The Shifting Sands of AI Demand and NVIDIA’s Stock Performance
NVIDIA, the long-reigning champion of the AI chip market, has experienced a significant downturn in its stock price since the beginning of 2025. This decline is particularly striking given the company’s outstanding financial performance in the fourth quarter of its fiscal year 2025. NVIDIA reported record revenue and profits, exceeding analyst expectations, primarily driven by the strong performance of its data center division. This seemingly paradoxical situation – stellar financial results coupled with a declining stock price – raises a crucial question: Is NVIDIA’s exceptional growth sustainable in the face of a rapidly evolving AI landscape?
While supply chain reports and statements from CEO Jensen Huang suggest continued strong demand for NVIDIA’s AI accelerator cards, especially in the Chinese market, a growing sense of unease permeates the market. Geopolitical risks are escalating, and investor focus is shifting from simply assessing the magnitude of NVIDIA’s growth to questioning its longevity. This shift in sentiment is reflected in NVIDIA’s forward price-to-earnings ratio, which has cooled considerably. This cooling trend coincides with a pivotal development in the AI field: the rise of DeepSeek and its R1 version.
DeepSeek: A Paradigm Shift in AI Computing
Launched in January 2020, DeepSeek’s R1 has emerged as a potential disruptor in the AI industry. This AI tool is characterized by its reduced computing power requirements while simultaneously emphasizing reasoning capabilities. This unique combination has fueled a growing belief that the global demand structure for AI computing power is undergoing a fundamental transformation.
DeepSeek’s R1 model utilizes a ‘chain of thought’ architecture. Although this approach increases the computing power consumption for a single inference request compared to traditional models, it achieves a remarkable 70% reduction in hardware costs through sophisticated algorithm optimization. The market’s response to DeepSeek’s unveiling was immediate and pronounced: NVIDIA’s stock price experienced a significant drop in the days following the public release.
The Ascendance of Reasoning in AI
The growing importance of reasoning capabilities in AI is further corroborated by research from Morgan Stanley. Their findings indicate a dramatic surge in the proportion of reasoning in the demand for computing power within U.S. data centers. As individuals and businesses increasingly seek AI applications that extend beyond the capabilities of current popular chatbots, reasoning is poised to become a central pillar of AI technology’s demand.
Industry analysts are projecting that capital expenditures for ‘frontier AI,’ specifically focused on reasoning, will surpass those allocated to training within the next two years. This represents a monumental shift in investment priorities within the AI sector, signaling a move away from the traditional emphasis on raw processing power for training large language models.
Cerebras Systems: Challenging NVIDIA’s Dominance
DeepSeek is not alone in its disruptive efforts. Other companies, most notably Cerebras Systems, are also challenging the established order and eroding the premium space that NVIDIA has long enjoyed. Cerebras, a relatively young company, has made significant strides in the reasoning chip market with its innovative ‘wafer-scale chip’ technology.
Cerebras’ latest offering boasts significantly faster performance than NVIDIA’s GPU solutions on specific models, while simultaneously achieving substantial cost reductions. This is achieved through a revolutionary approach that involves using an entire wafer as a single chip. This ‘All-in-One Wafer’ design eliminates communication bottlenecks between individual chips, resulting in a dramatic increase in memory bandwidth and computing density.
The commercial implications of this architectural shift are profound. In deployments of AI supercomputers, Cerebras systems have demonstrated significant reductions in training time and energy consumption. Furthermore, the company has launched an inference service that claims superior speed and cost-effectiveness compared to traditional GPU solutions. Cerebras is actively expanding its infrastructure, adding new AI data centers to significantly boost its inference capacity. This aggressive expansion underscores the company’s commitment to challenging NVIDIA’s dominance in the AI hardware market.
Software-Defined Hardware: A New Era in AI Computing
Beyond providing a cost-effective and high-performance AI inference solution, DeepSeek is also championing the trend of software-defined hardware. Through collaborations, DeepSeek is enabling mid-range GPUs to support high-end models, directly impacting the premium space occupied by NVIDIA’s high-end offerings. This approach democratizes access to advanced AI capabilities, potentially reducing the reliance on expensive, high-end hardware.
This confluence of factors – the rise of DeepSeek, Cerebras’ innovative technology, and the trend towards software-defined hardware – has led to a noticeable shortening of the global AI server delivery cycle. The market is interpreting this as a potential indicator of an impending oversupply of AI server capacity, further contributing to the downward pressure on NVIDIA’s stock price.
Shifting Alliances and the Rise of In-House Chip Development
Even staunch supporters of NVIDIA’s AI prowess are showing signs of recalibrating their strategies. Microsoft, a long-time ally and major customer of NVIDIA, has canceled leases for some data centers. CEO Satya Nadella has publicly acknowledged that the current return on investment for AI applications does not justify the existing investment intensity. This suggests a potential slowdown in the aggressive expansion of AI infrastructure that has fueled NVIDIA’s growth in recent years.
Other major players, such as Oracle, are diversifying their AI infrastructure investments, allocating a portion of their orders to Cerebras. Meanwhile, Meta is pursuing a path of self-sufficiency, increasing its investment in self-developed chips. This trend towards in-house chip development by major tech companies represents a significant threat to NVIDIA’s dominance, as it reduces their reliance on external suppliers.
These developments are collectively exerting downward pressure on NVIDIA’s stock price. Financial models suggest that a significant drop in NVIDIA’s market share of inference chips could lead to a substantial compression of its forward price-to-earnings ratio. Investment funds are also showing signs of reduced confidence, with some reducing their holdings in NVIDIA.
The Bullish Case for NVIDIA: A Counterargument
Despite the mounting challenges, some remain optimistic about NVIDIA’s prospects. Proponents point to the potential for continued growth in NVIDIA’s data center revenue, contingent on the expansion of manufacturing capacity for advanced packaging technologies, such as TSMC’s CoWoS (Chip-on-Wafer-on-Substrate).
CEO Jensen Huang remains steadfast in his belief in the enduring demand for NVIDIA’s solutions, emphasizing the importance of increased computation for both training and reasoning in achieving smarter AI models. Major orders and ambitious projects from industry giants, such as Tesla’s continued investment in NVIDIA hardware for its self-driving initiatives, continue to inject a degree of confidence into the market.
Some analysts argue that the current stock price adjustment is a temporary setback, a correction in a market that had perhaps become overly exuberant about NVIDIA’s prospects. They maintain that the long-term trend of global AI infrastructure investment remains intact, and that NVIDIA is well-positioned to benefit from this trend.
A Valuation Reconstruction Driven by Algorithmic Advancements
The ongoing adjustment in NVIDIA’s stock price is noteworthy for its underlying cause. It represents a valuation reconstruction driven not merely by incremental hardware iterations, but by a fundamental revolution in algorithms. DeepSeek’s ‘software-defined computing power’ model is challenging the long-standing paradigm of growth driven by Moore’s Law, which posits that the number of transistors on a microchip doubles approximately every two years, leading to exponential increases in computing power.
The shift towards software-defined hardware and the emphasis on reasoning capabilities suggest that algorithmic advancements are becoming increasingly important in determining the performance and efficiency of AI systems. This could potentially diminish the importance of raw hardware power, impacting NVIDIA’s traditional advantage.
NVIDIA’s Remaining Advantages: Ecosystem, Technology, and Cash Flow
In the near term, NVIDIA still possesses several key advantages that could help it weather the current storm:
Ecological Barriers: NVIDIA boasts a massive and deeply entrenched developer community around its CUDA platform, a parallel computing platform and programming model that allows developers to leverage the power of NVIDIA GPUs. This ecosystem advantage is significant, as it creates a high barrier to entry for competitors. Switching costs for developers who are already proficient in CUDA are substantial.
Generational Advantages: NVIDIA maintains a lead in advanced process technology, consistently delivering GPUs with cutting-edge performance and efficiency. This technological leadership allows NVIDIA to command premium pricing and maintain a competitive edge.
Abundant Cash Flow: NVIDIA’s strong financial position, fueled by its high profitability, enables substantial investments in research and development. This allows the company to continue innovating and developing new technologies to stay ahead of the competition.
However, the ultimate question is whether these advantages will be sufficient to withstand the ongoing paradigm shift in the AI landscape. The answer hinges on NVIDIA’s ability to adapt and potentially transform itself from a primarily hardware-focused company to a more diversified provider of AI solutions, perhaps even embracing the concept of ‘computing power service provider.’
A Seismic Shift in the AI Landscape: The Long-Term Outlook
In the long run, this industry upheaval, triggered by the evaporation of trillions in market value, has the potential to reshape the global AI power landscape. The rise of alternative approaches, such as those championed by DeepSeek and Cerebras Systems, the emphasis on reasoning capabilities, and the shift towards software-defined hardware are all contributing to a dynamic and evolving environment.
The coming years will undoubtedly be a defining period for NVIDIA and the entire AI industry. The established order is being challenged, and the future of AI computing is being rewritten. The emergence of new players, innovative technologies, and evolving market demands is creating a landscape ripe with both opportunities and uncertainties. NVIDIA’s ability to navigate this complex and rapidly changing environment will determine its long-term success and its position in the future of AI. The company’s response to these challenges – whether it embraces the shift towards software-defined hardware, invests heavily in reasoning-focused technologies, or doubles down on its existing strengths – will be crucial in shaping its destiny.