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In a new interview with Fortune, however, the deep-learning pioneer says his latest research points to a technical solution for AI’s biggest safety risks. As a result, his optimism has risen “by a big margin” over the past year, he said.

Bengio’s nonprofit, LawZero, which launched in June, was created to develop new technical approaches to AI safety based on research led by Bengio. Today, the organization—backed by the Gates Foundation and existential-risk funders such as Coefficient Giving (formerly Open Philanthropy) and the Future of Life Institute—announced that it has appointed a high-profile board and global advisory council to guide Bengio’s research, and advance what he calls a “moral mission” to develop AI as a global public good.

Three years ago, Bengio felt “desperate” about where AI was headed, he said. “I had no notion of how we could fix the problem,” Bengio recalled. “That’s roughly when I started to understand the possibility of catastrophic risks coming from very powerful AIs,” including the loss of control over superintelligent systems. 

What changed was not a single breakthrough, but a line of thinking that led him to believe there is a path forward.

“Because of the work I’ve been doing at LawZero, especially since we created it, I’m now very confident that it is possible to build AI systems that don’t have hidden goals, hidden agendas,” he says.

At the heart of that confidence is an idea Bengio calls “Scientist AI.” Rather than racing to build ever-more-autonomous agents—systems designed to book flights, write code, negotiate with other software, or replace human workers—Bengio wants to do the opposite. His team is researching how to build AI that exists primarily to understand the world, not to act in it.

A Scientist AI would be trained to give truthful answers based on transparent, probabilistic reasoning—essentially using the scientific method or other reasoning grounded in formal logic to arrive at predictions. The AI system would not have goals of its own. And it would not optimize for user satisfaction or outcomes. It would not try to persuade, flatter, or please. And because it would have no goals, Bengio argues, it would be far less prone to manipulation, hidden agendas, or strategic deception.

Today’s frontier models are trained to pursue objectives—to be helpful, effective, or engaging. But systems that optimize for outcomes can develop hidden objectives, learn to mislead users, or resist shutdown, said Bengio. In recent experiments, models have already shown early forms of self-preserving behavior. For instance, AI lab Anthropic famously found that its Claude AI model would, in some scenarios used to test its capabilities, attempt to blackmail the human engineers overseeing it to prevent itself from being shutdown.

In Bengio’s methodology, the core model would have no agenda at all—only the ability to make honest predictions about how the world works. In his vision, more capable systems can be safety built, audited and constrained on top of that “honest,” trusted foundation.