Guillermo Rauch has long promoted an open-source ecosystem for front-end and edge computing, with Vercel AI Gateway serving as a window to observe real production usage. Open source has risen from 11% in April to 36% in July, reaching 62% in a single day, driven by the weight of Chinese open source models like DeepSeek, Kimi, and GLM, reflecting a rapid migration of cost-sensitive workflows.

In terms of capital flow, closed source vendors lock in high-value scenarios (coding agents, high-risk decisions) with high prices, while open source handles a massive volume of low-profit Tokens. In Vercel's data, Anthropic captures most expenditures at about 4.4 times the average price, forming a "barbell structure" of "volume-price separation": open source captures volume, closed source captures money.

A similar pattern was seen in the early days of cloud computing: open source Linux dominated server volume, while commercial distributions and managed services captured profits. Currently, AI is transitioning from a "model capability race" to a "layering of inference costs and scenarios," with open source accelerating infrastructure demand rather than replacing premium cutting-edge models.

Essentially, this represents a transfer of pricing power. The volume of Tokens spreading towards open source lowers the average unit price, but cutting-edge intelligence still holds scarce premiums. The mechanism lies in the layering of task value: commodifiable tasks flow to low-cost models, while high-risk, high-reward tasks continue to pay premiums, leading to a high concentration of economic value.

ABAB News · Cognitive Laws

Victory in volume does not equal victory in money
Open source captures traffic, closed source captures premiums
Cutting-edge models always account for a minority of Tokens but capture the majority of value