Lessons from building an AI-native startup — every module anchored to a real, dated decision, with every identifying detail changed. This is a course, not a memoir: the deliverable is a transferable framework.
Understand the parse → chunk → embed → store → retrieve → synthesise pipeline that turns scattered AI conversation history into a queryable strategic asset, and identify the first step toward building an equivalent for your own decision-making history.
2Distinguish the three observable phases of AI adoption, recognise the concrete markers of moving between them, and diagnose which phase a given team or leader is actually in.
3Design and run an AI-assisted market-sizing, pricing, and qualitative-research exercise, and know how to validate AI-generated findings rather than just trusting them.
4Decompose a repetitive business process into an agent pipeline, produce a realistic cost model grounded in real figures, and anticipate the operational failure modes that come with running agents at scale.
5Explain why AI-driven search surfaces some businesses over others — as recommendability, not keyword-matching — and build a query-match style audit rather than relying on generic SEO habits.
6Apply a risk checklist to any AI-built prototype before shipping it, recognise the patterns where AI coding assistance genuinely earns trust, and track how your own judgement should change as you gain experience.
7Distinguish product risk from distribution risk, recognise the gap between channel reasoning and actual measurement, and understand how AI dialogue can surface real positioning pivots.
8Build a working, queryable knowledge base out of your own scattered decision history, and use it — through the frameworks from Modules 2–7 — to surface at least one real insight you'd otherwise have lost.