Students are required to memorize vast amounts of information but are rarely taught how to do so effectively. Teaching memory techniques as a foundational skill would reduce time spent on rote learning. This frees up students' cognitive resources to focus on higher-level analysis, context, and understanding—the actual goals of education.

Rather than causing mental atrophy, AI can be a 'prosthesis for your attention.' It can actively combat the natural human tendency to forget by scheduling spaced repetitions, surfacing contradictions, and prompting retrieval. This enhances cognition instead of merely outsourcing it.

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.

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.

The idea of separating "fact learning" from "skill learning" is a false dichotomy. Models need a base of internalized facts to reason effectively. The key is developing intelligence to compress what's important and discard what isn't, much like lossy human memory.

As AI handles more technical tasks, understanding fundamentals like math is still vital. This deep knowledge trains your own "neural network," enabling the creation of robust mental models and abstractions needed to reason at higher levels of complexity, much like learning arithmetic is necessary despite calculators.

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.

Just as crawling is a vital developmental step for babies even though adults don't crawl, some learning processes that AI can automate might be essential for cognitive development. We shouldn't skip steps without understanding their underlying neurological purpose.

When a basic fact (like 7x8=56) is fluent, it doesn't consume a 'working memory slot.' This is critical for complex, multi-step problems like algebra, where limited mental bandwidth is needed for higher-order thinking. A lack of fluency is the root of many academic struggles.