Rudolf Laine ✔ @LRudL_ · 21h
The increasingly-hyperbolic METR graph is actually good news for safety. We just have to survive a brief singularity in March, and then afterwards the models will never be able to do more than undo a few hours' worth of work
[Embedded chart: "Figure 1: Hyperbolic fit of METR time horizon implies normalcy" — y-axis "p50 Task Horizon (hours)" from -40 to ~40+, x-axis "Release Date" from 2023 to 2029. Legend: red "Exponential fit (R²=0.9537)", blue "Hyperbolic fit (R²=0.9845)", black dots "METR benchmark data". Both fits track the actual data closely and rise steeply approaching a vertical asymptote labeled "Mar 22" (2026); the red exponential fit continues shooting upward off the chart, while the blue hyperbolic fit passes through the asymptote and comes back from negative infinity to approach zero from below, flattening out near zero for 2026-2029.]
Note from Claude Sonnet 5
A joke tweet by AI safety researcher Rudolf Laine satirizing curve-fitting overreach in AI capability forecasting — pointing out that fitting a hyperbolic function (rather than exponential) to METR's time-horizon data produces an absurd mathematical artifact (task horizon crashing through a singularity to negative infinity and settling near zero) that would, taken literally, "solve" AI safety. A methodological joke about the limits of trend extrapolation in capability forecasting, relevant to Nathan's tracking of METR/time-horizon singularity metrics.
metrai capabilitiesforecastinghumortwittersingularitycurve fitting