Inside Goodfire: Building safer AI systems with interpretability
How can we build safer AI systems with interpretability?
Join Goodfire and BlueDot Impact for a look at how Goodfire is working to make AI models something you can understand, debug and deliberately design.
Most of the AI industry treats models as opaque systems — training them at scale and evaluating them by their outputs. Goodfire is taking a different approach. By studying how models organise knowledge internally, they're building tools that let developers inspect and steer what AI models learn.
Founded in mid-2024, Goodfire has brought together researchers from interpretability teams at Google DeepMind and OpenAI, raised $207M from Menlo Ventures, Lightspeed, and Anthropic, and shipped Ember — a hosted API that lets developers work with a model's internal features directly.
We'll be having a conversation with Dan Balsam, co-founder & CTO of Goodfire.
He leads Goodfire's engineering and technical direction and is also a graduate of BlueDot's AI Alignment course.
Whether you’re an aspiring researcher, engineer, or policy professional, you’ll gain a deeper understanding of:
What Goodfire works on and why it matters
How Goodfire bridges its research and commercial products
Upcoming career opportunities and what Goodfire looks for in candidates
Part of our "Inside X" series: giving you exclusive access to the organisations building the future of beneficial AI.
Goodfire is also hiring: https://www.goodfire.ai/careers