▶Twerk consistently argues that economic forces and market competition are the primary drivers shaping the AI landscape, forcing research convergence and dictating company strategy [1, 9].Apr 2026

▶He repeatedly emphasizes the power and potential of reinforcement learning (RL), stating it has 'basically no limits' for mastering specific skills and that OpenAI's early leadership in large-scale RL is a key advantage [17, 23].Apr 2026

▶Across multiple claims, Twerk identifies the static nature of current AI models as their single biggest limitation, highlighting their inability to learn from failure or update their internal knowledge as a critical barrier to AGI [2, 6, 19].Apr 2026

▶He views the scaling of pre-training and the targeted addition of new data as the core, predictable drivers of the quarterly improvements seen in models from major labs [5, 16].Apr 2026

▶Twerk's own perspective on AGI has evolved significantly; he explicitly states he has 'changed his mind' and now believes continual learning is a necessary component, rejecting the idea that a static model could ever qualify as AGI [6].Apr 2026

▶He presents a tension within AI research culture, noting that while many researchers are brilliant, they often lack the 'courage' to pursue unconventional research paths, contributing to the strategic convergence he observes [1, 3].Apr 2026

▶Twerk highlights a strategic conflict within OpenAI, suggesting its attempt to pursue many difficult product areas simultaneously is a 'very big risk' that conflicts with the focused execution that led to its initial success [15, 24].Apr 2026

▶He contrasts the immense, predictable benefits of scaling pre-training with the fundamental fragility of the deep learning training process itself, which requires significant effort just to maintain stability [5, 20].Apr 2026