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SemiAnalysis: AI Silicon Shortage — HBM Bottleneck and N3 Wafer Dominance · history

Version 14

2026-06-10 18:19 UTC · 165 items

What

HBM memory and TSMC N3 logic remain the two binding constraints on AI accelerator production. The central development over the past two days is that NVIDIA's interest in Intel's foundry has acquired a specific product name: multiple publications report that NVIDIA's 'Feynman' GPU architecture — its next generation after Vera Rubin — is planned to use Intel Foundry for some components, consistent with a multi-die chiplet approach [16][17][18][19]. SK Hynix and NVIDIA have formalized a multi-year HBM co-development partnership covering Vera Rubin [10], Bernstein projects HBM4 pricing to nearly double to $37/GB by 2027 [12], and Google's confirmed 3M+ TPU foundry order from Intel for 2028 [14][15] now sits alongside the Feynman GPU reports — together making Intel's 18A node a real, if limited, alternative production path for AI chips in 2028.

Why it matters

If NVIDIA's Feynman GPU uses Intel Foundry for meaningful silicon area at volume, Intel shifts from a single-hyperscaler alternative to a supplier for the dominant GPU maker — a qualitatively different foundry diversification outcome. TSMC's near-term dominance through 2027 is not in question, but the scope of Intel's role in Feynman (limited chiplet versus substantial die area) will determine whether this is a design footnote or a structural change to the 2028+ supply picture.

Open questions

  • Do NVIDIA Feynman GPU plans at Intel Foundry involve a limited chiplet engagement — 'some components' [18] — or does the multi-die design give Intel a meaningful share of total GPU die area?

  • Can Intel's 18A process meet both the Google TPU [15] and NVIDIA Feynman GPU commitments at volume by 2028, given that both deals target the same production window?

  • Does the SK Hynix–NVIDIA co-development partnership give NVIDIA preferential HBM4/HBM5 allocation, structurally disadvantaging AMD and other accelerator makers competing for the same supply? [10]

  • Does Bernstein's $37/GB HBM4 price projection by 2027 [12] hold if Samsung closes its HBM4 yield gap, or does it depend on SK Hynix maintaining its technical lead? [28]

Narrative

HBM wafer supply and TSMC's N3 process node are the two binding constraints on AI accelerator production. SemiAnalysis identified HBM wafer supply — not the earlier CoWoS packaging bottleneck — as the primary scarce resource [1]. SK Hynix has sold out its DRAM, NAND, and HBM capacity into 2026 [2], and Micron has similarly sold out its 2026 HBM allocation while committing roughly $200 billion to long-term memory capacity expansion [3][4]. SK Hynix plans to double DRAM wafer capacity by 2031 [5] but still expects memory supply to remain tight until at least 2030 [6]. On the logic side, AI is projected to consume approximately 60% of TSMC's N3-family output in 2026, rising to roughly 86% in 2027 [7], with TSMC posting +41% year-over-year Q1 2026 revenue at all-time-high margins [8][9].

The HBM supply relationship is consolidating around the dominant accelerator maker. SK Hynix and NVIDIA formalized a multi-year co-development partnership in June 2026 covering NVIDIA's Vera Rubin platform and future systems [10], making SK Hynix a design collaborator on next-generation AI memory rather than a pure supplier. Jensen Huang stated publicly that NVIDIA consumes essentially all available HBM output [11]. Bernstein analysts project HBM4 pricing will nearly double from $16.6/GB to $37/GB by 2027 [12], directly contra the consensus view that memory stocks have peaked. Micron crossed $1 trillion in market cap as investor conviction in the HBM supercycle thesis holds [13].

Intel is now tied to two distinct AI chip production engagements targeting 2028. Google confirmed an order of more than 3 million TPUs from Intel's foundry for 2028 delivery [14][15]. Multiple publications subsequently report that NVIDIA's Feynman GPU architecture — its next generation after Vera Rubin — will use Intel Foundry for some components, consistent with a multi-die chiplet design [16][17][18][19]; the Igor's LAB analysis identifies packaging as a potentially significant dimension of the arrangement [16]. In the near term, AMD's MI455 and VR200 systems remain engineering samples with incomplete software stacks [20], and Intel's Crescent Island targets AI inference by end of 2026 without HBM, competing on cost rather than training performance [21][22].

A third constraint layer sits below the GPU and memory discussion. Each AI server rack requires tens of thousands of MLCCs — multi-layer ceramic capacitors each costing under $1 — now experiencing price hikes and extended lead times [23]. Multiple industry publications confirm AI server demand as the driver of renewed MLCC shortages, with tantalum capacitors similarly strained as MLCC substitution falls short [24][25][26][27].

Timeline

  • 2026-03-01: SemiAnalysis publishes 'The Great AI Silicon Shortage,' identifying HBM wafer supply — not CoWoS packaging — as the binding constraint on AI accelerator production. [1][42]
  • 2026-05-30: SemiAnalysis projects AI consuming 60% of TSMC N3 output in 2026, rising to 86% in 2027. [29][43][7][44]
  • 2026-05-31: SK Hynix confirmed sold out of DRAM, NAND, and HBM into 2026; Micron confirms 2026 HBM sold out and commits roughly $200 billion to long-term memory capacity. [2][3][45][4]
  • 2026-05-31: SK Hynix reports delays to HBM4 mass production while ramping 1c DRAM 8-fold and building new HBM4 packaging plants. [33][46][47][48]
  • 2026-06-01: TSMC posts +41% year-over-year Q1 2026 revenue with all-time-high margins; Arizona Phase 1 fab profitable ahead of schedule. [32][8][9]
  • 2026-06-01: Intel announces Crescent Island GPU targeting AI inference by end of 2026, without HBM, competing on cost and thermal efficiency. [21][22]
  • 2026-06-02: SK Hynix discloses plans to double wafer capacity within five years while projecting memory supply tightness until at least 2030. [6]
  • 2026-06-02: SemiAnalysis shows H100 spot pricing stable in a $2.70–$3.01 band for 146 consecutive days, rejecting the Ornn index as methodologically misleading. [30]
  • 2026-06-03: SemiAnalysis clarifies AMD VR200 and MI455 racks at CoreWeave and Microsoft are engineering samples with incomplete software stacks and no production tokens. [20]
  • 2026-06-05: SemiAnalysis flags MLCCs as an overlooked AI server supply constraint; multiple industry publications corroborate AI-driven shortages of the sub-$1 passive components required in tens of thousands per rack. [23][24][25][26][27]
  • 2026-06-06: Micron crosses $1 trillion in market cap as investor conviction in the HBM supercycle thesis strengthens. [13]
  • 2026-06-08: SK Hynix and NVIDIA formalize a multi-year HBM co-development partnership covering NVIDIA's Vera Rubin platform and future AI infrastructure. [10][11]
  • 2026-06-08: Bernstein projects HBM4 pricing to nearly double from $16.6/GB to $37/GB by 2027, contra consensus that memory stocks have peaked. [12]
  • 2026-06-08: Google's order of 3M+ TPUs from Intel foundry for 2028 delivery confirmed across multiple publications; Intel stock surges on the news. [14][49][15][50][38]
  • 2026-06-09: TrendForce reports NVIDIA is evaluating Intel's 18A process node for a multi-die GPU design; SK Hynix DRAM capacity doubling target specified as 2031. [31][5]
  • 2026-06-10: Multiple publications report NVIDIA's 'Feynman' GPU architecture will use Intel Foundry for some components, naming the specific product behind the earlier 18A evaluation report. [16][17][18][19]

Perspectives

SemiAnalysis

AI's dominance of leading-edge semiconductor capacity is structural; N3 is the main supply bottleneck, H100 spot pricing is stable, the Ornn index is methodologically misleading, AMD MI455/VR200 racks are engineering samples, and MLCCs are an overlooked passive-component constraint in AI server supply chains.

Evolution: Consistent; the MLCC shortage flag has been corroborated by multiple industry sources, confirming it as an industry-wide phenomenon.

[29][7][1][8][30][20][23]

NVIDIA

Structurally dominant in AI accelerator production; the multi-year SK Hynix co-development partnership extends dominance into memory design; Jensen Huang states NVIDIA consumes essentially all available HBM supply; the Feynman GPU architecture is reported to use Intel Foundry for some components in a multi-die design.

Evolution: The Feynman GPU reports add a specific product name and chiplet framing to the previously reported 18A evaluation, making Intel foundry use more concrete though still bounded to 'some components.'

[10][11][31][16][17][18][19]

TSMC

AI demand is structurally robust through at least 2027–2028; Q1 2026 delivered all-time-high margins on +41% growth; TSMC Arizona is profitable ahead of schedule; AI chips are projected to consume 86% of N3 output by 2027.

Evolution: Consistent; NVIDIA Feynman reports add a competitive pressure signal for the 2028+ horizon, though TSMC's near-term dominance is not disputed by any source.

[7][32][8][9]

SK Hynix

Committed to HBM leadership through a multi-year co-development partnership with NVIDIA; targets DRAM capacity doubling by 2031; expects memory supply to remain tight until at least 2030; navigating near-term HBM4 mass production delays.

Evolution: Consistent; the 2031 DRAM doubling target adds specificity to the previously disclosed five-year capacity plan.

[33][2][34][6][10][5]

Micron

2026 HBM sold out; committed roughly $200 billion to an AI memory supercycle; crossed $1 trillion in market cap with analysts positioning it as a multi-year AI infrastructure beneficiary.

Evolution: Consistent.

[3][4][13]

Intel

On two foundry tracks targeting 2028: Google's confirmed 3M+ TPU order and NVIDIA's Feynman GPU reported to use Intel Foundry for some components; separately targeting AI inference with the HBM-free Crescent Island by end of 2026.

Evolution: The Feynman GPU reports expand Intel's foundry story from a single hyperscaler customer to the dominant accelerator maker's next-generation design, though the scope ('some components') keeps the magnitude of the engagement unresolved.

[21][22][14][15][31][16][17][18][19]

Samsung

Scaling HBM production to meet AI demand while facing HBM4 yield challenges and rollout delays that reinforce SK Hynix's structural lead in AI memory.

Evolution: Consistent; no new disclosures this pass.

[35][28][36][37]

Independent market analysts and investors

The HBM bottleneck is confirmed and investable; Bernstein projects HBM4 nearly doubles to $37/GB by 2027; Intel stock surged on the Google foundry news; institutional semiconductor rotation is reportedly accelerating on NVIDIA-Intel Feynman reports.

Evolution: The Feynman GPU reports have broadened the 'AI buyers diversifying from TSMC' investor thesis from a single confirmed Google deal to a pattern involving the dominant GPU maker's next-generation architecture.

[12][38][39]

Tensions

  • NVIDIA's Feynman GPU is reported to use Intel Foundry for 'some components' [18] while TSMC is projected to supply 86% of N3 wafers to AI by 2027 [7] — these are not mutually exclusive, but the scope of Intel's role (limited chiplet versus meaningful die area) remains unspecified and determines whether the NVIDIA-Intel foundry story is a design footnote or a structural shift in the 2028 supply picture. [16][17][18][19][7]
  • The NVIDIA–SK Hynix co-development partnership structurally concentrates HBM supply toward the dominant accelerator maker [10], while AMD's MI455/VR200 remain engineering samples [20] and Intel's Crescent Island deliberately avoids HBM [22] — neither competitor is positioned to access leading-edge HBM at scale in the near term. [10][20][22]
  • Investor framing positions Micron as an 'AI gatekeeper' [40][41], but SK Hynix holds dominant HBM market share, leads HBM4 development, and has formalized a co-development partnership with NVIDIA [10] — the two narratives assign structural primacy to different memory suppliers. [40][41][2][10]
  • SemiAnalysis's transaction-based H100 pricing shows a stable 146-day band [30], while the Ornn index swung from $1.80 to $3.22 to $2.63 in a single month — the two methodologies imply fundamentally different reads on current AI compute market conditions. [30]
  • SK Hynix is delaying HBM4 mass production [33] while targeting a 2031 DRAM capacity doubling [5] and spending $13.3 billion on equipment this year [34] — near-term execution risk coexists with the most aggressive long-term investment posture in the industry. [33][34][5]

Sources

  1. [1] The Great AI Silicon Shortage - SemiAnalysis — reactive:great-ai-silicon-shortage
  2. [2] SK Hynix sells out DRAM, NAND, and HBM capacity into 2026 amid ... — reactive:great-ai-silicon-shortage
  3. [3] Micron's Sold Out 2026 HBM And US$200b Bet On AI Demand — reactive:micron-hbm-bull-case
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  5. [5] SK hynix said to be planning to double DRAM capacity by 2031 - New Electronics — reactive:great-ai-silicon-shortage
  6. [6] SK hynix just said AI memory demand is now so large that it will double wafer capacity within 5 years, yet still expects… — Rohan Paul Twitter (2026-06-02)
  7. [7] Our work shows AI taking roughly 60% of N3 family wafers in 2026 and stepping up to about 86% in 2027, which is a regime… — SemiAnalysis Twitter (2026-05-30)
  8. [8] After posting +41% y/y growth with ATH GM and OM in 1Q26, TSMC is tracking to high-30s growth in CY26. We raised our TSM… — SemiAnalysis Twitter (2026-06-01)
  9. [9] The foundry industry hit a record $48.8B in 1Q26, +32% y/y and +3% q/q in seasonally soft Q1, marking the 9th consecutiv… — SemiAnalysis Twitter (2026-06-01)
  10. [10] SK hynix and NVIDIA just formed a multi-year memory partnership to build the chips behind the next wave of AI factories. — Rohan Paul Twitter (2026-06-08)
  11. [11] In Seoul, Nvidia CEO Jensen Huang handed out SK Hynix x 7-Eleven HBM Chips snack bags while addressing the crowd. — Rohan Paul Twitter (2026-06-08)
  12. [12] Most investors think memory stocks have peaked but they are completely wrong. (Save this). — Milk Road AI Twitter (2026-06-08)
  13. [13] Micron crossed $1 trillion in market cap and it is still undervalued (Save this). — Milk Road AI Twitter (2026-06-06)
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  20. [20] IMPORTANT: it is important to understand that the CoreWeave & Microsoft photos are still Engineering/Quality Samples… — SemiAnalysis Twitter (2026-06-03)
  21. [21] Intel: Our upcoming AI chip will be cheaper, run cooler than Nvidia, AMD options — Ars Technica AI (2026-06-01)
  22. [22] Intel's new inference chip, Crescent Island, doesn't use HBM. — reactive:great-ai-silicon-shortage (2026-06-05)
  23. [23] Nobody is asking who makes the <$1 multi-layer ceramic capacitor (MLCC) that keeps voltage stable across every chip i… — SemiAnalysis Twitter (2026-06-05)
  24. [24] MLCC Shortages Return as AI Server Demand Strains Capacity - Astute Group — reactive:great-ai-silicon-shortage
  25. [25] MLCC Consider Price Increase as AI Demand Outpaces Supply — reactive:great-ai-silicon-shortage
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  27. [27] AI drives MLCC shortage ... — reactive:great-ai-silicon-shortage
  28. [28] Samsung delays HBM4 rollout to 2026 due to yield challenges, all ... — reactive:aws-garman-a100-demand
  29. [29] It also explains why the bottleneck conversation is migrating away from CoWoS, which is finally easing, and onto memory,… — SemiAnalysis Twitter (2026-05-30)
  30. [30] The recent Ornn H100 index drop to $2.63 (-7.72%) is confusing. But mostly because it's a misleading index. Our H100 hou… — SemiAnalysis Twitter (2026-06-02)
  31. [31] [News] Intel Foundry Gains Momentum as Google Reportedly ... — reactive:great-ai-silicon-shortage
  32. [32] TSMC Arizona surprised. After ramping up strongly in CY25 ($2B+ revenue), Phase 1 net profit in 1Q26 alone exceeded the … — SemiAnalysis Twitter (2026-06-01)
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  38. [38] 🟢 Intel Surges After Report Google May Use Its Foundry for AI Chips — reactive:great-ai-silicon-shortage (2026-06-08)
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  43. [43] The broader implication, which we work through in detail in the piece, is that the supply curve for frontier accelerator… — SemiAnalysis Twitter (2026-05-30)
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