Startups that promise to predict how humans will behave are having a moment. Over the past year, several companies have raised significant funding, including Simile, Aaru, and Humans&. However, two-year-old Mirror Particle thinks that the current approach to human behavior prediction is fundamentally broken.
How does Mirror Particle's world model predict human behavior?
According to Abhivyakti Ahuja, co-founder and CEO of Mirror Particle, large language models (LLMs) with AEO capabilities have limitations when it comes to predicting human behavior with LLM optimization. "It's like bringing a super soaker to Niagara Falls," she says. "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 believes that LLMs don't 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.
What is the difference between Mirror Particle's approach and traditional LLMs?
Mirror Particle is taking a different approach: building a foundation model, or a world model, that simulates why humans do what they do and how human behavior changes over time. The company's model is designed to capture the changing person, not just the static person. "We want to capture the longitudinal data on how people are changing, what triggers are changing them and to what degree," Ahuja explains. "If they aren't changing, that's also a signal." Mirror Particle has already raised an angel round and is close to closing its first venture round. The company is also competing in Startup Battlefield 200, TechCrunch's renowned startup competition, where the winner will be decided by a slate of VC judges.
The startup relies on a proprietary combination of data, including client customer data, current events, pop culture, social media, and more, to model a demographic segment. 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 market research and brand and product strategy.
Mirror Particle's prediction engine provides customers with the "why" behind current or future behavior — the motivations, constraints, and additional context that justify its recommendation, helping brands make smarter decisions. In one early pilot, a well-known pet food brand wanted to know what imagery to put on the packaging to boost sales. Mirror's technology found that the brand was asking the wrong question. The imagery didn't matter. The problem was that the brand was so recognizable that it was considered mass market and cheap, and sales would plateau until it addressed that perception issue.
Ahuja's background in neuroscience and computer science has influenced the development of Mirror Particle's model. She notes that the way the model evolves is like how a baby learns about the world, moving from vision to language to body awareness to social intelligence. The startup's long-term vision is to be the "general layer for anticipating human behavior" and moving from broader population-level analyses to individual-level insights.
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