Python 2 to 3 is a good example of what can be expected to happen - very slow adoption of the new version since companies may just not have the resources or desire to rewrite existing code that is running without problems.
Epochs was given the usual WG21 treatment and that's the end of that. Rust shipped 2021 Edition, and 2024 Edition, and I see no reason to think 2027 Edition won't happen.
The current iteration of Bjarne's "Profiles" idea is in a similar ballpark though it got there via a very different route. This time because it will aid safety to outlaw things that are now considered a bad idea. If this goes anywhere its nearest ship vehicle is C++ 29.
Now, Python 2 to Python 3 did take a few years, maybe a decade. But just shipping the mechanism to reform C++ looks likely to take at least nine years. Not the reform, just the mechanism to enable it.
So what does the compiler chose, the edition of the caller, or the caller?
Crate A uses edition X.
Crate B uses edition Y + N, which has broken Rust language semantics between those editions, or changed a standard library type that Crate A uses on its public API in a backwards incompatible way.
Now an application on edition Y + M, with M >= N, would like to depend on Crate A and Crate B, and calling that incompatible API would be a requirement for the implementation goal.
So far editions haven't bothered covering such cases, deemed as not worthwhile thinking about.
Those in such scenarios get to hunt for another crate to replace Crate A, or have a local fork.
I’m not very sure, but maybe something like unforgettable types? Or linear types? Maybe negative trait bounds, or specialization?
People keep bringing this up when discussing backwards compatibility breaks, but I think the conclusion should be a bit more nuanced than just "backwards compatibility break <=> years (decades?) of pain".
IMHO, the problem was the backwards compatibility break coupled with the inability to use Python 2 code from 3 and vice versa. This meant that not only did you need to migrate your own code, but you also needed everything you depend on to also support Python 3. This applied in reverse as well - if you as a library developer naively upgraded to Python 3, that left your Python 2 consumers behind.
Obviously the migration story got better over time with improvements to 2to3, six, etc., that allowed a single codebase to work under both Python 2 and 3, but I think the big takeaway here is that backwards compatibility breaks can be made much more friendly as long as upgrades can be performed independently of each other.
The benefit of NOT maintaining backwards compatibility is that hopefully you end up with a cleaner language without all the baggage of the past, and "impedence mismatches" of new features that don't play well with old ones, etc, etc.
However, as a developer, I think I prefer the backwards compatible approach, knowing that:
1) Upgrading to latest version of the compiler hopefully isn't a big deal, and I can take advantage of any benefits that brings, including new language features, without having to sign up for rewriting existing tested code to remove deprecated features. In a large code base any such forced rewrites could be pretty onerous, and may involve a lot of retesting and potential for introducing bugs into code that was previously working.
2) It's nice to be able to take older projects, that you haven't worked on in a while (whether hobby projects, or legacy corporate code) and have the code still compile with the latest installed tool set, rather than to have developed "bit rot" due to using language features that have since been deprecated. Of course bit rot may also occur due to library changes, so language backwards compatibility is no guarantee of smooth sailing.
Maybe going forwards, with AI coding tools, this will become less of an issue, if they become capable of this sort of codebase updates without introducing errors. In fact, going forwards it may well be that choice of programming languages itself becomes seen as more something the tooling takes care of. Nowadays we write in high level languages and don't really think or care about what the generated machine code looks like. Maybe in the future we can write in high level instructions to the AI, without even having to care what the implementation language is?