The Next Generation of Sustainable AI

How SDG&E can leverage Extropic's thermodynamic computing to revolutionize energy management and achieve radical efficiency.

The Computational Energy Challenge

Modern AI workloads, from grid management to predictive analytics, are incredibly power-hungry. This creates a significant energy cost, a growing carbon footprint, and a hard "ceiling" on computational scalability for energy utilities.

10,000x

Potential energy reduction in compute workloads by switching from traditional GPUs to Extropic's Thermodynamic Sampling Units (TSUs).

The Energy Consumption Chasm

Extropic's thermodynamic hardware isn't an incremental improvement; it's a paradigm shift. By performing large-scale simulations using thermodynamics, TSUs consume a tiny fraction of the energy used by traditional GPUs.

This visualization (using a logarithmic scale to capture the immense difference) contrasts the relative energy consumption for the same AI task. This radical efficiency unlocks the potential for more sophisticated, real-time analytics without risking infrastructure overload.

Harnessing Efficiency: Key Application Areas

This new compute power can be strategically applied across SDG&E's most critical operations, transforming efficiency and resilience.

Compute Workload Focus

A potential allocation of new, efficient compute resources across primary analytics domains.

  • Grid Simulation & Prediction

    Running massive AI models for grid stability, forecasting peak demand, and integrating renewable energy sources becomes faster and hyper-efficient, enabling more sophisticated, real-time analytics.

  • Smart Infrastructure

    Thermodynamic hardware can support distributed sensors and edge devices across the grid, making local processing fast and energy-efficient without needing traditional power or cooling infrastructure.

  • Sustainability Analytics

    Probabilistic, generative AI algorithms can model complex scenarios like wildfire risk or infrastructure degradation with minimal energy, directly reducing costs and SDG&E's carbon footprint.

The Implementation Roadmap

A phased approach allows SDG&E to integrate thermodynamic computing seamlessly into existing data centers and workflows.

1

Hardware Deployment

Adopt XTR-0 TSU modules as expansion cards in data centers, supplementing or replacing power-hungry GPU racks within standard server infrastructure.

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2

Software Enablement

Adapt ML pipelines using Extropic’s `thrml` library, allowing engineers to migrate critical AI workflows and write new, tailored algorithms.

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3

Strategic Partnership

Launch pilot projects with Extropic to collaborate on algorithm development for industry-specific problems like energy pricing and weather modeling.

Potential Strategic Impact

This partnership future-proofs SDG&E's grid against next-generation AI workloads and energy challenges.

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Radical Efficiency

Achieve a radical reduction in compute-related energy usage, potentially 100x to 10,000x versus current AI and analytics platforms.

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Unlimited AI Scalability

Remove grid energy "ceilings" in data centers, allowing for unlimited AI growth without risking infrastructure overload or carbon emissions.

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Set a Global Precedent

Establish a national standard for sustainable AI adoption in the energy sector, aligning SDG&E with technology innovation for climate goals.