AI Product Strategy

Prioritize high-impact workflows and navigate non-deterministic development to build defensible AI products.

Help the user with ai product strategy using insights from 26 guests and posts across Lenny's Podcast and Newsletter.

How to Help

1. Define the wedge - Identify high-friction chores where AI can provide a disproportionate payoff for the user.
2. Select the architecture - Choose between retrieval-augmented generation (RAG) and fine-tuning based on the need for live data vs. specific behavior.
3. Scale agency safely - Design a graduated approach to autonomy that keeps humans in the loop before moving to full automation.
4. Build for the curve - Align product roadmaps with future model capabilities rather than building complex scaffolding for today's limitations.

Core Principles

Account for squishy outputs

Alex Komoroske: "LLMs allow writing shitty software to be significantly cheaper, not necessarily good software, but good enough in certain contexts. And also it means that there's certain software now that isn't plain old computing that can be run cheaply. It's relatively expensive marginal cost."

Design product experiences that assume AI is non-deterministic and imperfect rather than trying to force 100% accuracy into your UI.

Treat products as living organisms

Asha Sharma: "Because these models are so effective at this point, you want to start to tune them to certain types of outcomes. All of a sudden, these are these living organisms that just get better with the more interactions that happen. I think this is the new IP of every single company products that think and live and learn."

Measure success by the team's metabolism in ingesting data and improving learning loops rather than static feature releases.