FLOPs are usually discussed as an engineering or infrastructure measure. In regulated AI, they can also become a governance artifact. Compute used during fine-tuning helps describe model development, cost, environmental footprint, and in some contexts the scale of regulatory obligation.
FLOPs alone do not explain model risk. They belong to a broader evidence record: training data lineage, evaluation results, model changes, deployment scope, and monitoring controls. A mature MLOps process should be able to reconstruct how a model changed and what resources were consumed.
As AI regulation becomes more concrete, teams that already record these signals will have an advantage. Governance is easier when evidence is collected during development rather than reconstructed after legal or audit pressure appears.