Yes, agree. But I don’t think we are seeing here anything more than change in visibility for now, with AI systems enabling a greater number of people to express their beliefs more coherently + reaffirming them, but not causing them to hold these beliefs in the first place. But I could be wrong.

It is bad but can we also acknowledge that both Mack and Bergstrom are relatively well-known public intellectuals with very large followings who by default will receive more such emails? Which is not the same as everything suddenly thinking they can crack the world formula

Finally, the BBC has plans to generate more revenue in the US amid a tricky funding situation in the UK & did so by launching a new subscription service in June. Trump has multiple levers at his disposal to make the BBC’s life difficult, as James Warrington explains in the Telegraph: buff.ly/9X26Epb

On the BBC’s difficult governance structure, this piece by @arusbridger.bsky.social has already been widely shared but is worth a read: buff.ly/5U3iTeH

Audience research from @reutersinstitute.bsky.social shows that the BBC is the most widely used source of news in the UK, incl. across the political spectrum + one of the most trusted news brands in the UK. And it is the most trusted news brand in the US, ahead of newspapers and NPR. buff.ly/iLaMzHa

Thank you, Hannes :)

Feedback welcome, especially on the theoretical section and the discussion as well as literature that we will have missed! So feel free to plug your own or other people’s material, all of which will be appreciated as Liz and I work towards a journal submission.

🔗Pre-print: buff.ly/ObXx74j

We are very grateful to the team at the Financial Times, particularly @mattgarrahan.bsky.social, for supporting this study from the outset – and to the participants from the FT who volunteered their precious time to help us in understanding this issue.

Although this is a single‑organisation case study, we think there are some “no‑regret” principles that can be useful to other organisations:

Crucially, we argue that AI transparency is best seen as a spectrum: optimising one factor (e.g. maximum disclosure) can undermine others (e.g. perceived trust or revenue). There does not seem to be a one‑size‑fits‑all rule; instead transparency must adapt to org context, audiences and technology.

Internally, managerial and commercial logics push for efficient adoption and risk management; externally, professional journalism ethics and commercial imperatives drive an aim to remain trustworthy.

For those of you more academically interested in this, we argue that AI transparency at the FT is shaped by isomorphic pressures – regulations, peer practices and audience expectations – and by intersecting institutional logics.

Intriguing here is also the question of how much longer AI transparency will be required, especially with a view to the actions of tech companies.

4️⃣ Persistent challenges include achieving consistent labelling (especially on mobile), breaking organisational silos, keeping pace with evolving models and norms, guarding against creeping human over‑reliance, and mitigating against “transparency backfire” where disclosures reduce trust.

3️⃣ Nine factors shape what, when & how the FT discloses AI use. These include legal/provider requirements, industry benchmarking, the degree of human oversight, the nature of the task, system novelty, audience expectations & research, perceived risk, commercial sensitivities and design constraints.

No‑human‑in‑the‑loop features (e.g. Ask FT) get prominent warnings, whereas AI‑assisted, journalist‑edited outputs (e.g. bullet‑point summaries) get lighter labelling.

2️⃣ Disclosure is calibrated to context. Internally, full disclosure aims to reduce frictions and surfaces errors early; externally, labels are scaled with autonomy and oversight.

1️⃣ AI‑transparency ≠ a binary. At the FT it’s a hybrid of policy, process and practice. Senior leadership sets explicit principles, cross‑functional panels vet new applications, and AI use is signposted in internal/external tools and reinforced through training.

Link to the pre-print here, summary following below.

🔗Pre-print: buff.ly/ObXx74j