Counting AGIs

This project was the runner-up of the "Best Technical Governance" prize for our AI Governance (August 2024) course. The text below is an excerpt from the final project.

Artificial intelligence (AI) systems have become significantly more capable and general in the last decade, especially since the launch of ChatGPT in December 2022. Many people believe that the technological trajectory of AI will lead to the advent of artificial general intelligence (AGI), an AI system that can autonomously do virtually anything a human professional can do. Leading AI scientists, like Geoffrey Hinton, Yoshua Bengio, and Shane Legg, are publicly raising the alarm that such a system is incoming. There are several AI enterprises premised on the business model of creating AGI (Anthropic, OpenAI, Safe Superintelligence, to name a few).

The development of AGI will be a transformative technology, but the scale of transformation we should expect will hugely depend on how many copies of AGI we can run simultaneously. If AGI is computationally expensive, we might only be able to run a small number. If so, the immediate post-AGI world would be virtually unchanged. Alternatively, if AGIs are computationally cheap, we might be able to run hundreds of millions or more. This latter outcome would entail sudden and ubiquitous transformation. For a sense of scale, consider that a hundred million AGIs, each as productive as a typical American worker, would have an impact similar to doubling the US workforce, which in 2024 had 135 million full-time workers.

There are only a few calculations estimating the likely size of the initial AGI population. This post attempts to add some approaches, while also articulating major considerations for this kind of exercise along the way.

At a high level, our approach involves estimating two variables, namely, the total computing power (“compute”) that is likely to be available for instantiating AGIs, and the amount of compute likely to be required to run (“inference”) a single AGI. With these two variables, we can calculate the AGI population by dividing the available compute by a per-AGI inference rate:

Compute ÷ Inference per AGI = AGI Population

To read the full project submission, click here.