Is AILayer Farm Safe?
Risk Grade: C- (54/100)
AILayer Farm is rated as elevated risk — multiple novel mechanisms and notable interaction risks.
High risk — extreme TVL-to-FDV imbalance, minimal auditing, and incentive-driven TVL suggest this is primarily a farming play rather than a sustainable protocol
AILayer Farm is a yield farming protocol on AILayer, a Bitcoin Layer 2 blockchain that combines AI and blockchain. Users can stake tokens to earn AIL rewards. Despite claiming $71M in TVL and 1M+ active addresses, the AIL token has a tiny ~$1M valuation, suggesting most deposits are from yield farmers rather than genuine users. Its C grade reflects serious concerns about TVL sustainability and limited documentation.
TVL
$72M
Mechanisms
4
Interactions
4
Value Grade
F
Key Risks for AILayer Farm Users
The AIL token is worth almost nothing (~$1M total) despite claiming $71M in deposits — this usually means TVL is fake or driven purely by airdrop farming
Only one security audit (by ScaleBit) for a chain holding $71M — this is far below industry standards for the amount of money at risk
If the farming rewards stop or the AIL token drops further, most users will leave immediately since there is no other reason to keep money on this chain
Top Risk Factors
- •Bitcoin Layer 2 with $71M TVL but only ~$1M FDV creates an extreme TVL-to-FDV imbalance suggesting TVL may be artificially inflated through incentive farming
- •Minimal public documentation and audit information — single ScaleBit audit for a protocol managing $71M raises serious due diligence concerns
- •Staking-as-TVL model where depositing tokens counts as TVL may not reflect genuine protocol usage or economic activity
Risk Score Breakdown
AILayer Farm's highest risk area is Documentation Gaps (7/10). Here's how each dimension contributes to the overall 54/100 score:
Read the Full AILayer Farm Risk Report
This protocol has 2 collapse scenarios. 1 critical and 1 high-severity interaction risks identified. See the full mechanism classification, interaction matrix, and deep-dive recommendations.
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