Stop arguing about which LLM is smarter—there's a deeper problem nobody's talking about.

Most AI systems today operate like black boxes. You get an answer, cross your fingers, and hope it's accurate. But what if you could cryptographically verify that an AI's output was computed correctly, without revealing the underlying model?

That's where zero-knowledge proofs enter the picture. The technology enables verifiable computation—you can prove an AI result was actually computed as claimed, creating a layer of transparency and accountability. No more blind trust. Instead, you get mathematical proof.

This shift could reshape how we think about AI reliability. From trusting the vendor to verifying the computation itself.