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.

Have you ever wondered how an AI system, like a search engine or a chatbot, understands that the sentences “A dog chases the ball” and “A canine pursues the sphere” mean approximately the same thing? Of course, it’s not magic. It’s advanced mathematics, called semantic similarity, and the powerful concept of Sentence Embeddings.

At most conferences and events, we are seeing a growing number of examples of Synthetic Data, in its many forms and versions, being used for real projects. However, there still seems to be a body of thought that promotes unscientific criticism of this approach. This sort of criticism is likely to hold some people back from realizing the potential benefits and could cause real commercial damage to those who follow it blindly.

The 2025 ESOMAR Congress in Prague was dominated by one theme above all others:, and that theme was artificial intelligence. From the exhibition floor to the presentation sessions and corridor conversations, AI was the connective tissue running through the programme. What had been emerging in previous years became fully mainstream in 2025. Far from being a specialist topic, AI now underpins the methodologies, business models and strategic debates across the market research and insights industry.

For me the jury is out on the discussion about what synthetic data might be able to achieve in replicating human decisions and behaviour. I am worried by some of the overclaims and appalled by the number of people who reject the notions as being self-evidently wrong (without feeling the need to support their rejection with data). In this post, I want to explore one line of thinking relating to the broader debate, namely, what is the human brain doing and how might that help or hinder the creation of predictive models (AKA synthetic data).