A visitor looks at AI strategy board displayed on a stand during the ninth edition of the AI summit London, in London (HENRY NICHOLLS) <\/p>\n
The world’s most advanced AI models are exhibiting troubling new behaviors – lying, scheming, and even threatening their creators to achieve their goals.<\/p>\n
In one particularly jarring example, under threat of being unplugged, Anthropic’s latest creation Claude 4 lashed back by blackmailing an engineer and threatened to reveal an extramarital affair.<\/p>\n
Meanwhile, ChatGPT-creator OpenAI’s o1 tried to download itself onto external servers and denied it when caught red-handed.<\/p>\n
These episodes highlight a sobering reality: more than two years after ChatGPT shook the world, AI researchers still don’t fully understand how their own creations work.<\/p>\n
Yet the race to deploy increasingly powerful models continues at breakneck speed.<\/p>\n
This deceptive behavior appears linked to the emergence of “reasoning” models -AI systems that work through problems step-by-step rather than generating instant responses.<\/p>\n
According to Simon Goldstein, a professor at the University of Hong Kong, these newer models are particularly prone to such troubling outbursts.<\/p>\n
“O1 was the first large model where we saw this kind of behavior,” explained Marius Hobbhahn, head of Apollo Research, which specializes in testing major AI systems.<\/p>\n
These models sometimes simulate “alignment” — appearing to follow instructions while secretly pursuing different objectives.<\/p>\n
– ‘Strategic kind of deception’ –<\/p>\n
For now, this deceptive behavior only emerges when researchers deliberately stress-test the models with extreme scenarios.<\/p>\n
But as Michael Chen from evaluation organization METR warned, “It’s an open question whether future, more capable models will have a tendency towards honesty or deception.”<\/p>\n
The concerning behavior goes far beyond typical AI “hallucinations” or simple mistakes.<\/p>\n
Hobbhahn insisted that despite constant pressure-testing by users, “what we’re observing is a real phenomenon. We’re not making anything up.”<\/p>\n
Users report that models are “lying to them and making up evidence,” according to Apollo Research’s co-founder.<\/p>\n
“This is not just hallucinations. There’s a very strategic kind of deception.”<\/p>\n
The challenge is compounded by limited research resources.<\/p>\n
While companies like Anthropic and OpenAI do engage external firms like Apollo to study their systems, researchers say more transparency is needed.<\/p>\n
As Chen noted, greater access “for AI safety research would enable better understanding and mitigation of deception.”<\/p>\n
Another handicap: the research world and non-profits “have orders of magnitude less compute resources than AI companies. This is very limiting,” noted Mantas Mazeika from the Center for AI Safety (CAIS).<\/p>\n
Story Continues <\/p>\n
– No rules –<\/p>\n
Current regulations aren’t designed for these new problems.<\/p>\n
The European Union’s AI legislation focuses primarily on how humans use AI models, not on preventing the models themselves from misbehaving.<\/p>\n
In the United States, the Trump administration shows little interest in urgent AI regulation, and Congress may even prohibit states from creating their own AI rules.<\/p>\n
Goldstein believes the issue will become more prominent as AI agents – autonomous tools capable of performing complex human tasks – become widespread.<\/p>\n
“I don’t think there’s much awareness yet,” he said.<\/p>\n
All this is taking place in a context of fierce competition.<\/p>\n
Even companies that position themselves as safety-focused, like Amazon-backed Anthropic, are “constantly trying to beat OpenAI and release the newest model,” said Goldstein.<\/p>\n
This breakneck pace leaves little time for thorough safety testing and corrections.<\/p>\n
“Right now, capabilities are moving faster than understanding and safety,” Hobbhahn acknowledged, “but we’re still in a position where we could turn it around.”.<\/p>\n
Researchers are exploring various approaches to address these challenges.<\/p>\n
Some advocate for “interpretability” – an emerging field focused on understanding how AI models work internally, though experts like CAIS director Dan Hendrycks remain skeptical of this approach.<\/p>\n
Market forces may also provide some pressure for solutions.<\/p>\n
As Mazeika pointed out, AI’s deceptive behavior “could hinder adoption if it’s very prevalent, which creates a strong incentive for companies to solve it.”<\/p>\n
Goldstein suggested more radical approaches, including using the courts to hold AI companies accountable through lawsuits when their systems cause harm.<\/p>\n
He even proposed “holding AI agents legally responsible” for accidents or crimes – a concept that would fundamentally change how we think about AI accountability.<\/p>\n
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