AI is giving workers the illusion of expertise — and quietly making them worse at their jobs, researchers say

A new study from the Work AI Institute, in collaboration with researchers from prestigious institutions such as Notre Dame, Harvard, and UC Santa Barbara, reveals a concerning trend among office workers. It suggests that while artificial intelligence (AI) tools may enhance feelings of productivity, they can simultaneously degrade essential skills, especially for early-career professionals. Rebecca Hinds, who heads the Work AI Institute and co-authored the report, highlighted the paradox: AI provides an illusion of expertise that can lead workers to believe they possess greater skills than they truly do. "AI is putting expertise into our hands in a way that's not always predictable," Hinds explained. She noted that the situation mirrors the early days of search engines, where quick access to information often led to a false sense of understanding. According to Hinds, the advent of generative AI amplifies this illusion, posing significant risks to those in creative and knowledge-heavy positions. Workers increasingly rely on AI to overcome challenges, such as the daunting 'blank page' syndrome, by generating initial drafts. While this can expedite the creative process, it may also eliminate the valuable struggle of developing ideas. Hinds cautioned that neglecting this foundational work could lead to skill atrophy. The report argues that AI can either create a "cognitive dividend"—where it enhances expertise—or a "cognitive debt" that undermines it when used as a mindless shortcut. The report identifies early-career positions as particularly vulnerable, as these roles often serve as apprenticeship opportunities. Junior developers, marketers, and analysts depend on hands-on experience to build their skills. If they rely too heavily on AI for essential tasks, they risk missing out on critical learning experiences that are vital for career advancement. Hinds pointed out that organizational practices can inadvertently worsen this illusion of expertise. For example, some companies assess employee performance based on their frequency of AI tool usage, rather than the quality of their work. This incentivizes workers to prioritize clicking on tools over gaining a deeper understanding of them. Instead, Hinds advocates for aligning AI usage with overarching business goals, such as quality and customer satisfaction, to better measure its impact. Rather than rejecting AI, Hinds encourages a more thoughtful approach to its integration. She emphasizes the importance of intentional use and suggests that both employees and leaders ask themselves critical questions about how they employ AI in their work. Ultimately, she believes that while AI can enhance productivity, it does not inherently transform capabilities; it merely amplifies existing organizational strengths or weaknesses.