Earlier quoted context omitted.
The only real way to block specifically openAI generated content (or some other online generated LLM content) is for the company itself to store all of its outputs and compare to that database, like shingling / LSH for plagiarism detection. Other LLMs (local) are completely impossible to block, as it's a constant chase. Any system that tries to estimate the distribution of e.g. a specific LLM that performs beam searc…
Shazam-like fingerprinting for text. The complete LLM outputs wouldn't need to be stored, just the fingerprints along with some mechanism for trusted timestamping (could be Blockchain).
This has been done for a very long time. Blockchains are definitely not required (this isn't just the usual hate from HN of Blockchain, it just actually doesn't make sense here).
Fingerprinting by shingling (windows of text) with some normalization steps is pretty typical in plagiarism or similarity detection. A big database of docid-shingleid pairs along with weights for their frequency is often a very simple and fast way to do this analysis.
The big part is getting OpenAI/anthropic/etc to do it on their data and provide a service for that, but there's obviously a lot of unwanted consequences - specifically storing of all user data (even if the shingled and docids are hashes, it's still info).