A shorter update this week: seven presentations and workshops in ten days have left me genuinely tired, but they’ve also been the best kind of primary research about how people are encountering AI day-to-day. One theme came up again and again: how to use AI in content and marketing without losing your audience’s trust. That throughline runs through a lot of what follows.

There are two big themes this week: what new AI agents are making possible, and the need for a more nuanced discussion of AI’s impact on authors and publishers. In particular, Matt Webb’s first-person reflection on agents will resonate with any publisher shipping products internationally in 2026.

This week’s newsletter focuses on the economics of AI becoming more visible. Sometimes that’s positive for publishers: a new tool for estimating the value of licensing your content. Other perspectives are more challenging, particularly on the real cost of AI usage. For publishers, the lesson is that AI strategy must now include cost, value and platform-risk assumptions.

It’s been a week of jet lag, catch-up and presentations, with a workshop, a webinar for PW and my friends at Westchester Publishing Services, and the IPG Summer Summit. Before I shut down for the bank holiday weekend in the UK, some updates on what happened in AI and publishing this week:

Greetings from Portland, where I’m writing this on the Thursday of IBPA Publishing University—or, as my body clock is telling me, about 04:30 London time on Friday… It’s been a long day, but lots of good conversations with publishers have helped to sharpen my thoughts on this week’s news.

I’ve been on the road again this week, travelling to Portland, Oregon for the Independent Book Publishers Association Publishing University, where I’m speaking today. One quick observation, informed by email exchanges with my friend and his AI agent while I was travelling, and several conversations at the convention.

It was a quieter week for product releases, but there were significant legal, policy and research developments. I am in Portland, Oregon next week for IBPA Publishing University: I look forward to seeing some of you there. In the meantime, have a good weekend.

Earlier today I attended an online seminar hosted by the Creative Industries Policy and Evidence Centre, where Dr Paul Crosby from Macquarie University presented recent research on authors and AI carried out in Australia. Many of the sentiments expressed in the research were familiar: author concerns about the morality of AI training and the impact of AI outputs on their livelihood echoed many of the findings of the Cambridge research on AI and the novel published last November. What I found most interesting about the Australian research is that it was interpreted through economics and econometric analysis. One of the key questions was whether, if AI companies train on human creative work without compensation, the long-run incentives for cultural production are weakened. The concern was that more economically marginal forms of publishing such as literary, niche and experimental writing could be hardest hit. Framing the impact on authors and the creative industries as an externality is a helpful way of emphasising to policy makers that the economic benefits of training on copyrighted work accrue overwhelmingly to tech companies in the US, while the costs are potentially distributed across the wider cultural ecosystem globally. This is a textbook setup for undersupply of a public good (creative works).

Happy Friday. I’m catching up after a week on the road and there is a lot to process. This week my reading and thinking clustered around two themes: what happens when the cost of producing content falls, and whether that cost will be stable over time.

I’ve been leading training sessions on generative AI for nearly three years now, and over that time the advice that I’ve given on prompting has been relatively consistent: good results generally depend on a degree of detail and specificity about context, objective, and outputs. I was interested to see Ethan Mollick noting a change of emphasis in the developer documentation for GPT-5.5, OpenAI’s latest thinking model:

I’m writing this on the way home from Chicago, where I gave a keynote on AI and publishing at the ECPA Leadership Summit. As I said in my presentation, this is not an area of publishing I’ve worked in, and my role was to provide an outside perspective—less about the specifics of the sector, more about what AI might look like when viewed from beyond it. It was a really rewarding and thought-provoking experience, and I am very grateful to Jeff Crosby and his colleagues at the ECPA for their hospitality.

It’s been a big couple of weeks for AI image generation and design tools: with major updates from Canva, Google and Anthropic, users are spoiled for choice. The new tools speak to a trend I’m increasingly seeing—and write about below: the baseline for AI use is shifting from competent prompting to confidently delegating to agents. The exam question for publishers is getting used to managing systems of agents rather than using individual tools.

It has been a busy week: updating the materials for and delivering a lunch-and-learn session and another full day training course on AI for IPG members, and preparing a keynote presentation on the future of publishing for a conference in Chicago next week. To prepare for those things, I’ve been reading and talking to a lot of interesting people about how they are using AI at the moment. In between, I made a quick dash up to London for my ten year business school reunion—a room full of smart people from different industries who provide a triangulation to the publishing experience. There are three interesting patterns that kept coming up in all of those different contexts.

A slightly shorter newsletter than last time as it’s been a particularly busy week: several days of travel, and submitting the manuscript of my book to my publisher. More on that soon, but first, some perspectives on AI impact.

If you read my piece last year on sizing and plotting the UK publishing industry based on Companies House data, there’s an interesting coda today with the publication of the Office for National Statistics’ updated Standard Industrial Classification (SIC) codes for 2026. If you’re not familiar with what SIC codes are, you’ve probably given up reading by now—but the short version is that SIC codes are short numbers that describe what business activities companies engage in. Firms choose one or more SIC codes when they register with Companies House.

I don’t set out with a particular theme in mind for each week’s newsletter: sometimes the things I’ve found in my research cluster serendipitously around a particular area, and more often there’s a range of themes. As it happens, this has turned out to be Use Case Week: practical ideas from Anthropic, the BMJ, consultants Fathm, and Paul Ford. If you can’t find something practical to try as a result of this week’s newsletter, you can have your money back.*

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’m sending this week’s newsletter a day early, as Friday and Monday are public holidays here in the UK—judging by the out-of-office messages I’ve seen this morning, it looks as though a lot of publishing has already checked out for the long weekend. But if you’re still at your desk, some reading matter to take you into the Easter holiday: there’s a lot going on, and a surprising number of strategy signals in this week’s news.

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.

For authors, publishers and technology companies, it’s been a week that challenges the old axiom that there’s no such thing as bad publicity. The practical uses highlighted in the rest of this week’s newsletter need to be balanced against good strategy and judgement.

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.

It’s been a busy week, with significant developments in copyright policy and a steady drumbeat of new research and product launches. The key question posed by new research linked to below has stayed with me: for the right task, AI brings real benefits, but how do we determine what the right task is?

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’m catching up after three intense days at London Book Fair. That intensity has been mirrored in the AI world, with a slew of copyright and licensing developments, some new practical tools for publishers, and catastrophically misjudged AI developments at Grammarly, Amazon and McKinsey.

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.

It has been a week in which AI moved decisively out of product demos and into politics, newsrooms, courts, and war. Across very different domains, the same underlying tensions keep surfacing: control, accountability, and whether speed is crowding out judgement.

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.

A slightly shorter—and later—newsletter than normal, as I’ve been on the road this week (six webinars and courses in four days—welcome to new subscribers from those events). I started the week needing a productivity boost, and Google provided one…

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.

In recent months there has been a discernible shift in AI—and the pieces I feature—away from simple chatbots to agentic tools like OpenAI’s Codex and Claude Code. My smartest friends have moved from using AI for five or ten-minute microefficiencies to delegating complex coding or research tasks measured in hours. Chatbots will still have their place, but the rise of AI agents and big themes around trust dominate my thinking this week.

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.

Only two out of nearly three dozen publishing job ads I reviewed this week mentioned AI skills. Meanwhile, the marketing world has moved on entirely. This issue explores that disconnect, alongside ElevenLabs becoming the UK’s first AI and media decacorn, Amazon’s new publisher data marketplace, and what agentic search tools could mean for editorial archives.

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.

This week’s stories play out at a distance—in financial markets, academia and big tech—but they point clearly toward future directions for publishers. At the other end of the scale, I’ve written about my hands-on experience with Claude Code and agentic AI, which you could start using today.

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 are two big themes in this week’s newsletter: utility and accountability, and they don’t sit easily together. The productivity gains many of us are seeing from AI are increasingly in tension with unanswered questions about training and transparency.

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?

This week’s headline stories sit at the uncomfortable boundary between assistance and dependence. From lost intellectual scaffolding to new AI “coworkers”, the question isn’t just what AI tools can do—but how we should use them without dulling the human thinking they’re meant to support.

This week’s AI stories are less about capability and more about control: how models are trained, when licensing works, and where consent is quietly assumed rather than granted. For publishers, these mechanics are starting to matter more than the rhetoric.

Happy New Year. The first AI stories of 2026 aren’t about speed or scale, but about authenticity—who is speaking, what can be trusted, and where responsibility sits when things go wrong. That’s uncomfortable territory for publishers, but increasingly unavoidable.

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