In real-world life, incentive mechanisms often follow a simple principle: there needs to be a comprehensible connection between input and reward. However, in early Web3 products, incentive mechanisms were often designed to be overly financialized, leading to a disconnect between user motivation and actual behavior. The emergence of @easydotfunX has, to some extent, brought on-chain incentives closer to real-world logic.

Focusing more on the genuine participation process itself, rather than solely on the results, this approach is more similar to the learning, working, or creative processes in real life. People do not invest long-term solely for one-time rewards; rather, they participate because the process itself provides continuous feedback and a sense of growth. This design is more conducive to forming stable participation habits.

In practical life, this mechanism reduces the psychological burden on users. Participants no longer need to constantly calculate short-term gains but can focus on completing tasks or contributing content. This experience is closer to long-term behavioral patterns in the real world, rather than high-frequency gaming.

For projects and ecosystems, this also means healthier data structures. The behavioral traces left by genuine participants are easier to use for building long-term incentive systems, rather than being distorted by short-term arbitrage behaviors.

This exploration provides a valuable reference direction for how on-chain incentive systems can better align with real-world scenarios.