Leland+ for AI for Product Development

What to Learn When AI Can Do the Rest [8/13/2026] (Recording)

Everyone says to learn AI. Almost nobody tells you which parts, and guessing wrong costs you months. This session walks through the major AI career paths and how much technical depth each one actually needs. Some of them reward going deep on one narrow thing. Most reward pairing AI with domain knowledge you already have. The harder question is what's worth learning at all. A lot of what gets recommended right now is a tool that won't exist in a year, and it isn't obvious which is which from the outside. What we'll cover: - The major AI career paths and how to choose between them - How much technical depth you actually need - Specialist vs. generalist - Pairing domain expertise with AI - Durable skills vs. temporary tools You'll leave with a clear direction and a prioritized learning roadmap.

Build an AI Project Worth Talking About [7/30/2026] (Recording)

Anyone can spin up a demo in an afternoon now — which means a demo proves nothing. What gets remembered is the thinking around a project: a real problem, a defensible scope, and clear ownership of the decisions. We'll cover where strong ideas come from, what makes a project credible to employers across business, product, finance, operations, and technical roles, and how to scope something you'll actually finish. You'll leave with a shortlist of ideas and a way to pick the strongest one. What we'll cover: - Where strong project ideas come from - Starting with a real problem - What makes a project credible to employers - Right-sizing difficulty and scope - Explaining your decisions and contribution You'll leave with: A shortlist of project ideas and a framework for choosing the best one.