Something I did not expect to find interesting: OpenGradient lets developers choose how much truth they want to pay for. ZKML proofs cost the most, sometimes thousands of times slower, reserved for cases where being wrong is expensive. TEE attestation is faster, fits most medium workloads. Vanilla inference skips verification almost entirely.
That menu changes how I think about the network. It is not selling one level of trust. It is selling a spectrum, and asking builders to price their own risk tolerance against latency and cost. A trading bot might accept vanilla speed. An on-chain agent making irreversible decisions might demand zkML regardless of cost.
What I do not know yet is which tier developers actually choose once subsidies disappear. If most usage settles on the cheapest, least-verified option, the "verifiable AI" narrative gets thinner than the marketing suggests. If high-assurance tiers see real adoption, the thesis holds. Right now there is no public breakdown by verification type, only aggregate inference counts. I would rather see that split than another headline number.
#OPG $OPG @OpenGradient
That menu changes how I think about the network. It is not selling one level of trust. It is selling a spectrum, and asking builders to price their own risk tolerance against latency and cost. A trading bot might accept vanilla speed. An on-chain agent making irreversible decisions might demand zkML regardless of cost.
What I do not know yet is which tier developers actually choose once subsidies disappear. If most usage settles on the cheapest, least-verified option, the "verifiable AI" narrative gets thinner than the marketing suggests. If high-assurance tiers see real adoption, the thesis holds. Right now there is no public breakdown by verification type, only aggregate inference counts. I would rather see that split than another headline number.
#OPG $OPG @OpenGradient