Why zkML? Because Tencent’s latest developer report shows that over 50% of new production code is now AI-generated, with 90%+ of engineers using AI coding assistants daily.
Software is no longer manually authored — it’s produced by models at scale.
When AI shapes the logic that runs real systems, how do we verify the reasoning behind that code?
2/
AI-generated code defines:
- Service behavior
- Failure recovery paths
- System coordination
- Security boundaries
This isn’t “autocomplete.” It’s AI determining how systems operate in production.
Correctness becomes a reasoning assurance problem — not a formatting problem.
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zkML enables verifiable code generation:
- Proof the model followed the intended development policy
- Proof no unintended logic branches were introduced
- Without exposing proprietary architectures or internal repos
When execution logic is determined by a model, verifying its reasoning equals verifying the system itself.
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Imagine development where:
✅Every function ships with a cryptographic execution trace
✅Enterprises can audit behavior without revealing codebases
✅Software supply chains trust code through verifiability, not statements
zkML makes code production auditable at scale.
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As AI becomes the author of software, trust must shift from belief → to verification.
That’s what @PolyhedraZK is building: verifiable intelligence at the code execution layer.
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