Goodfire Raises $150 Million to Better Understand AI

Goodfire has raised $150 million to continue its research into understanding artificial intelligence (AI) models.

The company’s Series B funding round, announced Thursday (Feb. 5), values Goodfire at $1.25 billion, and comes as the startup continues its efforts in the field of interpretability. As Goodfire describes it, interpretability is the science of reverse engineering neural networks and turning those insights “into a universal, model-agnostic platform.”

“We believe that interpretability is the core toolkit for digital biology,” Goodfire wrote in its Thursday blog post. “As large foundation models become central to digital science, interpretability methods are our microscope for understanding what the models have learned from the vast data they’ve seen.”

In an interview with Bloomberg News, Goodfire CEO Eric Ho suggested that the technology field is moving too fast in developing AI models.

“I think what we’re doing right now is quite reckless,” he said. “How can we trust and rely on something that we don’t understand?”

Goodfire said its goal is to create a future in which it can understand the fundamentals of what AI models are doing and use that understanding to develop models in a “more principled, aligned, and useful” fashion.

“To that end, we’ve built a ‘model design environment’: a platform that uses interpretability-based primitives to get insights from models and data, improve model behavior, and monitor them in production,” the blog post said. “We use this environment internally for research, and deploy it forward with our customers, collaborating in a shared environment.”

As covered here last year when Goodfire announced its $50 million Series A round, the company’s Ember platform decodes the neurons inside an AI model, offers direct access to its “internal thoughts,” and lets users precisely shape the behaviors and boost the performance of their AI models.

In other AI news, PYMNTS wrote earlier this week about the technology’s gradual integration into finance departments. Early AI applications, that report said, were centered on pattern recognition like predicting demand, identifying late payments and bolstering forecast accuracy. But eventually, these systems gained confidence, fueled by larger datasets and reinforced by measurable wins.

“Folks are just starting to understand that AI isn’t just automation with kind of sexier marketing,” Finexio CEO and founder Ernest Rolfson told PYMNTS in December.

Research in a PYMNTS Intelligence report, “How Agentic AI Went From Zero to CFO Test Runs in 90 Days,” shows that nearly 7% of enterprise finance chiefs in the U.S. have already begun using agentic AI in live finance workflows, while another 5% are running pilots.