A certain AI data project recently adjusted its ranking mechanism by introducing stricter admission standards, and this approach is worth paying attention to. The new rules are very clear: to get on the leaderboard, you must meet any one of two conditions—

**First, look at reputation**—simply put, you need to continuously produce valuable content. This is easy to understand; genuine contributors will accumulate recognition within the community, and reputation reflects this long-term quality.

**Second, look at on-chain data**—directly verify your holdings or interaction records. This is more hardcore, using on-chain footprints to prove you are not just talking the talk but truly have a real economic relationship with the project.

From a mechanism design perspective, this combination is very clever. It speaks with data, filtering out a large number of bots and spectators. But there are also problems—does this approach potentially exclude many potential and sincere participants? Especially new users who haven't accumulated reputation or holdings, how can they gain a voice on the leaderboard? This is a **balance between elitism and inclusiveness**. In the long run, such strict participation thresholds can strengthen community quality, but in the short term, they might reduce new user engagement enthusiasm.