The promise of AI as an educational tool has led many down a problematic path: treating AI models as answer vending machines rather than learning companions. This approach is fundamentally flawed, and it's time we addressed it head-on.

Core Thesis: Effective AI-assisted learning requires treating AI as a teacher and guide rather than an answer provider, while maintaining a strong foundation in traditional learning methods.

The Foundation Problem

One of the most concerning trends in AI-assisted learning is the attempt to bypass fundamental knowledge acquisition. Take CUDA programming, for instance. As one practitioner pointedly noted:

"You cannot just prompt them to teach you; you need to know linear algebra first."

This observation cuts to the heart of the matter. Without proper foundations, AI-assisted learning becomes a house of cards - impressive at first glance but fundamentally unstable.

The Right Approach: Gradient-Based Learning

Instead of seeking quick answers, successful learners are adopting a gradient-based approach to AI-assisted learning:

"Adding gradients of what a CUDA level 0 programmer to level 5 should know helps."

The Practice Imperative

Perhaps the most crucial realization is that AI cannot replace practice. Complex skills require hands-on experience, regardless of how sophisticated our AI tools become. The role of AI should be to guide practice, not replace it.

A Call to Action

It's time to reshape our approach to AI-assisted learning. We must stop treating AI as a shortcut and start viewing it as a complementary tool in a comprehensive learning strategy. This means: