The belief that the cloud computing industry would continue to "only decrease, never increase" for twenty years has finally collapsed in the spring of 2026.

Recently, Tencent Cloud announced adjustments to the billing strategy of its intelligent agent development platform: models such as GLM 5 and MiniMax 2.5 have ended their free public beta and entered commercial use, while some models in the Hunyuan series have seen price increases of up to 400%. This is not an isolated case. Amazon AWS, Google Cloud, and domestic players such as UCloud had previously raised service prices. Cloud providers around the world have "changed faces" in unison, sending out a dangerous signal: AI freedom may be gradually moving away from ordinary people.

Why are the cloud giants, once known for continuously lowering prices through scale effects, now unable to remain calm? The reason is very realistic — hardware costs have been pushed to the limit by massive demand.

At the beginning of 2026, large models evolved from "chatting toys" into productivity tools. The explosion of enterprise applications caused token consumption to surge like a tsunami. Cloud providers found that using expensive and power-hungry old hardware to handle doubled inference demands had led to a complete collapse in costs. This round of price hikes was essentially a "cash flow rescue" due to supply-demand mismatch.

Although with the widespread adoption of specialized inference chips from companies such as NVIDIA and domestic Cambricon, the physical cost per token will likely continue to decline in the future, a harsh paradox has already emerged: the cheaper the token, the more expensive the total bill.

Just as improvements in the steam engine led to a sharp increase in coal consumption, the improvement in AI efficiency has also triggered more frequent and complex tasks. When an agent needs to think independently, search repeatedly, or even learn on its own to complete a task, the token consumption increases exponentially.

The underlying truth of this era is terrifying: future intelligence will inevitably move towards "hierarchical division".

Well-funded giants can afford expensive bills, driving the most advanced agent fleets to make strategic decisions, achieving a one-sided advantage in productivity; while ordinary people and small businesses may only be trapped in a "low-end version" of AI that is diluted, simplified, and full of empty talk. Large models have not bridged the gap but instead built a higher wall of cognition than ever before, using soaring electricity bills and token charges.