大模型全开源了，那到底咋挣钱啊？

Core Viewpoint - The article discusses the paradox of open-source large models in the AI industry, questioning how these models can generate revenue despite being freely available. It emphasizes that profitability is essential for business operations and suggests various monetization strategies that can be employed by companies in this space [5][8][41]. Group 1: Open Source Models and Revenue Generation - Open-source models have become mainstream, but there is skepticism about their ability to generate revenue [4][7]. - Companies can monetize open-source models through several strategies, such as charging for usage rights of certain models [12][18]. - Successful examples from the open-source world, like Red Hat, illustrate that companies can provide paid solutions around open-source products [9][10]. Group 2: Monetization Strategies - Companies can charge for customized B2B model deployments, which is a significant revenue source [20][33]. - Selling computational power, as demonstrated by DeepSeek, is another viable revenue stream, with reported daily profits of $470,000 and a profit margin of 545% [22][23]. - Open-source products often generate more revenue from services rather than direct product sales, creating an ecosystem that supports monetization [28][30]. Group 3: Market Dynamics and Challenges - The AI industry is still evolving, and many companies are struggling to achieve profitability, with significant investments in GPU resources yielding limited returns [45]. - The article highlights that the current focus for AI companies should be on gaining attention and user engagement rather than immediate profitability [47]. - The competitive landscape necessitates that companies adopt open-source strategies to remain relevant and avoid being overshadowed by leaders like DeepSeek [47][48].