AI product management
Use AI to move faster without skipping problem framing, customer evidence, or evaluation.
Practical guides for founders, first PMs, product engineers, and product builders: discover customer problems, decide what to build, and ship with AI.
Field guides for small teams building software, AI products, and better product habits.
Use AI to move faster without skipping problem framing, customer evidence, or evaluation.
Go from a real customer problem to an AI feature you can evaluate, release carefully, and improve.
Practical workflows for people close to the code: learn from users, choose a small bet, and give coding agents useful context.
Turn customer signals into a focused plan, and test whether people value what your team is building.
An AI product manager owns problem framing, AI behavior, evaluation, rollout, and outcomes. Learn the skills, responsibilities, and team boundaries behind the role.

Practical guides, product frameworks, and Specky updates — sent when it’s worth your time.
Give AI coding agents the context they need without copying stale docs. This workflow keeps product decisions in one shared source of truth and gives each task clear evidence, constraints, and review checks.

When AI makes prototypes cheap, discovery becomes the work that keeps small teams building the right thing. Use this two-week loop to test a real user problem before expanding a build.
The widely quoted 95% figure is not a universal AI failure rate. Here’s what the 2025 MIT NANDA report measured, what it leaves out, and how product teams can evaluate AI work against real outcomes.