AI “orders of magnitude” (OOMs), explained

What an OOM is

One order of magnitude = 10×. Two OOMs = 100×. Aschenbrenner reasons in OOMs because AI progress is exponential: it's easier to track "how many 10×s per year" than absolute numbers. His forecast is essentially an addition problem — stack enough OOMs of effective compute and you cross the AGI threshold.

The three sources of OOMs

The bet, and how it's tracking

He projected roughly 0.5 OOM/yr of effective compute, sustained. As of mid-2026 an independent audit calls the pace "roughly supported," with launches scattered within about ±0.5 OOM of the trend — graded On track. This OOM engine is what sits under every downstream claim: capability, AGI timing, and the intelligence explosion. If the OOMs stop stacking, the whole 2027 case slips.

Frequently asked questions

An order of magnitude (OOM) is a factor of 10. Aschenbrenner counts OOMs of 'effective compute' — raw compute plus algorithmic efficiency plus unhobbling — because AI progress is exponential and easier to track as '10×s per year.'

Roughly 0.5 orders of magnitude of effective compute per year, sustained over the decade. As of mid-2026 the pace has roughly held, graded On track.

Because his AGI forecast is essentially an addition problem: stack enough OOMs of effective compute and you cross the AGI threshold. If the OOMs stop stacking, the 2027 timeline slips.