Artificial intelligence may be making workers faster and more productive on the surface, but it’s quietly eroding their confidence and core job skills, according to Mehdi Paryavi, CEO of the International Data Center Authority. Paryavi warns that excessive and poorly designed AI implementation in workplaces is driving what he calls “quiet cognitive erosion” and “down-skilling” among employees.
The most immediate casualty of heavy AI reliance, according to Paryavi, is worker self-belief and confidence. When employees come to believe that AI writes better and thinks smarter than they do, they begin losing confidence in their own abilities. This loss compounds quickly as workers defer writing, analysis, and critical judgment to AI systems, gradually relying less on skills built through years of experience and learning.
Research from the Work AI Institute, produced with researchers from Notre Dame, Harvard, and UC Santa Barbara, supports this pattern. The study found that AI is creating workers who feel smarter and more productive while their underlying skills slowly erode. Rebecca Hinds, head of the Work AI Institute, told Business Insider that AI creates an “illusion of expertise,” which poses particular risks for early-career employees who haven’t yet established foundational skills.
Paryavi emphasizes that much of the problem stems from how leaders define productivity. AI’s biggest promise is speed—faster reports, launches, and analysis—but faster doesn’t always mean better. While AI can generate professional-sounding output, it often lacks the depth that comes from hands-on expertise. Anastasia Berg, a philosophy professor at UC Irvine, confirms that workers relying heavily on AI risk rapid skill atrophy, especially junior employees who never fully learn independent problem-solving.
To avoid cognitive dependency, Paryavi recommends tailoring AI access by job function rather than universal rollout. Companies should maintain human involvement at both ends of the workflow—leading creative thinking at the beginning and quality-checking AI output at the end. “What’s critical to note is that you, the human you, must quality check AI, not the other way around,” he emphasized. The key question, Paryavi suggests, is determining how much AI technology is truly needed and how far society should push its adoption.
Key Quotes
There used to be a notion called ’thinking outside the box.’ That notion will soon cease to exist when everyone draws on all their creativity, analytics, and innovation from a single box called AI.
Mehdi Paryavi, CEO of the International Data Center Authority, warns about the homogenization of thinking and loss of creative diversity when workers rely exclusively on AI for ideation and problem-solving.
If you come to believe that AI writes better than you and thinks smarter than you, you will lose your own confidence in yourself.
Paryavi identifies confidence erosion as the first and most immediate consequence of heavy AI reliance, setting off a cascade of skill degradation as workers increasingly defer to AI systems.
What’s critical to note is that you, the human you, must quality check AI, not the other way around.
Paryavi emphasizes the importance of maintaining human oversight and judgment in AI workflows, warning against reversing the proper hierarchy where humans become subordinate to AI systems.
How much technology do we really need, and how far are we willing to push the envelope? How much is enough?
Paryavi poses fundamental questions about the limits of AI adoption, challenging the assumption that more AI integration is always better for workers and organizations.
Our Take
This analysis represents a crucial counternarrative to the prevailing AI productivity hype. While most coverage focuses on efficiency gains and cost savings, Paryavi and the Work AI Institute research reveal a hidden cost: the gradual hollowing out of human cognitive capabilities. The concept of an “illusion of expertise” is particularly insightful—workers may feel more capable while actually becoming less so, creating a dangerous disconnect between perceived and actual competence. This has profound implications for organizational resilience: companies may discover too late that their AI-dependent workforce lacks the deep expertise needed for innovation, crisis management, or quality control. The solution isn’t rejecting AI but implementing it thoughtfully, with role-specific deployment and mandatory human oversight. The real productivity question isn’t how fast AI can work, but whether it’s building or eroding the human capital that drives long-term competitive advantage.
Why This Matters
This story highlights a critical blind spot in the AI adoption rush: while companies focus on productivity gains and efficiency metrics, they may be inadvertently creating a workforce that’s increasingly dependent on AI and losing fundamental cognitive skills. The concept of “quiet cognitive erosion” represents a long-term threat to workforce capability that won’t show up in quarterly productivity reports.
The implications are particularly significant for early-career workers who may never develop the deep analytical and creative thinking skills that previous generations built through hands-on experience. This could create a skills gap crisis where workers appear productive but lack the underlying expertise to handle complex problems, innovate independently, or adapt when AI tools fail or produce incorrect outputs.
For businesses, this raises important questions about sustainable AI implementation strategies. The research suggests that indiscriminate AI deployment could backfire, creating a workforce that’s faster but less capable of critical thinking, quality control, and genuine innovation—the very skills that drive competitive advantage in knowledge work.
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Source: https://www.businessinsider.com/ai-impacts-confidence-job-skills-career-think-tank-ceo-says-2025-12