Information Accessibility And Brainrot Coding

Information retrieval systems have evolved dramatically — from traditional city libraries to AI-powered tools.

The process of obtaining answers to questions is becoming increasingly streamlined. As tools become more technologically sophisticated, their capability to address specific queries grows proportionally. While every technological advancement comes with its trade-offs, we must examine the implications of evolving search technologies.

The evolution of information retrieval

In the library era, users inherently understood that libraries contained collections of “comprehensive” information units. One couldn’t simply approach a bookshelf and expect an immediate answer to “why is the sky blue?” — such specific questions rarely warranted entire books. To locate relevant information, one had to acquire substantial contextual knowledge. This peripheral knowledge inevitably led to a deeper understanding of the broader subject matter. For instance, understanding the blue sky phenomenon required familiarization with fundamental concepts of optics and physics.

In the Google era, users still needed to adapt their queries to match search engine indexing patterns. However, the information space now consists of numerous “granular” pieces of content. While humans still author this content, finding an answer to “why is the sky blue?” has become significantly more accessible — the democratization of publishing has enabled multiple authors to address such specific queries. The search engine era accelerated information retrieval but simultaneously reduced comprehensive understanding, leading to more fragmented knowledge acquisition patterns.

The LLM era has eliminated the “overhead” of understanding the broader subject domain — providing personalized responses to any query. This personalization has effectively created maximum isolation from contextual knowledge not directly related to the specific question at hand.

The cost of convenience

The flexibility of large language models allows people not only to get answers to questions but to find easely applicable solutions to their problems, without gaining knowledge of either the causes of these problems or the costs of the solutions.

This fact violates a crucial cause-and-effect relationship — previously, a necessary condition for solving a problem was understanding the problem. Now engineers who don’t understand the causes of problems solve them in ways whose risks they don’t understand either.

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More code leads to more errors, which results in more cognitive fatigue and a less conscious approach. Just as a piano doesn’t play without a pianist, complex instruments require mastery. In skilled hands, even primitive tools can become powerful, but if we rely too much on and abuse LLMs, we risk losing the depth of understanding and critical thinking skills.

10x bugs
10x LOC
10x • (-1’) understanding

Relying on Vibe Coding and LLMs without careful thought can create a false sense of productivity, while actually leading to a decline in our intellectual abilities. Instead of mastering our tools, we might end up being controlled by them. I remain optimistic about AI’s potential, but LLMs are most effective for those who have a solid understanding of the problem and can clearly define what they need. The main strength of LLMs is in turning detailed instructions into code, but this code can become problematic without thorough engineering review. When used without thoughtful consideration, even the most advanced technologies can become crutches that erode our capacity for deep and creative thinking. Unfortunately, many people voluntarily deprive themselves of this capacity.