What If AI Needed Receipts?
One thing I've been thinking about lately is how much trust is baked into the AI tools we use every day.
You send a prompt to an AI model, get a response back, and that's usually the end of the story. Nobody really asks what happened in between. Which model generated it? Were the weights updated? Was the output modified somewhere along the way? Most of the time we simply trust the API and move on.
That's why OpenGradient caught my attention.
From what I understand, the project is trying to make AI outputs verifiable rather than just believable. Instead of treating inference as a black box, it separates computation, proof verification, and execution into different layers. The end result is an AI response that comes with cryptographic proof showing how it was produced.
What I'm still trying to figure out is whether developers will eventually see this as a necessity or just an extra feature.
The idea makes sense, especially as AI starts handling more important decisions. At the same time, developers usually choose convenience until verification becomes impossible to ignore.
What makes me keep watching is that this isn't only a concept anymore. OpenGradient has already processed over 2 million verifiable inferences and more than 500,000 zkML proofs and TEE attestations. Those numbers suggest something is actually being used.
For now, I'm paying less attention to incentives and more attention to whether real applications start choosing verifiable AI because they need it. If that happens, the entire conversation around AI infrastructure could look very different.