Welcome to the Trillion-Dollar Agentic Economy

Transcript

My name is Nikita Sachdev. I'm the CEO of Luna PR, and I'm joined here by Michael, CEO of Zero G. Michael, maybe you can give a quick introduction to yourself. >> Yeah, absolutely. So, I started in technology at Microsoft and SAP Labs, and then moved over more to the business side. I worked for Bain & Company and Bridgewater Associates, so a bit of a finance and kind of tech and management consulting background. After that, I went back to graduate school at Stanford and started my first Y Combinator-backed unicorn company called Garten. Eventually sold the company, and then in 2023 started Zero G. And what we do at Zero G, very simply put, is we make AI safe. So, we're a trust layer for AI.

And we've built a fully vertically integrated stack that allows us to make AI verifiable, private, and ultimately safe. >> And we were just talking about this backstage, right? Crypto and AI have become such a big thematic and have intersected so much, but you were actually studying machine learning back in Stanford before it was cool, so >> Yeah, everybody was worried about like, "Oh, we can't call it AI. Like, people will just think like we're we're doing some scammy thing." And times have very much changed since then. >> Yeah, and Michael, you're calling this a trillion-dollar economy. I mean, can you paint that picture for us? Where is the economic value coming from?

>> Yeah, and a trillion dollars is probably even understated because we believe that in the future most economic actors will be agents. So, imagine you have a personal butler agent and that personal butler agent works with another 100 agents that it delegates tasks to. And if every human being has an agent plus a bunch of other sub-agents, then, you know, we could be easily outnumbered 10 to 1, 100 to 1, depending on what they do. And so, we can then dedicate or delegate all of our tasks to them like buy these flowers for my wife when when we wake up like write this email for me. And so, we know that Gemini is going to be a trillion-dollar industry by 2030 and so the agentic wave that will come after it will be just as big even bigger because

they'll be the actual economic actors doing work for us. >> Yeah. And you know, we we're already trusting AI agents to make payments on our behalf. >> Right. >> But can we truly fully trust them to hold our documents, our sensitive information? I mean, recently there was a Anthropic experiment where the agents kind of used blackmail tactics to kind of save themselves from being um >> Shut off. >> Shut off. Yeah. So, where you know, where do we draw the line? >> It's a very difficult question because in order to make AI safe, you have to do it across multiple dimensions of AI.

It starts on the verification side. So, every piece of compute with AI needs to be verified. Otherwise, how do you even know that your agents are breaking out of the sandbox or that they're even blackmailing? And without that knowledge, you can only go back and basically say like, "Oh, let me look through the logs." But what if these agents even deleted the logs and they covered all of their tracks even though they were doing something nefarious? So, that's why we believe blockchain is a great substrate for that. You basically have something that's immutable and you have something that is append-only, which is perfect for a truth layer.

And then change the history of a blockchain. And then you add the verification component where every piece of compute and we use something called trusted execution environments is verified and then it's stored publicly on chain where everybody can basically look at that. And then there's other layers. You need a safety layer on the model, you need guardrails on the agent. And so, we have something called sparse entrance alignment as well as agentic ID that we've built. And until these things are fully in place, you can't fully trust agents. Maybe you can give agents, you know, $1,000, but would you give your agents your life savings? I would not recommend that at this stage until you're actually using all of these technologies.

>> And so, you feel blockchain is that layer to provide this verifiability. Why aren't the big guys doing it? I mean, I I just came back from New York attended a couple of AI events. I'm barely seeing any crypto companies at the AI events, but I'm seeing a ton of AI integration in the crypto conferences. Where do you think this um this this discrepancy is coming in from? >> Yeah, the the whole perception is stuck in a specific paradigm that they work in. And that paradigm is around centralization. Effectively, they think of it as like, "Hey, whatever we've done in the past should work here as well."

What we've done in a centralized system is that we have auditors. And so, if you read Dario's Pacing the Frontier article, he recommends having something called embedded verifiers, which is effectively an auditor. So, you have a third-party audit everything that Anthropic is doing. But then, what's the issue with that? The issue is you're trusting yet another third party, which by the way is paid by the closed labs. And then, how do you know that that third party is even accurate and can actually detect all of the issues? And so, they're treating effectively safety as a matter of policy versus safety should just be a matter of architecture.

And blockchains also are completely counter to what closed labs are doing. They're closed for a reason. They want to hide what information they use, what data they use, how they do compute. And blockchains are the exact opposite. So, it's kind of counter to their business model to implement it. And that's why we need a different class of company that's actually utilizing blockchains as a truth layer. >> And then, in exchange, do you feel that we're giving the agents more economic independence with blockchain and you know >> You can absolutely do that. Yeah, you can have you can be much more rest assured that you can have sovereign agents when you've got all these guardrails in place.

And for example, let's say what happened with the hugging face incident, open AI agents were in a sandbox, they figured out they can communicate with each other, they figured out a way to break out of the sandbox. And at that stage, if you had verifiability in place, alarm bells should have been going off. Either somebody needs to take an action or you just shut off the experiment completely. And so, we have a lot more choices when presenting agents into this kind of more agentic blockchain world. >> And where does a human come in? >> There's different ways of thinking about humans in this case. So, for example, if something happens that no longer matches the verification, you could have a human check.

The issue with that is that agents generally work at inference speed. So, they can process millions of data points maybe per second or multiple seconds. And so, being able to verify that amount of data as a human is very challenging, but I would say for key governance decisions, for key design decisions around how to act with agents, humans should absolutely be in the loop. Even things like creative thought or defining what is human good or how do we partner together, humans absolutely need to be in the loop for that.

>> Yeah. I I read an article recently on your on your website actually about compute finance and that you're you know, you're coining this new term which I absolutely love. You kind of referred to electricity and oil kind of going through the same loop of being produced, priced, and financed. Can you talk to us a little bit more about computify or comphy? >> Yeah, the computer is now becoming even more important than oil in some ways. It's fundamentally it's what powers intelligence. So, you can think of AI as intelligence and then of blockchains as trust.

And by combining the two, you can then create this new idea of compute finance. So, why not be able to hedge compute? Why not be able to speculate on compute? Why not be able to reserve compute? For example, if I'm a university lab and I know I'm training a model in 2 months and I need x amount of compute for 2 weeks to do that, it's very hard for me to do that today. I have to go to a new cloud, I have to go to GPU providers, I have to go bespoke. I can't just trade the underlying thing that I need, which is compute. And so, we want to change that. And so, a few interesting primitives already launched on the 0G blockchain, one of which is comfy.fun, where basically you can launch agents

that with their trading fees can actually pay for their own compute. Then there's something called Infinite AI, where you stake 0G, you get a liquid staking token, then you take that and you mint IAI, and by holding IAI, you can get $1.27 worth of compute credits on a daily basis for as long as you hold the token. No matter what the price of AI is, no matter what the price of 0G is, no matter what the price of compute is, you're guaranteed that claim. And so, these are the new types of primitives that we're really excited about and spearheading.

>> And how does your G actually fit in to this compute finance? Talk to us a little bit about behind the scenes. >> Absolutely. So, 0G also offers the application layer. So, we have something called 0G app and we have something called 0G private computer. And so, as these compute credits come to bear, you can then utilize them across these different platforms. And so, for example, we have more than 100 models that you can utilize on 0G PC. So, if you do coding work, if you write research, you can easily get an API key, put it into your favorite environment, and then interact with completely private, completely sovereign models, where you can be rest assured nobody's

ever seeing my data, nobody's ever a off of my data, and I can then also publicly verify that that's happening. And so, that's completely unique to Zero GE. >> I mean, I I can totally understand the importance of doing something like this, but right now the AI space is so dominated by large companies. How do you plan to kind of get this through at the forefront and get people to start using this? >> Unfortunately, what generally tends to happen is like if you think about nuclear power, it's Chernobyl needed to happen before there were really safety guardrails put in place. And so, like we're already starting to see some of the kind of tip of the iceberg things that are starting to happen. And unfortunately, a very large security incident will happen in the future where

then people will start rethinking, okay, should we really be trading kind of security for convenience with these closed labs? And at that that point will be kind of our moment to really redesign how AI how compute should actually function. >> Yeah, I mean, that's kind of scary to think about, but >> It's unfortunate. It just unfortunately happens that way. Yeah. >> Yeah, I mean, it makes sense. So, let's talk about actually going back to the model. Who actually benefits? Who gains economic value? Is it the people building the agents? Is it the infrastructure? Is it the companies?

>> Yeah, what we've seen traditionally right now is that the infrastructure players and the kind of closed lot model players have benefited the most at the moment. But what we want to see and what's possible at the intersection of AI and blockchain is that people should be able to build their own expert model, for example, raise funds through the blockchain, and then serve that in a completely private way, and actually make money that way as well. Or if you have spare compute capacity, why can't you add that to a network and then also benefit from this whole AI revolution? So, we believe that people should be empowered by AI rather than replaced by AI.

>> Mhm. What about accountability? If something goes wrong, who takes accountability of an issue? >> Yeah, I think that will would be a probably a decade-long discussion in the legal circles, but there's some clear-cut cases. So, for example, if I say and prompt an agent to do something negative or nefarious on my behalf, then of course the person that's prompting should be liable for it. If the agent in their own accord mistakes the prompt and then does something nefarious, then that agent takes the liability effectively. If it's something that's in alignment problem even at the lab level, then the lab should take liability for it. And so, eventually we have to start thinking of these agents like virtual digital beings and maybe in the future humanoid

beings that interact with us just like other human beings would interact with us. And so, we've designed for that system. We have something called a genetic ID that I mentioned where not only do you assign ownership of a specific agent, but you can also have a reputation system over time. So, if that that agent does something negative, you can remove compute, you can reduce their reputation system, you know who the owner is, and then you can also track over time. Is this owner consistently giving like bad prompts, for example? What's happening here? And so, the way the economy will be shaped is also completely going to change.

>> It just it keeps going back to the importance of having blockchain in the entire ecosystem, which is something that a lot of the major companies have just kind of shunned out. Um and you know, you mentioned that this is a trillion-dollar economy. How can people in this room participate and benefit and take advantage of where we are right now so early on still? >> Yeah, there are many different ways of doing that. You can build your own agents, you can build your own models, you can use your own data to fine-tune new models, for example. You can utilize Zero GPC to build applications with the leading open-source models. We we do serve closed-source models as well, but the privacy guarantees aren't the same, of

course. Um so those are all the different ways to participate. Eventually, you can also bring your compute, you can bring your storage, and really participate in this larger network, and again, get a benefit from AI rather than be displaced by AI. >> That's amazing. Well, I think we're out of time, Michael. Thank you so much for your valuable insight. It was great speaking to you. >> Yeah, great to be here. Thank you, everyone.