Open Telemetry as a First Metrological Layer for Technical AI Governance

TL;DR: Researchers and leaders at frontier labs are increasingly calling for the collective development of governance tools to pace the frontier of AI development. Current on-chip GPU telemetry could provide a starting point towards an international standard for compute verification. GPUs already expose telemetry signals that can distinguish training from inference with high accuracy. The main limitation is trust: in an adversarial setting, these signals can still be manipulated, forged, or replayed. I argue that publicly funded compute clusters around the world could start publishing labelled telemetry from real training runs now, creating a large, openly available measurement layer. Researchers could build on this shared database to develop classifiers, compare measurements across hardware, red-team attacks, and eventually work towards common verification standards. This first layer would be imperfect, but that is part of the point. Creating it now could demonstrate that large-scale international measurement and coordination is possible, build technical and institutional momentum around a common standard, and provide the empirical basis for developing harder-to-forge verification tools later. Intro I am currently working on an inspection tool (verifier) able to distinguish training from simply 'using the AI model' (inference), to help in AI compute verification. I'm new to the field, so feedback is especially appreciated. The hope is that these tools could help policymakers and governments regulate AI training. It may particularly help with pacing the frontier: if large-scale training can be detected and monitored, it would be clear who is doing what, so a broad agreement across frontier labs may be easier to achieve. This post explores telemetry counters, which already exist on current hardware. Their presence and availability suggest that a first layer of technical AI governance (Baker et al., 2025) could be built around open, crowd-based GPU telemetry: on-chip telemetry first and off-chip monitoring later. Public compute clusters around the world could start contributing measurements, establishing a shared metrological baseline on which researchers and, eventually, policy could build. And, most importantly, we can start building it now, without waiting for verification hardware that does not yet exist. This first thin verification layer could start by making it possible to distinguish between three categories of compute: training (building the model), inference (running it), and non-AI compute. This distinction is key, as any future agreement on large-scale training runs or slowing down AI development would be difficult to verify without an agreed way of telling these three apart. A quick analogy borrowed from satellite navigation In July 2019, the entire Galileo constellation went down for about a week, and the official channels were almost silent about what was happening. According to Bert Hubert, during the outage, there was no public, independent way to see what the constellation was doing (more can be read here and here). Within roughly two months, he and a group of volunteers built Galmon, an independent, crowd-sourced network of consumer receivers that reconstructs the satellites' clocks and orbits and flags jumps or anomalous behaviour, in public. It soon achieved continuous coverage and expanded to monitor four GNSS constellations. Galmon shows how quickly an independent measurement layer can emerge when the underlying signal is publicly available and interpretable. There was no treaty behind it, and there still isn't. Two things made this possible. First, the navigation message was broadcast by design: the operator cannot withhold it without switching off the service. Second, it is intelligible because the EU publishes the Galileo Open Service Signal-In-Space Interface Control Document (OS SIS ICD), which specifies the full format of the broadcast signal. The first is the technical layer. The second is a policy decision. Why this might be a stepping stone for regulators The relevant lesson from Galmon is that making a signal public and interpretable allowed an independent measurement ecosystem to emerge around it. The capability for independent observation must exist before any agreement can even be discussed, because we cannot govern what we cannot measure. Making these data available, at least for public infrastructure, would give the scientific community something concrete to build on: understanding what needs to be measured, developing metrics, and assessing what is actually feasible to determine from the signal. Policy could then build on top of this measurement layer. There is an important break between the Galmon example and monitoring AI training, and it concerns exactly the adversarial case: nobody was trying to spoof the Galileo signal to actively fool the volunteers. We briefly discuss this in the next section. What GPU telemetry can already measure and its limitations Rahman & T