Israeli startup 42AI says it is developing a behavioral world model designed to simulate human decision-making, emerging from stealth with $62.5 million in seed funding. The company is initially deploying its predictive system at financial brokerages to model how individual traders respond to volatile market conditions.
According to CEO and co-founder Amit Gal-Or, human behavior is probabilistic rather than deterministic. The startup aims to measure underlying psychological tendencies and convert them into data that a machine learning model can process to project future choices.
Model Architecture
The system examines reactions to seemingly unrelated events during onboarding, support interactions, and financial transactions to infer internal motivations. These observations are transformed into mathematical trait representations and fed into a transformer-based world model.
42AI spent three years developing the framework, incorporating psychological research, neuroscience, genetics, and academic advice. Initial training utilized psych assessments and behavioral scenarios alongside government and biobank datasets covering millions of individuals.
Performance and Deployment
In financial market forecasting, 42AI reports achieving an F1 score between 0.8 and 0.85 for predicting trading direction and volume. The startup currently collaborates with 10 large brokerages for liquidity management and hedging operations, generating seven-figure revenues.
Funding and Expansion
The seed funding round was supported by the founders and Infinity Group over a three-year period. Beyond financial services, 42AI is exploring applications in gaming, fraud prevention, cybersecurity, and robotics.
Critics note potential ethical concerns regarding profiling and the use of predictive advantages against consumers. Company representatives point to regulatory compliance, internal monitoring, and an ethics committee as safeguards.
The company then converts those observations into mathematical representations of traits, which are then fed into a transformer-based world model that lays out probabilities for possible decisions under different circumstances. (The company uses a separate language model to generate explanations for its predictions.)
Ezra said the model is trained to preserve a person’s underlying tendencies during simulations. So if someone is usually risk-averse, but the model suddenly treats them as a risk-taker, “the target is off,” he said, explaining how the model’s training rewards are structured.
42AI spent three years developing this approach, combining psychological research with neuroscience and genetics, as well as advice from experts in psychology and PTSD, and Dan Shechtman, who was awarded the 2011 Nobel Prize in Chemistry.
Gal-Or said the team first studied thousands of people via psych assessments and behavioral scenarios, before adding government and biobank datasets covering more than 2 million people. Its customer datasets have since expanded to cover almost 30 million people, he said.
In live deployments, the model analyzes customers’ existing records, including transactions and support calls. Gal-Or pointed out the system doesn’t need personally identifiable information, arguing that “the model never knows who the person is.” However, it seems the company still creates individual behavioral profiles from linked customer activity.
In predicting market trades, 42AI claims its model achieves 98% accuracy, but Gal-Or clarified that this is more a measure of whether trading flows fall within a predicted range, not if each trade was predicted correctly.
He also said the model consistently achieves an F1 score of 0.8 to 0.85 for predicting trading direction and volume. (F1 score is a metric in machine learning that measures how well a classification model can balance correct predictions against false alarms and missed events.)
And it’s not like the model can work everywhere, given it’s still a machine in a box. “There’s a natural ceiling because you really don’t have access to everything that a person experiences,” Gal-Or said. Ezra explained that the system’s accuracy also suffers when it misses important situational context, like if someone chooses to change their trading choices based on advice from a friend or a colleague.
Gal-Or said 42AI is working with 10 large brokerages, who are using it to manage liquidity, margin requirements, and hedging exposure. The company is onboarding more customers, he said, and is generating more than “seven figures” in revenue, though he wouldn’t specify a number.
Customers pay the startup a flat fee, or a percentage of the value the platform helps them gain. He claimed one brokerage generated approximately $30 million in additional value through dynamic hedging.
The $62.5 million seed funding came from the founders and Infinity Group over three years, and wasn’t entirely raised from external investors, Gal-Or said. Infinity was founded by his father, Amir Gal-Or, who is also 42AI’s chairman and a co-founder. 42AI now plans to raise more money.
Beyond finance, 42AI is now exploring applications for its model in gaming, fraud prevention, and cybersecurity, and one day wants to use it to help robots and AI agents understand people. But that may be a ways off yet: Ezra acknowledged that the model needs to be trained on rules and context for each new domain.
And like with all AI tools, there’s a potentially troubling side to 42AI’s technology: If a brokerage can anticipate what its customers may do, what’s to stop someone from using that knowledge to their own benefit? Ezra admitted that this was “a valid concern” when I raised it, and the founders pointed to regulations, monitoring, and an internal ethics committee, though they didn’t explain what, if anything, would stop their customers from using the technology to gain an advantage over the very people it’s profiling.
Amit Gal-Or, however, was confident that the technology could help address such risks. “We are very proud that actually we are part of the solution; we are the solution to this problem,” he said.
