The Four AI Scaling Phases

For years, the AI race followed a simple formula: performance was a function of parameters, data, and compute. Add more GPUs, feed in more tokens, expand the model size, and performance climbed. That law—elegant in its simplicity—drove the exponential rise of large language models.

But the curve is bending. We are entering a new scaling regime where the old formula no longer captures the real drivers of capability. The fourth scaling phase isn’t speculation—it’s being actively engineered across frontier AI labs.

The way modern AI systems handle ‘working memory,’ token management, and extended thinking represents a fundamental shift from raw parameter scaling to architectural intelligence.