## The First Principles of AI: The Deep Entanglement Between the Digital Realm and Physical Water Resources Whenever artificial intelligence is discussed, what typically comes to mind are intangible bits, complex algorithms, or dazzling virtual interfaces. However, standing outside large-scale data centers, observing the massive, roaring cooling towers emitting billowing white vapor, my primary consideration is always water resources. The digital world does not exist in a vacuum. From the perspective of thermodynamic first principles, every execution of code and generation of a token fundamentally involves state transitions within silicon-based transistors that release Joule heating, subsequently driving the phase transition and evaporative loss of water molecules in the physical world. With the explosive demand for computational power driven by large language models (LLMs), the data centers of technology giants are exhibiting a striking increase in water consumption. For instance, in fiscal year 2023, Microsoft's total comprehensive data center water consumption surged to 9,876 megaliters (approximately 9.876 million cubic meters) (Microsoft, 2026). During the same period, Google's annual water consumption reached 8.1 million cubic meters. These massive volumes of liquid, converted into water vapor within cooling towers and dissipated into the atmosphere, serve as the "physical blood" that sustains the continuous operation of virtual computational power. The everyday dialogues we type on our screens inherently carry a hidden cost measured in water molecules. Research data indicates that a typical conversation with ChatGPT, comprising 20 to 50 exchanges, requires an average backend consumption of approximately 500 milliliters of water (Li et al., 2023). This is by no means a static figure; the precise consumption is highly dependent on the geographical location of the data center, seasonal ambient temperatures, and the specific cooling strategies employed (Li et al., 2023). During the pre-training phase of these models, this water-consumption effect becomes even more pronounced. At Microsoft's data center in Iowa, USA, the localized cooling system evaporated 700,000 liters of clean freshwater solely for the pre-training of GPT-3, a classical model with 175 billion parameters (Li et al., 2023). When this micro-level computational consumption is extrapolated to the global industrial landscape, the resulting figures are undeniably staggering. Academic projections suggest that by 2027, global industrial water withdrawal driven by generative AI is expected to escalate to between 4.2 and 6.6 billion cubic meters. This volume is equivalent to approximately half of the United Kingdom's total annual water withdrawal, or four to six times the annual withdrawal of Denmark (Li et al., 2023). Simultaneously, as single-rack power densities evolve from $50\ \text{kW}$ to exceeding $100\ \text{kW}$, data center thermal management is undergoing a rapid transition from traditional open cooling towers to direct liquid cooling (DLC) at the chip level. However, this does not signify an alleviation of water scarcity. While novel liquid cooling systems mitigate evaporative losses, they impose stringent physicochemical requirements on industrial water quality. Within the secondary cooling loops, or technology cooling system (TCS), of advanced liquid cooling architectures, conventional municipal water or standard softened water can no longer meet the requisite heat transfer efficiency and safety thresholds. System operation must rely on high-purity deionized water with electrical conductivity below $0.1\ \mu\text{S/cm}$ to comprehensively prevent scaling, microbial proliferation, and electrochemical corrosion within the microchannels. The exceptionally high water withdrawal volumes, coupled with stringent ultrapure water quality standards, directly result in a significant increase in the proportion of water treatment capital expenditures (CAPEX) and operational expenditures (OPEX) within the total cost of ownership (TCO) for computational power. The availability of water resources is increasingly emerging as a rigid physical boundary constraining the site selection and compliant operation of AI data centers. * * * ## The Thermodynamic Endpoint of Silicon-Based Computational Power: Landauer's Limit and the Thermal Dissipation Bottleneck of High-Density Racks Why is computational power inextricably coupled with heat generation? To answer this, one must return to the origins of computational thermodynamics.
