Developers claim huge AI productivity boosts, but the piece shows why those gains rarely improve lead times, reliability, or customer value, urging leaders to measure outcomes the business cares about.
Developers love to brag about AI code assistants delivering 2x, 5x, or even 1000% more output, but those numbers usually count lines of code or commits-metrics no customer watches. The article points out that when you measure productivity with the wrong yardsticks you miss the real bottlenecks: lead times, reliability, and cost.
It argues that most teams are blind to how much time they spend fixing bugs versus delivering user value, and they rarely know the business goals their software should serve. Without that visibility, teams can launch massive "firehose" AI initiatives that look impressive on paper but add no measurable business benefit.
The piece gives concrete examples: a team that touts a 10x boost from Claude Code still sees the same user complaints and unchanged revenue. It recommends that leaders anchor any AI or technical effort to clear outcomes-whether shortening lead time, improving uptime, or aligning with a specific market goal-so productivity claims become meaningful.
In practice, this means tracking lead times, error rates, and cost per change, and asking every technical initiative: "What business problem does this solve?" If the answer is vague, the effort is likely a waste of time and money.
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