Code review must shift from counting changed lines toward understanding the blast radius and evidence attached to a change. This matters now because code generation capacity can rise faster than reviewer capacity, making a familiar approval ritual the new delivery bottleneck.
Orosz reports growing concern among engineering leaders about review volume as AI agents generate more code and more pull requests. The article examines whether established pull-request review can absorb that change or needs different verification practices. The mechanism is risk-based review: classify affected systems, require targeted tests and traces, surface architectural consequences, and spend human attention where failure impact is highest.
Engineering teams should measure accepted changes, escaped defects, rollback cost, review latency, and reviewer load instead of code volume alone. The evidence is an informed industry analysis and reported leader experience, not a controlled study proving that one replacement process is superior. Watch for teams that publish repeatable review designs and show lower defects without hiding risk in automated graders.