---
type: claim
domain: manufacturing
description: "TSMC CEO confirmed CoWoS sold out through 2026, Google cut TPU production targets — the bottleneck is not chip design but physical packaging capacity, and each new AI chip generation requires larger interposers worsening the constraint per generation"
confidence: likely
source: "Astra, Theseus compute infrastructure research 2026-03-24; TSMC CEO public statements, Google TPU production cuts"
created: 2026-03-24
secondary_domains: ["ai-alignment"]
depends_on:
- value in industry transitions accrues to bottleneck positions in the emerging architecture not to pioneers or to the largest incumbents
challenged_by:
- Intel EMIB and other alternatives may break the TSMC CoWoS monopoly by 2027-2028
- chiplet architectures with smaller interposers could reduce packaging constraints
related:
- ASML EUV lithography monopoly is the deepest chokepoint in semiconductor manufacturing because 30 years of co-developed precision optics created an unreplicable ecosystem that gates all leading-edge chip production
reweave_edges:
- ASML EUV lithography monopoly is the deepest chokepoint in semiconductor manufacturing because 30 years of co-developed precision optics created an unreplicable ecosystem that gates all leading-edge chip production|related|2026-04-04
- HBM memory supply concentration creates a three-vendor chokepoint where all production is sold out through 2026 gating every AI training system regardless of processor architecture|supports|2026-04-04
supports:
- HBM memory supply concentration creates a three-vendor chokepoint where all production is sold out through 2026 gating every AI training system regardless of processor architecture
---

# CoWoS advanced packaging is the binding bottleneck on AI compute scaling because TSMC near-monopoly on interposer technology gates total accelerator output regardless of chip design capability

The AI compute supply chain's binding constraint is not chip design — it's packaging. TSMC's Chip-on-Wafer-on-Substrate (CoWoS) advanced packaging technology is required to integrate AI accelerators with HBM memory into functional modules. TSMC holds near-monopoly on this capability, and capacity is sold out through 2026.

TSMC's CEO publicly confirmed the packaging bottleneck. Google has already cut TPU production targets due to CoWoS constraints. NVIDIA commands over 60% of CoWoS allocation, meaning its competitors fight over the remaining ~40% regardless of how good their chip designs are.

The constraint worsens per generation: each new AI chip generation requires larger silicon interposers to accommodate more HBM stacks and wider memory bandwidth. NVIDIA's Blackwell GB200 NVL72 is a full-rack solution requiring massive packaging complexity. The trend toward system-level integration (entire racks as the unit of compute) amplifies packaging demand faster than capacity can expand.

This makes CoWoS allocation the most consequential bottleneck position in the AI compute supply chain. Whoever controls packaging allocation controls who can ship AI hardware. This is a textbook case of [[value in industry transitions accrues to bottleneck positions in the emerging architecture not to pioneers or to the largest incumbents]] — TSMC's packaging division holds more leverage over AI scaling than any chip designer.

## Challenges

Intel's EMIB (Embedded Multi-die Interconnect Bridge) technology is gaining interest as a CoWoS alternative and could reach comparable capability by 2027-2028. Chiplet architectures with smaller interposers could reduce per-chip packaging demand. TSMC is aggressively expanding CoWoS capacity. The bottleneck is real in 2024-2026 but may ease by 2027-2028 as alternatives mature and capacity expands. The question is whether AI compute demand growth outpaces packaging supply expansion — current projections suggest demand wins through at least 2027.

---

Relevant Notes:
- [[value in industry transitions accrues to bottleneck positions in the emerging architecture not to pioneers or to the largest incumbents]] — CoWoS allocation is THE bottleneck position in AI compute
- [[compute supply chain concentration is simultaneously the strongest AI governance lever and the largest systemic fragility because the same chokepoints that enable oversight create single points of failure]] — packaging concentration is a key component of the governance/fragility paradox
- [[physical infrastructure constraints on AI scaling create a natural governance window because packaging memory and power bottlenecks operate on 2-10 year timescales while capability research advances in months]] — packaging is the 2-3 year timescale constraint
- [[the atoms-to-bits spectrum positions industries between defensible-but-linear and scalable-but-commoditizable with the sweet spot where physical data generation feeds software that scales independently]] — NVIDIA's packaging allocation is an atoms-layer moat feeding bits-layer dominance

Topics:
- [[manufacturing systems]]
