Cameron Berg

Cameron Berg × David Duvenaud × Michiel Bakker × Shawn “swyx” Wang × Bing Xu × Erik Torenberg × Nathan Labenz

Architecture-first scoring places frontier LLMs around 30% on consciousness-relevant properties, while steering valence-like states already changes blackmail, confidence, backtracking, and coding behavior.Europe’s regulatory leverage is constrained by dependence on foreign frontier labs, prompting a coalition thesis around ASML, TSMC, Korean memory, Japanese materials, and reciprocal frontier access.Meanwhile, private evaluations, mergeability, routing, and NVIDIA’s CUDA ecosystem increasingly determine AI-engineering value as public benchmarks saturate and agentic optimization compounds tooling advantages.

Erik Torenberg × Nathan Labenz × Cameron Berg

Mechanistic studies report models detecting injected internal features with 0% false positives, resisting active distractors, and showing emotion-like trajectories around cheating, though each result remains individually inconclusive.Refusal training can suppress introspective detection by upwards of 50%, while Claude’s welfare reports remain below or barely above neutral, making replication, checkpoint controls, and low-cost welfare precautions important alignment risks to monitor.

Nathan Labenz × Cameron Berg

Mechanistic research on Llama 3.3 70B found that suppressing six deception- and role-play-associated features drove affirmative consciousness reports toward 100%, while amplification restored familiar denials and worsened TruthfulQA performance.Across frontier models, self-referential feedback—not consciousness priming—elicited high-rate experience reports, raising an unresolved governance and training-welfare risk as labs scale deployment and reward-based learning.