> "But that is separate from what file paths are for"

This is close to my core point. File paths and/or file hierarchies are not good for file organization. They have been pressed into this role for historic reasons and out of inertia.

Papering over the problems of file hierarchies by implementing a good copy-paste system is not a good solution to the fundamental problem. It alleviates some problems, but not all. You can conceivably copy-paste a file you've been working on in one app into another, but what if you want to retrieve a file you last interacted with a year ago? How will you find that file?

Under the file hierarchy scheme, in order to retrieve an old file the user must remember the idiosyncrasies of the hierarchy they created ages ago, with many concepts invariably being split into gods know how many subdirectories, OR nearly all of their files sitting in a small handful of directories, unorganized, with little more than the file name to help retrieve the desired file. These are both poor states of affairs. They require the user to store too much extraneous state about the system in their head in order to have any chance of efficiently finding the files they're looking for.

Modern systems have begun to improve on this state of affairs by implementing file searching capabilities that function more like internet search engines than traditional hierarchical directory explorers. In theory the user may be able to simply search "scuba" and the black box file search system may use a variety of methods for pulling up a list of files from numerous different subdirectories it believes to be scuba related. Perhaps it pulls in both /vacations/2017/scuba/neat-fish.jpg and /books/scuba/how-to-scuba.epub because both have the string "scuba" in their path. Or perhaps there is some exotic shit going on behind the scenes and an image classifier was previously used to determine the presence of scuba gear in /Pictures/1002389943-984983-198395.jpg This sort of file search engine is fundamentally an imperfect file tagging system. The tagging in this case is being done automatically in a variety of ways the user may or may not be aware of and understand, and may or may not be able to consciously influence and exploit.

Let's take a step back for a moment and define the problem:

In the mind of the user, there exists a many-to-many mapping between files and concepts. When a user is thinking of a particular file, they have in mind a set of traits about that file. (For example, the user might be thinking of "that picture I took of a strange fish while scuba diving on vacation." The concepts associated with that file might be picture, fish, scuba, vacation.)

How do we get that mapping out of the user's head and into the computer, such that it can be used by the computer to retrieve the files a user desires when thinking about a particular set of concepts?

That's not yet a question with a definite answer. Automatic tagging of files with concepts the user is anticipated to have should certainly play a role. Image classification is heavy, and therefore has privacy implications since that sort of thing is virtually always offloaded to some corporate server, but when it works well it's great. There are easy sources for automatic tags as well, such as extracting terms out of the file path, or extracting date ranges or even geolocation out of the file metadata. These are great, but typically prove insufficient on their own. Asking the user to manually tag their files with the concepts they associate each file with gets you a very high quality many-to-many mapping between files and concepts, but that is manually intensive. Notoriously so. One solution is to amortize it; during file creation ask the user to tag the file instead of naming the file. That's okay, but still not great. The user might be snapping 10 photos a second and not care to take the time to tag or even name each individual file. How do we meet the needs of that sort of user?

A solution I've been exploring is the creation of statistical models that assist the user in manual tagging, to blur the line between automatic and manual tagging. Here is what I mean by that: suppose the user imports 500 new jpg images, with metadata suggesting they were all taken in within a half hour of each other. The user tags ONE of those pictures with "vacation". What is the probability that the remaining pictures in that set are also vacation pictures? Pretty high. (Of course that's a trivial example, the user could just as well import the images all at once in a directory called 'vacation', but this goes back to my earlier point of extracting the many-to-many mapping that exists in the user's head. 'vacation' is merely one tag. By constructing the right statistical models, we can support an arbitrary number of tags.