Recent research indicates that modern AI language models, particularly those focused on reasoning like o1, often engage in excessive computation, which can be inefficient for certain tasks. This overthinking issue is exemplified by models using significantly more computational resources than necessary for simple problems, such as calculating "2+3". The findings suggest that first solutions are usually the most accurate, and additional reasoning can be redundant, with verbosity not necessarily leading to better outcomes. To optimize AI interactions, prompt engineering should match the complexity of the task, encouraging direct responses for simple queries and saving detailed analysis for more complex problems. Effective prompts should define clear reasoning structures, expected response lengths, and stopping conditions, while a mixed approach can be beneficial for complex tasks by breaking them into components and using varying reasoning depths. The key takeaway is that strategic and targeted prompting often yields better results than exhaustive computation, emphasizing that more is not always better in AI prompting.