How @OpenGradient could lower the barriers to AI innovation, the architecture argument is genuinely solid. $OPG operates as a specialized AI coprocessor, letting applications, blockchains and agents outsource heavy AI tasks to a network of GPU and TEE nodes.
The network runs EVM compatible and hosts thousands of models with verifiable inferences, meaning a Solidity developer can call a model much like a contract function without managing separate AI SDKs or API keys.
That's a meaningful reduction in setup costs for builders who want to integrate AI into DeFi applications or autonomous agents without maintaining their own GPU infrastructure.
A model hub with more than 2,000 models from over 100 developers, alongside millions of verifiable inferences and hundreds of thousands of cryptographic proofs, represents a level of live activity that many AI crypto projects have yet to demonstrate.
But there's a structural tension worth considering. The entire value proposition centers on verifiable inference yet two very different trust models sit under that umbrella: zkML proofs, which are mathematically trustless and TEE attestations, which depend on trusted hardware environments.
Since zero knowledge proving for large models remains computationally expensive, the more demanding and commercially relevant workloads are likely to rely on TEE rather than zkML.
That creates an important distinction. The inference jobs developers most want to run at scale may also be the ones that depend more heavily on hardware trust assumptions than on mathematical verification. For builders working on high stakes applications such as lending systems or autonomous trading agents, that difference could matter significantly.
For developers evaluating the network, a key question remains: if an application cannot tolerate hardware trust assumptions, how much of the current inference activity would qualify as strictly verifiable and is that breakdown publicly available?