Sustainability Atlas
AI & Emerging Tech·13 min read··...

Explainer: Compute, chips & energy demand — what it is, why it matters, and how to evaluate options

A practical primer: key concepts, the decision checklist, and the core economics. Focus on unit economics, adoption blockers, and what decision-makers should watch next.

In 2024, global data centers consumed approximately 415 terawatt-hours (TWh) of electricity—equivalent to the entire annual energy consumption of Thailand and representing 1.5% of worldwide electricity demand. By 2030, projections from the International Energy Agency suggest this figure could surge to 945 TWh, effectively tripling the sector's share of global power consumption to 3%. The catalyst behind this exponential growth is artificial intelligence: a single ChatGPT query consumes roughly 0.3 watt-hours of energy, approximately 1,000 times more than a traditional Google search. With AI workloads projected to account for 50% of data center energy growth through 2030, the intersection of compute infrastructure, semiconductor design, and energy systems has emerged as one of the most consequential sustainability challenges of our time.

Why It Matters

The sustainability implications of compute energy demand extend far beyond carbon accounting exercises. In the United States alone, data centers consumed 183 TWh in 2024—representing 4.4% of national electricity demand—with projections indicating this could reach 426 TWh (12% of U.S. electricity) by 2030 according to the U.S. Department of Energy's Lawrence Berkeley National Laboratory. This trajectory creates material risks across multiple dimensions.

Grid stability and infrastructure strain. The rapid scaling of AI infrastructure is outpacing grid capacity in key markets. In 2024, a grid incident in Virginia forced 60 data centers to switch to backup power, temporarily removing 1,500 megawatts from the system—equivalent to Boston's entire electricity demand. Ireland, home to significant hyperscaler presence, now sees data centers consuming 22% of its national grid capacity.

Consumer energy costs. Research indicates that data center expansion is already driving electricity price increases. In the PJM Interconnection market serving the Mid-Atlantic region, wholesale prices rose 20% in 2025, translating to an average $18 monthly increase for Maryland households. Nationally, the Congressional Research Service projects an 8% average household electricity bill increase by 2030 attributable to data center demand.

Carbon emissions trajectory. Despite aggressive renewable energy procurement, major technology companies have seen emissions rise significantly. Google's emissions increased 48% between 2019 and 2024, while Microsoft's rose 30% since 2020—largely driven by the embodied carbon of data center construction and the gap between renewable energy certificates and 24/7 carbon-free power.

Water resources. Data center cooling consumed an estimated 17 billion gallons of water in 2023, with projections reaching 33 billion gallons by 2028. In water-stressed regions, this creates direct competition with agricultural and municipal water supplies.

Key Concepts

Understanding the compute-energy nexus requires familiarity with several foundational concepts that shape both the problem and potential solutions.

Power Usage Effectiveness (PUE) measures total facility energy divided by IT equipment energy. The industry average PUE of 1.56 means that for every watt powering servers, an additional 0.56 watts goes to cooling and infrastructure. Leading hyperscalers achieve PUEs of 1.09–1.2, while legacy enterprise facilities often exceed 1.8. This metric remains the primary efficiency benchmark, though it fails to capture embodied carbon or water consumption.

GPU power density evolution. AI workloads have fundamentally altered data center thermal profiles. Traditional CPU servers drew 150–200 watts per chip; NVIDIA's H100 GPU consumes approximately 700 watts, while next-generation chips approach 1,200 watts. This translates to rack power densities of 40–100+ kilowatts for AI-optimized infrastructure, compared to 5–15 kilowatts for conventional racks. The average data center rack density is projected to reach 50 kilowatts by 2027, up from 36 kilowatts in 2023.

Training versus inference energy profiles. Large language model training involves one-time, extremely energy-intensive computation—training a model like GPT-4 may require thousands of GPU-hours over weeks or months. Inference (generating outputs from trained models) occurs continuously and, at scale, represents the larger share of total energy consumption. NVIDIA claims 100,000-fold efficiency improvements in inference over the past decade through hardware and software optimization, yet aggregate inference energy continues to grow as deployment scales.

Accelerated versus conventional servers. Accelerated servers featuring GPUs or purpose-built AI chips are growing at 30% annually, compared to 9% for conventional CPU-based servers. This architectural shift concentrates energy demand in fewer, higher-power systems, creating both efficiency opportunities and grid integration challenges.

KPIBaseline (2023)Current (2025)Target (2030)
Global DC electricity (TWh)340415–450<700 (efficiency scenario)
Average PUE1.581.56<1.3
AI chip power (watts)7001,200800–1,000 (efficiency gains)
Rack density (kW)364250+
Renewable energy share24%30–35%>60%
Water usage (billion gallons)172225 (with recycling)

What's Working

Hyperscaler efficiency leadership. Amazon Web Services, Google, and Microsoft have achieved PUEs approaching 1.1 in their newest facilities through advanced cooling systems, optimized airflow management, and custom power distribution. AWS announced achieving 100% renewable energy matching in 2023—seven years ahead of its original 2030 target. These gains demonstrate that significant efficiency improvements are technically achievable at scale.

Specialized silicon for inference. Purpose-built AI inference chips are delivering substantial efficiency gains over general-purpose GPUs. Cerebras Systems' wafer-scale engine achieves approximately 2x better energy efficiency than NVIDIA's GB200 for applicable workloads. Groq's Language Processing Unit claims 10x energy efficiency improvements for inference through on-chip SRAM memory that eliminates energy-intensive data transfers. These architectures signal a transition from the GPU-dominated training era toward inference-optimized, energy-conscious silicon.

Liquid cooling adoption. Direct liquid cooling systems can reduce cooling energy requirements by 30–50% compared to traditional air cooling while supporting higher power densities. Major deployments are underway: Microsoft's Azure datacenters have implemented immersion cooling for high-density AI racks, and NVIDIA's DGX systems increasingly ship with liquid cooling as standard for H100 configurations.

Nuclear power procurement. Recognizing that intermittent renewables cannot guarantee the 24/7 baseload power required for AI workloads, hyperscalers have executed significant nuclear energy agreements. Microsoft's 20-year, $16 billion agreement with Constellation Energy to restart Three Mile Island's Unit 1 will deliver 800+ megawatts starting in 2028. Amazon has secured 1.9 gigawatts through 2042 from the Susquehanna nuclear facility and invested $500 million in X-Energy's small modular reactor development. Google's partnership with Kairos Power will deliver 500 megawatts across six to seven small modular reactors by 2035.

What's Not Working

Renewable energy gaps. The distinction between renewable energy certificate matching and actual 24/7 carbon-free power remains significant. When Google commits to "100% renewable energy," it purchases certificates equivalent to its total consumption but may still draw fossil fuel-generated power during periods when renewable sources are unavailable. True 24/7 matching—where every hour of consumption is matched to carbon-free generation—remains years away for most operators.

Grid interconnection bottlenecks. New data center projects face interconnection queue delays of 3–5 years in many U.S. markets. This constraint is pushing development toward "neoclouds"—specialist operators in secondary markets with available power capacity—and offshore locations, creating geographical fragmentation that may undermine efficiency gains from consolidation.

Embodied carbon blind spots. Current sustainability metrics focus overwhelmingly on operational energy while underweighting the embodied carbon of server manufacturing, construction materials, and end-of-life disposal. Semiconductor fabrication is extremely energy and water-intensive, yet rarely appears in hyperscaler emissions accounting.

Efficiency gains absorbed by demand growth. Despite dramatic improvements in computational efficiency per operation, total energy consumption continues rising because demand growth outpaces efficiency gains. This "rebound effect" suggests that technology-driven efficiency alone cannot stabilize compute-sector energy demand without complementary demand-side interventions.

Regional grid stress. Certain markets face acute capacity constraints. Ireland's grid operator has warned that data center expansion is incompatible with national climate targets. Mexico's weak grid has forced Microsoft to rely on gas generators for 70% of power at some facilities. These regional bottlenecks highlight the limits of market-driven expansion without coordinated infrastructure planning.

Key Players

Established Leaders

NVIDIA dominates the AI accelerator market with approximately 80% market share for training workloads. Its H100 and forthcoming Blackwell architectures define the performance-per-watt frontier, though power consumption per chip continues rising. NVIDIA achieved 100% renewable energy for its own operations in 2025 and powers 23 of the top 30 most energy-efficient supercomputers on the Green500 list.

Intel is pivoting toward AI accelerators with its Gaudi and Xeon product lines while maintaining significant market share in conventional data center processors. Its focus on process technology advancement and chiplet architectures aims to recapture efficiency leadership.

AMD has gained meaningful GPU market share with its Instinct MI300 series, offering competitive performance-per-watt in specific workloads and providing hyperscalers with supply chain diversification beyond NVIDIA.

TSMC manufactures the vast majority of advanced AI chips across all fabless vendors. Its transition to 3nm and 2nm process nodes will fundamentally shape chip efficiency trajectories for the next decade.

Emerging Startups

Cerebras Systems (Sunnyvale, CA) produces the world's largest AI chip—a 4-trillion-transistor wafer-scale engine delivering 2x energy efficiency versus NVIDIA's latest for training and inference. The company is negotiating a $1 billion funding round at a $22 billion valuation and has secured DARPA contracts for advanced co-packaged optics integration.

Groq (Mountain View, CA) developed the Language Processing Unit specifically for inference, claiming 10x energy efficiency and one-third the power consumption of comparable GPU solutions. Following a $640 million Series D in 2024 at a $2.8 billion valuation, Groq is targeting enterprise and government AI deployment.

Etched (San Francisco, CA) raised $500 million in January 2025 for its Sohu transformer-specific ASIC, which trades programmability for extreme inference efficiency on transformer architectures—the foundation of large language models.

Key Investors & Funders

Breakthrough Energy Ventures, Bill Gates' climate-focused fund, has invested across the compute-energy nexus including in long-duration storage and next-generation nuclear technologies essential for clean data center power.

The U.S. Department of Energy is allocating significant resources to data center efficiency research and grid modernization, including the $45 million DARPA program partnering Cerebras and Ranovus on co-packaged optics.

Tiger Global, Andreessen Horowitz, and Sequoia Capital have led major rounds for AI chip startups, viewing energy efficiency as a key differentiator as power costs become binding constraints on AI scaling.

Examples

1. GlaxoSmithKline: Accelerating Pharmaceutical Research

GlaxoSmithKline deployed Cerebras wafer-scale engines for epigenetic research, achieving 160x speedups compared to traditional GPU-based approaches. Beyond raw performance, this translated to proportional reductions in energy consumption per research task—enabling computational experiments that would have been prohibitively expensive both financially and energetically. The deployment demonstrates that efficiency gains in specialized AI workloads can fundamentally alter the economics of computational science.

2. Amazon Web Services: Nuclear Baseload Strategy

AWS's multi-pronged nuclear strategy exemplifies hyperscaler evolution beyond renewable energy certificates toward firm, carbon-free baseload power. The 1.9-gigawatt Susquehanna agreement through 2042 provides 24/7 clean energy that solar and wind cannot guarantee. Complementary investments in small modular reactors with X-Energy and Energy Northwest position AWS for expansion as SMR technology matures in the early 2030s. This approach acknowledges that AI-scale compute requires dispatchable clean energy.

3. Microsoft Ireland: Renewable Transition Case Study

Microsoft's commitment to run all Irish data centers on 100% renewable energy by 2025 required direct power purchase agreements rather than certificate-based matching due to Ireland's acute grid constraints. The company invested in dedicated wind and solar capacity that feeds directly into facilities, providing a model for markets where grid-based procurement is insufficient. This case illustrates both what's possible with adequate investment and the premium costs required for true carbon-free operation.

Action Checklist

  • Audit current compute carbon footprint including Scope 2 (purchased electricity) and Scope 3 (embodied carbon in hardware) emissions across on-premises and cloud workloads.
  • Evaluate cloud provider sustainability commitments by examining actual 24/7 carbon-free energy percentages rather than annual renewable matching claims, and compare PUE metrics across providers.
  • Assess workload optimization opportunities including model distillation, quantization, and inference batching that can reduce compute requirements by 2–10x without proportional capability loss.
  • Implement procurement criteria for efficient hardware by specifying performance-per-watt requirements and end-of-life recycling commitments in vendor contracts.
  • Model grid carbon intensity impacts by evaluating the marginal emissions rates of data center locations rather than average grid emissions, as marginal rates can be 2–3x higher.
  • Establish monitoring and reporting protocols aligned with emerging standards including the EU Energy Efficiency Directive's data center requirements and proposed SEC climate disclosure rules.

FAQ

Q: How does AI energy consumption compare to other major electricity uses?

A: In 2024, U.S. data centers consumed approximately 183 TWh—roughly equivalent to the electricity used by all residential air conditioning nationally. AI-specific workloads accounted for an estimated 53–76 TWh, sufficient to power 7.2 million American homes. By 2028, AI energy consumption could power 22% of U.S. households based on DOE projections.

Q: Are renewable energy commitments from cloud providers meaningful for reducing emissions?

A: Current renewable energy commitments vary significantly in quality. Annual matching—purchasing renewable energy certificates equal to total consumption—does not guarantee carbon-free electricity at any given moment. True 24/7 carbon-free energy matching, which Google is targeting by 2030, ensures every hour of consumption corresponds to actual clean generation. Enterprises should scrutinize whether providers report 24/7 matching percentages alongside annual matching claims.

Q: What efficiency improvements can organizations achieve through software optimization alone?

A: Software-level optimizations can deliver substantial efficiency gains without hardware changes. Model quantization (reducing numerical precision) can reduce inference energy by 2–4x with minimal accuracy impact. Distillation (training smaller models to replicate larger ones) achieves 5–20x efficiency improvements for specific tasks. Batching inference requests increases throughput per watt by 30–50%. These techniques are generally underutilized relative to their impact.

Q: How will small modular reactors change data center energy sourcing?

A: Small modular reactors (SMRs) promise scalable, dispatchable carbon-free power that can be co-located with large data center campuses. However, commercial SMR deployments are not expected before the early 2030s. NuScale's VOYGR design received U.S. regulatory approval in 2025, with Google, Amazon, and Microsoft executing agreements for SMR capacity. If commercialization succeeds, SMRs could provide 20–40% of hyperscaler energy needs by 2040.

Q: What policy interventions are most likely to affect compute energy consumption?

A: The EU Energy Efficiency Directive now requires data centers above 500 kilowatts to report energy and water consumption, PUE, and renewable energy share. Proposed SEC climate disclosure rules would mandate Scope 2 emissions reporting for U.S. public companies. Ireland has implemented de facto moratoriums on new data center grid connections in constrained areas. These regulatory trends indicate increasing policy attention that enterprises should factor into expansion planning.

Sources

Topics

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