{"podcast":{"title":"Latent Space: The AI Engineer Podcast","slug":"latent-space-ai-engineer","podcast_index_feed_id":6058902,"rss_url":"https://api.substack.com/feed/podcast/1084089.rss","website_url":"https://www.latent.space/podcast","image_url":"https://substackcdn.com/feed/podcast/1084089/ca7468da5614a246d2906ee8926f6de7.jpg","author":"Latent.Space","episode_count":204,"summary":"The AI Engineer newsletter + Top technical AI podcast. How leading labs build Agents, Models, Infra, & AI for Science. See https://latent.space/about for highlights from Greg Brockman, Andrej Karpathy, George Hotz, Simon Willison, Soumith Chintala et al!","last_synced_at":null,"page_url":"https://stenobird.com/podcast/latent-space-ai-engineer"},"episode":{"title":"The First Mechanistic Interpretability Frontier Lab — Myra Deng & Mark Bissell of Goodfire AI","slug":"the-first-mechanistic-interpretability-frontier-lab-myra-deng-mark-bissell-of-goodfire-ai","published_at":"2026-02-06T22:45:00+00:00","page_url":"https://stenobird.com/podcast/latent-space-ai-engineer/the-first-mechanistic-interpretability-frontier-lab-myra-deng-mark-bissell-of-goodfire-ai","show_page_url":"https://stenobird.com/podcast/latent-space-ai-engineer","url":"https://www.latent.space/p/goodfire","audio_url":"https://api.substack.com/feed/podcast/187000315/45fdd1fce3ff7c69d24a13281311b152.mp3","summary":"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…","meta_description":"From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staf…","key_points":[],"chapters":[],"topics":[],"duration_seconds":4081,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"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","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/latent-space-ai-engineer/the-first-mechanistic-interpretability-frontier-lab-myra-deng-mark-bissell-of-goodfire-ai.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}