Excerpt

Examples of economic implications from statistical structure.

Here are a few brief cases in which the equilibrium economic effect of AI is determined by the underlying statistical structure of the domain. My conjecture is that these types of observations could be formalized in a common framework.

1. The concentration of the market for AI depends on the dimensionality of the world. If the world is intrinsically high-dimensional then the returns to model scale will be steadily increasing, and so we should expect high concentration and high markups. If instead the world is intrinsically low-dimensional then the returns to scale will flatten, and there should be low concentration (high competition) and low markups.
2. The effect of AI on scientific progress depends on the structure of the world. I give this argument below: if the world has a simple latent structure then progress will be bottlenecked more by intelligence than by data, and so advances in AI will dramatically accelerate scientific progress, without being bottlenecked on more data collection.
3. The wages paid to an occupation depends on the work’s latent dimensionality. If the work consists of tasks with high latent dimensionality then the returns to experience and ability will be high, and so wages will be high. As AI changes the incremental effect of human experience and intelligence we should expect it to change the structure of wages.
4. The demand for compute will depend on the self-similarity of the world. If 7 billion people all have very different problems then there are few efficiencies we can make in inference (through caching and distillation) and the share of GDP paid to compute will be high. If instead they have similar problems then the returns to additional compute will fall rapidly (demand will be inelastic) and the share of income paid to compute will be small.