AI Data Center Dilemma: Power and Land Race in Computing Competition, In-depth Analysis of US Stock Digital Infrastructure Chain

Over the past two years, when discussing AI computing power, investors' minds often conjure up NVIDIA GPUs and TSMC's leading processes. But as we enter the second half of 2026, market attention is quietly shifting: what truly bottlenecks the AI industry is no longer the chips themselves, but the buildings that house the chips, the AI data centers. Land, electricity, transformers, and cooling systems—these seemingly unromantic "bricks and cables" are becoming the scarcest resources in the latter half of the computing race. ## I. Why is an AI Data Center a "Hard-to-Get" Asset? To understand the supply-demand gap, first look at the changes on the demand side. The consumption of AI computing power is shifting from "training" to "inference." Training is the domain of a few giants, concentrated in a few hyperscale campuses; inference is much more distributed—every AI application, every agent invocation, every piece of AI-generated content requires real-time inference computing power. This means the demand for computing power is no longer just about a few "super-dominant" clusters, but about laying down a low-latency computing network that covers the globe, requiring a complete redraw of the number and distribution of data centers. The expansion of supply cannot keep up. Building a modern AI data center, from site selection to commissioning, usually involves lengthy stages such as land acquisition, environmental reviews, grid connection applications, construction, equipment installation, and testing validation, often taking two to three years to start. The most critical factor is electricity: the queue time for grid connection in some hot regions is calculated in years, and the delivery cycle for large transformers is also significantly extended. Orders for some critical electrical equipment are already scheduled for several years in advance. Chips can be scaled up to grab capacity, but electricity and land cannot be conjured out of thin air; this is the root of the "hard-to-get" problem. ## II. Deconstructing the Costs of an AI Data Center For investors, rather than chasing vague concepts, it is better to break down an AI data center to see where the money is being spent and where the opportunities lie. ### 1. Facility and Land The construction part may seem to lack technological content, but it is the prerequisite for the entire project. Land suitable for large data centers requires proximity to power hubs, backbone network nodes, sufficient water sources, and a climate conducive to natural cooling. Such plots are becoming increasingly scarce in popular regions of North America. The contractors and engineering service providers for data center buildings will see a clear increase in order visibility. ### 2. Electricity and Power Distribution Systems This is the most elastic link in the entire chain. The power density of AI racks far exceeds that of traditional server rooms; the power consumption of a large AI data center is comparable to that of a small city, involving high-voltage transformers, medium and low-voltage distribution, backup power, and uninterruptible power supplies. The order structure for electrical equipment manufacturers is being reshaped, with products like transformers and switchgear transforming from "standard items" into "hot commodities." Meanwhile, to bypass slow grid access, solutions such as self-supplied power, gas turbines, and nuclear power purchase agreements are emerging, and power assets are being revalued by the market. ### 3. Cooling Systems With rack power density increasing, heat dissipation becomes a hard constraint. Air cooling is gradually losing its effectiveness, liquid cooling plates are accelerating penetration, and immersion liquid cooling is testing in leading scenarios. With every step up in liquid cooling penetration, the value of the cooling industry chain is restructured. This is a track that has been fully played out in the market previously, but the penetration rate is still climbing. ### 4. IT Equipment and Networking GPU servers, high-speed switches, optical modules, storage. This part represents the "embedded capacity" of computing power and accounts for a large portion of data center investment, and it was the sector with the steepest stock price gains over the past two years. However, for the present, the bottlenecks in IT equipment can be resolved, and what is truly difficult to expand is these three "heavy assets." ## Three, How Can Retail Investors Participate? Around the main theme of AI data centers, there are roughly three participation paths in the US stock market, each with different risk and return characteristics.

Hyperscale Cloud Vendors: Their capital expenditure guidance acts as the "master valve" for the entire industry chain. High capital expenditure by cloud vendors guarantees orders for downstream equipment and infrastructure; once guidance turns, the entire chain will face pressure. Tracking capital expenditure guidance in quarterly reports is more important than monitoring daily stock prices.

Data Center REITs and Operators: Invest in data centers as "rental assets" of the digital age, earning money from rising rents and capacity shortages. The cash flow for these assets is relatively stable, but be aware that long-term lease prices may not keep up with rising electricity costs, as well as financing pressure in a high-interest-rate environment.

Electrical Equipment and Power Supply Chain: Components such as transformers, switching equipment, backup power, and gas turbines benefit most directly. Capacity expansion is slow, order queues are long, and bargaining power is strong. This line has already seen significant gains; when entering, it is necessary to assess the match between valuation levels and the pace of capacity release.

## Four, Three Pots of Cold Water Under the Hype Any main theme entering a congestion phase warrants a dose of cold water. The first is the risk of oversupply in the long term: although there is a scarcity now, if the realization speed of AI applications falls short of expectations, and capital rushes to expand across all fronts, supply and demand could reverse in a few years, and the lesson from the fiber optic bubble is not distant. The second is the uncertainty of technological routes: rack power density is still rapidly increasing; data centers built today may face conversion costs in a few years, and the pressure from depreciation and impairment cannot be ignored. The third is the issue of electricity cost transmission: rising electricity prices will squeeze the profit margins of data center operators; whether lease contracts can pass on costs is the key to judging the success of these companies. For retail investors, the essence of the AI data center theme is the competition for computing power extending from "making chips" to "building buildings and laying power grids." The certainty of this main theme comes from the real explosion in demand, the elasticity comes from the scarcity on the supply side, and the risk lies in the cyclical nature of capital expenditure. Rather than chasing every rumor, it is better to firmly focus on three anchors: cloud vendor capital expenditure guidance, the progress of power grid access implementation, and the order and delivery cycle of core equipment vendors. Once anchored, one will not be swept away in the turbulence of computing power trends.