  The diffusion of new technologies is crucial for the realization of social
and economic returns to innovation. Tracking and mapping technology diffusion
is, however, typically limited by the extent to which we can observe technology
adoption. This study uses website texts to train a multilingual language model
ensemble to map technology diffusion for the case of 3D printing. The study
identifies relevant actors and their roles in the diffusion process. The
results show that besides manufacturers, service provider, retailers, and
information providers play an important role. The geographic distribution of
adoption intensity suggests that regional 3D-printing intensity is driven by
experienced lead users and the presence of technical universities. The overall
adoption intensity varies by sector and firm size. These patterns indicate that
the approach of using webAI provides a useful and novel tool for technology
mapping which adds to existing measures based on patents or survey data.
