Back in 2007, Jason Cohen was an aspiring political scientist studying in China. As it turned out, locals—and the Chinese government—weren’t too enthusiastic about political science students from America asking lots of questions.
Luckily for Cohen, that initial pushback from Chinese officials was the beginning of a circuitous path that would eventually lead him to tea and, surprisingly, to developing AI tools that help food brands accelerate their path to market. The Spoon recently caught up with Cohen to hear about his journey from the tea markets of Yunnan province to his current role at Simulacra Data.
A Serendipitous Start in the Tea Markets
Shortly after Cohen arrived in China as a young prodigy who had graduated high school early and was sent to study politics, things quickly unraveled.
“Turns out, blonde hair, blue eyes, and bad Chinese don’t really endear you to asking about the government in rural southwestern China,” Cohen said. With his political studies cut short, Cohen was drawn to the local tea markets, where he encountered Ji Hai, a fermentation master at the Communist-era tea conglomerate CNNP. It was here that Cohen’s fascination with tea took root.
“I started hanging out in the tea market, originally out of a mix of interest in practicing Chinese,” he said. “But pretty quickly, I realized there was something more going on here.” This unexpected immersion in tea tasting honed Cohen’s palate and laid the foundation for his future endeavors in understanding consumer preferences.
From there, Cohen went to live at the Makaibari Tea Plantation in India, where he continued to study tea. He then embarked on a long journey from Guangzhou, China, through Tibet and Nepal into India, visiting tea places and picking up odd jobs along the way.
Eventually, Cohen returned to the United States, where he attended Penn State on a political science fellowship. However, as in China, his interest in politics was pushed aside by his passion for tea. “Like everything I touch, it kind of spiraled out of control,” Cohen says, describing how a small research group he started evolved into a full-fledged tea research institute, where he did his studies in sensory science and artificial intelligence. Cohen’s research at the Tea Institute eventually became the basis for his first company, Gastrograph AI.
Gastrograph AI: A Pioneering Venture in Flavor Prediction
In 2011, Cohen took the learnings from the tea institute and used them to found Gastrograph AI. At the time, he thought he could build an AI model to predict consumer preferences based on flavor. Over time, Gastrograph built a proprietary dataset of over 100,000 product evaluations from 35 countries, which Cohen claims allowed the company to accurately forecast which flavors would appeal to specific consumer segments.
“We were building a foundation model for flavor,” Cohen explained.
As CEO, Cohen helped Gastrograph AI secure large CPG brands as customers, where the company’s model helped fine-tune their products to meet the tastes of different demographics. Around this time, Cohen observed that AI researchers began to build large language models using neural networks and deep learning, but he wasn’t yet convinced of the power of generative AI for CPG research.
“I had always been a skeptic of the use of traditional neural networks and deep learning models,” he said. “In consumer research, you deal with small, expensive, and difficult-to-collect data sets. You can’t just throw a deep learning model at it and expect good results.”
The Turning Point
Cohen’s skepticism about generative AI shifted as he observed the rapid advancements in new tools based on LLMs over the past couple of years. One particular tool that caught his eye was Midjourney, the generative AI tool that creates lifelike images with simple prompts.
“The moment that the switch flipped was with the release of MidJourney,” Cohen said. “If you can generate images based on a text prompt, you should be able to do that with tabular business data.”
Once Midjourney led Cohen to reconsider the potential of AI in consumer research, he began to think about how generative AI could enable companies to generate synthetic data for scenarios that would otherwise be too costly or time-consuming to study. “It became very, very clear to me in 2022 that generative AI was going to change what’s possible to achieve in consumer research,” Cohen said.
It wasn’t long after this realization that Cohen stepped back from his role at Gastrograph and founded Simulacra Synthetic Data Studio.
Simulacra: Redefining Consumer Research with Generative AI
According to Cohen, Simulacra uses AI in a significantly different way than what he and his team pioneered at Gastrograph; instead of relying on proprietary data, Simulacra uses a “bring your own data” model. This allows companies to input their existing consumer data into the company’s model, which then uses generative AI to create synthetic data for a wide range of scenarios.
“We built an AI that learns to build a synthetic data generation model on whatever data is uploaded,” Cohen said. He explained that this allows companies to simulate outcomes—from market reactions to new products to optimizing pricing strategies—without extensive market research. “It’s much more mathematically accurate. It’s much more correct for drawing direct statistical inference,” he said.
At the core of Simulacra’s technology is diffusion modeling, which Cohen describes as challenging conventional thinking about AI models. “Synthetic data generation turns a lot of what we think about models on its head,” he said. By treating all variables as both dependent and independent, Simulacra’s AI can create a more holistic and accurate model of consumer behavior.
The Impact of Generative AI on the Food Industry
Cohen believes that generative AI will have a profound impact on the food and consumer goods industries.
“We’ve seen the market fracture, and we’ve seen a greater number of consumer cohorts than there had previously been.”
Cohen believes that in a fast-changing market, traditional market research is often too slow and expensive to keep up with changing consumer preferences. Because of the rising cost of traditional research, companies are forced to rely on smaller studies with less statistical power, making decisions based on incomplete data or gut instinct. Simulacra, Cohen explains, offers companies a way to make data-driven decisions that are both accurate and affordable.
“That’s where Simulacra is really going to make an impact.”
Beyond Digital Twins
According to Cohen, there is a big difference between Simulacra’s approach and traditional digital twin technology. While digital twin technology typically involves creating exact virtual replicas of specific entities or datasets to model and predict behaviors, Simulacra uses survey data—ranging from hundreds to hundreds of thousands of observations—to synthetically generate new data or incorporate new knowledge. He believes this approach allows Simulacra to adjust and predict outcomes with more mathematical accuracy and statistical relevance. Rather than producing textual outputs like those from large language models (LLMs), Simulacra returns quantitative and categorical data that companies can use for rigorous statistical analysis.
Looking Ahead: The Future of AI in Consumer Research
As AI technology evolves, Cohen envisions a future where AI-driven consumer research—including synthetic data—is the norm rather than the exception. He predicts that tools like Simulacra will help companies reduce the high failure rates associated with new product launches by providing more reliable data and insights earlier in the development process.
Despite the transformative potential of this technology, Cohen is quick to dismiss concerns that using AI model and synthetic data will lead to consumer product homogenization.
“The idea that this technology is going to be a convergent force across different product development cycles, I don’t think that’s the case,” he said. Companies will still have different goals, constraints, and consumer segments, leading to diverse outcomes even when using similar technologies.
You can watch Cohen’s full interview below. If you’d like to hear him talk about Simulacra and meet him in person, he will be at the Food AI Summit on September 25th!
