Showing 25–36 of 49 articles tagged AI Product Strategy

The 'never rewrite' doctrine was based on rewrite cost. AI has collapsed that cost to days. Pre-launch rewrites are now a product strategy, not a failure.

Per-seat pricing is dying but the replacement is not simple. A practical framework for AI pricing that covers usage-based, outcome-based, and hybrid models.

Going from zero to end in hours sounds like progress. It's also how you ship a product nobody can navigate. The real skill is knowing when to stop.

Most AI governance is either theatre or a bottleneck. A risk-tiered framework built from shipping AI features to AFSL-regulated Tier 1 banks in production.

AI coding is the sixth abstraction layer in 80 years. Every previous layer was dismissed as not real programming by the practitioners of the one below.

DAU, time-in-app, and NPS were built for a world where humans do the work. AI products need different metrics. A framework for what to measure and why.

Scaffolding gives you 10-20% gains that the next model wipes out. The bitter lesson for product builders: give the model tools and a goal, not a workflow.

AI features that work in demos fail in deployment because adoption is a product problem, not a training problem. A playbook from rolling out AI to Tier 1 banks.

Weekend build to 145K GitHub stars to acquisition in weeks. The pattern: agents that execute locally instead of chatting in a browser window win on adoption.

Your AI product market fit depends on a model that has not shipped yet. Build your product architecture for the capability curve, not today's snapshot.

I built AI voice receptionists that handle real phone calls for real businesses. Latency, conversation flow, graceful handoff. Here's what actually matters.

AI's biggest obstacles are not technical. They are structural: professional guilds, regulatory capture, procurement inertia, and incumbents profiting from it.