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Technology / SaaS
April 27, 2026
Technology / SaaS

AI-Era Semiconductor Sourcing: 3nm & CoWoS Bottlenecks, Taiwan Risk, Foundry Partnerships

Analyzes advanced-node semiconductor sourcing, highlighting TSMC concentration, AI-driven CoWoS bottlenecks, export-control constraints, and limited near-term diversification beyond Taiwan.

60 Mins
Former Vice President
Singapore
Public
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Companies Discussed
AMD (AMD), Amkor (AMKR), Apple (AAPL), ASE (ASX), ASML (ASML), Broadcom (AVGO), Cadence (CDNS), Foxconn (2317.TW), Google (GOOGL), Infineon (IFNNY), Intel (INTC), KLA (KLAC), Meta (META), NVIDIA (NVDA), NXP (NXPI), Qualcomm (QCOM), Samsung (SSNLF), SMIC (0981.HK), TSMC (TSM), UMC (UMC)
Executive Summary
Topics Covered
Methodology
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This transcript examines advanced-node semiconductor supply, where TSMC remains the dominant supplier for 3nm and 2nm chips, with AI demand intensifying allocation pressure. The key bottleneck is advanced packaging, especially CoWoS, rather than wafer capacity alone, with NVIDIA securing much of available supply. Mobile customers retain protected capacity, while new fabs in Japan and Arizona offer relief only by 2028. Export controls limit China’s progress beyond 7nm, and sourcing resilience increasingly depends on deep foundry partnerships, packaging expansion, and Taiwan utility reliability.

Topics Covered
  • AI-driven demand shock and advanced-node capacity allocation
  • TSMC’s dominance in 3nm and 2nm supply
  • Capacity prioritization across AI, mobile, auto, and industrial customers
  • CoWoS and advanced packaging as the key bottleneck
  • Structural vs cyclical nature of advanced-node tightness
  • Impact of U.S. export controls on sourcing strategies
  • China+1 workarounds and limits of supply chain decoupling
  • Taiwan concentration risk and disruption planning
  • Realistic timelines for global fab diversification
  • Single-foundry partnerships vs multi-foundry resilience strategies
  • Southeast Asia’s role in OSAT, packaging, and design ecosystems
  • Failure points including capacity access, yield ramp, and forecasting errors
  • Hidden bottlenecks across utilities, materials, energy, and logistics
  • Pricing power across foundries, packaging players, and suppliers
  • Future outlook for concentration, second-tier foundries, and AI scale economics
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Q: Can you walk us through the current GPU allocation framework at your organisation? How are you deciding between internal AI workloads and enterprise customer commitments? A: Sure. So the fundamental tension right now is that our internal AI teams — the ones building our own foundation models and inference services — are consuming GPUs at a rate that nobody anticipated even 18 months ago. We're talking about 3-4x the original projections. And that creates a real squeeze on what's available for enterprise customers. The allocation committee meets weekly now, which tells you everything. It used to be quarterly. We have a scoring matrix that weighs revenue potential, strategic importance, and internal capability gaps. But honestly, internal teams almost always win because the economics of our own AI services are so compelling compared to renting compute to enterprises...

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Expert Profile
Former Vice President at ams AG
Duration
60 Mins
Call Date
April 27, 2026
Geography
Singapore
Transcript Tier
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Companies Discussed
NVIDIA (NVDA)
Microsoft (MSFT)
AMD (AMD)
Google (GOOG)

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