Designing How AI Grows — Tom McGrath

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. --- 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? --- 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 --- RESCRIPT: https://app.rescript.info/share/846cfee4131b664fd09209cc3b98018e