// ENERGY CRISIS AT INTERFACES
The Hidden Energy Crisis
at Every Interface
The world's computing infrastructure wastes enormous energy where systems meet — at interfaces. We're building the tools to fix it.
TWh / year
Projected data center energy by 2026
// THE SCALE
The Problem Is Enormous
The energy crisis isn't just about data centers — it's about every interface where data crosses a boundary, every query routed to an oversized model, every watt lost at the seam between systems.
416
TWh / year
The internet's annual energy consumption — more than the entire United Kingdom's electricity usage.
$2T
Global AI Spend / 2026
Global AI infrastructure spending is projected to reach two trillion dollars — the majority consumed by inference, not training.
80–90%
Inference Compute
Of a model's total lifecycle compute is spent on inference — continuous, 24/7, at every interface between user and machine.
// THE FRAMEWORK
Where Energy Dies
Every complex system has five fundamental types of interfaces. Energy is lost at each one — through mismatches, conversions, and systemic blind spots that compound across scale.
We call them the Five Interface Tracts.
Data Tract
Protocol mismatches, redundant serialization, format conversions. Every unnecessary transformation burns energy at the boundary.
Energy Tract
Power domain crossings, voltage conversion losses, thermal runaway. Heat dissipation alone consumes 30–40% of data center energy.
Material Tract
CTE mismatch at junctions, thermal resistance between materials. Physical interfaces leak energy as heat through poorly matched boundaries.
Logistics Tract
Inefficient routing, over-provisioned paths, wasted hops. Data and compute travel unnecessary distances through poorly designed topologies.
Supply Chain Tract
EOL churn forcing redesigns, wasteful component replacements, and the mounting e-waste crisis — 110% CAGR for AI hardware waste.
In tracked non-recurring engineering costs from interface failures
Across 30+ projects analyzed. These aren't bugs — they're systemic blind spots where energy, time, and money vanish at the boundaries between systems.
// THE INFERENCE CRISIS
The Token Crash Paradox
Token prices have dropped 280x since 2023. Yet AI bills are rising. Volume exploded faster than prices fell.
96% of organizations report generative AI costs higher than expected. 43% face significant cost overruns. 25% of IT leaders bust their budgets by more than 50%.
The fundamental problem? We're routing every query — no matter how trivial — through the largest, most energy-hungry models available.
"We are using massive, general-purpose models to do simple arithmetic."
An agentic AI decision cycle costs 100–1,000x more than a traditional query. As AI agents proliferate, this multiplier transforms waste into catastrophe.
$ analyze --query-distribution Query Distribution Reality ───────────────────────────────── Simple queries: 45-60% → Need: 1-7B model Medium queries: 25-35% → Need: 7-20B model Complex queries: 15-25% → Need: 20B+ model ───────────────────────────────── Current approach: Route ALL → largest model Energy wasted: ~70% $ analyze --cost-per-query Cost Multiplier: Agentic AI ───────────────────────────────── Traditional LLM call: $0.001 Agentic decision: $0.10 - $1.00 Multiplier: 100-1000x $ _
INFRASTRUCTURE REALITY
Training is a one-time cost. Inference is continuous — 24/7, at every API call, at every interface between user and machine. It accounts for 80–90% of total compute costs.
// THE BOTTLENECKS
Three Walls Between Compute and Efficiency
These are interface problems — they exist at the boundary between compute and memory, between hardware and software, between throughput and latency.
GPU Memory Crisis
KV CACHE EXPLOSION
The KV cache grows linearly with sequence length and must reside in fast GPU memory. For Llama 3 70B with 128K context, it alone consumes ~40 GB.
Traditional systems waste 60–80% of KV cache memory through fragmentation. Batch sizes collapse. Context is truncated. Throughput drops.
Memory Bandwidth Wall
COMPUTE IS NOT THE BOTTLENECK
LLM inference is memory-bound, not compute-bound. The decode phase runs at 1–10 FLOPs/byte — far below the GPU's compute threshold of 208 FLOPs/byte.
GPUs spend more time waiting for memory than computing. Buying faster GPUs yields diminishing returns. The interface between compute and memory is the wall.
Throughput–Latency Tradeoff
THE IMPOSSIBLE BALANCE
Low concurrency (batch=1) means low latency but wasted compute. High concurrency (batch=64) gives 14x throughput but 4x latency.
Achieving both simultaneously is impossible with current architectures. This is a fundamental interface mismatch between serving and computing.
// OUR SOLUTIONS
Software That Treats Energy as a First-Class Concern
Just as organisms don't expend maximum energy for simple tasks, AI systems shouldn't route trivial queries through billion-parameter models. We're building the tools to make that possible.
Joule
The energy-aware systems programming language
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Energy Budgets in the Type System
Know exactly how much power your code consumes. Enforce limits at compile time.
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Thermal-Aware Runtime
Automatic adaptation based on hardware thermal state. Sustainable operation by default.
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Heterogeneous Computing
First-class CPU, GPU, TPU, and NPU support. One language for all accelerators.
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Tri-Backend Architecture
Cranelift for fast dev builds, LLVM for optimized releases, MLIR for energy-dialect heterogeneous compute.
// Energy budgets enforced at compile time #[energy_budget(max_joules = 0.001)] fn process_sensor_data(input: &[f32]) -> Vec<f32> { // Compiler rejects if budget exceeded input.iter().map(|x| x * 0.95).collect() }
JouleDB
The self-optimizing, energy-aware database
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Energy-Aware Query Routing
Routes queries to right-sized compute based on complexity. 40% cost reduction through intelligent classification.
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Hardware Telemetry Integration
Decisions grounded in physical reality: CPU/GPU temperature, power draw, thermal headroom — not just software metrics.
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Adaptive Complexity Classification
Thresholds shift with system state. Under thermal pressure, route to smaller models. With headroom, enable larger ones.
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Deployment Profile Adaptation
Automatically adapts strategy across environments — datacenter, edge, and MCU/mobile — each with optimal energy profiles.
$ jouledb --show-profiles Deployment Profiles ────────────────────────────────── Datacenter Quality-first Speculation: ON Edge Balanced Speculation: Conditional MCU/Mobile Max efficiency Speculation: OFF Active: Edge | Thermal headroom: 72%
Joule Energy Meter
What you can't measure, you can't improve. The Joule Energy Meter brings real-time power monitoring to every website you visit — measuring energy in millijoules, grading efficiency from A+ to F, and showing CO₂ impact.
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Real-time power monitoring — see watts, millijoules, and CO₂ as you browse
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Energy grades (A+ to F) — instant efficiency ratings for any website
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Issue detection — identifies heavy JavaScript, unoptimized images, autoplay video
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Privacy-first — all analysis runs locally, no data collection or tracking
Joule Energy Meter — Site Grades ────────────────────────────────── A+ text.npr.org 8 mJ A lite.cnn.com 12 mJ B wikipedia.org 45 mJ C github.com 78 mJ D medium.com 234 mJ F news sites (avg) 890 mJ ────────────────────────────────── Gap: over 100x between best and worst Every page view uses energy. Let's measure it, optimize it, and reduce it together.
// THE IMPACT
Numbers That Matter
When energy becomes a first-class concern in software, the results compound at every scale.
72.3%
Energy Savings
$720K saved per $1M inference spend through hardware-aware adaptive routing.
35–100x
Neural-Symbolic Efficiency
Energy savings with Cortex: 110J per task reduced to 3.2J — a 97% reduction.
$8.64M
Annual Enterprise Savings
For enterprises spending $1M/month on inference — 72% cost reduction scales immediately.
10.3 MWh
Saved Per Billion Requests
Annual energy savings per billion code execution requests using optimized runtimes.
56M tons
CO₂ Reduction Potential
If every website reduced energy consumption by just 20% — equivalent to taking millions of cars off the road.
100x
Best vs. Worst Practice
The energy difference between optimized web experiences (8 mJ) and bloated ones (890 mJ). The gap is entirely software.
At $2 trillion in global AI spending, even modest efficiency improvements represent hundreds of billions in potential savings — and a meaningful reduction in the environmental cost of intelligence.
// TAKE ACTION
The Energy Crisis at Interfaces
Is Solvable
But it requires a new generation of tools, languages, and systems that treat energy as a first-class concern — not an afterthought.
Interested in partnering or funding this research?
contact@openie.dev