The AI cycle is not over. It is entering a tougher phase: cost engineering.

In this episode of Dazi AI Cycle Watch, we unpack the latest validation behind the compute restructuring thesis. Marvell and Synopsys point to the rise of custom ASICs and EDA demand. Huawei’s “Tau Scaling” highlights a shift from geometric scaling to time and system-level efficiency. 800V DC power, CPO, silicon photonics testing, and high-end PCB upgrades show that AI data centers are becoming heavy industrial systems. Snowflake, DeepSeek, and Zhipu AI point to inference cost reduction and data infrastructure. HP, Xiaomi, and humanoid robotics reveal how AI is not just creating new profit pools, but also redistributing margins across the hardware and labor stack.

The next stage of AI is not about who tells the best model story. It is about who can build the most efficient system.

Prices are only the result. Variables define the stage.
We don’t predict prices. We observe the cycle.