A common trap is starting with the assumption that AI must be used, leading to a search for a place to tack it on. This results in superfluous features like a generic "AI assistant," rather than solving a real user need. The correct approach begins with the user's pain.

An analyst argues fans watch sports not for perfect fairness, but for human elements like drama, dialogue, and quirks. This is a lesson for product design: optimizing for pure efficiency can strip a product of the very 'inefficiencies' and imperfections that make it engaging and beloved by users.

It's easy to let edge cases and non-ideal user paths lower the ceiling of an experience. It's often better to downplay the impact on a small percentage of users if it means creating a truly special and optimized experience for your core target persona.

fMRI research revealed that averaging multiple brain scans creates a composite image that represents no single individual's brain activity. This fallacy of averages extends across society, from education to medicine, proving that systems designed for the 'average' fail to serve the individual.

The classic case of military jet crashes reveals a critical design flaw: cockpits were built for the "average" pilot. Out of 4,000 pilots, none fit the average on ten key dimensions. This illustrates how designing for an abstract average can fail everyone in practice.

The current trend of building huge, generalist AI systems is fundamentally mismatched for specialized applications like mental health. A more tailored, participatory design process is needed instead of assuming the default chatbot interface is the correct answer.

Drawing from service dog training, building trust requires designing for the edge scenario, not the average use case. A system's value is proven by its ability to handle what goes wrong, not just what goes right. This is where user confidence is truly forged.

"Work harder" advice is often consumed by Type A personalities who least need to hear it, reinforcing their unhealthy patterns. Conversely, those who would benefit most are least likely to seek it out. This selection bias means popular advice can inadvertently harm its most avid consumers.

Technologists often assume AI's goal is to provide a single, perfect answer. However, human psychology requires comparison to feel confident in a choice, which is why Google's "I'm Feeling Lucky" button is almost never clicked. AI must present curated options, not just one optimized result.