Tom McGrath is co-founder and Chief Scientist at Goodfire, and a former Google DeepMind researcher. He joins Tim Scarfe to ask what neural networks actually learn, whether their internal representations converge on structures in the world, and whether interpretability can extract new scientific knowledge rather than merely explain model outputs.

Beginning with AlphaZero and learned modularity, the conversation moves into neural geometry: concept manifolds, reusable computation inside Llama, and why activation steering can fail when it pushes a model off-manifold. McGrath then makes the case for intentional design, using interpretability as part of the training loop. They examine controlled generalisation, features as rewards, predictive data debugging, and the uncomfortable fact that a model may recognise a hallucination or reward hack and still produce it.

The discussion closes on grader awareness, oversight and collusion between adaptive agents, then returns to sparse autoencoders. SAEs are useful, McGrath argues, but they may fracture the higher-dimensional structures networks actually use. This episode was made with support from Goodfire.

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TIMESTAMPS:

00:00:00 Introduction: Can interpretability speed-run science?

00:02:03 The invisible grader

00:06:51 What AlphaZero learned from the world

00:12:24 Interpretability as a control loop

00:21:54 The forbidden method and safer interventions

00:37:36 Why models catch hallucinations too late

00:46:19 Debug the dataset before training

00:50:44 Why neural networks become modular

00:55:57 Finding the geometry inside a network

01:02:55 Why steering falls off the manifold

01:12:10 A reusable calculator inside Llama

01:17:19 From abstractions to goals

01:25:28 Reward hacking, oversight and collusion

01:37:23 Are sparse autoencoders dead?

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REFERENCES:

paper:

[00:05:45] Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

https://arxiv.org/abs/2502.17424v7

[00:11:05] Acquisition of Chess Knowledge in AlphaZero

https://arxiv.org/abs/2111.09259

[00:25:30] Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning

https://arxiv.org/abs/2507.16795

[00:29:30] Persona Vectors: Monitoring and Controlling Character Traits in Language Models

https://arxiv.org/abs/2507.21509

[00:41:14] Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability

https://arxiv.org/abs/2602.10067

[00:47:03] Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal

https://arxiv.org/abs/2606.12360

[01:00:26] Do Sparse Autoencoders Capture Concept Manifolds?

https://arxiv.org/abs/2604.28119

[01:03:04] Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior

https://arxiv.org/abs/2605.05115

[01:14:20] Arithmetic in the Wild: Llama uses Base-10 Addition to Reason About Cyclic Concepts

https://arxiv.org/abs/2605.01148

[01:29:35] Measuring Reward-Seeking via Contrastive Belief Updates

https://arxiv.org/abs/2607.18966v1

other:

[00:15:44] Intentional Design

https://www.goodfire.com/blog/intentional-design

[00:56:12] The World Inside Neural Networks

https://www.goodfire.com/research/the-world-inside-neural-networks

[01:37:28] A Pragmatic Vision for Interpretability

https://www.alignmentforum.org/posts/StENzDcD3kpfGJssR/a-pragmatic-vision-for-interpretability

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RESCRIPT:

https://app.rescript.info/share/846cfee4131b664fd09209cc3b98018e