Trusta.AI

• Trusta.AI scores the trustworthiness of blockchain addresses and AI agents using on-chain behaviour.

• Its MEDIA score assesses accounts across monetary activity, engagement, diversity, identity and age.

• TA is the network token.

• Supply allocated across community, ecosystem and investors.

• Used for accessing scoring services and governance.

Pros:

• Sybil detection has direct commercial demand from airdrop and incentive programmes.

• Behavioural scoring avoids the privacy problems of identity document verification.

• Established client usage before the token launched.

Cons:

• Behavioural scoring is adversarial: farmers adapt as soon as criteria become known.

• Demand is concentrated among token distribution events, which are cyclical.

• Extending from wallets to AI agents is a claim rather than a demonstrated capability.

• Exposure to sybil resistance, which has recurring demand across airdrops.

• Privacy-preserving relative to identity verification alternatives.

• Weigh the adversarial dynamic that erodes any scoring model over time.

• Reputation scored from on-chain behaviour, requiring no identity documents from the user being assessed.

• Distributing tokens fairly requires distinguishing real users from farmed addresses.

• Behavioural analysis does this without demanding personal identification.

• Founded by Simon Li and colleagues.

• Backed by ecosystem investors including Binance Labs.

• No public ICO. Distributed via Binance ecosystem programmes in 2025.

• Sybil farmers adapt to published scoring criteria.

• Demand is cyclical with token distribution activity.

• False positives penalise legitimate users with no appeal mechanism.

• The AI agent extension is unproven.