Ian Mulvany has a good piece in the latest issue of InPublishing examining challenges and opportunities for publishers. A couple of his points really stuck with me. Under pressure from investors to signal progress, companies are forcing AI into products and services without a clear sense of value. The result is a proliferation of new features that are individually justifiable but risk being collectively incoherent. And while generative AI can speed up the creation of content, code or even products, it doesn’t create additional time—in other words, opportunity cost still applies. I’ve started thinking of this pattern as strategic debt.

I took part in a webinar on AI and publishing earlier today, hosted by the Crius Group, alongside my friends and colleagues Cameron Drew and Simon Mellins. One of the topics that came up was what mistakes we saw organisations making with AI. The conversation was about publishing, but the more we talked, the clearer it became that these failure modes aren’t industry-specific. They show up everywhere. Here are eight of the most common. If you recognise more than two or three of them in your organisation, you may not have an AI problem—you may have a strategy and operating model problem.

This piece was commissioned by James McConnachie and first published in the Spring 2026 issue of The Author, the journal of the Society of Authors. It is reprinted with their permission.

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.