Jensen Huang says DeepSeek accelerated global shift toward open source AI
Nvidia highlights open source momentum at CES
Nvidia chief executive Jensen Huang has credited Chinese start up DeepSeek with accelerating a global shift toward open source artificial intelligence. Speaking during his keynote at the Consumer Electronics Show in Las Vegas, Huang said DeepSeek’s work had helped activate broader adoption of open models at a critical moment for the AI industry.
The comments came as Nvidia unveiled its next generation Rubin hardware platform, designed to support the training of increasingly powerful AI systems. By linking hardware innovation with the rise of open source models, Huang signalled that the future of AI will not be shaped by proprietary software alone, but by ecosystems that encourage wider participation and experimentation.
DeepSeek’s impact on the AI landscape
DeepSeek rose to prominence last year after releasing a series of open source models that demonstrated strong performance while requiring significantly fewer computing resources. Its R1 model, in particular, drew attention for lowering the cost barrier associated with training advanced AI systems. According to Huang, this approach helped energise the open source community and pushed the industry to reconsider assumptions about scale and efficiency.
The release of DeepSeek’s models also had immediate market effects. Nvidia shares experienced a brief sell off after investors questioned whether demand for high end chips could weaken if efficient models reduced computing needs. Huang acknowledged the reaction but framed it as short lived, arguing that efficiency gains ultimately expand the market rather than shrink it.
Open source versus closed systems
Huang’s remarks reflect a broader debate shaping the AI sector, the balance between open source and closed, proprietary systems. Closed models have dominated commercial deployments, offering companies tighter control over intellectual property and monetisation. Open source models, by contrast, prioritise transparency, collaboration, and rapid innovation.
By praising DeepSeek’s contribution, Huang suggested that open source development does not threaten Nvidia’s business model. Instead, it increases demand for diverse hardware configurations as more organisations experiment with AI. Lowering entry barriers allows startups, researchers, and enterprises to participate, expanding the overall ecosystem.
Rubin hardware and the next phase of AI
Alongside his comments on open source AI, Huang used the CES stage to introduce Nvidia’s Rubin platform. The new hardware is aimed at supporting more complex training workloads, particularly for reasoning intensive and multimodal AI systems. Nvidia positions Rubin as a response to the growing sophistication of AI models rather than simply their size.
Huang emphasised that as AI systems become more capable, they will require new forms of acceleration and memory architecture. Open source models, he argued, benefit especially from advanced hardware because they are adapted and deployed in a wide range of environments, from cloud data centres to edge devices.
Why efficiency matters to the industry
The success of DeepSeek’s models highlights a shift in how AI progress is measured. Instead of focusing solely on scale, developers are increasingly valuing efficiency, adaptability, and accessibility. Models that achieve strong results with fewer resources can be deployed more widely, including in regions and industries previously excluded by cost.
For policymakers and enterprises, this trend has strategic implications. Open source AI reduces dependence on a small number of dominant providers and encourages domestic innovation. Huang’s acknowledgement of DeepSeek suggests that even leading hardware companies see value in this diversification.
Market reaction and long term outlook
While Nvidia’s stock reaction underscored investor sensitivity to efficiency breakthroughs, Huang remains confident about long term demand. He argues that more efficient models will lead to more applications, more users, and ultimately more computing. In this view, open source AI acts as a multiplier rather than a constraint.
The convergence of open models and powerful hardware could reshape competitive dynamics across the AI industry. Companies that succeed will be those able to support diverse development paths rather than enforcing a single approach.
A signal of changing priorities
Huang’s comments at CES marked a notable moment. By openly crediting a Chinese open source startup for influencing global AI trends, Nvidia acknowledged that innovation is becoming more distributed. The shift toward open source is no longer a fringe movement but a central force shaping how AI is built, shared, and scaled worldwide.
