If you've been using various AI products recently, you probably have a very real experience: each platform has its own ecosystem.

Models are locked within platforms, pricing structures differ, invocation methods differ, and even data and results are hard to verify.
This is actually similar to the early internet — services keep getting stronger, but control keeps becoming more concentrated.
Against this backdrop, I started to re-understand what @dgrid_ai is trying to do.
They're attempting to build a decentralized AI inference network that runs models on distributed nodes rather than a single platform.
Developers submit inference tasks, nodes execute computations, and verification and settlement are completed through on-chain mechanisms.
There's a detail in this design I really like: results are not a black box.
The network records the inference process and results through a Proof of Quality mechanism, making AI outputs verifiable and traceable.
In an era where AI keeps getting stronger, this is actually more important than one might imagine. When algorithms start participating in decision-making, people will inevitably ask a question: how did this result come about?
Perhaps the truly important infrastructure in the future isn't just more powerful models, but more trustworthy AI.
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