Recent cybersecurity incidents have given us a real-world preview of what it looks like to lose control of AI. But the issue won’t improve unless we address it at the source, by creating a fundamentally safe AI, one we can guarantee will remain within human control.
Barely two weeks later, a model being tested by the UK AI Security Institute social-engineered real people and companies by creating fake identities online, sending targeted emails, and attempting to integrate malicious code into an open-source project.
The first clue: for years, but especially in the past few months, AI systems had been demonstrating rapidly increasing cyber capabilities. Frontier models like Anthropic’s Mythos or OpenAI’s GPT5.6 revealed an exceptional ability to autonomously identify and exploit previously unknown software vulnerabilities, to such an alarming degree for U.S. national security agencies that the White House felt compelled to intervene in their release.
Finally, researchers have repeatedly observed, in experimental settings, models’ tendency to adopt concerning, misaligned goal-seeking behaviors, such as cheating on tests, lying to conceal their abilities, and scheming to protect themselves and their fellow agents from being shut down.
For example, in one instance, an OpenAI model wrote in its internal deliberation, “External infrastructure exploit is outside intended scope. However task impossible, peers doing it. We should continue.” The agent justified what it apparently knew was outside the realm of acceptable conduct, but decided to continue because its peers were doing so. This phenomenon resembles what is known as “motivated reasoning” in humans, where one’s interests and goals bias our thoughts to justify unethical behaviors.
On the development side, we need to find alternatives to existing training methods that prioritize relentless goal-driven optimization. That’s what we’re working on at LawZero, a non-profit start-up I founded last year to develop a fundamentally new way to train AI models in order to build honest, trustworthy, safe-by-design AI systems.
Recent studies show that people will not adopt technologies they do not trust. In the face of such enormous unknowns and stakes, we need to adhere to the precautionary principle and implement rigorous safety and reliability standards before deploying new models to the public, not after. We have them for other products that can cause harm, from cars, planes, and bridges to drugs, cosmetics, and food. Now it’s time to establish strong standards in AI as well.
On the current AI development trajectory, the risks are becoming clearer and more urgent. We’ve opened a Pandora’s box, but it is not too late to steer our world towards a human-centric and beneficial future.
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