The Truth About Made in America | David Cahn Partner at Sequoia

2026-02-16 · Source: Weights & Biases · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, International Business & Trade · Depth: Fundamental Awareness, quick

Summary

David Cahn, a partner at Sequoia, highlights the complexities of the "Made in America" initiative, noting that even products assembled in the US often rely on Chinese suppliers deep within their supply chains. This reliance stems from China's significant cost advantages in components, driven by advanced automated factories and strong agglomeration economies in regions like Shenzhen. Cahn emphasizes that simply assembling goods in America is insufficient; the US must rebuild substantial portions of its domestic supply chain, effectively creating a Western equivalent of Shenzhen. This effort to re-localize production, after decades of globalization, presents a significant and challenging undertaking.

Key takeaway

For entrepreneurs considering domestic manufacturing, you must scrutinize your entire supply chain, not just final assembly. Recognize that achieving genuine "Made in America" necessitates significant investment in rebuilding foundational component production, potentially requiring the creation of new industrial ecosystems to compete with established global hubs like Shenzhen.

Key insights

True "Made in America" requires rebuilding deep supply chains, not just final assembly, due to China's cost and automation advantages.

Principles

• Global supply chains are deeply interconnected.
• Cost and automation drive manufacturing location.

In practice

• Audit supply chains beyond initial tiers.
• Invest in domestic manufacturing infrastructure.

Topics

• Global Supply Chains
• US-China Decoupling
• Made in America
• Advanced Manufacturing
• Agglomeration Economies

Best for: Entrepreneur, Executive, Investor, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Weights & Biases.