Grayscale Research Director: Cheaper Proxy AI Will Drive Up Computing Power Demand

Grayscale's Research Director Zach Pandl stated in his column The Stack on October 8 that the rising demand for AI is due to decreasing usage costs and technology's ability to perform tasks more autonomously, thereby increasing the demand for computing power and the physical infrastructure that supports AI. Lower costs can unleash more AI activities, and the low-cost competition of open-source weight models is driving down token prices, making cheaper intelligence economically viable for more applications and automated workflows.

The article cites that Anthropic's latest Sonnet model claims to reduce the cost of each task by up to 30%; OpenAI's always-on agent dots, launched on September 29, can work towards user goals around the clock, potentially significantly increasing the demand for computing power. Pandl compares that chatbots primarily answer questions, while agents execute multi-step processes to achieve goals, with tasks like preparing for client meetings potentially consuming 5 to 50 times the tokens of a single chat interaction, and always-on agents may extend processing beyond a single conversation.

Pandl cites Goldman Sachs' prediction that by 2030, token consumption could increase 24 times, primarily driven by agents; even with improved efficiency, if usage grows faster, the total demand for computing power may still rise. He points out that digital activities still rely on physical infrastructure that requires time to deliver, such as electricity, data centers, and GPUs, and cheaper AI can coexist with tight computing power supply, mentioning that CoreWeave recently signed new contracts at higher computing power prices. His conclusion is that cheaper intelligence and stronger agents may expand use cases and usage enough to offset efficiency gains, thereby supporting the demand for scarce computing power and benefiting its owners.