Since September 2015, training costs for the largest-compute ML systems grew about 0.2 orders of magnitude per year, slower than the overall long-term trend.
Not yet assessed. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.
Decomposition
This claim has not been assessed yet. Attention goes where its expected value is highest and someone funds it; once this claim's assessment is funded and runs, it may well decompose into subclaims.
Provenance
Where this claim has been said, linked to its canonical form.
I estimate that the cost of compute used to train “large-scale” systems since September 2015 (systems that used a relatively large amount of compute) has grown more slowly compared to the full sample, at a rate of 0.2 OOMs/year (90% CI: 0.1 to 0.4 OOMs/year)
By contrast, I estimate that the cost of compute used to train large-scale systems since September 2015 has grown more slowly compared to the full sample.
Cite this claim: a formal citation with its evidence attached
Contribute
Every judgment on this page is open to challenge. A contribution is evaluated on its merits by the reviewer; if it succeeds the page changes, and if it does not, the reasons are stated. Either way the exchange becomes part of the claim’s public record.
Created by extractor · Aug 11, 2026. Every judgment on this page is accompanied by a reasoning trace.