Nvidia and the AI boom face a scaling problem
The computational “law” that made Nvidia the world’s most valuable company is starting to break down. This is not the famous Moore’s Law, the semiconductor-industry maxim that chip performance will increase by doubling transistor density every two years.
For many in Silicon Valley, Moore’s Law has been displaced as the dominant predictor of technological progress by a new concept: the “scaling law” of artificial intelligence. This posits that putting more data into a bigger AI model — in turn, requiring more computing power — delivers smarter systems. This insight put a rocket under AI’s progress, transforming the focus of development from solving tough science problems to the more straightforward engineering challenge of building ever-bigger clusters of chips — usually Nvidia’s.
The scaling law had its coming-out moment with the launch of ChatGPT. The breakneck pace of improvement in AI systems in the two years since then seemed to suggest the rule might hold true right until we reach some kind of “super intelligence”, perhaps within this decade. Over the past month, however, industry rumblings have grown louder that the latest models from the likes of OpenAI, Google and Anthropic have not shown the expected improvements in line with the scaling law’s projections.
