DeepSeek has disclosed some financial insights, revealing that its theoretical profit margins could be more than five times its costs. The 20-month-old company, which has gained attention for its affordable and innovative AI models, shared on X (formerly Twitter) that its V3 and R1 models achieved a 545% cost-profit margin based on a 24-hour inference cost-to-sales ratio at the end of February.
Inference costs refer to the computing power, electricity, and data storage required to run AI models in real-time. However, while the reported margins appear striking, DeepSeek clarified on GitHub that actual revenues are significantly lower. The company cited several reasons, including:
- Limited monetization, as only a fraction of its services generate revenue
- Discounts during off-peak hours, reducing overall earnings
- Exclusion of R&D and training costs, which are critical expenses for AI development
This disclosure comes at a time when investors are scrutinising AI business models, questioning their long-term profitability and return on investment (ROI). Startups like OpenAI and Anthropic are experimenting with different revenue strategies, including subscriptions, pay-per-use fees, and licensing agreements, but profitability remains uncertain.
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Beyond financial figures, DeepSeek also shared insights into its infrastructure and optimization techniques. The Hangzhou-based company explained how it manages computing loads by distributing AI processing across multiple servers. optimises data processing to enhance efficiency and reduce latency, ensuring faster response times for AI-generated outputs.
Unlike major U.S. AI firms that guard their proprietary technologies, DeepSeek has embraced open-source AI, making some of its advancements publicly accessible. By openly discussing its operations and business model, the company is challenging industry norms and sparking discussions about the future of AI commercialisation and financial viability.
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