New research is questioning the effectiveness of large context windows in large language models (LLMs), suggesting that smaller context windows might be more beneficial for future AI models. LLMs are being recognized for their ability to transform collective intelligence within teams, organizations, markets, and online communities by solving complex problems. These models generate potential meanings that require human interpretation to convert into concrete insights, thereby reshaping both individual and collective knowledge. John Nosta, in an article for Psychology Today, explores how LLMs interact with human knowledge and the importance of human interpretation in making sense of AI-generated outputs. However, there are challenges associated with the quality of data these models are exposed to, which directly impacts the results they generate. Researchers from Nature's Human Behavior journal have highlighted the opportunities and challenges presented by LLMs in information access and transmission.