Future opportunities are shifting from pure software to AI-driven physical products. The rarest and most valuable professionals will be those who can bridge the gap between software and hardware by combining open-source AI, physical prototyping, and manufacturing knowledge.

In SaaS, value was delivered through visible UI. With AI, this is inverted. The most critical, differentiating work happens in the invisible infrastructure—complex RAG systems and custom models. The UI becomes the smaller, easier part of the product, flipping the traditional value proposition.

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.

Ankur Crawford's thesis posits that as AI automates software creation, the cost to create code approaches zero, eroding incumbents' moats. The economic value will migrate from software and IT services to the underlying hardware and networking layers that enable this exponential innovation.

As large AI models absorb functions of traditional SaaS products, investors and entrepreneurs are shifting focus back to tech-enabled services. Integrating AI deeply into physical services and workflows is now seen as creating more defensible, lasting value than pure software, reversing a years-long trend.

Value in the AI stack will concentrate at the infrastructure layer (e.g., chips) and the horizontal application layer. The "middle layer" of vertical SaaS companies, whose value is primarily encoded business logic, is at risk of being commoditized by powerful, general AI agents.

The 50-year supremacy of asset-light software may be an anomaly. If AI makes software creation nearly free, economic value will shift back to the historical mean: tangible assets like infrastructure, energy, and regulated, liability-bearing businesses that touch the physical world.

Maverick Capital predicts the AI value chain, which shifted from software to hardware, will swing back. Value will move from upstream bottlenecks (fabrication, materials) downstream to the application/infrastructure layer as AI agents integrate with existing enterprise workflows rather than replacing them.

Every layer of the AI supply chain is constrained, from energy and data centers to turbines, transformers, and rare earth minerals. This is a shift from software limitations to hard physical constraints. As a result, the price of intelligence may stop decreasing and could even rise.