AI has made it dramatically easier to prototype software. Features that once took weeks can now be sketched out in hours.
But in early-stage products, I keep seeing the same pattern repeat:
teams add AI before they’ve nailed the fundamentals.
Not because AI is bad — but because uncertainty is still high.
Before product-market fit, teams are often still unclear on:
who their core users really are
which problem actually matters
whether simpler logic would ship faster and teach more
In that phase, AI doesn’t reduce ambiguity.
It often amplifies it.
More configuration.
More edge cases.
Harder debugging.
Slower feedback loops.
AI works best as a multiplier of clarity, not a substitute for it.
Once user behavior is understood and the core workflow is stable, AI can meaningfully accelerate development. Before that, it can quietly become the bottleneck.
I recently wrote a deeper breakdown on when AI helps early-stage products — and when it slows them down.
Curious how others here decide when AI belongs in a product, especially before PMF.