Goodfire blog: Understanding, Learning From, and Designing AI: Our Series B

Understanding, Learning From, and Designing AI: Our Series B 
 

 Blog 
 
 
 

 

 
 
 Understanding, Learning From, and Designing AI: Our Series B

 

 
 
 
 
 February 5, 2026

 
 
 

 
 

 
 
 
 

 
 Contents

 
 What we believe 
 What we're building 
 Intentional design 
 Scientific discovery 
 Foundational research 
 Towards alignment 
 Our team 
 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 , reduced hallucinations in an LLM by half using interpretability-informed training, and identified a novel class of Alzheimer's biomarkers 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 to be done.

 What we believe

 Never in history have we deployed such a transformative technology so quickly with such limited understanding. Foundation models are now writing significant amounts of production code , running loose on the internet , and integrated into the military . Yet their internal decision-making remains opaque, and the black-box approach to shaping their behavior is ham-fisted and prone to bizarre failures .

 In order to unlock the promise of safe, powerful AI, we need a deeper and more principled understanding of what we're building.

 Luckily, the black-box approach is an unnecessary handicap: there is deep, intricate structure inside of models wherever we look, and we would be remiss not to use it. We can use interpretability tools to instruct and shape models, giving us more deeply aligned systems, and we can also use them as microscopes to understand the vast new knowledge that models learn about our world.

 
 “Interpretability, for us, is the toolset for a new domain of science: a way to form hypotheses, run experiments, and ultimately design intelligence rather than stumbling into it.”

 Eric Ho 
CEO of Goodfire

 

 What we're building

 At Goodfire, we'

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