In just over a year, DeepSeek has stopped sounding like a rarity in the Chinese industry to become one of those names that already appear every time we talk about the global race in artificial intelligence. We first looked at it for its models, for its efficiency and for the shock it caused beyond China. Now the question begins to move to another terrain: what happens when a company that competes in software understands that the next advantage may be in the chips that make it possible to execute that AI on a large scale.
The jump to hardware. The information that opens this new front comes from Reuters. The agency assures, citing three people familiar with the matter, that DeepSeek is developing its own artificial intelligence chip, aimed at inference tasks and not the training of new models. We will see the technical nuance immediately, because it changes the reading of the movement quite a bit. For now, caution is mandatory: DeepSeek has not publicly confirmed the project would be in an early phase and the company did not respond to the agency’s request for comment.
The key is in inference. The easiest way to understand this is to think about what happens after training. Once the model is built, every question we ask and every answer we receive requires putting it to work again. It is not an isolated operation, but a routine that is repeated millions of times if the product works. That is why a chip designed for that phase does not aim so much at technical prestige as at something more earthly: making using AI cheaper, faster and less dependent on third parties.
The move is best understood if we look at what DeepSeek has depended on so far. The company has used chips from NVIDIA and Huawei to train and run its models, including the base that held R1, trained on NVIDIA H800, a chip designed for the Chinese market whose export to China was banned by Washington in late 2023. Since then, DeepSeek has increasingly leaned on Huawei: in April it launched its V4 model adapted to Ascend and Huawei said its processors were used in part of the V4-Flash training.
DeepSeek is no longer a footnote: Until not so long ago, the global debate on AI seemed to revolve almost entirely around American companies such as OpenAI, Google, Microsoft, Meta or Anthropic. DeepSeek changed part of that conversation by demonstrating that China could also produce models capable of circulating outside its domestic market and forcing the industry to look to Hangzhou. Recall that the company was widely celebrated in China as a national AI champion.
The trend is already seen in a good part of the sector. Google has been developing its TPUs for years, Amazon has Inferentia for inference loads, Microsoft has Maia and Meta works on MTIA. Reuters also cites two recent movements that are especially close to the case: OpenAI announced its Jalapeño chip in June with Broadcom, also aimed at inference, and Anthropic was considering designing its own chips. The pattern is quite clear: large AI companies want to rely less on third-party providers and better control the cost, performance and availability of the computing that powers their services.
The big obstacle is manufacturing it. Designing a competitive chip is not the same as wanting to have it. Developing an AI accelerator typically requires years, a lot of capital, and a network of design, foundry, and memory partners. For a Chinese company, furthermore, the problem does not end at the technical level: US export controls limit access to the most advanced foreign factories and also to high-bandwidth memory, a key component for this type of chips.
Times change. NVIDIA arrived at the AI boom with an advantage built over decades: in 1999 it launched the GeForce 256, presented by the company itself as the industry’s first GPU, and in 2006 it launched CUDA, the architecture that helped take the parallel processing of its chips beyond graphics. When the models started requiring huge amounts of compute, I already had the hardware and ecosystem in place. For years, for much of the industry, competing in AI meant going through its chips. What the DeepSeek case suggests, with all caution, is that this dependency is beginning to have cracks.
Images | with Nano Banana
In | Samsung earns 19 times more than a year ago. Investors have reacted by sinking the stock 7%
