When Should AI Companies Report on Safety?

This article analyses when AI safety reports should be triggered. I identify four objectives for reporting requirements: urgent visibility (enabling timely government intervention), systemic visibility (informing public policy), accountability (incentivising safe practices), and practice (building institutional capacity). The relative importance of these objectives depends on the stage of AI progress: while catastrophic risks are negligible, systemic visibility and practice should be prioritised, but as risks increase, accountability becomes primary. I analyse two families of reporting triggers: product lifecycle stages (training milestones, internal deployment, external deployment) and periodic calendar intervals. Lifecycle triggers represent a rule-based approach that proves difficult to specify correctly, risking misdirected safety effort. Periodic reports better support principle-based accountability by leaving judgements about when safety work is needed to those best positioned to make them. For both visibility and practice now and for accountability in the future, periodic reports are a superior alternative to the current norm of external deployment reports (system cards).