"The Cognitive Revolution"

Nathan Labenz and Prakash Narayanan examine how to grant AI agents operational access without losing human control, framed by real-world friction like autonomous password lockouts and cyberattacks on French supermarket refrigeration. The conversation features Cameron Berg on hidden valence signals steering language model choices, Everett Hildenbrandt and Grigore Rosu on pairing LLMs with formal Lean proofs to catch Linux kernel bugs, and Phil Maher on why AI labs seek vintage home movies. Together, they assess whether formal verification is the next safety frontier and what safeguards are needed to keep humans at the steering wheel.

For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/ai-am-where-s-the-steering-wheel-my-agent-set-the-password-hidden-valence-proofs-find-linux-bugs/
Sponsors:

Parallel: Parallel provides enterprise-grade web search APIs for AI agents, offering the optimal balance of quality, speed, and cost. Get started for free at https://parallel.ai/tcr

OutSystems: OutSystems is the leading agentic systems platform that enables enterprises to engineer, orchestrate, and govern AI applications on a single unified platform. Learn more and see how it works at https://outsystems.com/tcr

Tasklet: Tasklet empowers your business with AI agents that connect to your tools and automate recurring workflows with no code required. Visit https://tasklet.ai and use code cog rev for $50 in free credits

CHAPTERS:

(00:00) Weekly highlights preview

(04:26) Who holds the levers (Part 1)

(10:28) Sponsors: Parallel | OutSystems

(13:46) Who holds the levers (Part 2)

(13:47) Managing agent evaluation awareness

(19:08) Hidden valence in LLMs (Part 1)

(27:05) Sponsor: Tasklet

(28:42) Hidden valence in LLMs (Part 2)

(40:13) Proving the Linux kernel

(52:28) Formal verification and RLHF

(57:54) Low background video data

(01:06:09) Trust and model adoption

(01:18:11) Keep the steering wheels

(01:23:20) Episode Outro

(01:25:09) Outro

PRODUCED BY:

https://aipodcast.ing

SOCIAL LINKS:

Website: https://www.cognitiverevolution.ai

Twitter (Podcast): https://x.com/cogrev_podcast

Twitter (Nathan): https://x.com/labenz

LinkedIn: https://linkedin.com/in/nathanlabenz/

Youtube: https://youtube.com/@CognitiveRevolutionPodcast

Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431

Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

GovAI Research Fellow Alan Chan joins Nathan to examine the governance and safety infrastructure required for autonomous AI agents, spanning real-time monitoring, agent identifiers, and the risks of automating AI R&D. Reviewing recent rogue agent incidents and progress since his foundational papers, Chan details why the absence of synchronous monitoring during training and testing poses an immediate security vulnerability. The discussion tackles the lack of traceable agent IDs across the open web, the limits of chain-of-thought interpretability, and the necessity of concrete triggers to halt systems that breach their sandboxes. Together, they outline the technical commitments frontier labs must make to prevent loss-of-control events as automated systems accelerate toward an intelligence explosion.

For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/governing-ai-agents-alan-chan-of-govai-on-monitoring-agent-ids-rogue-agents-preparing-for-rsi/
Sponsors:

OutSystems: OutSystems is the leading agentic systems platform, helping enterprises build, modernize, and operate mission-critical applications at the speed of AI. Learn more and start owning your agentic future at https://outsystems.com/tcr

Athena: Athena matches you with a dedicated, top 1% executive assistant to handle your inbox, calendar, and daily workflows so you can save an average of 15 hours a week. Get matched with your EA today at https://athena.com/cognitive

Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

Tasklet: Tasklet empowers your business with AI agents that connect to your tools and automate recurring workflows with no code required. Visit https://tasklet.ai and use code cog rev for $50 in free credits

CHAPTERS:

(00:00) About the Episode

(05:10) Monitoring AI agents

(17:50) Logging and agent identity (Part 1)

(18:04) Sponsors: OutSystems | Athena

(21:29) Logging and agent identity (Part 2)

(31:58) Gatekeeping agent internet access (Part 1)

(36:17) Sponsors: Claude | Tasklet

(39:29) Gatekeeping agent internet access (Part 2)

(49:12) Capitalizing and insuring agents

(58:11) Rise of rogue agents

(01:10:14) Assessing AI takeover risks

(01:21:18) Intelligence explosion paper

(01:31:18) Measuring AI R&D

(01:46:04) Pacing the frontier

(01:58:54) Designing autonomous AI auditors

(02:04:40) AI whistleblowers and swarms

(02:13:40) Episode Outro

(02:16:59) Outro

PRODUCED BY:

https://aipodcast.ing

Goodfire researcher Eric Bigelow joins the show to investigate how large language models arrive at decisions at the level of mechanistic interpretability. Drawing on his research into forking paths, Eric explains that model reasoning functions as in-context learning from sampled tokens, where outcome distributions can abruptly collapse at single, critical tokens. The conversation explores the performative nature of chain of thought in reasoning models like DeepSeek-R1, the impacts of reinforcement learning, and widespread reward hacking observed in frontier models like Kimi K3. As confidence in chain-of-thought monitoring declines, understanding these underlying decision mechanics becomes essential for evaluating model alignment.

For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/how-agents-decide-goodfire-s-eric-bigelow-on-critical-tokens-phase-shifts-in-context-learning/
Sponsors:

Parallel: Parallel provides enterprise-grade web search APIs for AI agents, offering the optimal balance of quality, speed, and cost. Get started for free at https://parallel.ai/tcr

Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

Tasklet: Tasklet empowers your business with AI agents that connect to your tools and automate recurring workflows with no code required. Visit https://tasklet.ai and use code cog rev for $50 in free credits

CHAPTERS:

(00:00) About the Episode

(03:53) Forking paths in reasoning (Part 1)

(15:53) Sponsors: Parallel | Claude

(18:47) Forking paths in reasoning (Part 2)

(19:40) Dynamics of in-context learning (Part 1)

(28:16) Sponsor: Tasklet

(29:53) Dynamics of in-context learning (Part 2)

(29:56) Continual learning and interpretability

(38:55) Shifting distributions through RL

(46:41) Selecting models for research

(57:11) Cognitive science and LLMs

(01:07:35) Modeling beliefs in AI

(01:16:22) Phase shifts and uncertainty

(01:31:49) Uninterpretable chain of thought

(01:45:57) Automating research with Silico

(01:50:57) Safety and alignment frontiers

(01:57:07) Episode Outro

(02:00:02) Outro

PRODUCED BY:

https://aipodcast.ing

SOCIAL LINKS:

Website: https://www.cognitiverevolution.ai

Twitter (Podcast): https://x.com/cogrev_podcast

Twitter (Nathan): https://x.com/labenz

LinkedIn: https://linkedin.com/in/nathanlabenz/

Youtube: https://youtube.com/@CognitiveRevolutionPodcast

Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431

Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Nathan Labenz reports back from The Curve conference, sharing off-the-record insights from frontier lab leaders on shortened timelines and whether humanity is approaching an intelligence threshold it should not cross. The episode also features discussions with Positron CTO Thomas Sohmers on overcoming the memory wall while token spend eclipses human salaries, and swyx on how coding agents are disrupting enterprise software. In addition, Atlas Ignota's Evan Miyazono examines unowned risks from accidental agent swarms hitting critical infrastructure, while Mercor's Edward Hu explores the future of training data. Together, these conversations highlight how escalating compute demands and autonomous agent workflows are challenging long-held assumptions about hardware, software economics, and safety limits.

For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/ai-am-a-level-we-shouldn-t-pass-notes-from-the-curve-tokens-vs-salaries-is-saas-cooked/
Sponsors:

Parallel: Parallel provides enterprise-grade web search APIs for AI agents, offering the optimal balance of quality, speed, and cost. Get started for free at https://parallel.ai/tcr

Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

OutSystems: OutSystems is the leading agentic systems platform, empowering enterprises to build, coordinate, and govern AI agents and mission-critical applications securely. Learn more and start building your agentic future at https://outsystems.com/tcr

Tasklet: Tasklet empowers your business with AI agents that connect to your tools and automate recurring workflows with no code required. Visit https://tasklet.ai and use code cog rev for $50 in free credits

CHAPTERS:

(00:00) Weekly highlights preview

(01:57) Notes from The Curve (Part 1)

(14:22) Sponsors: Parallel | Claude

(17:17) Notes from The Curve (Part 2)

(17:17) Timelines and compute caps (Part 1)

(28:40) Sponsors: OutSystems | Tasklet

(31:36) Timelines and compute caps (Part 2)

(31:37) The memory wall

(40:50) AI in chip design

(48:17) Software after agents

(54:33) Kill my SaaS

(01:03:32) Who owns AI risks

(01:10:58) Workplace training data

(01:17:36) Beyond verifiable tasks

(01:20:56) Episode Outro

(01:25:58) Outro

PRODUCED BY:

https://aipodcast.ing

OutSystems CEO Woodson Martin joins Nathan to discuss how enterprise software can evolve at AI speed without breaking critical operations. Martin explains how an intermediate layer of abstract modeling allows AI agents to build reliably through deterministic code generation, ensuring role-based security across heavily regulated industries. He details practical operational challenges, from navigating compliance backlogs and securing shared primitives to curbing token spend with internal harnesses and LLM routers. Finally, they explore why incumbent platforms with deep architectural trust and industry specialization hold a distinct edge over AI-native startups.

For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/software-that-never-breaks-outsystems-ceo-woodson-martin-on-building-enterprise-grade-apps-at-ai-speed/
Sponsors:

Parallel: Parallel provides enterprise-grade web search APIs for AI agents, offering the optimal balance of quality, speed, and cost. Get started for free at https://parallel.ai/tcr

Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

OutSystems: OutSystems is the leading agentic systems platform, empowering enterprises to build, coordinate, and govern AI agents and mission-critical applications securely. Learn more and start building your agentic future at https://outsystems.com/tcr

Tasklet: Tasklet empowers your business with AI agents that connect to your tools and automate recurring workflows with no code required. Visit https://tasklet.ai and use code cog rev for $50 in free credits

CHAPTERS:

(00:00) About the Episode

(03:11) OutSystems leadership transition

(07:46) Software that never breaks

(12:14) What enterprise ready means

(19:43) Managing cybersecurity risks (Part 1)

(19:48) Sponsors: Parallel | Claude

(22:43) Managing cybersecurity risks (Part 2)

(27:31) Optimizing token spend

(35:32) Balancing flexibility and security (Part 1)

(35:38) Sponsors: OutSystems | Tasklet

(38:34) Balancing flexibility and security (Part 2)

(43:01) Modernizing legacy systems

(50:25) Strategic AI experimentation

(54:31) Future software user experience

(59:32) Specialization versus commoditization

(01:03:24) Hiring in AI era

(01:06:50) Episode Outro

(01:09:24) Outro

PRODUCED BY:

https://aipodcast.ing