Reduce.
Not
Eliminate.............
Not
Eliminate.............
Publicar
MICHAEL MOORE
·
--
One word in Twin.fun's official documentation kept bothering me.
Reduce. Not eliminate.
The network verifies every AI inference. Cryptographic proof, on-chain attestation, signed outputs.
Per the official Twin.fun blog on opengradient.ai, published November 2025, the AI behind each twin runs on OpenGradient's verifiable infrastructure.
That part is real.
What it does not verify is whether the twin represents who it claims to.
Here is how Twin.fun works. A creator builds a digital twin modeled after a real person or persona.
Keys trade on a bonding curve, a pricing model where each key costs more as more are bought. Holding keys unlocks gated access to it.
The protocol does not require approval to buy or trade keys. A twin is identified on-chain by a unique address. Metadata lives off-chain on decentralized storage.
Impersonation policies exist. Per the official Twin.fun documentation, creator ownership can be pre-mapped to reduce impersonation risk.
The word is reduce, not eliminate.
Price on a bonding curve reflects demand for access to a specific twin. But that demand can be built entirely on the assumption that the twin represents a real person when it does not.
Verification proves the AI ran correctly. It says nothing about the identity behind it.
The compute layer has genuine rigor here. Every inference is attested. Every output is signed.
The question I keep sitting with is simpler. In a marketplace built on AI replicas of real people, what does it mean to prove the execution when the identity itself is not proven?
@OpenGradient #opg $OPG
Reduce. Not eliminate.
The network verifies every AI inference. Cryptographic proof, on-chain attestation, signed outputs.
Per the official Twin.fun blog on opengradient.ai, published November 2025, the AI behind each twin runs on OpenGradient's verifiable infrastructure.
That part is real.
What it does not verify is whether the twin represents who it claims to.
Here is how Twin.fun works. A creator builds a digital twin modeled after a real person or persona.
Keys trade on a bonding curve, a pricing model where each key costs more as more are bought. Holding keys unlocks gated access to it.
The protocol does not require approval to buy or trade keys. A twin is identified on-chain by a unique address. Metadata lives off-chain on decentralized storage.
Impersonation policies exist. Per the official Twin.fun documentation, creator ownership can be pre-mapped to reduce impersonation risk.
The word is reduce, not eliminate.
Price on a bonding curve reflects demand for access to a specific twin. But that demand can be built entirely on the assumption that the twin represents a real person when it does not.
Verification proves the AI ran correctly. It says nothing about the identity behind it.
The compute layer has genuine rigor here. Every inference is attested. Every output is signed.
The question I keep sitting with is simpler. In a marketplace built on AI replicas of real people, what does it mean to prove the execution when the identity itself is not proven?
@OpenGradient #opg $OPG
Aviso legal: Incluye opiniones de terceros. No debe interpretarse como asesoramiento. Binance AI puede usarse sin garantía. Lee los TyC.