Expired50% confidenceFactExact time
The main components of AI inference costs are depreciation and electricity for high-end GPUs such as the A100/H100, which are capital-intensive rigid expenditures that are difficult to compress quickly as revenue declines.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/18/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/16/2026(expired)
Sources
Related Claims
UnverifiedThe marginal cost of AI models decreases as scale expands; the more users there are, the lower the average cost per inference after optimization through batching and GPU utilization.76% similarUnverified虽然大模型单位Token成本在下降，但企业总体AI支出可能因使用量增长而不降反升75% similarUnverifiedWhen AI clusters scale to tens of thousands of GPUs, overall efficiency can plummet to below 40%, with more than half of computing power consumed in data transmission and waiting.73% similarUnverifiedAI推理的边际成本不趋于零，因为每次推理需在GPU显存中加载完整模型权重，且每次生成的输出唯一无法缓存复用73% similarUnverifiedUsing low-end AI models appears to save money, but due to low output quality and high rework costs, the total cost ultimately ends up being higher.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/555280API
curl https://kongchang.com/api/v1/knowledge/claims/555280MCP
get_claim(id=555280)