Jensen Huang Defines the AI Era: The "Five-Layer Cake" Framework Unveiled at Davos 2026

At the World Economic Forum in Davos this week, Nvidia CEO Jensen Huang delivered a defining thesis for the artificial intelligence industry, moving the conversation beyond chatbots and into the realm of critical global infrastructure. In a high-profile dialogue with BlackRock CEO Larry Fink, Huang introduced a comprehensive "Five-Layer AI Framework"—a strategic roadmap that categorizes the AI ecosystem into distinct, interdependent strata.

This framework comes at a pivotal moment. As 2026 begins, the industry faces scrutiny regarding massive capital expenditures and the sustainability of the so-called "AI bubble." Huang’s counter-argument was precise and structural: we are not merely building software; we are executing the largest infrastructure construction project in human history. From energy grids to physical robotics, the scope of AI has expanded far beyond the digital constraints of previous years.

For industry observers and enterprise leaders, Huang’s "layer cake" offers more than just a metaphor; it provides a rubric for understanding where value will be captured in the coming decade. As Creati.ai analyzes this new paradigm, it becomes clear that the focus is shifting from training models to deploying them in the physical world.

The Five-Layer Infrastructure Framework

Huang’s central contribution to this year’s forum was the delineation of the AI stack into five essential layers. Unlike previous models that focused heavily on algorithms, this framework emphasizes the physical and logistical realities required to sustain AI at a global scale.

According to Huang, the economic value of AI is realized only at the very top layer, but this success is predicated on the robustness of the four layers beneath it. The "buildout" he describes implies that the current trillions in investment are not speculative bets but necessary capital for foundational utilities, comparable to the electrification of the 20th century or the construction of the interstate highway system.

The following table details the five layers of Huang's framework as presented at Davos:

Table 1: Jensen Huang’s Five-Layer AI Infrastructure Stack

Layer	Description	Strategic Importance
1. Energy	The foundational requirement; power generation, cooling, and sustainable energy grids.	AI cannot exist without massive, consistent power; shortages here bottleneck the entire stack. Investment focus: Nuclear, renewable, and grid modernization.
2. Compute & Chips	The hardware layer; GPUs, custom silicon, and manufacturing plants (fabs).	This is Nvidia’s stronghold; includes new fabs by TSMC, Foxconn, and Micron. Supply remains constrained despite record production.
3. Cloud Infrastructure	Data centers, sovereign clouds, and massive server clusters.	The distribution network for intelligence; requires physical land, steel, and skilled labor. Sovereign AI clouds are emerging as national priorities.
4. AI Models	The intelligence layer; foundational models, open reasoning models, and "digital brains."	Includes proprietary giants and open-source breakthroughs like DeepSeek. Democratization allows industries to build without starting from scratch.
5. Applications	The value layer; AI agents in healthcare, finance, manufacturing, and robotics.	Where economic ROI is generated; AI solves specific domain problems. Shift from "chatting with AI" to "AI doing work."

The Democratization of Intelligence: The DeepSeek Effect

A significant portion of the dialogue focused on the fourth layer—AI models—and the radical shift occurring within it. Huang explicitly highlighted the release of DeepSeek and similar open reasoning models as a "huge event" for global industry. Looking back at the trajectory from 2025 to 2026, the availability of high-performance, open-source reasoning models has fundamentally altered the competitive landscape.

"DeepSeek was a pivotal moment because it was the world's first open reasoning model," Huang noted. This development shattered the barrier to entry for enterprises. Previously, companies believed they needed to train massive proprietary models from scratch—a prohibitive cost for most. Today, the "intelligence" is increasingly commoditized and accessible.

This "DeepSeek Effect" has allowed industries outside of traditional tech—such as pharmaceuticals and heavy manufacturing—to integrate advanced reasoning capabilities directly into their workflows without needing a team of PhDs to build a model. The result is a surge in layer-five applications, where the utility of AI is fine-tuned for specific, high-value tasks rather than general-purpose conversation.

Beyond Digital: The Rise of Physical Intelligence

Perhaps the most forward-looking aspect of Huang’s Davos address was his emphasis on "Physical AI." While the first wave of Generative AI was about mastering language and pixels (text, code, images), the current wave is about mastering the laws of physics.

"AI systems now understand far more than language," Huang explained. "They are learning protein structures, chemical interactions, fluid dynamics, and particle physics."

This transition marks the entry of AI into the physical world, or what Huang terms "Physical Intelligence." This is critical for the manufacturing and robotics sectors. In this vision, AI does not just write a poem or debug code; it simulates a wind turbine to optimize airflow, predicts how a new drug molecule will bind to a protein, or controls a humanoid robot on a factory floor.

For Europe and industrial nations, Huang flagged this as a "once-in-a-generation opportunity." Unlike the consumer internet era, which favored software-centric regions, the Physical AI era favors regions with deep industrial engineering roots. "You don't write AI anymore; you teach it," Huang said, suggesting that domain experts in mechanical engineering and biology are becoming the new AI architects.

Addressing the "AI Bubble": A Construction Story

The elephant in the room at Davos was the question of financial sustainability. With hundreds of billions of dollars poured into GPUs and data centers, skeptics—and even some investors—have begun to fear a bubble. Larry Fink, representing the capital allocators, posed the question directly.

Huang’s defense was rooted in supply and demand mechanics. "Try renting an Nvidia GPU right now," he challenged. The difficulty in securing compute power, even for older-generation hardware, indicates that demand still far outstrips supply.

However, his deeper argument was about the nature of the spending. Huang reframed the "bubble" as a necessary infrastructure cycle. He argued that we are witnessing the replacement of $100 trillion worth of traditional computing infrastructure with accelerated computing. Furthermore, this buildout is creating jobs, not just in coding, but in the trades.

"We need plumbers, electricians, construction workers, steelworkers," Huang said, highlighting that the physical construction of data centers is a massive economic engine in itself. He described the scenario as a "construction story" rather than just a tech story. The capital being deployed is buying tangible assets—land, power plants, shell buildings, and silicon factories—essential for the next century of economic activity.

Creati.ai Perspective: The Road Ahead

From the vantage point of Creati.ai, Jensen Huang’s 2026 framework serves as a stabilizing narrative for an industry in hyper-growth. By breaking the ecosystem down into five layers, he provides a checklist for organizational readiness.

For our readers and clients, the implications are three-fold:

1. Infrastructure is Key: Organizations must ensure they have access to the necessary compute (Layer 2) and cloud (Layer 3) resources, as scarcity will persist.
2. Leverage Open Models: The value is no longer in hoarding a model but in applying an open model (Layer 4) to proprietary data.
3. Prepare for Physical AI: The next competitive frontier is not in better chatbots, but in AI that understands the physical operations of your business (Layer 5), whether that is supply chain logistics, drug discovery, or automated manufacturing.

As the "Five-Layer Cake" is baked, the focus for 2026 will be on the icing: the application layer. This is where the theoretical trillions in investment must finally convert into tangible productivity gains. According to Huang, we are just getting started.