"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis
"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

Thinking in Silico: Goodfire CTO Dan Balsam on Concept Manifolds & a $1000/Month ML Research Agent

08 August 2026 1:57:21 Erik Torenberg, Nathan Labenz

Listen to episode

About this episode

Goodfire co-founder and CTO Dan Balsam returns to discuss where interpretability research now stands and to introduce Silico, the $1,000-per-month research platform Goodfire built for itself. He and Nathan explore Predictive Data Debugging, including the idea that fine-tuning and RL often amplify behaviors already latent in pre-training, and that interpretability can identify the data and features driving unwanted updates. The conversation centers on concept manifolds: Dan argues that models do not store concepts as simple one-hot features, but as sparse mixtures of meaningful subspaces whose geometry determines what kinds of steering and control work. The stakes are practical as well as conceptual, from debugging training data and RL to understanding why steering can fail off-manifold and why modern interpretability may be moving beyond its reputation as a toy-model science.


Silico: https://www.goodfire.com/silico Predictive data debugging: https://www.goodfire.com/research/predictive-data-debugging# Neural Geometry: https://www.goodfire.com/research/the-world-inside-neural-networks#



For full show notes, links, and references, read the episode page:
https://www.cognitiverevolution.ai/thinking-in-silico-goodfire-cto-dan-balsam-on-concept-manifolds-a-1000-month-ml-research-agent/

Sponsor:

Claude:

Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr


CHAPTERS:

(00:00) About the Episode

(03:22) Predictive data debugging

(12:36) Concept manifold geometry

(21:22) Finding concept manifolds (Part 1)

(21:28) Sponsor: Claude

(22:57) Finding concept manifolds (Part 2)

(33:24) Factoring model internals

(49:32) Introducing Silico platform

(57:10) Research taste and credits

(01:06:19) Silico research use cases

(01:16:37) Skills and open models

(01:24:57) Guardrails and bio risk

(01:32:09) Training interventions and monitoring

(01:42:04) Grants and AI consciousness

(01:50:41) Episode Outro

(01:55:47) Outro

PRODUCED BY:

https://aipodcast.ing

SOCIAL LINKS:

Website: https://www.cognitiverevolution.ai

Twitter (Podcast

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.