DesignArena creators raise $7.9 million to bring human taste to AI models

The company behind DesignArena, dubbed Intelligence, announced a $7.9 million seed round led by Index Ventures, with participation from Conviction (Sarah Guo and Mike Vernal), A*, Valkyrie, and others, TechCrunch reported.

DesignArena is an AI tool now used by 5.3 million people around the world. It grew out of an AI game-engine project: co-founder Grace Li and a handful of college friends found their models could build functional games, but none of the games were fun — raising the question of how you can tell whether a game will be fun without human judgment.

For non-enterprise users, DesignArena works like a sophisticated model router: a ChatGPT-style window for prompts, dropdowns for websites, images, and a dozen other visual formats, and then a series of "A vs. B" choices until the user has ranked the handful of outputs from best to worst.

The real value is on the enterprise side, where participating models treat the platform as an endless source of instant feedback for their media-generating models. Users tend to be indifferent to which models they are ranking — they just want the best output — so their rankings give critical input on what users actually want.

"It was the missing bottleneck for a lot of these models to make improvements in the design space," Li said. "About a week later, we closed our first major deal with a frontier lab." She added that the site is currently generating $60 million in ARR, solidifying its position as a key source of human-led evaluation data for the AI industry.

Crucially, users must log in to get their output, so Intelligence can track how tastes change across continents and over time — Li notes that web dashboards in Asia tend to favor a more maximalist design style. These human measures complement automated benchmarks, which scale further but are often subject to being gamed or manipulated.

The raise lands as demand grows for "taste" and human feedback in AI. As models converge on capability, the ones that best understand human preference are the ones most likely to ship products people actually enjoy.

What to watch: whether Intelligence invests the capital in scaling evaluations, serving more frontier labs, or blending human feedback more deeply with automated testing — and whether it becomes the default infrastructure for AI aesthetics.