Professions like law and medicine rely on a pyramid structure where newcomers learn by performing basic tasks. If AI automates this essential junior-level work, the entire model for training and developing senior experts could collapse, creating an unprecedented skills and experience gap at the top.

AI makes 'yesterday's expert competence' cheap, leading to an abundance of decent but generic outputs (e.g., code, essays). This devalues standard work and increases demand for true experts who can add nuance, create systems, or produce something genuinely novel that stands out.

The drive for AI efficiency is eliminating entry-level jobs, breaking the traditional apprenticeship model. This dynamic risks creating a future deficit of skilled experts ("verifiers") needed to manage complex AI systems, while simultaneously accumulating hidden systemic risks.

Meta is monitoring employee mouse movements and keystrokes to train AI agents. This practice mirrors 'Taylorism,' the historical method of measuring and optimizing factory workers' physical movements, with the modern parallel being knowledge workers training their own digital replacements.

Unlike past technologies that automated specific tasks, AI threatens to automate all economically valuable human labor. This removes the fundamental, non-seizable leverage that the general populace holds, creating a power vacuum that can be filled by capital owners.

AI models are trained on past human work (code, articles, designs), making those skills cheap and accessible. This abundance creates homogenous, default outputs or "slop." Consequently, the market develops an urgent demand for human experts who can create something novel and differentiated, moving beyond the model's defaults.

By replacing junior roles, AI eliminates the primary training ground for the next generation of experts. This creates a paradox: the very models that need expert data to improve are simultaneously destroying the mechanism that produces those experts, creating a future data bottleneck.

AI will create near-perfect transparency into employee productivity, eliminating stable, salaried roles for those who are merely "competent enough." The ability to hide in a large organization will disappear, creating a barbell economy of elite performers and an "unproductive class" reliant on support.

The 'augmentation trap' shows that while AI can boost immediate productivity, it leads to cognitive offloading. This causes existing employees' skills to atrophy and prevents new employees from ever developing crucial discernment, creating a less capable workforce in the long run.