## The challenges in predicting the future of AI Making predictions about artificial intelligence (AI) has become increasingly difficult. Several factors contribute to this growing difficulty, making the future of AI uncertain. One of the main elements of uncertainty concerns the future of large language models (LLMs). These models are the basis of many AI applications, from chatbots to customer service systems. If their evolution were to slow down, the impact on the entire sector would be significant. ## Public opinion and political response Public opinion towards AI is predominantly negative. Ambitious projects such as the construction of data centers for training AI models are meeting strong opposition from local communities. Big Tech is facing a major challenge in gaining public support. The response from lawmakers is also far from unanimous. While some politicians favor federal regulation, others are more inclined to state-level interventions. This lack of cohesion makes it difficult to predict how AI companies will be regulated in the future. ## AI and concrete applications Despite the progress of AI in the scientific field, with tools such as AlphaFold that have revolutionized biology and image recognition models that are increasingly accurate in identifying cancer cells, the effectiveness of LLM-based chatbots remains doubtful. Although these tools are able to analyze large amounts of data and summarize information, some claims about their discovery capabilities have proven unfounded. Furthermore, the use of chatbots for medical self-diagnosis can have negative consequences. In summary, predicting the future of AI is a complex undertaking due to multiple factors, including uncertainty about the evolution of LLMs, negative public opinion, and the fragmented political response.