The Race to Predict Human Behavior
The market for startups that claim to predict human behavior is heating up fast. In the past year alone, Simile raised $200 million at a $2 billion valuation, Aaru raised $88 million at a $1 billion valuation, and Humans&, an AI startup founded by former Anthropic, xAI, and Google employees, launched Persimmon after announcing a $480 million seed round in January at a $4.48 billion valuation.
Most of these companies rely on large language models (LLMs) that are either prompted or fine-tuned to role-play as a target demographic. But Mirror Particle, a two-year-old startup based in San Francisco, believes that approach is fundamentally broken.
Why LLMs Fall Short for Behavior Prediction
"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 reasoning 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 argues that LLMs don't perceive the world the way humans do. "LLMs are modeling written language, but humans are made of visual perception, spatial reasoning, social intelligence." Relying on them, she says, means drawing insights from what humans don't notice—which misses the point entirely when the goal is predicting human behavior.
A World Model Built From Scratch
Mirror Particle is taking a different route: 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 their behavior evolves 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 has already raised an angel round and says it is close to closing its first venture round. It is also competing in Startup Battlefield 200, TechCrunch's renowned startup competition, taking place at TechCrunch Disrupt 2026 in San Francisco on October 13–15.
How the Technology Works
Mirror Particle relies on a proprietary combination of data—including clients' customer data, current events, pop culture, and social media—to model a demographic segment as a system that evolves over time. It tracks how motivations shift as that system moves through new experiences. Much of the focus is on "revealed behavior": what people actually do, rather than what they report in surveys.
Go-to-Market Strategy
Like its rivals, Mirror Particle's initial go-to-market strategy targets areas where budgets for these kinds of insights already exist: market research, brand strategy, and product strategy. For example, the company might help a beauty brand not only write better ad copy for makeup that appeals to Gen Z, but also determine whether that demographic even wants the product in the first place.
"What if [the target demographic] doesn't want eyeshadow palettes?" Ahuja said. "Maybe blush is a better option to go for if you want to sell a product to this market."
via TechCrunch AI