# The First Mechanistic Interpretability Frontier Lab — Myra Deng & Mark Bissell of Goodfire AI

Page: https://stenobird.com/podcast/latent-space-ai-engineer/the-first-mechanistic-interpretability-frontier-lab-myra-deng-mark-bissell-of-goodfire-ai
Text version: https://stenobird.com/podcast/latent-space-ai-engineer/the-first-mechanistic-interpretability-frontier-lab-myra-deng-mark-bissell-of-goodfire-ai.md
Podcast: [Latent Space: The AI Engineer Podcast](https://stenobird.com/podcast/latent-space-ai-engineer)
Published: 2026-02-06T22:45:00+00:00
Episode link: https://www.latent.space/p/goodfire
Audio file: https://api.substack.com/feed/podcast/187000315/45fdd1fce3ff7c69d24a13281311b152.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/latent-space-ai-engineer/episodes/the-first-mechanistic-interpretability-frontier-lab-myra-deng-mark-bissell-of-goodfire-ai
Duration seconds: 4081

## Resource

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation . In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire’s core bet : that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire’s answer is to build a bi-directional interface between humans and models: read what’s happening inside , edit it surgically , and eventually use interpretability during training so customization isn’t just brute-force guesswork. Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models. We discuss: * Myra + Mark’s path: Palantir (health systems, fo…

## Actions

- request_transcript: `POST https://stenobird.com/v1/public/podcasts/latent-space-ai-engineer/episodes/the-first-mechanistic-interpretability-frontier-lab-myra-deng-mark-bissell-of-goodfire-ai/transcription-requests` — Idempotently request low-priority transcript generation for this episode.
- read_markdown: `GET https://stenobird.com/podcast/latent-space-ai-engineer/the-first-mechanistic-interpretability-frontier-lab-myra-deng-mark-bissell-of-goodfire-ai.md` — Read the agent-friendly Markdown representation of this episode resource.

A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed.

## Transcript

Full transcripts are not published on public pages unless there is a clear rights basis.