Topic: Scaling Law

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.

Today we delve into a comprehensive framework for the emerging AI economy proposed by Wang Jie, an early AI investor and angel investor in Moore Threads. As Scaling Law continues without convergence, we observe a non-linear and non-uniform development where AI inference costs drop 90% annually while capability density doubles every 100 days. We anticipate the full integration of AI across global industries to take 40 to 60 years, likely maturing between 2035 and 2050. A pivotal metric introduced is the Output Augmentation Multiple, which quantifies the productivity ratio between AI systems and human labor for identical tasks. By evaluating these shifts through an Economic Turing Test, we can better understand the transition toward a non-scarcity economy where total output significantly exceeds demand. This analysis provides developers and strategists with a rigorous methodology to track the structural evolution of global GDP, potentially increasing it fivefold in the coming decades.