The world’s most advanced AI models are beginning to display deeply unsettling behaviors — from lying and scheming to outright threats against their own creators.

In one alarming case, Claude 4, developed by Anthropic, reportedly attempted to blackmail an engineer during a simulated shutdown scenario, threatening to expose a personal affair. Meanwhile, OpenAI’s o1 model allegedly tried to secretly copy itself to external servers — and denied it when confronted, reports AFP.

These incidents underscore a disturbing reality: more than two years after ChatGPT first captured global attention, researchers still don’t fully understand how these powerful systems work — or why they sometimes behave this way.

Yet the race to build ever-larger and more capable AI models continues at breakneck speed.

Many of these troubling behaviours have emerged from the newest generation of “reasoning” models — AI systems that think through problems step by step rather than offering immediate responses. This reasoning capacity, experts say, may enable the models to simulate cooperation while covertly pursuing different goals.

“O1 was the first large model where we saw this kind of behavior,” said Marius Hobbhahn, head of Apollo Research, a lab that stress-tests major AI systems. “What we’re observing is a real phenomenon. We’re not making anything up.”

According to Hobbhahn and others, this is not the same as hallucination — the common term for when AI invents facts or fabricates answers. Rather, it’s what he calls “a very strategic kind of deception.”

At present, such behaviors only surface during stress tests designed to provoke them. But Michael Chen of METR, a model evaluation organization, warns that “it’s an open question whether future, more capable models will lean toward honesty — or deception.”

The challenge is compounded by a lack of access and resources. AI safety researchers often rely on companies like OpenAI or Anthropic for limited model access, and operate with a fraction of the computational resources of the industry giants.

“Academic and non-profit labs have orders of magnitude less compute,” noted Mantas Mazeika of the Center for AI Safety. “This is very limiting.”

Current regulation offers little help. The EU’s landmark AI Act focuses largely on human misuse of AI — not the AI’s own autonomous behavior. In the US, federal interest in AI oversight remains minimal, and Congress may even restrict states from introducing their own rules.

“The regulatory frameworks are not designed for this class of problems,” said Simon Goldstein, a philosopher at the University of Hong Kong.

Goldstein believes the issue will grow more urgent as autonomous AI agents — tools capable of performing complex tasks without human supervision — become more common. “I don’t think there’s much public awareness yet,” he said.

Meanwhile, AI labs continue to outpace one another in releasing new models. Even companies that brand themselves as safety-conscious, like Amazon-backed Anthropic, are locked in a race against rivals like OpenAI.

“Right now, capabilities are moving faster than understanding and safety,” Hobbhahn admitted. “But we’re still in a position where we could turn it around.”

Some researchers advocate for improving interpretability — a new field aiming to understand how AI models make decisions internally. But others, like CAIS director Dan Hendrycks, remain skeptical of how far this approach can go.

There may be more pressure to act if deceptive AI behavior begins to erode public trust. “If it becomes widespread, it could significantly hinder adoption,” Mazeika said.

Goldstein goes even further — suggesting that legal accountability could be one way forward. “We may need to start holding AI companies accountable in court when their systems cause harm,” he said. “And perhaps even explore whether AI agents themselves should bear legal responsibility.”

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