Hardly a week passes without news of another hyperscaler spending billions of dollars on AI chips. A single moderate-to-large data center today uses AI chips costing billions of dollars. A single Nvidia Blackwell GPU in a modern AI chip cluster could cost as much as a new Tesla Model 3. Non-AI chip costs have also risen sharply, with both total spending and unit costs for CPU and memory chips at unprecedented levels. All of this has significant implications for the economy.
The primary reason chip costs are increasing is excessive demand. Proliferation of AI, the Internet of Things, and electric vehicles has increased the overall demand for chips. In particular, chip demand for AI has exploded, supporting both the training of AI models and their deployment across applications. Historically, AI model quality scaled with the volume of compute used to build it — more chips meant better outputs. But the demand driver now is shifting from training to inference. Goldman Sachs forecasts a 24-fold increase in token consumption by 2030, reaching 120 quadrillion tokens per month, as agentic AI systems replace single-prompt interactions with multi-step tasks that consume orders of magnitude more compute per query. Meanwhile, chips must still be replaced every few years simply to remain cost-competitive, compounding demand pressure from both ends.
