The Jevons Paradox of AI Compute

Table of Contents

• Introduction: When Efficiency Creates More Demand
• Revisiting Jevons Paradox — From Steam to Silicon
• AI’s Efficiency Explosion: Cheap Compute, Costly Consequences
• The Hidden Cost of Storage Growth
• Why Data Sprawl Is the Real Jevons Effect
• How Infrastructure Leaders Can Break the Cycle
• Conclusion: Designing for Efficiency Without Excess

Introduction: When Efficiency Creates More Demand

Technological progress is supposed to make things cheaper and cleaner — but history often proves otherwise. In 1865, British economist William Stanley Jevons discovered that improvements in coal engine efficiency actually increased total coal consumption. Today, AI is replaying that paradox. As models become more efficient and cost-per-token falls, total compute and data storage demand are exploding. This is the Jevons Paradox of AI compute — and its hidden victim is storage.

Revisiting Jevons Paradox — From Steam to Silicon

The Jevons Paradox states that when efficiency rises in a resource’s use, overall consumption often increases due to expanded demand.

• Steam engines burned more coal as they improved.
• Cars consumed more gasoline as mileage rose.
• AI now consumes more energy and storage as compute becomes cheaper.

In the BOND 2025 report, NVIDIA’s Blackwell GPU delivers 105,000× more energy efficiency than its 2014 Kepler generation. Yet global data center electricity use continues to grow ~12% per year. Every leap in compute efficiency fuels new workloads — and with them, new data.

AI’s Efficiency Explosion: Cheap Compute, Costly Consequences

The efficiency gains in AI hardware are extraordinary:

• 225× GPU performance increase (2016–2024).
• 30,000× expansion in theoretical token capacity.
• 105,000× energy efficiency improvement per token.

But this efficiency drives exponential data growth: more tokens → more context → more logs → more fine-tuning datasets. Each new model release compounds data gravity, increasing embeddings, vector databases, and interaction histories. Compute may be cheap, but every bit of intelligence generated must be stored — often forever.

The Hidden Cost of Storage Growth

Every AI workload creates new persistent data layers that consume storage and energy:

• Training datasets: Augmented and synthetic data ballooning petabyte scales.
• Model checkpoints: Replicated across GPUs, clusters, and regions.
• Logs and telemetry: Retained for safety, compliance, and analytics.
• RAG systems: Duplicating corpora into vector databases.

While compute grows efficient, storage efficiency stagnates — making data retention AI’s largest unmeasured cost center.

Why Data Sprawl Is the Real Jevons Effect

Storage sprawl mirrors the classic Jevons effect — efficiency drives expansion, not reduction.

• Horizontally: More models and applications generating new datasets.
• Vertically: Each model retains prior checkpoints and metadata.
• Temporally: Legal and compliance mandates delay deletion indefinitely.

This unchecked sprawl increases not only storage costs but also energy demand and cooling loads. Hidden inside cloud Opex, this energy cost remains largely invisible — the digital equivalent of coal dust during the industrial age.

How Infrastructure Leaders Can Break the Cycle

The Jevons loop can be broken only through disciplined architecture — efficiency must be paired with constraint:

1. Treat data like emissions: Track and reduce your storage footprint (watts/TB, CO₂/petabyte).
2. Automate lifecycle governance: Use Data Storage Management Services (DSMS) for classification and deletion.
3. Disaggregate compute and storage: Optimize locality to minimize idle data replication.
4. Leverage sovereign or colocation environments: Keep data near inference nodes to reduce energy and egress costs.
5. Design deletion into systems: Make defensible data deletion a default sustainability function.

Conclusion: Designing for Efficiency Without Excess

The Jevons Paradox reminds us that unchecked efficiency drives excess. AI’s compute revolution — faster GPUs, cheaper inference, smarter models — risks repeating that cycle unless storage evolves too. True sustainability demands new metrics:

• Compute efficiency per watt.
• Storage efficiency per byte.
• Energy intensity per insight.

The next generation of AI leaders will measure not just how fast they can compute — but how responsibly they can store intelligence.