42AI launches with $62.5m and a system that predicts human behaviour
Israeli startup 42AI has emerged from stealth with $62.5 million in seed funding to deploy a behavioural world model capable of forecasting human actions.
The system is currently operational at a number of financial brokerages, where it aims to anticipate how individual traders will react to volatile market conditions.
“Behaviour is not deterministic; it’s constantly probabilistic,” Amit Gal-Or, CEO and co-founder, told TechCrunch when asked to explain the claim. He argues that people possess underlying psychological tendencies that drive decisions even when circumstances shift. The startup seeks to measure these tendencies and convert them into data the model can process.
“A week from now, you might be a different person, but your tendencies will always play out in certain areas to a probability or distribution,” co-founder and CTO Ehud Ezra added. The company states its model functions by showing the probability of a person making a specific decision.
42AI claims the model identifies internal motivations rather than extrapolating patterns from past actions. To achieve this, the system examines reactions to seemingly unrelated events. For an investor or trader, these events might include completing KYC processes, onboarding, support calls, money transfers, or trading stocks.
“When the person is talking to support or sales in the initial process […] we’re looking at the sentiment, we’re looking at the voice, we’re looking at a lot of interactions that they’re doing, how many times do they call, and so forth,” Ezra said. “And all of those data points are going into the model. Later on, you have the interactions: how you buy and sell, and when, and we’re looking at the market at that stage.”
“So we’re trying to look at how they’re looking at the world. In a world model, every individual is the world,” he added.
The company converts these observations into mathematical representations of traits, which feed into a transformer-based world model. This system lays out probabilities for possible decisions under different circumstances. A separate language model generates explanations for the predictions.
Ezra noted the model is trained to preserve a person’s underlying tendencies during simulations. 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, alongside advice from experts in psychology and PTSD, and Dan Shechtman, who won the 2011 Nobel Prize in Chemistry.
Gal-Or said the team first studied thousands of people via psych assessments and behavioural scenarios, before adding government and biobank datasets covering more than 2 million people. Customer datasets have since expanded to cover almost 30 million people, he said.
In live deployments, the model analyses customers’ existing records, including transactions and support calls. Gal-Or noted the system does not require personally identifiable information, arguing that “the model never knows who the person is.” However, the company still creates individual behavioural profiles from linked customer activity.
When predicting market trades, 42AI claims its model achieves 98% accuracy. Gal-Or clarified this measures whether trading flows fall within a predicted range, not if each specific trade was forecast 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.
It is not like the model can work everywhere, given it is 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 accuracy also suffers when the system misses important situational context, such as if someone changes their trading choices based on advice from a friend or colleague.
Gal-Or said 42AI is working with 10 large brokerages, who use it to manage liquidity, margin requirements, and hedging exposure. The company is onboarding more customers and generating more than “seven figures” in revenue, though he would not 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 was not 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 is a potentially troubling side to 42AI’s technology: if a brokerage can anticipate what its customers may do, what stops someone from using that knowledge to their own benefit? Ezra admitted this was “a valid concern” when raised, and the founders pointed to regulations, monitoring and an internal ethics committee, though they did not explain what, if anything, would stop their customers from using the technology to gain an advantage over the very people it is profiling.
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.
