Cysic Founder: Why The Compute Market Is Getting Ready to Explode (Biggest In The World)

**Nick** (0:00)
Alrighty guys, welcome back to AI Supercycle, episode number 43, our premiere weekly AI show, powered by our friends at Nier. Visit nier.ai, nier.com to get started on your on-chain journey, start using their private inference tools, and more fully quantum proof release happened recently.
Today we have Leo Fan, the founder of Cysic, a full-stack compute network, turns GPUs, ASICs and spare compute into a variety of different assets, focusing on verifiable AI.
Leo is a Cornell, CIS, PhD and a deep researcher in both the ZK landscape as well as AI. Leo, good to have you back as always. The state of AI is quite rapidly shifting. There is new sectors being created, memory, storage, compute, inference. There's investing happening in the stock market. It's going crazy. There's the nationalization of AI between the US and China. There's the compute bubble. There's the open source versus closed source. There's just a lot to cover. Leo, good to have you, man. Thanks for joining us.

**Leo Fan** (1:03)
Hello. Hey, how are you, Nick?
Nice to hear a lot of like afterwards, like we were here to throw them out.

**Nick** (1:12)
Yeah, beautiful, Leo. Well, great to have you back. I guess just to start off, man, this is what the people want to hear about. There has been a release of Kimi K3 model as of late, which is challenging the assumption that the value and the pricing structure of inference and compute both, but primarily the actual intelligence that you're paying for with a given model is significantly overpriced by the labs out west in San Fran and the frontier labs are kind of in this race to figure out the best way to kind of rival what's happening out in China, meaning that there is a lot of similar intelligence being produced at a significantly lower cost with these open source models. And it's kind of causing a lot of rhetoric around, is this AI bubble over? Because we've gone through this token maxing phase and people are really just looking for answers.
Leo, from your perspective, what is happening right now with the pricing of these models and of this intelligence as it relates to the open source kind of push out of China and the frontier labs pushing back against these models and at the expense of their profit margins, they would like to keep their dominance.

**Leo Fan** (2:25)
Yeah, so to be honest, I saw a grad student coming out of China. I honestly don't think right now the open source model from China is catching up with the closed source model from the United States. Yeah, as you know, although the Kimi or the DeepSeq, they are catching up, but there are still some gaps there, probably several months gaps there to be catch up with the latest model from, say, OpenAI or Anthropic. You can always engineer this model, say, like Kimi or DeepSeq to be performed very well in some benchmarks. But if you do it in a very broad way, to do it in a different task, I don't think it's still right now on the same path for the US closed source model. So which means I don't think right now the bubble is over, and it's still several months, we are catching up from the open source model. Yeah, so that's, we try a lot.
We get a lot of this native model from China.
And based on this model, based on our experiments for all the different tasks, and I, right now, still several months gap between. Probably, you can see, Kimi, right now, is on the same level of Opus 4.8, but it's not as good as Fable 5 in general tasks. Of course, you can engineer this model to be performed very well in some tasks, but if you want to do it in a general task, then I don't think it's like on the same par.

**Andy** (4:16)
And let's talk about how these models are affecting the compute market, because right now, you know, there was a, Kimi came out, there was a massive influx of users that went over there and started using it, and then they had to shut down because they realized that their servers were getting maxed out. You know, there was, you know, they couldn't keep up with the compute demand, and so they didn't just didn't have it in order to supply.
You know, you guys are on the compute side, and the theory is that like, hey, why am I going to pay for the premium compute of, you know, one of these frontier models when, you know, in a couple of weeks, you know, they're going to catch up and I can just use maybe, you know, maybe it's 5% degradated performance, but it's like 50% or 80% degradated cost, right? And so how does this look on the compute side? I would imagine that every time there's a frontier model comes out, everything gets maxed out, right? And then there's a compute bottleneck. Do you think compute is still the bottleneck or has it been commoditized? What is the, how does the compute market react when there's a new model that comes out?