NVIDIA’s Leadership in AI: Key Insights from Jensen Huang’s GTC Keynote

We’ve explored the evolution of AI, NVIDIA’s strategic positioning, and its impact at each stage. The breakthrough of the GeForce 5090 will drive the shift from Perceptual AI to Generative AI.

Next, Agentic AI will evolve into Physical AI, and these two will eventually merge, creating a profound real-world impact.

While NVIDIA has established itself as the dominant player in the AI ecosystem, the varying hardware needs across industries and use cases will spur competitors to find more cost-effective alternatives. As technology advances, this competition will only intensify.

Our Perspective

1.  What Did We Learn from Jensen Huang’s Keynote?

Last year, NVIDIA made its return to in-person events, creating an atmosphere akin to a rock concert. Some even called it the “Woodstock of AI,” while this year’s event has been dubbed the “Super Bowl of AI.” As the GPU Technology Conference (GTC) continues to grow in scale and influence, AI has firmly positioned itself at the forefront of global technological advancement.

In Jensen Huang’s keynote, we mapped the evolution of AI, NVIDIA’s strategic positioning, and its industry impact at each stage. As illustrated in Table 1, AI development unfolds in several key phases, with NVIDIA playing a crucial role at each stage by providing the essential computing power through its GPU technology.

Table 1   NVIDIA’s AI Strategy

Source: Researcher and Research

NVIDIA is evolving into the central supplier of the AI ecosystem, moving beyond its role as a GPU manufacturer. By actively integrating technologies such as CUDA, Omniverse, AI agents, and robotics, it is creating an irreplaceable software-hardware moat.

This shift suggests that Agentic AI could disrupt SaaS, enterprise software, marketing models, and decision-making processes, with significant implications for e-commerce strategies. More importantly, the next phase of AI development will extend beyond software, influencing hardware devices, automation applications, and physical-world AI technologies. For example, Physical AI is poised to transform industries like manufacturing, logistics, autonomous vehicles, and other automation sectors.

Next, we will delve deeper into these topics.

2.  How NVIDIA Bridges Perceptual AI to Generative AI

In his keynote, Jensen Huang highlighted the technological advancements of the GeForce 5090, including a 30% reduction in size, a 30% improvement in cooling efficiency, and performance that far exceeds the 4090. We view this as a critical “computational infrastructure” for NVIDIA, bridging the development path from Perceptual AI to Generative AI.

Through advanced chip fabrication, packaging technologies, and optimized architectures, the 5090 significantly boosts GPU performance, accelerating AI computations across applications such as gaming rendering, 3D design, and AI-generated content.

NVIDIA has deeply integrated AI into the core of its GPUs, incorporating AI-assisted rendering techniques like 100% path tracing and AI-based pixel completion to demonstrate AI’s evolving role in graphics processing. This has the potential to disrupt workflows in the gaming and animation industries, while also transforming computational methods used across software and content creation sectors. This breakthrough is poised to make a lasting impact on game development, animation production, professional computing, and AI training.

3.  Will the Next Wave Be Agentic AI or Physical AI?

The next phase in the evolution of Generative AI will likely move toward either Agentic AI or Physical AI. While there is no strict order, their relationship can be understood from a technological development perspective.

Agentic AI refers to AI systems with autonomous decision-making capabilities. These systems can perform tasks based on goals, environmental changes, and context, rather than simply responding to commands. It primarily develops in the “digital world,” providing AI with autonomy but without direct influence over the “physical world.”

In contrast, Physical AI involves AI systems capable of performing actions in the real world, interacting physically with their environment. Common applications include robots, autonomous vehicles, and smart factories. Physical AI depends on Agentic AI for decision-making and incorporates technologies like sensors and motion control to facilitate real-world interactions. Therefore, Physical AI can be seen as the natural evolution of Agentic AI.

Ultimately, these two areas will converge. The future of Physical AI will likely be driven by advanced Agentic AI, enabling robots and autonomous vehicles to make independent decisions and exert real-world influence. This is why NVIDIA is initially focusing on developing technologies related to Agentic AI before advancing into Physical AI.

3.1  Agentic AI Will Redefine Software Companies

Agentic AI represents the next evolution in AI, shifting from a passive “responder” to an active, autonomous decision-maker. In the future, AI will not only react to commands but will also perceive its environment, understand context, and engage in reasoning, planning, and action. It will even use tools to execute tasks. With the ability to browse the web, read texts, watch videos, and learn from these interactions, Agentic AI will evolve beyond relying on fixed datasets and will possess “self-learning” capabilities.

This transition will have a profound impact on industries like search engines, digital marketing, SEO, and e-commerce recommendation systems, as AI moves from being a supportive tool to becoming the ultimate decision-maker. For instance, AI could autonomously optimize marketing campaigns or adjust e-commerce strategies based on real-time data and insights.

Furthermore, this shift will challenge traditional SaaS systems, such as CRM and ERP software, which businesses currently rely on. AI will no longer be just an enhancement to these systems; businesses will require AI agents with decision-making abilities to directly execute tasks. Traditional software, which relies on manual inputs and predefined workflows, will no longer meet the demands, pushing companies toward more adaptive and intelligent solutions driven by Agentic AI.

3.2  Physical AI and the Robotics Revolution: A Turning Point for Manufacturing and Logistics

NVIDIA envisions Physical AI not only enabling AI to understand data but also allowing it to influence the physical world. This technological leap will drive advancements in robotics, transitioning from rigid, fixed production lines to more flexible, intelligent robots. Physical AI’s ability to understand concepts like friction, inertia, causality, and object permanence (i.e., objects don’t disappear, they are just temporarily hidden) demonstrates how AI is beginning to comprehend the physical world in a way that empowers robots to learn and adapt autonomously.

This evolution will significantly impact industries such as smart logistics, autonomous vehicles, warehouse management, and even urban planning. As AI becomes more adept at interacting with the real world, it will enhance systems reliant on precise physical interactions. Autonomous vehicles and robots within logistics and warehouse operations will be able to navigate complex environments, performing tasks with greater efficiency, safety, and flexibility.

Consequently, logistics and supply chain companies must closely track the development of robotics and AI computing solutions. These innovations are set to reshape operations and business models in industries that depend on physical tasks and movements.

4.  Conclusion and Discussion

4.1  NVIDIA: From GPU Manufacturer to AI Ecosystem Leader

Jensen Huang highlighted how GeForce played a key role in promoting CUDA globally, catalyzing the rise of AI, and now, AI itself is revolutionizing the world of computer graphics. This signifies that NVIDIA has evolved beyond its roots as a graphics computing company, positioning AI computing as its central competitive advantage.

In the past, real-time graphics rendering relied heavily on path tracing technology, where each pixel was rendered mathematically, with AI inferring the other pixels. Today, AI directly participates in graphics creation, suggesting that future GPUs will likely integrate AI computing even more deeply. This shift is not only transforming gaming but is poised to extend into fields like medical imaging, scientific simulations, and 3D design.

NVIDIA has fully integrated AI with its GPUs, creating a powerful synergy between hardware and software. Moving from CUDA to AI computing, and now to AI-enhanced graphics, NVIDIA has transitioned from being a graphics card company to a leader in AI computing. AI is no longer just influencing computation methods; it now plays an active role in decision-making and data processing, understanding context, generating responses, and retrieving information to enhance understanding.

NVIDIA’s software strategy—spanning CUDA, Omniverse, and generative AI models—has become its core strength, shifting away from a hardware-centric business model. Instead, NVIDIA has locked industries into its software ecosystem, creating a robust competitive advantage. Specifically:

• CUDA forces developers to run AI workloads on NVIDIA chips, reinforcing its market dominance.
• AI Agents enable businesses to deploy AI decision-making systems directly on NVIDIA platforms, enhancing operational efficiency.
• Omniverse leads the charge in the industrial metaverse, making NVIDIA the exclusive leader in this space.
• Physical AI opens new opportunities in robotics, positioning NVIDIA as a key player in the automation future.

Through this integrated hardware-software strategy, NVIDIA has evolved from simply “selling GPUs” to controlling the entire AI industry’s computational and development landscape, making it increasingly difficult for competitors to challenge its dominance.

4.2  How Will the AI Industry’s Competitive Landscape Evolve?

The AI industry has evolved into a core technology domain, transitioning from a specific application to a disruptive force across multiple sectors. Each wave of AI innovation presents three primary challenges: data, training, and scalability. These factors will play a pivotal role in shaping the future competitive landscape, particularly in areas like data acquisition, computing performance, and large-scale deployment.

NVIDIA has established a near-monopoly in the AI training market, competing with industry leaders such as Google, Meta, and OpenAI in both AI training and inference. While NVIDIA maintains its dominance in AI training, the focus of future competition will shift towards inference—reducing costs, enhancing efficiency, and developing specialized chips. As ASIC (Application-Specific Integrated Circuit) technology progresses, competitors are intensifying efforts to create cost-effective solutions and secure breakthroughs in the inference market.

4.2.1  High-Performance Inference Chips (ASIC vs. GPU)

NVIDIA’s GPUs remain dominant in the high-performance computing sector. However, with the rise of specialized AI chips, particularly in cloud AI inference and edge computing, the cost advantages of ASIC technology are becoming more apparent. Notable examples include Google’s TPU, AWS’s Inferentia, Meta’s custom AI chips, and Tesla’s Dojo, all of which are increasingly challenging NVIDIA’s market leadership. As these competitors’ solutions mature, GPUs may face increasing challenges from ASICs, especially in inference applications, and potentially even in training.

4.2.2  Software-Hardware Integration and Ecosystem Development

Beyond hardware competition, the AI software ecosystem has become a crucial battleground. NVIDIA leverages its ecosystem, including CUDA and TensorRT, to strengthen its market position. However, Google, AWS, and Meta are actively developing their own AI software frameworks, such as TensorFlow and PyTorch, to reduce reliance on NVIDIA’s technology. In the future, as AI software and hardware become more tightly integrated, major companies will focus on building their own ecosystems while attempting to weaken NVIDIA’s influence in the software domain. The competition will center not only on hardware performance but also on the ability to establish seamlessly integrated software ecosystems.

4.2.3  Cloud vs. Edge AI Computing

Cloud computing remains the primary platform for AI training, but the rise of Edge AI is driving a significant shift. As applications like smart vehicles, automated factories, and IoT devices expand, the demand for edge computing continues to grow. Products such as NVIDIA Jetson and Tesla’s FSD chips are increasingly establishing strong footholds in the Edge AI market. As Edge AI progresses, these chips—designed for autonomous driving, smart devices, and industrial automation—will compete with large-scale cloud AI training platforms, collectively accelerating the adoption and evolution of AI technology.

4.3  Why Do So Many Companies Challenge NVIDIA Despite Its Unshakable Dominance?

NVIDIA’s unshakable dominance in AI stems from its highly integrated ecosystem, making it difficult for competitors to surpass in the short term. However, many companies continue to challenge its position, reflecting the complexity of the industry’s competitive landscape.

Technology evolves rapidly, and different applications have distinct hardware requirements. In some areas, specialized ASICs offer more cost-effective solutions than GPUs. Consequently, companies like Google, Meta, and AWS are developing their own custom chips to reduce reliance on NVIDIA’s products.

In summary, while NVIDIA’s leadership in AI remains formidable, competitors persist in challenging its position due to the diverse hardware demands across industries and applications, as well as the drive for more cost-efficient alternatives. This means NVIDIA will continue to face competition, particularly in the AI inference market.

This article is part of our Future Scenarios and Design series.
It explores how possible futures take shape through trend analysis, strategic foresight, and scenario thinking, including shifts in technology, consumption, infrastructure, and business models.

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