India is building its AI ecosystem across five distinct layers: energy, infrastructure, compute, model development, and deployment. This 'full-stack' approach treats energy as the critical base layer, recognizing that massive compute needs require a robust and scalable power supply, which is a key national advantage.

Instead of betting on specific AI models like ChatGPT, a more robust strategy is to invest in the underlying infrastructure that all AI development requires. This 'onion' approach focuses on second-order essentials like semiconductors and data centers, which are poised to grow regardless of which consumer-facing application wins.

Huang reframes massive AI spending not as a bubble but as essential infrastructure buildout. He describes a five-layer stack (energy, chips, cloud, models, applications), arguing that large investments are necessary to build the entire foundation required to unlock economic benefits at the application layer.

When a new technology stack like AI emerges, the infrastructure layer (chips, networking) inflects first and has the most identifiable winners. Sacerdote argues the application and model layers are riskier and less predictable, similar to the early, chaotic days of internet search engines before Google's dominance.

Jensen Huang's analogy frames AI not as a single technology but a full stack: energy, chips, infrastructure, models, and applications. This powerful mental model clarifies the distinct roles and investment opportunities at each layer of the AI economy, from utility companies to consumer-facing software.

The AI value stack has evolved from chips (NVIDIA) to models (OpenAI). The next critical phase is the application layer. It's unclear if value will be captured by new application companies or if the underlying model providers will absorb all the profits, a key question for investors and founders.

In 2026, the AI investment narrative will expand from foundational model creators to companies building applications and services. It also includes sectors enabling AI growth, such as energy generation and data centers, offering a wider range of investment opportunities beyond the initial tech giants.

Despite massive investment in chips (NVIDIA) and models (OpenAI), it is not yet clear where long-term value will concentrate. The entire stack is in flux. Models could be commoditized by open source, chips could face historical commoditization cycles, and new AI-native apps could capture the most value. We are only in the early innings of a 30-year shift.

The new AI technology landscape is a layered 'Collaborative Intelligence Stack.' It starts with hardware and models but culminates in 'AI teammates'—agentic AIs that augment human workers. The largest future value lies in this top layer, which could capture 10-20% of the $30 trillion global knowledge worker spend.