In today’s tech industry few individuals have as much influence as Nvidia CEO Jensen Huang. The entire industry hangs on his every word reminiscent of earnings calls or presentations made by the last generation of tech greats such as Microsoft’s Bill Gates, Apple’s Steven Jobs, Intel’s Andy Grove, Oracle’s Larry Ellison or Cisco’s John Chambers.

Given the speed at which AI is transforming business and tech, it is important to study the words of Huang or risk falling behind in this new era. With that in mind, we wanted to share Huang’s statements made during the Nov. 19 Nvidia earning call. What follows are his opening statements made during the earnings call followed by some of his answers to questions from investors and analysts who attended. We used The Motley Fool site along with an AI tool that our parent company Informa deploys code-named “Elysia” to produce the following.

Huang touched on some major themes that included:

• AI Bubble Rejection
• Demand for GPUs
• AI as the New Electricity
• Market Leadership
• Revenue and Growth
• Three Transformative Shifts

In the three major shifts fueled by AI he discussed transitioning from traditional computing infrastructure to GPU-based system, the integration of generative AI into applications like search, and the rise of agentic AI and physical AI that power coding assistants and robotics.

Nvidia reported $57 billion in revenue for the quarter, with significant growth in data center operations. Huang also mentioned a $500 billion forecast for AI chip sales over 2025 and 2026, driven by strong demand and new deals with companies like Anthropic as well as Saudi Arabia.

Huang: “There has been a lot of talk about an AI bubble. From our vantage point, we see something very different. As a reminder, Nvidia is unlike any other accelerator. We excel at every phase of AI, from pre-training and post-training to inference. With our two-decade investment in CUDA-X acceleration libraries, we are also exceptional at science and engineering simulations, computer graphics, structured data processing to classical machine learning.

“The world is undergoing three massive platform shifts at once, the first time since the dawn of Moore's Law. Nvidia is uniquely addressing each of the three transformations. The first transition is from CPU general-purpose computing to GPU accelerated computing as Moore's Law slows. The world has a massive investment in non-AI software, from data processing to Science and Engineering Simulations, representing hundreds of billions of dollars in compute cloud computing spend each year. Many of these applications, which ran once exclusively on CPUs, are now rapidly shifting to CUDA GPUs. Accelerated Computing has reached a tipping point.

“Secondly, AI has also reached a tipping point and is transforming existing applications while enabling entirely new ones. For existing applications, generative AI is replacing classical machine learning in search ranking, recommender systems, ad targeting, click-through prediction to content moderation. The very foundations of hyperscale infrastructure.

“Meta's GEM, a foundation model for ad recommendations trained on large-scale GPU clusters exemplifies this shift. In Q2, Meta reported over a 5% increase in ad conversions on Instagram and 3% gain on Facebook feed driven by generative AI-based GEM. Transitioning to generative AI represents substantial revenue gains for hyperscalers.

“Now, a new wave is rising: agentic AI systems capable of reasoning, planning, and using tools. From coding assistants like Cursor and Claude Code to radiology tools like iDoc, legal assistants like Harvey, and AI chauffeurs like Tesla FSD and Waymo, these systems mark the next frontier of computing. The fastest-growing companies in the world today—OpenAI, Anthropic, xAI, Google, Cursor, Lovable, Replit, Cognition AI, OpenEvidence, Abridge, Tesla—are pioneering agentic AI. There are three massive platform shifts. The transition to Accelerated Computing is foundational and necessary, essential in a post-Moore's Law era.

“The transition to generative AI is transformational and necessary, supercharging existing applications and business models. The transition to Agentic and Physical AI will be revolutionary, giving rise to new applications, companies, products, and services. As you consider infrastructure investments, consider these three fundamental dynamics. Each will contribute to infrastructure growth in the coming years. Nvidia is chosen because our singular architecture enables all three transitions, and thus so for any form and modality of AI across all industries, across every phase of AI, across all of the diverse computing needs in a cloud, and also from cloud to enterprise to robots. One architecture.”

What follows are Huang’s responses to questions we selected:

“Well, as you know, we've done a really good job planning our supply chain. Nvidia Corp.'s supply chain basically includes every technology company in the world. And TSMC and their packaging and our memory vendors and memory partners and all of our system ODMs have done a really good job planning with us. And we were planning for a big year. You know, we've seen for some time the three transitions that I spoke about just a second ago, accelerated computing, from general-purpose computing and it's really important to recognize that AI is not just agentic AI, but generative AI is transforming the way that hyperscalers did the work that they used to do on CPUs.”

“Generative AI made it possible for them to move search and recommender systems and, you know, add recommendations and targeting. All of that has been generated has been moved to generative AI. And it's still transitioning. And so whether you install Nvidia GPUs for data processing, or you did it for generative AI for your recommender system, or you're building it for agentic chatbots and the type of AIs that most people see when they think about AI, all of those applications are accelerated by Nvidia. And so when you look at the totality of the spend, it's really important to think about each one of those layers. They're all growing. They're related, but not the same.

“But the wonderful thing is that they all run on Nvidia GPUs. Simultaneously, the quality of the AI models are improving so incredibly. The adoption of it in the different use cases, whether it's in code assistance, which Nvidia uses fairly exhaustively, and we're not the only one. The combination of Cursor and Claud Code and OpenAI's codex and GitHub Copilot, these applications are the fastest growing in history. And it's not just used by software engineers. Because of vibe coding, it's used by engineers, marketeers and supply chain planners all over companies. That's just one example, the list goes on. Whether it's work that they do in health care or the work that's being done in digital video editing, Runway and a number of really, really exciting start-ups are taking advantage of generative AI. And agentic AI is growing quite rapidly, and we're all using it a lot more.

“And Gemini 3 takes advantage of the scaling laws, it received a huge jump in quality performance model performance. And so we're seeing all of these exponentials running at the same time. And just always go back to first principles and think about what's happening from each one of the dynamics that I mentioned before. [From] General-purpose computing to accelerated computing, generative AI replacing classical machine learning, and, of course, agentic AI, which is a brand-new category.”

“We use this concept called co-design across our entire stack, across the frameworks and models, across the entire data center, even power and cooling optimized across the entire supply chain in our ecosystem. Each generation, our economic contribution will be greater. Our value delivered will be greater. The most important thing is our energy efficiency per watt is going to be extraordinary every single generation. With respect to continuing to grow, our customers' financing is up to them. We see the opportunity to grow for quite some time.

“And remember, today, most of the focus has been on the hyperscalers. One of the areas that is really misunderstood about the hyperscalers is that the investment in Nvidia GPUs not only improves their scale, speed, and cost. Because Moore's Law scaling has really slowed. Moore's Law is about driving cost down. It's about deflationary cost, the incredible deflationary cost of computing over time. That has slowed. Therefore, a new approach is necessary for them to keep driving the cost down. Going to Nvidia GPU computing is really the best way to do so.

“The second is revenue boosting in their current business models. Recommender systems drive the world's hyperscalers every single, whether it's watching short-form videos or recommending books or recommending the next item in your basket to recommending ads to recommending news to—it's all about Recommenders. The internet has trillions of pieces of content. How could they possibly figure out what to put in front of you and your little tiny screen unless they have really sophisticated Recommender systems. That has gone generative AI.

“The first two things that I've just said, hundreds of billions of dollars of CapEx is going to have to be invested, is fully cash flow funded. What is above it, therefore, is agentic AI. This is net-new new consumption, but it's also net-new applications. These new applications are also the fastest-growing applications in history. I think that you're going to see that once people start to appreciate what is actually happening under the water, if you will, from the simplistic view of what's happening to CapEx investment, recognizing there's these three dynamics.

“Lastly, remember, we were just talking about the American [cloud providers]. Each country will fund their own infrastructure. You have multiple countries. You have multiple industries. Most of the world's industries haven't really engaged agentic AI yet, and they're about to. All the names of companies that you know we're working with, whether it's autonomous vehicle companies or digital twins for physical AI for factories and the number of factories and warehouses being built around the world, just the number of digital biology startups that are being funded so that we could accelerate drug discovery. All of those different industries are now getting engaged, and they're going to do their own fundraising. Do not just look at the hyperscalers as a way to build out for the future.”