SPEAKER_00: Hello and welcome back to Twist. Today is March 25th, 2026. My name is Alex and I'm joined today

SPEAKER_01: by my dear friend Lon Harris. Lon, how you doing? I'm doing pretty good. What day AO is it? What day

SPEAKER_04: after Claw is it? Is it Alex? We didn't... It's like 40, 47, 48. It's in the late 40s or something

SPEAKER_00: folks. We kind of lost track of that. Much like Lon himself in the late 40s and has somewhat lost track. Importantly though, we are not going to be spending all of our time on Open Claw today. Instead, we are going to be drilling down even further into the world of BitTensor. We have three different subnets on the show today. Metanova, BitCast and Score. I'm actually really excited about each one of these companies for different reasons, Lon. But I love getting a diversity of projects all part of BitTensor because it shows what the project can do as a whole.

SPEAKER_04: It's a really cool thing about doing... We were so focused on Open Claw for a while and it's cool.

SPEAKER_09: I still enjoy Open Claw. I've bonded with my agent. But a lot of the Open Claw projects are kind of similar. It's people doing kind of similar things. Here's how you can use your Open Claw. Here's how you can make multiple agents. Here's how you can do this workflow and that. And the great thing about making a show about BitTensor is that every one of these subnets, they're doing different things with the core technology. It's one core concept or idea, but then you can use it in a whole lot of creative ways for all these interesting applications. And that's what I think is

SPEAKER_04: interesting about today's show is that it's three wildly different projects, but all built on the same

SPEAKER_13: kind of ecosystem. Yeah. Drug discovery, social media for creators, and then also vision models for commercial applications. So we're going to be getting through quite a lot of things.

SPEAKER_00: Balan, I think before we bring up our first guest, we should do a little PSA about our fun little

SPEAKER_03: devices that are listening to us as we speak. Yeah. We should talk about how we applaud plod.

SPEAKER_09: You may notice that Alex is wearing one on his wrist. I have one right here on my collar. These are applaud pins. And all you do is you hit the button. It doesn't just record you and keep track of notes on everything that you said and the people around you said. It organizes them so that you can go through it easily later, figure out what you said, search through what was said. It identifies the people in the room with you. It gets to know the people in your life. So it really is kind of this magical device that takes interactions that you're doing throughout the day in your regular spoken, allowed life and then sort of saving them for you and making them searchable and easy to look through later so that you never sort of miss anything in conversation ever again. Absolutely. I use it for

SPEAKER_16: less of that lawn and more as my personal scribe for when I'm like holding a child and I want to

SPEAKER_13: remember a thought, an idea, a task, a to-do. And I just kind of hit the button, drop it in, turn it off. And then I go back and I have kind of a list of things that I need to get done.

SPEAKER_00: An absolute lifesaver on my end. I'm a huge fan. And if you want to get applaud, you can do so. Go to plaud.ai slash twist, P-L-A-U-D dot AI slash twist. Use the code twist to save 10%,

SPEAKER_01: look super fly. And then Lon and I will give you high fives when we see you. Or online. We'll give you a virtual high five if you get applauded. If you send us a tweet, we'll follow you back or

SPEAKER_20: whatever the digital equivalent of that is. All right. Let's dive in. We're going to talk to

SPEAKER_00: Metanova or as I like to call them Lon, subnet number 68. So please welcome Michaela Bazo and Pedro Penna to the show. Just for folks out there who are less familiar with BitTensor and maybe don't know quite what we're talking about. So I don't know who wants to take this, but the way we think about it, BitTensor is a decentralized network that uses crypto incentives to reward individuals who contribute useful AI models, compute or results to task specific subnets. Thoughts guys, how can we

SPEAKER_24: improve that? Tighten it up so everyone can follow along. I mean, I think as you were saying day 47 after Claw, we're seeing that now it's humans and agents, right? So it's a marketplace for

SPEAKER_25: intelligence production. And there is a wide range of applications. Today we're going to be talking about drug discovery. But as you were saying earlier, one of the things that makes it very unique is the fact that you can use this network to train any kind of AI use case or to develop any kind of digital commodity that you can think of from renting compute to vision to drug discovery. And we're all in it together and benefiting from each other's success, which I think is something really nice that's coded into the way that the protocol works. Now on that protocol point, I want to talk

SPEAKER_00: about the individual actors and players inside of BitTensor. So can you explain to me miners and

SPEAKER_13: validators as they relate to individual subnets? Basically, there's like three main actors, let's

SPEAKER_24: call it in the way that each subnet works. You've got the subnet owner slash operator. In this case,

SPEAKER_25: it would be us who's basically designing the challenges. And then miners can be anyone from around the world or increasingly any form of intelligence from around the world that's solving those problems. Validators are then given a scorecard, let's call it, and basically selecting,

SPEAKER_24: reaching a consensus and selecting which ones are the winners for each competition. So these are like

SPEAKER_25: competitions that are running 24-7. Think about a hackathon that never sleeps, that you can apply to any problem that you would like to solve.

SPEAKER_13: Pedro, tell me about staking and how Tau and Alpha tokens fit into this. Again, for folks out there who are just tuning in and learning this for the first time.

SPEAKER_34: So I think the best way to talk about staking is to see this as a way to vote on the subnets that you believe. Depending on the amount of stake that is flowing through the different subnets, the chain is going to define how much emission the subnets are going to be receiving. So it is also a mechanism for you to vote with your Tau on the different, very different projects that are running on Bitensor of the 128.

SPEAKER_17: And just to clarify, emissions are, that's how people are getting paid. Like that the Tau gets, or the tokens get admitted to them based on their work, and that's how their fortunes rise over time.

SPEAKER_34: Yeah, it is. It works as, uh, also an incentive from the chain to, to everyone that is involved in the project.

SPEAKER_20: Got it. And just like Bitcoin, there's a hard cap of 21 million tokens, uh, for Tau, correct?

SPEAKER_41: Yeah, for Tau and for, for the Alpha tokens that are linked to every subnet.

SPEAKER_20: And just to make sure that I'm, I'm tracking this correctly, each subnet has their own 21 million token Alpha cap.

SPEAKER_13: Yes. Got it. Okay. Uh, explain the goal and economic structure, please, of subnet 68.

SPEAKER_24: Subnet 68 is part of this grand decentralized platform that we're building for drug discovery. Why? Drug discovery is a very difficult, very expensive problem. Most people are describing

SPEAKER_25: it as being in a state of crisis with the average drug taking about $10 billion and 2.2, no, sorry,

SPEAKER_24: 10 years and $2.6 billion. Well, we see different estimates. Um, and a lot of people are kind of

SPEAKER_25: shooting in the dark. It's a really hard question. There's a lot of points of failure. And so what

SPEAKER_24: we're trying to do is improve the virtual screening process so that we can make the best bets so that they don't even feel like bets so that we're de-risking what is really like the most asymmetrical bet that you could make. And it has an impact, not just like financially, but also on people's lives. So we launched March 1st of last year, and it was first of all, proof of concept of, can we even do

SPEAKER_25: this in a decentralized way? Like nobody had ever tried to do that before. And since then, um, we've

SPEAKER_24: grown to have two different incentive mechanisms. So right now our miners are doing two things simultaneously. They're either submitting molecules of interest based on whatever target we set for the competition. So we're like, Hey, find us the most interesting molecules that bind to serotonin and, or they're competing on their second incentive mechanism. That's focused on chemical search algorithms. Why? Because chemical search algorithms allow us to basically look within the possibilities of the chemical universe in a very flexible way. And we can plug it to any kind

SPEAKER_25: of state of the art model and also keep certain information private that, you know, might be sensitive

SPEAKER_47: from like our partner's perspective. Is the second mechanism a way to automate the first or are they

SPEAKER_34: distinct? They talk a lot with each other. And indeed we, we can see that miners can get a lot of inspiration from the second mechanism to compete in the, in the first mechanism, because you can actually choose, do you want to open source or code that you're using to win in, in the first mechanism? Um, or, or do you want to like keep it to yourself? So we have these two kinds of incentives and it can, you can go for a more open source route of winning with the code itself, or you can use this code to

SPEAKER_41: structure something that will really allow you to explore very, very vast chemical universes. And then

SPEAKER_51: submit that when you're launching a brand new company and designing your first product, you don't

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SPEAKER_13: that bind to serotonin, just stick with that example, as a non-scientist myself, little economics, little philosophy, not a lot of chemistry and biology, how many possible molecules are we talking about? Like what's the library we're flipping through

SPEAKER_25: to then select from? So the number of possibilities, bigger number than I can even enumerate. And honestly, it's a theoretical combination of atoms. We're trying to focus specifically on what's synthesizable so that it is actionable, so that our miners are not just, you know, submitting something that then we can't get tested in the lab and advanced into eventually like a drug candidate. We started off with a data set of a billion molecules, and then we layered on top of it, five combinatorial reactions. So our miners are essentially recreating, like they're using these generative approach to recreate what it would be like to synthesize molecules in the lab. And that

SPEAKER_24: brought it up to about 65, we estimate 65 billion possibilities. That's a lot of possibilities.

SPEAKER_04: Can I jump in here, Alex? I have a question. So being a non-biologist, after this process is done, when we've identified strong candidate molecules through this system, what's the next step towards

SPEAKER_65: actually synthesizing them into like a treatment that we could sort of test out on people?

SPEAKER_34: It is indeed the synthesis of the molecules. So miners are submitting a lot of potentially interesting molecules. There is a heat picking process, that is the name, that you basically go through the best submissions and also consider a few other parameters. So like maybe a molecule could work really well, but you have evidence based on the chemical structure that it could be super toxic. So we're not picking that one for synthesis. So during the heat picking process, we consider a lot of other parameters to pick the ones that really go for synthesis. And then wet lab validation that it is doing what it should be doing. Because every model will have some level of success, but it is not perfect. But it is so much better than testing billions of molecules.

SPEAKER_04: So after the candidate molecule you've discovered, it passes the test, it's not toxic, it's worth trying to, do you send it off to a lab? I mean, what's the actual next step in the process towards getting

SPEAKER_34: these drugs made? Yes. So we operate as what is called a virtual biotech. That means we are running with a very lean team focusing on increasing efficiency and avoiding a lot of overhead for running internal wet labs. So there are companies called contract research organizations that are specialized in synthesizing what you want them to synthesize and then testing those molecules exactly in the assay that you want it tested. That is how the core format that will operate.

SPEAKER_75: And that's something that you guys are taking on inside of your company. So BitTensor to generate

SPEAKER_13: the possible candidates, and then your company then takes those out to a CRO and then runs the test

SPEAKER_00: to see if they are compelling. And then at that point, Pedro, would you sell it to a different biotech company? Would you manufacture it yourself? What's that final step look like?

SPEAKER_41: So there are multiple paths that you can follow. Since we're talking about a process that is very long and

SPEAKER_34: very expensive, it is a game of risking the assets and generating IP. So you're creating IP along the way and risking the potential drug that you are creating. So there are multiple points where you can be interacting with the industry and they might be interested in, for example, licensing of assets at earlier or later stages, depending on how interesting is the target, the indication, in the validation that was generated in order for them to want to acquire or indirect this kind of

SPEAKER_25: novel IP. So if I can add, basically the core motivation was to support our own R&D process, but we specifically or very intentionally built something that is completely flexible, target agnostic so that we could also become a gateway for anybody else's drug development process. So it's very flexible, allows us to enter into any kind of co-development or even offer screening as a service. We have one partner who's based in Shanghai, Yalotain, and thanks to them, we're expanding beyond just small molecules into another therapeutic class called nanobodies. And together with them, we will be validating 50 of the candidates that are coming out of the, out of the, the subnet.

SPEAKER_24: So it's really about maximizing shots on goal.

SPEAKER_13: Okay. Can I ask about that? Because, and this is going to show my ignorance of the actual science at work here, but let's say there's 65 billion possibilities. You guys are looking for serotonin binding say, and people compete and compete and compete and they find the best possible molecule for it. Haven't you solved the problem? Why do you need 65 different candidates? Does this narrow to a a single molecule or is there still going to be a range at the end for different variations in use?

SPEAKER_34: So that is the thing you can go up to a point in terms of prediction. If your asset is going to be tested is going to be efficient and safe, right? In some cases, you are going to need to test it to, to really be sure in many kinds of essays. So the idea is that you can, if you can improve versus what could be random, there are a lot of gains that can be made and you can truly accelerate getting to cures. But as of now, um, I don't think there is any kind of approach that can get to the exact molecule from the, the first moment. It does need refinement along the way.

SPEAKER_80: Well, you gotta start giving it to people and seeing if they get sick, see if they get sick.

SPEAKER_24: Right. But also it's like each person may respond differently. So the goal ultimately is finding something that's safe and effective for broad populations, or at least that's usually how, I mean, there's also a whole future in which we're like delving a little bit more into personalized medicine and we deviate from that. But at the current time, what we're trying to do is find things that are safe and effective. And commonly they, they describe this process of drug development as like a funnel because you're essentially losing options along the way, as you move from cell lines to animals

SPEAKER_25: and eventually humans. Uh, and what we're trying to do is improve the predictive power of step one so that you don't have to go three steps in and figure out that you've been wasting a bunch of time

SPEAKER_13: and money on the wrong thing. And we've all seen biotech companies long go public and then their phase three trials fall apart and then their stock goes to zero and then you, you kind of cash it in,

SPEAKER_04: right? Exactly. Yeah. We're, we're always hearing about like, oh, they're, they're testing a new Alzheimer's drug. We'll see, you know, that's a, it's always this, we're sort of very hypothetical. Uh, speaking of hypotheticals, I, I have one. So, uh, to me, it feels like we're going to use AI to make new drugs. It's going to cure all of these diseases like diabetes and depression and cancer. That's like the cornerstone argument for the AI optimism. Like Americans are very down on AI. We keep telling them like, no, no, no, you don't understand. We're going to, we're going to cure these diseases. What is in your mind? Cause you guys are sort of on the, on the cutting edge of this. What in your mind is the timeline? Like, when are we actually going to start being able to offer people therapies that were developed by AI so we could start to actually make this argument

SPEAKER_72: for real? So we have a few assets developed, uh, using, using AI, uh, in, in late clinical trials

SPEAKER_34: right now. So we could be seeing this like earlier than, than, than we imagined. But the thing is, even though for you to prove that something that takes a long time and a lot of resources is working, especially when they're doing this the first times, it will take some time.

SPEAKER_04: Sure. Yeah. How much is some time? Oh, I don't expect it to be tomorrow. I'm just curious, David, in your mind,

SPEAKER_09: like when, what is this, is this a, you know, in the future we'll, we'll be dead, but our

SPEAKER_94: grandchildren will be fine. Or is this like five years from now?

SPEAKER_34: No, I think, I think we'll be seeing some, some interesting things in the next. Three to five years, uh, considering everything that is being, uh, developed right now. And that is undergoing clinical trials. Wow. It is becoming kind of the standard to go for these kinds of techniques because again, it is truly, truly expensive. Uh, so if you can improve in any way, uh, this is really relevant. You can bet more.

SPEAKER_20: That that's a perfect segue to my next question. Michaela, you mentioned, uh, 10 years and 2.6 billion

SPEAKER_13: dollars to get a drug to market today. How much do you think that can be reduced using Pedro's three to five year timeline? Do you think it could be five years and 1.3 billion? Could it be one year and a hundred million? Like how far can we compress the time and cost to get new drugs to market?

SPEAKER_101: This is where Pedro is going to be like, do not throw a number because then we're not being accurate. So I'm going to, I'm going to refrain from, from falling into the honey pot. You're allowed to

SPEAKER_103: just rip. No one's going to, no one's watching. I'll tell you some things. I'll tell you some

SPEAKER_106: things. Yeah. But also it's like, we need, we need to be rigorous or else like everything falls apart.

SPEAKER_25: Like, do you want to scam? We can scam, but that's not what we're here for. No, no. What I can tell you that's really interesting is a, um, there are many things to accelerate. So we are a decentralized company. We are decentralizing, not just virtual screening, but also the whole R and D process. And that means we think that we can do geographic arbitrage cut through red tape, accelerate timelines and slash costs even further, um, by choosing the right place to get the tests done by working with CROs. And for example, there was like a very exciting news a few years ago from a treaty between the Brazilian health department and visa and the FDA, because the FDA is still kind of the gold standard for drug approval and like the gateway into the largest, like addressable markets. I can't tell you exactly how many years. Um, but we know that I mean, the clinical trials done outside of the U S drastically reduces the budget. Right. And if we can prove, or if we can work in locations and with partners that don't compromise the quality of that testing process, that means maybe we can get the best of both worlds, right?

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SPEAKER_13: Every.io. Does the FDA's work in this case mean that we can do essentially American responsive trials outside of the country? Just to make sure that I'm getting this right. So the FDA would accept results say from Brazil in this case, so you can go faster and more cheaply, but still get that gold

SPEAKER_34: standard seal of approval. Yeah. Yeah. For, for some specific cases, we're, we're seeing that this is where they're moving to. So I think it is really encouraging, very reasonable to imagine that we're going to have this substantially more integrated and more common as we are getting to harder and harder

SPEAKER_07: diseases to tackle. I want to go back to bedtensor because I have one more question I want to ask

SPEAKER_13: about this. When I think about other subnets like shoots, for example, you know, providing compute, it's pretty easy. It's kind of fungible. People could just bring it. When I think about finding the

SPEAKER_20: right molecule from such an enormous set of possibilities, it feels like a different type

SPEAKER_31: of challenge to me. And so I'm curious, how many people out there are potential miners for Metanova? Is that a large group of people? Is this a problem that a lot of folks can attack or is it a relatively

SPEAKER_121: small number of people with a niche set of skills and information?

SPEAKER_46: I think the, the beauty of this system is that, and the way that we have designed this problem,

SPEAKER_25: is that you don't need a background in this field to participate in it. We've like reduced it to a search problem. And that's actually led to very interesting results. So for example, in our second incentive mechanism that's focused on chemical search algorithms, we saw somebody apply an optimization strategy that's never before been used in drug discovery, outperform a well-established industry technique across a number of targets, across a number of challenges. So we're seeing innovation in being able to change the competition format so that it can allow for this like, um, cross-pollination of ideas, right? So going back to this, we need like a hybrid intelligence model to truly automate science. At least that's kind of where we're coming at it. And that means humans, experts and non-experts, agents, and machine learning competitions that are hosted within Bitensor. If we can bring all of these things together, I think we are in the most solid ground to really accelerate the timelines and hopefully get surprises along the way. You know, we, we want to be cautiously optimistic, but at the same time, it's truly fascinating to see how much the world has changed since we launched the subnet, like everything's truly accelerating and we're seeing like amazing new tools being developed that totally change and force us to update our priors on what we believe

SPEAKER_13: can be possible. So Bitensor then has been the right choice for your company to democratize this work that underpins your future commercial prospects. Absolutely. Absolutely. Has it, has it met exceeded expectations? I'm curious about like the enthusiasm you had on day one when you launched the subnet to where we are today better than expected? Better than expected. Yeah, no, I would, I would say

SPEAKER_25: the day we launched the subnet, it was more like, um, so it, it's interesting to have a workforce that a, you don't know, and that is in an adversarial relationship with you, right? Like, um, it's not your usual office job. Like the miners are actually quite unruly. So when you first launch incentive mechanisms, there's this, this warmup period or this learning curve where they're trying to exploit you. They're trying to break your subnet and they're trying to game it. But there's actually, we noticed what we thought, you know, it's, it's actually a feature, not a bug. So one thing that we found is that at the very early stages, what the miners were doing is they were finding the shortest path to a reward. And that meant that they were pointing to us, the areas of low confidence and these state of the art models, because they don't have the same bias, right? And everyone else is training this, like in a private company that's trying to protect their valuation or in a research institution that's trying to publish good results. Our miners do not care. They want to make a token. They want to earn their tokens. And, um, that can, that can be, you know, a little tense and it can be a high stress environment, but ultimately it can add resilience to the system. And if we can use that information to build something that has a higher predictive capacity, then I think we have a competitive edge in technology that we're training in a decentralized way that goes beyond just resource efficiency and tapping a global network of like really cracked engineers that are competing for your token and for creating increasingly more valuable commodities. The true challenge becomes, can you program their behavior and align them in a way that generates valuable inputs?

SPEAKER_131: Yeah. You're, you're productizing the unruliness essentially.

SPEAKER_25: Yeah. And we believe the answer is yes, but it's a very dynamic system. But at the same time, wouldn't it make sense that a dynamic system would be the one that would create the most interesting technology? Like every, we're constantly responding to what they're submitting and being like, actually we need to steer them this way and that way. And, and that means that ultimately we, we have a living mechanism that can learn from, uh, the results from their behavior and you can tweak

SPEAKER_24: the incentives so that they can yield different results. And that's very interesting in and of itself outside of the context of drug discovery.

SPEAKER_20: Do you need like an in-house economist to help manage the stuff that you're describing? I'm not even

SPEAKER_13: really kidding, Michaela. Like, like, to me, it sounds like you could have, you know, just get yourself a Chicago school PhD in econ and set them loose to tune and tweak and improve your economic

SPEAKER_94: incentives on the platform. Oh, I thought you were pitching yourself for this game.

SPEAKER_126: No, no, I, I dropped that back. You're a cohost. I mean, this is actually more like Pedro's I think

SPEAKER_25: best suited to really comment on this because, um, you know, he's constantly tracking submissions and also recently recruited an agent to help, uh, look at some of, some of the work that we've been getting from our miners. And Pedro, do you want to talk a little bit more about, you know, how, how to iterate on the incentives and the submissions? Yeah, no, it is, it is actually very, very interesting

SPEAKER_34: because, um, miners, they have a power of breaking like really well published methods in a way that is insanely fast. And that is actually really, really good because in many cases, you just don't know what are the potential problems in your scoring function, right? Right. With the, with what you're using to, to do the predictions. And so after you know that and you understand how to consider that, um, in the, in the structuring of the challenges, then it really, it really goes well, right? But it is constant iteration. It means we need to be checking what is being submitted to be sure it is aligning with the long-term value generation, right? This is absolutely our responsibility. Um, at the same time, a lot of interesting things comes from that, right? We started with one mechanism and then the second mechanism was also a way for us to increase the competitiveness and kind of have everyone sharing, um, their, their super interesting new approaches to look for molecules. And so you need to be creative on, on how to do that. But when, when you get it right, you can get really, really good things very fast, right? The way we are going to be plugging all of those things with the, the agent economy in this wave of agents is also something very interesting because we are considering what is the best way to also make the outputs, um, that we, that we are creating integrated with agents, because we do envision a lot of agents doing a bunch of applied science in the next few years, right? So we need to be able to integrate all of that. Um, we, we are, we are implementing some very, very interesting agents. There are some that are already in production. And for example, helping me select the molecules that are going for synthesis or helping me check

SPEAKER_72: which ones might already be covered by patents. So not ideal for, for us to explore. Um, this is,

SPEAKER_13: this is like really, really interesting. Yeah. I was thinking about some combination of auto researcher, um, from, uh, Andre Carpathy, my local, uh, OpenClaw setup, and then somehow doing useful work for Tau subnets or BitTentra subnets. Like I, I feel like I should be able to put my agents to work somehow to help with something here. Uh, so I, I wonder in time how much the ratio of humans to agents doing work at the minor level will, uh, will shift, but, uh, long that'll probably take a

SPEAKER_82: couple of years. I think mine's still just writing my tweets for me, but. Oh, well that explains why

SPEAKER_00: they're so bad. URL is metanova-labs.ai. You can take a look at it. Michaela and Pedro, thank you so much for coming on. I appreciate it. And I think I fully understand it. So rock and roll,

SPEAKER_03: you guys are the best. Up next from subnet 93, we've got Tom Blears. He's the co-founder of

SPEAKER_09: BitCast Network. This Alex is a subnet where miners compete over who could generate the most social media views for a brand, a product, an individual, whatever you, whatever your project is. This is a way to crowdsource user generated content about whatever you're working on. Tom, thank you so

SPEAKER_53: much for being here. Thanks guys. Yeah. Really good to be on. AI models can produce stunning, realistic video, but going from an idea to something polished enough to publish that still takes a long time. Even the pros spend half their time managing tools and jumping between models rather than actually creating. But now there's Luma and Luma Agents. Luma's not just an aggregator of third-party models. That's the future of AI. They know the best tools for that task. If you're doing a website, if you're doing a promotional video, while you stay focused on bringing your vision to life. And Luma's just introduced their powerful new model, Uni1, which understands your full context and turns your original idea into a beautiful finished work. Text to image was just a demo, but reasoning to image, that's the real product in the future. You're not just typing in a prompt and walking away. Luma puts you in the director's chair. Luma is going to 10 extra creativity, not try and replace it. To try Luma's Agents for free, go to lumalabs.ai slash twist. That's L-U-M-A-L-A-B-S dot A-I slash twist.

SPEAKER_13: The thing that really grabbed me about BitCast when I was prepping for you coming on was the idea of mining being something so far away from the proof of work, you know, Bitcoin OG setup

SPEAKER_07: that I'm familiar with. So before we dive too deep into this, Tom, can you explain just the economics of BitCast and how the value and tokens flow? You're spot on. We're very unique

SPEAKER_154: in a way on BitTensor and our miners are YouTubers. So people essentially mine crypto with YouTube content and the platform, we can go into a lot more detail on how it all works, but essentially takes care of automating the whole process from creating the briefs to actually measuring attention. Yeah. And the more attention that you can generate, the more rewards that get issued to creators.

SPEAKER_09: This was so after years of working on YouTube, working for creators, working on YouTube channels, the brand deal advertising part of it is such a huge part of the job and it's such a time suck. And it's really kind of like, there's, there's so much uncertainty about it. You get like a little brief from a brand. Here's what we want the video to be, but then you're always sending it in and you're kind of waiting on pins and needles. Are they going to like it? Are they going to reject it? Did I miss it? Did I miss saying the one sentence? And so I, I love the efficiency here of figuring out a model where it's like, here's exactly what we want in your video. And if you do a good job and you hit these three metrics, you, you get a, you get a little towel out of it.

SPEAKER_20: Okay. So then how does the validation then do validators determine if the views that are accrued to content made from briefs on BitCast are high quality? Tom, talk to me

SPEAKER_13: through how you ensure that people aren't just putting out slop and trying to, you know, stick 15 views together a hundred thousand times to make money. Yeah. A hundred percent. So, um,

SPEAKER_154: when we release a brief, uh, essentially, uh, that will say a video needs to talk about point a, b and c as, as an example, and then creators create content that matches that. And they're basically scored on how much watch times, nothing to do with views is to do with how long they can keep people on the videos. So that comes down to, obviously the competition here is to make the most engaging visit videos possible, get the information across as accurately as possible. And the more you can keep people watching, the more that you will be rewarded. Okay. So it's length of time. Okay.

SPEAKER_09: And it's, so it's not like if a video is made by AI versus having people in it, it, you're not really

SPEAKER_04: that concerned about it. As long as the watch time is keeping up with the correct metrics. Yeah. And

SPEAKER_154: interestingly, when, when we launched the subnet at the beginning and Michaela touched on it earlier about people trying different things and, and exploits, et cetera. Um, we did have a lot of people creating AI videos. Um, but it just generally, it doesn't seem to convert very well. Like people like to see people, they like to talk to people. So that has been experimented with, I don't know why the future is going to land us with that. Um, but yeah, it's generally people, um, bringing, bringing stories, bringing information to life and putting their own

SPEAKER_13: created twists on it. Does the system result in a little bit less brand control? Because in the scenario that lawn mentioned, you know, no one wants to submit their video or a piece of content and then wait to hear back. And then maybe you have to do it again. That process is terrible,

SPEAKER_31: but it does leave kind of a lot of the power authority in the brand, Tom, in their, in their hands, versus in bitcast, it feels more like the brief is put out. People react against it. And then they earn

SPEAKER_07: a share of emissions via view time, but the brand in question has less control over,

SPEAKER_171: over what goes on. Does that worry them? It all comes down to the design of the brief,

SPEAKER_154: doesn't it? So the briefs, um, they do go through all the points that you want creators to go into detail on. Um, alongside the brief, there's an information pack. So if the brief says talk about point A, B, and C of how they achieve this, the information pack will give you all of that information. So that's all accurate information provided by the, the brand. Um, but then we're also leaning on the fact that the, these creators are, these are very, very well established creators. They have their own reputations and that they are leaning on and they are, they're, they're providing their opinions. So, so like all of that combined, it results in really, really good, uh, videos. And we've not had any instances where people haven't been happy with the videos. Oh, yes. The whole, I mean, this technology, yeah, this, this technology, um, it really, what it can achieve is, is not really been done before. So we can now get, you know, hundreds, if not thousands of videos created or a push of a button. And if you think about the admin saving on that, it's all validated against the creator, against the brand's messaging. So you can get waves and waves of content at the click of a button. Whereas previously that would have taken weeks, months, maybe even years to get that many videos out. So it's extremely powerful in that regard.

SPEAKER_134: So do you think this is going to lead to an overall increase in brands that wanted to work with

SPEAKER_07: creators and creators that can monetize their videos, or does this more replace the current, the current ecosystem and marketplace of creators and brands that we have?

SPEAKER_154: Well, it's a tool that can be used by brands and marketing agencies. We're not here to completely replace the way that people do things. Um, but it's a very, very effective way of generating a lot of content with reducing all of the admin. Yeah.

SPEAKER_183: And what industries are the best for that, that fit? Because I presume that, you know,

SPEAKER_13: if you're selling $2 million watches, you don't really want a mass audience with a very target niche audience, but there's probably a lot of stuff that does fit into this. So what's the best or what's the current most popular brand type that want to use BitCast?

SPEAKER_154: So currently we're working within crypto. We're working with a lot of BitTensor subnets and we're starting to work with some exchanges as well. Um, where we're still developing the software, developing our AI that analyzes all the videos as they come in. Um, but beyond this, we see sort of AI, general AI and general tech as very, very good niches to go down. So if you've got a new product release, let's say your Microsoft, you've got a new product release, you can get loads of targeted YouTubers to give a breakdown on how that product works. Again, you can sort of apply that to most industries really, but you are right. Like a, a unique, uh, watch, um, really high ticket item might be more aligned to go in with your Leonardo DiCaprio. But if you want to get information out and breakdowns and demos of how products work and you want to get a lot of people to understand you've got a new feature, you've got a new product that you're releasing. Um, yeah, we can release

SPEAKER_188: that into a targeted, uh, pool of creators to bring that story to life. I mean, the one that jumps out

SPEAKER_189: at me immediately is like fast food. Like every time a new fast food restaurant introduces a new item,

SPEAKER_09: you see the wave of people who've obviously been compensated on Instagram to like, go try it out. Like, Hey, I've got the new BK chicken, whatever. And like, I feel like that is the sort of thing that this was just like purely designed for you. You could just think Burger King could just pay for

SPEAKER_17: thousands of people to sort of pretend to enjoy their new chicken sandwich at once.

SPEAKER_191: Whoa, whoa, whoa. You don't like chicken sandwiches? What the hell? Not for Burger King.

SPEAKER_31: Someone grew up fancy. All right. Sorry, Tom, would Burger King be a good fit for BitCast if they wanted to promote their new chicken Whopper?

SPEAKER_154: Probably not right now. Um, but no, we, we, we're keeping it to quite, um, sort of, uh, technological, um, sort of topics at the moment. Um, if you think about what YouTube is really good at is, is sort of putting faces behind names and explaining how things

SPEAKER_170: work. And so we're leaning, we're leaning into those sort of tech versus, um, with where we're sort

SPEAKER_17: of going to market. We're doing it on this week at AI with demos. That's that's YouTube loves a demo.

SPEAKER_196: YouTube does love a demo. What is the health of the creator economy writ large? Uh, there was a way

SPEAKER_13: of startups, some number of years ago, three, four, five, maybe Tom, when the creator economy is going to be this big thing. And everyone thought that there's going to be, you know, 10 times as many creators, but it seemed to kind of end up like the power law, like the top 1% made 80% of the money and things seem to have gotten a little bit quieter. So I'm not as familiar or

SPEAKER_152: aware today of just how strong the creator economy is that BitCast is going to tap into to create the

SPEAKER_154: videos, if that makes sense. So the creative economy is absolutely booming. Um, I know what you're sort of touching on now with the top 1% taking a lot of the revenue, which is a problem that we are solving. I'll come on to a minute, but the, the creator economy is absolutely booming. Um, and the, I mean, I think the projections at the moment is that it's about $250 billion worldwide. Um, it's growing much faster than any other, um, much faster than any other ad medium. So paper clicks and, you know, um, sort of traditional ads that you see, um, in newspapers, et cetera, it's growing much, much faster than any of those. And it's returning on investments much, much higher. Um, this all boils down to trust. You trust the people that you follow and you, uh, lend your attention to, if you will. Um, but on your, on your point about the top 1% taking a lot of the earnings. So that's actually, um, a result of the, the admin behind launching marketing campaigns with, um, creators. So if you were to go and work with, let's say you had the budget and you, you, you want to go and work with 10 creators, the effort to go and get 10 creators to talk about your brand, run through the brief with them, get them to understand what you're doing is, is very onerous. So you're only going to go for the top creators because you bang for the buck on the admin is way better spent. However, what BitCast actually unlocks is, is really powerful when you think about it because we now have removed all of the admin and the return of investment and the actual trust and, um, engagement for smaller creators is much higher than big creators. It feels a lot less commercialized. Now you can activate all of these creators at the push more button. So you're actually managing to tap into that 99% of creators with your same budget. And, um, yeah, it's, it's, it's, it's,

SPEAKER_200: it's a theory and it's a sort of point that we're sort of leaning into.

SPEAKER_13: Yeah. So it democratizes who can participate, who can make money from being a creator. And it probably also evens out the payments a little bit because if it's just done on view time to your earlier point

SPEAKER_20: versus, you know, Mr. Beast probably has a premium on a per impression basis because he's Mr. Beast. Um, so it makes it more fair as well. I, Lon, this seems very democratic to me. I like it.

SPEAKER_146: Yeah. I mean, I think you're, you're hitting the long tail of creators. There's a ton of creators

SPEAKER_09: out there who have, you know, a few thousand, a few tens of thousands of really dedicated fans who will listen to anything they say and who, if they started to give brand messages would probably pay a lot of attention. And if you sort of cast a big enough net over that group, you're still talking about millions and millions and millions of people. I think right now, everything is so consolidated on like, you know, the Mr. Beast of the world, the people who have those incredibly high profiles, a system like this is amazing for like scooping up a whole bunch of those people that are in the middle. You know, they're not, they don't have nobody following them. They have a lot of people. It's just not record breaking competing at the very top of the charts numbers.

SPEAKER_154: Another thing that we can actually do, which is, um, very, very novel. And I think we are the only people in the world that can actually do it is that our system can work with obviously any creator in any place, but any language as well. So we could have a hundred different languages all on the same campaign because it's AI that validates what they're talking about. It, it, it's language agnostic. So, um, as we sort of build this up and introduce much more create, many more creators. Um, and as we grow into other software verticals, like the, the reach that we can do would possibly take multiple teams all speaking different languages or speaking to different creators. So the time savings on the

SPEAKER_13: admin side are yeah. It's the AI verification though, that, that I keep getting a little bit stuck on Tom, because if I'm thinking about a creative brief to me, that's a very human document. Cause you know, humans are trying to talk to other humans through the medium of a creator.

SPEAKER_31: So I, I, I guess, you know, as Mark said, AI checks the videos that are created. How, how strong is that? Are there problems with it? Have you improved it? Is that an easy problem

SPEAKER_154: that I'm overestimating the difficulty of? That's essentially what we've been learning and that's what we've been developing. And you know, that's part of the benefits of being on bit sensor. We've got access to some of the best AI tools in the world. So, um, yeah, we, we are always iterating our process. Um, and the, the video, like at the moment we're sort of checking, you know, 50 to 100 different videos a day against the brief, um, that system needs to scale up and we're getting it to a very, very stable position now where we could scale up way beyond that. Um, but yeah, it's, it's a problem that we're working on and, um, we're getting really, really good results now. Um, and you know, it, it, it has to, it has to scale up to sort of a hundred thousand videos a day, really. Um, and based on what we're seeing at the moment, we're, we're, we're getting close to that.

SPEAKER_13: I want to close with growth because when you guys wrapped up your 2025, you said in your Substack post, uh, that in the last two months of the year, I think you said your, uh, hours, watches up 60%, views were up 56% in the same period. So a lot of growth going into 2026.

SPEAKER_07: How has the new year treated you? How's Q1 gone?

SPEAKER_154: Really, really well. So I think we sort of flipped the switch over new year. So we were developing

SPEAKER_170: a lot last year. We flipped it into sort of scaling up the network. Um, we're now, our creator networks now at 2 million subscribers, uh, and, uh, 50, 50 different YouTube creators.

SPEAKER_154: Um, but we are accelerating about 40, 50% a month on creators and our watch time and views are,

SPEAKER_170: yeah, about 60, 50, 60% month on month at the moment. And that's no sign of slowing down. Um, in fact, we are getting creators every, every day trying to join the network.

SPEAKER_134: Is there enough brand demand inside of your current niche? Cause you're targeting a particular slice

SPEAKER_13: of the market to start. Is there enough brand demand there to support that many creators, or do you need to start opening up to other niches, internet technology sometime soon?

SPEAKER_154: The whole of last year, we were just working with BitTensor subnets. Um, the start of this year, we decided to outreach and we've started working with one of the biggest exchanges in the world. We're now in conversations with many others. And, um, yeah, so we're now starting to attract capital from outside of BitTensor and the demand for this product is, um, yeah,

SPEAKER_170: there's a lot of demand. There's a lot of custom out there just within the crypto industry. Um, obviously move that out beyond that. Um, we've got creators. If we get creators talking about tech, like I mentioned before, uh, or AI, there's, there's thousands of creators and there's

SPEAKER_154: massive, massive budgets there from centralized labs, the biggest companies in the world. Um, and you know, they're, they're going to be very interested in, in what they can achieve with BitCast.

SPEAKER_04: They're coming for Oliver's AI demos. Oliver, look out. Well, when you are ready for, uh, fried chicken demos, that that's when I'll be ready to jump in. That's my expertise. Tom, thank you so much for joining us. BitCast.network is where to go.

SPEAKER_09: If you want to check it out and become a creator, become a miner, become a validator, just learn more about it. Uh, Tom, thank you so much for being here with us. We appreciate it.

SPEAKER_214: Subnet 93, everybody. We love to see it.

SPEAKER_04: We love a numbered subnet, don't we, Alex? We love a numbered subnet.

SPEAKER_20: Well, you know, it's okay. It's, it's, it's, I have a lot of thoughts about this. One, there's a lot of numbers using company names in China. So something that I've become more accustomed to just as time goes along. Yeah. Also, it just strikes me as slightly science fiction to have like a series of numbered subnets in this way. And so, you know, I, I'm a big nerd lawn. And so this actually kind of works

SPEAKER_04: for me. Like I, it does feel a little sci-fi, like a community where everybody is identified by their number there. It's like the prisoner, but for tech, before we jump into our interview

SPEAKER_20: with score lawn, I want to bring up my new favorite polymarket of all time, because

SPEAKER_31: often polymarkets are a little bit binary, you know, like, okay, will Elon Musk tweet five times

SPEAKER_222: before noon or whatever it is specific now they get so specific. You can literally prediction market

SPEAKER_31: like a five minute Bitcoin price changes, for example. But, uh, this one is called AI bubble burst

SPEAKER_00: by it's question. It's a question about when things will turn if they will. And, uh, if you're watching

SPEAKER_31: the video version, you can see there's a 24% chance, according to the sharps over at polymarket,

SPEAKER_20: that'll happen this year. But what's interesting lawn is the terms here. Talk us through it.

SPEAKER_09: We always say you got to look at the rules. And I think this is maybe of all the polymarkets we've ever looked at the most important to look at the rules because just will the bubble burst it's like, well, how, what does that mean? How are we defining it? And here's how they're defining it. So, uh, the AI industry will be considered to have experienced a downturn once at least three of the following events have occurred within 90 days of the timeframe. So by December 31st, 2026, uh, 24% of the people on polymarket are betting that three, not one, but three of the following things will happen. And they are Nvidia's closing stock price is down 50% from its all time high. Uh, the I shares P H L X semiconductor ETF. Uh, that's S O X X. If you're looking at the stock ticker, uh, that closing price is down 40% from its all time high open AI or anthropic declare bankruptcy. Open AI gets acquired, uh, uh, the rental price for an H 100 chip falls to a dollar or lower for five days straight or, uh, uh, some of these major AI hardware suppliers, their stock price goes down 50% from its all time high. That includes, you know, Taiwan, semi-connected TSM, ASML, uh, Broadcom, Supermicro, the, the, the big players in the, in the chip world. So I could see one of those things happening by the end of this year, but for three of those things happening by the end of the year,

SPEAKER_231: it strikes me as pretty remote, more remote than a one in four chance.

SPEAKER_20: Well, that's, well, that's what you and I think, but you know, people can take the other side of that bet if they want, but what I appreciate here about this is if three of these things happen, I think it's actually very fair to say, yes, the AI bubble has burst so much as there was a

SPEAKER_31: bubble to begin with. So like, this is a really well-framed bet. Now, I don't think that we're going to see open AI or anthropic declare bankruptcy because they have like, they would have to go on

SPEAKER_233: like a, a wild, insane, like Vegas spendings for like, like they'd have to buy Guatemala. Yeah. Like, I mean, I don't know. I don't know how that could happen.

SPEAKER_20: So that's not going to happen. Now, Nvidia share price losing 50%. I did the math before the show.

SPEAKER_31: So this is probably a little bit out of date now, but it's off about 15% from its all-time highs, which is not that much given how bubbly that stock has been, but to lose 35% with the 75% gross margins, 2 trillion in spending for this latest CPU, blah, blah, blah, blah. I don't really see it.

SPEAKER_233: We're talking three fiscal quarters. We're not talking, like if it was 10 years from now, you know, anything could happen, but that's just not that long a time period.

SPEAKER_31: Yeah. It's not that long a timeframe. Yeah. Also, if you're curious, an H100 hourly rental today is about 750, according to the data source they're using for this bet. So to have a collapse to $1 would imply an absolute inference and AI compute collapse lawn. Yeah. Not super likely, I don't think, but the OpenAI acquisition thing caught your eye. Tell me why.

SPEAKER_03: I, I, well, I just, I, yeah, I mean, that one feels like a weird rule. Like OpenAI being acquired,

SPEAKER_09: it could be a sign of a, like a collapse. Like it's worth so much less now. Its value has plummeted.

SPEAKER_04: Everybody stopped using ChatGPT. Yahoo's going to pick it up. Like I could, like, I'm kidding about

SPEAKER_246: you, but like, I used to work for Yahoo. So like, you know, but like, uh, but you know, I,

SPEAKER_09: I could envision also a scenario where OpenAI gets acquired and it doesn't necessarily mean AI

SPEAKER_04: is dead and over. It just means like another mega deal happened and two companies. It could be super bullish. Right.

SPEAKER_248: Someone buys it for $3 trillion and then, I mean, then Sam Altman's walking on the moon.

SPEAKER_09: Exactly. So, so that one struck me as, as, as kind of weird, but I don't know, obviously not financial advice, but overall to me, uh, you know, 76% feels like a pretty strong wager at this point.

SPEAKER_04: You probably make up 24 cents on your, on your dollar there.

SPEAKER_20: Yeah. But this is, this is like, this is actually what I want people to use prediction markets for. I know this is my old man yells at cloud thing, but like, this is a hedge. If you have a lot of like exposure to like AI stocks, you could buy the other side of this contract and literally just hedge your, hedge yourself. And that's super duper cool to me. That's, that's the type of thing that I'm most excited about from Polymark.

SPEAKER_09: I also, I feel like there's a lot of culture in like, if you're inside the tech industry, the idea that any of this could happen by the end of this year is like, you sound insane. Everybody would be like, what are you, what are you talking about? Like, we're, it's just like, we were, we were just talking with Metanova, like things are accelerating. That's from the perspective here at this, we could startups, things are moving faster than ever. This stuff is being adopted more than ever. Compute in tokens are more valuable than gold. Literally like, that's how people in the industry feel. So I think this is a lot of like, if you're outside the industry, you're like, I don't even use chat GPT that much. If you're not coding, so you're not using Claude code or you're not on codex or whatever, you're not on open claw for a lot of people, everyday people, I guess it still seems like, well, this could all just go away at any time for Elizabeth's attack. That sounds crazy. It's like, it's too embedded in our everyday lives for it to ever go away.

SPEAKER_249: The average person has asked chat GPT to write them a poem four times at this point in time. And

SPEAKER_13: that is not enough, I think, to get your feet wet enough to learn how fast the water is rising.

SPEAKER_146: Exactly. I think that's, I think that's a big part of what we're seeing here is people outside of the

SPEAKER_82: tech industry wagering on this without really understanding how deep it goes already.

SPEAKER_20: I want them to fire up Claude code and have it build them an app and then run it. Because I think if you, once you discover that that's possible, then AI does feel like the old bicycle for your mind thing. Like, here's the thing that lets you go and do so much more.

SPEAKER_00: Anyways. All right, let's do our third interview because I'm stoked about this one. I did the pre-interview for this lot. And let me tell you, the technology is astounding. So we're going to talk to SCORE, or as we call it, Subnet44 over on BitTensor. Please welcome to the show. It's Max Sebti. And critically long, he is in Paris. And we have had people from around the world on the show today, but it's always nice to see France show up, the home of Mistral, one of the world's leading

SPEAKER_260: AI labs. Max, hey. Yeah, thanks for having me, guys. Definitely. Great to have you.

SPEAKER_00: Happy to be with you today. So let's start with the same question we're asking everybody. What is the goal of the SCORE project? And also how did the economics work?

SPEAKER_262: Yeah, definitely. So the goal of our project is to literally give AI sites. So our subnet is

SPEAKER_263: building a vision skills, let's say, for agents or human users to build vision AI apps. So the whole thing about, you know, via coding and stuff, it's really, you know, you just said it, you know, you can download codecs, for instance, or court codes, and you can start building something and it's, and it's, you know, it's empowering. It's crazy. It's a beautiful experience. And we think that if we want to go a step further and go beyond text-based intelligence, we need to give agents and people

SPEAKER_265: the ability to build with vision as well. I love that. Lon?

SPEAKER_146: I was just going to say, as somebody who has constantly tried to get his agent to watch YouTube

SPEAKER_09: videos and have them rely exclusively on captions or a transcript, I could not agree with this more. Like I want my agent to be able to see the video, not just read what was said.

SPEAKER_269: Yeah. And you just mentioned Polymarket a few seconds ago. Imagine someone being able to stream something like mention markets or even sports markets, and then get an agent to act differently

SPEAKER_263: based on what's happening on screen. This is the type of thing we want to unlock.

SPEAKER_20: Yes. But this brings up the question of what type of vision model are we talking about here? Because there's a lot of things you could watch. So how broad are the vision models that people are

SPEAKER_31: bringing to score via BitTensor or how narrow are they, I suppose?

SPEAKER_276: So it's exactly like your second question about, you know, what's the economy around the subnet?

SPEAKER_263: The economy is pretty simple. If you try to use a state of the art VLM, which is a vision language model, it's actually quite accurate, but it's not built to run in production. It's built to kind of be very accurate on very specific things. And most of the time to access computer vision, you would need to be an expert, you would need to know how to code, you would need to know how to train a model. And you would probably end up with something very accurate, but then way too expensive to run in production anyway. And this is like the biggest problem in vision at the moment. So the way we build our subnet is to actually distill big models into very specific and tiny skills. So then people can use them and can buy them the way they want. And instead of running them on very large GPUs, you mentioned H100s, for instance, they could run it locally on a CPU, which unlocks then a lot of vision use cases that were so far not really profitable or not really interesting from a unit economics perspective. So we are incentivizing our miners because now we all know the different terms around BitTensor. We're incentivizing them to take big models and to cut them into chunks that are working, for instance, for person detection, car detections, yada, yada, yada.

SPEAKER_04: Right. That's what I wanted to get into. Can I ask a sort of a doofus question, jumping off point just before we go further? How does a VLM differ from an LLM just in terms of like

SPEAKER_09: the training process and putting it? I think we all have a pretty good conception of what an LLM is and how you make one. How do you train up a good quality VLM? So most of the time, and that's a bit

SPEAKER_282: of my background as well. I was working for a data annotation company like a few years ago.

SPEAKER_263: You use human annotators to tell you captures, for instance. Captures were the best way to start building VLMs because you're using human beings to tell you if there's a bicycle within the image, right? I could spot a crosswalk like that. Yeah. Like crosswalk now. Okay. So that's pretty much how you're trying to like the first, you just build like a very, you know, a good data set of human annotated pictures. So that was the first step. And that was a big hurdle for us as well, because we wanted this to kind of really scale. We had to find a way to create very precise data sets that could bring also what we call ground truth. So I don't, I don't want to go too much into the details, but basically if you want to validate something in vision, you actually need to know if miners are going to produce quality, you know, data as well. So you need to find a way to automate this process of, you know, annotating content. And that's a bit technical, so I can probably, you know, answer two more questions there. But just to your first point, you need to collect a lot of data. So the

SPEAKER_285: computer would know exactly how a crosswalk looks like, for instance. And this is why even before you

SPEAKER_13: launched the subnet formally, you were already getting partnerships with other companies to collect that data you needed, Max, right? Yeah, it comes from, I mean, there's two reasons

SPEAKER_263: around that. First one is obviously, we needed to test our approach. But now I would say that, you know, the day and age of just needing people to solve captures to get a good model, I mean, are over. What you need to know now is you need to, you need to get, basically you need to train AI models to understand how human beings are solving problems in the real world. So you're not just extracting a, an annotation, like this is a bicycle, this is a crosswalk, this is a horse, you're also codifying the way someone is solving a problem, you know, in their day to day operations. So it goes a bit further. So you want, you want, you want reasoning, you want, you know, you want models to kind of say, okay, so when that happens, well, Bob did this, so it's the right way to solve it.

SPEAKER_134: And then I can kind of remind that. So you mentioned distillation earlier. And then also,

SPEAKER_13: I think you said chunking a model. So it sounds like what you're doing is taking a general VLM and then letting your miners essentially cut it down and heavily tune it. So that way it does one thing. Well, I think you mentioned people detection, but the models that the miners create are problem specific. So if I made a model as a miner for subnet 44, uh, to help identify, let's just say chickens crossing

SPEAKER_20: the road to keep it nice and generic and neutral, then would I win all the emissions from that if my

SPEAKER_274: model stayed the best for that specific task? So for each task, each skills, we've got a winner

SPEAKER_263: takes all, um, okay, mechanism. So we always want to have the best model and to pay all the rewards that we are reallocating to, to, to, to, to task, uh, to one individual model. So we can also run this model through our front end, but I'll show you later on. Yeah. Well, actually that's where I wanted

SPEAKER_31: to get to next because we're talking about the, the BitTensor backend here, but the front end of your company, which is called Manico is a pretty, and I say this with love, standard looking software

SPEAKER_263: service. So the non-standard part of things is, is that our front end is actually, uh, collect, you know, um, collecting all the skills from the subnet. So from the infrastructure and, and putting them together automatically. So it's a full agentic platform that knows exactly what you're trying to achieve just from a chat with you. So you just come with a prompt and from the prompt, this platform is going to build a full computer vision pipeline from fine tuning your model. So this is the chat from fine tuning your model to creating your computer vision pipeline and also your deployment, and you don't have to know anything about computer vision. So as you can see, we can create, we can generate the code for you. We can generate an SDK that you can plug into your, your own app. Um, and also, yeah, and maybe, maybe you have a question. Sorry. I saw you raising your hand.

SPEAKER_16: Yeah. I want to, I want to clarify something for folks out there who may be a little bit less

SPEAKER_13: familiar with this, but when you say fine tuning that is taking the, uh, Alex incorporated information and giving it to the BitTensor selected best model for my task. So that way it has the base intelligence

SPEAKER_276: for the task that I have. And it knows my company's context, right? Yeah, exactly. And in this case, for instance, I was mentioning, um, car detection. I was mentioning, you know, person detection.

SPEAKER_263: This is something one of our partners created, you know, for you using the, the alpha version of our platform. They wanted to know across all their stations, uh, they're running, they're running, um, gas stations. They wanted to know, um, every time something like, like a car or a truck would kind of crash into a pump. Um, and they, and they, and they started building their own custom model. And, and, and within, within a few minutes, they realized that they actually found something that happened, you know, across one station, one of their stations, which is a truck completely smashing the roof of a pump. Um, and I'm, and I'm, and I'm laughing, but in reality, when that happens, um, without things like Manico and agents plugged into subnet 44, um, they would have to wait until someone would realize that something happened, you know, called someone at the station, because most of the time in Europe that those stations are completely, you know, automated. And then the time to action would be in hours, sometimes 24 hours with this system, you can literally get a message

SPEAKER_285: on Slack, WhatsApp, whatever in a few seconds. Yeah. Lon, this brings back our prior points about

SPEAKER_13: agents eventually playing a large role here, but I can absolutely see like, you could have an agent running these for you and then just passing the information to you and kind of getting the go

SPEAKER_189: between from your agent. Like, Hey, a truck just hit our roof. Yeah. Yeah. Yeah. And I mean,

SPEAKER_263: when, so like all businesses, they have kind of a, like a, you know, a timeframe to kind of file a complaint, you know, uh, to their insurance, uh, company. Sure. And if you miss that, it's like a hundred thousand dollars every time a truck, you know, crushes into a roof. So, uh, for them, that's, that's really cool to have access to that type of thing. Uh, but in general, just for you to know, uh, and maybe you guys have other questions, our subnet is built for other agents to also access to those skills and we built it in a way where we have a twin competition. We have the public track, so it's fully open source. So any of your, um, you know, uh, open claw agents would be able to use what miners would produce in open source. And then you have a private track that is going to be launched this week where, you know, Manico is going to be trained on the actual real customers we have

SPEAKER_304: so they can, um, have access to their own, uh, skills. Does that mean that some of the winning

SPEAKER_13: vision models over on the business or competition will actually be different by the time they reach

SPEAKER_263: production on the customer scale? Yeah. But depending on, on which track they are processed through. So if, if they processed on the public track, they're going to be generalized approach to computer vision problems and they're going to be open source. So you would be able to grab them and make them, you know, tweak them the way you want on the private track. They would be tweaked based on the prompts people would, you know, write on Manico. This actually brings up a question

SPEAKER_289: that I had, which is who's paying for the inference because on the use of, of BitTensor competitions

SPEAKER_13: and token emissions to help find the best vision models for a specific task, I'm totally with you. But when Manico serves them to a customer, you guys are handling the inference costs thereof.

SPEAKER_276: Yeah. So we, we kind of fixed that problem. Um, we created an app that people can download

SPEAKER_285: on their computer and the inference is then running on their CPU.

SPEAKER_78: But I'm shocked that it's that efficient. What am I missing?

SPEAKER_09: If you had your open claw agent running in like a Mac mini, it could like run this on its own. It wouldn't need to. Wow. That's amazing.

SPEAKER_263: Yeah. And the reason for that is because what this kind of, um, let's say, you know, decompose approach allows us to move from a model like SAM3, which is like 3.4 gigabytes to a model for the, for the gas station that is like 50 megabytes because it's, because it's an expert

SPEAKER_299: model. So you can run it on CPU. Versus a mixture of experts model. You're just grabbing the one

SPEAKER_317: slice of it. Yeah. Wow. That's neat. I mean, this is what I'm glad I actually read the MOE papers.

SPEAKER_09: Yeah. That feels to me like a very futuristic fit. Like that's what we all need to do all the time.

SPEAKER_04: Like I, I, I rarely need Opus 4.6 for my problems. I need a very specialized model that my agent could use just to help me write tweets. Yeah. A hundred cents. And I think we should

SPEAKER_320: build AI that is smart enough to just use the exact amount of resources you need to use. You're

SPEAKER_203: right. Yeah. That makes so much more sense. Yeah. 50 megabytes is nothing. 50 megabytes is like I sneeze 50 megabytes. I mean, often all the Chrome tab that uses a gigabyte, like that's like a casual

SPEAKER_324: game is more than that. That's why. No, that was, that was one of the, that was,

SPEAKER_263: that was one of the biggest blockers in computer vision as well, because you could, you could come to a client like the gas, you know, the fuel distribution company, but the minute you, you tell them that they have to buy a machine, like a specific machine that they would just install on site. And then they have to like buy like a hedge 100 or something like that. You know, the conversation is over, you know, you can't, you can't talk to, yeah. Yeah. Well,

SPEAKER_16: that's one thing I like about a lot of these budgets or projects, uh, Max that we're talking to is that they,

SPEAKER_13: they take all this really complicated economics and tokenomics, and then it's kind of abstracted,

SPEAKER_20: uh, behind the scenes. It's almost feels like, like, like an API hook that takes away, you know, the difficulty of telephony in the case of Twilio. But in this case, it's just like, do you need a custom tuned VLM? Well, cool. Yeah, we'll do it. You don't need to know that BitTensor is behind it. And so to me, that's just so powerful. I, I, I, I freaking love it, but it implies demand on both sides. Um, clearly you've shown that you can help make better,

SPEAKER_13: uh, vision models for commercial use, but how do you go about finding the customers who want to tap

SPEAKER_152: into it? Traditional business problem, but still applies in this case. Yeah. I mean, and also

SPEAKER_263: traditional, uh, I would say traditional, I mean, not so traditional, but we do believe that, and we're building a community of, um, enthusiasts, I would say at the moment, we, we think that we would get more clients by letting people vibe code with, with the product. So we believe in vision, vibe coding, this is one of our kind of strongest opinion on how our go-to market strategy should look like. Uh, the second thing is we, we do have, and one of my co-founders is a, is an expert in business sales. Um, we also have from within our team, people that are already connected to a lot of, um, large companies. Um, and also I can't talk about this right now, but we also managed to sign a

SPEAKER_331: very, um, big agreement with a large corporation that is going to help us fix our distribution when it

SPEAKER_333: comes to large enterprises. And, uh, is that a technology company that you're partnering with? Give me, give me one little hint. Sprinkle some hints on me. Sort of.

SPEAKER_320: Oh, it's IBM. Okay. Got it. No, no, no, it's not. No, it's not. No, no, no. I don't want to do

SPEAKER_263: like an announcement of an announcement, but basically they, they help a lot of businesses, um, uh, you know, implementing, uh, tech, you know, in the, in their day-to-day operations. I know who it is.

SPEAKER_183: Lawn. Max is in Paris. The company is based out of, uh, out of London, European company. Who is it

SPEAKER_339: going to be? It's going to be SAP. But I bet you, I bet you. I'm not going to say anything.

SPEAKER_343: Stop putting our guests on the spot. It's, it's fun. We're into the show, being a little

SPEAKER_344: loose. We're getting loose because the show's wrapping up. That's what, that's what's happening.

SPEAKER_20: So, so as you bring on more partners, is this kind of the year of, uh, commercial growth for

SPEAKER_285: Manico and score and the subnet? Yeah, definitely. This is how we see our, uh, kind of 2026,

SPEAKER_263: um, you know, year plan rollouts. We, we need to go to market quickly. We need the app to be used by a lot of people. And also we need to show that it's actually bringing more value to the whole

SPEAKER_290: ecosystem. So yeah, this is the, the 2026 is definitely our kind of commercial year for us.

SPEAKER_13: Yeah. Well, between small models, great technology and economics that I understand, I fricking love it.

SPEAKER_20: If you want to learn more about what Max and his team are working on, go to Manico.ai,

SPEAKER_13: M-A-N-A-K-O.ai, or you can go through the various BitTensor world and look up subnet 44. Max, I think you've taught me more than anyone that I've interviewed in the last two months. So thank you

SPEAKER_31: very, very much. I learned a lot. And, uh, we'll have you back on when, uh, you announced that major

SPEAKER_349: partner. Amazing. And it's SAP. Thanks for having me, guys. Thanks, Max. Thanks, man. Cheers.

SPEAKER_31: Lon, this has been a real treat. I really hope people like the BitTensor focus. It's a cool new ecosystem. It's an interesting project. Jason's made a couple of bets and we're doing our standard

SPEAKER_04: learning as we go. I'll tell you who does love it. The BitTensor community. They have been very supportive throughout our exploration of Tau so far. Uh, and you know, we, we, we love lots of enthusiasm,

SPEAKER_20: lots of passion from the Tau. It's gotten to the point now when I'm thinking about asking the spouse if I can take some chunk of cash, buy some Tau and stake it as a learning experiment. Very similar to

SPEAKER_04: how back in like, I, I said it on this very podcast. I don't know, 2012? I'm, I'm, I'm putting, I'm putting one stack into Tau, I think. I think I'm, I'm dipping my, dipping my toe in. I'm wetting my beak

SPEAKER_360: a little bit here. A stack of hundreds, a stack of tens, or a stack of ones? A stack is 10K. A stack is 10K,

SPEAKER_94: Alex. That's, that's, that's how, that's how we talk on the street. That's, that's our, that's our,

SPEAKER_31: I'm from the mean streets of rural Oregon where we didn't have stacks. Um, anyways, Lon, an absolute treat as always, guys. Twist is back on Friday. My name is Alex at Alex on Twitter. He's Lon Harris at Lon's on Twitter. We think you're fantastic. Thanks for hanging out and we'll see you next time. Bye-bye.