Clearly, providing only the input data and results in a data archive, even if fully FAIR-data compliant, is not enough for reproducibility, if part of the results are obtained in an incompletely documented way and/or via some custom-tailored analysis software, which is not properly stored and versioned. The NOMAD AI toolkit already enables re-run AI software on FAIR data for a relatively small set of Jupyter notebooks at the price of human-intensive maintenance. The grand-challenge is to develop a strategy to scale up such maintenance in a (semi-)automatic fashion, so that all AI tools from the community can be preserved according to FAIR practices, fully achieving scientific reproducibility. Clearly, these reproducibility concepts and the use of Jupyter notebooks also imply that newcomers to AI can use the software that already exists at the NOMAD infrastructure, train themselves and adjust and advance the analysis tools towards their own but different applications.