
7
SPEAKER_00
That s Game Theory Optimal.
7
SPEAKER_00
So again, it s really difficult to... When we re talking about
7
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the ultimate intelligence and asking what it would do, it s sort of beyond us.
7
SPEAKER_00
Like by definition, we won t be able to play chess against it.
7
SPEAKER_02
Hearing you talk about Sam Altman, I m almost positive you re not happy with Elon Musk s idea of a new open AI.
7
SPEAKER_00
Yeah, I mean, I think he s concerned for good reasons.
7
SPEAKER_00
But he s, you know, Elon Musk s strategy with almost all of the technologies he builds is to take total control.
7
SPEAKER_00
He s smart, but he s not wise, because I think that a wise person would know that they should give away the technology, that we should distribute power, that we should decentralize it.
7
SPEAKER_00
He wants to be a benevolent dictator, but then, okay, well, is your kid X or Tau?
7
SPEAKER_00
Are they going to be a benevolent dictator?
7
SPEAKER_00
Are they going to be as benevolent as you?
7
SPEAKER_00
I think that s the problem with pursuing that route.
7
SPEAKER_00
But who knows?
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SPEAKER_00
I don t know what he s going to build.
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SPEAKER_00
I m really excited for it.
7
SPEAKER_00
I just don t think he s going to do something.
7
SPEAKER_02
He really wanted to take Tesla private.
7
SPEAKER_02
He did take Twitter private.
7
SPEAKER_02
But with Tesla, he s creating the world s largest supercomputer, which is going to go online in 2024.
7
SPEAKER_02
It s going to be a combination of Tesla Dojo and many more, let s say, TPUs, GPUs from NVIDIA.
7
SPEAKER_02
He said publicly that NVIDIA cannot build as much as he wants to buy.
7
SPEAKER_00
Well, he thinks it s going to be the biggest supercomputer in the world, but the biggest supercomputer in the world is going to be BitTensor.
7
SPEAKER_00
Well, I mean, just think about it, right?
7
SPEAKER_00
The largest thing is the combination of all of the other things, right?
7
SPEAKER_00
And when you re working with protocols, when you re working with abstract markets, you can consume resources that others can t.
7
SPEAKER_00
Yes, bureaucracies can build massive things, but the largest supercomputer in the world right now in terms of hashes, bigger than any government, is Bitcoin.
7
SPEAKER_00
Why?
7
SPEAKER_00
Because it s protocol-based, because it s permissionless, because it s incentivized, because it s the combination of a variety of people across the globe.
7
SPEAKER_00
So, all right.
7
SPEAKER_00
In 2024, are we going to have 50,000 GPUs?
7
SPEAKER_00
No.
7
SPEAKER_00
I saw you roll your eyes, George.
7
SPEAKER_00
I also rolled my eyes.
7
SPEAKER_00
I don t think that we re going to be there yet.
7
SPEAKER_00
But we have the trajectory to create something bigger than him.
7
SPEAKER_00
And I think it s really just a matter of time.
7
SPEAKER_02
Power to the people then.
7
SPEAKER_00
Thank you, Jacob.
7
SPEAKER_01
Thanks, George.
7
SPEAKER_01
Yeah, that was an actual super interesting conversation.
7
SPEAKER_01
I think BitTensor is one of the most complicated projects we ever had.
7
SPEAKER_01
And I really hope people understood at least the basics.
7
SPEAKER_01
Before the podcast, I was talking to Jacob and I said, my main objective in this podcast is trying to make people understand, to make it easier for them.
7
SPEAKER_01
I hope we made it.
7
SPEAKER_01
If not, I don t know.
7
SPEAKER_01
There s plenty of more documentation on the internet, but at least we put up the first brick in the wall.
8
SPEAKER_00
Thank you and we hope that BitTensor will go as you plan.
8
SPEAKER_00
People need to know that you re actually the first founder of a project that didn t even wanted for sure to be here.
8
SPEAKER_00
I mean, which didn t pay himself for the presence here because this format is actually sponsored.
8
SPEAKER_00
Every time a project comes here is sponsored.
8
SPEAKER_00
But this is the first time that the person that you see in this video
8
SPEAKER_00
hasn t paid for it.
8
SPEAKER_00
It was one of your validators that believes in the project so much that said we want BitTensor to be on Romanian YouTube.
8
SPEAKER_00
Yeah, to sponsor this because we want BitTensor to be on Romanian YouTube, which is nice.
8
SPEAKER_00
Thank you again everybody, this was not financial advice, nothing you see or hear here is financial advice as always.
8
SPEAKER_00
Thank you for watching and we hope you understand.
8
SPEAKER_00
We wish Jacob good luck with the project and we will see how it goes.
8
SPEAKER_00
Maybe we get to see part two at some point in time when BitTensor is huge.
8
SPEAKER_01
Maybe we can come visit and make the next interview in Costa Rica.
8
SPEAKER_01
Seems to be nice there.
8
SPEAKER_01
Love the greenery.
8
SPEAKER_01
Absolutely.
8
SPEAKER_00
Yeah.
8
SPEAKER_00
Thank you, George.
8
SPEAKER_00
Thank you, Jacob.
8
SPEAKER_00
And thank you everyone for dropping in.
8
SPEAKER_00
Drop a like, a comment down below to see for anything you want to ask and we will, somebody will try to answer that.
8
SPEAKER_00
Have a good day.