How would we know a prototype will have PMF until it’s tested in the wild? You said prioritization and alignment is the bottleneck—I feel that AI enabling fast prototypes enables a bottoms up prioritization rather than top down (which I consider better / higher signal). At least, that’s what i’m seeing at my company. What are your thoughts?
This is such a good question. At a high level, I think AI can and should enable bottoms-up prioritization; the logic being: build faster → test in the wild faster → ship the right things based on tighter feedback loops.
But in large enterprise environments, I'm skeptical. "How well a concept performs in the wild" is just one of several inputs into the ship decision. The others tend to be top-down: platform considerations, security, compliance, stakeholder alignment, budget cycles. None of those get faster just because prototyping does.
So maybe the real unlock isn't faster building, it's whether faster learning can shift how top-down decisions get made. If anything, the volume and speed of insights coming out of AI-enabled builds puts more pressure on product leadership to make clearer, faster trade-off calls. The bottleneck moves up the chain, not away.
I wonder if this is also depending on a product led vs eng led company?
How would we know a prototype will have PMF until it’s tested in the wild? You said prioritization and alignment is the bottleneck—I feel that AI enabling fast prototypes enables a bottoms up prioritization rather than top down (which I consider better / higher signal). At least, that’s what i’m seeing at my company. What are your thoughts?
This is such a good question. At a high level, I think AI can and should enable bottoms-up prioritization; the logic being: build faster → test in the wild faster → ship the right things based on tighter feedback loops.
But in large enterprise environments, I'm skeptical. "How well a concept performs in the wild" is just one of several inputs into the ship decision. The others tend to be top-down: platform considerations, security, compliance, stakeholder alignment, budget cycles. None of those get faster just because prototyping does.
So maybe the real unlock isn't faster building, it's whether faster learning can shift how top-down decisions get made. If anything, the volume and speed of insights coming out of AI-enabled builds puts more pressure on product leadership to make clearer, faster trade-off calls. The bottleneck moves up the chain, not away.
I wonder if this is also depending on a product led vs eng led company?