[Cross posted on windowsontheory; see here for my prior writings]

There have been several studies to estimate the timelines for artificial general intelligence (aka AGI). Ajeya Cotra wrote a report in 2020 (see also 2022 update) forecasting AGI based on comparisons with “biological anchors.” That is, comparing the total number of FLOPs invested in a training run or inference with estimates on the total number of FLOPs and bits of information that humans/evolution required to achieve intelligence.^[1] In a recent detailed report, Davidson estimated timelines via a simulation (which you can play with here) involving 16 main parameters and 51 additional ones. Jacob Steinhardt forecasted how will large language models look in 2030.

In this blog post, I will not focus on the timelines or risks but rather on the shape we could expect AI systems to take and their economic impact. I will use no fancy models or math and keep everything on the level of cartoons or back-of-envelope calculations. My main contention is that even post-AGI, AI systems will be incomparable to humans and stay this way for an extended period of time, which may well be longer than the time to achieve AGI in the first place.^[2]

The cartoon below illustrates the basic argument for why we expect AGI at some point. Namely, since capabilities grow with log compute, and compute (by Moore’s law) grows exponentially with time; eventually, AI systems should reach arbitrarily high capabilities.

Figure: Left: Cartoon of capabilities of AI systems as a function of computational resources. To a first approximation, historical improvement in capabilities scaled with the logarithm of commute invested. There are scenarios of either “hitting a wall” (e.g., running out of data) or “self improvement” (once we get near AGI, we can start using AIs to design better AIs) that could break this line in one direction or the other. Still, the baseline scenario is a straight line. Right: Cartoon of computational resources available per “moonshot investment” (e.g., hundreds of Billion dollars) as a function of time. We expect the computational resources per dollar to increase exponentially with time, eventually reaching the threshold needed for AGI.

Notes: There are established estimates for the slope of the graph on the right (comp...

Stu Chuang Matthews

Stu Chuang Matthews