I keep circling this idea because I’m not sure the market is framing it correctly. People keep looking at AI tokens like they should mostly reward compute, the machines doing the actual work. But I keep noticing something else. In systems that survive repeated use, the expensive part isn’t always thinking. Sometimes it’s attribution. Knowing what actually helped the output exist.
“Participation is cheap. Recognition is where scarcity starts.”
That changes behavior. A lot. If thousands of agents, datasets, prompts, validators, or feedback loops touch an AI output, not every interaction deserves economic memory. Some actions are noise. Some are timing luck. Some actually changed the result. That filtering layer feels more important than people admit.
It reminds me a bit of game economies. Tons of players farm, move, repeat loops, burn energy, touch the system constantly, but only certain behaviors get surfaced by reward logic. Activity isn’t the same as selection. Off-chain effort, meaning behavior happening before final settlement or token recognition, can be huge and still invisible.
So maybe $OPEN doesn’t price raw AI labor. Maybe it prices attribution pressure. The right to economically mark who mattered after the fact.
And honestly, that sounds harder to scale than compute itself.