LessWrong (30+ Karma)

“Analyzing A Critique Of The AI 2027 Timeline Forecasts” by Zvi

55 min • 24 juni 2025

There was what everyone agrees was a high quality critique of the timelines component of AI 2027, by the LessWrong user and Substack writer Titotal.

It is great to have thoughtful critiques like this. The way you get actual thoughtful critiques like this, of course, is to post the wrong answer (at length) on the internet, and then respond by listening to the feedback and by making your model less wrong.

This is a high-effort, highly detailed, real engagement on this section, including giving the original authors opportunity to critique the critique, and warnings to beware errors, give time to respond, shares the code used to generate the graphs, engages in detail, does a bunch of math work, and so on. That is The Way.

So, Titotal: Thank you.

I note up front that at least Daniel Kokotajlo has indeed adjusted his estimates, and has [...]

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Outline:

(02:00) The Headline Message Is Not Ideal

(09:19) An Explanation of Where Superexponentiality Is Coming From

(10:24) Three Methods

(10:53) Time Horizon Extension Method

(17:53) The Public Versus Internal Gap

(19:41) The Difficulty Gap

(21:38) Recent Progress

(23:36) Infinite Time Horizons

(24:53) Intermediate Speedups

(28:02) Is There A Flawed Graph Still Up?

(29:07) Some Skepticism About Projection

(29:58) Part 2: Benchmarks and Gaps and Beyond

(30:03) Benchmarks

(35:16) The Time Horizon Part of the Second Model

(37:44) Why The Thresholds?

(39:14) The Gap Model

(39:42) Eli Responds On LessWrong

(45:10) On Eli's Recent Update

(46:09) Conclusion

(50:38) Perhaps The Most Important Disagreement

The original text contained 1 footnote which was omitted from this narration.

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First published:
June 24th, 2025

Source:
https://www.lesswrong.com/posts/5c5krDqGC5eEPDqZS/analyzing-a-critique-of-the-ai-2027-timeline-forecasts

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Narrated by TYPE III AUDIO.

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Images from the article:

Graph showing
Probability distribution graph showing expected date to reach score 1.5, median 2026
Graph titled
Graph showing AI model task completion times from 2019-2027, with various models plotted.
Graph showing
Graph showing AI coding task completion times from 2021-2028, with projections.

The graph displays both METR's historical data (various GPT and Claude models) and future projections (Agent-0, Agent-1, Agent-2), showing an exponential increase in the length of coding tasks AI can complete autonomously, ranging from seconds to years.

The trendline suggests each capability doubling becomes 15% easier over time.

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