A counterpoint to the idea that AI is better than you at generating ideas — something I’ve been hearing at work and reading here and there.
What this study found is that while at the individual level it might seem to help generating more ideas, in the aggregate those ideas look alike:
First, having access to generative AI effectively equalizes the evaluations of stories, removing any disadvantage or advantage based on the writers’ inherent creativity. That generative AI particularly benefited less able writers is paralleled in recent studies focusing on other domains in which generative AI has been shown to help less productive workers. Second, one might ask whether the generative AI ideas can push the upper bound of creativity of produced stories, beyond what particularly creative humans are capable of on their own. We do not find evidence of this possibility in this study. […]
While these results point to an increase in individual creativity, there is risk of losing collective novelty. In general equilibrium, an interesting question is whether the stories enhanced and inspired by AI will be able to create sufficient variation in the outputs they lead to. Specifically, if the publishing (and self-publishing) industry were to embrace more generative AI-inspired stories, our findings suggest that the produced stories would become less unique in aggregate and more similar to each other. This downward spiral shows parallels to an emerging social dilemma: If individual writers find out that their generative AI-inspired writing is evaluated as more creative, they have an incentive to use generative AI more in the future, but by doing so, the collective novelty of stories may be reduced further. In short, our results suggest that despite the enhancement effect that generative AI had on individual creativity, there may be a cautionary note if generative AI were adopted more widely for creative tasks.
I wonder if two things are happening. One, the model is trained on something, it can only think with what it’s trained upon. Two, the optimisation pushes the model to converge on similar outcomes. Variety gets whittled out — unusual perspectives, diverse viewpoints, rare knowledge. It converges to the safe, statistical average outcome. AI researcher Andrew Peterson defines this as knowledge collapse.
Abstracting away from models, Lauren Leek recently wrote about what happens when prediction shapes the choices in front of us:
[V]ariety is where new options come from. The strange Georgian-Japanese street-food fusion restaurant nobody can classify, the pub that doesn’t fit the template, the song that sounds unlike what came before, most will never matter. But some will. A world that keeps removing the unlikely also removes the things that could have become the next likely. Ecologists have known this for a long time already and even priced it: they call it the insurance value of biodiversity, the standing reserve of rare species that cost energy and contribute nothing, until the climate shifts and one of them turns out to be what survives. A monoculture is efficient right up until the weather changes.
And there’s a second cost, harder to see, and it’s the one that actually a bit scared of. Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want.