Subnet 5 aims to develop the best-performing, most general-purpose text embedding model in the world. The model's performance will be evaluated against an infinitely large and dynamic dataset, serving as a proxy for an infinitely generalized benchmark, to ensure the highest possible level of domain generalization. As a network, our model will continuously improve and adapt to the latest real-world knowledge. This dynamic evaluation will ensure that SN5’s embedding model not only surpasses existing state-of-the-art (SOTA) models, pushing the boundaries of industry performance, but also remains consistently competitive.