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.
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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.
Unlike their first company Meraki, the Samsara founders entered the physical operations industry as novices. Their conviction came from identifying compounding technology waves—connectivity, compute, and sensors—and trusting these would unlock future value, even if the exact path was unclear.
Glean spent years solving unsexy enterprise search problems before the AI boom. This deep, unglamorous work, often dismissed in the current narrative that credits AI for its success, became its key competitive advantage when the category became popular.
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.
For fragmented, tech-averse industries, GC funds startups to first build an AI automation platform. Then, instead of a difficult sales process, the startup acquires traditional service businesses, implementing its own AI to dramatically boost their margins, providing immediate distribution and data.
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.
Alex Sacerdote's investment thesis identifies technologies at their adoption inflection point (S-curve), finds companies with strong competitive advantages within that trend, and capitalizes on the resulting exponential, often overlooked, earnings growth. This three-part framework guides their entire investment process for technology stocks.
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.
Beyond AI infrastructure providers (NVIDIA, AWS), a key opportunity lies in the 'layer below'—companies like Uber and Spotify. They leverage big tech's tools but dominate specific verticals because they possess superior, niche-specific user data, which AI then supercharges for monetization and personalization.
Businesses previously considered non-venture scale due to service-based models and low margins, like Managed Service Providers (MSPs), are becoming investable. By building with an AI-first core, these companies can achieve the high margins and scalability required for venture returns, blurring the line between service and product.
