Xiao Jin Dou saw that Sapien released the White Paper, which contains a lot of key information.

I carefully looked at the tokenomics section, and it is indeed designed very cleverly.
The entire system considers data quality, contributor incentives, and long-term development through a closed-loop mechanism of staking + rewards + penalties.

Several key points:

1. Staking Mechanism:
It is not just simple collateral, but rather linking the staked amount to the quality of the task.
If you perform well, you will get back your principal + rewards; if you perform poorly, you will be slashed.
This economic constraint directly improves data quality.

2. Reward Multiplier:
This design is very clever, dynamically adjusting the reward multiplier based on the contributor's performance.
The better the performance, the higher the reward multiplier, and the longer the staking time, the greater the returns.
This mechanism allows high-quality contributors to receive excess rewards.

3. Long-term Incentive:
Through an on-chain reputation system and DAO governance, contributors can continuously participate in platform development.

It's not a one-time deal, but rather the establishment of a long-term cooperative relationship.

4. Token Distribution:
53% to contributors, 47% to the team and investors, the allocation ratio is reasonable.
Moreover, most tokens are released gradually, avoiding short-term selling pressure.

Overall, Sapien's economic model addresses the most troublesome quality control issues in the AI data industry while providing contributors with a fair and transparent incentive mechanism.
This design is considered a relatively innovative attempt in the field of decentralized data.

Little Golden Bean is more concerned about the execution strength of the Slash mechanism after going live and the activity level of community governance.
If all of these can be done well, it may become a new standard in the field of AI data. Let's look forward to it together.
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