Introduction

In June, a Substack/LessWrong/EA Forum user named titotal wrote “A deep critique of AI 2027's bad timeline models”. Our original model that they were critiquing can be found here, with a brief overview below.[1]

In a nutshell, we disagree with most of titotal's criticisms. While they pointed out a few mistakes, for which we are grateful, on the whole we think the problems that they pointed out do not add up to the model being “bad.” In this post we will explain why.

While we initially responded via a comment, we wanted to make a more in-depth response. We apologize for the delay; we are planning to release a new timelines+takeoff model soon and thus thought this was a good time to polish and then publish our thoughts.

We thank titotal for the time and effort they put into the critique. We gave them $500 for their efforts for pointing out some bugs and mistakes. The primary correction we made to our model based on titotal's critique would have pushed back the median Superhuman Coder arrival time by about 9 months. Though our upcoming model release represents a significant restructuring of the old model, some considerations raised by [...]

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Outline:

(00:13) Introduction

(01:54) Brief overview of our AI 2027 timelines model

(03:51) What types of futurism are useful?

(03:55) Summary

(05:36) Decision-making without strong conceptual or empirical support

(08:45) Details regarding why our model should inform decisions

(14:13) Was our timelines model bad?

(16:22) Many future trajectories

(17:37) Concrete criticisms of our model

(17:41) Summary

(20:53) Superexponential-in-effective-compute time horizon growth

(21:05) Summary

(23:34) Clarifying the notion of time horizons

(26:27) Argument from eventually infinite time horizons

(33:45) Argument from long-horizon agency generalization

(36:41) The recent speedup in time horizon growth

(37:07) Functional form and parameters

(41:14) How changing the present time horizon should affect behavior

(45:17) Backcasts

(45:20) How much should we care about backcasts?

(49:49) Original time horizon extension model

(51:42) Backcasts with AI R&D progress multiplier interpolation bug fixed

(57:34) Comms criticisms

(57:38) Summaries

(57:41) Misleading presentation

(01:00:04) The false graph

(01:03:03) Misrepresentations of the model code

(01:05:40) Misleading presentation of rigor, specific claims

(01:09:42) The false graph, details

(01:09:47) What we intended to do with our original plotted curve

(01:10:47) Fixing a bug in the original curve

(01:12:17) How big of a problem is it that neither the original nor intended curve was not an actual model trajectory?

(01:15:49) Claims regarding new evidence

(01:17:36) Responses to all other concrete criticisms

(01:23:18) Our upcoming model release

(01:25:08) Acknowledgments

(01:25:20) Appendix

(01:25:23) Defining effective compute

(01:25:58) Forecasting AI R&D progress multipliers based on an interpolation between GPT-3 and Claude 3.7 Sonnet

(01:27:33) Median parameter estimates vs. median of Monte Carlo simulations

(01:28:57) On the Benchmark and Gaps model

(01:29:19) Effect of addressing issues

(01:29:23) Effect of fixing AI R&D interpolation bug on top of original model

(01:29:59) Effects of fixing AI R&D interpolation bug on top of May update

(01:30:32) Comparing graph curves against model with AI R&D interpolation bug fixed

The original text contained 61 footnotes which were omitted from this narration.

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First published:
December 15th, 2025

Source:
https://www.lesswrong.com/posts/G7MmNkYADKkmCiumj/response-to-titotal-s-critique-of-our-ai-2027-timelines

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Narrated by TYPE III AUDIO.

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Images from the article:

Graph showing probability distributions for superhuman coder arrival timelines across three forecasts.

Graph showing AI success probability versus time taken by humans on long-horizon tasks.

Graph showing coding time horizon versus log(effective compute) increase with three exponential curves comparing different parameter scenarios.

Line graph showing AI R&D progress multiplier backcast with intended interpolation method.

Probability density graph showing superhuman coder arrival and time horizon extension predictions.

Graph showing model trajectories versus graph curves for March 2027 SC arrivals, coding time horizons.

Graph showing AI coding task completion time versus model release date, with exponential and other trendlines.

Graph showing AI coding task completion times from 2022 to projected 2027.

Graph showing AI agents' autonomous coding task completion times from 2019 to 2028 projections.

Graph showing time horizon projections from 2019 to 2030, titled

Graph showing progress multiplier over time titled

Graph showing probability density of superhuman coder arrival over time with two scenarios.

Graph showing probability density of superhuman coder arrival time horizon extension predictions.

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“Response to titotal’s critique of our AI 2027 timelines model” by elifland, Daniel Kokotajlo

LessWrong (30+ Karma)