#opg $OPG @OpenGradient
The TEE attestation for the LLM Proxy Node proves one thing: the enclave has executed the approved code correctly, and the request and response were not tampered with while relaying through OpenGradient's infrastructure. However, it cannot and does not prove that GPT-4 or Claude within OpenAI or Anthropic's API hasn’t been silently altered between calls made a week apart. This is a fundamental difference between proving the pipe and proving the brain.
Compared to the Local Inference Node, which runs an open-source model directly on OpenGradient's hardware, the distinction is much clearer. With the open-source model, the weights can be hashed and verified against the publicly released version, meaning you can accurately prove which model has run at the byte level. In contrast, with the LLM Proxy Node calling OpenAI, OpenGradient doesn't have access to the internal weights to do the same. x402 LLM Inference is charging $OPG for both types of requests under the same billing logic, but the level of verification actually received is completely different, and most users may not recognize that boundary when they see a "verified" label pop up.
If OpenAI or Anthropic silently changes the model behind the API while the TEE attestation of the LLM Proxy Node continues to confirm the pipe integrity as normal, users are paying $OPG for something tagged "verified" without truly knowing what they are verifying, or if they are just confirming that the pipe isn’t lying about a brain that may have changed?
The TEE attestation for the LLM Proxy Node proves one thing: the enclave has executed the approved code correctly, and the request and response were not tampered with while relaying through OpenGradient's infrastructure. However, it cannot and does not prove that GPT-4 or Claude within OpenAI or Anthropic's API hasn’t been silently altered between calls made a week apart. This is a fundamental difference between proving the pipe and proving the brain.
Compared to the Local Inference Node, which runs an open-source model directly on OpenGradient's hardware, the distinction is much clearer. With the open-source model, the weights can be hashed and verified against the publicly released version, meaning you can accurately prove which model has run at the byte level. In contrast, with the LLM Proxy Node calling OpenAI, OpenGradient doesn't have access to the internal weights to do the same. x402 LLM Inference is charging $OPG for both types of requests under the same billing logic, but the level of verification actually received is completely different, and most users may not recognize that boundary when they see a "verified" label pop up.
If OpenAI or Anthropic silently changes the model behind the API while the TEE attestation of the LLM Proxy Node continues to confirm the pipe integrity as normal, users are paying $OPG for something tagged "verified" without truly knowing what they are verifying, or if they are just confirming that the pipe isn’t lying about a brain that may have changed?