I study how interacting with AI reshapes the problems people think they're solving
Most work on AI focuses on what it helps us produce. I focus on how interaction with AI changes the problem itself, and how those shifts evolve over time.
This work examines how problem representations evolve during human-AI interaction.
Across iterative exchanges, small contributions can change into meaningful shifts in how a problem is defined.
These shifts shape what gets created, what gets pursued, and ultimately what gets solved.
When people interact with AI, the problem they are working on isn't static.
It evolves through the interaction itself.
A single suggestion may seem small. A reframing. An added constraint. A new direction.
But across multiple exchanges, these small shifts can accumulate.
Over time, the problem can become meaningfully different from where it began.
I refer to this process as generative drift.
The gradual reshaping of problem interactions through iterative human-AI interaction.
This matters because these shifts are often subtle. They may not be experienced as change in the moment, even as they reflect on the trajectory of the work.
Understanding generative drift helps us examine how AI shapes the problems we think we are solving.