Provenance or It Didn't Happen
When a model produces an answer, a diagnosis, or a merge request, one question decides whether it matters: can you prove it? We make the case for deterministic, verifiable AI outputs — signed, reproducible, auditable end to end. Provenance is not compliance theater; it is the line between software you operate and software you merely hope about.
Show Notes
Verification is the difference between a demo and infrastructure. We make the case that AI outputs need provenance the way code needs version control: signed inputs, deterministic replay, and an audit trail that survives contact with a regulator.
Builders working on reproducible inference explain what breaks when you try — and why trusting the vendor stopped being an answer.
Topics Covered
The Knowledge Tree
Seven debates, connected — each one a node in the same system.
- 01
Deterministic AI outputs
- 02
Signed and reproducible inference
- 03
Audit trails that hold up
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