Goodfire is a San Francisco-based AI research company founded in June 2024 with the mission to advance humanity's understanding of AI in order to build safe and powerful systems. The company specializes in mechanistic interpretability — the science of reverse-engineering how neural networks represent and process information — and translates those insights into a commercial platform called Ember. Ember enables researchers and engineers to detect, steer, and surgically edit model behaviors without treating AI as a black box. Goodfire's team draws from foundational interpretability work at Google DeepMind, OpenAI, and leading academic institutions, and has attracted clients including Arc Institute, Mayo Clinic, and Microsoft.
Funding Details
- Annual Budget
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- Monthly Burn Rate
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- Current Runway
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- Funding Goal
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- Funding Raised to Date
- $207,000,000
- Fiscal Sponsor
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Theory of Change
Goodfire believes that the primary bottleneck to safe and beneficial AI is not capability but comprehension: we cannot reliably align, audit, or improve systems we don't understand. By advancing mechanistic interpretability — decoding the neurons and internal features of neural networks — Goodfire aims to give researchers and engineers the tools to detect misalignment (sycophancy, deception, unfaithful reasoning), surgically edit unwanted model behaviors, and trace reasoning for high-stakes applications. The causal chain runs from interpretability research to practical tools (Ember), to widespread adoption by AI developers who use these tools to build safer and more reliable systems. A secondary path to impact is scientific discovery: by extracting insights from superhuman foundation models in domains like genomics and medicine, Goodfire's work could accelerate beneficial science while demonstrating that understanding AI internals is tractable at scale.
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Details
- Last Updated
- Apr 2, 2026, 10:10 PM UTC
- Created
- Mar 19, 2026, 10:30 PM UTC