The concept of using a "teacher" Large Language Model (LLM) to train smaller AI systems has sparked controversy and debate. On one hand, this technique could potentially accelerate the development of more efficient and specialized AI models by leveraging the knowledge and expertise of existing, highly advanced LLMs. However, there are also significant concerns surrounding the use of "teacher" LLMs. For instance, the reliance on a pre-trained model can lead to a lack of diversity in the training data, which may result in biased or inadequate performance of the smaller AI systems. Furthermore, this approach could also perpetuate the problem of over-reliance on a single, dominant AI paradigm. Ultimately, as with any emerging technology, it is crucial to weigh the benefits against the risks and consider the potential consequences of such an approach. A more nuanced discussion is necessary to ensure that the development of smaller AI systems prioritizes transparency, accountability, and responsible innovation.