A thing that I think about is that what you have said kinda compares itself to current math crisis. If we are only focused on the outcome and not the path it took to get there (i.e. understanding mechanisms of how the drug works) we might lose additional paths for discovery that would have bloomed from that process. Maybe an approach that did not yield a usable drug had a mechanism that would aid somewhere else. Yet the failure would be discarded as this does not grant the outcome for a model that attempts to optimize. Same is for math.
As aside I agree with you, but also worth noting that long horizon reasoning is difficult and AI, as good as it is, cannot predict something like 5-10 year drug interaction. Can it heal the patient now but cause a more aggressive cancer to come back and kill them in 5 years? You might say it is cruel to deny a person this treatment but if the doctor and manufacturer don't know the long term outcomes would it not be dishonest to tell the patient they are out of the danger? My point is, when we accelerate, we still should make sure we won't accelerate into a wall.
As aside I agree with you, but also worth noting that long horizon reasoning is difficult and AI, as good as it is, cannot predict something like 5-10 year drug interaction. Can it heal the patient now but cause a more aggressive cancer to come back and kill them in 5 years? You might say it is cruel to deny a person this treatment but if the doctor and manufacturer don't know the long term outcomes would it not be dishonest to tell the patient they are out of the danger? My point is, when we accelerate, we still should make sure we won't accelerate into a wall.