For leading AI companies, the biggest expense is not talent. It is compute.
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.
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.
Across all threeAI companies, compute is the main cost center.Epoch AI estimates that R&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&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.
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.
Source: ZeroHedge News
