The limit protects Meta's learning phase. Sudden or very frequent changes can restart learning and temporarily reduce performance.

For this reason, AI Optimization applies safeguards when raising budgets and may split a change into stages. The reason appears in history. Reductions meant to cut wasteful spend follow the safeguards configured for the optimization.