This is not even close to singularity-level AI scaling.

The way that Nature is phrasing this is quite disingenuous because they make it sound like AI algorithms are designing the chips that the same AI algorithms are running on, which would logically lead to an exponential increase as predicted by Kurzweil and in science fiction.

AlphaChip (the AI model described in the paper) is only performing chip layout. This is the stage of chip design where you already have the chip design at a functional block level, and the layout algorithm must then decide where to place all of the elements on the silicon so that it can connect them all together correctly in the most efficient way (in terms of space used and connection lengths required).

Placement is important but it we have been using algorithms for placement and routing for decades (possibly since the very beginning of VLSI), so the only new thing here is that it's a reinforcement learning model which is doing the placement instead of a human-constructed algorithm.

In order for us to reach a runaway singularity level of AI self-improvement, we first need an AI that can actually design the chips from the functional level. I have no doubt that Google are working on that but for now chip design, just like every other form of engineering design, is very much a human activity.

AlphaChip is just the beginning. We envision a future in which AI methods automate the entire chip design process, dramatically accelerating the design cycle and unlocking new frontiers in performance through superhuman algorithms and end-to-end co-optimization of hardware, software, and machine learning models

The poor phrasing of this article is just more AI hype, designed to appeal to the people who believe that the singularity is near.

LLMs are not really AI

Fascinating and quite terrifying.