AI Data Center Value Chain Analysis 2025 — Interactive Supply Chain Map
Comprehensive interactive analysis of the AI data center ecosystem covering 52 companies and 86 supply chain relationships across 10 value chain layers. This research maps the competitive dynamics between GPU makers, hyperscaler custom ASICs, ARM and x86 CPUs, ASIC co-designers, startups, OEMs, cable/optical interconnect suppliers, and cloud providers in the $1.2 trillion AI data center market projected for 2030.
Key Market Statistics
NVIDIA data center AI share: 86% → ~75% (2024→2026E). Source: Mercury Research, SemiAnalysis.
AI data center TAM by 2030: $242B → $1.2T (2025→2030E). Source: Yole Group 2025.
Hyperscaler AI capex in 2025: $380B+ (2025E). Source: Company earnings calls.
NVIDIA's data center revenue hit $115.2B in FY2025 (+142% YoY), but market share is projected to decline from 86% to ~75% by 2026 as custom ASICs scale.
Hyperscalers are spending $380B+ on AI capex in 2025 while simultaneously building custom chips (TPU, Trainium, Maia, MTIA) that offer 40-65% TCO advantages over GPUs.
Broadcom and Marvell control ~95% of the custom ASIC co-design market — Google alone spends ~$8B/year with Broadcom on TPU development.
ARM CPUs have grown from 5% to ~20% of data center server share since 2020, driven by 30-60% energy efficiency gains. NVIDIA's own next-gen platform (Vera Rubin) is ARM-exclusive.
The AI data center total addressable market is projected to grow from $242B in 2025 to $1.2T by 2030 — a 5x expansion in five years.
NVIDIA's NVLink Fusion strategy lets hyperscalers plug custom ASICs into NVIDIA's rack architecture — ensuring NVIDIA stays embedded even when it isn't the primary compute chip.
Cable and optical interconnects are an emerging bottleneck — AI racks require 10-36x more fiber than traditional setups, DAC/AOC lead times exceed 20 weeks, and the $2.7B interconnect market is projected to reach $10.7B by 2034.
Value Chain Layers
The AI data center value chain is organized into 10 layers: GPU Makers, ARM CPU, x86 CPU, ASIC Co-designers, Established ASIC, ASIC Startups, Interconnects, OEMs / ODMs, Hyperscalers / Cloud, On-Premise / Private.
GPU Makers
Companies designing and selling general-purpose GPU accelerators for AI training and inference.
NVIDIA: Dominant AI accelerator supplier with 75-86% data center AI market share. Key metric: $115.2B DC rev. Revenue: $130.5B FY2025 total; $115.2B data center. Products: H100, H200, B200, GB200, Vera Rubin (2026).
AMD: Second-largest GPU supplier, gaining share with MI-series accelerators. Key metric: ~$10B DC rev. Revenue: ~$10B data center 2025E. Products: MI300X, MI325X, MI350 (2025), MI400 (2026).
Intel: Third GPU contender via Gaudi line and upcoming Falcon Shores. Key metric: ~$2B AI accel. Revenue: ~$2B AI accelerator 2025E. Products: Gaudi 2, Gaudi 3, Falcon Shores (2025).
ARM CPU
Custom ARM-based server CPUs displacing x86 in data centers with superior energy efficiency.
AWS Graviton: ARM server CPU designed by Annapurna Labs for AWS infrastructure. Key metric: 50%+ of new AWS CPU. Products: Graviton4 (96 cores), Graviton5 (192 cores, 2025).
Ampere: Independent ARM server chip company serving cloud and enterprise. Key metric: 192 cores. Products: Altra Max (128 cores), AmpereOne (192 cores).
Google Axion: Google custom ARM CPU for internal cloud workloads. Key metric: GCP custom CPU. Products: Axion (Neoverse V2-based, 2024).
Microsoft Cobalt: Microsoft custom ARM CPU for Azure infrastructure. Key metric: 128 cores. Products: Cobalt 100 (128 Neoverse N2 cores).
NVIDIA Grace/Vera: NVIDIA ARM CPU for GPU-attached data center use. Key metric: GPU-paired ARM. Products: Grace (72 Neoverse V2), Vera CPU (2026).
Fujitsu: Japanese ARM chip designer behind Fugaku supercomputer. Key metric: HPC leader (JP). Products: A64FX (Fugaku), next-gen 2nm planned with Rapidus.
x86 CPU
Traditional x86 server CPU suppliers facing share erosion from ARM and accelerator-first architectures.
Intel: Largest x86 server CPU maker but losing share to AMD and ARM. Key metric: ~$12B DC CPU (↓). Revenue: ~$12B DC CPU 2025E (declining). Products: Xeon 6 (Granite Rapids), Sierra Forest (E-cores).
AMD: Fastest-growing x86 server CPU supplier, approaching #1 share. Key metric: 30%+ server share (↑). Revenue: ~$8B DC CPU 2025E (growing). Products: EPYC 9005 (Turin, Zen 5), EPYC 9006 (2026).
ASIC Co-designers
Companies providing custom ASIC design services to hyperscalers building their own AI chips.
Broadcom: Largest custom ASIC partner; co-designs TPUs for Google, ASICs for Meta & others. Key metric: ~60% ASIC co-design. Revenue: ~$12B AI revenue FY2025. Products: TPU co-design, XPU, custom networking.
Alibaba Cloud: Largest cloud provider in China. Key metric: #1 China cloud.
Baidu AI Cloud: Baidu AI infrastructure powering Ernie and PaddlePaddle. Key metric: Kunlun chip.
IBM Cloud: Enterprise hybrid cloud with AI integration. Key metric: Watsonx.
On-Premise / Private
Companies building massive private AI compute infrastructure outside public cloud.
Apple: Building private AI cloud infrastructure with custom M-series chips. Key metric: Private Cloud. Products: M2 Ultra, M4 clusters for Private Cloud Compute.
Meta: Operates one of the largest private AI compute fleets globally. Key metric: ~600K GPUs. Products: ~600K NVIDIA GPUs (H100/B200), MTIA for inference.
Tesla: Building Dojo supercomputer and Cortex data centers for autonomous driving AI. Key metric: 100K+ H100s. Products: Dojo D1 chip, Cortex cluster (100K+ H100s).
Supply Chain Relationships
This analysis maps 86 relationships between companies, categorized as: supply relationships (60 edges), competitive relationships (20 edges), and co-design partnerships (6 edges).
8 Power Shifts Reshaping the AI Data Center Ecosystem
Power Shift 1: NVIDIA Becomes a Systems Company
$115.2B — FY2025 data center revenue (+142% YoY)
NVIDIA is no longer just a chip company. The DGX Pod and SuperPod platforms, combined with the NVLink Fusion interconnect strategy, signal a pivot toward selling complete rack-scale systems. The upcoming Vera Rubin platform bundles six co-designed chips into a single architecture. NVLink Fusion is the most strategically significant move — it lets hyperscalers plug their own custom ASICs into NVIDIA's rack architecture, ensuring NVIDIA stays embedded even when it isn't supplying the primary compute chip.
Source: NVIDIA FY2025 earnings, GTC 2025 keynote
Power Shift 2: The Great Decoupling: Hyperscalers Build Their Own
40-65% — TCO advantage from custom ASICs vs GPUs
Google's TPU v7 Ironwood delivers 4,614 TFLOPS with 192GB HBM3e — purpose-built for Gemini. AWS Trainium 3 moves to 3nm with 2x the training performance of its predecessor. Microsoft Maia 200 and Meta MTIA are ramping. The economics are compelling: custom silicon offers 40-65% total cost of ownership advantages at scale. But adoption remains at roughly 15-20% of internal workloads — CUDA lock-in and the sheer pace of NVIDIA's roadmap keep most training on GPUs.
Source: Google I/O 2025, AWS re:Invent, SemiAnalysis
Power Shift 3: Established Companies Lean on Co-designers
~$8B/yr — Google's annual spend with Broadcom on TPU development
The custom silicon revolution runs through a handful of ASIC design houses. Broadcom controls roughly 60% of the custom AI ASIC co-design market, with Marvell holding about 35%. Google alone spends an estimated $8 billion annually with Broadcom on TPU silicon development. Alchip and GUC handle the complex tapeout process at TSMC's most advanced nodes. This creates a parallel semiconductor ecosystem that routes entirely around NVIDIA — from design through fabrication to deployment.
Source: Broadcom earnings, Raymond James estimates
Power Shift 4: Startups Intensify the Race
4T — transistors on Cerebras WSE-3 (largest chip ever)
The startup landscape is defined by architectural bets against NVIDIA's dominance. Cerebras built the largest chip ever — a full-wafer engine with 4 trillion transistors and 900,000 AI cores. Groq's LPU achieves 800+ tokens per second on inference. SambaNova's reconfigurable dataflow architecture targets enterprise AI. But most face the fundamental challenge of competing against NVIDIA's 20-year CUDA ecosystem with over 4 million developers. Cerebras withdrew its IPO in October 2025. The startup path is viable but treacherous.
Source: Company disclosures, TechCrunch
Power Shift 5: Hyperscaler Vertical Integration Accelerates
$380B+ — combined hyperscaler AI capex in 2025
The four largest cloud providers are spending over $380 billion on AI infrastructure in 2025 alone — and the trend is accelerating. These companies are evolving from chip customers to chip designers, building custom CPUs (Graviton, Axion, Cobalt), custom accelerators (TPU, Trainium, Maia), and custom networking silicon. The most telling signal: AWS Trainium 3 will use NVIDIA NVLink Fusion, showing that even direct competitors cooperate when the architecture demands it. Custom silicon is projected to capture 15-25% of total data center AI compute by 2030.
Source: Company earnings calls, Bernstein Research
Power Shift 6: ARM CPUs Rise in the Data Center
~15-23% — ARM share of data center CPUs (2025, up from 5% in 2020)
ARM's data center penetration has quietly reached 15-23% of server CPU shipments — up from roughly 5% in 2020. AWS Graviton 5 with 192 cores now runs over half of all new AWS CPU capacity. Google Axion and Microsoft Cobalt 200 add further momentum. NVIDIA's own Vera CPU is ARM-based, and the Blackwell/Rubin NVL72 rack pairs exclusively with ARM Grace/Vera CPUs. The driving force is 30-60% better energy efficiency versus x86, which matters enormously at data center scale.
Source: Ampere, Arm Holdings earnings, SemiAnalysis
Power Shift 7: x86 Decline Accelerates
~30%+ — AMD's approaching server CPU share (record high)
Intel's data center CPU dominance has collapsed from 90%+ to below 70%, and the trajectory is worsening. AMD is approaching its highest-ever server CPU share above 30%, but the total x86 pie is shrinking relative to ARM. The most significant structural shift: NVIDIA's NVL72 rack architecture pairs its GPUs exclusively with ARM-based Grace and Vera CPUs, not x86. As GPU-attached compute grows faster than general-purpose servers, the GPU ecosystem itself is pulling the market toward ARM.
Source: Mercury Research, Intel/AMD earnings
Power Shift 8: The Interconnect Bottleneck Emerges
20+ wks — DAC/AOC cable lead times for large GPU deployments
As AI data centers scale, the physical layer — cables, connectors, and optical transceivers — has become a critical chokepoint. AI-focused racks require 10-36x more fiber than traditional CPU-based setups. NVIDIA's GB200 NVL72 alone uses 5,184 copper cables per rack. Corning's fiber inventory is sold out through 2026 after a $6B Meta deal. The DAC/AOC market is projected to grow from $2.7B to $10.7B by 2034. Amphenol, which custom-engineered the NVLink spine cartridge, saw IT Datacom revenue surge 134%. Meanwhile, InnoLight holds over 50% of NVIDIA's 800G optical transceiver orders. The transition to 1.6T optics in 2026 will intensify supply pressure further.
Source: Corning earnings, Amphenol Q3 2025, industry reports
Interactive Tools
This page includes four interactive tools: (1) A supply chain flow diagram visualizing all 86 relationships between 52 companies with hover highlighting and edge type filtering. (2) A value chain explorer with tabbed navigation across all 10 layers, company detail modals with ego-network graphs, connection statistics, and power shift references. (3) Market data dashboards showing NVIDIA vs AMD vs Intel quarterly data center revenue, ARM vs x86 CPU share trends, AI data center total addressable market projections, and hyperscaler AI capex by company. (4) A scenario modeling tool with 4 preset scenarios and 6 adjustable parameters to project NVIDIA GPU share, Broadcom ASIC revenue, ARM CPU adoption, and custom ASIC compute share through 2030.
Methodology
This analysis synthesizes data from company earnings reports (NVIDIA, AMD, Intel, Broadcom, Marvell, hyperscaler quarterly filings), industry research (Yole Group, Mercury Research, SemiAnalysis, TrendForce, Bernstein Research, Goldman Sachs), and publicly available product announcements. Last updated: 2026-02-18.
Interactive Research
Updated 2026-02-18
The AI Data Center Chess Game
How the $1.2 trillion AI infrastructure boom is triggering the biggest semiconductor supply chain realignment in decades
86% → ~75%
NVIDIA data center AI share
2024→2026E
$242B → $1.2T
AI data center TAM by 2030
2025→2030E
$380B+
Hyperscaler AI capex in 2025
2025E
40-65%
Custom ASIC TCO advantage vs GPUs
At scale
Key Takeaways
1NVIDIA's data center revenue hit $115.2B in FY2025 (+142% YoY), but market share is projected to decline from 86% to ~75% by 2026 as custom ASICs scale.
2Hyperscalers are spending $380B+ on AI capex in 2025 while simultaneously building custom chips (TPU, Trainium, Maia, MTIA) that offer 40-65% TCO advantages over GPUs.
3Broadcom and Marvell control ~95% of the custom ASIC co-design market — Google alone spends ~$8B/year with Broadcom on TPU development.
4ARM CPUs have grown from 5% to ~20% of data center server share since 2020, driven by 30-60% energy efficiency gains. NVIDIA's own next-gen platform (Vera Rubin) is ARM-exclusive.
5The AI data center total addressable market is projected to grow from $242B in 2025 to $1.2T by 2030 — a 5x expansion in five years.
6NVIDIA's NVLink Fusion strategy lets hyperscalers plug custom ASICs into NVIDIA's rack architecture — ensuring NVIDIA stays embedded even when it isn't the primary compute chip.
7Cable and optical interconnects are an emerging bottleneck — AI racks require 10-36x more fiber than traditional setups, DAC/AOC lead times exceed 20 weeks, and the $2.7B interconnect market is projected to reach $10.7B by 2034.
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8 Power Shifts Reshaping the Value Chain
From NVIDIA's systems pivot to ARM's data center rise — the structural forces redrawing the competitive map.
1
NVIDIA Becomes a Systems Company
$115.2B
FY2025 data center revenue (+142% YoY)
NVIDIA is no longer just a chip company. The DGX Pod and SuperPod platforms, combined with the NVLink Fusion interconnect strategy, signal a pivot toward selling complete rack-scale systems. The upcoming Vera Rubin platform bundles six co-designed chips into a…
Source: NVIDIA FY2025 earnings, GTC 2025 keynote
2
The Great Decoupling: Hyperscalers Build Their Own
40-65%
TCO advantage from custom ASICs vs GPUs
Google's TPU v7 Ironwood delivers 4,614 TFLOPS with 192GB HBM3e — purpose-built for Gemini. AWS Trainium 3 moves to 3nm with 2x the training performance of its predecessor. Microsoft Maia 200 and Meta MTIA are ramping. The economics are compelling:…
Source: Google I/O 2025, AWS re:Invent, SemiAnalysis
3
Established Companies Lean on Co-designers
~$8B/yr
Google's annual spend with Broadcom on TPU development
The custom silicon revolution runs through a handful of ASIC design houses. Broadcom controls roughly 60% of the custom AI ASIC co-design market, with Marvell holding about 35%. Google alone spends an estimated $8 billion annually with Broadcom on TPU…
Source: Broadcom earnings, Raymond James estimates
4
Startups Intensify the Race
4T
transistors on Cerebras WSE-3 (largest chip ever)
The startup landscape is defined by architectural bets against NVIDIA's dominance. Cerebras built the largest chip ever — a full-wafer engine with 4 trillion transistors and 900,000 AI cores. Groq's LPU achieves 800+ tokens per second on inference. SambaNova's reconfigurable…
Source: Company disclosures, TechCrunch
5
Hyperscaler Vertical Integration Accelerates
$380B+
combined hyperscaler AI capex in 2025
The four largest cloud providers are spending over $380 billion on AI infrastructure in 2025 alone — and the trend is accelerating. These companies are evolving from chip customers to chip designers, building custom CPUs (Graviton, Axion, Cobalt), custom accelerators…
Source: Company earnings calls, Bernstein Research
6
ARM CPUs Rise in the Data Center
~15-23%
ARM share of data center CPUs (2025, up from 5% in 2020)
ARM's data center penetration has quietly reached 15-23% of server CPU shipments — up from roughly 5% in 2020. AWS Graviton 5 with 192 cores now runs over half of all new AWS CPU capacity. Google Axion and Microsoft Cobalt…
Source: Ampere, Arm Holdings earnings, SemiAnalysis
7
x86 Decline Accelerates
~30%+
AMD's approaching server CPU share (record high)
Intel's data center CPU dominance has collapsed from 90%+ to below 70%, and the trajectory is worsening. AMD is approaching its highest-ever server CPU share above 30%, but the total x86 pie is shrinking relative to ARM. The most significant…
Source: Mercury Research, Intel/AMD earnings
8
The Interconnect Bottleneck Emerges
20+ wks
DAC/AOC cable lead times for large GPU deployments
As AI data centers scale, the physical layer — cables, connectors, and optical transceivers — has become a critical chokepoint. AI-focused racks require 10-36x more fiber than traditional CPU-based setups. NVIDIA's GB200 NVL72 alone uses 5,184 copper cables per rack.…
Source: Corning earnings, Amphenol Q3 2025, industry reports
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This analysis synthesizes data from company earnings reports (NVIDIA, AMD, Intel, Broadcom, Marvell, hyperscaler quarterly filings), industry research (Yole Group, Mercury Research, SemiAnalysis, TrendForce, Bernstein Research, Goldman Sachs), and publicly available product announcements. Market share estimates reflect the best available consensus from multiple analyst sources. Projected figures (marked "E") represent consensus analyst estimates and company guidance as of the publication date.
The scenario explorer uses a simplified model based on observable market dynamics. It is intended for directional analysis only, not precise forecasting. Actual outcomes will depend on technology roadmap execution, software ecosystem evolution, and macroeconomic factors.
Last updated: 2026-02-18|Silicon Analysts Research
AI Data Center Value Chain FAQ
What is the AI data center value chain?
The AI data center value chain encompasses the full ecosystem of companies that design, manufacture, assemble, and operate the computing infrastructure powering artificial intelligence workloads. This includes GPU and AI accelerator designers (NVIDIA, AMD), custom ASIC builders (Google TPU, AWS Trainium), ASIC co-design houses (Broadcom, Marvell), CPU makers (Intel, AMD, ARM-based), server OEMs (Supermicro, Dell, HPE), cable and optical interconnect suppliers (Amphenol, Corning, InnoLight), and hyperscale cloud operators (AWS, Google Cloud, Microsoft Azure). The total addressable market is projected to reach $1.2 trillion by 2030.
Why are hyperscalers building custom AI chips instead of buying NVIDIA GPUs?
Hyperscalers like Google, Amazon, Microsoft, and Meta are developing custom AI accelerators primarily for cost efficiency — custom ASICs can deliver 40-65% lower total cost of ownership versus GPUs for specific workloads at scale. Additionally, custom chips reduce dependency on a single vendor (NVIDIA), can be optimized for specific model architectures (e.g., Google's TPU for Transformer workloads), and provide supply chain control during GPU shortages. However, CUDA ecosystem lock-in and NVIDIA's rapid innovation pace keep most training workloads on GPUs.
What is NVIDIA NVLink Fusion and why does it matter?
NVLink Fusion is NVIDIA's interconnect strategy that allows third-party chips — including hyperscaler custom ASICs — to plug into NVIDIA's rack-scale architecture (NVL72 and beyond). This is strategically significant because it ensures NVIDIA remains embedded in data center deployments even when customers use competing compute chips. By controlling the interconnect fabric, NVIDIA shifts its value proposition from supplying the only viable AI chip to providing the system architecture that connects all AI chips.
How is ARM disrupting x86 in data centers?
ARM-based CPUs have grown from approximately 5% of data center CPU shipments in 2020 to 15-23% in 2025. Key drivers include 30-60% better energy efficiency versus x86, which translates to significant cost savings at data center scale. AWS Graviton now runs over half of all new Amazon CPU capacity, Google and Microsoft have deployed custom ARM CPUs (Axion, Cobalt), and NVIDIA's own Grace/Vera CPUs are ARM-based. The trend is accelerating because NVIDIA's NVL72 rack pairs GPUs exclusively with ARM CPUs, pulling the GPU ecosystem toward ARM.