Four views of AI automation: model, researcher, lab, economy

Every serious AI lab wants to automate themselves. I believe this sentence to hold predictive power over AI timelines and all other predictions about the future. In particular, I believe taking the AI-lab-centric view is the right way to think about automation.

In this post, I want to present the different levels of abstraction at which AI automation can be thought of:

1. Model: The model is being optimized by the lab. The right measure of acceleration is how much a model can autonomously do on tasks of a given purpose; ideally, research on future versions of itself. Examples of metrics are the METR time horizons study and OpenAI MLE-bench.
2. Researcher: The researcher uses tools and builds automations to make the research process faster. The right measure of acceleration is the productivity of the researcher on their core tasks. (METR uplift study)
3. AI lab: The lab trains models and hires researchers to be faster; allocates resources on bottlenecks; builds capabilities for external purposes to raise money and earn revenue; spends compute on external inference, internal inference, and training. (There are no public studies.)
4. Economy: Researchers and practitioners share models, automations, ideas; the labs earn money; the overall pace of progress increases, or keeps pace in case the technical problems become more difficult to solve. (GDP proxies, automation studies)

All views except the lab view have important drawbacks:

• Model: as of 2025, the abilities of the model to do AI R&D do not imply that much about the future capabilities of the model, because many other things influence model capabilities; it's not a self-reinforcing loop. The model's capabilities do not directly translate to future model capabilities.
• Researcher: Again, a researcher's work is not self-reinforcing; the researcher's productivity results in general model capabilities. This only rarely feeds back into their future productivity as a first-order effect. Researchers do work on their own automation; but this is a smaller fraction of their time.
• Economy: society-wide metrics depend a lot on the capabilities that are not directly accelerating model R&D. Of course, higher total productivity does accelerate the compute buildup and hence AI progress at large; but this is a second-order effect. Additionally, society is ruled by democratic and other slow-moving institutions that have a difficult time optimizing for future productivity.

The AI lab is the unit with the most power over its own level of acceleration, because:

1. Labs know where their bottlenecks are and can decide to train models to complement the researchers;
2. Labs can record researcher actions and train models to do those actions;
3. Improvements that the lab makes to its own productivity very directly translate to its future ability to do more of these productivity improvements;
4. Labs benefit from scale (and hence can expend mental cycles to do proper allocation of resources towards productivity improvements);
5. Labs benefit from power concentration (and can just make decisions to redirect resources towards productivity improvements);

The concepts of "AI lab" and "model" will get closer as time goes on

In the future, a larger and larger fraction of decisions inside the lab will get taken over by the model. This is due to two separate processes:

1. imitation learning approaches to automation (existing activities done by people get optimized away to be done by the model).
2. reinforcement learning on new tasks makes activities possible that were never actually done by people in the first place.

As we get closer to certain notions of AI self-improvement, the model will do actions to improve itself. For instance, in the end, most actions of Anthropic, internal or external, will be taken by Claude. In an efficient world where AI labs are aiming to automate AI R&D, "researcher" view goes away, and metrics tracking "model" and "AI lab" views start tracking the same thing.