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:

1. The extent to which AI is building the next version of itself, as opposed to being built by humans
2. Our ability to oversee and intervene in actions that AI agents take on Anthropic’s systems
3. The resources that power the development of more capable models

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.

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.

Reasons to track these measurements

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.

(1) Measuring AI-led AI R&D

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).

What we measured. We built a prototype index of how much of Anthropic’s AI research and development (R&D) is performed by Claude, called the Anthropic R&D Automation Index. It’s built by cataloguing every kind of AI R&D work done at the company, rating how automated each task currently is, and aggregating those ratings.

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).

As of August 2026,

• Claude is not operating fully autonomously for any measured subset of AI R&D work.
• Claude “leads” 26% of Anthropic’s AI R&D work.
• The share of work at or above “AI collaborates” is above 90%.

(2) Measuring oversight of AI agents

Why measure oversight of agents? Like other frontier developers, Anthropic employees increasingly delegate tasks to agents that work semi-autonomously for long stretches, and that delegate work to one another. As work becomes increasingly automated, from “AI collaborates” toward “AI leads,” agents could make more consequential decisions, such as which research direction to pursue next.

What we measured. We’ve built a system that lets us oversee and intervene in actions AI agents take on Anthropic’s systems. Here, we consider three different metrics: coverage, which describes the share of an agent’s actions that pass through a monitor before or after execution; review latency, which is the time between an action and its review; and escalation rate, which is the share of agent activities that are either blocked/redirected or flagged for further review.

What we found. As of August 2026, there were approximately 30,000 agents doing research and engineering work at Anthropic at any one time in our most-used internal platform.

(3) Measuring compute allocation

Why measure compute allocation? Broadly speaking, AI developers use compute for building more powerful models, serving customers, and safety-focused work. Understanding how AI developers allocate their compute can tell you where a developer is focusing its resources and how that focus changes over time.

What we measured. We examined a snapshot of how Anthropic used all of its compute from July 13 to July 20. We sorted every workload into a small number of categories, then asked how much of the compute going to AI R&D was safety work.

What we found. Over the examined week, about 6% of compute that went to AI R&D was allocated toward safety, and about 12% of compute that went to AI-driven AI R&D was allocated toward safety.

Conclusion

As the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows. This means better measuring the development of AI, reporting on it publicly, and giving society an opportunity to decide how to use this information.