Absolutely there are real situations where the new stuff is going to last longer than the old stuff, even the old stuff that's been through a selection process.

Absolutely there are real situations where the new stuff is going to last longer than the old stuff, even the old stuff that's been through a selection process. E.g. modern manufacturing techniques have improved overall longevity of all 2022 models. But I think you are outside the Lindy effect model at that point. To put this in the example of the original post: The author says visual studio code is expected to last less time than VIM. You could counter by saying: "hey, maybe, uh, the rise of Product Management as a discipline has meant that modern software overall will have longer lifetimes, and hence it's not fair to guess that VIM will outlive VSCode". And that'd be a fine position. But imo the right way to frame that isn't "the author did an incorrect logical inversion of the Lindy effect model"; rather it would be "I don't think the Lindy effect model applies to this domain". (No one is saying the Lindy model is universal.) I guess you could say you want to apply it only within a given year of software; so, we're happy to look backwards and apply the Lindy model to software written in 2011, but we've no idea how to think about the lifespan of software written in 2022, and aren't allowed make any inferences from software written before 2022. That's fine, but that's an additional constraint we've added, is outside the Lindy model, and, really, we're in "all models are wrong, some are useful" territory here, where I'd ask "is it really useful to throw away all that previous data? Wouldn't it be a better starting point to use the lifetimes of previous years as at least a prior?" And if you grant that, then I think theres no logical error here.