CONCEPT
Innovation Instability
The structural problem Rogers's framework does not anticipate: AI is not a stable innovation diffusing through time but a continuously transforming trajectory, resetting the adoption curve with each capability leap.
Innovation instability is the second major limit Rogers's framework encounters in the AI transition. His adopter categories assume a fixed innovation against which adopters can be ranked by timing. An innovator adopts the same innovation the laggard will later adopt — the innovation is stable through the diffusion process. AI tools are not stable. Each model release, each capability expansion, each new application domain transforms what the innovation is. The innovator who adopted GPT-3 in 2020 adopted a categorically different technology than the majority evaluating GPT-4 or Claude 3.5 in 2024. The innovation is a moving target, and Rogers's framework, designed for stable innovations, needs substantial extension to accommodate this.
In The You On AI Field Guide
The instability operates on multiple timescales. Short-term: models are updated weekly or monthly, with each update introducing capabilities that change how the tool should be used. Medium-term: major model generations (GPT-3 to GPT-4 to GPT-5) represent qualitative shifts in capability. Long-term: the entire paradigm of how