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: