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Goodfire blog: Understanding, Learning From, and Designing AI: Our Series B
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Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
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Goodfire is a startup focused on mechanistic interpretability; this Series B announcement reflects the commercialization of interpretability research and is relevant to tracking the AI safety industry landscape.
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Importance: 38/100blog postnews
Summary
Goodfire announces its Series B funding round, outlining the company's mission to advance mechanistic interpretability research to understand, learn from, and design AI systems. The post highlights the company's vision for making AI internals legible and controllable, positioning interpretability as central to safe and beneficial AI development.
Key Points
- •Goodfire raises Series B funding to scale its mechanistic interpretability research and product development.
- •The company's mission centers on making AI model internals understandable, enabling learning from AI behavior and deliberate system design.
- •Interpretability is framed as a core enabler of AI safety, allowing humans to inspect and steer model behavior.
- •The announcement signals growing investor and industry interest in technical AI safety and interpretability as a commercial field.
- •Goodfire positions itself as bridging academic interpretability research and practical deployment-ready tools.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Goodfire | Organization | 68.0 |
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Blog
# Understanding, Learning From, and Designing AI: Our Series B
February 5, 2026
Goodfire - YouTube
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### Contents
[What we believe](https://www.goodfire.ai/blog/our-series-b#what-we-believe) [What we're building](https://www.goodfire.ai/blog/our-series-b#what-were-building) [Intentional design](https://www.goodfire.ai/blog/our-series-b#intentional-design) [Scientific discovery](https://www.goodfire.ai/blog/our-series-b#scientific-discovery) [Foundational research](https://www.goodfire.ai/blog/our-series-b#foundational-research) [Towards alignment](https://www.goodfire.ai/blog/our-series-b#towards-alignment) [Our team](https://www.goodfire.ai/blog/our-series-b#our-team) [Build with us](https://www.goodfire.ai/blog/our-series-b#build-with-us)
* * *
Today, we're excited to announce a $150 million Series B funding round at a $1.25 billion valuation. The round was led by B Capital, with participation from Juniper Ventures, DFJ Growth, Salesforce Ventures, Menlo Ventures, Lightspeed Venture Partners, South Park Commons, Wing Venture Capital, Eric Schmidt, and others.
We started Goodfire because we saw a problem with how today's AI models are built: every frontier model is a black box, making critical decisions while behaving in unpredictable ways not well understood by science.
We decided to change that by pushing forward the frontier of interpretability—the science of how neural networks work internally—letting us “open the black box” to understand and edit models.
Since then, we've built a world-class team drawn from frontier labs and top research universities, and partnered with industry leaders like Arc Institute, Mayo Clinic, and Microsoft to deploy our technology. We've developed novel techniques to [decompose model internals](https://www.goodfire.ai/research/stochastic-param-decomp#), reduced hallucinations in an LLM by half using interpretability-informed training, and [identified a novel class of Alzheimer's biomarkers](https://www.goodfire.ai/research/interpretability-for-alzheimers-detection#) by reverse-engineering a biological model.
Along the way, we've only grown stronger in our conviction that interpretability is critical to building models that are powerful, yet also reliably steerable and safe. While we've accomplished a lot, there is a lot left t
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