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

Deep dive: Compute, chips & energy demand — the fastest-moving subsegments to watch

What's working, what isn't, and what's next — with the trade-offs made explicit. Focus on unit economics, adoption blockers, and what decision-makers should watch next.

In 2024, global data center electricity consumption reached 415 TWh—approximately 1.5% of global electricity demand—and projections from the International Energy Agency suggest this figure could surge to 945 TWh by 2030 (IEA, 2024). In the United States alone, data centers consumed 183 TWh in 2024, representing 4% of national electricity usage, with AI-specific workloads accounting for 53-76 TWh of that total (Berkeley Lab, 2024). The AI boom has fundamentally altered the energy calculus: a single ChatGPT query consumes roughly 0.3 watt-hours compared to 0.0003 kWh for a traditional Google search—a 1,000-fold increase in computational intensity. As hyperscalers collectively announced over $350 billion in data center investments for 2025, the intersection of compute infrastructure, semiconductor innovation, and energy systems has become the defining sustainability challenge of the decade.

Why It Matters

The exponential growth in AI compute demand presents an unprecedented challenge to global decarbonization efforts. According to Goldman Sachs, data center power demand is projected to increase by 165% by 2030, with AI workloads driving the majority of this growth. This trajectory directly conflicts with net-zero commitments made by corporations and governments worldwide.

The stakes extend beyond environmental concerns. In Virginia—home to 26% of the state's electricity consumption from data centers—the PJM capacity market saw prices surge from $28.92/MW in 2024-2025 to $329.17/MW for 2026-2027, a tenfold increase that will translate to residential electricity bill increases of $18-25 per month in affected regions (PJM Interconnection, 2025). Ireland, where data centers now consume 22% of national electricity (projected to reach 32% by 2026), faces similar grid stability challenges.

For policymakers and corporate sustainability leaders, understanding the fastest-moving subsegments in this space is essential. The decisions made today regarding chip architecture, cooling technologies, and energy procurement will determine whether the AI revolution accelerates or undermines climate goals.

Key Concepts

Power Usage Effectiveness (PUE)

PUE remains the primary metric for data center energy efficiency, calculated as total facility power divided by IT equipment power. Industry-leading hyperscalers have achieved PUEs of 1.08-1.12, while traditional data centers average 1.56. A PUE of 1.1 means only 10% of energy is lost to cooling and overhead—a significant improvement from the 1.8-2.0 ratios common a decade ago. However, PUE does not capture the carbon intensity of the power source, creating a blind spot in sustainability reporting.

Compute Density and Thermal Envelopes

AI accelerators have fundamentally changed data center thermal requirements. Traditional server racks draw 5-15 kW, while AI-optimized racks consume 40-100+ kW, with next-generation configurations projected to reach 1,000 kW per rack by 2029 (Deloitte, 2025). This density shift requires transitioning from air cooling to liquid or immersion cooling systems, which can reduce cooling-related power consumption by 15-30%.

Renewable Energy Matching vs. 24/7 Carbon-Free Energy

Most corporate renewable claims rely on annual matching—purchasing renewable energy certificates (RECs) equivalent to annual consumption. Google has pioneered a more rigorous standard: 24/7 carbon-free energy (CFE), which requires matching clean energy supply with consumption on an hourly basis at each location. This distinction matters enormously; annual matching can mask significant fossil fuel reliance during peak demand periods.

Performance Per Watt Economics

The AI chip market has shifted focus from raw performance to performance-per-watt metrics. NVIDIA's H100 GPUs consume up to 700 watts per unit, creating operational expenditure challenges that increasingly favor more efficient architectures. AMD claims its MI355X accelerators deliver 40% more tokens per dollar than competitors—a metric that directly addresses the unit economics driving procurement decisions.

Sector-Specific KPIs

Metric2024 Baseline2025 Target2030 ProjectionIndustry Leader
Average PUE1.561.401.20Google (1.09)
AI Rack Density (kW)40-6080-100200-1000Meta, NVIDIA
% Renewable Energy40%60%100%AWS (achieved 2023)
Water Usage (L/MWh)1.81.5<1.0Microsoft
Carbon Intensity (gCO2e/kWh)350250<50Google Cloud
Chip Efficiency (TFLOPS/Watt)2.54.010+NVIDIA Blackwell

What's Working and What Isn't

What's Working

Hyperscaler renewable procurement at scale. Amazon Web Services achieved 100% renewable energy matching in 2023, two years ahead of its 2025 target, contracting over 20 GW of clean energy globally. Google has operated carbon-neutral data centers since 2007 and now pursues 24/7 CFE matching. These commitments have created substantial demand signals that accelerate renewable energy deployment globally.

AI-driven efficiency optimization. Google's DeepMind-powered cooling systems reduced cooling electricity consumption by approximately 30%, demonstrating how AI can optimize its own infrastructure footprint. Similar machine learning approaches now optimize workload scheduling, shifting compute to times and locations with higher renewable availability.

Next-generation chip architectures. The transition from NVIDIA's H100 to Blackwell architecture delivers significant performance-per-watt improvements, while AMD's MI350 series promises 4x generation-over-generation efficiency gains. Intel's commitment to 10x energy efficiency improvement by 2030 for its processor lineup reflects industry-wide recognition that power consumption is now a primary competitive differentiator.

Liquid cooling adoption. The shift from air to liquid cooling is accelerating, with estimates suggesting that configurations with 75% liquid cooling deliver 15.5% power savings compared to air-cooled equivalents. Companies like Equinix and Digital Realty have deployed large-scale liquid cooling infrastructure for AI workloads.

What Isn't Working

Grid interconnection bottlenecks. Over $162 billion in data center projects were blocked or delayed as of mid-2025 due to grid constraints (S&P Global, 2025). The average time to bring new power generation online exceeds four years, creating a fundamental mismatch between AI demand growth and clean energy supply.

Natural gas as a "transitional" solution. Despite renewable commitments, many hyperscalers are deploying hydrogen-ready natural gas turbines to meet immediate power needs. While marketed as future-proof infrastructure that can transition to clean hydrogen, this approach locks in fossil fuel infrastructure and emissions during the critical 2025-2030 window for climate action.

Scope 3 emissions growth. Microsoft's 2024 sustainability report revealed that Scope 3 emissions increased 26% from its 2020 baseline, primarily driven by data center construction materials and supply chain impacts. The embodied carbon in concrete, steel, and semiconductor manufacturing remains largely unaddressed even by industry leaders.

Water consumption trajectories. U.S. data centers consumed 17 billion gallons of water in 2023, with projections reaching 16-33 billion gallons for hyperscale facilities alone by 2028. In water-stressed regions, this creates conflicts with agricultural and municipal water needs that regulations have not adequately addressed.

Efficiency gains offset by demand growth. Despite dramatic improvements in PUE and chip efficiency, absolute energy consumption continues to rise. The Jevons paradox applies: efficiency improvements reduce per-unit costs, stimulating additional demand that overwhelms efficiency gains.

Key Players

Established Leaders

NVIDIA Corporation dominates the AI accelerator market with over 80% market share in training workloads. Its Blackwell and forthcoming Rubin architectures set the benchmark for AI compute performance, though the company faces increasing pressure on power consumption metrics. NVIDIA has committed to 100% renewable electricity for its operations and announced a $100 billion partnership with OpenAI for next-generation chip supply.

Google (Alphabet) leads in data center efficiency with the industry's lowest PUE metrics and pioneering 24/7 carbon-free energy commitments. Its custom Tensor Processing Units (TPUs) offer 60% better energy efficiency than conventional CPUs for AI workloads. Google's $9 billion Virginia expansion and investments in advanced geothermal and small modular reactors signal long-term clean energy infrastructure commitment.

Amazon Web Services achieved 100% renewable matching earlier than any major hyperscaler and operates as the world's largest corporate buyer of renewable energy for five consecutive years. AWS maintains average PUE of 1.2 and is deploying battery storage paired with solar installations at scale.

Microsoft pursues carbon-negative operations by 2030 and has pioneered mass-timber data center construction projected to reduce embodied carbon by 65% compared to traditional concrete structures. Its $10 billion renewable energy deal with Brookfield Asset Management represents one of the largest corporate clean energy commitments to date.

Emerging Startups

Cerebras Systems has developed the largest chip ever built (wafer-scale integration), eliminating the memory bottlenecks that constrain GPU efficiency. Its CS-3 systems demonstrate that alternative architectures can deliver competitive performance with different power profiles.

Groq specializes in inference optimization, achieving dramatically lower latency and power consumption for deployed AI models compared to training-optimized hardware. As inference increasingly dominates AI compute demand, Groq's approach addresses the fastest-growing segment.

Tenstorrent offers open-source AI accelerator architectures, providing alternatives to the NVIDIA CUDA ecosystem. Founded by former AMD and Intel engineers, the company addresses both cost and supply chain diversification concerns for enterprises.

RunPod and Lambda Labs provide cloud GPU infrastructure with transparent carbon intensity reporting, enabling researchers and enterprises to make informed decisions about the environmental impact of their compute workloads.

Key Investors and Funders

Brookfield Asset Management has emerged as the leading infrastructure investor in clean energy for data centers, with over $30 billion in renewable energy partnerships with hyperscalers including Microsoft and Google.

The U.S. Department of Energy funds research through ARPA-E and national laboratories on next-generation cooling technologies, chip efficiency, and grid integration solutions for data centers.

The European Investment Bank provides preferential financing for data center projects meeting stringent sustainability criteria under the EU Taxonomy for Sustainable Activities.

Breakthrough Energy Ventures (Bill Gates' climate investment fund) backs multiple startups in advanced nuclear, geothermal, and energy storage technologies directly relevant to data center power supply.

Examples

1. Google's Fervo Energy Partnership (Nevada, USA): In 2024, Google became the first major tech company to power data centers with advanced geothermal energy through its partnership with Fervo Energy. The 115 MW project in Nevada uses enhanced geothermal systems (EGS) technology to provide 24/7 carbon-free baseload power—addressing the intermittency challenges of solar and wind. This approach demonstrates that 24/7 CFE is achievable with current technology, though at higher cost than annual renewable matching.

2. Meta's Odense District Heating Integration (Denmark): Meta's Odense data center captures waste heat from servers and distributes it to 11,000 homes through the city's district heating network, offsetting approximately 100,000 MWh of natural gas annually. This circular approach transforms the thermal output typically wasted through cooling into a community energy asset, demonstrating how data center siting decisions can create rather than extract value from local communities.

3. AMD's MI350 Series Enterprise Deployment: A Fortune 500 financial services company transitioned from NVIDIA H100 infrastructure to AMD MI350X accelerators for inference workloads in late 2024, reporting 35% reduction in power consumption and 40% improvement in cost-per-inference. While NVIDIA retains advantages for training workloads, this deployment demonstrates that competitive alternatives exist for the inference workloads that constitute the majority of production AI compute.

Action Checklist

  • Audit current data center PUE metrics and establish improvement targets aligned with industry leaders (target: <1.3 by 2027)
  • Evaluate 24/7 carbon-free energy procurement options rather than relying solely on annual renewable matching
  • Assess liquid cooling infrastructure requirements for AI workload density projections over the next 3-5 years
  • Incorporate Scope 3 emissions from construction materials and supply chain into sustainability reporting
  • Develop water stewardship policies for data center siting, particularly in water-stressed regions
  • Evaluate alternative chip architectures (AMD, Intel, custom ASICs) to reduce NVIDIA supply chain concentration and optimize power profiles
  • Engage with grid operators and policymakers on interconnection timelines and clean energy infrastructure development

FAQ

Q: How does AI compute energy demand compare to cryptocurrency mining? A: While both are computationally intensive, AI training and inference now significantly exceed cryptocurrency energy consumption in absolute terms. Bitcoin mining globally consumes approximately 120-150 TWh annually, while AI data centers alone are projected to reach 165-326 TWh in the U.S. by 2028. Critically, AI compute demand is accelerating while cryptocurrency mining has plateaued, and AI workloads are concentrated in facilities with sophisticated efficiency measures rather than distributed mining operations.

Q: Can renewable energy scale fast enough to meet data center demand growth? A: Current grid interconnection timelines (4+ years for new generation) create a fundamental mismatch with AI demand growth. The $162 billion in delayed or blocked projects due to grid constraints demonstrates this challenge. Solutions include behind-the-meter power generation, advanced nuclear (SMRs), and geothermal—but none can scale sufficiently by 2027 without regulatory acceleration. The interim period will likely see continued natural gas deployment despite renewable commitments.

Q: What regulatory frameworks are emerging for data center energy consumption? A: The EU Energy Efficiency Directive requires data center operators to report PUE, water usage, and renewable energy metrics starting in 2024, with potential efficiency mandates forthcoming. Ireland has implemented de facto moratoriums on new data center connections in grid-constrained areas. Singapore requires operators to achieve PUE below 1.3 for new facilities. In the U.S., regulatory approaches remain fragmented at the state level, though the DOE has increased research funding and reporting requirements.

Q: How do Scope 3 emissions from data center construction compare to operational emissions? A: For a typical hyperscale data center with a 15-year operational life and clean electricity supply, embodied carbon from construction can represent 30-50% of lifetime emissions. Concrete and steel production, semiconductor manufacturing, and cooling equipment all carry significant carbon footprints. Microsoft's mass-timber construction approach and increasing focus on low-carbon concrete demonstrate that industry leaders are beginning to address this previously overlooked category.

Q: What is the outlook for on-site nuclear power at data centers? A: Multiple hyperscalers have announced nuclear energy partnerships. Amazon acquired a data center adjacent to a nuclear plant in Pennsylvania. Google has invested in small modular reactor (SMR) developer Kairos Power. Microsoft has explored SMR partnerships for campus power. However, no SMRs are commercially operational in the U.S. as of early 2026, and regulatory timelines suggest 2028-2030 as the earliest realistic deployment window. Advanced geothermal may reach scale faster for 24/7 baseload power needs.

Sources

Topics

computechipsdata qualitycompute costgovernanceMRVopex

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