Topic: AI Efficiency

We analyze the growing shift away from the "compute-first" paradigm, as highlighted by former Google Brain researcher Sara Hooker. While the past decade prioritized scaling parameters and data, we observe that deep neural networks are increasingly inefficient, consuming massive resources to learn rare long-tail features with diminishing returns. Recent trends show that smaller models are frequently outperforming their massive counterparts, driven by superior data quality, architectural breakthroughs, and algorithmic optimizations like model distillation and Chain-of-Thought reasoning. We emphasize that existing Scaling Laws primarily predict pre-training loss rather than downstream task performance, often failing to account for architectural shifts or varying data distributions. As the cost of training reaches astronomical levels, we believe the industry must move beyond brute-force scaling to focus on efficiency and better learning methods. This pivot is crucial as redundancy in large models remains high, with 95% of weights often predictable by a fraction of the network.