Here are three big themes from last week’;s AI news. AI moves from conversation to action: three developments for insights teams
The past week’s AI coverage points to three connected themes: increasingly capable frontier models, a shift from prompting chatbots to managing autonomous agents, and the rapid embedding of AI-led interviewing into mainstream research platforms.
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 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.
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.
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.
Here is my attempt at an updated Lexicon and Taxonomy for Synthetic Data. I would love to hear your thoughts and suggestions.
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 […]
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.
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.
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
By continuing to use the site, you agree to the use of cookies. more information
The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.