Agentic AI Era: NVIDIA’s Technological Revolution and Ambitions

In the field of artificial intelligence, technological advancements often reshape our world at an astonishing pace. Today, we stand on the cusp of a new technological revolution – the dawn of the Agentic AI Era. In this revolution, NVIDIA is not just a participant but a leader. Jensen Huang’s speech at CES 2025^[1] was akin to a technological feast, showcasing NVIDIA’s latest products and technologies, while also hinting at the future direction of AI development. Let us unveil NVIDIA’s technological blueprint and explore how it is redefining the future of AI through Agentic AI, solidifying its leadership position in the global AI industry.

Jensen Huang’s Stunning Appearance at CES: The Prelude to a New AI Era

At the CES 2025 exhibition, NVIDIA founder and CEO Jensen Huang’s speech shocked the global tech community. This was not just a product launch, but a declaration of the beginning of a new era in AI. Huang’s speech focused on three main aspects: Blackwell GPU, Cosmos World Model, and Project Digits.

Blackwell GPU: A Leap in Performance

Huang first revealed NVIDIA’s latest GPU series – the RTX Blackwell. The performance specifications of this GPU are staggering:9.2 billion transistors, 4,000 TOPS of AI computing power, three times the improvement over the previous Ada architecture. Even more astonishing, Blackwell is not just a graphics processor; it is an AI super engine. According toNVIDIA’s official press release^[2], the performance of the Blackwell architecture is three times that of the Ada architecture, and about 100% better than the Hopper architecture.

By integrating neural network processing capabilities, Blackwell achieves neural texture compression and neural material shading, meaning it can generate unprecedentedly realistic images. Huang showcased real-time rendering scenes powered by Blackwell, demonstrating the incredible potential of AI-driven computer graphics. In terms of performance, the gaming performance of the Blackwell architecture’s RTX 5090 is 35-50% better than the RTX 4090 (source^[3]).

The above image illustrates the performance improvement of the Blackwell GPU compared to its predecessors. The green part represents the Ada architecture, the blue part represents the Hopper architecture, and the orange part represents the Blackwell architecture. We can clearly see that the performance of the Blackwell architecture is three times that of the Ada architecture, and about 100% better than the Hopper architecture. This leap forward provides a strong hardware foundation for the development of Agentic AI.

Cosmos World Model: A Milestone in Physical AI

Next, Huang unveiled NVIDIA Cosmos – the world’s first foundation model for the world. This model has been trained on 20 million hours of real-world video, focusing on understanding the dynamic nature of the physical world. This video data includes 90 trillion annotations, covering various aspects such as human interactions, environments, industries, robotics, and driving (source^[4]).

The significance of Cosmos lies in its ability to provide AI with the capability to understand and simulate the real world. Whether for testing autonomous vehicles or training robots, Cosmos can generate physically accurate simulated scenarios. This breakthrough technology will greatly accelerate the development of AI applications in the physical world.

NVIDIA’s official blog^[5] provides detailed insights into how Cosmos works and its potential applications. For example, in the autonomous driving field, Cosmos can generate countless complex traffic scenarios, helping autonomous systems better respond to various situations. In robotics, Cosmos can simulate various physical environments, accelerating the learning process of robots. According to the information provided in the blog, developers can use the NVIDIA Omniverse platform to build physically accurate simulation scenarios and input the rendering data of these scenarios into the Cosmos model to generate realistic synthetic data.

Project Digits: The “Pocket Version” of AI Supercomputing

The climax of the speech was the release of Project Digits – a desktop-level AI supercomputer. This compact device is equipped with NVIDIA’s latest GB110 chip and can run the complete NVIDIA AI software stack.

The significance of Project Digits is that it brings enterprise-level AI computing power to personal desktops. This means that in the future, every engineer, researcher, and even creative worker may have an AI supercomputer on their desk. According toreports^[6], Project Digits boasts an AI computing power of 1 petaflop, capable of running AI models with up to 200 billion parameters. This level of computing power was previously only available in large data centers, but now can be placed on a desk, which will undoubtedly accelerate the pace of AI innovation significantly. According to NVIDIA’s official press release^[7], the starting price of Project Digits is $3,000, and it is expected to be officially released in May 2025.

Agentic AI: Maximizing Test Time Scaling

Agentic AI is the concept emphasized by NVIDIA at this CES. It represents a new stage in AI development, taking the idea of Test Time Scaling to the extreme. According to NVIDIA’s official blog^[8], Agentic AI refers to an AI system capable of autonomously understanding, reasoning, planning, and executing complex tasks.

Test Time Scaling: The “Dynamic Cooking” of AI

Test Time Scaling can be likened to AI’s “dynamic cooking”. Traditional AI models typically use fixed resource allocation during inference, much like cooking according to a fixed recipe. In contrast, Test Time Scaling allows AI to dynamically adjust resource allocation based on the complexity of the task, similar to a flexible chef adjusting cooking methods based on different dishes. According to a Forbes article^[9], by allocating more computing resources during the testing phase, Test Time Scaling can significantly enhance model performance.

NVIDIA has taken this concept to new heights with Agentic AI.Agentic AI can autonomously analyze problems, formulate multi-step plans, and continuously adjust strategies during execution. This is akin to a top chef who can flexibly adjust cooking plans based on ingredients, diners’ preferences, and current kitchen conditions.

The above image illustrates the workflow of Test Time Scaling. When the system receives a task, it first evaluates the complexity of the task. For simple tasks, the system performs standard inference. For complex tasks, the system reallocates resources, performs multiple inferences, and then merges the results. This ability to dynamically adjust allows the AI system to handle tasks of varying complexity more efficiently. Specifically, it can save computing resources on simple tasks while allocating more resources for complex tasks to improve accuracy.

NVIDIA Nemo: Intelligent Resource Allocation for AI

To support the development of Agentic AI, NVIDIA has launched the Nemo framework. Nemo is not just a development tool, but an intelligent manager for AI resource allocation. According to NVIDIA’s official documentation^[10], the Nemo framework is specifically optimized for building large language models and generative AI models.

Nemo employs various parallelization strategies, including data parallelism, tensor parallelism, and pipeline parallelism. These strategies allow AI models to automatically optimize the allocation and utilization of computing resources based on task requirements and available hardware resources.

Moreover, Nemo supports dynamic tensor parallelism, allowing it to adjust the tensor parallelism degree during training to meet the computational needs of different layers. For example, certain layers of the model may require more parallel computation to accelerate processing, while others may need less parallelism to conserve resources. The Nemo framework can automatically adjust parallel strategies based on these needs. This flexibility enables Agentic AI to achieve unprecedented efficiency when handling complex tasks. According to the documentation, the Nemo framework also supports memory optimization techniques such as mixed precision training and activation recomputation, enabling the training of larger-scale models.

Breaking the AI Ceiling: Agentic AI Ignites a New Round of Revolution

The emergence of Agentic AI signifies that AI technology has broken through the limitations of traditional models, ushering in a new stage of development.

The “Fixed Menu” Dilemma of Traditional AI

Traditional AI models face many challenges when handling complex tasks. They often rely on predefined rules and algorithms, making it difficult to respond to dynamically changing environments. It is like a fixed menu, unable to adjust according to diners’ real-time needs and taste preferences. According to an article from Dataversity^[11], traditional AI models typically perform well on tasks with clear rules and patterns, but exhibit limitations in tasks requiring creativity, adaptability, and understanding of dynamic environments.

For example, in the field of autonomous driving, traditional AI models may struggle to handle complex road conditions. When faced with rare traffic situations or unexpected events, these models may fail to respond appropriately due to their lack of flexible strategy adjustment capabilities.

Agentic AI: A “Personal Chef” Tailored for Every Task

In contrast, Agentic AI is like a personal chef who can customize menus for each guest. It possesses the following revolutionary features:

1. Autonomous Decision-Making: Agentic AI can autonomously analyze situations, formulate plans, and execute actions without continuous human supervision.
2. Complex Task Handling: It can break down complex tasks into manageable steps and systematically execute these steps. For example, it can autonomously plan a route from point A to point B and dynamically adjust based on real-time traffic conditions.
3. Continuous Learning: By learning and improving through experience, Agentic AI can optimize its decision-making processes over time. This means it can gradually master more efficient solutions through continuous experimentation and feedback.
4. Goal-Oriented: It has a clear goal orientation, actively seeking to achieve specific objectives. For example, it can set a goal such as “delivering goods to the destination in the shortest time” and autonomously find the best route.
5. Context Awareness: It can understand complex contextual information and make decisions based on this understanding. This means it can make the most appropriate decisions based on the current specific context.

These features enable Agentic AI to adapt to various complex scenarios and tasks, greatly expanding the scope of AI applications.

Revolutionary Applications: A New Paradigm in Autonomous Driving, Robotics, and Drug Development

The potential of Agentic AI has been fully demonstrated in multiple fields:

In the autonomous driving sector, NVIDIA’s collaboration with Toyota is a typical example. They are jointly developing the next generation of autonomous driving systems. Agentic AI can better handle complex traffic scenarios, adjust decisions in real-time, and enhance safety and efficiency. For instance, when encountering road construction or unexpected accidents, Agentic AI can quickly assess the situation, formulate alternative routes, and coordinate with other vehicles to ensure smooth and safe traffic. According to Toyota’s official press release^[12], Toyota plans to use the NVIDIA DRIVE AGX Orin platform to develop its next-generation autonomous vehicles.

In the robotics field, NVIDIA’s Isaac Groot platform demonstrates the powerful potential of Agentic AI. Through this platform, robots can learn from a small number of human demonstrations and significantly expand training datasets through synthetic data generation techniques. This means robots can learn complex tasks more quickly and exhibit greater flexibility and adaptability in practical applications. For example, robots can learn how to assemble furniture quickly by observing humans.

In the drug development field, Agentic AI is changing traditional research and development processes. It can autonomously analyze vast amounts of data, predict drug-target interactions, and optimize screening strategies. This not only accelerates the drug development process significantly but also increases the success rate. For instance, during the COVID-19 pandemic, Agentic AI systems were able to analyze a large number of potential drug compounds in a short time, quickly identifying promising candidates and significantly shortening development cycles. According to an article from Akira AI^[13], Agentic AI can reduce the drug discovery process from years to months, or even days.

These applications not only showcase the powerful capabilities of Agentic AI but also signal that it will fundamentally change the way these industries operate and innovate. We can expect to see more groundbreaking innovations driven by Agentic AI in the near future.

Redefining Human-Machine Interaction: When AI Becomes Your Digital Twin

The emergence of Agentic AI is not just a technological advancement, but a profound transformation in human-machine interaction models. It will fundamentally change the way we interact with AI systems, turning AI from a passive tool into an active partner.

Goodbye to Passive Responses: A New Era of Proactive AI

Traditional AI assistants typically respond passively to user commands. However, Agentic AI possesses proactivity and predictive capabilities, enabling it to anticipate users’ needs.

In the retail sector, Agentic AI has demonstrated remarkable proactivity. For example, according to a report from CXTODAY^[14], a leading e-commerce platform developed an intelligent shopping assistant utilizing Agentic AI technology that can proactively identify customers abandoning their shopping carts. The system analyzes customers’ browsing history, purchasing patterns, and current market trends to automatically generate personalized reminder emails and offer targeted discounts. This not only improves conversion rates but also significantly enhances customer satisfaction.

In the healthcare sector, the proactivity of Agentic AI is even more pronounced. According to an article from Itechnotion^[15], an AI health management system developed by a well-known medical technology company can actively monitor patients’ health records. It not only reminds patients of routine check-ups but also predicts potential health risks based on their medical history, lifestyle, and the latest medical research, providing personalized preventive advice. For example, for a middle-aged patient with a family history of diabetes, the system may recommend more frequent blood sugar monitoring and suggest suitable diet and exercise plans.

Personalized to the Extreme: How AI Truly “Understands” You

A key feature of Agentic AI is its powerful personalization capability. It can not only understand users’ immediate needs but also gain insights into their long-term preferences and behavior patterns. According to a blog from Zingly AI^[16], Agentic AI can build user profiles by analyzing historical interaction data, thus providing more personalized services.

In the travel industry, Agentic AI is redefining the standards for personalized services. An internationally renowned online travel platform has developed an intelligent travel planning system using Agentic AI technology. This system can not only recommend destinations based on customers’ travel histories and preferences but also consider their personality traits, current mood, and interactions on social media. For instance, for a customer who has just experienced work stress, the system might recommend a tranquil beach vacation destination rather than a bustling city; for a food lover, the system would pay special attention to the destination’s specialty restaurants and cooking classes.

In the workplace, Agentic AI exhibits astonishing adaptability. An AI work assistant developed by a leading productivity tool company can learn users’ work habits and automatically adjust task priorities. It is not just a simple scheduling tool but a truly intelligent partner that understands users’ working styles. For example, it can identify when users are most productive and schedule the most important tasks for that time. It can also predict potential problems and prepare solutions in advance, significantly enhancing work efficiency.

Future Scenarios: Disruptive Changes in Smart Homes, Personal Assistants, and Office Collaboration

Agentic AI will bring disruptive changes to our daily lives:

In the smart home sector, AI will no longer just be a tool for executing simple commands, but a “smart housekeeper” for the home. Imagine your home AI system being able to automatically adjust the temperature, lighting, and music ambiance based on the weather forecast, your schedule, and your health condition. It could even predict your mood, preparing your favorite relaxation method for you when you return home, whether it’s a cup of hot tea or a movie. According to a blog from Daffodil^[17], future smart homes will be driven by Agentic AI, achieving comprehensive automation and personalization.

In terms of personal assistants, future AI assistants will become our “digital twins”. They will not only handle daily chores like scheduling meetings and filtering emails but also represent us in making preliminary decisions. For instance, they might automatically reply to less important emails based on your preferences and schedule or attend certain online meetings when you are busy, summarizing the key points for you.

In office collaboration, Agentic AI will serve as a valuable assistant to team members. It can automatically assign tasks, coordinate the work of team members, and even participate in creative discussions, providing new ideas and insights. For example, in a product development meeting, the AI assistant might propose innovative product feature suggestions based on market trends and competitor analysis.

The above image illustrates three main application areas of Agentic AI in daily life: smart homes, personal assistants, and office collaboration. These three areas are closely interconnected, forming the intelligent ecosystem of our future lives. In this system, Agentic AI will seamlessly connect our home, personal, and work lives, providing unprecedented convenience and efficiency.

This transformation not only improves efficiency but, more importantly, allows us to focus more on tasks that truly require human wisdom and creativity.The goal of Agentic AI is not to replace humans but to enhance human capabilities, enabling us to better realize our potential..

NVIDIA’s Technological Fortress: A Full-Stack Layout from Chips to Cloud

NVIDIA’s success is not only due to its outstanding hardware technology but also its unique full-stack approach. From chips to software to cloud services, NVIDIA has built a complete AI ecosystem, laying a solid foundation for its leadership position in the Agentic AI era.

How Scaling Laws Guide the Design of the Blackwell Architecture

The design of NVIDIA’s Blackwell GPU architecture is heavily influenced by AI scaling laws. These laws reveal the relationship between model size, data volume, and computing power, guiding the development direction of AI hardware. According to NVIDIA’s official press release^[18], the Blackwell architecture reflects a profound understanding of scaling laws in multiple aspects:

1. Significant Increase in Computing Power: A single Blackwell GPU can provide up to 20 petaFLOPS of FP4 precision AI computing power, and 2,500 TFLOPS of FP16/BF16 computing power, which is five times that of the previous Hopper architecture. This substantial increase in computing power makes training and running larger-scale AI models possible.

2. Significant Increase in Memory Capacity and Bandwidth: Each GPU’s HBM3e high bandwidth memory capacity is up to 192GB, which is 2.4 times that of the previous generation, with a memory bandwidth of up to 8 TB/s. This enhancement addresses the memory bottleneck issues of large-scale AI models, making it possible to handle more complex tasks.

3. Innovative Multi-Chip Design: Adopting a dual-chip design, forming a single unified GPU through a 10 TB/s chip interconnect, breaking through the physical limits of single-chip designs. This design paves the way for future larger-scale AI systems.

These design considerations reflect NVIDIA’s awareness of the continuous growth in AI model scale, computational demands, and system complexity. Through this forward-looking design, NVIDIA not only meets the current demands of AI applications but also prepares for future more complex and larger-scale AI systems. According to an article from Anandtech^[19], the dual-chip design of Blackwell achieves extremely high inter-chip communication bandwidth through NVLink-C2C technology.

Nemo Framework: The Magic Wand that Turns Agentic AI into Reality

NVIDIA’s Nemo framework is a key tool for realizing Agentic AI. It not only provides various functions required for developing AI agents but also optimizes resource allocation and performance through various innovative technologies:

1. Diverse Parallel Strategies: Nemo supports various parallel computing methods, including data parallelism, tensor parallelism, and pipeline parallelism. This flexibility allows developers to choose the optimal parallel strategy based on specific tasks and hardware resources, making full use of available computing resources.

2. Dynamic Tensor Parallelism: It can dynamically adjust the tensor parallelism degree during training to meet the computational needs of different layers. This ability for dynamic adjustment greatly enhances training efficiency, especially when dealing with complex and hierarchically variable models.

3. Memory Optimization Techniques: Including mixed precision training and activation recomputation, effectively reducing memory usage. These techniques make it possible to train larger-scale models on limited hardware resources, significantly improving hardware utilization.

4. Distributed Optimizers: Such as Fully Sharded Data Parallel (FSDP), optimizing the efficiency of training large-scale models. This technology is particularly suitable for training ultra-large-scale language models, significantly reducing training time and resource consumption.

These features make Nemo an ideal framework for developing complex, autonomous AI agent systems. Through Nemo, developers can more easily build and optimize Agentic AI systems, accelerating the process from concept to practical application.

Perfect Coordination of Hardware, Software, and Cloud Services

NVIDIA’s full-stack approach is also reflected in the seamless integration of its hardware, software, and cloud services:

1. Hardware Foundation: From GPUs to DGX systems, NVIDIA provides a comprehensive range of hardware solutions. These hardware not only have powerful performance but are also specifically optimized for AI workloads, providing a solid computational foundation for the development of Agentic AI.

2. Software Ecosystem: The CUDA platform, AI Enterprise suite, and NGC catalog form a complete AI software stack. This software ecosystem greatly simplifies the AI development and deployment process, allowing developers to focus more on innovation and application development. According to NVIDIA’s developer blog^[20], NVIDIA offers over 150 SDKs tailored for various industries, greatly enriching its software ecosystem.

3. Cloud Services: DGX Cloud provides instant access to cloud AI supercomputer services, in deep collaboration with major cloud providers, making NVIDIA’s technology omnipresent. This cloud service allows even small businesses and individual developers to easily access powerful AI computing resources.

This comprehensive layout not only provides customers with highly optimized and integrated AI solutions but also significantly reduces the complexity of development and deployment. Through this full-stack approach, NVIDIA has created powerful network effects and customer stickiness, establishing an unshakeable competitive barrier for itself in the Agentic AI era.

The above image illustrates NVIDIA’s full-stack layout. From hardware to software to cloud services, NVIDIA has built a complete and coordinated AI infrastructure. This comprehensive ecosystem not only provides NVIDIA with a strong technological advantage but also offers developers and businesses a one-stop AI solution, greatly accelerating the pace of AI innovation and application.

Agentic AI Reshapes the Industrial Landscape: Winner Takes All or Blooming Diversity?

The rise of Agentic AI is not just a technological advancement but a profound industrial transformation. It will reshape the job market, give rise to new business models, and potentially alter the competitive landscape of the entire tech industry.

Emerging Fields: New Careers such as AI Agent Trainers and AI System Engineers

With the proliferation of Agentic AI, we will see a series of brand new professions emerge:

AI Agent Trainers have become a hot new profession. These professionals are responsible for training and optimizing the performance and decision-making capabilities of AI agent systems. For instance, at a leading autonomous driving company, AI agent trainers need to design complex training scenarios that simulate various extreme road conditions and traffic situations to ensure the autonomous driving systems can cope with real-world challenges. According to a Forbes report^[21], AI agent trainers will be one of the hottest professions in the next decade. These trainers need to have a deep understanding of machine learning, especially expertise in reinforcement learning, as well as in-depth knowledge of specific application areas such as autonomous driving, robotics control, or customer service.

AI System Engineers are also becoming increasingly important. In large enterprises, these experts are responsible for quality assurance and supervision of AI agent systems. They need to ensure the stable operation of AI systems, monitor their performance, and intervene when necessary. Imagine a global logistics company where AI system engineers need to design and maintain complex AI agent systems, ensuring that multiple AI systems for cargo tracking, route optimization, and customer service can work together seamlessly to ensure smooth supply chain operations. This position requires expertise in AI as well as experience in systems engineering and project management.

AI Ethics Experts are also in high demand. With the widespread application of Agentic AI across various industries, ensuring the ethical and fairness of these systems is becoming increasingly important. These experts need not only a deep understanding of AI technology but also multidisciplinary knowledge in philosophy, law, and sociology to assess the social impact of AI systems and establish corresponding ethical guidelines. They will be responsible for formulating usage norms for AI systems, ensuring the fairness and transparency of their decision-making processes, and avoiding potential bias and discrimination issues.

Human-Machine Collaboration Experts have become key roles connecting human employees and AI systems. In the healthcare industry, these experts may need to design a system that enables doctors and diagnostic AI to collaborate seamlessly, leveraging AI’s advantages in data analysis while retaining the clinical experience and intuitive judgment of physicians. This role requires a deep understanding of human-machine interaction principles and the workflows of specific industries to design efficient and natural human-machine collaboration models.

AI Explainability Experts are tasked with ensuring the transparency and explainability of AI systems’ decision-making processes. This role is particularly crucial in the financial industry. When AI systems make loan decisions or investment recommendations, AI explainability experts need to clearly explain the basis for these decisions to meet regulatory requirements and earn customer trust. This position requires experts to be proficient in AI algorithms as well as possess excellent communication skills to translate complex technical principles into easily understandable explanations.

These emerging professions not only require a strong technical background but also demand interdisciplinary knowledge and insights. They represent a new role for humans in the AI era: not to be replaced by AI, but to collaborate with AI, leveraging human creativity, judgment, and ethics.

The Fate of Traditional Giants: Survival of the Fittest or Breaking the Old to Establish the New?

The emergence of Agentic AI poses severe challenges to traditional tech giants. It may disrupt existing business models and product lines:

Software companies face pressure to redefine their products. Traditional SaaS products may be replaced by more flexible, intelligent AI agents. For example, an established customer relationship management (CRM) software company may need to transform its product into an AI-driven intelligent platform that not only records customer data but also proactively predicts customer needs and provides personalized sales strategies. According to a Forbes report^[22], traditional SaaS companies are actively exploring how to integrate Agentic AI into their products.

Cloud service providers must quickly adapt to the computing needs of AI-native applications. This may involve significant infrastructure overhauls to support large-scale AI training and inference workloads. Giants like Amazon AWS, Microsoft Azure, and Google Cloud are racing to launch services specifically optimized for Agentic AI, such as dedicated AI accelerators and AI model marketplaces.

Hardware manufacturers face pressure to optimize hardware architectures for Agentic AI. Traditional CPU manufacturers like Intel are increasing investments in AI chips to respond to NVIDIA’s strong position in AI computing. At the same time, we are also witnessing the rise of AI chip startups like Graphcore, which focus on designing entirely new processor architectures for the next generation of AI workloads.

However, this transformation also brings enormous opportunities. Companies that can quickly adapt and innovate will stand out in the new era. We have already seen companies like Microsoft and Google actively embrace Agentic AI, integrating it into their existing products. Microsoft’s deep collaboration with OpenAI and Google’s launch of the Gemini model demonstrate these giants’ determination to transform in the AI era.

This transformation is not only a technological update but a complete restructuring of business models and organizational structures. Traditional giants need to rethink their core competencies, which may require significant organizational restructuring or even a complete overhaul of their business models.

NVIDIA’s Strategic Positioning in the New Landscape

In this transformation, NVIDIA is cleverly positioning itself:

Technology Leader: Through continuous innovation, such as Blackwell GPU and Cosmos World Model, NVIDIA maintains its technological leadership. This is reflected not only in hardware performance but also in NVIDIA’s forward-looking judgment and layout regarding AI development trends. For instance, the launch of the Cosmos World Model demonstrates NVIDIA’s foresight in the field of physical AI, paving the way for developments in autonomous driving, robotics, and more.

ecosystem Builder: Through the Nemo framework and NGC catalog, NVIDIA is nurturing a strong developer community. This ecosystem not only provides a wealth of tools and resources but also creates an innovative platform that allows developers to more easily develop and deploy Agentic AI applications. NVIDIA’s NGC catalog^[23] provides a large number of pre-trained models and SDKs, greatly lowering the barriers to AI development.

Strategic Partner: NVIDIA’s collaborations with companies like Accenture, ServiceNow, and SAP are penetrating Agentic AI technology into various industries. This partnership model not only expands the application range of NVIDIA’s technology but also provides NVIDIA with valuable industry insights, aiding its technology’s further optimization and development. According to Accenture’s official press release^[24], they will collaborate with NVIDIA to help enterprise clients develop and deploy Agentic AI solutions.

Industry Standard Setter: Through open-source projects and technical specifications, NVIDIA is influencing the direction of Agentic AI’s development. For example, NVIDIA’s CUDA^[25] has become the de facto standard for GPU computing, and its efforts in the field of Agentic AI are likely to shape the technological standards of this emerging field.

NVIDIA’s goal is not only to be a technology provider in the Agentic AI era but also to be a shaper of this new era. Through its comprehensive technological layout and strategic vision, NVIDIA is building a complete ecosystem around Agentic AI, covering everything from hardware to software, from development tools to application platforms.

The above chart clearly illustrates NVIDIA’s multi-dimensional strategic positioning in the Agentic AI era. From technological innovation to ecosystem building, from strategic partnerships to standard setting, NVIDIA is comprehensively shaping the future of Agentic AI. This comprehensive strategy not only solidifies NVIDIA’s leadership position in the AI industry but also lays a solid foundation for its continued success in the Agentic AI era.

NVIDIA’s ambition is not limited to its own development but aims to drive the progress of the entire AI industry. Through initiatives such as the open-source Nemo framework, NVIDIA is actively promoting the popularization and application of Agentic AI technology. This open and cooperative attitude will accelerate the pace of AI innovation, allowing more businesses and developers to participate in this technological revolution.

However, NVIDIA also faces competition from other tech giants and emerging AI chip companies. For instance, AMD’s MI300 series AI chips^[26] and AI accelerators developed by companies like Google are challenging NVIDIA’s dominance in the AI hardware field. At the same time, NVIDIA needs to continue innovating in software and ecosystems to maintain its competitive advantage.

In the field of Agentic AI, NVIDIA’s main competitors include:large tech companies^[27] such as Microsoft, Google, Meta, Amazon, and OpenAI, all of whom are actively investing in and developing their own Agentic AI technologies and platforms; as well as AI startups like Groq^[28], which focus on rapid LLM inference.

In the face of intense market competition, NVIDIA needs to continue leveraging its comprehensive advantages in hardware, software, and ecosystems, continuously driving innovation and application of Agentic AI technology. At the same time, NVIDIA must actively address the ethical and social issues brought about by Agentic AI, collaborating with all parties to ensure the healthy development of this technology.

Conclusion: Standing at the Starting Point of a New AI Era

The emergence of Agentic AI marks an important turning point in AI development. This is not just a technological advancement but a revolution in thinking. It will change our understanding of AI, redefine the ways we interact with machines, and potentially reshape the operational models of society as a whole.

NVIDIA, through its forward-looking technological layout and strategic vision, is actively shaping this new era. From Blackwell GPU to Cosmos World Model, from the Nemo framework to Project Digits, NVIDIA is building a comprehensive infrastructure for the Agentic AI era. These innovations not only push the boundaries of technology but also provide powerful tools and platforms for digital transformation across various industries.

However, technological advancement also comes with great responsibility. How to ensure that the development direction of Agentic AI aligns with human interests, how to balance efficiency and ethics, and how to promote innovation while protecting privacy are all important questions we need to consider seriously. NVIDIA and the entire tech industry need to engage in discussions on these critical issues while promoting technological progress and actively collaborate with governments^[29] and regulatory bodies to establish reasonable policies and regulations.

The arrival of the Agentic AI era is not only a challenge for tech workers but also a call to everyone. We need to embrace this transformation with an open and inclusive mindset while remaining vigilant and critically thoughtful. Only in this way can we truly seize the opportunities presented by Agentic AI and create a smarter and more humane future.

In this exciting new era, NVIDIA will undoubtedly play a key role. But ultimately, the future of Agentic AI will be shaped by all of us together. Let us move forward hand in hand, writing a new chapter of human wisdom in this technological revolution.

Agentic AI is not just a technology; it is the beginning of a new era. It represents an extension of human wisdom and a new tool for exploring cognitive boundaries. In this future full of infinite possibilities, everyone has the opportunity to become an innovator and a shaper. Let us embrace this AI new era with curiosity and a sense of responsibility, creating a better world together.