"The quality of those learning loops depends on the quality of the signals we build into them. As we move from static interfaces to generative ones, understanding the signals produced by our experiences and how those signals tie back to a user's intent becomes foundational. These signals help generative systems reduce model loss, meet expectations, and continue doing what we care about most: helping people make progress in ways they find meaningful."
"I discussed how AI concepts overlap with the work of product designers as we optimize across three key levels: the interfaces we aim to make frictionless, the journeys that help users find value, and the connection to the business outcomes we hope to achieve. That work is becoming even more important today. As AI-driven and generative tools move into our day-to-day workflows, the experiences we design no longer stop at the interface. Interfaces can now generate, adapt, and learn from what users do next."
AI-driven and generative tools enable interfaces to generate, adapt, and learn from user behavior, extending experiences beyond static screens. Learning loops depend on the signals embedded in experiences and require mapping those signals back to user intent. Clear instrumentation aligns telemetry with high-level business goals and prepares systems to translate signals into intent for iterative optimization. Well-defined signals help generative models reduce loss, meet expectations, and support meaningful user progress. Measurement should prioritize defining the meaning of events before collecting data. Design responsibilities shift toward specifying the signals and feedback that shape intelligent, adaptive product experiences.
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