The launch of Chinese AI company DeepSeek’s latest model in January broke through usually niche technical research spheres to make international headlines. The surprise: an upstart Chinese AI company had produced a model that was not only on par with reasoning models released by frontier US labs, but seemingly at one thirtieth of the cost. In doing so, it reset fundamental assumptions about where AI innovation could come from in the future. For Mexico and other emerging markets, this has re-opened the window of opportunity in what was seen as a two-horse AI race.
DeepSeek’s success suggests that competitive, homegrown AI platforms are possible, even in countries that do not enjoy the US’ unrestricted access to advanced chips or its vibrant technology ecosystem. Raw computational power remains undoubtedly important, but DeepSeek’s high efficiency and cost model suggests countries, like Mexico, have an opportunity to reap the benefits of AI even as the US and China dominate the AI frontier.
There is more nuance to DeepSeek’s innovations, as well as lingering questions about its real significance. There are misgivings about the startup’s reliance on open-source US models and methodology for evaluating unprecedently low training costs. DeepSeek said it spent $5.6 Mn and used around 2 000 NVIDIA chips to train its model, a fraction of what OpenAI and Google spend to train comparably sized models. However, analysts have suggested the true figure may be closer to $500 Mn once other necessary costs, such as training runs and R&D, are considered. Hardware, engineering talent, and access to capital remain important building blocks for AI innovation. Nevertheless, DeepSeek’s success has expanded the Overton window for many countries beyond the US and China as they consider where their AI ambitions fall relative to their resources.