As the annual NVIDIA GTC developer conference approaches next week, this event, recognized as the annual bellwether for the AI sector, is gaining even greater significance and attention this year. When CEO Jensen Huang steps into the packed ice hockey arena next Monday (local time March 16th), global investors will be focused on the strategic moves he will unveil to tackle intensifying market competition and reinforce the company's position as the leading provider of artificial intelligence chips.

This four-day conference serves not only as a platform for NVIDIA to showcase its latest advancements in chips, data centers, the CUDA software platform, AI agents, and physical AI areas like robotics, but also as a crucial test of the company's strategic direction. Following a recent earnings report that surpassed expectations yet failed to significantly boost the stock price, investors are seeking reassurance that NVIDIA's strategy of reinvesting profits into the AI ecosystem is yielding results.

Jacob Bourne, an analyst at market research firm eMarketer, stated, "I expect NVIDIA to present an updated full-stack roadmap, from Rubin to Feynman, while emphasizing inference, agentic AI, networking technologies, and AI factory infrastructure."

**Competitive Focus in the "Post-Training Era": Inference Chips**

As the AI industry accelerates its transition from the "training" phase for large models to the "inference" phase where AI agents execute tasks within applications, the competitive landscape is undergoing significant changes. Although NVIDIA currently holds over 90% market share in both training and inference, analysts widely believe a loss of market share is inevitable, particularly in the inference segment.

Sid Sheth, Founder and CEO of inference chip startup d-Matrix, noted that while NVIDIA will maintain dominance in training, "inference is a whole different story." He added that CUDA, the core software that supports most AI training and locks developers into NVIDIA's ecosystem, provides a weaker "moat" in the inference field. Developers can more easily switch to competitors because running completed AI models does not require the complex programming needed to train them.

To address this trend, NVIDIA is expected to introduce new products optimized specifically for inference workloads. Reports suggest a new inference chip, potentially incorporating technology from AI startup Groq, acquired last December for $1.7 billion, may be unveiled, aimed at delivering fast and cost-effective inference computing capabilities. Groq's ultra-fast AI technology is expected to be integrated into NVIDIA's vast CUDA ecosystem, thereby strengthening its software moat.

**Potential Threats and NVIDIA's Defensive Measures**

Nevertheless, challenges remain significant. On one hand, key NVIDIA customers, including OpenAI and Meta Platforms, have initiated their own chip development projects, with Meta explicitly planning to release a new AI chip every six months. The rise of Application-Specific Integrated Circuits (ASICs), customized for specific functions, is seen as a long-term threat to NVIDIA's general-purpose GPUs, as they demonstrate higher efficiency advantages in inference scenarios.

KinNgai Chan, Managing Director at Summit Insights Group, stated that compared to a year ago, NVIDIA will undoubtedly face fiercer market competition. He anticipates that by 2027, as companies scale up their own ASIC chips, NVIDIA's market share will decline, especially in the inference chip market.

To counter these challenges, NVIDIA is strengthening its defenses on multiple fronts. Beyond the Groq acquisition, the company recently invested $2 billion in optical communications companies Lumentum and Coherent, aiming to advance the application of "co-packaged optics" (CPO) technology. This technology uses light instead of electrical signals to transmit data between chips, potentially greatly enhancing connection efficiency and reducing power consumption in hyperscale data centers.

William Blair research analyst Sebastien Naji anticipates that CPO will be a core breakthrough area for the next-generation Feynman chip architecture. eMarketer's Bourne added that NVIDIA will likely position CPO technology at GTC as critical for efficiently connecting large-scale AI clusters. However, the current production scale of this technology cannot yet match the shipment volume of NVIDIA's chips, and the cost and feasibility of its large-scale deployment will also be a key focus for investors.

On another front, the role of Central Processing Units (CPUs), long dominated by Intel and AMD, is regaining importance in AI tasks. William McGonigle, an analyst at Third Bridge, pointed out that with the rise of agentic AI, the "agent orchestration layer" handled by CPUs is becoming a new performance bottleneck. Consequently, the analyst expects NVIDIA to showcase server products utilizing only its own CPUs in response to this new trend.

**AI Agents and Robotics: Driving the Next Wave of Growth**

Beyond hardware competition, the market is equally focused on whether the prospects for AI applications can support sustained demand for computing power. Jensen Huang has previously emphasized that agentic AI will become the next major driver of inference demand. d-Matrix's Sheth indicated that as the potential of voice, video, and multimodal AI agents is gradually unlocked, this field could bring a new wave of inference computing.

Robotics is seen as another area for growth. Daniel Newman, CEO of The Futurum Group, noted that NVIDIA reported approximately $6 billion in revenue related to robotics last quarter and predicted a very "aggressive" development timeline for humanoid robots. This suggests that physical AI might become a reality sooner than anticipated.

**Geopolitics: The Sword of Damocles Hanging Over the Chip Giant**

Apart from technological competition, geopolitical factors are increasingly becoming a critical variable influencing NVIDIA's future. As the US considers further expanding export restrictions on AI chips, and access to key markets like China becomes limited, NVIDIA's global sales map is being reshaped. Reports indicate that after facing significant challenges in the Chinese market, NVIDIA has halted production of the H20 chip and shifted capacity to the next-generation Rubin platform.

In this context, massive AI infrastructure investments in Middle Eastern countries like Saudi Arabia and the UAE hold substantial importance for NVIDIA. However, factors such as regional conflicts, energy costs, and the pace of data center construction add uncertainty to the demand from these emerging markets.