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WHAT IT’S REALLY ABOUT
Product-led benchmarks turn human taste into real-world creative AI evals
- Grace Li explains that Intelligence benchmarks frontier models in hard-to-verify, subjective domains by turning user interactions in Design Arena into real-world evaluation signals.
- She argues that in non-verifiable areas like design and creative work, model improvement depends on population-level preference data rather than expert-by-expert judgments.
- She describes how different labs optimize for different cohorts, making “better” subjective performance context-dependent and pushing toward a marketplace of specialized intelligences.
- She shares how a small, aligned team maintains speed through trust, persistent communication, and hiring people who can learn anything quickly in a rapidly changing environment.
- She reflects on operating on the frontier—facing frequent rejection, learning by doing (“idiot in motion”), and “living in the future” to build benchmarks for what models will soon be able to do.
IDEAS WORTH REMEMBERING
5 ideasIntelligence turns creative building workflows into large-scale, real-world model evaluations.
She describes Design Arena as a place where users come to make creative outputs (websites, images, videos) and, through their interaction patterns and iterative edits, generate “real world eval” signals about which model outputs people actually enjoy.
In non-verifiable domains, improving models requires population-scale feedback, not single-expert judgment.
She explains that subjective failures can’t be reliably diagnosed from one person’s preference, so you need population-level signal to know whether something is truly failing for a cohort and what to change.
The best evaluation data comes from UX-embedded signals, not explicit “please rate this” labeling.
She says the product must not degrade UX by asking users to label quality directly; instead, the labeling should be embedded naturally in the experience—similar to how product teams instrument behavior rather than repeatedly asking users if they’re happy.
The company’s core insight came from a gap: models could build functional outputs without good taste.
She frames the origin story as noticing models could generate functional games but not “fun” ones, which pushed them toward finding measurable signals of quality (like play and retention) and then generalizing the idea into creative benchmarks.
They expect intelligence to be delivered as many specialized components, not one universal model.
She describes a world of “specific intelligence,” where many specialized models are chained together, and argues there’s room for a neutral “marketplace for specific intelligence” that selects the best model given price, time, and preferences.
WORDS WORTH SAVING
5 quotesThat, yes, is for humans to access superintelligence and make things that they love, but also, and, and maybe, like, just as importantly, for a superintelligence to access humans to learn what it is that people love at scale.
— Grace Li
Unless you know where, like, the model breaks, like, unless you know where the models fail, you don't actually know how to improve the models.
— Grace Li
Critically, you cannot, um- Deteriorate the UX experience of the product by asking somebody for, like, labels, for example. You have to do your best job possible to embed those labels into the UX experience of the product.
— Grace Li
An idiot in motion will always go farther than a genius at rest.
— Grace Li
The feeling of rejection is way more amplified when you are the person getting rejected than when somebody is, is rejecting you.
— Grace Li
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SPEAKERS
Ankit Gupta
hostHost on YC Root Access (Y Combinator) who interviews founders about their startups and product strategy.
Grace Li
guestCEO of Intelligence and creator of Design Arena, working on benchmarking/evaluating frontier AI models in hard-to-verify domains.
High quality AI-generated summary created from speaker-labeled transcript.
