Before the bottom line: Nvidia ( NVDA ) CEO Jensen Huang, in his role as the primary company leader behind the ongoing technology movement, says many people don’t realize how big the AI revolution is. He says there are currently more than 1.5 million AI models worldwide, from healthcare and drug discovery to common names like Elon Musk’s Grok and Sam Altman’s ChatGPT.
Details: Nvidia founder and chief executive Jensen Huang continues to frame artificial intelligence as an infrastructure-scale transformation rather than a single technological breakthrough. Dr. President and CEO of the Center for Strategic and International Studies (CSIS). In a fireside chat with John Jay Hamre, Huang continued his layered view of AI, emphasizing the breadth of systems, capital and applications that underpin the technology’s long-term impact.
Building on earlier discussions of energy, chips, and systems, Huang described AI’s current infrastructure build as multi-layered. The first layer is simple: energy. Huang frames power generation as the most fundamental part of the entire AI revolution.
Next is Nvidia itself: the chips. Without the chips, there is nothing to use electricity and do the AI compute processing needed to run AI models.
Huang then highlights the third layer, which “includes financial services, because everything we do requires a large amount of capital.” His remarks highlight that AI development at scale is not only a technical challenge, but also a financial one, requiring sustained investment in data centers, networking, and long-term computing assets.
Huang then turned to the fourth layer that receives the most public attention: the models themselves. He acknowledged the prominence of well-known systems, citing examples like ChatGPT, Anthropic’s Claude, Google’s Gemini, and xAI’s Grok, describing it as “a huge focus in this space when people talk about AI.” However, he put those systems in a much broader context, saying, “They are four out of a million and a half AI models in the world.” The statement reframes popular generative models as an incredibly small subset of a much larger and more diverse ecosystem. This statement shifts the risk away from larger, more widely known AI models, and instead frames Nvidia as an infrastructure play in the midst of a technological revolution that is being implemented in every industry in the world.
The broader point of Huang’s comments was that artificial intelligence is not limited to language or consumer interaction. He emphasized that “AI is not just intelligence that understands English or language” but includes systems that “understand genes, proteins, chemistry, the laws of physics”, as well as AI that “understands quantum”, body movement and robotics, long-term patterns, financial services, and healthcare in many data types. By listing these domains, Huang emphasized the role of AI as a general-purpose technology applicable to science, industry, and services.
This perspective is consistent with Nvidia’s development over the past decade. The company’s hardware and software platforms are widely used not only in consumer-oriented AI, but also in drug discovery, climate modeling, industrial automation, and financial analytics. Huang’s authority on the subject stems from Nvidia’s position at the center of these applications, supplying the computing infrastructure that enables a wide range of AI workloads beyond high-profile chat interfaces.
In the broader market and policy context, Huang’s comments speak to a recurring challenge in the technology cycle: Public focus often narrows to the most visible applications while underestimating the diversity of underlying infrastructure and use cases. As investment flows into AI-related companies, the distinction between model developers, infrastructure providers, and domain-specific applications becomes increasingly important. His layered framework offers a way to evaluate AI development that accounts for capital intensity, specialization, and long-term deployment rather than short-term innovation.
Describing AI as a platform with many layers and millions of specialized models, Huang positions the technology as a fundamental change comparable to previous industrial transformations. His comments suggest that the long-term impact of AI will be shaped less by any one model and more broadly by how intelligence is applied across disciplines, industries and economic systems.
As of the date of publication, Caleb Nesmith did not have positions (either directly or indirectly) in any of the securities mentioned in this article. All information and data in this article is for informational purposes only. This article was originally published on Barchart.com
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