The current AI track involves models in training and applications in deployment, but the infrastructure layer is actually the most scarce and also the most easily underestimated part.

Especially in the Web3 scenario, AI is expected to make executable decisions rather than simply generate content, which places higher demands on data authenticity and consistency.

The value of @useTria lies in providing an composable data collaboration model for AI.

Data is no longer just a consumable resource but can be tagged, measured, and reused as a productive element, making the relationship between data contributors, AI systems, and applications clear and sustainable.

This also explains why $TRIA is more like an infrastructure asset rather than a sentiment-driven target.

Its growth path will not be explosive but will be gradually called upon and accumulate value as the number of AI Agents and the complexity of on-chain intelligent systems increase.

If you believe that AI will take on more complex judgment, collaboration, and execution roles on the chain, then the problem Tria addresses itself is already positioned correctly.

@KaitoAI @cookiedotfuncn @cookiedotfun @MindoAI #TriaTreasure @easydotfunX