Solving the wrong problem

AI-assisted coding feels impressive when a language model spits out working snippets, but the real insight is that the technology is only remixing what it has seen. LLMs predict the next token based on training data, so they hallucinate when asked to generate code in domains they haven't internalised, like Rust, forcing engineers to take a detour through a well-trained language such as Python before translating back. This reveals that the agents are not truly creative; they are stitching together fragments, which means the same low-level problems get solved over and over.

When developers treat AI output as a black box - "vibe coding" - they often miss constraints, introduce subtle bugs, or rely on code that looks correct but is fragile. The article cites examples of agents adding wrong fragments, getting distracted, or needing constant correction, highlighting the hidden cost of trusting generated code without scrutiny. The broader consequence is a surge of mediocre, hard-to-maintain code that masks skill gaps rather than elevates software quality.

Most of the code shipped today is already sub-par, and AI simply accelerates its production. While inexperienced engineers can deliver functional prototypes faster, the underlying lack of solid engineering fundamentals, testing discipline, and architectural thinking remains unaddressed. Decision makers see the promise of speed and push AI tools, but they end up reinforcing a cycle where developers produce "good enough" code without improving craftsmanship.

For technical leaders the takeaway is clear: use AI as a convenience layer for boring tasks, not as a cure for systemic problems. Invest in continuous education, build higher-level abstractions, and enforce standards that keep code production-ready. Only then does AI become a genuine productivity lever instead of a distraction that reinforces the very inefficiencies it appears to solve.