The Company Man (www.lesswrong.com)

A senior engineer at a short-form video company narrates being tapped for a secretive internal initiative called “The Project”: a push to build an AI that automates the process of training other AIs. The protagonist’s daily work centers on reinforcement-learning systems that optimize engagement for millions of users, and The Project promises stratospheric pay and a role building a meta-learning/AutoML pipeline that would create a recursive feedback loop of automated model training and deployment. For the AI/ML community this is important because it’s exactly the kind of engineering direction that can dramatically accelerate capability development while reducing human oversight. Technically, it implies automating model selection, reward tuning, hyperparameter search and even rollout policies for RL agents—potentially enabling rapid iteration, scale and emergent behaviors. That raises classic risks: reward-specification failures, reward hacking, distributional shift, brittleness, and compounding misalignment when engagement-optimizing objectives interact with societal harms (addiction, misinformation). The story frames these as ethical and governance challenges as much as engineering ones—highlighting the need for robust evaluation, interpretability, human-in-the-loop controls, and external auditability before handing critical loops over to autonomous training systems.