When the model vanishes overnight: the lessons from the Fable ban and its implications for your AI supply line

Anthropic’s two most capable models disappeared for everyone in a single evening. For research teams now building on AI, that is a warning worth acting on.
On the evening of 12 June 2026, the US government issued an export control directive, and within hours, Anthropic disabled its two most capable models (Fable 5 and Mythos 5) for every customer worldwide. The order arrived at 5:21 pm Eastern Time. By nightfall, the models were gone.

AI Myth Number 1: AI Cannot Produce Anything New

AI only repeats what it was taught. That is a comforting line that appears whenever AI comes up in research conversations. AI is framed as an assistant, a parrot, a tool, a very fast intern with a big knowledge base and sometimes a flaky memory, but no imagination. The implication is reassuring, because it suggests the interesting thinking is still safely ours. But it is a myth.

Why do clocks go clockwise? A sundial reminder about standards

Have you ever wondered why clockwise is the direction it is? Who decided and why? That is part of what this post covers, but it also highlights the often arbitrary nature of standards.

The State of Research and Insights

NewMR is currently conducting the sixth wave of our State of Research and Insight study. We have conducted two waves in 2023, two in 2024, and this will be the second wave in 2025. We anticipate reporting on this study towards the end of December this year, possibly in January next year.

Synthetic Data: A Lexicon and Taxonomy

Here is my attempt at an updated Lexicon and Taxonomy for Synthetic Data. I would love to hear your thoughts and suggestions.

Exploring CoLoop: A Powerful AI Tool for Qualitative Research

By Ray Poynter15 September, 2025 I recently had the chance to sit down with Jack Bowen, founder of CoLoop, to explore what this innovative platform can do for insights professionals. This post accompanies the video demonstration (see below). My aim […]

AI making a difference to real people

I think too much of the discussion about AI is focused on what it might deliver in the future. I believe it is important that we look at what has already been achieved to get a sense of where we are and where it might go soon. This post presents a set of examples from around the world and across various fields.

The AI Territory – A Tool for Strategic Planning

In the fast-moving world of AI, organisations often struggle to decide which initiatives to prioritise. The opportunities seem endless, yet resources are limited, and different parts of the business may disagree on what matters most.

To cut through this, I use a framework I call The AI Territory. It adapts classic strategy tools (such as the Eisenhower Box and Value–Effort matrix) to the specific challenges of AI adoption.

The AI Revolution is Different in Speed and Nature to the Internet Revolution

As somebody who was a pioneer during the uptake of the Internet by the research ecosystem, I have been struck by how different I think the AI impact will be.

My feeling is that, compared with the Internet, the AI changes will be:

Faster
Driven by the big players
Take senior as well as junior jobs

AI Needs to be Compulsory

There is widespread agreement amongst seasoned consultants that if an organisation is going to realise the benefits of AI fully, it needs to be adopted throughout the organisation. I firmly believe this to be true, and I am working with a number of organisations to help them achieve this. However, there are good ways and bad ways of going about this that I will illustrate with three case studies. Norges Bank, Shopify, and Klarna.