A growing phenomenon known as the Great AI Deskilling suggests that while artificial intelligence is boosting workplace output, it is simultaneously eroding the core skills of employees. Recent anecdotes and expert analysis indicate that heavy reliance on AI tools may lead to a dangerous regression in human capability, particularly for junior workers who lack the opportunity to build foundational experience. Josh Anderson, a software consultant with 25 years of experience, recently documented his use of AI to build a mobile app without writing a single line of code. Initially, the process felt effortless, with features appearing in minutes. However, as the project grew, Anderson found the pace slowing and the collaboration with the chatbot becoming a struggle. When he eventually attempted to intervene and modify the code manually, he experienced significant hesitation and a loss of confidence, despite his deep expertise. He described the feeling as his mental muscle memory having atrophied after months of inactivity, noting that while he understood the logic, his ability to execute the work had diminished. This personal experience highlights a broader concern among researchers. John Nosta, founder of NostaLab, calls this the "AI rebound effect," where improved performance masks declining ability. He argues that AI reverses the natural human cognitive process, which typically moves from confusion to structure and finally to confidence. By providing answers before questions are fully understood, AI creates an overinflated sense of competence while the underlying skill set falls below baseline levels. Rebecca Hinds of the Work AI Institute warns that fluency generated by AI can create an illusion of expertise, making it difficult to distinguish between a worker's knowledge and the technology's output. She distinguishes between two outcomes: using AI intentionally to free up time for high-level judgment, which creates a cognitive dividend, versus using it reflexively as a shortcut, which accumulates cognitive debt. The latter makes people faster at tasks but silently erodes their skills. The risk is most acute for early-career workers. Traditional entry-level roles have served as training grounds for breaking down messy problems and defending one's thinking under pressure. When AI handles these foundational tasks, new employees can appear competent on paper while lacking the resilience and deep understanding required when tools fail. Ben Eubanks of Lighthouse Research & Advisory notes that AI widens the gap between learning concepts in school and applying them in the real world, as employees no longer need to wrestle with problems to find solutions. Furthermore, corporate evaluation metrics are shifting, with some companies now rewarding the frequency of AI usage rather than deep understanding. This incentive structure accelerates deskilling. To counteract this trend, experts like Mehdi Paryavi are calling for the creation of "mental gyms." These would be dedicated spaces or training programs where employees deliberately practice problem-solving without AI assistance, much like physical exercise builds muscle. Until such measures are adopted, the workforce risks facing a future where it can generate output but struggles to repair the systems it creates when the technology is unavailable.