2 min lesson
Prototype-with-AI as a core skill
Connect "The prototype is not a mockup someone else codes later" to the decision you would make.
Step 1 of 2
The builder-PM bar this track prepares you for is simple to state: you build the first version of a feature yourself, get it in front of users and iterate. The prototype is not a mockup someone else codes later. It is the artifact you hand to engineering.
This reframes the whole role. At a company with no traditional PMs and no project managers, a spec that ends at a Figma file is half a deliverable. The bar is a thin slice a real user can click, running on a stack you can deploy without fumbling. Most of your interview prep for this role is reps at exactly that.
Cursor's growth team has a phrase for the shift: deliver demos, not memosThe shift from writing a text spec to building a live, clickable prototype to convey product intent; what gets cheap to make moves the leverage to vision and taste. Press Enter for the full definition.. The build step is collapsing, so the opportunity moves to expressing product vision concretely and fast. That means a live, clickable prototype you hand to engineers and designers in place of a text PRDProduct Requirements Document. The spec describing what to build, for whom and why, before implementation starts. Increasingly the artifact you hand an AI agent as the source of truth for a feature. Press Enter for the full definition. they have to imagine.
Deliver demos, not memosThe shift from writing a text spec to building a live, clickable prototype to convey product intent; what gets cheap to make moves the leverage to vision and taste. Press Enter for the full definition..
The onsite small project is where this gets tested live. Treat it as a prototype-with-AI exercise even if the prompt is written as a spec, because that is how the team actually works.
The reason Plan modeA mode that makes no edits: it researches the codebase and produces an editable plan you review before any code changes. Press Enter for the full definition. pays off for non-engineers: you carry years of pattern-matching in your head, and the underlying models are smart but don't know how you do your work. So you brain-dump the problem and what good looks like, let the agent research the docs and codebase and write a Markdown plan, correct it, then let it execute and test on a few examples. The modes are worth knowing by name: agent (the default), plan, ask (read-only) and debug, plus Projects, where a coordinator agent plans the work and delegates it to other agents.
A lot of it just came from brain-dumping. The models are so smart, but they often don't understand the way we do our work.