Technological progress has always fed on electricity. Yet the digital civilization of today is suffering from the hunger of the very power it consumes. Humanity has entered an era where it must build not faster computers, but 'sustainable' ones.

The evolution of AI, the explosion of data centers, and the expansion of blockchain and quantum computing all revolve around a single resource: electricity. Power is both the blood and the heart of IT civilization. But as technology accelerates, that heart overheats, and the power demand curve begins to draw unpredictable spikes.

A single large-scale model training session can consume as much power as a mid-sized city uses in an entire day. Even during inference, as the number of users grows, per-request energy usage accumulates. Real-time multimodal models that combine speech and vision are even more energy-intensive, since they consume both memory bandwidth and network switching power. As data centers expand, they drag with them new transmission lines, substations, and transformer upgrades—an ever-rising cost that feeds back into energy prices and carbon emissions.

The surge of demand during peak hours also threatens grid stability. Traditional generation sources like thermal and nuclear power can withstand such shocks with inertia, but as renewable penetration grows, variability increases, and IT’s sudden spikes drive up 'frequency regulation costs'. If technology is the brain of civilization, then the grid is its circulatory system—when one loses balance, the other falters.

For decades, IT was misunderstood as an “immaterial” industry. In reality, it depends on physical infrastructure no less than steel, oil, or cement. Cooling towers, UPS units, battery strings, diesel backups, racks, switches, fiber optics, undersea cables, and high-voltage substations—digital technology rests upon massive material foundations. High-density racks now exceed 20–30 kW each, with GPU clusters surpassing 60 kW. Air cooling can no longer handle the heat, driving a rapid transition to 'direct liquid cooling (DLC)' and 'two-phase immersion' systems.

Urban planning is being reshaped. Amsterdam and Dublin have suspended new data center permits to prevent power grid overload. In South Korea, Seoul’s 2026 power shortage was traced to six hyperscale cloud data centers in Pangyo starting up simultaneously. Beneath the comfort of digital convenience lies an enormous 'spatial and energetic externality'.

Water use is another cost. Evaporative and adiabatic cooling systems improve energy efficiency but consume large amounts of water, causing tension in drought-prone regions. Sustainability can no longer be judged by PUE alone—it must include 'WUE (water), CUE (carbon), and TUE (total utilization efficiency)'.

Electronic waste adds yet another shadow. The typical server life cycle is under three years, and GPU generations move even faster. Rare-earth recovery, lead solder, flame retardants, and secure disk destruction all weigh on sustainability. The performance metric of the future is not just TCO but 'TEO—Total Environmental Ownership'.

Big Tech now treats energy and carbon as key performance indicators. 24/7 'Carbon-Free Energy (CFE)' matching, carbon-aware workload scheduling, and 'demand-response participation' are becoming standard practice. The three major cloud providers are turning data centers into 'Grid-Interactive Efficient Buildings (GEBs)' that shift AI traffic to regions with renewable energy surpluses.

Corporate power procurement has also evolved. Beyond traditional PPAs, firms are combining on-site solar and fuel cells with off-site wind and geothermal, and adopting 'granular energy certificates (GECs)' that verify the 'time purity' of power usage. This has diversified data center locations—from hydro-rich Scandinavia to U.S. Midwest wind corridors and offshore wind sites along Korea’s eastern coast.

At the same time, companies are addressing the water–carbon trade-off. Seawater and lake cooling, graywater recycling, low-GWP refrigerants, and 'waste heat recovery' for district heating are all expanding. In cities like Copenhagen and Stockholm, waste heat from servers now warms residential buildings in winter and drives absorption chillers in summer.

Hardware stands at a clear inflection point. The limits of miniaturization and the problems of heat and leakage are forcing a shift from sheer speed to 'energy-per-computation' optimization. Intel’s RibbonFET and PowerVia, Samsung’s GAA, and TSMC’s backside power delivery all aim to achieve equal performance at lower voltages and switching losses.

In accelerators, the metric is now 'bandwidth-per-watt'. GPU-CPU coherency and optical interconnects like NVLink and CCIe reduce the energy cost of data movement. Photonic computing uses light for matrix operations, cutting 'Joules per operation' by an order of magnitude. Analog 'In-Memory Computing (IMC)' and 'spiking neuromorphic architectures' present both challenges (accuracy and calibration) and promises (ultra-low power).

At the edge, ARM and RISC-V NPUs, event-based cameras, and 'on-device quantization and sparsification' reduce server round trips and latency. The age of “centralized giants” is giving way to 'distributed swarms' that collectively cut total energy consumption.

The convergence of renewable energy and digital systems is transforming the logic of operation itself. Because wind and solar fluctuate over time, digital services must become 'load-flexible'. Model training jobs can be scheduled for nighttime wind surpluses, while inference tasks are routed to low-carbon regions in real time.

At the grid level, AI integrates forecasts of generation, demand, price, temperature, wind, and cloud cover to ease transmission bottlenecks. Battery energy storage (BESS) and 'thermal energy storage (TES)' are combined to make data centers hubs of 'sector coupling' between electricity, heat, and data. Some regions use geothermal or seawater cooling to move heat rather than power, while others experiment with 'SMRs (small modular reactors)' and 'green hydrogen fuel cells' as backup sources. Despite differing risks and public perceptions, the goal is clear: '24/7 year-round low-carbon availability'.

Meanwhile, Asia’s long-distance HVDC grid projects aim to balance renewable abundance and digital demand across borders. The economic, political, and environmental costs remain under debate, but the attempt to align renewable geography with digital geography marks a civilizational experiment in itself.

Cooling has become the intersection of engineering and ecology. From air to 'direct liquid cooling', and now to 'two-phase immersion', systems are achieving rack densities of 80 kW or higher. Though challenges remain—non-conductive fluid leakage, GWP, fluid lifetime, and component compatibility—these systems have become the de facto path to PUE below 1.1 in the GPU era.

AI-driven cooling analyzes real-time temperature, humidity, and load data to optimize compressor cycles and fan speeds. Wet-bulb temperature and microclimate inputs guide systems to the best operating points each hour. Water-intensive methods are restricted in arid regions, while 'heat recycling for district heating, greenhouse agriculture, and aquaculture' expands rapidly. In Scandinavia, waste heat from servers now serves urban heating in winter and air conditioning in summer through absorption chillers.

Subsea data centers balance natural cooling against corrosion and maintenance difficulty. What matters most is the 'right to experiment' and the 'sharing of learning'. Cooling innovation works best not as isolated corporate optimization, but as 'co-design between industry, cities, and ecosystems'.

Sustainability cannot be achieved by hardware alone. 'Algorithmic efficiency' has improved faster than hardware for a decade and will continue to do so. Reducing energy footprints of large models requires optimization across all three phases—before, during, and after training.

Before training, 'data curriculum design' and 'deduplication' remove redundancy and reduce epochs. 'Weighted sampling' and 'hard-negative mining' achieve the same accuracy with fewer samples. 'Retrieval-Augmented Generation (RAG)' injects external knowledge instead of retraining massive parameters.

During training, 'low-precision formats (FP8, INT8)', 'sparse tensors', and 'Mixture-of-Experts (MoE)' architectures minimize compute load. Communication optimization (ZeRO, FSDP, pipeline parallelism) and 'carbon-aware orchestration' shift jobs to regions and hours with cleaner grids.

After training, 'knowledge distillation', 'pruning', '4-bit quantization', 'KV-cache optimization', and 'token-efficient prompting' reduce both power and latency per inference. Engineering teams now practice 'GreenOps', managing carbon and energy metrics alongside FinOps. Continuous Integration pipelines include 'energy regression tests', and pull requests are reviewed for changes in “millijoules per inference.”

Real-world results abound. An e-commerce company replaced its ranking model with MoE-distilled versions, cutting inference energy by '62%' at identical accuracy. A media streaming platform redesigned adaptive bitrate policies to maintain perceived quality while lowering 'network transmission energy by 28%'. A Korean fintech firm used a 'carbon-aware SDK' to reschedule batch and ETL jobs, reducing cloud power bills by 17% and emissions by 35%. 'Efficiency has risen from a subcategory of performance to the very definition of ethics.'

Policy is shifting from punitive to constructive. The EU’s 'Energy Efficiency Directive' mandates disclosure of PUE, WUE, and CUE, while the 'Green Data Directive (2026)' will price excess emissions. The U.S. combines the 'Inflation Reduction Act (IRA)' with state-level incentives to support low-carbon power procurement. California will require 'carbon disclosure' for all cloud providers by 2027. South Korea’s 'Digital Carbon Neutral Roadmap (2025)' mandates 30% renewable power in public data centers. Japan’s 'GX League' ties voluntary corporate reductions to national emissions trading.

Finance has moved quickly. 'Green and sustainability-linked bonds' now fund data center retrofits, photonic chips, and cooling upgrades. 'SBTi' and 'TCFD/ISSB' frameworks pressure IT firms to manage Scope 2 and 3 emissions. Clients demand '24/7 CFE certification' and 'granular RECs', while cloud quotes now list estimated 'gCO₂e' beside vCPU and GPU costs. A new metric has emerged: 'Carbon SLA'—optimizing carbon, latency, and cost together.

Standardization is accelerating. 'ISO 30134' (PUE/WUE/CUE), the 'Green Software Foundation patterns', and 'W3C time-based energy attributes' are being aligned, bridging the languages of energy, software, and accounting. This alignment is not surveillance—it is the 'market signal for innovation'.

The true stage of sustainability is neither laboratory nor boardroom, but philosophy and community. Data center siting intersects with land, water, and landscape; grid expansion crosses communities and ecosystems. Thus, 'environmental justice (EJ)' and local consent are central. Chasing cheap power without social acceptance leads to opposition and risk. 'Early consultation, compensation, and benefit-sharing' are now prerequisites for a technology’s social license to operate.

Labor and education are also transforming. “Green IT” is not just for hardware engineers. New professions—'energy data analysts, GreenOps engineers, carbon-aware schedulers, and cooling-AI tuners'—are emerging. Universities and polytechnics must teach 'electricity, thermodynamics, and code' together. The old debate that digitalization kills jobs is giving way to data showing that 'sustainable transition creates them'.

Ethics, too, becomes clearer. Are there communities excluded by our pursuit of efficiency? Do renewable projects conflict with local ecosystems? Does e-waste export outsourcing hide moral evasion? The key performance indicator is shifting from “How large?” to “How meaningful?” The new question is 'how much knowledge, healthcare, and education we can deliver with less energy'.

Even users have agency. Choosing 'low-carbon cloud regions', enabling 'eco modes', scheduling 'batch jobs overnight', selecting 'efficient codecs', and 'extending device lifespans'—small defaults can change massive systems. Transformation often begins not with revolutions, but with default settings.

Sustainable IT is the condition for civilization’s survival. Technology loves speed, but life demands balance. We must learn to create greater meaning with less energy, not just more computation.

Civilization’s competitiveness will soon be measured not in FLOPS but in 'Meaning per Joule'. Data centers will contribute to city energy loops, chips will raise intelligence per watt, software will refuse wasteful computation, and finance will reward efficiency as innovation. Local communities will no longer be bystanders but co-designers of technology’s benefits.

Technology borrows time from the Earth; therefore, its speed must be tuned by responsibility. Sustainable IT is not an adjective of ecology but the 'default state of civilization itself'. The 'technology of coexistence' is the most enduring legacy humanity can leave to the generations that follow.