Version 3

What

Why it matters

Open questions

• Nvidia securing approximately 60% of CoWoS capacity [4] and reports that supply is being fully 'snapped up' [3] appear to resolve the mid-2025 utilization gap [1] in the bullish direction—but does concentrated hyperscaler demand reflect genuine AI workload consumption, or preemptive capacity reservation that itself risks overshooting?

• Gartner's prediction that 40%+ of agentic AI projects will be canceled by 2027 [10] raises the question of whether enterprise deployment will scale sufficiently to validate the infrastructure buildout—or whether hyperscaler demand and enterprise adoption will decouple structurally, with the former remaining strong while the latter stalls.

• Power and grid constraints are reported to be halting data center growth in 2026 [14], and S&P Global identifies grid dependency as a growing financial vulnerability [15]—does physical energy scarcity function as a natural cap on speculative overbuilding, or as a new category of stranded-asset risk when construction commitments outpace grid access?

• CoWoS ASPs rising to near 7nm-wafer-equivalent levels [6] signal strong pricing power in advanced packaging—does this reflect genuine supply scarcity consistent with structural AI demand, or does it indicate TSMC is extracting rents from hyperscalers locked into near-term capacity constraints regardless of end-demand fundamentals?

Narrative

Timeline

• 2025-06-25: Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027, citing implementation difficulty, unclear ROI, and governance gaps [10]
• 2025-08-05: Digitimes reports CoWoS capacity utilization at only 60% amid the AI boom, suggesting AI-driven demand has not kept pace with packaging supply buildout [1]
• 2025-12-15: Digitimes reports TSMC plans to expand Chip-on-Wafer orders in second half of 2026 as OSAT firms develop CoWoS-equivalent alternatives [5]
• 2026-01-30: Guardian publishes commentary arguing the AI bubble will pop and that responsibility lies in managing the transition responsibly [24]
• 2026-02-05: Report published that TSMC will quadruple advanced packaging capacity to 130,000 CoWoS wafers monthly by late 2026 [28]
• 2026-02-27: Forbes characterizes the AI buildout as a '$1.7 trillion bubble' in analysis titled 'The End of Thinking' [21]
• 2026-03-12: Forbes publishes analysis identifying hidden costs—integration, maintenance, retraining, data preparation—as systematically undermining enterprise AI ROI [11]
• 2026-03-26: Time publishes analysis calling on investors and policymakers to prepare for an AI bubble now [23]
• 2026-04: NY Fed and CEPR publish academic framework examining AI's cyclical vs. structural transmission effects on monetary policy and financial stability [37][38][39]
• 2026-04-28: TrendForce reports CoWoS wafer ASPs approaching 7nm-wafer-equivalent levels, with advanced packaging positioned as a key TSMC profit driver [6]
• 2026-05-18: SemiAnalysis publishes internal token-spend ROI data, reporting 10–90x returns on AI-assisted tasks and arguing demand is structural [7][8]
• 2026-05-20: Milk Road AI summarizes Gavin Baker's framework: watch TSMC capacity decisions as the primary bubble-detection signal for AI investment [2]
• 2026-05-21: SemiAnalysis draws screw-manufacturing analogy to argue AI's transformative impact comes from unlocking new use cases, not cheapening existing ones [9]
• 2026-05-21: Digitimes publishes investor analysis arguing TSMC's cautious capex approach is actively averting an AI bubble rather than feeding one [27]
• 2026-05: Multiple reports indicate TSMC CoWoS capacity is being 'snapped up,' with Nvidia securing approximately 60% of available supply and ASPs rising sharply [3][4]
• 2026-05: Reports emerge that power and grid constraints are halting AI data center growth, with S&P Global identifying grid dependency as a growing financial vulnerability [14][15]

Perspectives

Tensions

• Micro-level productivity economics vs. macro-level capital cycle risk: SemiAnalysis argues that 60–90x task-level ROI makes AI adoption behaviorally irreversible, implying demand is structurally sound [7][8]. Baker's framework counters that strong underlying economics do not preclude speculative overshoot—genuine breakthroughs have historically still produced capital bubbles [2]. The TSMC utilization recovery partially favors the structural side, but Baker's framework was designed precisely to warn that even genuine breakthroughs can attract misallocated capital. [7][8][2][4]
• Hyperscaler semiconductor demand vs. enterprise deployment failure: TSMC's CoWoS capacity is being secured at the hyperscaler tier—Nvidia holds roughly 60% of supply [4], utilization has recovered from 60% in mid-2025 [1]—while enterprise AI deployment is deteriorating, with Gartner predicting 40%+ of agentic projects canceled by 2027 [10]. The structural demand thesis requires productivity gains to replicate broadly, but hyperscaler conviction and enterprise deployment outcomes are diverging rather than converging. [4][1][10][7]
• Internal firm ROI data vs. systematic enterprise failure rates: SemiAnalysis's 60–90x ROI claims derive from its own research-intensive workflows [7], but third-party analyses place enterprise AI project failure rates at 95% for scaling attempts [12] and Gartner predicts 40%+ of agentic AI projects will not survive to full deployment [10]. These bodies of evidence cannot simultaneously be representative—either research-intensive workflows are unrepresentative outliers or the failure-rate analyses are capturing structurally avoidable implementation errors. [7][8][12][10]
• Physical infrastructure constraints as bubble-prevention vs. new stranded-asset risk: Power and grid limitations are halting data center growth in 2026 [14], which could moderate the speculative overbuilding that Baker's framework warns against [2], or could instead create a new class of stranded capital when committed construction investment outpaces actual grid access timelines [15]. [14][15][2]