> The surge of AI, large language models, and generated art begs fascinating questions. The industry’s progress so far is enough to force us to explore what art is and why we make it. Brandon Sanderson explores the rise of AI art, the importance of the artistic process, and why he rebels against this new technological and artistic frontier.
Do watch the video as it makes a compelling argument against this exact kind of thing. From a product design perspective, you're asking people to read a bunch of slop and organize it into slop piles. What's the point of that? Honestly it seems like a huge waste of everyone's time.
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.
Thanks Josh! I tried GEPA previously back when it was still 1-shot generation. It actually ended up working really well for some models and horrible for others, so I decided to scrap for a more generic prompt instead to make the benchmark a bit more rigourous.
Hard to find the signal in the noise and know what stories I should even read to get a sense of baseline quality; partially because that's just a hard problem inherent to floods of any content, but also because the recommendation system seems to lack enough data (and also might be weighting the wrong things, e.g. the rank #1 story is also the lowest-rated...).
A very cool idea in theory and something very hard to pull off, but I think in order to get the data you need on how readable each story is you'll need to work on presentation and recommendation so those don't distract from what you're actually testing.
Thanks for the feedback - looking at the rest of the comments, I definitely agree it seems to be a common theme. Will do better to fix those issues so there's less noise.
It would be interesting to consider composite systems where human brainstorming feeds AI writing, as well as vice versa, to see what kind of engagement with AI writing people like the most. At least in my case, I find plot writing good fun, and actual writing slightly less good fun.
Definitely on the to-do list! Right now, there's smth called fork (inspired by Github fork), where it lets you remix the story with a given input. It might be cool for you to mess around with.
1. UI is terrible. Paragraphs are extremely far apart, and most paragraphs are 1 short sentence (e.g. "I glare."). On mobile, I can only see a few words at a time, and desktop's not much better.
Thanks for letting me know - the UI issues are definitely on me (fixing asap). Feel free to generate a story or two - right now there's not enough annotations to make "top-rated" a valid moniker.
Did you skip Anthropic models? I honestly can't take this seriously if you're not looking at all the leading providers but you did look at some obscure ones.
There's 151 models there right now (with all the latest Anthropic models), it's all randomized, it's just that there aren't enough annotations for the anthropic models to be elicited right now.
I have a lot of engagement data on LLMs from running a creative writing oriented consumer AI app and spending s lot of time on quality improvements and post training
have you read any of the generated stories? if you can honestly tell me this is not complete drivel (even worse, wildly generic and poorly written) then i will consider giving real feedback but i would find that hard to believe.
I hope it's clear that the stories aren't being generated one-shot. I'm sure there are flaws that I haven't perfectly accounted for in the agent-loop, but because we randomize the models for each of the brainstorming -> writing -> memory parts, bad intermediate outputs will affect the final output as well. That's why unless we have above average models across all 3 stages, it might be worse than what you're used to. It's a trade-off to get more granular results. Hope you can give it a chance.
> The surge of AI, large language models, and generated art begs fascinating questions. The industry’s progress so far is enough to force us to explore what art is and why we make it. Brandon Sanderson explores the rise of AI art, the importance of the artistic process, and why he rebels against this new technological and artistic frontier.
What It Means To Be Human | Art in the AI Era
https://www.youtube.com/watch?v=mb3uK-_QkOo
Do watch the video as it makes a compelling argument against this exact kind of thing. From a product design perspective, you're asking people to read a bunch of slop and organize it into slop piles. What's the point of that? Honestly it seems like a huge waste of everyone's time.
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.
This is super cool! Have you tried GEPA?
Thanks Josh! I tried GEPA previously back when it was still 1-shot generation. It actually ended up working really well for some models and horrible for others, so I decided to scrap for a more generic prompt instead to make the benchmark a bit more rigourous.
Hard to find the signal in the noise and know what stories I should even read to get a sense of baseline quality; partially because that's just a hard problem inherent to floods of any content, but also because the recommendation system seems to lack enough data (and also might be weighting the wrong things, e.g. the rank #1 story is also the lowest-rated...).
A very cool idea in theory and something very hard to pull off, but I think in order to get the data you need on how readable each story is you'll need to work on presentation and recommendation so those don't distract from what you're actually testing.
Thanks for the feedback - looking at the rest of the comments, I definitely agree it seems to be a common theme. Will do better to fix those issues so there's less noise.
It would be interesting to consider composite systems where human brainstorming feeds AI writing, as well as vice versa, to see what kind of engagement with AI writing people like the most. At least in my case, I find plot writing good fun, and actual writing slightly less good fun.
Definitely on the to-do list! Right now, there's smth called fork (inspired by Github fork), where it lets you remix the story with a given input. It might be cool for you to mess around with.
I took a look at the "top-rated" story.
1. UI is terrible. Paragraphs are extremely far apart, and most paragraphs are 1 short sentence (e.g. "I glare."). On mobile, I can only see a few words at a time, and desktop's not much better.
2. Story is so bad that it's not even amusing.
Thanks for letting me know - the UI issues are definitely on me (fixing asap). Feel free to generate a story or two - right now there's not enough annotations to make "top-rated" a valid moniker.
Did you skip Anthropic models? I honestly can't take this seriously if you're not looking at all the leading providers but you did look at some obscure ones.
There's 151 models there right now (with all the latest Anthropic models), it's all randomized, it's just that there aren't enough annotations for the anthropic models to be elicited right now.
Quick feedback: Website is basically unusable on mobile
Ah shoot - thanks for letting me know. I'm still a noob on frontend so still learning as I go.
I have a lot of engagement data on LLMs from running a creative writing oriented consumer AI app and spending s lot of time on quality improvements and post training
Do you have a contact email?
Would love to chat! Here's my email: team@narrator.sh
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Happy to engage if you have concrete criticisms.
have you read any of the generated stories? if you can honestly tell me this is not complete drivel (even worse, wildly generic and poorly written) then i will consider giving real feedback but i would find that hard to believe.
I hope it's clear that the stories aren't being generated one-shot. I'm sure there are flaws that I haven't perfectly accounted for in the agent-loop, but because we randomize the models for each of the brainstorming -> writing -> memory parts, bad intermediate outputs will affect the final output as well. That's why unless we have above average models across all 3 stages, it might be worse than what you're used to. It's a trade-off to get more granular results. Hope you can give it a chance.
so you havent read any of them, very nice product you got there. i stand by my original statement.