The measurement layer that turns "sustainable AI" from a claim into a fact.
Anyone can say their AI is sustainable. Almost no one can prove it — because there's no shared, independent way to measure energy use, water draw, and emissions across the AI and cloud infrastructure the whole industry runs on.
This project is that measurement layer: a telemetry and data pipeline that aggregates real environmental-impact data across AI services and infrastructure providers, at scale. It's the evidence base behind AIforE's mission — the open specifications, benchmarks, and certification standards that let a company's sustainability claim be checked, not just taken on faith.
It's also what powers the ROI & Sustainability Client-Subscriber Plugin & API — it reads its numbers from this pipeline. Build the measurement layer, and every tool built on top of it gets more credible.
The same electricity has a different cost and a different carbon footprint depending on the hour. Prices move with wholesale markets, demand-response events, and time-of-use tariffs, and the grid's carbon intensity swings with whatever is generating at that moment. So wherever time changes the cost, our telemetry records the timestamp and the grid region alongside the number, rather than a single annual average.
The grid signals come from real-time sources such as Electricity Maps (generation mix and carbon intensity) and WattTime (marginal emissions, meaning what actually responds when demand changes). We use both because average and marginal intensity answer different questions.
Once the hour is measured, it becomes something engineers can act on. Batch training runs, embeddings, and other asynchronous workloads can wait for the hours when the grid is cleanest or when renewable generation would otherwise be curtailed, at no cost to anyone waiting on an answer. We're building the telemetry so an operator can see what a workload would have cost, in dollars and in carbon, had it run at a different time.
Before a chip computes anything, it has already cost something: mining, fabricating silicon wafers, and manufacturing high-bandwidth memory (HBM). AI accelerators are also replaced quickly, often within two to four years, which spreads that upfront cost over a short working life. Ignoring it makes a workload look cleaner than it is.
Our lifecycle measurement already accounts for upstream hardware manufacturing. We're extending it so embodied carbon is attributed per workload across the hardware's useful life, with the system boundary and confidence recorded each time. Some of these figures can't be traced today, so every value carries a confidence tag, and we'll work directly with AI infrastructure operators to close the gaps.
Our Recycled pillar started with heat and water. Hardware belongs there too. We're working toward a lifecycle standard for data-center hardware: how long it runs, whether it's redeployed or refurbished, and how it's recovered and recycled at the end. Recycling of electronics and other materials is already tracked as its own impact type, with recovery attested by certified recyclers.
This is early work with real gaps, and it will be shaped with the operators who run the equipment.
The goal is a standard that works anywhere in the world, not a proprietary format. Our telemetry is designed to align with OpenTelemetry, so it flows through the observability tools operators already use, and it maps to existing standards rather than competing with them, including the Software Carbon Intensity specification (ISO/IEC 21031), the GHG Protocol, and ISO/IEC 30134 data-center KPIs. It's developed in the open as Project Stardust.
Building out the data pipeline and telemetry standards across major cloud/AI providers.
Publishing the benchmarks in the open, so any company — or any other nonprofit — can build on them.
The research and validation work behind AIforE's certification program.
This is infrastructure for the whole field, not just one product — support here underwrites the standard everyone else will eventually measure against.
AIforE is a 501(c)(3) tax-exempt nonprofit organization. EIN: 42-2272946. Donations are tax-deductible to the extent allowed by law.