Disarm the AI Arms Race

The Problem

The $2 trillion infrastructure arms race

Routing every query through 700-watt GPUs seems like progress, and the results are impressive. But projections show AI data centers consuming 945 TWh by 2030, while the vast majority of that compute is wasted on questions that never needed a large language model.

80-90%

of AI cost is inference

75%

of queries need no LLM

96.7%

energy reduction possible

1970s

The CFC Problem

Non-toxic. Non-flammable. Cheap. CFCs seemed like pure engineering success. They were silently destroying the ozone layer, creating a hole that took decades to detect and 30 years of global policy to begin healing.

2020s

The AI Problem

Routing every query through billion-parameter models seems like progress. But the vast majority of that compute is wasted, a $2T infrastructure arms race burning the grid to answer questions that never needed a large language model.

The Solution

A different architecture

We built an AI architecture designed from the ground up for energy efficiency. Most queries resolve without ever touching a large language model. When an LLM is needed, our architecture routes to the most efficient path available. The result: faster answers at a fraction of the energy cost.

We don't have to choose between advancing AI and sustaining our power grids. By fixing the leaks at the interfaces, we make intelligence both frictionless and sustainable.

The Physics

Shannon, Landauer, and the thermodynamic floor

1948

Shannon proved that information is physical and measurable. Every communication channel has a maximum rate at which information can be transmitted reliably, measured in bits per second.

1961

Landauer proved that erasing information costs energy. This is not an engineering estimate. It is a consequence of the second law of thermodynamics.

2026

OpenIE completes the sequence: measure every operation, route to the most efficient physics-permitted path.

E_min = k_BT ln 2 ≈ 2.87 × 10^-21 J per bit Landauer's limit at room temperature (300K)

Modern computation operates roughly 10 billion times (10¹⁰) above the thermodynamic minimum. The space between where we operate and where physics says we could operate is vast. Every step down that curve represents recoverable energy, energy that is currently wasted because no one measures it.

You cannot optimize what you cannot see. JoulesPerBit makes the energy cost of computation visible.

The Compounding Win

Intelligence gets more efficient the more you use it

Unlike traditional AI, which costs the same on day one and day one thousand, our architecture learns. Answers are cached and reused. Common queries are answered faster and cheaper as usage grows. The system gets more efficient over time.

Where It Matters

Not a meter. A force multiplier.

When you can measure, budget, and route every joule per decision, intelligence becomes feasible where it was previously impossible.

+5 hrs

Mission endurance. A soldier carries 250 Wh of battery. A 5W AI processor cuts a 20-hour mission to 14 hours. Our architecture at 1W average recovers 5 hours. That's not efficiency. It's capability that didn't exist.

+300 min

Persistent coverage. A 20-drone swarm on 100W power budgets. 20-80% compute savings means 200-300 extra minutes of ISR coverage per day across the swarm.

7.1W

Onboard intelligence. A satellite's full solar budget. The difference between a 10W AI chip and a 2W adaptive runtime is having onboard intelligence or not having it.

Browser

Last-mile intelligence. Intelligence that runs in the browser. No data center, no cloud, no subscription. A health worker in rural Malawi with a $100 phone covers what used to require three.