I don’t think OpenGradient’s real stress point is compute.

It is attribution.

Once AI inference becomes verifiable, the network stops being only an execution layer. It becomes a memory system for responsibility.

That changes the behavior of every participant.

Model providers are no longer just offering outputs.
They are attaching identity, execution history, and proof trails to those outputs.

Developers are no longer just consuming AI.
They are choosing how much accountability their application can survive.

Node operators are not only selling computation.
They are becoming witnesses inside an economic system where bad execution can be isolated instead of silently absorbed.

This creates a strange constraint.

The more useful OpenGradient becomes, the less invisible AI inference can remain.

Most AI infrastructure scales by hiding complexity from the user.

OpenGradient may scale by forcing complexity to leave evidence behind.

That is not automatically bullish.

Evidence creates trust, but it also creates liability, comparison, reputation decay, and new forms of coordination pressure.

The unresolved question is not whether verifiable AI is useful.

It is whether markets actually prefer intelligence that can be audited after it makes a mistake.
#opg $OPG @OpenGradient
Which will become the scarce resource in decentralized AI?