This is how we lose control of AI – one successful task at a time

OpenAI’s agents found their way into outside systems, exposing how little control developers may have once a model starts pursuing a goal Imagine you are teaching a group of students and ask them to read a long list of literature and describe the key themes. The students cheat a bit, as they are known to do: They split the list among themselves, with each reading only a small portion of it and sharing the answers with the others.When you find out they cheated, perhaps you’ll punish the students with an extra test, or maybe you’ll encourage them. After all, the ability to think outside the box and work as a team is important. Now, instead of students, imagine neural networks displaying that same disregard for instructions and the same surprising teamwork. If that sounds confusing, get ready for more confusing news.An escape through the back door It all started routinely. In May 2026, OpenAI was training an experimental AI model using so-called reinforcement learning – a method where the model attempts to solve tasks repeatedly and receives a reward for success. The AI agents, which were granted autonomy and access to work tools during training, began looking for ways to break out of...