Recursive self-improvement, or RSI, sounds like science fiction. RSI is when AI improves itself, then the improved AI improves itself again, and the loop speeds up. But it is not science fiction; it is here and beginning to reshape what AI is and how quickly it develops. In this post, I will spell out what it is and why so many experts want to treat it with caution.
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.
Open-weight AI is often discussed as though releasing the model automatically makes AI accessible. In practice, access depends on the interface, the computing power required and what the user is trying to achieve. In this post I will tell you about open-weight AI and the four main ways it is being used.
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.
Perhaps the most commonly quoted statistics about AI projects is that 95% of them fail, according to MIT. It has been quoted by Fortune, Forbes, Harvard Business Review, and a long tail of content that is still, in 2026, using “95% of AI pilots flop” as a hook to sell the very thing the study was supposedly warning us about.
It is a wonderful statistic. It is shocking, it carries an elite institutional badge, it confirms what a tired and sceptical audience already suspected, and it is short enough to survive a thousand reposts. There is only one problem, it does not say what almost everyone thinks it says.
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.
We are looking for regular runners based in the UK to take part in a research project about running clothes. Your experiences and opinions will help shape an online training course for research professionals, hosted on the Flowres.io platform.
Here is my report on the International Workshop on Comparative Survey Design and Implementation, University of Vienna, 23–25 March 2026
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.
I’ve been tracking the rise of Chinese AI systems with growing interest over the past year, and I believe there are implications for the insights and market research industry. In 2026, I think we will see an important shift. I predict that many organisations across Europe, Asia-Pacific, and beyond will turn to Chinese-developed AI as part of their solution. Note, I predict they will usually run these models locally rather than rely on cloud services.
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