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Interpretability Infrastructure at Frontier Scale: Harvesting Activations from a Trillion-Parameter Model

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At Goodfire, our mission is to move past the era of black boxes by advancing the science of interpretability: developing and applying techniques that let us see inside models, learn from them, and make them more reliable. Executing on that mission means applying interpretability at the frontier, ensuring that our methods work with the AI systems actually being deployed in the world. These models are more challenging to work with than simpler 'model organisms' — they have more complex architectures (like mixture-of-experts layers), reason over many steps, make tool calls to interact with the world, and, perhaps most importantly of all: they are big. When Anthropic published Towards Monosemanticity in 2023, they deliberately worked with a toy model — a single-layer transformer with 512 neurons — to develop foundational techniques. Today's frontier models exceed the trillion parameter threshold. Our mission therefore requires solving hard engineering problems in addition to research on ne

Interpretability Infrastructure at Frontier Scale: Harvesting Activations from a Trillion-Parameter Model Blog Interpretability Infrastructure at Frontier Scale: Harvesting Activations from a Trillion-Parameter Model Authors Michael Anderson Tucker Fross Michael Byun Published February 25, 2026 --> At Goodfire, our mission is to move past the era of black boxes by advancing the science of interpretability: developing and applying techniques that let us see inside models, learn from them, and make them more reliable. Executing on that mission means applying interpretability at the frontier, ens

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