ab 17 March 2026 Global Research NVIDIA Corp GTC – Keynote and Day 1 Highlights GTC Day 1 Highlights Inference, infrastructure and a heavy dose of robotics are the key themes this year. Product announcements from the keynote were generally as expected, though the tone around CPUs was more bullish. NVDA updated and extended the backlog slide it showed at GTC last fall, now indicating its prior $500B (excluding Hopper) for C2025+2026 is now $1T+ for C2025-2027. On the basis of our numbers (which are far above ...

Welcome to GTC. The inference inflection has arrived. I believe that computing demand has increased by one million times in the last two years.AI now has to think. In order to think. it has to inference. In order to do, it has to inference. AI has to read.In order to do so, it has to inference. Finally, AI is able to do productive work and therefore the inflection point of inference has arrived. Tokens are the new commodity.On the vertical axis is throughput. On the horizontal axis is token rate. And so thi ...

Nvidia just made a major strategy shift and most of Wall Street is missing it. >> This is definitely the next chat should GPT. >> Everyone knows Nvidia for its chips, but that's last gen Nvidia.Its new evolution is all about open claw and open source. Jensen Wong is going allin. So, let's back up a second.Nvidia is the most valuable company in the world for a reason, and that's thanks to its chips. They dominated the first era of AI that was all about training and building models from scratch. But that's ol ...

(0:00) Jensen Huang joins the show! (1:00) Acquiring Groq and the inference explosion (9:27) Decision making at the world's most valuable company (11:22) Physical AI's $50T market, OpenClaw's future, the new operating system for modern AI computing (17:12) AI's PR crisis, refuting doomer narratives, Anthropic's comms mistakes (21:22) Revenue capacity, token allocation for employees, Karpathy's autoresearch, agentic future (31:24) Open source, global diffusion, Iran/Taiwan supply chain impact (40:19) Self-dr ...

Let's bring in CNBC contributor Big Technologies, Alex Canerix. Alex, good to see you. Saw you here at the exchange yesterday off camera, but let's talk about Nvidia.It's weird. Uh Nvidia sort of has to make the case at this point. It's been doing so well that it continues to do well and maybe can do even better.What What have you found to be the most notable points out of GTC. >> Well, first of all, it just highlights the expectations on this company. Only Nvidia could tell you that it's going to make a tr ...

Core Viewpoint - Nvidia is expected to generate approximately a trillion dollars from two products over the next two years, which some investors view as disappointing given the current market environment and capital expenditure trends in the tech industry [2][4]. Group 1: Nvidia's Market Position - Nvidia is poised to capture a significant share of the projected $700 billion capital expenditure among major tech companies this year, up from $400 billion last year [3][4]. - The company is well-positioned to benefit from the accelerating adoption of AI technologies, with new product applications emerging rapidly [5]. - Despite a recent stagnation in revenue forecasts for 2026, there remains potential for growth in Nvidia's business [7]. Group 2: Competitive Landscape - The transition from AI training to inference presents Nvidia with increased competition, which could impact its market position [10]. - There is uncertainty regarding which players in the AI ecosystem, including chip makers and application developers, will ultimately capitalize on the growth opportunities [8][10]. - The economic value of advanced AI models, such as artificial general intelligence, raises questions about pricing power and market commoditization [9]. Group 3: Importance of Data Management Software - Companies focused on data management software, such as Snowflake and Datadog, are expected to play a crucial role in the AI ecosystem, as effective data management remains essential [11][12]. - The functionality of these middle-layer companies should not be overlooked, as they provide core infrastructure that supports AI applications [13].

Chipmaking giant Nvidia’s (NVDA) closely followed four-day GPU Technology Conference (GTC) 2026 is set to commence later today with a keynote address from CEO Jensen Huang. Analysts and investors alike are looking for updates on several key areas from the annual developer conference. This includes a possible announcement on an inference chip with Groq, a roadmap for the Vera Rubin successor Feynman, a strategy to rival Intel (INTC) and Advanced Micro Devices (AMD) in the central processing unit (CPU) marke ...

Summary of Key Points from the Conference Call Company and Industry Overview - The conference call primarily discusses NVIDIA, a leading company in the AI and computing industry, focusing on advancements in AI technologies and their implications for the market. Core Insights and Arguments - **AI Inflection Points**: The speaker identifies three key inflection points in AI development: generative AI, reasoning, and the current focus on agentic systems, which can operate autonomously and perform tasks beyond answering questions [6][14]. - **Token Economy**: The concept of a "token budget" for engineers is introduced, emphasizing that engineers now require tokens to perform their jobs, which are produced by the company's computing systems [7][14]. - **Revenue Visibility**: NVIDIA has strong visibility of over $1 trillion in demand for its products, specifically mentioning Blackwell and Rubin systems, with expectations to close and ship more business by the end of 2027 [14][15]. - **Value Proposition**: The company emphasizes that the price of its computers is justified by their ability to produce tokens at a low cost, thus delivering significant value to customers [17][18]. - **Market Dynamics**: The speaker notes that the IT industry, valued at approximately $2 trillion, is expected to transform rather than be disrupted, integrating AI technologies from companies like OpenAI and Anthropic [39][40]. - **Growth of AI Models**: The growth of open-source models and their integration into the IT industry is highlighted, with NVIDIA positioned as a leader in this space [20][21]. Additional Important Content - **Customer Diversity**: NVIDIA is seeing significant customer diversity beyond hyperscalers, including regional clouds and industrial enterprises, which are growing rapidly [23][24]. - **Future Projections**: The speaker predicts that the current 40% of the market not dominated by hyperscalers could grow significantly as industries related to physical AI expand [51][52]. - **Investment Strategy**: NVIDIA plans to balance investments in growth, ecosystem partnerships, and shareholder returns, with a focus on maintaining a strong supply chain [93][94]. - **Technological Advancements**: The introduction of new architectures, such as Groq, is expected to enhance performance and efficiency in AI workloads, with Groq projected to capture 25% of inference workloads [80][90]. - **Token Cost Dynamics**: The cost of tokens is expected to decrease while the smartness per token increases, indicating a favorable trend for customers [102]. This summary encapsulates the key points discussed during the conference call, providing insights into NVIDIA's strategic direction, market positioning, and future growth potential in the AI industry.

Core Insights - The AI industry is at an "inference inflection point," where the demand for computing power is rapidly shifting from training AI models to running them in real-world applications [5][17] - Nvidia's CEO Jensen Huang highlighted that AI computing could approach a trillion dollars in data center infrastructure investments between now and 2027 [5] - The concept of "AI factories" is emerging, which are specialized data centers designed to generate AI outputs at scale, with intelligence tokens becoming the new currency [15][17] Inference and Token Economy - Inference is the process where trained AI models generate responses, and the demand for computing resources during inference can exceed that needed for training [6][7] - Tokens are the basic units of AI-generated text or data, and the efficiency of generating tokens at scale is becoming crucial for the long-term economics of AI [6][7][11] - Huang emphasized that "inference is your new workload, tokens are your new commodity," indicating a shift in how companies should optimize their architecture for future demands [11] AI Factories and Infrastructure Boom - Nvidia introduced its next-generation AI computing platform, Vera Rubin, which aims to deliver up to 10 times higher inference performance per watt and reduce token generation costs by approximately 90% [16] - The shift towards inference-driven workloads is transforming the technology industry's approach to computing infrastructure, moving from periodic model training to continuous token generation [17] - Huang stated that the future of computing will revolve around AI factories, fundamentally redefining the economics of computing [17]

Core Insights - Nvidia's CEO Jensen Huang announced a new inference system at the GTC conference, marking a significant step to maintain its leadership in the AI sector as inference becomes a critical area of competition [1] - The company anticipates a surge in demand for its AI systems, projecting at least $1 trillion in demand for its Blackwell and Rubin systems by 2027, a substantial increase from the previous estimate of $500 billion through 2026 [1] Group 1 - Nvidia introduced the Groq 3 LPX chip, which can accelerate inference workloads by up to 35 times, integrating technology from AI startup Groq with Nvidia's Vera Rubin architecture [2] - The Groq chip is manufactured by Samsung, with expectations for shipping in the second half of the year [2] - Huang emphasized that the "inflection point of inference has arrived," indicating a pivotal moment in the AI landscape [2] Group 2 - The new inference system builds on a $20 billion deal with Groq, which involved licensing Groq's technology and hiring its top engineers [7] - Nvidia's GPUs continue to dominate the AI market, serving both training and inference purposes, but competition is increasing from various companies developing specialized, cost-effective systems for inference tasks [8] - The emergence of AI agents could significantly boost the demand for inference capabilities [8] Group 3 - Companies like OpenAI are exploring alternatives to Nvidia's hardware due to dissatisfaction with its inference chips, with OpenAI reportedly signing a $10 billion deal with Cerebras for compute resources [9]