I think the most underexamined risk in OpenLedger's attribution model is not technical failure. It is incentive capture.
Suffix-array attribution rewards data whose specific tokens appear in model outputs. That creates a measurable optimization target. A contributor who understands the measurement can craft contributions designed to be memorized rather than to genuinely improve model capability.
Both produce on-chain transactions. Both generate attribution scores. Both receive OPEN rewards. The difference only becomes visible when models trained on gamed contributions are evaluated against real-world performance.
That evaluation loop is slower than the contribution incentive loop. Which means the system may be rewarding gaming behavior before it can detect it.
Still figuring out whether Proof of Attribution is robust enough to survive participants who understand it well enough to optimize toward it.