The widespread adoption of efficient Small Language Models (SLMs) like Gemma2 is poised to have a significant impact on the development and deployment of larger, more resource-intensive AI models. Firstly, the success of models like Gemma2 demonstrates that high performance can be achieved with smaller models, challenging the notion that bigger models always equate to better performance. This shift in perspective may lead to a reevaluation of the necessity of extremely large models, potentially redirecting resources towards more efficient and sustainable model architectures. Moreover, the adoption of efficient SLMs could democratize access to advanced AI technologies, as smaller models are more accessible to a wider range of developers and organizations due to their reduced computational requirements. This democratization could foster innovation and diversity in the AI landscape, moving away from a reliance on a few large models towards a more varied ecosystem of models tailored to specific use cases.