Analyst(s): Brendan Burke
Publication Date: January 28, 2026
At the World Economic Forum in Davos, NVIDIA CEO Jensen Huang described AI as the foundation of the largest infrastructure buildout in human history. Framing AI as a five-layer stack, Jensen Huang argued that economic value, job creation, and productivity gains will increasingly be driven by the application layer built on top of massive investments in energy, compute, and cloud infrastructure.
What is Covered in this Article:
- Jensen Huang’s “five-layer cake” framework for AI infrastructure
- Why AI is driving large-scale investment across energy, compute, and cloud
- How AI-native startups and VC funding are accelerating application development
- The impact of AI on jobs, productivity, and workforce purpose
- AI’s role as national infrastructure and its implications for global participation
The News: At the World Economic Forum Annual Meeting in Davos, NVIDIA founder and CEO Jensen Huang described artificial intelligence as the foundation of what he called “the largest infrastructure buildout in human history.” Speaking alongside BlackRock CEO Larry Fink, Huang framed AI as a five-layer stack spanning energy, chips and computing infrastructure, cloud data centers, AI models, and the application layer.
Jensen Huang said the buildout is already driving job creation across energy, construction, advanced manufacturing, cloud operations, and application development, while venture capital funding in 2025 exceeded $100 billion globally, with most capital flowing into AI-native companies building applications on top of increasingly capable AI models.
AI Is the Largest Infrastructure Buildout Ever—Are Investments Keeping Up?
Analyst Take: NVIDIA CEO Jensen Huang’s remarks at Davos position AI not as a single technology wave but as a full-stack infrastructure transformation that simultaneously touches energy systems, industrial manufacturing, cloud operations, and application development. His framing challenges the narrow focus on AI models by emphasizing that every layer beneath them must be built, operated, and scaled. According to Jensen Huang, the economic payoff does not sit in the models themselves but in the application layer, where AI is integrated into healthcare, manufacturing, and financial services. This perspective reframes current AI spending as foundational rather than speculative. The core question raised is not whether AI investment is excessive, but whether it is sufficient to support all five layers.
AI’s Five-Layer Stack Reframes Where Value Is Created
Jensen Huang’s “five-layer cake” starts with energy as the base, followed by chips and computing infrastructure, cloud data centers, AI models, and applications at the top. He stressed that while most public attention focuses on models, those models depend entirely on the lower layers being built at scale. The application layer is where Jensen Huang believes economic benefit will ultimately materialize, as AI is embedded into real-world workflows. This framing highlights why infrastructure investment must precede and scale alongside application development. The implication is that underinvestment at any layer constrains the entire AI platform.
Job Creation Comes From Infrastructure, Not Just Software
Jensen Huang argued that AI is already creating demand for skilled labor across energy, construction, manufacturing, networking, and cloud operations. He specifically cited demand for plumbers, electricians, construction workers, steelworkers, and network technicians involved in building and operating AI infrastructure. These jobs are not peripheral but central to enabling AI systems to function at scale. In healthcare, he pointed to radiology and nursing as examples where AI has increased demand rather than eliminated roles. The consistent theme is that AI shifts work from repetitive tasks toward higher-value purposes, reinforcing workforce demand rather than eroding it.
AI Productivity Gains Are Expanding, Not Contracting, Employment
Using healthcare as an example, Jensen Huang noted that AI has become a core tool in radiology, yet the number of radiologists has increased rather than declined. By automating scan analysis, radiologists can see more patients and spend more time on diagnosis and care, driving higher hospital throughput. A similar dynamic is playing out in nursing, where AI is used to automate charting and transcription, addressing a U.S. shortage of roughly 5 million nurses. As productivity improves, hospitals can serve more patients and hire more staff. This challenges the assumption that task automation inevitably leads to workforce reduction.
AI as National Infrastructure Raises the Stakes for Participation
Jensen Huang framed AI as essential national infrastructure, arguing that countries should treat it like electricity or roads. He emphasized that AI is highly accessible, describing it as the easiest software to use in history, with adoption reaching nearly a billion people in just two to three years. This accessibility underpins his view that AI can help close technology divides, particularly for developing economies. He also urged countries to build AI systems rooted in local language and culture, integrating AI into national ecosystems. The conclusion is that participation, not exclusion, will determine who benefits from the AI-driven economic shift.
What to Watch:
- Whether infrastructure investment across energy, compute, and cloud keeps pace with application-layer growth
- How AI-native startups translate record VC funding into sustainable application deployment
- The extent to which labor shortages in healthcare and skilled trades continue to shape AI adoption
- How countries act on AI being framed as national infrastructure rather than imported capability
See the complete blog on NVIDIA CEO Jensen Huang’s comments on AI as the largest infrastructure buildout in human history at the World Economic Forum.
Declaration of generative AI and AI-assisted technologies in the writing process: This content has been generated with the support of artificial intelligence technologies. Due to the fast pace of content creation and the continuous evolution of data and information, The Futurum Group and its analysts strive to ensure the accuracy and factual integrity of the information presented. However, the opinions and interpretations expressed in this content reflect those of the individual author/analyst. The Futurum Group makes no guarantees regarding the completeness, accuracy, or reliability of any information contained herein. Readers are encouraged to verify facts independently and consult relevant sources for further clarification.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
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Author Information
Brendan is Research Director, Semiconductors, Supply Chain, and Emerging Tech. He advises clients on strategic initiatives and leads the Futurum Semiconductors Practice. He is an experienced tech industry analyst who has guided tech leaders in identifying market opportunities spanning edge processors, generative AI applications, and hyperscale data centers.
Before joining Futurum, Brendan consulted with global AI leaders and served as a Senior Analyst in Emerging Technology Research at PitchBook. At PitchBook, he developed market intelligence tools for AI, highlighted by one of the industry’s most comprehensive AI semiconductor market landscapes encompassing both public and private companies. He has advised Fortune 100 tech giants, growth-stage innovators, global investors, and leading market research firms. Before PitchBook, he led research teams in tech investment banking and market research.
Brendan is based in Seattle, Washington. He has a Bachelor of Arts Degree from Amherst College.
