AI is growing. So is its footprint.
Every AI query consumes energy, water, and compute. Most workflow tools ignore this, running every task as a fresh inference call with no compression, no caching, and no reuse. CadeLume is built differently.
The environmental cost of unoptimized AI.
The IEA projects global data center electricity will nearly double from 485 TWh in 2025 to 950 TWh by 2030. AI infrastructure is consuming resources at an unprecedented rate.
Energy
A simple text query uses about 0.3 Wh, modest alone, but at billions of queries a day, data centers now draw 1–2% of global electricity, projected to reach about 3% by 2030. Reasoning and agent queries use far more.
Water
Independent estimates put AI data-center cooling water in the hundreds of billions of gallons a year. Google alone disclosed about 8 billion gallons across its data centers in 2024, and most operators still do not report theirs.
Carbon
Within weeks of deployment, cumulative inference emissions surpass a model’s entire training footprint, and keep compounding with every query served.
Compression is not a feature. It is a responsibility.
CadeLume is built on MicroPrompt, a runtime that tracks prompt versions, execution history, and workflow provenance, so it can reuse what has already been computed instead of paying full price every time.
- Version-controlled workflows avoid re-running validated inference
- Fact-lock compression strips redundant tokens, up to 60% fewer
- Approved outputs cached and reused; evidence exported once
Even a nudge compounds across thousands of reviews.
Token reduction
Prompt compression cuts inference cost up to 60% with under 5% accuracy drop, documented in the peer-reviewed CompactPrompt pipeline.
Real-world savings
Caching plus smart context management delivers 70–80% savings at high cache-hit rates in enterprise implementations.
Software beats hardware
Google cut the energy of a median AI prompt 33x in one year, almost all from model and software optimization (about 23x), versus 1.4x from better hardware use.
Small per review. Enormous across an industry.
A regulated team reviews 100 documents a month, five inference calls each. At about 0.3 Wh per call that is 150 Wh a month, modest on its own. Across 50 teams in one pharmaceutical company it becomes 7,500 Wh a month, roughly 90 kWh a year, for a single review type.
CadeLume’s workflow compression cuts token use by up to 60%, dropping that to about 36 kWh. Over five years, across many review types, kilowatt-hours compound into megawatt-hours. Multiply it across the industry and the nudge becomes a force.
What we are, and are not, claiming.
We believe in accurate positioning. CadeLume is a workflow tool that, by design, consumes fewer resources than alternatives that run every task as a fresh, uncompressed inference call.
We are claiming
- Version-controlled workflows reduce redundant inference calls
- Prompt compression reduces tokens processed per review
- Evidence package reuse eliminates re-verification compute
- These techniques are documented in peer-reviewed research
We are not claiming
- CadeLume will solve AI’s environmental crisis
- Our product is “carbon neutral” or “green AI”
- We have measured our exact carbon reduction (we are working on it)
- Using AI is environmentally free, it is not
Every figure here is sourced.
Every claim on this page is backed by peer-reviewed research, government reports, or corporate sustainability disclosures. We update these figures as new data becomes available.
Nobody knows what AI really costs. Let us fix that together.
The public numbers are estimates, and the people with the real data have little reason to share it. We are inviting companies to contribute environmental data so the whole industry can get accurate, real-time measurement of what frontier models and AI agents actually cost. Not our model alone. A shared one, built on real numbers.
Bring one document-heavy process.
We will map where your team reviews, approves, and defends critical documents, then identify where controlled AI can help, with lower environmental cost.
Become a design partner