If an AI's work is easy to check, people use it — even when the task is hard — but avoid it when they can't verify the answer.

This explores how workers decide when to use AI and when to do the work themselves, and whether being able to check the result is what decides it. The most direct evidence in the collection comes from a small study: in interviews, thirteen junior developers said their main test was whether they could verify the output, not deadlines or how hard the task was Do junior developers choose AI based on their ability to verify results?. They avoided AI for work they couldn't evaluate. They used it most where they already had expertise. That runs against the usual idea that AI helps most where you know least. For these developers, it was useful mainly where they could already have done the work.

The same rule shows up on the machine side too. Wei's "verifier's rule" argues that AI systems get good at tasks in proportion to how easily a solution can be checked. He also argues the gap can be closed ahead of time with answer keys, test suites or measurement tools Does task verifiability determine what AI systems will learn to solve?. Put the two studies together and verifiability is doing the work twice: it shapes what AI learns to do well and what humans trust it with. That may help explain why real delegation has clustered in information-heavy jobs and follows what AI can actually do, not simply where chatbots are popular Where have workers actually delegated tasks to AI?.

The catch is that people are often bad at judging whether they can verify something. In WalkMe's survey, 90% of workers said they feel confident with AI. Only 25% said it works on the first try, and half had spent more time with AI than the task would have taken by hand Why do workers feel confident with AI but get poor results?. One explanation is the "LLM fallacy." When AI output reads smoothly, people blur the line between what they produced and what the tool produced, and start crediting themselves with skills they don't have Do AI-assisted outputs fool users about their own skills?. A worker who thinks they can check the work may only be recognizing that it sounds right.

The less obvious problem is that the strategy can wear itself down over time. The junior developers' rule depends on expertise, and research mapping thousands of workplace AI risk scenarios found that relying on AI agents can gradually erode both skills and the ability to oversee the AI meaningfully Does AI augmentation protect workers from skill erosion?. So the more a worker delegates in the area where they can verify, the more that area may shrink. Social pressure adds to this: people who use AI expect to be seen as less competent and often don't tell colleagues Do people fear judgment when they use AI at work?. That leaves less room to talk openly about where checking actually works.

The collection's direct evidence is thin: one interview study of thirteen people. The broader pattern comes from linking it to related work, not from large studies that measure verification-driven choices directly.