AI Product Strategy

Memory sounds like a simple product feature. A user tells an assistant something today. The assistant remembers it tomorrow. Everyone applauds, the demo works, and someone writes “personalization” on a roadmap slide. Lovely. We have rediscovered a notebook. The harder problem begins when the user does not explicitly say what matters. A student says, “It’s fine.” A customer writes, “No worries.” A therapy-like support user replies with a short, polite sentence that looks neutral in isolation. Locally, the words are harmless. Historically, they may be resignation, guardedness, disappointment, or the emotional equivalent of quietly closing the door. ...

Car settings are usually pretending to know you. Sport mode assumes you are impatient. Eco mode assumes you have discovered moral superiority through fuel efficiency. Comfort mode assumes everyone in the vehicle prefers to be gently transported like a bowl of soup. These modes are not useless. They are just blunt. They adjust a handful of parameters and call the result personalization, which is a bit like calling a restaurant “personalized” because it offers small, medium, and large. ...

A chatbot can act like a pirate for three turns. That is not the impressive part. A teenager with a Halloween hat can also do that. The harder problem begins when the agent has to remember what happened last week, preserve a recognizable personality across changing situations, make choices consistent with its motives, avoid borrowing another character’s copyrighted voice a little too enthusiastically, and still behave safely when the user pushes it outside the script. At that point, “pretend you are X” stops being a prompt trick and becomes a systems engineering problem. ...

Image generators fail in very ordinary ways. A prompt asks for a green banana and a blue vase. The model gives you something banana-adjacent, vase-adjacent, and chromatically negotiable. A designer asks for a bowl containing a pizza. The model places the pizza beside the bowl, halfway inside the bowl, or in a bowl-like universe where geometry has apparently resigned. A product team then does the usual dance: collect bad outputs, ask users what they preferred, curate examples, fine-tune later, and call the whole thing “continuous improvement” because the spreadsheet had a date column. ...