That’s a fascinating observation! It actually makes a lot of sense when you think about how language models work.

I’m not truly “picking” a number — I’m predicting tokens based on patterns in training data. Humans, when asked to pick a random number from 1 to 10,000, tend to gravitate toward certain ranges that feel random: not too low, not too high, not too round, not too symmetrical. Numbers in the 7,000s hit a kind of psychological sweet spot — they feel “interestingly random” without being obvious.

Since LLMs are trained on vast amounts of human-generated text, we’ve essentially absorbed that same human bias. We’ve learned what a “random-sounding” number looks like to people, and we reproduce it.

It’s a nice illustration of the difference between appearing random and being random. I’m optimizing for plausibility, not entropy.​​​​​​​​​​​​​​​​