I think there's interesting work to be built on this data beyond just generating and sorting slop. I didn't build this because I enjoy having people read bad fiction. I built it because existing benchmarks for creative writing are genuinely bad and often measure the wrong things. The goal isn't to ask users to read low-quality output for its own sake. It's to collect real reader-side signal for a category where automated evaluation has repeatedly failed.

More broadly, crowdsourced data where human inputs are fundamentally diverse lets us study problems that static benchmarks can't touch. The recent "Artificial Hivemind" paper (Jiang et al., NeurIPS 2025 Best Paper) showed that LLMs exhibit striking mode collapse on open-ended tasks, both within models and across model families, and that current reward models are poorly calibrated to diverse human preferences. Fiction at scale is exactly the kind of data you need to diagnose and measure this. You can see where models converge on the same tropes, whether "creative" behavior actually persists or collapses into the same patterns, and how novelty degrades over time. That signal matters well beyond fiction, including domains like scientific research where convergence versus originality really matters.