<div id="readability-page-1"><div>For leading AI companies, the biggest expense is not talent. It is compute.This chart from Visual Capitalist’sAI Week, sponsored byTerzo, usesEpoch AIdata to compare spending at Anthropic, Minimax, and Z.ai across R&amp;D compute, inference compute, and staff plus other costs.In every case, compute accounts for the majority of total spending, underscoring how capital-intensive it has become to build and serve frontier AI models.<img width="500" height="651" src="https://imageproxy.pbkrs.com/https://files.zhedge.com/cdn-cgi/image/width=1920,quality=75,format=auto/https://assets.zerohedge.com/s3fs-public/inline-images/AI-Company-Costs_03-web.jpg?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" original-src="https://imageproxy.pbkrs.com/https://files.zhedge.com/cdn-cgi/image/width=1920,quality=75,format=auto/https://assets.zerohedge.com/s3fs-public/inline-images/AI-Company-Costs_03-web.jpg"/><h2>How AI Company Costs Break Down</h2>Despite differences in scale, all three companies allocate the largest share of their budgets to a single category: compute.The data below compares spending composition across Anthropic, Minimax, and Z.ai. Anthropic’s figures are for 2025, while Minimax’s are from Q1 to Q3 of 2025 and Z.ai’s are for H1 2025.<img width="500" height="163" src="https://imageproxy.pbkrs.com/https://files.zhedge.com/cdn-cgi/image/width=1920,quality=75,format=auto/https://assets.zerohedge.com/s3fs-public/inline-images/2026-04-23_06-29-08.jpg?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" original-src="https://imageproxy.pbkrs.com/https://files.zhedge.com/cdn-cgi/image/width=1920,quality=75,format=auto/https://assets.zerohedge.com/s3fs-public/inline-images/2026-04-23_06-29-08.jpg"/>Across all threeAI companies, compute is the main cost center. Epoch AI estimates that R&amp;D compute and inference compute together account for57%to70%of total spending, making infrastructure more expensive than staff and other costs in every case.Among the three, Z.ai has the most R&amp;D-heavy profile, with58%of spending tied to compute powering model development and training.Anthropic stands out for sheer scale. Epoch AI estimates the company spent$9.7 billionin 2025, including $6.8 billion on compute alone across training and inference.Its costs are significantly higher than Minimax’s and Z.ai’s, even if the two Chinese AI companies’ figures were annualized to match Anthropic’s full-year period.Both Chinese companies release many of their models asopen source, meaning the model weights are freely available for anyone to download, modify, and run. This strategy helps them compete with better-funded U.S. labs by building developer adoption at a fraction of the cost.<h2>AI Talent Costs Less Than Chips and Compute</h2>One of the clearest takeaways is that talent costs less than compute in this comparison. Even though top AI labs pay some of thehighest salaries in tech, staff and other costs still account for less than half of total spending at each of the three firms.While the chart focuses on costs, Epoch AI estimates these labs are currently spending around 2–3x more than they generate in revenue, even as some expect economics to improve over time.<h2>How These Estimates Were Built</h2>This dataset comes with a few important caveats. Anthropic’s figures are based on reporting from The Information and are more speculative, while Minimax and Z.ai figures come from IPO filings released in January 2026.The time periods also differ: Anthropic data is for the full year of 2025, Minimax covers 2025 Q1–Q3, and Z.ai covers 2025 H1. Epoch AI says its expense totals include operating expenses, cost of goods and services, and non-cash items such as stock-based compensation.If you enjoyed today’s post, check outThe Soaring Revenues of AI Companieson Voronoi.</div></div>

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國泰中證計算機主題ETF

納斯達克人工智慧與機器人 ETF - First Trust

領先的人工智能公司發現，計算成本超過了人才費用，計算佔總支出的 57% 至 70%。Anthropic、Minimax 和 Z.ai 將其預算中最大的一部分分配給計算，Anthropic 在 2025 年的支出為 97 億美元，其中 68 億美元用於計算。儘管薪資高昂，員工成本仍然不到總支出的 50%。這些數據突顯了開發人工智能模型的資本密集型特性，目前公司支出是其收入的 2-3 倍

在人工智能領域，計算成本比人才成本更高