Of course, if we say that the GPT-3 model is successful, it only implies that we have established that the GPT-3 model has identified correlations in the current training data that can be successfully exploited to make predictions. However, this does not imply that the correlations are always meaningful or related to cause-effect relationships. Hence, our research does not stop here. The next step will be to use GPT-3 to identify these correlations and ultimately get a deeper understanding. In this context, we argue that GPT-3 is only a tool to make more effective use of the knowledge scientists have collected over the years. It is also important to mention that while the training corpus contains chemistry information, many, if not most, scientific articles and results (including all failed or partially successful experiments ) have not been seen by GPT-3. Hence, one can expect an even more impressive performance if these data are added to the training data.