While professional engineers focus on craft and quality, the average user is satisfied if an AI tool produces a functional result, regardless of its underlying elegance or efficiency. This tendency to accept "good enough" output threatens to devalue the meticulous work of skilled developers.
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When deploying AI tools, especially in sales, users exhibit no patience for mistakes. While a human making an error receives coaching and a second chance, an AI's single failure can cause users to abandon the tool permanently due to a complete loss of trust.
Once AI coding agents reach a high performance level, objective benchmarks become less important than a developer's subjective experience. Like a warrior choosing a sword, the best tool is often the one that has the right "feel," writes code in a preferred style, and integrates seamlessly into a human workflow.
The trend of 'vibe coding'—casually using prompts to generate code without rigor—is creating low-quality, unmaintainable software. The AI engineering community has reached its limit with this approach and is actively searching for a new development paradigm that marries AI's speed with traditional engineering's craft and reliability.
Using AI to code doesn't mean sacrificing craftsmanship. It shifts the craftsman's role from writing every line to being a director with a strong vision. The key is measuring the AI's output against that vision and ensuring each piece fits the larger puzzle correctly, not just functionally.
Users mistakenly evaluate AI tools based on the quality of the first output. However, since 90% of the work is iterative, the superior tool is the one that handles a high volume of refinement prompts most effectively, not the one with the best initial result.
With AI agents automating raw code generation, an engineer's role is evolving beyond pure implementation. To stay valuable, engineers must now cultivate a deep understanding of business context and product taste to know *what* to build and *why*, not just *how*.
The process of struggling with and solving hard problems is what builds engineering skill. Constantly available AI assistants act like a "slot machine for answers," removing this productive struggle. This encourages "vibe coding" and may prevent engineers from developing deep problem-solving expertise.
Research highlights "work slop": AI output that appears polished but lacks human context. This forces coworkers to spend significant time fixing it, effectively offloading cognitive labor and damaging perceptions of the sender's capability and trustworthiness.
As AI generates more code, the core engineering task evolves from writing to reviewing. Developers will spend significantly more time evaluating AI-generated code for correctness, style, and reliability, fundamentally changing daily workflows and skill requirements.
The era of winning with merely functional software is over. As technology, especially AI, makes baseline functionality easier to build, the key differentiator becomes design excellence and superior craft. Mediocre, 'good enough' products will lose to those that are exceptionally well-designed.
