The Climate Modelling Intercomparison Project coordinates climate projections made by the world's flagship climate models which feed into reports such as the IPCC. It should remain a concern that even in the 6th version of the project, relatively few of the models have made their code accessible to other scientists, let alone the general public. Furthermore, the consideration of what may or may not constitute good code is of interest to all those involved in geoscientifc model development. This paper may come at an interesting point in time. Since their inception in the 1960s, prognostic climate models have always used fortran and similar languages to create algorithms which directly reproduce our basic geoscientific understanding of how the climate evolves, through differential equations. The authors propose that things may be evolving in a new direction, with the potential incorporation of machine learning and other forms of AI into future generations of the models. This will surely present new challenges to the concept and assessment of scientific reproducibility and code development.