I gave the opening keynote at the ALPSP University Press Redux conference in Liverpool earlier today, discussing how generative AI is affecting scholarly publishing. My professional background is in trade rather than scholarly publishing, so instead of focusing on the university press business model or specific AI tools, I tried to step back and look at a macro question: what happens when AI changes both how knowledge is produced and how it is discovered.

I’ve just had three very busy days with friends, colleagues and clients at the London Book Fair. I spent time with every company in my portfolio, and had a great discussion with my publisher about my forthcoming book (watch this space for more). While the show is still fresh in the mind, here are a few reflections—less a comprehensive account of the Fair than a set of personal observations, raw signals and conversations that struck me over the last few days.

A new Anthropic research paper on AI and employment is getting a lot of attention. At face value, it suggests that the sectors most vulnerable to disruption from AI are white-collar, analytical professions: law, finance, management, media and arts, many academic disciplines. The headline finding and a radar chart of affected areas have been repeated ad nauseam in my LinkedIn feed. But a careful reading of what is a genuinely interesting paper explains why I find it less alarming than first appearance might suggest, despite it affecting the fields closest to my own work.

Earlier this week, John Willshire posted on LinkedIn about Claude Code and the sudden proliferation of niche tools people are building with it. He wondered whether this “personal software”—quick, bespoke tools built to solve a very specific problem for a single person or team—might eventually develop into something scalable. Someone else replied that perhaps scaling isn’t the point. If the cost of building software has collapsed, tools no longer need to justify themselves as products. They can exist just because someone finds them useful, interesting, or entertaining to make.

Everything is now a data business, especially media companies. My friend Alex Boden’s analysis of the Washington Post’s pivot to WP Intelligence is characteristically sharp: editorial expertise converted into structured intelligence products, sold to professional audiences on enterprise contracts. The playbook works for a news publisher. The question for trade book publishers is what version of that pivot is available to them.

Thanks to Luis González, Javier Celaya, Christopher Kenneally and their colleagues for inviting me to give a keynote address on the impact of AI on audiobook publishing this morning at Parix Audio Day 2026 in Madrid. It’s one of the best conferences in the publishing calendar, and hands down the best venue.

I’ve been trying to write something here roughly once a week, largely but not exclusively about my experience working with AI in publishing. I hadn’t planned to post anything else this week, but John Willshire linked to a post from Rachel Andrew on managing publishing operations in the context of generative AI and I wanted to respond to it with some thoughts about how the issues it raises affect book publishing.

Andrew Savikas recently wrote that one of the best ways to see the future of knowledge work is to watch how software developers work. It made me smile because I got to know Andrew nearly twenty years ago through his work at O’Reilly Media and their Tools of Change for Publishing conference—“watching the alpha geeks” was a core philosophical and strategic tenet for O’Reilly.