Instead of selling software to traditional industries, a more defensible approach is to build vertically integrated companies. This involves acquiring or starting a business in a non-sexy industry (e.g., a law firm, hospital) and rebuilding its entire operational stack with AI at its core, something a pure software vendor cannot do.

Mala Gaonkar's firm gains an advantage by deeply analyzing the technology infrastructure of companies in traditional sectors like aerospace or finance. This reveals scalability and quality often overlooked by investors focused solely on the core business, treating every company as a technology company.

Unlike software, where customer acquisition is the main risk, the primary diligence question for transformative hardware is technical feasibility. If a team can prove they can build the product (e.g., a cheaper missile system), the market demand is often a given, simplifying the investment thesis.

Technology is permeating every industry and blurring the lines between them, making traditional sector-based research obsolete. Wood advocates for structuring investment research departments around foundational technologies like AI, robotics, and blockchain to accurately analyze future growth drivers.

Instead of only investing in tech, Sequoia builds it. The firm employs as many developers as investors to create proprietary tools. This includes an AI system that summarizes business plans, analyzes team quality, and maps competitive dynamics, giving partners an immediate, data-rich overview of opportunities.

Significant disruption often comes from applying mature technologies in novel contexts, not just from new inventions. Gaonkar points to 1970s lithium-ion batteries revolutionizing EVs and old gaming GPUs now powering the AI boom as prime examples of this powerful investment thesis.

Gaonkar favors businesses with complex, "systemic" moats derived from deeply integrated processes, like TSMC's manufacturing expertise. She argues these are more durable than moats based on a single advantage, comparing it to owning the process of gold extraction rather than just owning the mine.

Unlike SaaS startups focused on finding product-market fit (market risk), deep tech ventures tackle immense technical challenges. If they succeed, they enter massive, pre-existing trillion-dollar markets like energy or shipping where demand is virtually guaranteed, eliminating market risk entirely.

Unlike competitors who specialize, Google is the only company operating at scale across all four key layers of the AI stack. It has custom silicon (TPUs), a major cloud platform (GCP), a frontier foundational model (Gemini), and massive application distribution (Search, YouTube). This vertical integration is a unique strategic advantage in the AI race.