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Aakash Gupta and Austin Miller posted new notes

To become an AI PM, you need six skills on top of core PM. Here's the order to learn them. The pay gap is why it's worth it. Glassdoor puts the average AI PM at $198K in total pay, against about $151K for PMs overall. Most AI PM learning maps I see are generic PM concepts with "AI" in the title. They skip what hiring managers screen for, which is proof you've done the work. So in this map, every stage ends in something you can show. 1. Learn how the models work Tokens, context windows, embeddings, tool calls, agents. You don't need to train a model, but you should be able to sketch everything that happens between a user's prompt and the reply. A PM I coach hit exactly this depth check in technical screens at both Nvidia and Glean. 2. Engineer the context Everyone rents the same models, so the edge is what you feed them. Climb only as far as you need. Prompting first, RAG when answers depend on your data, fine-tuning when a style has to stick. Your proof is a Claude Project loaded with…
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You get interviewed at the level your stories describe. The title on your resume has little to do with it. The table is the ladder most tech companies run, Associate PM to CPO. The column people skip is the one that matters: what you own at each level. An enhancement. Then a feature. Then a problem. Then a problem area. Now take your six best interview stories and ask what each one is about. A PM-level story is about a thing you owned. A senior-level story is about a call you made when two teams wanted opposite things. A lead-level story is about a problem area nobody had handed you yet. A candidate targeting senior who answers with PM-level stories gets downlevelled. The recruiter note reads "strong execution, unclear judgment." Mark the level each story is written at. If they all sit one rung below the job you want, that is your prep list for the week. Which rung are your stories written at?
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