Increasing AI compute power does not automatically yield higher-quality software, as evidenced by a controlled experiment using Claude Code to build a mission control dashboard. Testing across six effort levels revealed that the "Extra" setting achieved the optimal balance of functionality and polish in 45 minutes, while "Max" and "Ultra" settings resulted in diminishing returns, excessive pixel-polishing, and longer runtimes that disrupt developer flow. High-effort settings often lead to unnecessary complexity rather than superior architecture. Effective AI integration requires treating effort levels as a throttle to maintain momentum, as superior results stem from efficient upfront planning rather than brute-force computation. Ultimately, the most valuable skill for future software engineering involves determining the precise amount of machine effort required to solve a problem without sacrificing productivity or clarity.
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