I think it's actually often worse in AI papers. Fortunately at least some bigger journals/conferences encourage or require releasing source code, which makes it easier to track down subtle details that the authors didn't clearly mention in the paper.
On top of that due to its dependence on data and the ability to 'fudge' statistics, a lot of AI papers aren't really that replicable even if there aren't any implementation subtleties. For example, I've run into papers on image generation which describe some trick to improve quality, but focus entirely on standardized scores without providing any visual comparisons (and thus as feared turning out to not have as much of a visual improvement as the scores would suggest on other datasets).
While in a lot of sciences or engineering many things can be attributed to being standard practice for experts in the field, AI moves too fast to have such standards and tends to be a bit too arbitrary for such standards to mean much.
On top of that due to its dependence on data and the ability to 'fudge' statistics, a lot of AI papers aren't really that replicable even if there aren't any implementation subtleties. For example, I've run into papers on image generation which describe some trick to improve quality, but focus entirely on standardized scores without providing any visual comparisons (and thus as feared turning out to not have as much of a visual improvement as the scores would suggest on other datasets).
While in a lot of sciences or engineering many things can be attributed to being standard practice for experts in the field, AI moves too fast to have such standards and tends to be a bit too arbitrary for such standards to mean much.