Given how chaotic the AI track is, it's still necessary to clarify the thinking process. One perspective is to break down several projects by functional layers—especially when facing institutional investors asking about the differences between these projects, this framework becomes particularly useful.

First, let's talk about Worldcoin($WLD). The core of this project is summarized in one sentence: in a world full of machines, how do you prove you're a real person? By establishing identity verification through iris scans, forming a "human proof" mechanism. From an ecosystem perspective, it occupies the identity layer. But there's a problem—its value chain is actually quite fragile, entirely dependent on how deep people's fear of AI threats runs. Optimistically, no one will want this; pessimistically, it could become a necessity. In a sense, this is more like issuing a "certificate of good conduct" for humans.

Next, consider projects like Bittensor($TAO) and Fetch.ai($FET). They aim to connect global computing power and algorithms to form a decentralized intelligent production network—understood as a distributed AI factory. This layer is called the production layer, solving the question of "how to create AI models." But there's also a bottleneck—no matter how smart a model is, if it can't access real-time external data, it's no different from a bookworm locked in an ivory tower. It doesn't know how the market is doing or what is happening in the world.

If we must make an analogy, Worldcoin is a defensive identity barrier, while projects like Bittensor are offensive production bases. The former says "I want to protect humanity," while the latter says "I'm here to create stronger AI." Their logical directions are fundamentally different, and their target audiences are completely different.

APRO, within this framework, aims to fill another gap—it's actually about handling the layer where models interact with the external world. This is the critical but overlooked link. So, it's not simply about which is stronger, but about their different positions within AI infrastructure.