Scaling's Ceiling: Why Compute Alone Won't Build AGI
Source: Transformer
A growing cohort of AI researchers and their funders are abandoning the assumption that throwing more data and parameters at neural networks will automatically produce artificial general intelligence. Companies like OpenAI, Anthropic, and DeepSeek are now investing in architectural innovation, training techniques, and inference optimization rather than simply building larger models. This suggests the scaling laws that drove GPT-2 to GPT-4 may be flattening sooner than expected. The shift has concrete effects: it redirects capital from compute infrastructure toward research teams, favors labs with scientific depth over those with larger cloud budgets, and indicates that current transformer architectures face diminishing returns on the path to general intelligence.