diff --git a/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco b/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
index 5ba3d8b..f648325 100644
--- a/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
+++ b/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
@@ -17,7 +17,7 @@ Based on [[https://schlockmercenary.fandom.com/wiki/The_Seventy_Maxims_of_Maxima
 *. Only you can prevent reward hacking.
 *. Your model is in the leaderboards: be sure it has dropout.
 *. The longer your Claude Code runs without input, the bigger the impending disaster.
-*. If the optimizer is leading from the front, watch for exploding gradients in the rear.
+*. If the researchers are leading from the front, watch for hardware failures in the rear.
 *. The field advances when you turn competitors into collaborators, but that’s not the same as your h-index advancing.
 *. If you’re not willing to prune your own layers, you’re not willing to deploy.
 *. Give a model a labeled dataset, and it trains for a day. Take its labels away and call it “self-supervised” and it’ll generate new ones for you to validate tomorrow.