While most programmers have reached this conclusion, they're generally not day-in day-out jupyter users. They need to understand *everything* is transient for scientists who optimize for proof-of-concept and publish-and-forget-it paper writing.
Which itself is a huge problem.
Happily this mindset is changing, at least in some scientific all fields. For example, in particle physics proposals a document ("data management plan") much be written describing how that unconscionable attitude will not be taken with the experiment's data and software. That said, this transient mindset and derision of real software skills is still fairly prevalent in this field.
More "nature of the beast" in my opinion. Science measures itself by how many alluring women it can date; engineering, by how long it can keep the wife happy.
Even for short-lived experiments reproducibility is important. So much of today's experiments ultimately rely on complex stacks of software to get their results and on data which humans can afford to acquire only once. Preserving both is necessary for future re-validation or reuse.
This problem should not be excused.