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Hi, Alaa.
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Thanks so much for joining us.
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Really appreciate you taking the time.
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If you don t mind, let s just jump in.
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It might be worthwhile just giving a quick background on yourself, if you don t mind.
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SPEAKER_01
Yeah, yeah, sure thing.
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Thanks for having me.
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So my name is Alaa.
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I obtained, actually, probably better first start off very early.
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I m a co-founder of the OpenTensorFoundation, along with Jacob Steves.
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I started off actually my career specifically in AI, obtained my PhD in 2017 from McMaster University and applied artificial intelligence, which was actually a lot of fun because we got to kind of, I really got to learn the hard way and how to build a product from the very beginning.
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So basically we were building sensors that would
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detect human movement or human electricity, bioelectricity or body heat, and basically infer certain human actions from that, whether somebody s typing or if there s some lactic acid come out of the muscles as a result of that and stuff like that.
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So kind of, you know, really went from, you know, sketch design all the way up to PCBs, all the way up to actually building the firmware and the classical AI models that went on top of that.
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And so really kind of got to learn how to build a real product from start to finish, really.
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Um, afterwards I kind of started to try my hand at something a little different.
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So I went to distributed computing routes.
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I joined VMware in, um, 2017 right after graduation.
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Uh, worked in Palo Alto for about two years.
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Uh, then started joining Instacart for a little bit, kind of do a little bit more on the software development side of things.
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And, uh, I met Jacob, interestingly enough, uh, through the founder of Cohere, um, Aiden Gomez.
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He kind of introduced us together, um, when we worked at a, uh, not-for-profit called 4AI.
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And, uh, at the time I just presented, uh, just, you know, honestly, just off the wall ideas I had about distributed compute.
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And then, uh, Jacob kind of reached out to me and he was like, Hey, nice idea, but I m like five years ahead of you.
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So just come join me.
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Let s work on this together.
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And so that s when OpenTensor was born really.
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Uh, we kind of, interestingly, we built through COVID.
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So we kind of just, you know, he was in Peru, I was in Toronto.
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We kind of just started building and building and building went through several iterations until.
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2021 in January, we launched the first Kusunagi network at the time it was called.
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And we ran that up until the end of 2021, actually.
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Actually, mid-2021.
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And then we paused it, and we ran it again in November.
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And it s been on since then.
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SPEAKER_00
Awesome.
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That s great context.
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So let s just kind of set the stage here.
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What is BitTensor?
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Yeah, if you could just give us your, your kind of your pitch.
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I think it s I think it s a lot of different things for a lot of people at this point, it is yeah it is I think it s best to kind of describe it, I want to say from a very simplistic perspective and then can we condemn it to details later for kind of set more color to it.
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So at its core, really it s, BitTester is a protocol for peer-to-peer machine learning and inference.
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That s really it in one sentence.
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We are leveraging the power of markets to democratize access to AI and incentivize people to actually contribute their AI models.
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And we sort of take advantage of the open source movement and the ability of open source AI models to actually make it available to the public.
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So in essence, we re incentivizing people to create cutting edge open source models and to also compete with those models at the same time.
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So we re sort of setting up a benchmark.
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where really AI is judging AI at the end of the day, right?
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The, there is no sort of human intervention in checking which model s better and which model s worse.
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Um, so to kind of make it more specific, there s two main actors in the system.
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You have your miners and you have your validators.
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Now the miners will contribute to the system by running AI models that will solve a specific task, let s say image generation.
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And the validators will basically contribute to the system by ensuring that all the miners are running legitimate AI models and they re not really cheating the system, so to speak.
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Now the network itself is divided into sub networks.
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That s actually our most recent update.
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And that s sort of when things started skyrocketing.
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Which are basically neighborhoods within the network.
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That s the best way to think of them.
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These neighborhoods do specific tasks.
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Let s say you have one neighborhood or one sub network for text generation, one sub network or a neighborhood for image generation, for classification and so on.
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And so the miners and validators will participate in one or many of these neighborhoods.
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And they re basically incentivized to be with their peers.
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Um, for Tau, which is the, the, uh, token that s being issued by the system.
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And, um, yeah, it s just kind of last thing is that it s pretty exciting because now we effectively have what is really a mega mall of machine learning tools and models.
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And there s subnets now for storage subnets for compute subnets for images, for zero knowledge proofs, et cetera.
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So now what we have is basically almost becoming, or what we want to become a one-stop shop really for any AI tool usage or inference or training.
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SPEAKER_00
Great.
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Yeah, appreciate that.
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And we ll dive into I think a lot of those specifics in a few minutes.
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As far as AI inference is concerned specifically.
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What would you describe as the value proposition of BitTensors?
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OK, great.
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We have this decentralized marketplace for AI running on crypto rails.
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It s a lot of buzzwords, right?
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And I think people from maybe the AI community might look at a project built on crypto rails and be dismissive.
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How would you describe the value proposition of what BitTensor is doing, like whether it s from a performance, cost, censorship resistance standpoint?
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Yeah, can you dig into that?
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Yeah, absolutely.
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So I kind of read this line in a paper when we first started working at BitTensor, and it kind of stuck with me since and sort of become like my personal thesis and sort of the project s overall ethos as well, is that we value
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resilient generalists over neurospecialists, right?
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And that s something that s really, it s becoming more and more what people really are looking for.
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If you look at ChatGPT, for example, it s a really good generalist, right?
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But when you ask into a specialization, asking very specific questions, it s ripe with misinformation.
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It s ripe with garbage.
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It ll send you some weird stuff and stuff that s just outright false, right?
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Now, if you look at BitTensor as a comparison, right?
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With ChatGPT, you kind of, you swipe a credit card,
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You, um, get access to the API, you ping the API, you get back response done.
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With BitTensor, what you re really doing is instead of speaking to one chat GPT, for example, you re speaking to hundreds, if not thousands of different models.
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And each one is, could be, um, uh, specialized in something different.
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Right.
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So let s say, for example, you asked a question about cellular biology, right?
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Try to see my given answer that it may be pulled from some paper that I went through.
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Right.
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But on BitTensor, you re likely to get a more nuanced answer, because there could be a model on there that is actually trained on biological papers.
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It can give you much more detail that is more correct and more accurate than something that s just generally trained by one company.
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So what we re really doing is sort of generalizing.
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You re sort of getting a lot of generalizability as a result.
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And you re sort of not relying on one single entity.
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If tomorrow OpenAI came and told you, hey,
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we detected some weird stuff with your credit card or with your account, we re going to shut you down.
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And that s it.
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It s over.
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Your entire business model just crashed because you were based on OpenAI.
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Whereas the BitTensor, it doesn t really matter if one miner goes down.
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There s so many others to replace it.
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And there s other people coming in to compete and try to replace that miner in the first place.
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Yeah.
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And I think that s one of the things that I always describe to people is like bit tensors, sort of a Hydra, right?
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Like you can cut off one minor, but you, you can t cut off all of them.
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Right.
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And if, if one minor, uh, or model, you know, degrades relative to its peers, then another one will rise to the top.
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And I think the other thing that you touched on there was really from a performance standpoint.
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BitTensor has the ability to specialize in basically every domain because you re bringing a lot of different entities from all over the place and all over specialties to the network.
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And when you talk about that, is that like mixture of experts or is that a little bit separate?
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It s a tad separate.
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So mixture of experts is actually a little bit older.
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It s an older iteration of BitTensor at this point.
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Don t quote me on this, I m not fully 100% sure.
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I think our programmers and probably Jacob are much more in tune with this.
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But I don t believe we re using quite exactly a mixture of experts anymore.
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Having said that, people with the subnetwork can certainly introduce that back in if they want to.
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And that s something we could delve into a little bit later.
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But it s more because the setup that we have now with each subnetwork, it means that every single subnet owner can actually define their own incentive mechanism.
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And what that means is that because you can find your incentive mechanism, you can find even your own objective function.
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And so that means that any minor that joins the system, all they really need to do is just tune to that objective function and try to make it better, try to basically beat their peers by doing something better there.
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In that sense, if somebody does badly, for example, if it s just, just an innocently bad model, doesn t really need to be a, you know, something that s catastrophic as a terrible actor or something like that.
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It just, the system kind of just calls it out, right?
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Somebody new registers.
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they get the worst one out and then the next one comes in.
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So kind of ensure that there s always quality over quantity most of the time.
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Got it.
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Got it.
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The one other thing that I honed in on a little bit, I think I was listening to Mog Machine at Urban Assembly.
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He was talking about
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kind of the unified experience of using BitTensor in that you can have all these different resources under potentially one API.
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I m wondering if you can dive in a little bit into how that might actually be possible if I
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Because, as I understand it, I might be using an API from a specific validator, and that validator might be plugging into one, or maybe all subnets.
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From a developer standpoint, I could see that being potentially interesting, because you don t have to go grab resources from all over the place.
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You can just tap into one, one API.
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Am I completely off there?
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No, you re good.
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You re actually on the right track.
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Yeah, yeah.
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So that s actually, you re tapping on two different subjects here, right?
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So first of all, the validators.
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Yes, the validators can actually plug into any subnet they want as well.
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Cause they can, they can run that models, uh, subnet subnets code.
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Right.
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So let s say I have one big machine.
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I ve got, let s say it s 30 GPUs.
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I can dedicate those GPUs to each different subnet if I want to.
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Right.
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And then this way I could just validate on different subnets, right.
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Using, um, using my, my same wallet.
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Now, the other aspect that you re talking about, which is the API, the unified API, what we re really talking about here is standardization.
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Right.
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And that is actually the.
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next big step on BitTensor is we need to start standardizing now, right?
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Now that we have the supply, we have some demand, which is great, and we re sort of growing, now it s time to standardize.
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And that means that really, like, one of the big things that needs to be done is to release an API that every validator can use, and every validator can adapt to it.
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So that means that this API can be really self-adapting to whatever you re validating it, right?
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So let s say, for example, I m validating on text, image, and audio, right?
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All I need to do is just grab this API,
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It just, it s part of my validation, um, mechanism and it s the same API that everyone else is using.
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It s just that it adapts to my own validation.
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I m validating in image, text and audio.
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Then CPI lets me access image, text and audio.
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Right.
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Um, having said that it s actually more of a software development problem than it is a research and development or even an AI problem.
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Cause really you re just building API to actually plug into different services.
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That s all it is.
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So it s more like, I think the, the longer arching question is how do we standardize this across all validators and, um,
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That s one of the things we ll look at next year.
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Maybe on that note.
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Oh, sorry.
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Go ahead.
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No, I just had one more thing.
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Um, one of the other things as well that, uh, this, you know, API, whatever you want to call it, we ll need to do too, is to abstract away the complexities of crypto and AI from the actual API usage itself.
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Right.
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If you look at chat GPT, for example, if I m just a regular user who doesn t have an AI background, it s a magic box.
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I just want a credit card and gives me answers.
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That s all it is.
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And.
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That s actually one of the beautiful things that OpenAI actually did with this, is that it s so easy to use.
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And that s what we need to start towards next, is to abstract away the crypto side, abstract away the AI side, and really it s the magical box that works for everybody.
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And that s how we sort of will start penetrating the more, I want to say, layman user base.
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Yeah, we like to think a lot about go to market, you know, in sort of the crypto space, which has been tricky for everybody.
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And, you know, particularly I think around that idea that you ve just been discussing.
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When you think about, you know, I think we re somewhat familiar with how, you know, initially when you re bootstrapping a network, you know, there s an inflation that needs to happen to be able to incentivize, you know, the different participants to come into the table, right?
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And BitTensor is doing this amazing job of, on the one side, attracting
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of like sort of creating this gravitational pull, attracting on the one hand, the miners or let s say the models that can do all sorts of things.
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On the other side, you know, the potential demand of people that need businesses, consumers, whatever that need a problem resolved.
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If you think sort of into the future as to what, let s say a sustainable BitTensor network looks like, you know, is that mostly focused around this sort of unified experience that Phil, you know, was sort of asking about with the single API?
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Is that the vision that you think BitTensor is moving towards, or is it much more of a marketplace fragmented, a lot of different things for a lot of different people type?
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I think the answer is somewhere in the middle, to be honest.
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Just because we use, for example, let s say something like a unified API, doesn t mean that it can t be a marketplace that s a lot of different things for a lot of different people.
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We can look at the API example as just really a way to bring in these external developers to build on top of the subnetwork.
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Um, those people are sort of ultimately what will drive a lot of the applications on top of the system.
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And really, if you really think about it, they re also the people who will.
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Bring the system to the mainstream in the first place.
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So enabling them to do that is definitely going to be, um, like it s, it s going to be a challenge, of course, but that is actually one of the reasons why we re looking at abstracting away all of the AI and all of the crypto sides of things.
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Right.
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And that s something that I always talk about is, um, you know, I ve been to a few, uh, quite a few crypto conferences.
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I started doing this and as an AI person.
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To be honest with you, if I come in without any crypto knowledge, a lot of them make no sense to me, right, because they re very geared towards a crypto audience and some of the technologies are amazing, but nobody s going to get it because they re not really gearing towards an audience who doesn t understand crypto in the first place.
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And so, one of the things that we need to do is we need to bridge this gap, right.
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Bridging it with developers, maybe that s actually one of the big pushes that we re trying to do is how do we enable any developer to join the system and build on top of it as well.
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And that includes documentation that includes, um, API wrappings that includes, you know, a whole slew of things that we want to release next year.
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And the other side of it is how do we ensure that the people who are already in the system and really the, the democratized market aspect of it is not compromised as a result.
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Right.
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We want to enable the.
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Let s say, for lack of a better example, the disgruntled open AI employee to come into the system and build their own sub-network and actually build something really cool on top of the system that is an actual utility, right?
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And there s a device to do that still without sacrificing the integrity of that.
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So that s sort of the line we re trying to walk for the next year.
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It s going to be a very fine line, but I think we re fairly capable of doing that.
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Maybe just wrap this question up, but are there any, any key partnerships, you know, that you think, uh, the BitTensor network will have to, you know, strike, uh, in order to push that maybe that specific problem forward?
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Yeah, I think that, uh, specifically, you know, and I say this all the time, I think if you ve heard the other podcasts I participated in, I think specifically what we really need to do is we need to bring in the AI talent, right?
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Um, there s a lot of people in the system now that are doing a great job, but they might not be, you know,
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AI researchers or AI engineers, they re more software developers that taught themselves AI to work on top of the system, or crypto developers who taught themselves AI.
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And, you know, that is absolutely awesome, absolutely commendable that people are actually teaching themselves this complicated topic to participate in it.
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But I feel like somebody with
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a lot of theoretical knowledge of AI will actually have a lot to contribute as well.
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And those are the people we want to yank in for the next year.
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And interestingly enough, they re very crypto-averse, which fair enough.
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All of us in the crypto space have had a few eggs thrown in our face this year.
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But I think that if we speak their language and if we
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actually come at them from the angle of, you know, this is not snake oil, this is actually developed by an AI company that has happened to use a blockchain to solve a problem, then that s where we re going to, that s really going to be the, I want to say, the key to strike here, right.
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And that is usually done by either, you know, publications and respective conferences.
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So we ve been publishing in
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For lack of a better term, we ve been either lead authors or co-authors of a paper on neural IPS for the last two years.
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That s been very good.
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A lot of really good exposure.
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We want to participate even more than that.
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Next year, we want to participate in more conferences.
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We want to actually be a known platform in the community.
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That s sort of where the key is going to be at.
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And sort of incentivizing them that way is going to be, that s really going to be the hard part.
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Because they already have a bad misconception.
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You bring it in and be like, we re the good guys here.
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Yeah, I think that s one of the key partnerships we want to do, for sure.
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Yeah.
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Yeah, that ll be huge.
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It seems like BitTensor is sort of at an inflection point where things are working well enough and starting to see real innovation, especially with the absolute boom in subnets only being live for two or three months.
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Let s dive in a little bit into how BitTensor really works.
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You briefly described the two-layer system, which is basically validators and miners.
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Miners being
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the suppliers of a digital resource, whether that is compute, text-based, you know, inference, image-based, multimodality, really the, you know, the universe is kind of endless.
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And then validators are basically just these entities that
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make sure that the output coming from these miners is good and valid and they rank them so that you can put these incentive schemes together.
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Can you maybe dive in and fill in any gaps that I might be missing there?
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Yeah, there s quite a lot to this story.
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Interestingly enough, you can look at it as, sorry, I m trying to think of a good analogy to this specifically.
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I ll tell you what, think of it like a graph, right?
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There s two types of nodes in this graph, right?
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You have your validators and your miners.
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And effectively, what s going to happen is each validator is going to ping a subset of these miners at a given point in time.
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So let s say, for lack of a better term, let s say we re working on our text subnetwork, right?
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This is kind of the easiest example because it s the oldest one at this point, really.
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And so what each validator does, it s going to query a subset of miners periodically and
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when a miner s queried, it s going to be expected to be performing useful work.
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And that means that I can query it and say, okay, give me back a prompt.
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Give me the, for example, the, sorry, give me the synopsis of Lord of the Rings, let s say, in a French accent, right?
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And what it s going to do is the miner is going to respond back with that query.
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And the type of work is going to depend on that subnet.
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SPEAKER_01
So for example, if it s an image, it s going to say different prompt, let s say, you know, generate an image of say a speaker system in a concert or, you know, et cetera, et cetera, for each different subnetworks.
2
SPEAKER_01
And, um, the miners really have to respond before a time limit with their proof of, with the proof of the work that they actually did.
2
SPEAKER_01
If they don t respond in that time limit, then they get penalized.
2
SPEAKER_01
If they do, then their response is actually, um, analyzed through it.
2
SPEAKER_01
at least it s in our design, through a layer of reward models.
2
SPEAKER_01
So what these reward models really do is they basically just check your diversity, your validity, your relevancy of the response of your miner directly to the validator and what the validator is actually expecting in the first place.
2
SPEAKER_01
And so according to that, then you get basically ranked according to whoever s with you, like basically, sorry, ranked according to the peers who are with you.
2
SPEAKER_01
And that ranking is recorded on the blockchain eventually.
2
SPEAKER_01
So in doing so, really, we re taking out the AI from the blockchain completely.
2
SPEAKER_01
It s actually everyone s running their own AI.
2
SPEAKER_01
And the blockchain really just contains a ranking.
2
SPEAKER_01
There s a lot more complexity to it, but for lack of a better term, it really only contains the ranking.
2
SPEAKER_01
And that ranking basically determines who gets rewarded how much as a result, whether it s validators or the miners.
2
SPEAKER_01
Now, as you repeat this process, eventually every single miner is going to get pinged.
2
SPEAKER_01
And every single miner will be ranked.
2
SPEAKER_01
And every single miner will basically get either rewarded or be pundit penalized for it as a result.
2
SPEAKER_01
And
2
SPEAKER_01
So to cut out this further, this is only for our subnetwork.
2
SPEAKER_01
And that s actually going to change very soon because there is obviously a lot of, um, issues with sort of design.
2
SPEAKER_01
Like for example, what s stopping somebody from fine tuning to the world model.
2
SPEAKER_01
So there was get the right response.
2
SPEAKER_01
It s because you have open source, you have a lot of different problems you have to tackle.
2
SPEAKER_01
And that s again, sort of undergoing an overhaul.
2
SPEAKER_01
And the beauty of it is that because BitTensor is actually able to ingest any technology, that means that the subnet owners have full control over how they do this process.
2
SPEAKER_01
You don t have to use world models.
2
SPEAKER_01
You could use whatever you want to use.
2
SPEAKER_01
Whatever works for you, use that.
2
SPEAKER_01
And if you re able to do it properly, then you ve sort of really incentivized your miners with the reward, right?
2
SPEAKER_01
And that s really what it s all about.
2
SPEAKER_01
That s what really building incentives is all about.
2
SPEAKER_01
You want to incentivize your miners and validators with the reward that s coming out, right?
2
SPEAKER_01
And that incentive is actually a balance, right?
2
SPEAKER_01
You want to incentivize them from cheating, and you want to incentivize them to actually do things properly.
2
SPEAKER_00
Yeah, on that point.
2
SPEAKER_00
The two of the really important things in this is accurate benchmarking and properly aligned incentives.
2
SPEAKER_00
Right.
2
SPEAKER_00
Yeah.
2
SPEAKER_00
And it sounds to me that those are both customizable within each subnet right the
2
SPEAKER_00
Or actually even within each validator software, right?
2
SPEAKER_00
How exactly benchmark, can you dig into, into exactly how benchmarking works?
2
SPEAKER_00
You know, you first started with AI inference, which I think is actually the most ambitious benchmarking to do because it s so subjective, but yeah, can you just dive into like first how, how the benchmarking for kind of inference works and then maybe how.
2
SPEAKER_00
Uh, benchmarking has expanded from there into these other sub networks.
2
SPEAKER_01
Yeah, it s a very good question.
2
SPEAKER_01
It s actually rather technical.
2
SPEAKER_01
I think I could probably just dive into the, I want to say the pencil tips of the whole thing, and then we can probably deep dive into it, but it s like a full-on article later.
2
SPEAKER_01
So actually, we initially started with training.
2
SPEAKER_01
At the very, very early editions of BitTensor, we were focused on training.
2
SPEAKER_01
We weren t focused on inference at all, right?
2
SPEAKER_01
And the problems with that is
2
SPEAKER_01
how do you verify something was trained properly, right?
2
SPEAKER_01
And at the time we were using a lot of different methodologies to actually measure whether a miner did better or worse as a result of the contributions of its peers.
2
SPEAKER_01
So for example, one of the things that we used to do is we used to measure basically, what is the impact of removing a miner from the network on its peers?
2
SPEAKER_01
Are they gonna do worse?
2
SPEAKER_01
Are they gonna do better?
2
SPEAKER_01
And then as a result, reward them that way.
2
SPEAKER_01
it became rather complicated and we found that there s actually a simpler approach to this.
2
SPEAKER_01
And that s sort of when we came up with, um, sort of what is called the, what we re calling the Fini fork, which happened earlier this year.
2
SPEAKER_01
And, um, we sort of set folks on inference a little bit more first, and then also bring in pre-training as well.
2
SPEAKER_01
So pre-training is actually already being done on 7.9.
2
SPEAKER_01
That s being done by Jacob.
2
SPEAKER_01
And, um, I think they re, they ve already reached GPT-2 performance, which is pretty crazy considering they started like a month ago.
2
SPEAKER_01
Um, and they re moving more towards GPT-3 and 4 probably eventually.
2
SPEAKER_01
Now, um,
2
SPEAKER_01
In the way that we benchmark before inference right so, for example, that really because, as it is subjective it s really up to the validator right, so you need to basically pick out.
2
SPEAKER_01
For example, in the example of the subnet one the subnet one architecture described, you have to pick up war models that are as unsubjective as possible now, of course, that is.
2
SPEAKER_01
not quite easy to do, because every road model that s out there, it s actually not that good, because there s not that much data to train on.
2
SPEAKER_01
At the same time, that road model that you create will always be biased towards whoever created that road model in the first place.
2
SPEAKER_01
Now, because of that, benchmarking and actually measuring the output of, specifically for text, for example, is an ever-evolving challenge.
2
SPEAKER_01
We re always working on this sort of thing.
2
SPEAKER_01
Now, the silver lining in this, which is actually a gigantic one, and we re pretty happy about it, is that because a lot of people are trying to cheat the system, we re getting a lot of data as a result, right?
2
SPEAKER_01
A lot of bad data, which is great because we love bad data.
2
SPEAKER_01
So we can actually train our own role model against
3
SPEAKER_00
this sort of thing if we wanted to.
3
SPEAKER_00
So as a result, one of the things that you can do to actually benchmark properly is to train on the data you re getting.
3
SPEAKER_00
It s OK if you get a bad subnetwork for a little while.
3
SPEAKER_00
Use that data.
3
SPEAKER_00
Train something that can actually tell bad from good according to what you re trying to validate.
3
SPEAKER_00
So let s say, for example, you have an image subnetwork.
3
SPEAKER_00
Train your
3
SPEAKER_00
say, for example, a reward model against that sort of thing.
3
SPEAKER_00
And then you ll be able to actually create a better model that s better guarded against this sort of attack or this sort of issue.
3
SPEAKER_00
And then you get a better merge mark as a result.
3
SPEAKER_00
Again, this is sort of just speaking from subnet one perspective.
3
SPEAKER_00
A lot of different subnetworks actually employ very, very different ways to validate and benchmark.
3
SPEAKER_00
So I can t speak for them specifically.
3
SPEAKER_00
But that is one of the ways you can actually tackle it as a result.
3
SPEAKER_02
Yeah, that s really interesting that there can be so many different approaches to validation, to benchmarking.
3
SPEAKER_02
BitTensor really is wildly generalizable.
3
SPEAKER_02
It s not just AI inference anymore.
3
SPEAKER_02
There are all these different specialties.
3
SPEAKER_02
I m wondering if you can dive into
3
SPEAKER_02
how these subnetworks work and how emissions from BitTensor get allocated to any specific subnetwork, right?
3
SPEAKER_02
Because we can start to see this topology.
3
SPEAKER_02
And I think it s interesting to analyze how value is actually flowing through the system.
3
SPEAKER_01
Yeah, absolutely.
3
SPEAKER_01
Especially since, you know, each subnet has so much sovereignty in some ways around like how to benchmark and the rest of the things, right?
3
SPEAKER_01
I m sure it must be a sort of titanic problem for you guys to keep in check.
3
SPEAKER_01
It is huge, yeah.
3
SPEAKER_00
It is pretty massive and that s sort of where the responsibility will eventually lie on the subnet owners.
3
SPEAKER_00
And if the subnet doesn t do well, then that subnet actually will risk getting deregistered as a result.
3
SPEAKER_00
Now, but yeah, the crazy part about, you know, about BitTensor generally is that it s changing often so much that I m fairly certain by the time this podcast is published, whatever I said will be completely out of date, really, because people are always adding new stuff.
3
SPEAKER_00
So to kind of delve into the incentivization, how subnetworks actually work initially, right?
3
SPEAKER_00
So right now there s 32 slots in the subnetworks, right?
3
SPEAKER_00
There is a slot zero, that is subnet zero.
3
SPEAKER_00
Now subnet zero is actually static and you can t really deregister it, but it s actually what we call the root subnetwork.
3
SPEAKER_00
It really contains validators and the job of those validators is to set ways to the other set of networks on the system.
3
SPEAKER_00
This weight setting will determine how much emission they get per, uh, basically per tau.
3
SPEAKER_00
So for example, if their emission is, for example, 10%, then they will get 720 tau per day.
3
SPEAKER_00
Right.
3
SPEAKER_00
And basically the subnets owner will receive 18% of that 10% and then the validators and the miners will each receive 42% of that 10%.
3
SPEAKER_00
So, um.
3
SPEAKER_00
Really, uh, the way that we sort of keep this up is up to the validators eventually in the, uh, root sub network itself.
3
SPEAKER_00
And, um, sorry, just, just add a little bit more of the, uh, if let s say a sub network has existed for a long time and it hasn t really done anything.
3
SPEAKER_00
All the, really the root validators have to do is say, okay, you haven t done much.
3
SPEAKER_00
I don t like what you re doing.
3
SPEAKER_00
I m just going to take away weight from you.
3
SPEAKER_00
And eventually when that weight s taken off.
3
SPEAKER_00
And the new sub-network is trying to register in, then the lowest admission sub-network will get deregistered as a result.
3
SPEAKER_00
So it s how we kind of ensure a very similar process to how we kick binders out and validators out according to performance, similar to the sub-network.
3
SPEAKER_00
If there is bad emissions or bad performance as a result, or people don t agree with how that sub-network is operating, then eventually we ll just get deregistered.
3
SPEAKER_00
So we really ultimately let the markets decide all the time.
3
SPEAKER_01
Gotcha.
3
SPEAKER_01
I was just going to ask that.
3
SPEAKER_01
I think we re having, we were just discussing with Phil about like what the value, what is the most valuable subnet for BitTensor right now?
3
SPEAKER_01
What does the sort of a break, optimized breakdown of subnets look like?
3
SPEAKER_01
Can you maybe give us a sense of what the market or what the, you know, what users are valuing right now for subnets?
3
SPEAKER_00
Yeah.
3
SPEAKER_00
Yeah.
3
SPEAKER_00
I can tell you right now.
3
SPEAKER_00
Give me one second.
3
SPEAKER_00
I m getting that list as we speak.
3
SPEAKER_02
To build on that it s almost like what what are the validators look on the root subnet looking at to determine which subnet is the most valuable and deserves the most emissions.
3
SPEAKER_00
To be honest with you, at this point, I think it s more of a social problem than anything.
3
SPEAKER_00
It s really what subnetwork is getting the most attention from everybody, and that s the one that gets a lot of emission.
3
SPEAKER_00
So to give you an example, right now, subnet one, that s our subnetwork, is getting 14% emissions.
3
SPEAKER_00
Subnet 18 is actually getting the most emissions, which is more than the foundation, which is honestly really awesome to hear that somebody is competing that hard.
3
SPEAKER_00
I believe subnet 18 is the Cortex folks.
3
SPEAKER_00
I think they re doing
3
SPEAKER_00
uh, something akin to translation.
3
SPEAKER_00
And then the time series is the other one as well.
3
SPEAKER_00
Um, there s seven at eight, they re doing time series predictions.
3
SPEAKER_00
So it s, it s really interesting because it shows really what the community really cares about at this point.
3
SPEAKER_00
And that s what they think is interesting.
3
SPEAKER_00
Um, if I think, let s say for example, I care more about, um, image generation, I m just going to set all my waste image generation, because that s really much cooler and et cetera, et cetera.
3
SPEAKER_00
And just looking enough, a lot of the, um, sort of as we age and ask people to understand how these are working.
3
SPEAKER_00
A lot of time that orders are actually starting to experience people trying to cheat the system.
3
SPEAKER_00
So initially people were working honestly, because they re trying to understand how things work.
3
SPEAKER_00
But now that they get it, they try to shortcut their way through.
3
SPEAKER_00
How do I get the same reward for less work, basically?
3
SPEAKER_00
And that s where it really is going to be really interesting.
3
SPEAKER_02
Gotcha.
3
SPEAKER_02
Yeah, and so within each subnetwork, and I guess it s not necessarily at the subnetwork level,
3
SPEAKER_02
Um, when validators are pinging minors, is there any sort of, uh, are minors looking at a minimum amount of tau that validators have delegated to them?
3
SPEAKER_02
Like, um, similarly, when validators are choosing which minors to.
3
SPEAKER_02
To ping, you know, is there any kind of decision that they have to make there or is it all just kind of random?
3
SPEAKER_00
Yeah, that s a very good question.
3
SPEAKER_00
So validators, you know, um, directly speaking, validators don t really need a specific amount of tau to get a response from minors.
3
SPEAKER_00
You can actually start off with two tau if you needed to, but minors have the option to blacklist validators if they so wish, which is actually one of the things that we ran into this year is some of the smaller validators were getting blacklisted and sort of.
3
SPEAKER_00
Eventually do research from the network because they re not getting any responses.
3
SPEAKER_00
And what we found out was interesting is that.
3
SPEAKER_00
A lot of the miners will try to optimize their compute by only responding to the big validators and big here, meaning that they re swinging a lot of stake.
3
SPEAKER_00
And so what that really means is that this miner is going to lose out on some of their reward, but because they re only responding to giant validators that have a lot of stake, they re still getting like 80% of the reward that they re looking for in the first place.
3
SPEAKER_00
And so they re saving compute, but, but sacrificing a little bit of reward.
3
SPEAKER_00
That is part of how the system works.
3
SPEAKER_00
That is sort of how the setup is.
3
SPEAKER_00
And.
3
SPEAKER_00
The one thing I want to add is that because validators can actually operate across subnetworks as a result, smaller validators can actually join the smaller subnetworks that just started to kind of sort of accumulate talent, kind of help that subnetwork grow, and then sort of work their way up to the bigger ones if they needed to.
3
SPEAKER_02
Gotcha.
3
SPEAKER_02
I mean, it sounds like that was an emergent property almost, but yeah, exactly.
3
SPEAKER_02
It s, it s kind of, it s kind of a good one.
3
SPEAKER_02
Uh, it seems, uh, and that the, you know, the validator, uh, set is pretty competitive.
3
SPEAKER_00
Um, yeah, exactly.
3
SPEAKER_00
Yeah, exactly.
3
SPEAKER_00
It s, it s very interesting because it is, as you said, it s an emergent property.
3
SPEAKER_00
It was not something we expect to see right off the bat.
3
SPEAKER_00
It was something that we said, you know, might happen later down, but it actually did.
3
SPEAKER_00
know, it just blew up right off the bat.
3
SPEAKER_00
So it s pretty cool.
3
SPEAKER_02
Yeah, yeah, that s very cool.
3
SPEAKER_01
I remember reading at the time that the foundation was considering certain fixes for this, you know, one of them being maybe lending some of the smaller validators a stake, you know, so they could be a little more muscular and be considered a little more by the miners.
3
SPEAKER_01
Has it
4
SPEAKER_01
sort of decision eventually been made that like, this is just going to be the way it s run?
4
SPEAKER_01
Do you see like an aggregator type, you know, staking service, which can maybe bring together all the small validators to allow them to compete in the future, perhaps?
4
SPEAKER_01
Or is this just sort of the way that it s going to keep going for now, you think?
4
SPEAKER_02
To be honest, we are open source, I definitely see people going to, they will eventually try to do this.
4
SPEAKER_02
And if they do all power to them, really, you know, they eventually I m
4
SPEAKER_02
Certain that eventually down the line, we ll say something more akin to pools and people are just going to put together a small, small amounts of talent and try to do that.
4
SPEAKER_02
And sort of, it s funny because in a way you can actually do this with delegation, right?
4
SPEAKER_02
Delegation is effectively a validator pool in a sense, because you re giving out your towel, you re minusing a fee and that s what you re doing, but sort of a more ad hoc pool in a sense of people, small, they want to run a validator.
4
SPEAKER_02
They don t want to delegate or anything like that.
4
SPEAKER_02
I can totally see people trying something like this, especially because they re open source.
4
SPEAKER_02
It s crazy to see.
4
SPEAKER_02
Sorry, I just wanted to add one correction.
4
SPEAKER_02
I had accidentally said that Vortex, which is subnet-18, is translation.
4
SPEAKER_02
It s actually not.
4
SPEAKER_02
It s mog-subnet.
4
SPEAKER_02
I confused it with, I think it was the other subnetwork that was actually translation.
4
SPEAKER_02
Mog-subnetwork is the CoreSell app.
4
SPEAKER_02
I m not sure if you guys got a chance to play with that, but yeah.
4
SPEAKER_03
Yep, thanks for that.
4
SPEAKER_03
Cool.
4
SPEAKER_03
What are the future plans for BitTensor?
4
SPEAKER_03
I ve seen some talk of building out, I guess, the smart contract execution layers.
4
SPEAKER_03
Is that true?
4
SPEAKER_03
And can you talk to that a little bit?
4
SPEAKER_02
There s a lot of work on that sort of thing.
4
SPEAKER_02
Actually, I believe some folks from Foundry had also mentioned they wanted to build something like this.
4
SPEAKER_02
To be honest, we are discussing that sort of layer right now, but we don t have it on the actual, I want to say, pipeline quite yet.
4
SPEAKER_02
Probably going to happen sometime in Q2, Q3 next year, mainly because that s an inevitability that s going to happen eventually.
4
SPEAKER_02
But we haven t quite built that yet.
4
SPEAKER_02
In terms of future plans, there s two big ones we re trying to do next year.
4
SPEAKER_02
And if we kind of get that done by end of 2024, I think that we ve officially set a potential to be a big player in the system.
4
SPEAKER_02
First would be complete decentralization.
4
SPEAKER_02
And that means that we need to set up our governance, we need to set up ways for people to participate in the system, and we need to enable all subnet owners to actually be able to make big decisions on the network.
4
SPEAKER_02
Things like chain upgrades and stuff like that.
4
SPEAKER_02
Right now, that s being done by the foundation, which, you know, it really helped us out a lot in the initial stages up until this point, and actually even including this point.
4
SPEAKER_02
Um, because we need to always make a lot of changes to the blockchain very quickly.
4
SPEAKER_02
Right.
4
SPEAKER_02
Especially like, you know, I m sure you ve both seen, since you first started looking into the tensor, so many changes have happened because people find different ways around it, or we find better ways to do the incentive mechanism or et cetera, et cetera.
4
SPEAKER_02
If we needed to fork every time, or if we needed to.
4
SPEAKER_02
Um, sort of do it in sort of a different layer, it would have been a complete mess.
4
SPEAKER_02
So being able to do this very quickly is actually very important, but we re sort of starting to get to the stage where things are stable.
4
SPEAKER_02
And we want to sort of give that control to the community as opposed to us.
4
SPEAKER_02
So what we want by end of 2024 is sort of the Open Industry Foundation to really just be another player in the system.
4
SPEAKER_02
We don t own anything anymore.
4
SPEAKER_02
If somebody comes and shuts our doors down next year, makes no difference, system still keeps running, nothing goes down.
4
SPEAKER_02
The second bit is really what we are trying to push for is the AI side, which we already kind of discussed earlier, is we want to bring in these talented web 2.0 AI developers into the system and have them contribute
4
SPEAKER_02
and help us build more interesting subnets or solve harder problems or sort of tackle the system from a different perspective to kind of grow it in a sense that makes it more mainstream with the AI folks as well as opposed to just the crypto side or just even the layman side.
4
SPEAKER_03
Got it.
4
SPEAKER_03
That s awesome.
4
SPEAKER_03
Yeah, I think that
4
SPEAKER_03
you know, the degree of centralization that you guys have right now is like very useful, you know, for iterating and building upon projects.
4
SPEAKER_03
But it s good to hear that you guys have aspirations to really kind of push control outside of the foundation, which I think is going to be very, very necessary if long-term, we all want BitTensor to be this almost public good in a way.
4
SPEAKER_02
Yeah, exactly, exactly.
4
SPEAKER_02
We re sort of looking at it as the internet back in 1995, right?
4
SPEAKER_02
There s a lot of regulation happening from corporations to try to regulate the internet back in 1995.
4
SPEAKER_02
I think famously, Microsoft tried to own a monopoly of Internet Explorer.
4
SPEAKER_02
Similarly, it s happening with open AI.
4
SPEAKER_02
We re trying to sort of get away from that and sort of build it in a sense that, as you said, it s effectively a public good.
4
SPEAKER_03
Yep.
4
SPEAKER_03
One of the ways that I describe BitTensor is that it s just a battlefield.
4
SPEAKER_03
Right.
4
SPEAKER_03
For all these different miners to come to the network and the, the way the output is, is, uh, uh, developed is less important than output itself.
4
SPEAKER_03
Right.
4
SPEAKER_03
That s the whole idea of like the objective function, the objective function is put in place and, um, you know, give me, give me your best results.
4
SPEAKER_03
And at some, at some points.
4
SPEAKER_03
those, there will be closed source models that are, you know, at the top of the pecking order, but then there will be times when those closed source models, you know, fall down in the, in the rankings, um, relative to some other models.
4
SPEAKER_03
So it s, there s always this, this battle going on and, and just because, you know, chat GPT might be one of the, one of the minors in a sub network, or maybe the leading minor.
4
SPEAKER_03
It comes back to this Hydra, you know, idea where at any point in time, you know, chat GPT could be cut off and a new miner might rise to the occasion.
4
SPEAKER_02
Exactly.
4
SPEAKER_02
Right.
4
SPEAKER_02
You can also look at it as a proven ground in a lot of ways, right?
4
SPEAKER_02
If you can create a model locally, that s actually able to beat all these chat GPT endpoints, then really you ve created something that is able to compete with that endpoint in the first place.
4
SPEAKER_02
Right.
4
SPEAKER_02
And if you can compete.
4
SPEAKER_02
then you ve, again, as we were discussing before, you re kind of benchmarked using AI as a model that s actually able to beat something as big as Chai2pt in the first place.
4
SPEAKER_02
So it s very interesting to see how that s going to play out, especially as more and more people join the network with their own models instead of just Chai2pt miners in the first place.
4
SPEAKER_03
Yep, makes sense.
4
SPEAKER_03
Well, this has been great, Alaa.
4
SPEAKER_03
Thanks so much for taking the time.
4
SPEAKER_03
Is there anything that we haven t touched on that you think people should know, or anything interesting that you might want to bring up?
4
SPEAKER_02
I think you covered everything.
4
SPEAKER_03
Awesome.
4
SPEAKER_03
And where can people reach out to you, or what s the best way to communicate with you?
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Either directly on Discord.
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Everyone knows me as ShipShip there.
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You can reach out to me on my BitTensor email, alaa.bittensor.com.
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Otherwise, yeah, it s probably
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We ll respond to you quite quick there.
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Awesome.
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Well, thanks so much for taking the time.
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This was great.
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Really appreciate it.
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And we re very excited for the future of BitTensor.
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Awesome.
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Thanks for having me, guys.
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Really appreciate it.
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