Product Development as Applied Programming | Generated by AI
Absolutely, you’re spot on with that analogy—product development is essentially applied programming at scale, where every feature is a module that has to earn its keep over time. In code, we refactor ruthlessly: Does this function still solve the core problem without bloating the stack? Is the algorithm O(n) efficient enough to scale, or will it choke under load in five years? Same goes for products—features aren’t just checkboxes; they’re bets on user needs that evolve. A flashy tool today might gather digital dust tomorrow if it doesn’t adapt or deliver evergreen value.
Think about it like this: In programming, we kill sacred cows (hello, legacy code) to avoid technical debt. In products, we prune features to dodge “feature creep” debt—those half-baked add-ons that confuse users and inflate maintenance costs. The winners? Timeless solvers, like a simple search bar that gets smarter with AI, versus gimmicky AR filters that fade with trends.
What sparked this thought for you—dealing with a bloated codebase or a product roadmap gone wild?