These two categories of tool get compared as if choosing one replaces the other. They don’t overlap as much as the comparison implies, because they solve different stages of the same problem.
What a reference manager is built for
A reference manager’s core job is storage and formatting: keeping a personal library of sources, generating citations and bibliographies in the format your document needs, and syncing that library across devices and collaborators. It is a librarian’s tool — excellent at organizing what you already have and have already decided matters.
What an AI research assistant is built for
An AI research assistant’s core job is discovery and synthesis: finding sources you didn’t already know to look for, comparing findings across a set of papers, and drafting a first-pass account of what a body of literature says. It is a reading tool — useful before you’ve decided what belongs in your library, not after.
Where the overlap actually is
The genuine overlap is citation formatting — most tools in both categories can generate a formatted reference. That narrow overlap is why the categories get conflated, but formatting was never the hard part of either job.
Where each one fails without the other
A reference manager with nothing in it can’t help you find what belongs there. An AI assistant with no persistent library forces you to re-discover and re-verify the same sources every session. In practice, most serious research workflows end up using something from each category — one for finding and synthesizing, one for storing and formatting what survives that process.
A sensible division of labour
Use a reference manager as the durable, shareable record for the sources you cite; use a discovery and synthesis workspace while you are evaluating what may belong in that record. The research workspace includes a Saved Papers folder for studies you want to revisit during discovery, but it is not a substitute for the library, collaboration, and citation-quality controls of a dedicated reference manager.