I love technology. I love librarianship. I love the intersection of the two—where innovation meets information, and where we, as librarians, help people navigate the ever-evolving landscape of knowledge. But lately, I’m tired. Tired of what feels like the AI wars.
We’re in a moment of rapid transformation, and while I know it’s essential to stay current with advances in AI and information retrieval, the pace and fragmentation of it all is exhausting. Every week seems to bring a new tool, a new policy, a new platform—each promising to revolutionize research, while simultaneously complicating the very ecosystem we’ve spent decades building.
The Fragmentation of AI Tools
Let’s start with the publisher-created AIs. These tools are often limited to the publisher’s own content, creating silos that lack the diversity and breadth of scholarship needed for real, comprehensive research. They’re polished, yes—but narrow. They reinforce the walled information gardens we’ve been trying to break down in libraries for years.
Then there are the LLM-powered platforms that promise to assist with literature reviews and synthesis. These tools are exciting, but they often hit paywalls. They can’t access the full range of scholarly content behind institutional subscriptions, and most don’t integrate with our link resolvers—the very systems designed to connect users to full-text content we’ve already paid for. So we end up with tools that are powerful in theory but incomplete in practice leaving our users disconnected from the information.
The Copyright Conundrum
Meanwhile, users—well-meaning and curious— seem to want to upload everything into the AI of their choice. Library subscriptions, PDFs, paywalled articles, open access content… if it’s digital, they want to feed it into ChatGPT, Claude, Copilot or whatever tool they’re experimenting with. But copyright doesn’t disappear just because the interface is conversational. Additionally, any content created by AI is not subject to copyright – many users don’t realize this either.
We’re now spending more time educating users on copyright, fair use, and licensing than ever before. And it’s not just students—faculty and researchers are also navigating this new terrain, often unaware of the legal and ethical implications of how they use AI tools.
The Shifting Sands of Licensing
And just when we think we’ve got a handle on things, the publishers change the rules. Contracts that once allowed for text and data mining (TDM) are now being rewritten with restrictive AI clauses. These clauses often limit or eliminate our ability to mine content for research or innovation—something we’ve supported for years as part of open science and discovery.
Each publisher and company has their own unique rules regarding the licensing of AI (or inability to license AI). At MLA one librarian discussed trying to license a few journals (less than 100) to use with an institutional AI. Those journals had different publishers and different licensing requirements and fees, requiring hours/days/weeks of negotiating. This is impractical when dealing with the thousands of resources a library subscribes to. It feels like we’re being pushed out of the very conversation of connecting users to information that we helped start.
Wishing for the Fast Forward Button
Some days, I wish I could just hit the fast forward button—skip ahead to when the dust has settled, the standards are clearer, and the tools are interoperable. I want to get to the part where AI is a seamless part of the research process, not a battleground of competing interests, legal gray areas, and technological silos.
But I know that’s not how progress works. We’re in the messy middle. And as much as I’m tired, I also know this is where librarians are needed most.
We are the translators, the educators, the advocates. We understand metadata, licensing, access, and equity. We know how to ask the hard questions about bias, transparency, and sustainability. And we care—deeply—about helping people find, use, and trust information.
So yes, I’m tired. But I’m also still here. Still learning. Still advocating. Still believing that librarianship has a critical role to play in shaping the future of AI in research.
Let’s just hope that future gets here soon.
**1st Note** I want to be transparent that AI was used to aid in the creation of this post as I continue to attempt to learn ways to use AI better.
**2nd Note** My exhaustion is not regarding any specific company’s AI or type of AI, just tired of living in the messy middle.