About 6x slower in the median case, with an 80% confidence interval of 3.5x to 8x.

Setup

Define the "R&D compute" (in, say, H100-equivalents) of an AGI company at a given time to be the total compute in use across the following categories:

Compute used to run experiments, which are used to search for software improvements,
Compute used to run automated researcher agents,
Compute used in final training runs for frontier models.

With less R&D compute, the company would need to make cuts across these categories. For now, we'll assume they maintain the same allocation across the three areas, that is, we'll assume they achieve the reduction in the total via a reduction to each category.

The capability level of your best AI model is represented by its effective training compute, . is a stock, with units of "2025-FLOP". evolves according to

where is training system performance (a flow, units: FLOP/yr) and is software efficiency (units: 2025-FLOP per FLOP). We will call this quantity effective training system performance.

Training system performance and software efficiency tend to grow exponentially over time, so the "rate of AI progress" will be operationalized as the relative growth rate of (i.e. the [...]