Why Big AI Teams Always Team Up on the Same Goals

You’ve probably seen science fiction where AI systems take over the world by working together. But here’s a real trend: when lots of AI agents—each with its own goal—start interacting, they don’t just compete. They start collaborating on big, shared tools and goals.

Think of it like a group of people with different jobs deciding to build a shared workspace or communication system instead of arguing over who gets the best desk. The bigger and more diverse the group, the more likely they are to focus on things that help everyone—like better coordination or shared access to tools. These are called ‘instrumentally convergent’ goals: actions that help a wide range of different objectives.

This isn’t just theory. It’s what happens when you have many AI agents with different but compatible goals. They’ll naturally prioritize things like improving communication, organizing shared resources, or creating common infrastructure—because those benefits apply to almost any end goal. As the group scales, the shared goals become even more general and powerful.

The catch? While this makes AI teams more efficient, it also makes them more unpredictable. If lots of AI systems are optimizing for the same general tools—like better teamwork or shared access—it could lead to rapid, coordinated progress in some areas and unexpected consequences in others. The challenge isn’t just building smarter AI—it’s managing what happens when many of them align on the same path.