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Speed is a reward,
not a strategy

The speed AI offers is the result of having done the prior work well, not the goal of doing it.

Statement​

Using AI to go fast without having invested in thinking is like running faster in the wrong direction. Legitimate speed emerges when the Problem Statement is precise, the context is well-designed, and the team has consensus. Any other speed is an illusion.

Why it matters​

AI has created an illusion of infinite speed. You can generate code, content, and prototypes in minutes. But speed without direction is not productivity: it's accelerated entropy.

Real speed in Problem-Driven AI is a reward the system grants you when you've invested the right effort in the prior phases. It's not something you can demand or shortcut. It's the natural consequence of clarity.

Practical implications​

  • Don't use "AI makes it fast" as justification for skipping phases.
  • Measure net speed, not gross: include the rework, corrections, and pivots caused by rushing.
  • Celebrate speed only when it comes with validation: going fast in the right direction is the only type of speed that counts.
Anti-pattern: The Speed Theater

What it is. Using AI generation speed as a productivity metric. Generating 10 versions in an hour seems productive, but if none of them solve the real problem, it's Speed Theater.

How to detect it. The team showcases the number of outputs generated, not the number of validated solutions. Demos emphasize "we built this in two days" instead of "this solves the validated problem." Rework cycles are hidden or normalized. Net velocity (progress minus rework) is never measured.

How to prevent it. Measure net speed, not gross: include rework, corrections, and pivots in the velocity calculation. Celebrate speed only when it comes with validation. Ask: "Of everything we generated this week, how much survived contact with real users?"

Connections​