As AI models automate increasingly complex tasks, a de-skilling loop may emerge where reliance on AI reduces human learning and skill development, potentially creating economic hardship. The article argues that while AI capabilities will advance rapidly, humans working alongside AI rather than being replaced by it can achieve better outcomes than either alone.
ScienceBuddy is an interactive scientific research workspace that enables continually improving AI agents through recursive-in-recursive self-improvement, combining harness evolution with model reinforcement learning. The system transforms researcher feedback into training tasks and evaluation rubrics, supporting scientific workflows across multiple task families while advancing toward discovery intelligence through sustained human-AI collaboration.