Startups that promise to predict how humans will behave are having a moment. Over the past year, Simile raised $200 million at a $2 billion valuation; Aaru raised $88 million at a $1 billion valuation; and Humans&, an AI startup that announced a massive $480 million seed round in January at a $4. 48 billion valuation, launched Persimmon to model human behavior. The status quo for human behavior prediction today relies heavily on large language models (LLMs) that are prompted or fine-tuned to role-play as a target demographic.
But two-year-old, San Francisco-based Mirror Particle thinks that approach is fundamentally broken. "It's like bringing a super soaker to Niagara Falls," says Abhivyakti Ahuja, co-founder and CEO of Mirror Particle, which provides brands with an AI engine that predicts consumer behavior and the reasons behind it. "LLMs have been trained on hundreds of billions of data points. How much can you influence its behavior by [fine-tuning] with such a small amount of data? It's still stuck in the past. " Ahuja doesn't think LLMs see the world the way a human does.
“LLMs are modeling written language, but humans are made of visual perception, spatial reasoning, social intelligence. ” Relying on them, she says, means getting insights based on what humans don’t notice, which is beside the point when trying to predict human behavior. Mirror Particle is taking another approach: building a foundation model, or as Ahuja describes it, a world model built from scratch that simulates why humans do what they do and how human behavior changes over time. “We don’t want to capture the static person,” Ahuja said. “We want to capture the changing person.
That means capturing the longitudinal data on how people are changing, what triggers are changing them and to what degree. " If they aren't changing, she added, "that’s also a signal. ” The startup relies on a proprietary combination of data that includes its clients' customer data, current events, pop culture, social media, and more to model a demographic segment, thinking of it as a system that evolves over time and tracking how motivations shift as it moves through experiences. Much of the focus is on “revealed behavior” — what people actually do rather than self-reported survey answers.