  At face value, this essay is about understanding a fairly esoteric governance
tool called compute thresholds. However, in order to grapple with whether these
thresholds will achieve anything, we must first understand how they came to be.
To do so, we need to engage with a decades-old debate at the heart of computer
science progress, namely, is bigger always better? Does a certain inflection
point of compute result in changes to the risk profile of a model? Hence, this
essay may be of interest not only to policymakers and the wider public but also
to computer scientists interested in understanding the role of compute in
unlocking breakthroughs. This discussion is timely given the wide adoption of
compute thresholds in both the White House Executive Orders on AI Safety (EO)
and the EU AI Act to identify more risky systems. A key conclusion of this
essay is that compute thresholds, as currently implemented, are shortsighted
and likely to fail to mitigate risk. The relationship between compute and risk
is highly uncertain and rapidly changing. Relying upon compute thresholds
overestimates our ability to predict what abilities emerge at different scales.
This essay ends with recommendations for a better way forward.
