Your Code is Burning the Planet
Every line of code consumes energy. Every algorithm has a carbon cost. Yet programmers have been blind to this for 70 years. Until now.
# How much energy does this use?
# Nobody knows. Nobody asks.
for i in range(1000000):
result = expensive_computation(i)
# Cost: $0.002 in cloud compute
# Cost: ??? in energy
# Cost: ??? in CO2// THE SCALE
Computing's Dirty Secret
of global electricity consumed by data centers
Equal to the entire aviation industry
of global CO2 emissions from ICT
Growing 9% annually
Year computing could consume 20% of global power
If current trends continue
Training a single large AI model produces as much CO2 as five cars over their entire lifetimes. A Google search uses enough energy to power a lightbulb for 17 seconds. And it's getting worse.
// THE ROOT CAUSE
The Problem Starts at the Programming Language
Every abstraction layer adds energy overhead. Every language design decision has thermal consequences. Yet no programming language has ever treated energy as a first-class concern.
Languages Ignore Energy
- ✗
No energy visibility
Programmers can't see how much energy their code uses
- ✗
No energy constraints
No way to set or enforce energy budgets
- ✗
No thermal awareness
Code runs the same whether the CPU is cool or thermal throttling
- ✗
No hardware telemetry
RAPL counters exist but languages don't expose them
# Energy to run the same algorithm:
C 1.00x (baseline)
Rust 1.03x
Java 1.98x
Go 3.23x
JavaScript 4.45x
Python 75.88x
# Source: Energy Efficiency across
# Programming Languages (2017)
# Pereira et al.// THE CASCADE
Uninformed Choices Compound
Developer chooses Python for convenience
"It's faster to write, we can optimize later"
+75x energy overhead locked in
Algorithm chosen without energy consideration
"Big-O complexity is what matters"
+2-10x energy overhead from cache misses, branching
Code runs on thermal-throttled hardware
"The cloud handles scaling"
+30-50% energy overhead from inefficient scheduling
Scaled to millions of users
"Success!"
= Megawatts of unnecessary power consumption
The Result
A single inefficient choice, made once by one developer, multiplied across billions of executions, becomes a climate problem.
// THE SOLUTION
Joule: Energy as a First-Class Citizen
What if the programming language itself understood energy? What if every function had an energy cost, and exceeding your budget was a compile error?
// Energy-aware computation
fn main() {
energy_budget(100.millijoules) {
let result = compute_efficiently();
print("Energy used: {}", energy_consumed());
}
}Joule Changes Everything
- ✓
Energy budgets in the type system
Compiler enforces energy limits at compile time
- ✓
Real-time hardware telemetry
RAPL counters, thermal sensors as language primitives
- ✓
Thermal-aware execution
Code adapts to hardware state automatically
- ✓
Performance of C, safety of Rust
No compromise on speed or memory safety
Energy vs C (Rust-level efficiency)
Memory safety bugs (borrow checking)
Backends (Cranelift, LLVM, MLIR)
Accelerators (CPU, GPU, TPU, NPU)
// OPENIE FRAMEWORK
Energy is an Interface Problem
At OpenIE, we've spent years studying the Five Tracts—the fundamental interfaces where systems fail. Energy is one of them.
Just like mismatched data formats cause integration failures, mismatched energy assumptions cause thermal throttling, battery drain, and climate impact.
Joule is interface engineering applied to the most fundamental tool of computing: the programming language itself.
Learn about the Five TractsData Tract
Bits, protocols, APIs
Energy Tract
Power, thermal, efficiency
Material Tract
Mechanical, chemical
Logistics Tract
Assembly, routing
Supply Chain Tract
Sourcing, vendors, lifecycle
// WHERE IT MATTERS MOST
Built for Energy-Critical Applications
Edge AI & Medical Devices
Implantable devices, wearables, and portable diagnostics where battery life is measured in years, not hours. Every millijoule extends patient care.
FDA/EU AI Act compliance ready
Satellites & Space Systems
Solar-powered systems where energy budgets are fixed and thermal management is life or death. Joule's constraints map directly to mission requirements.
10x mission duration potential
Data Center Optimization
Hyperscalers spend billions on cooling. Thermal-aware code reduces hotspots, enables higher density, and cuts operational costs.
30% cooling cost reduction
Regulated AI & ESG Compliance
Auditable energy consumption for sustainability reporting. Prove your AI's environmental impact with compile-time guarantees, not estimates.
Built-in energy auditing
The Future of Computing is Energy-Aware
Joule launches Q2 2026. Join the waitlist to be notified when we go live and help build software that respects planetary boundaries.