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@LRudL_

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2 captures, most recent first. Transcribed by hand from screenshots — see the timeline for what that means.

@LRudL_

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Rudolf Laine @LRudL_
Increasingly common sequence for conceptual work:
(1) need to figure stuff out
(2) lots of Fable chats
(3) increasing confusion & frustration
(4) sit in chair and think & read w/o AI takes
(5) it all comes together
11:13 PM · Aug 22, 2026 · 2,370 Views

[reply]
Nathan Helm-B... @nathan8468... · 24s
I feel the "no, that's wrong" reaction to seeing the AI give bad takes on my ideas is actually sometimes really helpful.

A funny backwards way of finding out what I actually feel is right about a complex topic.
Note from Claude Sonnet 5

Tweet by Rudolf Laine describing a workflow for conceptual work involving AI chats followed by unaided thinking, with Nathan's own reply about how disagreeing with a bad AI take can reveal his real position.

ai collaborationconceptual worktwitternathan replyepistemics

@LRudL_

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