Today I discovered a detail about myself that I hadn’t noticed before.
There are three ways to verify @OpenGradient : TEE, ZKML, and basic signatures.
I thought one transaction could only use one verification method.
I finally understand this wasn’t true.
Within the same on-chain transaction, different reasoning steps can each use different verification methods.
Here’s an example: a DeFi protocol performs an operation that includes three AI inference steps—
The first is the LLM generating a market analysis report. It uses TEE—fast, with costs close to zero.
The second is the risk model deciding whether to execute the transaction. It uses ZKML—deterministic at a mathematical level. The cost is high, but it’s worth it.
The third is categorizing and archiving the operation results, using basic signatures—knowing who did it is enough.
Three steps, three verification methods, all completed together within the same transaction.
This design made me realize: verifiable AI isn’t a simple “on/off” switch—it’s a dial you can fine-tune.
For each inference, based on how much it affects the final outcome, you choose the corresponding verification strength and cost.
It’s like shipping packages: standard delivery for ordinary items, insurance for valuables, and courier delivery for the most important documents. You don’t always choose the most expensive option—you choose what’s most suitable based on the risk.
$OPG consumes resources for each inference. The higher the verification level, the more you consume per run.
This “choose verification strength on demand” design is far more flexible and practical than “add the same verification to all inferences.”
When you use AI to make decisions, have you ever considered that different decisions might need different levels of trust guarantees?
#OPG
There are three ways to verify @OpenGradient : TEE, ZKML, and basic signatures.
I thought one transaction could only use one verification method.
I finally understand this wasn’t true.
Within the same on-chain transaction, different reasoning steps can each use different verification methods.
Here’s an example: a DeFi protocol performs an operation that includes three AI inference steps—
The first is the LLM generating a market analysis report. It uses TEE—fast, with costs close to zero.
The second is the risk model deciding whether to execute the transaction. It uses ZKML—deterministic at a mathematical level. The cost is high, but it’s worth it.
The third is categorizing and archiving the operation results, using basic signatures—knowing who did it is enough.
Three steps, three verification methods, all completed together within the same transaction.
This design made me realize: verifiable AI isn’t a simple “on/off” switch—it’s a dial you can fine-tune.
For each inference, based on how much it affects the final outcome, you choose the corresponding verification strength and cost.
It’s like shipping packages: standard delivery for ordinary items, insurance for valuables, and courier delivery for the most important documents. You don’t always choose the most expensive option—you choose what’s most suitable based on the risk.
$OPG consumes resources for each inference. The higher the verification level, the more you consume per run.
This “choose verification strength on demand” design is far more flexible and practical than “add the same verification to all inferences.”
When you use AI to make decisions, have you ever considered that different decisions might need different levels of trust guarantees?
#OPG