AI Act enforcement lands, and documentation becomes a moat
Enforcement of high-risk obligations has moved from theory to audit. Teams that treated model documentation as an afterthought are now retrofitting it under deadline.
The transition period for high-risk system obligations has closed and the first formal information requests have gone out. The requests are unglamorous: training data provenance, evaluation methodology, human oversight design, and incident logging.
Nothing in that list is technically difficult. What makes it expensive is retrofitting. Provenance records cannot be reconstructed after the fact, and evaluation methodology written to satisfy an auditor rarely resembles the evaluation a team actually ran.
Our AEGIS governance module exists precisely because we assumed this year would arrive. Every model call across the platform emits a structured record — prompt class, model version, reasoning budget, human review status — into an append-only store.
The strategic read is that compliance overhead is becoming a barrier to entry rather than a tax. Incumbents with clean records can enter regulated verticals that newer entrants cannot serve at any price.
Investor takeaway
Governance infrastructure built early converts a regulatory cost into a competitive advantage.