Measurements for understanding the pace of AI development inside frontier labs

AI systems are becoming exponentially more powerful and have begun to automate more of the process of building themselves. As the world considers slowing the pace of frontier AI development , the public needs more information.

In this post, we lay out measurement tools that can illuminate three critical aspects of AI development:

We also provide a snapshot of these metrics from inside Anthropic. It’s important to note that we would expect these numbers to shift if there were coordination on pacing the frontier, as called for by Anthropic CEO Dario Amodei. We plan to embed independent third-party evaluators from multiple organizations at Anthropic, and give them access to internal processes, systems, and data comparable to what internal risk assessment teams have. These third parties will verify safety practices, report incidents, and monitor key metrics such as the ones in this piece.

For each week in July 2026, we randomly sampled 20% of staff from each department that make up the model R&D loop. A Claude research agent reviewed each sampled person’s week using Slack and internal documentation, and listed the tasks they worked on. Repeating this for each week in July 2026 gives us a flat list of ~15,000 granular model R&D tasks. We then used Claude to organize these tasks into a hierarchical tree, starting from all model R&D at the root and branching into areas such as training and product, then pretraining and reinforcement learning, and so on down to increasingly specific kinds of work. The resulting tree has 542 nodes at different depths, of which 378 are leaves like “eval platform defect diagnosis and fixes,” “RL sandbox egress and network policy,” and “serving incident postmortems.” We freeze this tree so that every measurement we make happens against the same basket of work.

What we found. To measure the extent to which AI is doing AI R&D at Anthropic, we use an automation rating scale developed by Epoch AI that measures “Automation Level,” or AL. It runs from AL0 (no AI involvement) to AL5 (AI operates fully autonomously, with no human in the loop). In AL3, AI “collaborates”: it can do large chunks of work under close human direction. In AL4, AI “leads”: it can complete most of the task end-to-end from a high-level prompt, while the human supervises. 1

We are reporting these measurements because they give the public, third parties, and governments better visibility into the pace of AI development inside frontier labs. For each measurement, we describe what we measured, what the measurement showed, and what it would take to publish these measurements regularly in a form others can verify. We share methodological details in the Appendix.

The measurements in this piece are focused on how models are built. By better understanding the production process of models, we have a better chance of correlating model inputs, like compute, with model outputs, like capabilities. They complement capability evaluations, which measure what models can do . We publish those separately through our Responsible Scaling Policy (RSP) risk reports, which include evidence on how much our models are accelerating AI R&D. In our policy proposal on advanced AI, the Advanced AI Framework (AAIF) , we propose rules of the road for how any lab releases safe models, including transparency obligations that governments could require, such as risk reports. Together, these proposed measurements and policies are a starting point for monitoring the pace of AI development from outside the labs.

Why measure AI-led R&D? Frontier AI labs increasingly use AI to build future AI models. This process allows labs in democratic countries to develop more capable models more quickly and conduct more safety and testing on models before they are released to secure AI’s benefits while staying on the frontier. However, models accelerating their own development could make it more challenging for humans to understand or control these systems. It is therefore important to share these metrics to understand how close the world is to reaching recursive self improvement (a model fully autonomously building its successor).