AI is dramatically increasing how fast teams can produce code, tests, and pull requests. But an unexpected problem is starting to show up in many pipelines: the faster development becomes, the more QA becomes the bottleneck. Traditional quality gates were designed for human-scale development, not AI-scale output. So what happens when your backlog grows faster than you can validate it? Instead of trying to “test harder,” teams are starting to rethink the system itself. How much testing is actually necessary given the time-to-market pressure? What validations should exist inside the development context itself rather than waiting for QA? And how do you handle the remaining volume without overwhelming humans? This session walks through practical patterns teams are experimenting with, reducing validation backlog, and using AI-assisted or agentic workflows. Along the way we’ll look at the workflow ideas that work, the ones that don’t, and the surprising pitfalls teams encounter when AI starts producing more change than QA can realistically review. Because the real question isn’t how QA keeps up with AI – it’s how quality systems evolve when AI changes the speed of development itself.