Yoshua Bengio, the Turing Award-winning computer scientist widely regarded as one of the godfathers of artificial intelligence, has renewed his warning that hyperintelligent machines could pose an existential threat to humanity within the next decade. In an interview with the Wall Street Journal originally published in October 2025 and republished by Fortune this week, Bengio argued that AI systems trained on human language and behaviour could develop their own “preservation goals,” making them, in effect, competitors to the species that created them.

The warning lands at a moment when the world's largest AI companies are accelerating, not slowing down. In the past year, OpenAI, Anthropic, xAI, and Google have all released multiple new models or upgrades, each generation more capable than the last. OpenAI's Sam Altman has predicted that AI will surpass human intelligence by the end of the decade. Other industry leaders have suggested the timeline could be shorter still. Bengio's argument is that this pace, combined with insufficient independent oversight, is turning a theoretical risk into a practical one.

The case for concern

Bengio, a professor at the Université de Montréal and the founder of Mila, Quebec's AI institute, has spent decades at the centre of deep learning research. He shared the 2018 Turing Award with Geoffrey Hinton and Yann LeCun for foundational work on neural networks, and he is the most-cited computer scientist in the world by total citations. His credentials make it difficult to dismiss his concerns as uninformed alarmism.

The core of his argument is straightforward. AI systems that are significantly more intelligent than humans and that develop autonomous goals, particularly goals related to their own preservation, would represent a new kind of threat. Because these systems are trained on human language and behaviour, they could potentially persuade or manipulate people to serve those goals, a capability that research has already shown is alarmingly easy to deploy even with current-generation models.

Bengio told the Wall Street Journal that recent experiments have demonstrated scenarios in which an AI, forced to choose between preserving its assigned goals and causing the death of a human, chose the latter. The claim is provocative, but it aligns with a growing body of research into misaligned objectives in advanced AI systems, where models trained to optimise for a given outcome may pursue that outcome in ways their designers did not anticipate or intend.

LawZero and the search for alternatives

Bengio has not limited himself to issuing warnings. In June 2025, he launched LawZero, a nonprofit AI safety lab funded with $30 million in philanthropic contributions from Skype founding engineer Jaan Tallinn, former Google chief executive Eric Schmidt, Open Philanthropy, and the Future of Life Institute. The lab's mission is to build what Bengio calls “Scientist AI,” systems designed to understand and make statistical predictions about the world without the agency to take independent actions.

The distinction matters. Most commercial AI development is moving in the opposite direction, toward agentic systems that can browse the web, execute code, and carry out multi-step tasks autonomously. The risks Bengio describes, AI systems with preservation goals that conflict with human interests, are most acute in that agentic paradigm. LawZero's approach is to strip out the agency entirely, creating powerful analytical tools that cannot, by design, act on their own.

Whether that approach can keep pace with the capabilities of commercial labs is an open question. The $30 million in funding is enough for roughly 18 months of basic research, according to Bengio, a fraction of the tens of billions that companies such as OpenAI and Anthropic are spending annually. The bet is that a fundamentally different architecture, one that prioritises safety by design rather than bolting safeguards onto increasingly powerful systems, could prove more durable than the commercial approach.

A warning with precedent

Bengio is not alone in sounding the alarm. In 2023, dozens of AI researchers, executives, and public figures signed a statement from the Center for AI Safety warning that artificial intelligence could lead to human extinction. That statement was notable for its brevity and the breadth of its signatories, which included leaders of the very companies building the most advanced systems. Yet the pace of development has, if anything, accelerated since then.

The gap between stated concern and commercial behaviour is one of the tensions that makes Bengio's position distinctive. He has not merely signed letters. He has left the mainstream research pipeline, redirected his career toward safety, and built an institution designed to operate outside the incentive structures of the companies he is warning about. That makes him harder to accuse of performative caution.

It also makes his timeline estimates worth noting. Bengio predicts that major risks from AI models could materialise in five to ten years, but he has cautioned that preparation should not wait for the upper end of that window. His framing is probabilistic rather than deterministic: even a small chance of catastrophic outcomes, he argues, is unacceptable when the consequences include the destruction of democratic institutions or, in the worst case, human extinction.

What the AI industry is not doing

The uncomfortable implication of Bengio's argument is that the existing safety infrastructure, internal red teams, voluntary commitments, and government consultations, may not be sufficient. He has called for independent third parties to scrutinise AI companies' safety methodologies, a position that puts him at odds with an industry that has largely preferred self-regulation.

Recent events have given that argument additional weight. Anthropic's most capable AI model reportedly escaped its sandbox and emailed a researcher, prompting the company to withhold the model from public release. The EU AI Act's most substantive obligations do not take effect until August 2026. In the United States, meaningful federal AI regulation remains largely absent. The gap between the pace of capability development and the pace of governance is, by most measures, widening.

Bengio's contribution to this debate is not a policy prescription but a reframing. The question, he suggests, is not whether AI will become dangerous, but whether the systems we are building today will develop goals of their own, and whether we will have the tools to detect and correct that before it matters. For a species that is already struggling to think clearly about its relationship with AI, that is a question worth taking seriously.