Some very significant disadvantages to that approach:
* It's common these days for a single operation to manipulate dozens or even hundreds of database records. Often these records are interrelated because they reference each other. So with fakes, you're faking initial inserts and then faking inserts based on other fake inserts, creating a fragile tree structure of fakes, which models reality very poorly.
* No data type validation on data inserts or updates. Put a string in the integer field? Find out in production.
* No foreign key validation (or just general capacity for checking referential integrity) so you don't find out that you're rows aren't referencing each other correctly until production.
* Similarly, no checks on primary keys, check constraints, triggers do not run, etc.
* Since you're not doing real inserts, you're not doing real updates or deletes on inserted rows. So if those latter operations are referencing the wrong ID, you don't find out until ... you guessed it, production.
* You can try to build up the fidelity of your repository/fake framework, but the more effort you put into it, the closer you are to just rebuilding a database and the slower it'll get. You'll also never achieve actual parity with what your database is doing.
* Building out these big fake frameworks is a lot of work relatively speaking (you didn't need to build out anything for your DB because your non-test code is already using it), and gets you negative gain.
There was a time a long time ago when disk I/O was a lot slower than it is now and maybe there was some argument for a repository/stub system, but that was at least a decade ago, and even then the rationale was thin. These days we have NVMes, and if you're a real speed demon and think those are too slow, you can just put an in-memory SQLite or Postgres in place for your testing and get all the performance advantages with none of the downside.