Investors rarely witness a single trading day erase nearly $600 billion in market value from one company. Yet that exact scenario unfolded recently when Nvidia’s shares plunged as much as 18 percent, settling at a 17 percent decline and marking the largest one-day market capitalisation loss in US history.
The catalyst came from an unlikely source: DeepSeek, a Chinese AI laboratory that operates under severe restrictions on access to advanced Western hardware. The firm released DeepSeek-R1, an open-source reasoning model that rivals OpenAI’s o1 and Meta’s top offerings in benchmarks, yet required only a fraction of the computational resources and cost typically associated with frontier AI development.
DeepSeek achieved this breakthrough despite US export controls that limit China to older or downgraded Nvidia chips, such as the H800 series. Engineers optimised algorithms, frameworks, and training techniques, including innovative use of reinforcement learning and mixture-of-experts architectures. Reports indicate the model trained for roughly $5.6 million, using thousands rather than tens of thousands of GPUs, and in far less time than Western counterparts.
Markets interpreted the release as a direct threat to the prevailing AI investment narrative. Nvidia has profited immensely from the assumption that ever-larger models demand ever-more powerful and expensive hardware clusters. If capable alternatives emerge through efficiency gains and clever engineering, demand for premium GPUs could soften.
The sell-off extended beyond Nvidia. Broadcom dropped sharply, Taiwan Semiconductor fell 13 percent, and the Nasdaq Composite shed 3.1 percent overall. Tech giants like Microsoft and Tesla also recorded losses, reflecting broader concerns about sustained capital expenditure in AI infrastructure.
Executives who committed billions to Nvidia-powered data centres now confront uncomfortable questions. Does DeepSeek-R1 signal the peak of hardware-intensive scaling? Could open-source models from resource-constrained environments accelerate competition and compress margins across the supply chain?
Nvidia responded by describing DeepSeek-R1 as “an excellent AI advancement” and emphasising that even efficient training still relies on its GPUs. The company points to ongoing demand for inference and future workloads. Shares partially recovered in subsequent sessions, suggesting some investors view the rout as overreaction. Geopolitical tensions add another layer.
US restrictions aimed to preserve technological leadership, yet they appear to have spurred innovation in China. DeepSeek’s success demonstrates how constraints can drive ingenuity, much like businesses that thrive by maximising limited resources during economic downturns.
The episode forces a reassessment of AI economics. Lower barriers to high-performance models could democratise access, spurring broader adoption and new applications. For Nvidia and its peers, it underscores the need to adapt to a landscape where software efficiency challenges hardware supremacy. Investors must now weigh whether this marks a temporary shock or the start of a more competitive era in artificial intelligence.
Author:Oje. Ese