The article critiques the capabilities of large language models (LLMs) in handling complex programming tasks such as implementing algorithms, CSS animations, and codebase refactoring. It emphasizes that LLMs often produce overly complex and ineffective solutions that fall short of human-generated code. The author notes the frustration of debugging LLM-produced code and suggests that the underlying issue may relate to the economics of AI tools, specifically how their costs are tied to the volume of text processed, leading to inefficient outputs.
the highly complex tasks I've handed to them have largely resulted in failure: implementing a minimax algorithm... crafting thoughtful animations in CSS... completely refactoring a codebase.
the LLMs routinely get lost in the sauce when it comes to thinking through the high level principles required to solve difficult problems with computer science.
using the LLM to debug it requires sending the bloated code back and forth to the API every time I want to holistically debug it.
the problem runs deeper, and it has to do with the economics of AI assistance... Many AI coding assistants, including Claude Code, charge based on token count.
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