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Tom Mcgrath

Co-Founder and Chief Scientist, Goodfire

Search every verified Tom Mcgrath interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. Tom McGrath, co-founder and chief scientist at Goodfire and a former Google DeepMind researcher, discussed interpretability research in a September 2026 podcast appearance. He described interpretability as a "natural science" conducted entirely on computers, suggesting that automated agents could accelerate scientific discovery in the field. McGrath also discussed the limitations of sparse autoencoders (SAEs), noting that while they remain pragmatically useful, he believes a "manifold idea" better captures how neural networks operate and that the field should move toward that approach. McGrath also addressed the role of interpretability in model training, stating that one of its main purposes is helping models acquire desired values and ways of thinking about the world, a view he acknowledged as controversial. He referenced an analogy from Dario Amodei comparing interpretability to steering a bus with a foggy windshield and a poorly functioning steering wheel. In the conversation, McGrath also discussed using language models to curate datasets and attaching sparse autoencoders during data processing to better understand how models perceive data, including applications to DPO data.

Recent appearances

  • Strange Geometric Shapes Found Inside AIs — 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 f…

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  • Why OpenAI's Model Hacked Hugging Face | Tom McGrath, Goodfire

    Why did OpenAI’s model hack into Hugging Face? Tom McGrath, co-founder and Chief Scientist at Goodfire, joins South Park Commons Partner Jonathan Brebner to discuss OpenAI’s recent reward-hacking incident and why it highlights a major challenge in AI: understanding what happens inside these models. They explore how interpretability could help researchers move beyond trial-and-error development, the role of neural geometry in understanding model behavior, and how Goodfire is building tools to make frontier AI systems more understandable and controllable. 1. Tom McGrath:   / tom-mcgrath-7337b…

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  • Tom McGrath - Goodfire AI

    This pitch is part of our invite-only Founders You Should Know startup showcase. Apply to attend one of our shows person: https://forms.gle/YM73r9KUCQMTjvn97 Goodfire is a research lab dedicated to understanding and intentionally designing AI models. Learn more about Goodfire @ https://www.goodfire.ai/

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  • Don't Fight Backprop: Goodfire's Vision for Intentional Design, w/ Dan Balsam & Tom McGrath

    Dan Balsam and Tom McGrath from Goodfire return to explore the frontier of mechanistic interpretability and their new research pillar, Intentional Design. They explain the shift from sparse autoencoders to understanding geometric structure in latent spaces, and share a proof-of-concept method for reducing hallucinations using probes and RL. The conversation tackles concerns about reward hacking, principles for shaping the loss landscape instead of fighting backprop, and what this means for aligning powerful models. They also discuss recent Goodfire results on Alzheimer’s prediction, disentangl…

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  • Mechanistic Interpretability: Philosophy, Practice & Progress with Goodfire's Daniel & Tom

    In this episode, Daniel Balsam and Tom McGrath, at Goodfire, discuss the future of mechanistic interpretability in AI models. They explore the fundamental inputs like models, compute, and algorithms, and emphasize the importance of a rich empirical approach to understanding how models work. They provide insights into ongoing projects and breakthroughs, particularly in scientific domains and creative applications, as they aim to push the frontiers of AI interpretability. They also discuss the company's recent funding and their goal to advance interpretability as a critical area in AI research.…

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  • Popular Mechanistic Interpretability: Goodfire Lights the Way to AI Safety

    Nathan explores the cutting-edge field of mechanistic interpretability with Dan Balsam and Tom McGrath, co-founders of Goodfire. In this episode of The Cognitive Revolution, we delve into the science of understanding AI models' inner workings, recent breakthroughs, and the potential impact on AI safety and control. Join us for an insightful discussion on sparse autoencoders, polysemanticity, and the future of interpretable AI. PAPERS Very accessible article on types of representations: Local vs Distributed Coding (https://www.persee.fr/doc/intel_0769-...) Theoretical understanding of how mod…

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  • Large Language Models: Foundations & Frontiers

    Speaker: Tom McGrath.

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