Goodfire Secures $50M to Make AI Models More Transparent
Goodfire, a fast-rising AI interpretability startup, has announced a $50 million Series A funding round. The investment was led by Menlo Ventures, with additional participation from Lightspeed Venture Partners, Anthropic, B Capital, Work-Bench, Wing, and others. This funding arrives less than a year after the company’s founding, showing strong support for its mission to make artificial intelligence models easier to understand and control.
As AI systems grow more powerful, their inner workings remain largely mysterious. Goodfire aims to solve this challenge through mechanistic interpretability, a field focused on reverse engineering the internal logic of neural networks. Goodfire wants to reduce the risks of deploying large language models and other complex machine learning systems by shedding light on how AI systems think and fail.
Ember Platform Opens the Black Box
The company’s core product, Ember, is a universal interpretability platform that gives users direct insight into a model’s internal features. Rather than treating AI like an unpredictable black box, Ember helps developers see and shape how models process information. This unlocks new capabilities for training, aligning, and improving AI systems across many use cases.
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According to co-founder and CEO Eric Ho, the lack of understanding around model failures creates serious risks. His team believes the only way to safely scale foundation models is to make their internal logic fully visible and adjustable. Goodfire’s researchers, many of whom come from leading AI labs like OpenAI and DeepMind, are pushing the limits of what’s possible in this area.
Advancing Scientific Discovery and AI Safety
Goodfire is also working closely with frontier AI labs and research partners to apply Ember across diverse fields. In one project, the Arc Institute used Goodfire’s platform to uncover new insights from their DNA-based models. The tools helped reveal novel biological concepts that would have remained hidden without deep interpretability.
In the future, the company intends to publish additional research that showcases progress in areas such as image processing, language reasoning, and scientific modeling. These initiatives will further establish the company as a leader in AI transparency, safety, and responsible development.
