The siren song of LLMs is undeniable for developers: instant code generation, rapid documentation lookup, and seemingly effortless troubleshooting. Yet a cautionary perspective emerges from veteran developer Arya in a recent blog post, arguing that this convenience comes at a hidden cost—the erosion of genuine learning and conceptual internalization.

The Slippery Slope of the "LLM Assistant"

Arya paints a familiar scenario for newcomers: You start a toy project to learn a new concept, leaning on an LLM for guidance. The outputs seem correct, minor syntax errors get fixed by the model itself, and progress feels swift. But beneath the surface, a critical failure occurs:

"You didn't actually think about what the shape of that problem is, you didn't even play with it in your head or on paper to actually understand its contours... The most important part of learning anything is the ability to internalize concepts and build a mental model."

This passive consumption, Arya contends, bypasses the cognitive struggle essential for durable knowledge. Reading 20 lines of generated code isn't equivalent to wrestling with the problem space, experimenting with solutions, and forging your own mental pathways.

The High Cost of Shortcuts in a "Ship It!" Culture

The pressure to deliver quickly—whether driven by startup culture or job hunting—exacerbates the problem. LLMs become tools to accelerate output at the expense of understanding:

"Young people... start using LLMs to their advantage... to catch up to these slop-peddlers... I dislike seeing people led into this hole, it ends up hurting them almost every time."

The danger isn't just temporary confusion. Superficial understanding creates fragile knowledge foundations. Arya warns of "piecing together a puzzle with parts that aren't even from the same box," leading to:

  1. Hidden Knowledge Gaps: Misconceptions embedded early can persist unnoticed.
  2. Critical Failures: The potential for flawed logic or incorrect solutions to surface in production code.
  3. Wasted Time: Hours lost later debugging or re-learning concepts that weren't properly internalized.

Striking a Sustainable Balance

Arya isn't advocating abandoning LLMs. They remain "wonderful things and can be the most useful tools." The key is mindful usage:

  • Awareness is Paramount: Consciously track when and why you reach for the LLM. Is it for boilerplate, or to bypass understanding?
  • Embrace the Struggle: Allow time for genuine problem-solving before automating it away. Sketch solutions, write pseudocode, consult official docs first.
  • Use LLMs as Tutors, Not Oracles: Prompt them to explain concepts step-by-step rather than just generate code. Verify their outputs critically.

The Enduring Value of Deep Understanding

As LLMs grow more capable, the developers who thrive won't be those who delegate thinking, but those who leverage AI to augment their robust, deeply internalized knowledge. True expertise lies not in generating code fastest, but in possessing the mental models to understand why code works, how systems interconnect, and how to solve novel problems LLMs haven't seen. The most valuable investment remains the time spent building that foundation—even if it means typing fewer lines today.

Source: Adapted from insights by Arya (@aryvyo) in "On LLMs and Learning"