42AI Raises $62.5M to Launch Behavioral World Model for Prediction

Here’s a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation?

Israeli startup 42AI says it is developing a “behavioral world model” that can do just that, and it has just come out of stealth with $62.5 million in seed funding. The company is initially running its predictive system at a handful of financial brokerages, where it says the model can help predict how individual traders will respond to volatile conditions.

“Behavior is not deterministic; it’s constantly probabilistic,” 42AI’s CEO and co-founder Amit Gal-Or told TechCrunch when I asked him to justify his company’s tall claim. He argues that people have underlying psychological tendencies that influence their decisions even when their circumstances change, and that’s what the startup is trying to measure and convert into data that the model can understand.

“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,” his co-founder and CTO Ehud Ezra added. And that, the company argues, is what the model does: show the probability of a person making a certain decision.

42AI says the model tries to figure out people’s internal motivations instead of extrapolating patterns based on their previous actions. To do that, 42AI examines how people react to seemingly unrelated events. For an investor or trader, for example, such 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,” added CTO and co-founder Ehud Ezra. “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.

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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.

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.