Our practices for covering AI adoption have evolved meaningfully, reflecting a better sense of what adoption statistics actually measure.
We started specifying what adoption statistics actually count
Early coverage sometimes cited adoption figures without clarifying whether they measured access, trial, or sustained use. We now try to make this distinction explicit whenever we cite a statistic.
We became more skeptical of self-reported adoption figures
We used to treat company-reported adoption numbers with less scrutiny than we're now comfortable with. We now weight independently verifiable adoption evidence more heavily.
We got more comfortable flagging statistical ambiguity
When an adoption figure's methodology isn't clear, we now say so explicitly rather than presenting the number as more precise than it actually is.
Why this evolution matters
Adoption statistics shape the industry's overall narrative about AI's actual maturity, and we think being more precise about what these numbers actually measure serves readers better than repeating impressive-sounding figures uncritically.