LayerZero Completes Its Sybil Hunt: What Are the Results?
LayerZero’s month-long battle against dishonest airdrop hunters, initiated on May 1, has come to an end. This initiative sparked considerable debate and received mixed reviews. Let’s analyze the results of this campaign.
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How the LayerZero Sybil Hunt Unfolded
As we previously reported, under the program's terms, users who honestly tagged their wallets with the “Sybil” label by May 17 were allowed to retain 15% of their share in the airdrop. Additionally, those who conducted independent research and provided information about sybils were entitled to an extra reward—receiving 10% more than their original entitlement during the token distribution.
On May 18, LayerZero released their list of suspicious addresses, numbering 2 million. The developers suspected these accounts of foul play, therefore disqualifying them from receiving tokens.
Subsequently, on May 19, with input from analysts at Nansen and Chaos, the methodology for detecting sybils was refined to avoid falsely accusing users. Consequently, the list was reduced to 803,093 addresses, representing 13% of the 6 million users registered on the LayerZero platform since its launch over two years ago. Company representatives clarified that the remaining 85% would be considered qualified users.
When the program to identify unscrupulous participants was extended until May 31, the LayerZero team received an additional 3,000 reports of suspicious addresses in just three days. However, there has been concern within the community that incentivizing the hunt for sybils might inadvertently impact honest participants.
Bryan Pellegrino, the project’s founder, expressed concerns about time constraints, stating that processing all reports thoroughly would require an additional two months.
Is the Hunt Over or Is This Just the Beginning of the Story?
This situation resembled a large-scale social experiment, similar to the Stanford prison experiment, where one community was divided into “guards” and “prisoners,” performing respective roles, and raised several pressing questions:
- Why didn’t the project team tackle the issue of sybils on their own?
- Could the practice of employing sybil hunters harm community relationships?
- How did the verification model become a tool for user harassment?
- How fairly will LayerZero be able to evaluate the outcomes of this program?
- What are the long-term implications of this fight against sybils for the future of airdrops?
On one hand, projects distributing tokens to their early adopters must devise strategies to combat dishonest practices, as it protects the interests of honest participants.
However, the LayerZero experiment faced issues from the outset, both in planning and execution. The team failed to justify their economic model and distribution volumes adequately beforehand. The wallet verification process was opaque and inefficient, which necessitated further analysis and corrections. Additionally, the program generated significant controversy within the community and fostered a wave of negative sentiment towards the platform.
Some users continue to lodge complaints about the unjustified blocking of their wallets, and some sybil hunters are even willing to offer financial compensation to those excluded if they can prove their innocence.
It seems the initiative to mobilize the community to identify fraudsters was misguided. LayerZero found itself having to manually verify all reports, and the initiative ultimately led to division and mistrust within the community.
Alternative Approaches to Sybil Detection
Future projects could consider adopting the token distribution approach used by EigenLayer, where the team assumed full responsibility for blocking dishonest users' wallets.
Alternatively, an airdrop could be executed with minimal announcements and advertisements, simply distributing tokens to the wallets of eligible participants based on a snapshot.
Another approach could involve collaborating with external projects experienced in developing identification infrastructure and highly regarded in the Web3 ecosystem. This method could potentially address issues of bias and financial interest associated with “hired hunters.”
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