Sorry, but that is simply incorrect, on many levels.
Virtual environments are the fundamental way of setting up a Python project, whether or not you use uv, which creates and manages them for you. And these virtual environments can freely either use or not use the system environment, whether or not you use uv to create them. It's literally a single-line difference in the `pyvenv.cfg` file, which is a standard required part of the environment (see https://peps.python.org/pep-0405/), created whether or not you use uv.
Most of the time you don't need a different Python version from the system one. When you do, uv can install one for you, but it doesn't change what your dependency chain actually is.
Python-native tools like Poetry, Hatch etc. also work by managing standards-defined virtual environments (which can be created using the standard library, and you don't even have to bootstrap pip into them if you don't want to) in fundamentally the same way that uv does. Some of them can even grab Python builds for you the same way that uv does (of course, uv doesn't need a "system Python" to exist first). "system Python -> virtualenv Python -> poetry Python -> project" is complete nonsense. The "virtualenv Python" is the system Python — either a symlink or a stub executable that launches that Python — and the project will be installed into that virtual environment. A tool like Poetry might use the system Python directly, or it might install into its own separate virtual environment; but either way it doesn't cause any actual complication.
Anyone who "ends up installing their project dependencies globally" has simply not read and understood Contemporary Python Development 101. In fact, anyone doing this on a reasonably new Linux has gone far out of the way to avoid learning that, by forcefully bypassing multiple warnings (such as described in https://peps.python.org/pep-0668/).
No matter what your tooling, the only sensible "stack" to end up with, for almost any project, is: base Python (usually the system Python but may be a separately installed Python) -> virtual environment (into which both the project and its dependencies are installed). The base Python provides the standard library; often there will be no third-party libraries, and even if there are they will usually be cut off intentionally. (If your Linux comes with pre-installed third-party libraries, they exist primarily to service tools that are part of your Linux distribution; you may be able to use them for some useful local hacking, but they are not appropriate for serious, publishable development.)
Your tooling sits parallel to, and isolated from, that as long as it is literally anything other than pip — and even with pip you can have that isolation (it's flawed but it works for common cases; see for example https://zahlman.github.io/posts/2025/02/28/python-packaging-... for how I set it up using a vendored copy of pip provided by Pipx), and have been able to for three years now.