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.

I’m on my way back from the IPG Spring Conference in London, always one of the highlights of the publishing year. It was a really good mix of the inspirational, the practical, and a uniquely generous, welcoming and supportive community. I particularly enjoyed the presentations from former Shazam chairman and crime writer Ajay Chowdhury, National Literacy Trust CEO Jonathan Douglas, BBC media editor Katie Razzall, Illumicrate CEO Daphne Tonge and Bonnier UK Co-CEOs Sarah Benton and Jonathan Perdoni. I spoke in the morning about the IPG’s forthcoming short training courses (data, content creation, websites/GEO and AI risk/governance) and in the afternoon to give members an update on the policy and licensing landscape for AI.

I had a couple of conversations this week that inspired a quick coda to my previous post on using Claude Code for prototyping ideas. The day after I posted it, I spoke with Tom, one of my most thoughtful and creative friends—someone who is about as far away from AI boosterism as it would be possible to get. A decade ago, he had spent several thousand pounds with a web developer building a location-aware mobile website. He asked how much information Claude Code needed to build something similar. I copied his sixty-word WhatsApp, clarified half a dozen questions that Claude asked in response, and it took under two minutes to replicate the core functionality.

Last week, Ethan Mollick posted about creating a functioning game with Claude Code and a one-shot prompt. As a first project with Claude Code, it seemed ambitious but appealing. I started with a single paragraph prompt to develop a short, simple adventure game in the style of the Sierra On-Line games I remember from the late eighties, with an AD&D 2nd Edition vibe. Getting a first, testable version was almost indecently fast. It then took several rounds of iteration to improve it, including a separate workstream to create retro style screens (Claude doesn’t have native image generation). The total active investment of time was about twenty minutes, with Claude Code running in the background while I did other things.

There’s an uncomfortable paradox at the heart of Generative AI adoption: the promise from AI vendors and consultants like me is that the technology will boost productivity. And for the most part, this holds true with my clients. But the gains are unevenly distributed. Dan Hon wrote in his newsletter recently that his friends who use LLMs brilliantly in their coding work do so because they bring very particular skills and experience that don’t translate to the average user—a challenge amplified by what Ethan Mollick calls AI’s ‘jagged frontier’ of uneven capabilities. Scale this up to enterprise level and you hit a bigger problem: AI can create efficiencies for individual, power users, but it doesn’t necessarily deliver the company-wide transformation the C-suite was sold on. You’ve probably seen headlines claiming that most AI pilots fail to deliver measurable P&L impact. Are the promised gains genuinely elusive, or do we need to reframe what we’re looking for?

There’s an interesting new report out from the Minderoo Centre for Technology and Democracy at Cambridge University on the impact of AI on the novel, authored by Dr Clementine Collett. There’s much that I completely agree with and that accords with my experience of working with book trade on AI. But I have some constructive thoughts about the framing/communication.

Recently I’ve been doing some research on the size and scope of the UK book and journal publishing industry, based on data from Companies House. This research is a preparatory step towards building a database of companies, highly relevant for my work as an independent consultant and as policy advisor for the Independent Publishers Guild, which represents over 600 book and journal publishers of all sizes. The other trade body, the Publishers Association lists 168 members at the time of writing, particularly larger, corporate publishers—though there is some overlap between the two organisations’ memberships. The UK publishing sector is world leading, representing over £7 billion of revenue, and it represents the bulk of my client base. So it’s worth understanding.

Last September, BookNet Canada invited me to speak at their Tech Forum webinar on the use of AI in publishing. Looking back at my slides, it’s remarkable how much the world has moved on since then, though the eight principles I set out have held up pretty well.

I was invited to deliver a short webinar today for the Swedish Publishers Association and its international affiliates. It was great to see a very strong turnout and thank you to my hosts for arranging it. I talked about recent trends in AI, how publishers are approaching it, and examples of how AI tools are being used.

Thanks to Graham Bell from EDItEUR for inviting me to speak at the 45th International Supply Chain Seminar in Frankfurt this afternoon, to update the AI presentation I gave last year. Giving an update meant less time on first principles, and it was a really interesting prompt to step back, compare this year’s slides with the 2023 deck and see what had changed. Of course, massive development on the technology side, more legal uncertainty, and greater awareness of the environmental impact of AI. But some points, like the importance of keeping human decision makers in the loop, are perennial.

I gave a short presentation on how publishers are using AI at the Society of Indexers Conference today. It was great to give the publisher perspective ahead of a fascinating presentation from Tanya Izzard on experimenting with LLMs for indexing: the mix of general and specific themes led into a lively discussion.

Thanks very much to the team at BookNet Canada for inviting me to speak at their Tech Forum webinar this evening on the elements of a balanced and ethical AI strategy for publishers. It’s hard to do justice to such an expansive topic in a forty minute presentation, but I discussed a number of key aspects including the pace of change and capability overhang in AI, copyright and Fair Use, accuracy, provenance, the environmental impact of AI and its increasing integration with all elements of the publishing application stack.

I don’t use TikTok, but it has been hard to escape its impact on the publishing industry, where I spent the first part of my career. Nielsen research suggests that one in four UK book buyers used TikTok in 2022, accounting for 90 million purchases. As a collective phenomenon, BookTok was awarded the person of the year award at last year’s Futurebook conference. In the meantime, there’s little doubt that the platform has a huge impact on commissioning, marketing and selling books, and that publishers are investing time and money to make the most of that. As a proxy for the level of interest, a search on the Bookseller magazine’s website this morning showed eighty nine search results for ‘TikTok’ for stories published in the first quarter of the year, versus four results for ‘GPT’. (Long term, I know which one I would bet on being most significant and transformative, but that’s another story.)

It’s the start of a new academic year, and I’ve been thinking a lot about volatility and uncertainty. Developing strategy in an uncertain context is the challenge underlying my current research on games and scenario planning. But even without that focus, just looking at the world around me this summer brought the subject home. Many of my clients experienced disruption to supply chains and operations during the Covid-19 pandemic; most are now contending with serious inflationary impacts. My current projects involve business partners on five continents, and talking to them, some level of political instability is a consistent theme. Even going on a family holiday, our first time out of the UK in two and a half years, raised uncomfortable questions about the state of world and sustainability, as we travelled against the backdrop of heatwaves and wildfires at home and devastating floods overseas.

This piece was commissioned by James McConnachie and first published in the Autumn 2021 issue of The Author, the journal of the Society of Authors. It is reprinted with their permission.

I started a new project this week, doing a piece of business modelling for a client considering a new venture. As a result it was quite a different week to normal: very few Zooms or calls, and several days of intensely focused time researching and sorting the data I could find on the subject and building the first version of the model in Excel. Then testing the logic with a known set of data, before building a set of possible scenarios (base case, downside, upside) and running the model past a couple of senior stakeholders at the client for some useful feedback. Next week I’ll be working on improvements to the basic model.

Running a business has its ups and downs. The successes are heightened because they are your own, but the corollary is that the low points feel particularly personal. This week I had disappointing news on two projects within about half an hour of each other—a blow even if I concluded after some reflection that I wouldn’t have done anything differently on either.

This week was largely spent designing a strategy workshop for an ongoing client. The latter would have been much more straightforward if it were being delivered in person: as it is, I’ve been having to think how to adjust the format for online delivery. Part of that is designing the workshop around short, focused sessions with ample screen breaks. I’m also allowing for regular check-ins with the audience. In person, it’s generally possible to have a sense of the level of interest and focus in the room through watching body language and reactions, but that’s much harder on Zoom. So I’m building in prompts to explicitly ask participants if they’re happy with the material before proceeding, more so than I might if I could gauge a positive mood in the room.

I first heard of EBITDAC in mid-April when a business school classmate sent a meme to a WhatsApp group showing a new term that we definitely hadn’t learned in Corporate Finance classes: Earnings Before Interest, Tax, Depreciation, Amortization and Coronavirus. At the time it seemed pretty funny—well, at least to a bunch of MBAs—so I sent it to a few peopl and forgot about it.