Rohan Paul

AI is entering a volume economy: because token demand now has to outrun the price collapse underneath it. Per Goldman Sachs research, total token demand is accelerating (by 18x from December-2025 to Sept-2026 ), while frontier-model token demand is growing much more slowly, at roughly 8-9x on the same index. the bar for sustaining investment spending getting higher as inference becomes commoditized. That means the marginal AI token is increasingly coming from cheaper, smaller, open-source, or routed models, not necessarily from the most compute-heavy frontier model. This changes the infrastructure math quite a bit. That gap is so important for hyperscaler's capex-cycle, because frontier models remain an important source of hyperscaler compute demand, while increasingly competitive open-source models are helping push average token prices down. So token growth alone is no longer enough to read the AI infrastructure cycle. The relevant variable is the interaction between token volume, token pricing, and the amount of compute required to serve each token. What really matters is: tokens consumed × compute required per token If token volume grows 18x but more of those tokens move to models that need far fewer GPU cycles per token, total GPU demand can grow much slower than token demand suggests. Quantization, distillation, speculative decoding, better caching, smaller models, and model routing all push in that direction.