Leland+ for AI Services

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.

How to Make AI Actually Useful at Work [8/6/2026] (Recording)

Opening ChatGPT now and then isn't the same as working better. This session is about the gap between dabbling and real output: finding the repetitive, high-friction workflows worth automating, knowing when to reach for a prompt vs. a project vs. an agent vs. an API, and measuring what you actually save. With practical case studies, you'll leave with two or three workflows you can improve immediately — and a way to find more. What we'll cover: - Finding repetitive, high-friction workflows - Prompts vs. projects vs. agents vs. APIs vs. automation - How businesses are actually implementing AI - Practical case studies - Measuring time saved and output gained You'll leave with: Two or three workflows you can improve immediately, plus a framework for finding more.