Business Climate Indicators for the AI Chip Sector

Driven by the AI wave, the chip industry is entering a phase where structural changes far outweigh traditional cycles. Unlike past semiconductor cycles dominated by PCs or smartphones, demand for AI chips comes from data centers, cloud service providers, and large model training. Investment rhythms are more concentrated, volatility is more severe, and it is easier to form 'expectation gaps' in the capital markets. In this context, relying solely on revenue or individual company financial reports often means being half a step behind; what truly holds value is a system of leading indicators that can capture turning points early from the supply chain.

In the semiconductor cycle, 'orders' have always been one of the most leading indicators. For AI chips, this is particularly evident. Large cloud service providers typically coordinate capacity with chip suppliers and foundries six months to a year in advance when making capital expenditure decisions. Thus, changes in orders essentially reflect expectations of future computing power needs rather than current actual consumption.

For instance, when the market observes a sudden slowdown in advanced process accelerator orders, it often indicates that cloud customers are becoming more conservative about the pace of AI training expansion. Conversely, if order visibility keeps extending, or even 'overbooking' occurs, it usually signals that a new investment peak is brewing. This is why investors pay special attention to order commentary from companies like NVIDIA or leaders in advanced process contract manufacturing, as it reflects not just the company's condition but acts as a thermometer for overall AI computing power demand.

Compared to orders, inventory indicators lean more towards being a "confirmatory" signal. When demand rises rapidly, the supply chain often can't react in time, leading to swift inventory depletion. In the early stages of an economic downturn, companies typically rely on inventory to meet declining demand, which results in longer inventory days. For AI chips, due to their high unit price and concentrated customer base, inventory fluctuations tend to be more volatile and can have a greater impact on stock prices.

It's worth noting that interpreting inventory for AI chips cannot fully adhere to traditional logic. Even if some high-end accelerators show higher inventory levels, it might simply be because customers are waiting for specific applications or the launch of next-generation models. What investors should truly be wary of is the simultaneous occurrence of "order reductions + rising inventory," which often indicates an expanding mismatch between supply and demand and suggests that market optimism about future demand is waning.

If orders reflect short-term expectations and inventory verifies the state of the economy, then capital expenditure (CapEx) is the core indicator that determines the medium- to long-term supply and demand structure. AI chips heavily rely on advanced processes and packaging, and the CapEx plans of foundries and equipment manufacturers often reveal capacity trends two to three years ahead.

When wafer foundries and key equipment suppliers simultaneously raise their CapEx forecasts, it usually signals continued confidence in AI demand. Conversely, if CapEx growth slows or even decreases, it may indicate concerns about the sustainability of demand from the supply side. Take Taiwan Semiconductor as an example; its investment pace in advanced processes and packaging is often seen as a bellwether for the long-term health of the AI industry and directly impacts the valuation center of the entire chip sector.

Overall, the stock performance of the AI chip sector cannot be explained by a single data point but rather is the result of the interplay between orders, inventory, and CapEx. Understanding this system of economic indicators not only helps in determining the phase of the cycle but also assists investors in distinguishing whether fluctuations are just sentiment corrections or actual trend reversals. In the long race of AI, seeing earlier and more comprehensively is often more important than reacting faster.