Curriculum

Discovery and strategy for AI products

AI PM craft is the second strongest theme (88 percent, 73 companies) and building under ambiguity is 68 percent, both core. The report asks for Shreyas-style product sense so the AI PM makes correct macro and micro calls before the data exi

1. 04.01

   Product sense under ambiguity

   You make the big and small product calls before the data exists, treating each call as a prediction you will later check rather than a guess you defend.
2. 04.02

   Discovery for AI

   You run discovery on an AI product by mining real interviews and production logs for the one behavior you can actually move, not a wishlist of features.
3. 04.03

   The problem-first test

   Before you scope an agent, you apply the problem-first test to decide whether an agent, a plain workflow, or nothing at all is the right answer.
4. 04.04

   Qualify the opportunity

   You test whether an opportunity is hard enough and material enough to deserve an LLM, and when it is not, you name the classifier you should have shipped instead: labeled data, a threshold, and a retraining plan.
5. 04.05

   The capability horizon

   You judge the capability horizon as your strategy spine, run the model-upgrade drill on every frontier release to see what a new model just fixed, and use that to decide what compounds, what you refuse to build, what scaffolding to retire, and how you answer the Monday a competitor keynote makes your feature free.
6. 04.06

   Goals

   You turn strategy into cascading goals, sequence a roadmap around the smallest slice you can actually measure, and say no to the loudest request with a priced alternative instead of a flat refusal.
7. 04.07

   INTEGRATION CASE

   Based on source-backed evidence, you draft a falsifiable problem statement and a strategy memo a stranger can defend.