Every product moves through a lifecycle, and the right decision depends on where you are in it. What's smart at launch is often wrong at maturity.
Introduction (find product-market fit, learn fast), Growth (scale what works, build the engine), Maturity (defend the moat, optimise margins), and Decline (harvest, pivot, or sunset gracefully).
Early on you optimise for learning and speed. Later you optimise for reliability, unit economics, and defensibility. Using growth-stage tactics at introduction — or vice versa — burns time and money.
Killing a feature or product is a product decision too. Continuity for existing users and a clean migration path matter more than ego.
An AI product often re-enters 'introduction' with every model generation — a capability that was infeasible last year becomes a whole new growth curve. Treating the lifecycle as a loop, not a line, is an AI-era mindset.
Ask 'what stage am I in?' before choosing a tactic — the same move can be right or wrong depending on the answer.
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