This Bittensor Subnet Could Cut Drug Discovery Costs in HALF | E2267

25:38Right. 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 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.

26:19your 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? Yeah. You're productizing the unruliness, essentially. 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. We're constantly responding to what they're submitting and being like, actually, we need to steer them this way and that way. And that means that ultimately, we have a living mechanism that can learn from the results from their behavior, and you can tweak the incentive so that they can yield different results.

27:09And that's very interesting in and of itself outside of the context of drug discovery. Do you need like an in-house economist to help manage the stuff that you're describing? I'm not even really kidding, Michaela. 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 incentives on the platform. Oh, I thought you were pitching yourself for this game. No, no, I dropped out of economics. You're a co-host. I mean, this is actually more like, Pedro's, I think, best suited to really comment on this because, you know, he's constantly tracking submissions and also recently recruited an agent to help look at some of the work that we've been getting from our miners.

27:51And Pedro, do you want to talk a little bit more about, you know, how to iterate on the incentives and the submissions? Yeah, no, it is actually very, very interesting because 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 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.

28:45This is absolutely our responsibility. At the same time, a lot of interesting things come from that. 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 their super interesting new approaches to look for molecules. And so you need to be creative on how to do that. But when you get it right, you can get really, really good things very fast. The way we are going to be plugging all of those things with the agentic 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 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?

29:48So we need to be able to integrate all of that. 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 which ones might already be covered by patents, so not ideal for us to explore. This is really, really interesting. Yeah, I was thinking about some combination of auto-researcher from Andre Carpathy, my local OpenClaw setup, and then somehow doing useful work for Tau subnets or BitTensor subnets. I feel like I should be able to put my agents to work somehow to help with something here.

30:32So I wonder in time how much the ratio of humans to agents doing work at the minor level will shift. But Lon, that'll probably take a couple of years, I think. Mine's still just writing my tweets for me. Oh, well, that explains why they're so bad. The 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. You guys are the best. Up next from Subnet 93, we've got Tom Blears. He's the co-founder of BitCast Network. This, Alex, is a subnet where miners compete over who can generate the most social media views for a brand, a product, an individual, whatever your project is.

31:15This is a way to crowdsource user-generated content about whatever you're working on. Tom, thank you so much for being here. Thanks, guys. 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.

31:53And 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's 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. 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 that I'm familiar with.

32:37So 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 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. 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.

33:27And it's such a time suck. And it's really kind of like 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 saying the one sentence? And so 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 get a little tau out of it. 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?

34:05Tom, talk me through how you ensure that people aren't just putting out slop and trying to stick 15 views together 100 ,000 times to make money. Yeah, 100%. So when we release a brief, essentially, that will say a video needs to talk about point A, B, and C, as an example. And then creators create content that matches that. And they're basically scored on how much watch time. It's nothing to do with views. It's 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 videos possible, get the information across as accurately as possible.

34:43And the more you can keep people watching, the more you will be rewarded. Okay. So it's length of time. Okay. So it's not like if a video is made by AI versus having people in it, you're not really that concerned about it as long as the watch time is keeping up with the correct metrics. Yeah, and interestingly, when we launched the subnet at the beginning, Michaela touched on it earlier about people trying different things and exploits, et cetera. We did have a lot of people creating AI videos, but it just generally doesn't seem to convert very well. People like to see people. They like to talk to people.

35:20So that has been experimented with. I don't know why the future is going to land us with that. But yeah, it's generally people bringing stories, bringing information to life and putting their own creative twists on it. Does the system result in a little bit less brand control? Because in the scenario that Lon mentioned, no one wants to submit their video or a piece of content and then wait to hear back and then maybe have to do it again. That process is terrible, but it does leave kind of a lot of the power authority in the brand, Tom, in their hands. Versus in BitCast, it feels more like the brief is put out, people react against it and then they earn a share of emissions via view time but the brand in question has less control over over what goes on does that worry them the camp it all comes down to the design of the brief 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 that information so that's all accurate information provided by the the brand um but then we're also leaning on the fact that these creators are these are very very well established creators they have their own reputations and they are leaning on and they are they are 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 the 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.

37:14So 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. So do you think this is going to lead to an overall increase in brands that wanted to work with creators and creators that can monetize their videos? Or does this more replace the current ecosystem and marketplace of creators and brands we have? 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. But it's a very, very effective way of generating a lot of content with reducing all of the admin.

37:53Yeah. And what industries are the best for that fit? Because I presume that 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? 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. And we're still developing the software, developing our AI that analyzes all the videos as they come in. But beyond this, we see sort of AI, general ai and general tech is very very good niches to go down so if you've got a new product release let's say you're 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 unique uh watch um really high ticket item might be more aligned to go in with your leonardo dicaprios 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.

39:07Yeah, we can release that into a targeted pool of creators to bring that story to life. I mean, the one that jumps out at me immediately is like fast food. Like every time a new fast food restaurant introduces a new item, 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 could just think burger king could just pay for thousands of people to sort of pretend to enjoy their new chicken sandwich at once whoa whoa whoa you don't like chicken sandwiches what the hell not for burger king someone grew up fancy all right sorry talk would burger king be a good fit for bitcast if they wanted to promote their new chicken whopper probably not right now um But no, we're keeping it to quite sort of technological sort of topics at the moment.

40:01If you think about what YouTube is really good at is sort of putting faces behind names and explaining how things work. And so we're leaning into those sort of tech versus with where we're sort of going to market. We're doing it on This Week in AI with demos. That's YouTube loves a demo. YouTube does love a demo. know what is the health of the creator economy writ large uh there was a wave of startups some number here 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 one percent made 80 of the money and things seem to have gotten a little bit quieter so i'm not as familiar or aware today of just how strong the creator economy is that bitcast is going to tap into to create the videos if that makes sense so the creative economy is absolutely booming um i know what you're sort of touching on there with the top one percent taking a lot of the revenue which is a problem that we are solving i'll come on to a minute but the the creative economy is absolutely booming um and the i mean i think the projections at the moment is that it's about 250 billion dollars worldwide um it's growing much faster than any other ad medium, so paper clicks and traditional ads that you see in newspapers, etc.

41:24It's growing much, much faster than any of those, and its return on investment is much, much higher. This all boils down to trust. You trust the people that you follow and you lend your attention to, if you will. But on your point about the top 1 % taking a lot of the earnings, So that's actually a result of the admin behind launching marketing campaigns with creators. So if you were to go and work with, let's say you had the budget and 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 very onerous.

42:06so you're only going to go for the top creators because your 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 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 of a 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 a theory and it's a sort of point that we're sort of leaning into of 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 versus, you know, Mr.

43:04Beast probably has a premium on a per impression basis because he's Mr. Beast. So it makes it more fair as well. Lon, this seems very democratic to me. I like it. Yeah. I mean, I think you're hitting the long tail of creators. There's a ton of creators 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 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'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 all speaking to different creators so the time savings on the 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 because, you know, humans are trying to talk to other humans through the medium of a creator.

44:51So I guess, you know, as Mark said, AI checks the videos that are created. How strong is that? Are there problems with it? Have you improved it? Is that an easy problem 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 BitTensor. We've got access to some of the best AI tools in the world. So, yeah, we are always iterating our process. And the video, like at the moment, we're sort of checking, you know, 50 to 100 different videos a day against the brief. 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.

45:34But, yeah, it's a problem that we're working on and we're getting really, really good results now. and you know it has to it has to scale up to sort of 100 000 videos a day really and based on what we're seeing at the moment we're getting close to that 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 watch was up 60 percent views were up 56 percent in the same period so a lot of growth going into 2026 how has the new year treated you how's q1 gone really really well so i think we sort of flipped the switch over new year so we were developing a lot last year we flipped it into sort of scaling up the network um we're now our creator network's now at 2 million subscribers and 50 50 different youtube creators um but we are accelerating about 40 50 percent a month on creators and our watch time and views are yeah about 60 50 60 percent 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 is there enough brand demand inside of your current niche because you're targeting a particular slice of the market 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 the whole of last year we were just working with bit tensor 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 bit tensor and the demand for this product is um yeah there's a lot of demand there's a lot of custom out there just within the crypto industry.

47:26Obviously, move that out beyond that. We've got creators. If we get creators talking about tech, like I mentioned before, or AI, there's thousands of creators and there's massive, massive budgets there from centralized labs, the biggest companies in the world. And they're going to be very interested in what they can achieve with BitCast. They're coming for Oliver's AI demos. Oliver, look out. You've got some competition. Well, when you are ready for fried chicken demos, 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 if you want to check it out and become a creator, become a miner, become a validator, just learn more about it.

48:06Tom, thank you so much for being here with us. We appreciate it. Subnet 93, everybody. We love to see it. We love a numbered subnet, don't we, Alex? We love a numbered subnet. Well, you know, it's okay. I have a lot of thoughts about this. One, there's a lot of numbers used in company names in China. So something that I've become more accustomed to just as time goes along. Also, it just strikes me as slightly science fiction to have like a series of number subnets in this way. And so, you know, I'm a big nerd, Lon. And so this actually kind of works for me. It does feel a little sci-fi, like a community where everybody is identified by their number.

48:42It's like the prisoner, but for tech. Before we jump into our interview with Scorelon, I want to bring up my new favorite polymarket of all time because often polymarkets are a little bit binary. You know, like, okay, will Elon Musk tweet five times before noon or whatever it is. They're so specific now. They get so specific. You can literally prediction market like a five-minute Bitcoin price changes, for example. But this one is called AI Bubble Burst Buy. It's a question about when things will turn, if they will. And if you're watching the video version, you can see there's a 24 % chance, according to the Sharps over at Polymarket, that it'll happen this year.

49:22But what's interesting, Lon, is the terms here. Talk us through it. 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, what does that mean? How are we defining it? And here's how they're defining it. So 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 time frame. So by December 31st, 2026, 24 percent of the people on Polymarket are betting that three, not one, but three of the following things will happen.

50:02And they are NVIDIA's closing stock price is down 50 percent from its all time high. The iShares PHLX Semiconductor ETF, that's S-O-X-X if you're looking at the stock ticker, that closing price is down 40 % from its all-time high. OpenAI or Anthropic declare bankruptcy. OpenAI gets acquired. The rental price for an H-100 chip falls to$1 or lower for five days straight. or some of these major AI hardware suppliers, their stock price goes down 50 % from its all-time high. That includes Taiwan, TSM, ASML, Broadcom, Supermicro, the big players 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, it strikes me as pretty remote, more remote than a one-in-four chance.

50:59Well, 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 bubble to begin with. So, like, this is a really well-framed bet. Now, I don't think that we're going to see OpenAI or Anthropic declare bankruptcy because they have – It seems impossible, right? Like they would have to go on like a wild, insane, like Vegas spending spree. They'd have to buy Guatemala. Yeah. Like, I mean, I don't know.

51:33I don't know how that could happen. So that's not going to happen. Now, NVIDIA's share price losing 50%. I did the math before the show, 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. Yeah. But to lose 35 % with the 75 % gross margins,$2 trillion in spending for its latest GPU, blah, blah, blah, blah. So I don't really see it. 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.

52:01Yeah, it's not that long a time frame. 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 it collapse to$1 would imply an absolute inference and AI compute collapse lawn. Yeah. Not super likely, I don't think, but the open air acquisition thing caught your eye. Tell me why. Well, I just, yeah, I mean, that one feels like a weird rule. Like, OpenAI being acquired, it could be a sign of, like, a collapse. Like, it's worth so much less now. Its value has plummeted. Everybody stopped using ChatGPT. Yahoo's going to pick it up.

52:36Like, I could, like, I'm kidding about Yahoo. But, like. I used to work for Yahoo. So, like, you know. But, like, but, you know, I could envision also a scenario where OpenAI gets acquired. And it doesn't necessarily mean AI is dead and over. It just means like another mega deal happened and two companies. It can be super bullish. Right. Someone buys it for three trillion. Then, I mean, Sam Altman's walking on the moon. Exactly. So that one struck me as kind of weird. But I don't know. Obviously, not financial advice. But overall, to me, you know, 76 % feels like a pretty strong wager at this point.

53:10You probably make up 24 cents on your dollar there. Yeah. But this is like, this is actually what I want people to use prediction markets for. And 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. I feel like there's a lot of culture. 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.

53:46Everybody would be like, what are you talking about? It's just like we were just talking with Metanova. Things are accelerating. From the perspective here at this week at startups, things are moving faster than ever. This stuff is being adopted more than ever. Compute 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 ChatGPT 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 OpenClaw. For a lot of people, everyday people, I guess it still seems like, well, this could all just go away at any time.

54:23For Elizabeth and Tech, that sounds crazy. It's like it's too embedded in our everyday lives for it to ever go away. the the average person has asked chachi bt to write them a poem four times at this point in time and that is not enough i think to get your feet let enough to learn how fast the water is exactly i think that's i think that's a big part of what we're seeing here is people outside of the tech industry wagering on this without really understanding how deep it goes already i want them to fire up cloud code and have it build them an app and then run it because i think if you once Once you discover that that's possible, then AI does feel like the old bicycle for your mind thing.

55:00Like, here's a thing that lets you go and do so much more. Anyways, all right, let's do our third interview because I'm stoked about this one. I did the pre-interview for this, Lon, 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 Septi. And critically, Lon, 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 AI labs. Max, hey. Yeah, thanks for having me, guys.

55:32Definitely. Great to have you. 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 do the economics work? Yeah, definitely. So the goal of our project is to literally give AI sites. So our subnet is building 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 code codes, and you can start building something. And it's, you know, it's empowering, it's crazy, it's a beautiful experience.

56:11And we think that if we want to go a step further and go beyond text-based intelligence, we need to give agents and people the ability to build vision as well. I love that. Lon? I was just going to say, as somebody who has constantly tried to get his agent to watch YouTube 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. 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 based on what's happening on screen.

56:48This is the type of thing we want to unlock. 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 bringing to score via BitTensor or how narrow are they, I suppose? So it's exactly like your second question about, you know, what's the economy around the subnet? 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.

57:26And 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.

58:15So 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. 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 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?

58:53So most of the time, and that's a bit of my background as well, I was working for a data annotation company like a few years ago. So 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.

59:40So 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 data as well. So you need to find a way to automate this process of annotating content. And that's a bit technical, so I can probably answer two more questions there. But just to your first point, you need to collect a lot of data so the computer would know exactly how a crosswalk looks like, for instance. And this is why, even before you launched the subnet formally, you were already getting partnerships with other companies to collect that data you needed, Max, right?

1:00:19Yeah, it comes from, I mean, there's two reasons around that. First one is obviously, we needed to test our approach. but now I would say the day and age of just needing people to solve captures to get a good model are over what you need to know now is 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 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.

1:00:59So you want reasoning, 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. And then I can kind of remind that. So you mentioned distillation earlier. And then also, 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 to help identify, let's just say chickens crossing the road to keep it nice and generic and neutral, then would I win all the emissions from that?

1:01:45If my model stayed the best for that specific task. So for each task, each skills, we've got a winner-tech-sol mechanism. So we always want to have the best model and to pay all the rewards that we're allocating to a task to one individual model. So we can also run this model through our front-end, which I'll show you later on. Yeah, well, actually, that's where I wanted to get to next, because we're talking about 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 service. So the non-standard part of things is that our front end is actually collecting all the skills from the subnet, so from the infrastructure, and putting it together automatically.

1:02:34So 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. And also, yeah, and maybe maybe you have a question.

1:03:09Sorry, I saw you're raising your hand. Yeah, I want to clarify something for folks out there who may be a little bit less familiar with this. But when you say fine tuning, that is taking the Alex Incorporated information and giving it to the BitTensor selected best model for my task. So that way it has the base intelligence 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 car detection. I was mentioning, you know, person detection. this is something one of our partners created you know for using the alpha version of our platform they wanted to know across all their stations they're running they're running gas stations they wanted to know every time something like like a car or a truck would kind of crash into a pump 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 laughing but in reality when that happens um without things like manaco 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 on slack whatsapp whatever in a few seconds yeah lon this brings back our prior points about agents eventually playing a large role here but i can absolutely see like you could have an agent running these for you and then passing the information to you and kind of being the go-between from your agent like hey a truck just hit our roof yeah yeah yeah and i mean when so like all businesses they have kind of a like a you know a time frame 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 for them that's that's really cool to have access to that type of thing uh but in general just for 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 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 manaco is going to be trained on the actual real customers we have so they can have access to their own skills.

1:05:56Does that mean that some of the winning vision models over on the BitTensor competition will actually be different by the time they reach production on the customer scale? Yeah, but depending on which track they are processed through. So if they're 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 that I had, which is who's paying for the inference?

1:06:32Because on the use of BitTensor competitions 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. Yeah, so we kind of fixed that problem. We created an app that people can download on their computer, and the inference is then running on their CPU. I'm shocked that it's that efficient. What am I missing? 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. Yeah, and the reason for that is because this kind of, 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 gas station that is like 50 megabytes.

1:07:23Because it's an expert model. So you can run it on CPU. Versus a mixture of experts model. You're just grabbing the one slice of it. Wow, that's neat. This is what I'm glad I actually read the MOE papers. Yeah, that feels to me like a very futuristic fit. Like that's what we all need to do all the time. Like 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, 100%. And I think we should build AI that is smart enough to just use the exact amount of resources you need. You need to use. You're right. Yeah. That makes so much more sense.

1:08:0150 megabytes is nothing. 50 megabytes is like I sneeze 50 megabytes. I mean, often I'll have a Chrome tab that uses a gigabyte. Yeah, it's like a casual game is more than that. That's wild. That was one of the biggest blockers in computer vision as well. Because you could come to a client like the fuel distribution company, but the minute 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 buy a H100 or something like that, the conversation is over. You can't talk to them. it yeah well that's one thing i like about a lot of these budgets or projects uh max that we're talking to is that they they take all this really complicated economics and tokenomics and then it's kind of abstracted uh behind the scenes it almost feels like like it 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.

1:08:58And so to me, that's just so powerful. I freaking love it. But it implies demand on both sides. Clearly, you've shown that you can help make better vision models for commercial use. But how do you go about finding the customers who want to tap into it? Traditional business problem, but still applies in this case. Yeah, I mean, and also traditional, I would say traditional, I mean, not so traditional, but we do believe that and we're building a community of enthusiasts, I would say at the moment. We think that we would get more clients by letting people vibe code with the product. So we believe in vision vibe coding.

1:09:34This is one of our kind of strongest opinion on how our go-to-market strategy should look like. The second thing is we do have, and one of my co-founders is an expert in business sales. We also have within our team, people that are already connected to a lot of large companies. And also I can't talk about this right now, but we also managed to sign a very big agreement with a large corporation that is going to help us fix our distribution when it comes to large enterprises. And is that a technology company that you're partnering with? Give me one little hint. Sprinkle some hints on me. Sort of.

1:10:12Oh, it's IBM. Okay, got it. No, no, it's not. No, it's not. I don't want to do like an announcement of an announcement, but basically they help a lot of businesses, implementing tech in their day-to-day operations. I know who it is. Lawn. Max is in Paris. The company is based out of London. European company. Who is it going to be? It's going to be SAP. But I bet you... No comment. I'm not going to say anything. Stop putting our guests on the spot. It's fun. We're into the show. Being a little loose is good time. We're getting loose because the show is wrapping up. That's what's happening. So as you bring on more partners, is this kind of the year of commercial growth for Manico and Score and the subnet?

1:10:56Yeah, definitely. This is how we see our kind of 2026 year plan rollout. 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 ecosystem. So yeah, 2026 is definitely our kind of commercial year for us. Well, between small models, great technology and economics that I understand, I freaking love it. If you want to learn more about what Max and his team are working on, go to manako.ai, 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.

1:11:36So thank you very, very much. I learned a lot. And we'll have you back on when you announce that major partner. Amazing. And it's SAP. Thanks for having me, guys. Thanks, Max. Thanks, man. Cheers. 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 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. And, you know, we love lots of enthusiasm, lots of passion from the Tau.

1:12:08It'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 how back in like i i said it on this 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 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 alex that's that's that's how that's how we talk on the street that's that's our that's our well i'm from the mean streets of rural oregon where we have stacks.

1:12:41Anyways, 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 Lons on Twitter. We think you're fantastic. Thanks for hanging out and we'll see you next time. Bye-bye.