Using generative AI to produce work bypasses the reflection and effort required to build strong knowledge networks. This outsourcing of thinking leads to poor retention and a diminished ability to evaluate the quality of AI-generated output, mirroring historical data on how calculators impacted math skills.

Deep expertise is often built through a 'hazing process' of trial and error—a form of good friction. AI tools, by providing an 'easy button,' remove these critical learning opportunities, which can lead to a decline in high-caliber talent and an over-reliance on superficial, AI-generated solutions.

AI will make users intellectually weaker if it's used merely to obtain answers ('derivatives'). To enhance intelligence, AI must be used as a tool to deconstruct subjects into their foundational 'primitives' or first principles. Over-reliance on AI for final outputs circumvents the difficult cognitive work where true understanding is forged.

Like abundant dietary calories causing metabolic harm when self-control fails, unfettered access to automated intelligence can lead to cognitive atrophy if individuals passively offload thinking. Using AI simply to evade difficult cognitive tasks reduces individual capabilities. To build true operational competence, individuals must actively challenge AI outputs from first principles and treat models as thinking partners rather than intellectual escape hatches.

The process of struggling with and solving hard problems is what builds engineering skill. Constantly available AI assistants act like a "slot machine for answers," removing this productive struggle. This encourages "vibe coding" and may prevent engineers from developing deep problem-solving expertise.

A framework for AI use: delegate 'vicious friction' (tedious tasks like data entry) but retain 'virtuous friction' (challenging problems that require deep thought). Outsourcing the latter prevents the cognitive struggle necessary for learning, expertise, and building new neural pathways.

The struggle to learn, condense, and articulate an idea is more valuable for comprehension than the final output. Relying on AI shortcuts this cognitive "sweat equity," which studies show leads to poor recall, a loss of individual voice, and only a superficial understanding of the subject.

A key driver of AI adoption in the workplace is its ability to smooth over moments of high cognitive effort, like starting a document from a blank page. For brains already exhausted by constant context switching, this is a welcome relief but ultimately creates a dependency that further weakens the ability to focus.

Beyond "digital dementia" (outsourcing memory), "digital deduction" is outsourcing the act of thinking itself. Relying on AI to analyze and problem-solve for you prevents your brain from getting the necessary "exercise," causing cognitive skills to weaken over time.