GitHub made effort levels for Copilot code review generally available, letting teams configure how thorough an automated Copilot review pass should be, from a lightweight scan focused on obvious issues up to a deep review that digs into architectural and design-level concerns, rather than getting one fixed level of scrutiny regardless of context. This directly addresses a complaint that has followed AI code review tools since they first shipped: a single fixed review depth is wrong for most situations, because a one-line config change and a sprawling new feature branch warrant very different amounts of automated scrutiny, and teams that couldn't adjust that dial either tuned out noisy reviews on trivial changes or missed depth on complex ones. Configurable effort levels let a team match review intensity to the actual risk and complexity of a given change, which matters directly for the verification-bottleneck problem showing up in industry survey data this year: as AI-generated code makes up a larger share of what gets committed, and as developers report spending more time reviewing that code than writing their own, tooling that lets teams allocate review depth more intelligently, rather than uniformly, is a direct lever for getting review effort back under control instead of just adding more raw review time. For engineering teams already running Copilot code review, the practical move is to set higher effort levels on repositories or paths that see complex, high-risk changes, like core services or security-sensitive code, and lower effort levels on low-risk, boilerplate-heavy areas, treating the effort-level setting the same way a team would tune static analysis rule severity: matched to actual risk rather than applied uniformly everywhere.