In this paper, we propose the first training-free framework for robust and scalable text watermarking applicable across multiple text types (e.g., articles, code) and languages, for general as well as LLM text training data provenance. We highlight perspectives on text IP protection, such as using LLMs to enable better IP protection rather than viewing them as just sources of IP infringement, not relying on just major LLM providers, and the benefits of having a general framework that can be easily adapted to defend against new threats.