Building a Habit for Tracking AI Advances and Applying Them as a PM

How do you keep yourself current on what is changing in AI? ```hint Show a system, not a reading list Think about what makes your habit repeatable, how you tell a real shift in what models can do from hype, and how what you learn reaches your product work. ``` ### Clarifying Questions - Is the interest mainly in research progress, such as new models and techniques, or in how AI features are being built into products like a personal finance app? - Would you like a specific example of something I learned that changed a product decision, or an overview of my habits? - How far into the technical side should I go: model capabilities and limits, or product and market patterns? ### What a Strong Answer Covers - A repeatable habit built on primary material (model release notes, technical reports, documentation) more than on secondhand commentary. - Hands-on use of new models and tools, ideally tested against tasks from the candidate's own product area. - A filter for separating real, shippable progress from hype and for judging what matters to the product's users. - A concrete example, told as situation, action and result, in which staying current changed a decision, a roadmap item or how a team worked. - Spreading what is learned across the team, and honesty about the limits of one's own knowledge. ### Follow-up Questions - Name one recent AI development you think is overhyped and one you think is underrated, and explain why. - How would you decide whether a new model capability belongs on the roadmap of an app that people trust with their financial data? - How would you help engineers and designers on your team stay current without it eating into delivery time?