Computational can be a huge bottleneck. Some steps they really do take dozens of hours to run on cluster. And you are not the main character, others might be using the cluster and your jobs might be waiting in a queue. You might not be able to appreciate parameters need adjustment until the run is over and you evaluate output.
Another delay point is getting collaborators schedules to align for meetings on progress or potential directions.
Placing the results in context takes some time but not so much as you might guess if you are constantly reading and writing sourced paragraphs and skeleton papers needing only results plopped in when they are ready and some exposition in the discussion section.
Writing the code might be the fastest step in the process already.
In my computational niche the bottleneck was always writing up the results :) And wow does AI help there.... It's not hard to get a decent first draft written by AI based on my existing results.
Another delay point is getting collaborators schedules to align for meetings on progress or potential directions.
Placing the results in context takes some time but not so much as you might guess if you are constantly reading and writing sourced paragraphs and skeleton papers needing only results plopped in when they are ready and some exposition in the discussion section.
Writing the code might be the fastest step in the process already.