Analysts created a method to evaluate corporate AI adoption across six key areas: personalization, customer acquisition, product innovation, labor productivity, supply chain, and inventory management. Companies are then ranked on the breadth, depth, and proprietary nature of their AI initiatives.

Jon Gray outlines a tripartite market landscape shaped by AI. It includes clear AI winners, physical-world businesses like medical supplies that are largely immune, and a high-risk category of software and services companies whose moats are now uncertain. This framework guides investment toward clarity and away from ambiguity.

Like containerization, AI is a transformative technology where value may accrue to customers and users, not the creators of the core infrastructure. The biggest fortunes from containerization were made by companies like Nike and Apple that leveraged global supply chains, not by investors in the container companies themselves.

In the AI gold rush, don't bet on the "miners" like Google and Meta, who are spending billions on a new, high-risk game. Instead, invest in the "pickaxe makers"—the essential toll bridges like TSMC and ASML that every AI company must pass through, ensuring your investment has a higher probability of success.

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.

To manage the risk and opportunity of AI, LeadEdge ranks all its portfolio companies on their readiness. The score considers data structure, new AI product releases, and AI-driven revenue, facilitating knowledge sharing between high- and low-scoring companies.

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.

A useful framework for analyzing the AI landscape is a six-level stack: Energy (Level 0), Chips (1), Data Centers (2), Models (3), Software Infrastructure (4), and Apps/Services (5). This model helps investors map the ecosystem, understand dependencies, and identify where value is currently accruing.

Drawing a parallel to the early internet, where initial market-anointed winners like Ask Jeeves failed, the current AI boom presents a similar risk. A more prudent strategy is to invest in companies across various sectors that are effectively adopting AI to enhance productivity, as this is where widespread, long-term value will be created.