AI acceleration?

Modelling based on the METR report. By Eugene Earnshaw · November 11, 2026 · Originally published on Substack AI uplift in action I’ve seen a lot of discussion about the METR acceleration report. I think many of the models used to discuss it predict acceleration that is too fast. Here is a simple-ish model that is tied as closely as I can make it to relevant empirical data to predict how things will unfold given what we know of AI acceleration. Suppose that Anthropic uses some amount of compute to train models using their training pipeline. The amount of compute and the efficiency of the pipeline are both growing rapidly over time. The difficulty of improving models also increases rapidly as a function of model capacity, leading to the roughly linear growth in model capability we have historically observed. The rate of improvement in the pipeline is a function of average researcher productivity. Historical estimates suggest that training compute used to train models at the frontier has been growing 5x per annum and training efficiency has been growing at 3x. Given the historical growth rate of model capabilities (measured via ECI or AECI) is about 15 points a year, it suggests that multiplying the effective training effort by 15X results in a new model with ECI 15 points higher than the old one, or about 20% more effective training input needed for a model with 1 more ECI. Then in october 2025 we have a shift with 2 elements. Researcher productivity begins to be uplifted by AI assistance, and other unspecified factors also increase the rate of research efficiency increase. The net effect is that over the following year, capabilities improve almost twice as fast. Modelling training efficiency and capability We can express our first assumption with a simple equation: mjx-math { display: inline-block; text-align: left; line-height: 0; text-indent: 0; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacin