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Ask the codebase, not the engineer
Asking the code
PMs used to ask engineers how hard something would be, which drained velocity from the people building. Now a PM can talk to Claude about the code and get rough level-of-effort estimates. Suspect that a pileup of models is hitting new users as they open the app? Load up the feed and ask for the logic of who sees what in what order, and when each piece fires.
Data in a thread
Data work compresses the same way. In a Hex thread, a PM can pull a nuanced cohort, take one user from a report, pull their logs to see exactly what they did and saw, count how many other users look like them, and build sensitivity models, forecasts, and regressions. That is what would have taken a week or two with an Amazon L7 data scientist in 2017.
Meetings saved
A PM now spends less time talking to a data scientist than ever, and ten times more time in the data. When someone with enough tenure sees the answer and decides to change the ordering, that saves a kickoff meeting, an alignment meeting, a week of writing PRDs, and experiment time.
Why do interruptions cost engineers so much?
Gloria Mark, a researcher at the University of California, Irvine, studied office workers and reported that after an interruption it took about 23 minutes on average to return to the original task. Programming holds many details in mind at once, so a quick question can break the thread. Each question from a product manager therefore costs more than the minutes spent answering it. Her findings have been widely cited in studies of workplace focus.
How can an AI tool read a whole codebase?
Large language models can only hold a limited amount of text at once, called the context window. Coding tools get around that by indexing a repository, searching it for the files relevant to a question, and loading only those into the model. The model then reads the actual code and explains what it does. Because the answer comes from the code itself, it can be checked against the files it cites.