August 23, 2024

Can AI Scaling Continue Through 2030?

In this article:

In recent years, the capabilities of AI models have significantly improved.
Research suggests that this growth in computational resources accounts for a significant portion of AI performance improvements.¹
The consistent and predictable improvements from scaling have led AI labs to aggressively expand the scale of training, with training compute expanding at a rate of approximately 4x per year.
Here, we examine whether it is technically feasible for the current rapid pace of AI training scaling—approximately 4x per year—to continue through 2030.
We investigate four key factors that might constrain scaling: power availability, chip manufacturing capacity, data scarcity, and the “latency wall”, a fundamental speed limit imposed by unavoidable delays in AI training computations.

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