Decentralized AI as a Public Good

How can AI be a public good and still support a business?

0:00 Wavect, the Web3 software company that understands what you want.

Kevin 0:06 Hi everyone, today I’m going to talk with Michael Heinrich, CEO of 0G Labs. And they’re building basically an AI-native Layer 1 that lets you build high-scale and privacy-preserving AI applications. And it’s a super exciting episode. I definitely would love to hear your feedback on this session. And yeah, without further ado, enjoy the episode.

Kevin 0:30 You have said you want to make AI a public good. From a business standpoint, how do you reconcile that with building a profitable and at the same time venture-backed company?

Michael 0:43 Of course, making AI a public good where everybody can contribute to AI does not come at the expense of building a massive company. Because otherwise a company like Ethereum or Solana wouldn’t be as big as they are, because in a similar way, they’ve basically figured out a way to create a decentralized network that creates a bunch of value for the participants.

Michael 1:05 And those participants then actually truly own their assets. And so it’s not any different here from a 0G standpoint, just because people can provide their compute, their data, and then be able to train, let’s say, new models that can be tokenized, and they can then deliver kind of profits to themselves from it and get actual rewards as the model is being used, doesn’t mean that the platform can’t capture some of the value as well.

Michael 1:31 And so we very much think about a win-win consistently. So naturally, as you use those compute resources, you have to spend on compute, you have to spend on storage, you have to spend on gas fees for the chain. And so those kinds of small fees over time add up to something very significant as more and more people use it.

Michael 1:48 And maybe in the future, it’s not even people that will use the chain, it’ll be AI agents. And so potentially we will not only have billions of agents, but maybe even trillions at some point, who knows. And so from that perspective, they are not at odds with each other.

How does 0G handle privacy and data ownership?

Kevin 2:03 Okay. I mean, when we talk about privacy, data ownership and all these things, it’s usually kind of a spectrum, right? Of course, you guys might be on the [unclear] spectrum, on the privacy and ownership side, but to what extent, right? Because you usually have something like a base model and you have that ownable data, for example, on top of it, like maybe as part of a RAG or however it works.

Kevin 2:32 Could you shine a little bit of a light onto that, like how it works with your company, what you guys do different?

Michael 2:42 So our philosophy is, so we call ourselves kind of a modular stack, if you will. And so our philosophy is really it should be up to the developer or the end user in terms of what they’re comfortable with. So if the end user needs absolute privacy and they’re giving some type of personally identifiable information, let’s say biometrics, DNA data, then, of course, you know, I want that to be completely private and I want that to be stored in a kind of safety vault or wherever you want to store it.

Michael 3:13 And so we enable those options, again, kind of going back to this modular philosophy. If you need deep privacy, then instead of just storing it directly onto our storage network, you can actually use something like Beacon Protocol, which built on top of our platform and keeps everything completely encrypted.

Michael 3:30 And so the other benefit, too, is by being part of the decentralized storage network, for example, we don’t see the data unless we actively try and kind of actually scrape the data. And so there is a kind of privacy by design in that system. And you can, again, choose the spectrum that you want. Same on the, let’s say, training side.

Michael 3:52 You can decide, okay, well, I don’t mind building this model in public. So I want to give everything completely open source. I want to give the training data open source. I want to give the weights and biases completely open source. That’s a design choice for you. But if you have, again, some really sensitive data, I don’t know, maybe it’s like nuclear access codes, and you want to train a model off of that, not saying that’s a good idea, but let’s say that’s the case, then you could use something like [unclear] Labs, which is a decentralized federated learning, where, again, the data itself is kept completely private and nobody can interfere with it, but the model can still be built with other participants.

Michael 4:28 And so we enable for that entire spectrum. So that’s kind of by design.

Which developers is 0G built for?

Kevin 4:33 Okay, interesting. Like whenever I talk with founders about the product, there’s usually something like a user gap in terms of what kind of users they have or customers they have and what kind of users, customers they like to have. Could you share both of those?

Michael 4:54 Yeah, given that we’re still relatively young protocol, we’re about two years old, a lot of our initial users are more familiar with the Web3 and AI intersection. So they tend to be more crypto native by design. So a lot of people building in that space. So it could be, for example, somebody that’s building a DEX that is using AI for rebalancing of LP positions.

Michael 5:20 And so that’s not something that a regular AI developer would use, for example, unless they happen to be a secret degen and LP in their spare time. But so that’s kind of the early adopters, I would say. But really, over time, I think the early majority that we want to target is AI developers that are building high scale AI applications.

Michael 5:44 Now, there’s a few components that need to be part of that. One is the platform needs to be absolutely scalable. So there shouldn’t be any performance trade-off or very little for that matter. And it needs to add additional, like what at Y Combinator was known as, like, a 10x difference. And that needs to come in the form of cost, in the forms of certain superpowers like verifiability, as well as kind of AI alignment and safety features built in.

Michael 6:09 And so given our current stage of the journey, we’re not quite there yet where we can actually reach out to that early majority and say, hey, we have a killer use case for you. It’s super easy to use. You can plug it into whatever kind of workflow that you’re building. But we’re getting pretty close, I would say.

Michael 6:26 Give it another six months and I think we can start with the infrastructure that we’ve built to get there. Whether that’s being able to train very large scale models. For example, we’ve done one research paper recently where we’ve been able for the first time to train a 107 billion parameter model completely decentralized.

Michael 6:45 So we’re starting to get close to parity effectively. And eventually we also need a Web2 wrapper where right now you can use, I don’t know, even like Lovable or Replit or any of these systems and they find all the APIs for you to build the system for you. We need to get to a stage where it’s as easy as a simple API call and you can use all of 0G’s infrastructure.

Michael 7:09 And so again, I think it’ll take another six, maybe maximum 12 months for that maturity.

How does 0G create durable network effects?

Kevin 7:15 Okay, so developer accessibility also being a main building block or roadblock across the road, basically. Okay, got it. I mean, in general, right, like targeting developers is usually a great idea for such applications, right, or ecosystems because they are building products and those products then create basically an echo effect or like exponential growth hack, basically.

Michael 7:42 Yeah, it’s kind of the way I look at it, it’s sort of like network effects effectively. So you need to have some basic liquidity layer so that developers are attracted to build on your chain. Once they come and then they build a great application and users come, that bring more liquidity and it creates this kind of positive flywheel effectively.

Kevin 7:59 Yeah, 100%. I mean, whenever we talk about networks, chains, right, there are basically two kinds of network effects. Strong network effects that basically have something like a lock-in, and we have these weaker network effects, which typically EVM compatible networks fall into. Because it’s very easy to change networks by just replacing the RPC URL.

Kevin 8:27 Like how do you try to tackle that knowing that it’s a really, really challenging and somewhat unsolved problem?

Michael 8:37 It’s a good point around those network effects. And I think there’s also kind of single-sided and dual-sided network effects and they kind of determine the strength as well. But to answer the question simply, I would say it’s not just a layer one that we’re building, it’s an entire kind of approach to AI.

Michael 8:54 And just simply deploying on a layer one isn’t sufficient for building mass scale AI applications. You need, again, a really high performance storage network. You need a compute network that’s high performance. And so today, given what we’ve been able to accomplish, you can’t find that anywhere else.

Michael 9:11 And so you can use each individual component, but similar to using an iPhone, it’s great using it by itself. But if you’re in the entire ecosystem, everything kind of just works together. Like my MacBook pairs easily with my iPhone, and then, you know, I can do easy transmissions via AirDrop to other iPhones.

Michael 9:29 And so that’s kind of the type of network effect that we’re going for by building an entire ecosystem plus a stack that’s not just a layer one.

Kevin 9:37 Okay. Okay, cool. I want to do something like a role play really quick here. Let’s imagine I’m a CIO, like chief information officer or data scientist or whatever, and I’m evaluating like if I should go with with your chain, with your ecosystem, like decentralized AI model basically, versus a centralized one, or or even just go with cloud providers and train my own one there.

Why choose decentralized AI over a cloud provider?

Kevin 10:08 Like what should be my, let’s say, motivation to go with your approach?

Michael 10:18 The key question is, where do you want the locus of trust to lie? Do you want to trust an entity to do everything kind of for you? Or do you want to be able to be on a system where you can verify the trust? And what I mean by verifying the trust is, do you care about the type of values that the model exhibits?

Michael 10:38 Do you care about the type of training data that you give the model? Do you need your data to be private, for example? Like, do you have sensitive customer data? If you’re using something like OpenAI, you’re rest assured, anytime it goes onto their server, they will look at the data. It’s very hard to completely firewall that off, for example.

Michael 10:58 So there’s always a security concern. And that’s why a lot of major companies have actually forbidden usage of OpenAI, particularly for that, in the offices. And so is privacy an important concern? And then what about cost and efficiency? Some of these AI systems are very expensive on a per seat basis, especially if you’re using some of the more advanced features like 200 a month and so on.

Michael 11:21 Is that something that your enterprise can afford or do you want a solution that’s 90% less so that you can train whatever model you want off of the data that you do have? And as part of that, do you need to invest in your own infrastructure or do you actually want to use shared infrastructure that still keeps everything completely private and secure, but comes at a significant cost differential?

Michael 11:45 No CapEx and up to 95% less OpEx for you. So those are the types of questions I would ask. And then if there’s other kind of safety and security concerns, like if the model misbehaves, what is your recourse? Do you want to have certain guardrails in place so that if something negative happens, you can quickly remove resources from that model?

Michael 12:06 Or again, do you trust a single entity to kind of do that for you?

Kevin 12:12 Yeah, hopefully we won’t ever see those AIs actually go rogue, right? Like with all these safety, let’s say surveys or basically scenarios that they have played through and continue to play through, right? Let’s go a little bit more Web3 native, right? Because you mentioned your first users actually are more crypto native.

How does 0G attract developers and projects?

Kevin 12:39 Like it’s no secret that alt L1s, L2s, like basically any ecosystem usually needs tons of effort to actually go from zero to one, right? It’s an ecosystem that’s just part of the game. Like what’s your go-to-market strategy here? Because we have heard that distribution is the newest bottleneck, right?

Kevin 13:08 That’s also common knowledge. What’s your play here, aside from podcasts, of course?

Michael 13:16 It’s only podcasts and events all the time. That’s how we roll.

13:22 That’s it.

Michael 13:23 No, I’d say there’s three aspects at play. Incentives, business development, and then kind of the marketing key pieces to that. And as part of the marketing, I kind of will go backwards. It’s very important to have a differentiated position. So we’re very clear. We’re an AI chain. We’re built for extreme use cases, build whatever AI application that you want.

Michael 13:45 And it just so happens that we believe over time, not only will AI agents replace kind of most transactions, but also that most companies will end up becoming AI companies over time. And so it’s really a bet to the longer term future. And by being differentiated, we are able to attract the builders that really care about that and then want to launch as a result because they see, oh, there’s this ecosystem of other AI builders.

Michael 14:08 I can ask them some really interesting things. Versus going to another chain that’s maybe more general and where I won’t get the same answers, or where I will have a lot of failed transactions, which is not possible for my application because I need to have 100% guarantees that if this particular decentralized inference call comes in, that it actually goes through as well, because my application is just really mission critical.

Michael 14:32 And so the marketing is really much around that. It’s setting the brand and it’s consistently repeating the brand, which is a promise, and then being able to attract kind of the right builders by being on podcasts, events and, you know, great blogs and social, uh, appearances and, you know, kind of influencer engagement and all of that.

Michael 14:52 So that, of course, has to be there. Then the business development side is an interesting one because that’s where we can also be very strategic. So our head of business development comes from Chainlink. He was one of their best kind of sales people there, has a good grasp on both the institutional end and on the bit more degen end, if you will.

Michael 15:10 And so he basically knows everybody in the space, and as a result, for example, we’re going to make a big announcement on day one of mainnet. We’ll have 100 plus launch partners and some super well-known names. We’ll probably have one of the most impressive lists of wallets, for example, on day one out of any layer one to have launched.

Michael 15:29 And so that’s also a big kind of muscle and advantage that we have. And just being really strategic on who we want to attract and being able to reach them very, very quickly. And in fact, actually, we get a lot of inbound because we got the marketing pieces right as well, and get inbound not only from projects, but also from partners that then want to introduce us to other projects, whether it’s structuring liquidity campaign, right, or being able to have kind of agents that care about who’s on the chain, whether it’s, you know, a new travel agent that’s building an AI agent and wants to be associated with 0G to a company that’s doing biometrics like Dormint AI and giving you recommendations, and only the best place

Michael 16:18 to build is 0G because we have the full stack AI experience. So it gives you a bit of a sense of the BD side and then incentives naturally because every ecosystem needs to be seeded in some way. And so we have an 88.8 million ecosystem program, we call it, and the two key aspects of that, actually three key aspects, are kind of full lifecycle of developer, anything from deep hackathons that we’ve been doing a lot in the APAC region kind of recently to then being really hands-on through accelerators.

Michael 16:49 We’ve had three accelerator batches. First accelerator, we had CARV that came out of it, which at the height was about a billion-dollar project. Second accelerator, we had PlaysOut launch, which I think at the height around now is about a $300 million project that launched on Binance. And the third accelerator we just completed.

Michael 17:07 So let’s, let’s hope there’ll be a really great success story as part of that too. And then finally we have a Guild on 0G program, which is all about more of a bespoke, uh, solution. So if you’re a later stage builder and you just need help with how do I deal with these market makers? Well, we have two people who are ex-Jump Crypto on our team that can help you guide through that, and another guy that’s, you know, worked with Merrill Lynch for 12 years and then been in the crypto space for the last three and runs a market maker.

Michael 17:35 So like we can plug in the right resources for you as part of that program. And so we believe in this kind of, when you come to 0G, you’re gaining a competitive advantage. It has this kind of agency model, and that’s another big differentiating factor. Like I even do office hours for many founders because I just believe so deeply in building ecosystems.

Michael 17:56 They need the personal touch. Maybe they need just this one introduction to an investor that they really want to close. And so, yeah, that’s the incentive layer is absolutely part of that. So probably longer answer than you were looking for.

Kevin 18:09 No, no, I love that. Thanks. I want to have depth, right? Like not that shallow superficial stuff. So that’s all great. Sounds to me like you basically are quite well, like figured out the user acquisition stuff. What a lot of projects a little bit neglect is the user retention part. Like, what’s your strategy or approach here?

How does 0G turn developer acquisition into retention?

Michael 18:35 So it’s a little bit tricky when we’re just in testnet. So once we’re in mainnet, it’s a lot easier to kind of see that. But what we’ve put in place, at least at the moment, is we have a customer experience layer so that we have somebody who’s consistently following up and trying to understand where are you in this journey right now?

Michael 18:54 Where are you from a project standpoint, but where are you from a standpoint of utilizing the 0G ecosystem resources, as well as the different offerings that we have? And are there ways where we can enable your business model or your user growth with the different resources that we have? And so we’ve heavily invested in this kind of customer experience layer for that retention piece.

Michael 19:15 And so the way I think about it, it’s kind of like the get, keep, grow aspects. And so get is all about kind of the business development aspects or the marketing aspects, as I mentioned. Keep is making sure that the platform consistently performs and that the infrastructure actually works, and then grow is all about the customer experience layer effectively.

Kevin 19:36 Okay. Okay, so it’s basically aiming at, I wouldn’t call it a PLG approach on that sense, right? Because you do have BD and marketing and everything, but from a retention side you basically just try to build a really core, strong, user-friendly ecosystem and basically support founders enough or well enough so that they actually attract users, right?

Kevin 20:05 So that’s kind of the main plan here. Okay.

Michael 20:07 Exactly. And the, the other piece to that too is we have to listen to the people that are building our chain, and so we have to consistently innovate as well. If we’re just stagnant, I think that’s when you start seeing attrition as well.

How does 0G avoid token-incentive traps?

Kevin 20:20 Yeah. Yeah. I mean, if you don’t grow, you shrink. That, that’s the...

20:25 Yes.

Kevin 20:25 That’s the harsh truth, right, in business. Um, I mean, you, you’re just on testnet, right? But a lot of projects also use tokens as a means of user acquisition, right, with airdrops and all these other mechanics that we have. How do you avoid falling into that trap, or what’s your, well, because it’s generally seen as something negative, right?

Kevin 20:52 People see most of these tokens as VC tokens nowadays. What’s your approach and perspective on that?

Michael 20:59 We try not to give grants, for example. We try to look at it from an investment standpoint. And so the key thing that we want to see is not just giving a ton of incentive for anybody that’s coming to the chain, but for them to feel like this is the right home for me and then support them through that journey.

Michael 21:25 So we want to make sure it’s not just somebody that’s coming here because like, oh, I’ve gotten three grants from other chains and I’m going to get another one from 0G. It’s more about, hey, this has the right infrastructure that I need to be successful as a founder. And then once that’s there, that’s when we put our full support behind those founders.

Michael 21:44 And so that’s how we think about it. And so instead of giving a lot of upfront grants, we would actually do more like retroactive type of airdrops and have a more milestone kind of based approach, because then we get to know each other, and then to see how you as a founder kind of interact with 0G, what you contribute to the community, and then, you know, step by step be behind you as you go through that experience.

Michael 22:09 And I think that’s how you build a really more like a cult, less than a, not just a chain. So we think about it more from that perspective. So in my previous company, I went through Y Combinator and Alexis Ohanian was one of our investors. And he just ingrained this idea of like, he’s the co-founder of Reddit.

Michael 22:29 And so he ingrained this idea in me of like, build a cult, build a cult, like that’s what you need. You build cult through great experiences but also through kind of this personal touch and just obsession with the kind of developers and end users.

Kevin 22:44 I mean, that’s what you need nowadays, right? Let’s be honest, right? There’s just so much competition, right? Software is, it becomes easier to some extent, right, to build, but in the end it’s all about who is using it and why, right? And a lot of the why is, as you said, coming down to community.

Why is great engineering not enough?

Kevin 23:08 And of course, it needs to be a great product as well, right? So reliability, privacy, and all the things you mentioned.

Michael 23:16 Yeah, it’s not to be underestimated to build great technology. I mean, it’s very hard. Like getting, for example, getting to 11,000 TPS per second per shard and then being infinitely scalable. I mean, that’s a really hard engineering problem. There’s maybe a thousand people in the world that know how to solve that at kind of block times of less than 400 milliseconds.

Michael 23:36 I mean, that’s damn hard. And so not to be underestimated at all, but if you can’t communicate it and you can’t explain why you exist and why what we’re building matters, then it just ends up being technology that’s unused.

Kevin 23:50 100% right. That’s something a lot of founders actually get wrong, right? They build something and like just the TPS part, right? Like, and I totally agree, that’s super challenging. But for example, a small, low volume enterprise application might not need whatever thousand TPS, right, for example.

Kevin 24:13 Although you have that noisy neighbor problem, of course. So if there are other games that influence you, of course, there are tons of things to look at. So from a technical perspective, it’s super challenging, right, like as you said. Let’s look at it from a developer angle. What do you think is the most challenging aspect?

What makes Web3 development slow?

Kevin 24:39 Like when we, for example, are a software agency, right? When we work with a new network, the main challenge oftentimes is like what are the unique tweaks that you have to do in order to build safe and also native applications to that network using the tools and the tech available? Like what’s the main challenge?

Michael 25:03 I think in Web3 in general, it’s the startup time. Let’s say if you come from a Web2 environment and you’re used to, hey, I’ve, you know, used a front-end framework and then on the back end maybe I use like LangChain, a bunch of other things, and now I have to figure out, you know, it’s, it’s maybe a few services that you use, and maybe you have Vercel to like deploy very quickly.

Michael 25:26 On the Web3 side, you’re like, okay, well, where do I go for inference, and then where do I go for a security layer, and then how do I do this like RPC call, and when do I use a smart contract versus centralized kind of? So there’s just all this like startup cost basically. So how do I ensure that I can get from idea to production as quickly as possible?

Michael 25:49 And so we want to decrease that latency as much as possible. So that’s what we generally think about. How do we make the startup costs so small that any kind of Web2 developer will want to build on chain and gain the benefits of it? So I think that’s a particularly challenging problem because there’s just a lot of new concepts that come into play.

Michael 26:12 And so I don’t think we’re quite where we want to be. Ideally, I’d just have a, you know, you start up a Docker container, you have an entire environment and you can literally just deploy a smart contract, [unclear] AI agent onto our chain. I mean, that would be amazing. So I think that’s a key thing that we’re looking at.

Michael 26:33 The other thing is to also ensure that the developers have enough knowledge to understand why things like verifiability matter. And so there’s an education component, too, that people have to kind of take into account when they look through our developer documentations. Because it is quite different.

Michael 26:50 Like if you’re used to trusting an entity for your AI, then verifiability is not that important to you. But to me, it needs to be a first-class citizen. And that’s why decentralized AI exists in the first place. So that entire ethos, there’s a whole education component. So I think there’s a bit of, again, startup cost involved with that.

Michael 27:12 So I think those are the two key things that we think about. And then the third thing is, because there’s a lot of noise in Web3, it’s easy to fall into this trap of like, oh, I’m just going to go to all conferences and be active on Twitter and forget about product market fit. Like you actually still need to build something that people use and that people love.

Michael 27:30 And I think there’s sometimes too much noise in the space and helping guide kind of founders through that is another challenge sometimes.

When does product-market fit matter more than scaling?

Kevin 27:40 Okay, I love that. I mean, sounds a little bit like you personally see the main challenge mostly on the product management side, if I get that right, which means like, of course, our dev velocity, like time to value, like how fast they can move, right, like that plug and play factor basically.

28:00 Yes.

Kevin 28:01 Um, but at the same time guiding them a little bit on the business side as well in order to get self-sufficient entrepreneurs onto your network. So that’s basically like that incubation kind of approach. Do I get that right? Okay.

Michael 28:19 Yeah, I would say that because other people might argue with me and say like, well, we’re not there from an infrastructure side and, you know, the technology is not ready and so on. Sure, but it’s going to be in 12 months. I mean, 11,000 TPS, you can build a medium-sized Web2 application in a shard.

Michael 28:35 Can you build a, you know, WhatsApp in that environment with a billion plus users? No, but at 100,000 TPS, you could. And so in a year, we’ll effectively solve that. So at some point, it’s no longer about kind of scaling challenges. It’s much more about like, let’s build things that actually matter.

Michael 28:54 And so, yeah, Web3 still has a bit of this kind of product market fit aspect where certain things definitely product market fit, like speculation, trading, stablecoins now more so. And hopefully over time, it’ll be more and more use cases like medical history data and building actual AI models as a community so that we can all prosper together, building smaller expert models that we can string together that can enhance our life, maybe even aligning robotics so that it’s actually safe for human kind of consumption.

Michael 29:31 I get very worried when massive companies try to roll out robotics without safety switches and alignment kind of practices. And so, yeah, those are the types of problems that I want people to solve.

Which decentralized AI use cases matter now and next?

Kevin 29:44 Love to hear that. Cool. Maybe one last question. What do you think is one of the best use cases, like actual use cases for your infrastructure in the short run to make it easier?

Michael 30:02 Short run. Okay. I mean, short run, because we’re still in a Web3 environment, it’s definitely going to be kind of trading liquidity and all of those aspects. It’s basically things like, how do I make complex operations simple so that I can reap the benefit from it? So let’s say you’re a market neutral hedge fund and you’ve discovered an opportunity for like a great looping strategy.

Michael 30:27 It requires a lot of human involvement. You need to, let’s say you’re doing a play where you stake an asset, you get a liquid staking token in return, you then borrow USDC against it, and then you loop it multiple times. Or you hold a token that’s actually a return of a market neutral hedge fund or a long basis trade, and you loop that multiple times.

Michael 30:50 And it requires a lot of visibility consistently because if your borrow rate is higher than your return rate, then you need to unwind that trade very quickly and do a different trade. So why can’t an AI agent consistently monitor that for me and find the highest yield and then find the best opportunities consistently?

Michael 31:07 So I see aspects like that short term be very, very useful because they can kind of target and help companies manage their treasury more easily, for example, or generate the type of return that these kind of market neutral hedge funds want and then pass it on to their customers effectively. So really short term, I see a lot of kind of strong applications like that from building with us.

Michael 31:32 Medium term, what I love to see is exactly what I mentioned, like the community coming together and building expert models that outperform state-of-the-art models. Let’s say I know 20 people that are expert Solidity coders. We all get together, we share our code, we contribute compute. We go through the 0G launchpad, we tokenize that particular model.

Michael 31:56 Model outperforms on that particular benchmark, is used in production, I get paid for my contributions through the token. Like that’s what I want to see. That’s when decentralized AI will be on the map. And that’s when centralized AI will take us very seriously as a result. And when we then also have infinite compute, so to speak, I’ll give you an example on it.

Michael 32:19 So a few days ago, I did a calculation. Basically, Grok was trained with 200,000 H200s. Now they each do about 60 teraflops or so. Your iPhone 16 does about two teraflops. So it’s about a 30x difference. So you could say that Grok was trained with 6 million iPhones. Imagine if you had 6 billion iPhones at your disposal that you could lend compute to you.

Michael 32:42 What can you build with that? And it’s distributed, so you don’t need to have all of this like massive power in one centralized location. It could be across the world as you train these different models. You’re basically giving power back to the end user and smaller businesses and other companies that want to participate in AI and not just be disrupted by it.

Michael 33:02 And so that’s what I want to see kind of in the medium term, these types of applications, because that’s what will create our mission of making AI public good and really abundance for everyone and not just for a select few. And then you take kind of universal basic income checks, for example.

Where can people follow Michael Heinrich and 0G?

Kevin 33:20 Okay, super interesting. Hey, thanks, Michael. Really a lot. Super interesting. And yeah, I mean, anything left to say on your end? Any call to action for our audience?

Michael 33:36 Well, definitely follow me on X. I’m just Michael H underscore 0G. Always love interacting with our community, hearing great ideas, discussing kind of new business ideas and so on. So please follow me there. And then all resources around 0G are just on our website. It’s very well organized. All of our ecosystem programs are on there.

Michael 33:55 0G dot AI. So it’s the numeral 0G, just like Michael H underscore 0G is also the numeral. So very easy to find us and follow us and engage with us.

Kevin 34:06 Cool stuff. Okay, thanks again, Michael. And thanks everyone for watching.

Michael 34:11 Thank you.

34:12 Wavect, the Web3 software company that understands what you want.