Many reasons for the failure of AI × Crypto projects are not actually due to model capabilities, but rather the data itself.

Untrustworthy, non-reproducible, and unverified data sources ultimately lead to AI outputs that cannot be validated, let alone form long-term value on the chain.

@useTria's entry point is precisely here.

It is not about building models or application shells, but focusing on the most overlooked yet critical layer in AI training and inference: structured, verifiable data foundations.

Tria clearly records the source, contribution, and usage relationships of data through on-chain mechanisms, allowing AI systems to no longer rely on black-box data inputs, but to establish auditable and traceable trust pathways. This is especially important for AI Agents, on-chain decision systems, and future autonomous protocols.

From this perspective, the value of $TRIA does not come from short-term narratives, but from whether it can become a default layer in AI infrastructure that is routinely invoked.

If AI is to truly scale and operate on the chain, data layers like Tria are not optional but essential.

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