När når vi AGI?

The most recent moment that shook us came from a more fundamental shift: GLM is increasingly helping build AI itself. We watched the model complete an infrastructure task that would previously have taken a team of experienced infrastructure engineers weeks. When we realized that this work would directly change how the next generation of models is trained, we became even more convinced: our successors are the AI systems we are creating ourselves.
Frankly, before GLM-4.7, our internal use of GLM for coding involved a certain amount of obligation. It was, after all, our own creation. At that point, product-market fit for coding had yet to arrive. Today, GLM-5.3 has become an indispensable daily coding partner for everyone on the team, and it is moving steadily toward replacing us. If this trend continues, given enough compute and enough time, its endpoint is a system that can design and train its own successor entirely autonomously. This is known as Recursive Self-Improvement, or RSI.

There's a deeper implication too. A layered, verifiable feedback environment built on real infrastructure tasks is exactly what training the next generation of models needs most. Every task the agent completes can become training ground for its successor. We are still far from recursive self-improvement. But the smallest loop now exists. The model optimizes the system. The system serves the model.