2026, the trend of AI investment is changing. The previous two years of疯狂扫货GPU、拼命训练大模型的军备竞赛, now enters a more realistic stage—whether inference costs can be lowered is the key to victory.

Capital's focus is shifting again. All the money used to pour into hardware giants like Nvidia, but now investors are calculating a more detailed account: inference costs, electricity costs, cooling costs, and even network transmission costs. This is not just a technical issue, but an economic one.

**Where is the real bottleneck in computing power expansion?**

Is the chip enough? That's no longer the problem. The real bottleneck is electricity. A super-large data center requires a stable power supply, efficient cooling systems, and supporting network interconnections. Whether these infrastructures can be connected into a replicable capacity curve determines how far AI commercialization can go. In other words, the ceiling for factory-scale computing power is determined by industrial-grade equipment like power infrastructure, cooling capacity, and network switches.

**The power of the supply chain is shifting**

From a simple hardware arms race to a competition over the efficiency of the entire industry chain. The network interconnection layer (switches, optical modules), the power infrastructure layer (power purchase agreements, data center operations), and even the software layer (inference optimization, scheduling strategies)—these links directly impact gross profit margins and business competitiveness. An inference optimization software stack can improve hardware efficiency by 20%, and that 20% difference is the cash flow gap.

**AI bubble controversy heats up**

Wall Street now has two camps arguing. The optimists say AI is the second industrial revolution, and data center investments can last more than 10 years. The pessimists ask: when will these investments start to generate real returns? It seems that the next competition is not about who has more chips, but who can truly turn technological advantages into money.