DeepSeek and OpenAI: The cost of AI in China? A comment from Meta, the tech giant, and an openAI spokesperson

The performance and efficiency of DeepSeek’s models has already prompted talk of cost cutting at some big tech firms. One engineer at Meta, who asked not to be named because they were not authorized to speak publicly, says the tech giant will most likely try to examine DeepSeek’s techniques to find ways to reduce its own expenditure on AI. “We believe open source models are driving a significant shift in the industry, and that’s going to bring the benefits of AI to everyone faster,” a spokesperson for Meta said in a statement. Meta has over 800 million downloads on their Llama models and they want the US to remain the leader of open source artificial intelligence.

DeepSeek’s technology was developed by a relatively small research lab in China that sprang out of one of the country’s best-performing quantitative hedge funds. A research paper posted online last December claims that its earlier DeepSeek-V3 large language model cost only $5.6 million to build, a fraction of the amount its competitors needed for similar projects. Some of the models from OpenAI cost up to $100 million each. The most recent models from both Open and Anthropic likely cost more.

OpenAI told the Financial Times that it found evidence linking DeepSeek to the use of distillation — a common technique developers use to train AI models by extracting data from larger, more capable ones. It makes sense to train smaller models at a fraction of the cost of training GPT-4. While developers can use OpenAI’s API to integrate its AI with their own applications, distilling the outputs to build rival models is a violation of OpenAI’s terms of service. OpenAI has not provided details of the evidence it found.

The situation is rich with irony. It was Open AI that made huge leaps with its GPT model, by taking down all of the written web without consent.