Investors have been fleeing US artificial intelligence stocks amid surprise at a new, cheaper but still effective alternative Chinese technology. DeepSeek is a Chinese artificial intelligence AI company based in Hangzhou that emerged a couple of years ago from a university startup. Its stated goal is to make an artificial general intelligence β a term for a human-level intelligence that no technology firm has yet achieved.
Another reason it appears to have taken the low-cost approach could be the fact that Chinese computer scientists have long had to work around limits to the number of computer chips that are available to them, as result of US government restrictions. The company has been quietly impressing the AI world for a while with its technical innovations, including a cost-to-performance ratio several times lower than that for models made by Meta Llama and OpenAI Chat GPT.
They have been pumping out product announcements for months as they become increasingly concerned to finally generate returns on their multibillion-dollar investments. This model uses a different kind of internal architecture that requires less memory use, thereby considerably reducing the computational costs of every search or interaction with the chatbot-style system.
It has been praised by researchers for its ability to tackle complex reasoning tasks, particularly in mathematics and coding and it appears to be producing results comparable with rivals for a fraction of the computing power. Nevertheless it is vastly less than the billions that the Silicon Valley tech companies are spending to develop AIs and is less expensive to operate. A key character is Liang Wenfeng, who used to run a Chinese quantitative hedge fund that now funds DeepSeek.
In this wave, our starting point is not to take advantage of the opportunity to make a quick profit, but rather to reach the technical frontier and drive the development of the entire ecosystem β¦ We believe that as the economy develops, China should gradually become a contributor instead of free-riding. The timing was significant as in recent days US tech companies had pledged hundreds of billions of dollars more for investment in AI β much of which will go into building the computing infrastructure and energy sources needed, it was widely thought, to reach the goal of artificial general intelligence.
