Recent discussions in the AI community highlight the evolving landscape of large language models (LLMs) and the challenges associated with their scaling. Notably, models like GPT-4 have become significantly cheaper, with costs dropping by 240 times in just two years. However, experts are expressing concerns that the improvements in performance are diminishing despite the exponential increase in data, parameters, and training time. For instance, while the transition from GPT-3.5 to GPT-4 marked a substantial leap, subsequent advancements are yielding only minimal enhancements. Additionally, the energy demands for training these models are substantial, with the inference phase often requiring even more energy due to the volume of queries processed. This has led to discussions about the financial and environmental implications of deploying such resource-intensive technologies, emphasizing the need for smarter architectural approaches rather than simply larger models.