Large Language Models (LLMs) have shown remarkable progress, but their evolution into Multi-Modal LLMs (MLLMs), capable of processing images, audio, and more alongside text, presents new challenges. The key? Data. MLLMs rely heavily on massive, high-quality, and diverse datasets to achieve their emergent capabilities. This isn't a one-way street, though. As MLLMs become more sophisticated, they can be leveraged to improve the very data they are trained on, creating a synergistic cycle of co-development. This co-development loop is still nascent, but its implications are vast. Imagine MLLMs not only learning from data but also filtering, refining, and even generating new data, constantly boosting their own performance. This self-improving system can revolutionize MLLM training, making it more efficient and enabling the development of even more powerful models. For example, an MLLM could analyze its own weaknesses in understanding specific visual concepts and then search for or even synthesize new training examples to address those gaps. However, several challenges must be addressed to fully realize the potential of data-model co-development. Ensuring accurate alignment between different modalities within the data, mitigating ethical concerns around data privacy and bias, and developing robust evaluation methods are crucial. Overcoming these obstacles requires focused research and development of dedicated infrastructures for MLLM data-model co-development. The future of MLLMs isn’t just about building bigger models; it's about harnessing the power of data to drive a continuous cycle of improvement, creating truly intelligent and versatile AI systems.