What is the real bottleneck in the implementation of AI? It's not a lack of computing power, nor is it that the models are not smart enough, but rather the absence of an accountability mechanism.

When enterprises and institutions deploy automation systems, they must be able to clearly track—who made what decision, when, and based on what permissions. This is especially important for finance, healthcare, and government sectors.

$RENDER is advancing AI computing infrastructure, and $NEAR makes AI applications easier to deploy, but all of these require a foundational trust layer. That’s why autonomous identity frameworks are so critical. Through a verifiable credential system, every step of AI’s operation can be traced back to a real entity and specific permissions.

The same identity layer has already proven effective in cross-border trade and public services, and now it can be directly used to authenticate AI-driven decision-making processes. This is the key for AI to move from proof of concept to real production applications—not faster models, but verifiable chains of responsibility. This is both the problem and the solution.