The original Google Maps redesign simplified five search boxes into one. Years later, the app is again cluttered. This illustrates a natural product lifecycle: feature expansion leads to clutter, which necessitates a periodic, principles-based simplification to refocus on core user needs.
Tools like Descript excel by integrating AI into every step of the user's core workflow—from transcription and filler word removal to clip generation. This "baked-in" approach is more powerful than simply adding a standalone "AI" button, as it fundamentally enhances the entire job-to-be-done.
To launch in India, where navigation is traditionally landmark-based, Google Maps fundamentally changed its system from street names to culturally relevant landmarks. This required deep user research to identify what was prominent and noticeable from the street, like temples or specific shops.
Chatbots are fundamentally linear, which is ill-suited for complex tasks like planning a trip. The next generation of AI products will use AI as a co-creation tool within a more flexible canvas-like interface, allowing users to manipulate and organize AI-generated content non-linearly.
AI's unpredictability requires more than just better models. Product teams must work with researchers on training data and specific evaluations for sensitive content. Simultaneously, the UI must clearly differentiate between original and AI-generated content to facilitate effective human oversight.
A speaker's professional headshot was altered by an AI image expander to show her bra. This real-world example demonstrates how seemingly neutral AI tools can produce biased or inappropriate outputs, necessitating a high degree of human scrutiny, especially when dealing with images of people.
The magic of ChatGPT's voice mode in a car is that it feels like another person in the conversation. Conversely, Meta's AI glasses failed when translating a menu because they acted like a screen reader, ignoring the human context of how people actually read menus. Context is everything for voice.
Presented with the "LinkedIn for AI" problem, the designer's first step isn't visual design. It's product strategy: clarifying the core objective (e.g., matchmaking, certification), identifying the target user groups (job seekers, employers), and defining what "a good match" even means in this new context.