Executive Summary

The Google DeepMind mechanistic interpretability team has made a strategic pivot over the past year, from ambitious reverse-engineering to a focus on pragmatic interpretability:
- Trying to directly solve problems on the critical path to AGI going well[[1]]
- Carefully choosing problems according to our comparative advantage
- Measuring progress with empirical feedback on proxy tasks
We believe that, on the margin, more researchers who share our goals should take a pragmatic approach to interpretability, both in industry and academia, and we call on people to join us
- Our proposed scope is broad and includes much non-mech interp work, but we see this as the natural approach for mech interp researchers to have impact
- Specifically, we’ve found that the skills, tools and tastes of mech interp researchers transfer well to important and neglected problems outside “classic” mech interp
- See our companion piece for more on which research areas and theories of change we think are promising
Why pivot now? We think that times have changed.
- Models are far more capable, bringing new questions within empirical reach
- We have been [...]

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Outline:

(00:10) Executive Summary

(03:00) Introduction

(03:44) Motivating Example: Steering Against Evaluation Awareness

(06:21) Our Core Process

(08:20) Which Beliefs Are Load-Bearing?

(10:25) Is This Really Mech Interp?

(11:27) Our Comparative Advantage

(14:57) Why Pivot?

(15:20) Whats Changed In AI?

(16:08) Reflections On The Fields Progress

(18:18) Task Focused: The Importance Of Proxy Tasks

(18:52) Case Study: Sparse Autoencoders

(21:35) Ensure They Are Good Proxies

(23:11) Proxy Tasks Can Be About Understanding

(24:49) Types Of Projects: What Drives Research Decisions

(25:18) Focused Projects

(28:31) Exploratory Projects

(28:35) Curiosity Is A Double-Edged Sword

(30:56) Starting In A Robustly Useful Setting

(34:45) Time-Boxing

(36:27) Worked Examples

(39:15) Blending The Two: Tentative Proxy Tasks

(41:23) What's Your Contribution?

(43:08) Jack Lindsey's Approach

(45:44) Method Minimalism

(46:12) Case Study: Shutdown Resistance

(48:28) Try The Easy Methods First

(50:02) When Should We Develop New Methods?

(51:36) Call To Action

(53:04) Acknowledgments

(54:02) Appendix: Common Objections

(54:08) Aren't You Optimizing For Quick Wins Over Breakthroughs?

(56:34) What If AGI Is Fundamentally Different?

(57:30) I Care About Scientific Beauty and Making AGI Go Well

(58:09) Is This Just Applied Interpretability?

(58:44) Are You Saying This Because You Need To Prove Yourself Useful To Google?

(59:10) Does This Really Apply To People Outside AGI Companies?

(59:40) Aren't You Just Giving Up?

(01:00:04) Is Ambitious Reverse-engineering Actually Overcrowded?

(01:00:48) Appendix: Defining Mechanistic Interpretability

(01:01:44) Moving Toward Mechanistic OR Interpretability

The original text contained 47 footnotes which were omitted from this narration.

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First published:
December 1st, 2025

Source:
https://www.lesswrong.com/posts/StENzDcD3kpfGJssR/a-pragmatic-vision-for-inter

#19 Morgane Cadignan : « Ce n’est pas grave de perdre du temps »