Ask most carbon or water dashboards for a number, and you get one number. Ask what produced it, and the honest answer is usually: some of it was measured, and some of it was estimated — and the dashboard doesn't tell you which parts are which.
What's actually measurable at a data center
A handful of things are directly instrumented, no modeling required:
Facility-level power draw (metered at the utility connection)
PUE — power usage effectiveness, the ratio of total facility power to IT equipment power
Water intake at the cooling system
These are meter readings. They're as close to ground truth as this domain gets.
What isn't measurable — and has to be modeled
Nobody has a meter on a single inference request. Attributing a slice of that facility-level power draw, water intake, or carbon output to one API call requires a model: an assumption about how load is distributed across concurrent requests, how much of a GPU's draw belongs to your prompt versus the ten others sharing the batch, how cooling load correlates with compute load versus ambient temperature that day.
That's not a flaw — it's unavoidable. But it means every per-inference number you've ever seen from a carbon tool is, structurally, a modeled estimate wearing the same formatting as a measured fact. Nothing on the dashboard tells you which one you're looking at.
Why this distinction breaks silently
Two failure modes, both common:
Comparing a modeled number against a measured one as if they're the same kind of fact — e.g., benchmarking your model's per-inference carbon (modeled) against a competitor's facility-reported PUE (measured) and drawing a conclusion neither number supports.
Compounding modeling error invisibly — a model with a modest per-call error margin, aggregated across millions of inferences, can produce a monthly total that's confidently wrong by a wide margin, with no flag anywhere that the number was ever a model in the first place.
Neither failure is caused by bad math. It's caused by treating "modeled" and "measured" as if they're interchangeable once they hit a dashboard.
The fix: say which one you're looking at
This isn't a call for perfect measurement — direct per-inference metering doesn't exist yet, maybe doesn't exist ever, at reasonable cost. The fix is disclosure, not precision: tag every number with where it came from.
That's why CarbonLayer's API returns carbonSource, waterSource, and energySource alongside every figure — explicit, machine-readable flags for whether a given number is measured (from a meter) or modeled (attributed via an allocation method), so a router, a dashboard, or a compliance report downstream can decide how much weight to put on it instead of inheriting silent uncertainty as if it were fact.
If your current carbon tooling can't answer "was this number measured or modeled?" for every figure it gives you, that's the gap — not the precision of the number itself.
