What you are probably seeing
Your complex systems often produce edge cases where AI suggestions lack nuance, forcing you to manually patch code that looks correct but fails in production.
You spend more time verifying AI-generated logic than you do writing it yourself, indicating that the automated assistance does not reduce the total cognitive load.
Why it happens
Generative models optimize for likely patterns rather than specific architectural constraints, so they miss the unique edge cases inherent in large codebases.
Without a clear definition of your independent capabilities, you outsource decisions that require domain expertise, creating a dependency that slows down development.
What changed recently
Step by step
1. List three specific coding tasks you completed recently without any AI tool assistance.
2. Compare your manual time for those tasks against the time spent generating and reviewing AI equivalents.
3. Identify the specific architectural decisions where AI outputs required your significant human intervention.
4. Determine which tasks you can fully handle alone and which require expert human review for validation.
5. Adjust your workflow to use AI only for tasks outside your current independent capability set.
Ways to go further
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