From AI Adoption to AI Practice: Building Governed AI in Scholarly Publishing
How can a publisher make AI useful across the organization while keeping control of confidential information, editorial decisions, and accountability?
The AI Transparency Trap
The absence of AI doesn’t guarantee trustworthy research. And neither does disclosing AI use.
Who Keeps the Catalog of Connectors? The Coming MCP Discovery Problem
Libraries and their vendor partners can build registries and metadata feeds that make MCP connectors for scholarly content findable, accessible, and usable.
Guest Post — All this AI Signal and Noise: What Deserves Action Now
Amidst the onslaught of industry and global changes, opportunities, and advancements, which signals deserve action now, which are context, and which are weather?
From Content to Trusted Knowledge: Reflections on Frankfurt Book Fair 2026
Thoughts on the Frankfurt Book Fair 2026: how do we turn trusted content into useful knowledge — and help people act on it?
TRACE Project Launches to Advance AI interoperability
The community comes together to launch TRACE, a project to improve generative AI systems’ interoperability, verifiability and usage tracking.
The Representation Is Not the Scholarship
The problem with AI may not be that machines are growing more intelligent. It may be that we are becoming increasingly skilled at amplifying a highly capable, but limited form of cognition.
AI and Scholarly Reading: Foxes versus Hedgehogs
Some readers are foxes, and some are hedgehogs. What kind of reader is AI, and how will that impact both the practice of science and the business of journal publishing?
Ask the Chefs: Peer Review Capacity — Uncomfortable Truths and Overlooked Conversations
For Peer Review Week 2026, we asked the Chefs two questions: What is one uncomfortable truth about peer review that the scholarly publishing community doesn’t talk about enough? And, beyond recruiting more reviewers, what conversation about peer review capacity are we overlooking?
We Can Now Track AI-generated Content. It Doesn’t Solve the Real Problem.
Identifying AI content is now embedded in Claude models. It doesn’t solve the problem we have. It also creates others.
AI Use in The Scholarly Kitchen
Some thoughts on the reader response the Scholarly Kitchen has seen regarding AI use by authors, a recent Nature News article on the efficacy of AI detection tools, and why it’s important to keep experimenting to figure out what’s ethically acceptable.
Guest Post — AI Can Find the Article. Can It Tell Which Version It Used?
In this AI era, establishing trust and validating article versions with persistent identifiers (PIDs) is more important than ever.
Reading Between the Lines, Part 2: Cognitive Debt and the Adaptive Publisher
The central risk of AI in scholarly publishing is therefore not hallucination but cognitive debt: the gradual erosion of the human capacities that give scholarship its originality, integrity, and meaning.
Reading Between the Lines, Part 1: A Cognitive Framework for AI in Scholarly Publishing
AI is now a permanent feature of the scholarly landscape. The question is no longer whether it should be used, but how it should be understood and governed.
What Authors Expect Now, and Why Publishing Hasn’t Kept Up: An Interview with Jennifer Fleet of Aries Systems and Christian Grubak of ChronosHub
Today Alice Meadows interviews Jennifer Fleet (Aries Systems) and Christian Gruback (ChronosHub) about changing author needs and how their recently announced partnership is an effort to address them.