Readers can't identify watermarked AI text

In the last few weeks, I’ve been complaining that everyone is wrong about AI watermarking: it isn’t really anti-consumer and it doesn’t make the outputs any worse. The watermarking papers demonstrate¹ that this is true, but I thought it might be interesting to put it to a practical test. Given examples of watermarked and unwatermarked answers to the same prompt, could readers tell which is which?

To find out, I vibed up² https://sgoedecke.github.io/watermark-quiz/, a static site that quizzes readers. I used Qwen3-30B-A3B-Instruct-2507 on a rented H200 to generate thirty responses: three responses per question, one of which was secretly watermarked with SynthID-Text. The rented GPU cost around two dollars. To measure results, I just sent users to a different page for each score, and aggregated visitors-per-page in my analytics³. This would be easily spoofable if anyone cared enough to do so, but for a casual test I think it’s acceptable.

The first round of traffic I got to the quiz (278 participants) had these slightly puzzling results:

Pure random choice would lead to an average score of 3.33/10. However, the mean score here is 3.92. There is indeed a spike around 3/10, as expected, but there’s also a second weird spike at 6/10. Why is that? It turned out that the SynthID response was option A in six of the ten questions, so users who just selected the first answer for every question would get 6/10. Oops.

I re-shuffled the questions and got these results:

Now the mean is 3.4/10, much closer to the expected 3.333. There’s no spike around 6. We only had 73 people take the quiz after I shuffled the questions — most people saw it and took it immediately after I posted it to my LinkedIn and Hacker News — but given the previous results, I think that’s still enough to feel confident that people were just guessing randomly.

So no, people can’t identify the presence of AI watermarks. Obviously this wasn’t exactly a scientific study, but it’s still pretty suggestive. If watermarks were really choosing random words that the model would never pick, you’d be able to sometimes tell from three side-by-side responses which one went down the weird watermarked road, right? I also hope that something like this can serve as a persuasive tool: if you’re worrying about what impact watermarking is going to have, and your intuition is unmoved by the mathematical explanations, having a read of the watermarked and unwatermarked responses might convince you that there’s really no difference in quality.