AntSeed Founder: The AI Inference Boom That Is Set To Explode (Here's Why)

**Robbie Klages** (0:00)
Alrighty, guys, welcome back to AI Supercycle, episode number 40, our premier weekly AI show, powered by our friends at Nier.
Visit nier.com for one of the best portfolio interfaces in all of crypto, as well as nier.ai for their private inference product. Today, we've got Amos Meiri talking about the AI inference explosion. Amos is a longtime crypto builder and investor, was one of the first builders on Bitcoin with colored coins, and is now the co-founder of a company called AntSeed, which is an open-source peer-to-peer marketplace for AI inference. Amos, welcome to the show. Good to have you, buddy.

**Amos Meiri** (0:33)
Thank you, Trezor. Thank you for having me, man. Super happy to be here.

**Robbie Klages** (0:37)
Of course, man. Of course. So we've kind of seen the iterations of decentralized AI happen. We saw this iteration of decentralized training compute and inference about a year and a half, two years ago. Then we kind of ran through this like agentic framework with like these different forms of agents on the chain, as well as having like X accounts and meme coins associated with them.
Then we've gone through GPU-backed stable coins like USDAI.
And then now we're quite firmly in this open source AI era, where the CEO of Anthropic is saying that open source AI is progressively moving in a very scary direction and it's something that the US is also trying to onshore. Through this, there's been a persistent demand for compute across the board. We've seen memory stocks fly. We've seen chip stocks fly, semiconductor stocks fly. And through this, there's been a kind of new golden goose, which is inference. People are realizing the value of inference in the AI stack as being the most valuable part of the stack. And so in today's conversation, we just want to understand what is happening at the inference level, why it is so valuable, what AntSeed is building, open source AI and more. So maybe Amos, you could just give us a start of what is inference and why is it catching so much fire and traction in the AI world as being the most valuable part of the AI stack?

**Amos Meiri** (2:08)
Yeah. All right. So many things to talk about. But I'll start firstly, you mentioned like the crypto and how it evolved and the terrible meme coin period for crypto, which was very hard for me seeing being in the space for almost 14 years.
So yeah, I'm very happy to start seeing actually things that make sense in crypto. And for me, it was a very long journey. But I got really excited about AI just I would say a year ago, something like that, and really went deep into understanding where crypto and AI could actually work. There are many places that I think they won't, but we can talk about them.
And yeah, you had a lot of different attempts in stuff that were early, for example, think about Akash and projects like that, that were very early to market and now are booming, which is very, very nice to see people holding for that long and building something that is actually meaningful. And inference is one of the hottest topics, because it's kind of like the new oil, right? There are basically two types of works, I would say, in terms of AI. The one is like the training part, and the other part is the inference. The inference, to simplify it, it's like, when you're talking with JetGPT, Cloud, whatever it is, that work that the AI DLLM is doing is basically the inference. So you're paying for that service, that compute. And I look at inference really like the new oil, because, you know, if you look at airline companies, if they're, we're hedging oil prices, because that's their biggest expense, what we're going to see is that inference is going to be one of the biggest expenses of the new tech, the new company. So as we move forward, the market is going to evolve a lot, and we'll start seeing, you know, stuff. And I think we're already starting to see that, like people looking at hedging inference prices, and, you know, you have Vannes doing also something interesting in this intersection with Dian, which we can talk about as well. So yeah, I think inference is going to be a big thing, something with basically endless demand. If only like five or seven percent of the population is paying for it, think about what will happen when we'll have more, and you will have robotics, and everything will use compute and inference.

**Robbie Klages** (4:56)
Yeah, yeah. And so just a way to kind of classify the value of inference when you're thinking about using these tools, when they are like writing back to you like, oh, we're thinking that is the inference, you're producing tokens to get an outcome, like a response to your prompt. And so the inference explosion or the bull thesis for inference is the demand for tokens. Is that the right way to think about it?