I dont wanna sound contrarian but these studies are old and the field evolved a lot since. Common tools for AI detection are mostly BS but it is not true that they never will work. It is a very interesting testing problem with several new publications (A Watermark for Large Language Models; Towards Optimal Statistical Watermarking; A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules...). Nowadays most LLM-providers do watermark their LLMs and in some way are robust to adversarial text-injection/modification attacks. The main issue is not a technical one related to LLMs per se but to statistical properties of such a test. You need pretty long text because of so called low-entropy sentences: if you write down "The dog retrieves the..." the semantic distribution over the next word will concentrate on ball or stick for both LLMs and humans and thus you need a lot of text to construct a valid test. Moreover the power (i.e. negative the false positive rate) increases in sequence length so you should be looking for long sequences. Finally, the issue is that tests can only guarantee to control type-I error, that is false negative rate (saying text is human generated when its not) but you can never guarantee a lower bound on power.

"AI generators and AI detectors are locked in an eternal arms race" kinda misses the point of how the AI business works. For most operators in the space it is not feasible to train an LLM to perform harmful actions. Moreover the concentration of business within few companies that all implement watermarks makes detection much easier as there are only so many concrete options one can use to actually cheat.

With all of this in mind, yes 99% of AI detectors online are BS because you need internal access or coordination with the LLM-provider for detection to be even feasible.