Research shows how AI at work can degrade skills
The more staff relies on using AI at work, the further their skills degrade and that leads to a need for more AI use, according to new discussion papers from Yale University and Nanjing University. The papers argue that AI can create systems where individually rational behaviour leads to worse outcomes collectively.
If a person’s skills start out at a low level, they are rationally incentivized to use AI. But skills improve through practice and erode without it. So the more a person relies on AI, the further their skills degrade, which induces the need for more AI use, and the degradation cycle repeats, the authors argue.
They find that the skill degradation from AI use actually lowers people’s overall performance compared with the no-AI scenario.
Two Cowles Foundation Discussion Papers from Yale’s Nisheeth K. Vishnoi and collaborators examine the impact of individuals’ choices to use AI at work.
To understand what happens to a person’s performance when they begin to use AI, Vishnoi and Lingxiao Huang of Nanjing University examined the incentives to use AI.
A key to finding an answer is a person’s skills prior to their use of AI.
If a person’s skills in a certain area start out at a low level, they are rationally incentivized to use AI. But skills improve through practice and erode without it. So the more a person relies on AI, the further their skills degrade, which induces the need for more AI use, and the degradation cycle repeats, the authors say.
A “high-skill” person stays capable and independent, while a “low-skill” person becomes persistently dependent on AI. As with any technology, advancements in AI evolve the behaviours of the users. The papers argue that as AI models improve, the gap between high and low skill workers is exacerbated.
The authors find that the skill degradation from AI use actually lowers people’s overall performance compared with the no-AI scenario.
Improving AI capability can amplify short-term gains while persistent long-run means losses in both human skill and task performance.
A paper that Vishnoi also co-authored with Huang, as well as Wenyang Xiao, notes that workers lean on AI more for hard or uncertain tasks and less when things are easy or certain.
Their research finds that employers benefit from having employees who are good at evaluating AI outputs: their quality improves, and they may even meet standards they couldn’t reach before.
Workers who are less skilled at verification, however, might hand off more to AI than they should and will inadvertently create lower quality work. The employer ends up worse off as a result.
Conclusion is that AI changes not just how well people work but what it means to be a good worker, making the ability to oversee and evaluate AI outputs a central determinant of quality, alongside underlying task skill.
“From a policy perspective, our findings highlight the importance of sustaining practice and redesigning incentives, such as delaying high-quality AI assistance, rewarding independent problem solving, or valuing learning trajectories, to align short-term performance with long-term skill development as AI capability advances.”
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