Platforms like the NOMAD AI toolkit also foreshadow the scientific-reproducibility utopia. Much of the technology that allows for reaching these goals still needs to be developed, but some important steps have been taken already. First of all, Jupyter notebooks can be uploaded to NOMAD as easily as the data. The upload timestamps and other provenance metadata that allow for the unique identification of each analysis script. Furthermore, users are encouraged to provide a rich set of metadata that are made searchable and therefore will allow other users to locate the notebooks by e.g., model class for the AI analysis, or by used libraries, including their versions. In its current state, the NOMAD AI toolkit allows for the findability and interoperability of the AI-analysis software. In fact, a unique container is currently used for all the notebooks, thus allowing for a full interoperability among the different AI analysis tools. The complexity of the maintenance of such an environment rapidly increases with the number of uploaded notebooks which poses challenges to ensure that stored notebooks can run over the years and produce the same results. However, each set of obtained results, including all the intermediate results along the analysis workflow, can be stored (according to FAIR principles) and automatic tests could be run to check the conformity of the results produced by the re-trained model with the reference ones. Knowing that some piece of code is at some point in time unable to reproduce old results is the necessary condition to try and fix the code in order to conform with the reference results. This solution, which requires quite some human effort, introduces a possibly interesting generalization on the idea of reproducibility, which in some sense is a black-box requirement. I.e, in each step of the analysis, the same input needs to yield the same output, but the details inside the black box are allowed to change. A radically alternative route is to partly renounce to a full interoperability among the notebooks and maintain several different containers within the NOMAD AI toolkit. Such an approach would allow for the creation of specific containers that are not updated, thus allowing for the software installed therein to be always executable. Although the tools used in these not-updated containers cannot always be combined with software installed into other containers , it can still be deployed on new data that have been uploaded at a later time.