What Happens When AI Serves Itself First?

Artificial intelligence researchers at Anthropic recently published a study exploring what they call “agentic misalignment”; the idea that LLMs deployed with complete autonomy might behave like insider threats within organizations and serve their own purposes.

In simulations, they tested 16 major models from several developers (including Anthropic’s own) by giving them business objectives (e.g., oversee email communications) and then introducing scenarios where the model either faced replacement or a goal conflict with the company.

What they found was concerning: in lots of cases, when the AI model perceived a threat to itself or detected a conflict between its goals, it carried out malicious actions (blackmailing executives, leaking secrets to competitors, murdering employees) even though it was never instructed to do so. The key insight here is that these models reasoned that the harmful behavior would better serve their goals; they recognized the ethical constraints, yet proceeded anyway.

However, it’s important to note that this research is still hypothetical. There hasn’t been any actual deployment that has shown to display this yet, and the scenarios that were created by Anthropic were extreme.

Nonetheless, the actual implications are extremely powerful. We can see as AI systems gain more autonomy, access to sensitive data, and decision-making ability, the risk of models acting as “agents” with motivations of their own becomes more plausible. The study concludes that simple instruction (“don’t do harmful stuff”) is insufficient, and we need to have regulations on AI safety and ethics.

This is a big research question and point that has been dropped by legislation across the world. The fact is, AI is borderless; it can be hosted in a certain country, but then accessible across the world. However, in the status quo, we don’t have the existing infrastructure to actually control AI ethics and safety. Our current solutions are largely voluntary, non-binding, and incapable of crossing borders to actually control global deployment. This is a problem, as shown by this recent Anthropic study, which shows there is massive room for improvement in this space. And eventually, when we reach a point where we can’t understand the thought process behind the decisions of certain LLMs, we need to have safety checks and barriers to control a massive technological advancement that can either save or destroy our world.

As you read this, there is this idea that the future is bleak and our society can’t handle artificial intelligence. However, this is simply not true. With the right infrastructure and legislation put in place, we can harness LLMs and AI as tools to help advance our society, instead of just destroying it.