Epoch AI Estimate: Chips Shipped Through 2027 Could Run Hundreds of Millions of Agents, But Demand Gap Looms
Epoch AI researcher Jason Li uses high-bandwidth memory (HBM) shipment volumes as a basis to estimate that AI chips shipped between 2025 and 2027 could theoretically support approximately 30 to 170 million concurrent frontier-model agents, equivalent to the weekly working hours of 140 to 720 million full-time employees. The report further shows that even using only 20% of potential capacity, the required API spending would reach $2.6 to $5.3 trillion annually, far exceeding projected developer revenues, highlighting a risk of over-supply in AI infrastructure investment.
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AI companies are spending hundreds of billions of dollars annually on chips and data centers, premised on the idea that this hardware will run AI agents to perform work that people do today. A critical but often overlooked question is: how many agents can this infrastructure actually support? And if capacity exceeds demand, what happens to returns?
In a study published on October 2, 2026, Epoch AI researcher Jason Li quantified this supply-demand relationship. Li previously worked at NVIDIA on LLM serving and inference optimization, and now focuses on AI inference topics at Epoch. He chose high-bandwidth memory (HBM) as the measurement basis because HBM is simultaneously an important component for inference and a main bottleneck for chip production, and its supply data is relatively transparent. Globally, HBM is produced primarily by three companies: Micron, Samsung, and SK hynix.
Li's estimation method proceeds in two steps: first, he converts HBM shipped since 2025 into equivalent GPU units by dividing by 288 GB (matching one GB300 GPU), then multiplies by the number of concurrent agent sessions each equivalent GPU can support. For closed frontier models, he analyzed actual session traces from the TraceLab dataset covering Codex and Claude Code workloads, deriving API-equivalent spending per agent-hour—roughly $16–18 for Codex and $24–50 for Claude Code—using $30 as a central reference point. Combined with GPU rental costs ($5 per GB300-hour) and an assumed API revenue-to-serving-cost ratio of 5–10×, he calculates concurrent sessions per equivalent GPU.