The market for startups promising to predict human behavior is experiencing a boom. Over the last year alone, Simile secured $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.
Currently, predicting human behavior relies heavily on large language models (LLMs) that are prompted to roleplay as target demographics. However, two-year-old San Francisco-based startup Mirror Particle believes this approach is fundamentally flawed.
“It’s like bringing a super soaker to Niagara Falls,” says Abhivyakti Ahuja, co-founder and CEO of Mirror Particle, which sells brands an AI engine to predict consumer behavior and its underlying reasons. “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 do not perceive the world the way humans do. “LLMs are modeling written language, but humans are made of visual perception, spatial reasoning, social intelligence.” She contends that relying on them means getting insights based on what humans don't notice, which is counterproductive for predicting behavior.
Instead, Mirror Particle is taking a different path: constructing a foundation model, or as Ahuja describes it, a world model built from scratch that simulates why people act and how human behavior evolves over time.
“We don’t want to capture the static person,” Ahuja explained. “We want to capture the changing person. That means capturing longitudinal data on how people are changing, what triggers are changing them and to what degree.” She added that if people aren't changing, that lack of change is also a significant signal.
Mirror Particle has already raised an angel round and is close to closing its first venture round. The company is also competing next week in Startup Battlefield, TechCrunch’s renowned startup competition.
The startup relies on a proprietary mix of data, including clients' customer data, current events, pop culture, and social media, to model a demographic segment. It views this as a system that evolves over time, tracking how motivations shift as individuals experience new things. Much of the focus is on “revealed behavior”—what people actually do rather than self-reported survey answers.
Like its rivals, Mirror Particle’s initial go-to-market strategy focuses on sectors where budgets for these insights already exist: market research and brand and product strategy. For instance, the startup might help a beauty brand not just write better ad copy for makeup appealing to Gen Z, but also determine if that demographic even wants the product.
“What if [the target demographic] doesn’t want eyeshadow palettes?” Ahuja asked. “Maybe blush is a better option to go for if you want to sell a product to this market.”
Mirror Particle’s prediction engine also provides customers with the “why” behind current or future behavior, offering the motivations, constraints, and additional context that justify its recommendations to help brands make smarter decisions.
In one early pilot, a well-known pet food brand wanted to know what imagery to put on packaging to boost sales—chicken, beef, or vegetables? Mirror’s technology found the brand was asking the wrong question. The imagery didn't matter; the issue was that the brand was so recognizable that it was considered mass market and cheap, causing sales to plateau until it addressed that perception problem.
“The way we see our model evolving is like how a baby learns about the world,” Ahuja said, noting that babies move from vision to language to body awareness to social intelligence.
That fundamental interest in modeling the human brain stems from Ahuja’s background in neuroscience and computer science. Originally from India, she studied at the University of Toronto, where she became inspired by AI pioneer Geoffrey Hinton’s contributions to neural networks.
Following graduation, Ahuja worked at Amazon Robotics building robots that build other robots, where she met her co-founders, Will Song and Thomson Yen. Song has spent time building sales personalization engines, while Yen focused on using deep learning to understand how AI agents perceive human behavior.
The startup’s long-term vision is to become the “general layer for anticipating human behavior,” moving from broader population-level analyses to individual-level insights.
“We just need a better model of humans if we’re going to work alongside AI and with each other,” Ahuja stated.
TechCrunch readers can check out Mirror Particle and dozens of other vetted startups at Disrupt in downtown San Francisco next week. The winner of this year’s Startup Battlefield will be decided by a slate of VC judges on the afternoon of Thursday, October 15.
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