AI Compute Infrastructure: A Deep Dive into the CapEx Transmission Chain

You are reading an earlier report. A newer report on this company was published on May 20, 2026: AI Content Copyright and Data Licensing: Investment Research

Lead
The first landing point for AI CapEx is the "high-value compute unit"—GPUs/ASICs, HBM, advanced packaging, and leading-edge wafers—before the spend diffuses outward to rack-scale servers, networking, and liquid cooling and power distribution. The profit pool concentrates in the high-barrier component layer (NVDA / TSMC / SK hynix / Broadcom / Micron), while ODMs capture revenue on thin margins. Rating Watch: a sector where the durable profit pool and the real bottlenecks sit upstream, not in white-box data center floor space.

Core Conclusions

The first landing point for AI compute CapEx is not "data center buildings" but "high-value compute units": capital flows first into GPUs/ASICs/TPUs and their supporting HBM, advanced packaging, and leading-edge wafers, then diffuses outward to rack-scale servers, switch chips, switches, optical modules, liquid cooling, and the power-distribution side. What forms high revenue and profit earliest and most directly is still the accelerator, HBM, foundry, and advanced-packaging links. Microsoft's latest disclosure puts its AI business at an annualized revenue run rate above 37 billion dollars; NVIDIA's FY2026 data center revenue reached 193.7 billion dollars; Broadcom's Q1 FY2026 AI semiconductor revenue hit 8.4 billion dollars with Q2 guidance of 10.7 billion dollars; Micron's FQ2 2026 revenue was 23.86 billion dollars at a 74.4% gross margin. All of this reflects capital accelerating along the "compute chip—memory—network—infrastructure" chain.
The largest revenue elasticity over the next 12–24 months will not necessarily come from server OEMs, but from links that are "supply-scarce with high per-unit value": GPUs/ASICs, HBM, advanced packaging, switch ASICs/AI networking, plus high-power liquid cooling and power-distribution equipment will show higher ASP and profit elasticity than general-purpose server OEMs/ODMs. NVIDIA's full-year FY2026 GAAP gross margin still reached 71.1%, Broadcom's Q1 FY2026 adjusted EBITDA margin was 68%, and Micron's gross margin has climbed to 74.4% in the AI era—evidence that the profit pool clearly concentrates in the high-barrier component layer.
The most direct core beneficiaries remain NVIDIA, TSMC, SK hynix, Broadcom, and Micron: they correspond, respectively, to accelerators, leading-edge process/packaging, HBM, custom ASICs/AI networking, and AI memory. NVIDIA's FY2026 data center revenue was 193.7 billion dollars; TSMC will raise 2026 capital spending toward the high end of 52 billion to 56 billion dollars and expects AI-related demand to be "extremely strong"; SK hynix is widely viewed as the HBM leader, and as early as 2025 customers were racing to lock in 2026 capacity; Samsung also said in Q1 2026 that current customer demand far exceeds supply and that the 2027 supply-demand gap could widen further.
AI server ODMs are major revenue beneficiaries, but not the center of the profit pool: Quanta, Wiwynn, Foxconn, Foxconn Industrial Internet, and Celestica will clearly capture orders and incremental revenue, but their margins are constrained by their systems-integration nature, customer bargaining power, and NVIDIA's growing control over the full system. Citing supply chain information, Tom's Hardware reports that NVIDIA is pushing more complete system integration to a higher level, compressing ODMs' differentiation and margin room.
The most real and most lasting bottleneck in this round of AI expansion is not a single GPU shortage, but a "stacking of multiple bottlenecks": HBM/DRAM supply, CoWoS/2.5D packaging, leading-edge process capacity, high-power supply and distribution, liquid-cooling retrofits, land and grid access, and construction and electrical labor will all jointly limit how fast clusters come online. When Meta raised its 2026 CapEx to 125 billion to 145 billion dollars, it explicitly cited higher component prices and data center costs; Microsoft and the market both point to rising memory/component prices lifting CapEx; Oracle's OpenAI data center project has also been reported as delayed due to material and labor shortages.
"AI CapEx is very high" does not equal "newly added physical compute grows proportionally year over year": part of the 2026 CapEx surge at the hyperscalers stems from sharp price increases in HBM, DRAM, NAND, advanced packaging, and certain key components. Microsoft-related figures suggest roughly 25 billion dollars of CapEx increment is tied to hardware costs; Meta likewise attributes its CapEx increase to higher component prices, especially memory. In other words, within the AI infrastructure chain, both "price" and "volume" are now driving revenue growth simultaneously.
The links with the best margins and strongest bargaining power remain concentrated in companies built on "standard-setting + ecosystem lock-in + capacity scarcity": GPUs and the software ecosystem sit with NVIDIA; leading-edge process with TSMC; HBM in the SK hynix/Samsung/Micron oligopoly; the custom ASIC and switch-chip inflection with Broadcom; EUV equipment with ASML; and advanced-packaging equipment benefiting Applied Materials and others. Applied Materials has guided its 2026 packaging business to grow more than 50%, showing that advanced packaging has moved from a "supporting link" to a "core profit amplifier."
The inference era will not simply weaken hardware demand; it will redistribute the value: training leans more on GPU count, HBM bandwidth, and scale-up networking; inference emphasizes cost-per-watt, memory bandwidth, caching and storage layers, rack-level cooling, power efficiency, and larger-scale scale-out networking. NVIDIA has explicitly positioned Grace Blackwell with NVLink as the "king of inference," stressing lower token cost; AWS is also advancing Trainium and data center architecture upgrades, showing that the inference era is not about "buying less hardware" but "switching to a different hardware mix."
In-house ASICs from cloud providers will erode NVIDIA's "share narrative," but not total semiconductor spending: Google TPU, AWS Trainium, and Meta's in-house inference/training chips essentially shift part of the CapEx from NVIDIA to Broadcom, Marvell, TSMC, advanced packaging, HBM, and networking components. AWS has disclosed that its chip business runs at an annualized revenue run rate above 20 billion dollars; Broadcom states clearly that its AI growth comes from "custom AI accelerators and AI networking." This means ASICs are more about ecosystem redistribution than total-pool compression.
What the market is most likely to overvalue is not "AI fabs" or "AI optical modules" themselves, but companies "with murky disclosure, low AI revenue mix, yet already priced as pure AI plays": typical examples include certain general-purpose server OEMs, general-purpose IDC REITs, early CPO-concept beneficiary chains, and small-cap liquid-cooling/connector companies that have not disclosed their AI revenue mix. By contrast, companies like NVIDIA, Broadcom, Micron, Microsoft, and AWS have at least provided evidence of AI revenue, AI guidance, component price increases, or order visibility.
The most obvious overheating in valuation has already spilled over from "the GPU itself" to "high-elasticity small caps in the supporting chain": when Barron's reported on Applied Optoelectronics, it implied a forward 12-month valuation of about 55.1x; Nebius continued to sharply raise its CapEx guidance to 20 billion to 25 billion dollars amid high growth, while its customer concentration and profit delivery still require verification; some CPO/high-speed optical-interconnect concept stocks are already trading on 2027–2028 penetration rates.
The most important industrial catalysts over the next 12–24 months are not a single new product launch, but five threads validating at once: first, actual shipments and system ramp of NVIDIA's Blackwell/GB300/Rubin; second, HBM3E/HBM4 qualification and capacity lock-in; third, delivery on TSMC's advanced-packaging capacity expansion; fourth, whether hyperscaler CapEx continues to be revised upward; and fifth, whether power, liquid cooling, and gigawatt-scale campuses connect to the grid on time. A problem in any one link would change the revenue-recognition cadence of the entire chain.
The biggest risk is not "AI ending," but "misallocation and deferral under strong demand": this includes a 2027–2028 cyclical swing-back after GPU/HBM/packaging supply over-expands, or, conversely, delayed compute deployment driven by grid, construction, material, and labor constraints. This would keep upstream chips highly prosperous while deferring revenue recognition for mid-stream servers/optical modules/data center infrastructure, creating a scissors gap inside the chain.

Transmission Chain Panorama

AI Compute CapEx Transmission Chain Overview

Chain Position	Segment	Core Products	AI Demand Driver	Revenue Recognition	Key Customers	Supply Bottleneck	Margin Profile	Representative Companies	Public/Private	Benefit Strength	Investment Elasticity	Core Evidence
Upstream capital source	Cloud providers/model companies/NeoClouds	DC CapEx, GPU leasing, data center development	Training + inference scale-up, model companies building/leasing compute	CapEx deployment, long-term supply contracts, cloud service revenue	Microsoft, Meta, Alphabet, Amazon, Oracle, OpenAI, xAI, CoreWeave, Nebius	Power, land, construction, component prices	End-user ROIC depends on utilization, not hardware margin	Microsoft, Meta, Amazon, Alphabet, Oracle, Nebius, CoreWeave	Public + private	10	10	Meta 2026 CapEx of 125 billion to 145 billion dollars, Amazon maintaining about 200 billion dollars, Alphabet/Microsoft both around 190 billion dollars per market figures, Nebius raising annual CapEx to 20 billion to 25 billion dollars.
Core compute	GPU/AI accelerators	H100/H200/B200/GB200, MI series, TPU, Trainium, MTIA	The most central value in training and high-end inference	Chip/module/full-system shipments	Hyperscalers, NeoClouds, model companies	Wafers, HBM, CoWoS	Among the highest gross margins	NVIDIA, AMD, Google TPU, AWS Trainium, Meta MTIA	Public + non-public/internal projects	10	10	NVIDIA FY2026 data center revenue of 193.7 billion dollars, Q4 data center revenue of 62.3 billion dollars.
Memory	HBM/DRAM	HBM3E/HBM4, DDR5, LPDDR	Accelerator bandwidth needs, inference caching, server memory upgrades	Memory die/stack shipments	NVIDIA, AMD, hyperscalers, ASIC makers	Yield, TSV, wafers, packaging	Extremely strong margins in tight cycles	SK hynix, Micron, Samsung	Public	10	9	Micron FQ2 2026 revenue of 23.86 billion dollars at a 74.4% gross margin; Samsung says current supply is far below demand, with a larger 2027 gap.
Manufacturing	Wafer foundry	3nm/5nm/4N/N3/N2	Full ramp of GPUs/ASICs/switch chips	Wafer starts and long-term agreements	NVIDIA, Broadcom, Apple, AMD, hyperscalers	Leading-edge process capacity	High margin, capital-intensive	TSMC, Samsung Foundry, Intel Foundry	Public	9	8	TSMC raising 2026 capital spending to a high 52 billion to 56 billion dollars, AI demand "extremely strong."
Back-end manufacturing	Advanced packaging	CoWoS, SoIC, 2.5D/3D, interposer	Rising GPU+HBM packaging complexity	Package-unit shipments	NVIDIA, AMD, ASIC makers	CoWoS, substrates, test	Scarce supply, improving margins	TSMC, ASE, Amkor, Intel EMIB, Hanmi Semi, Applied Materials	Public	9	9	Reuters points to persistent advanced-packaging tightness; Applied Materials expects 2026 packaging revenue to grow more than 50% year over year.
Key materials	ABF substrates	High-layer-count substrates, FC-BGA	Large packages, high-IO chips	Shipments + long-term orders	NVIDIA, AMD, Broadcom, Taiwanese ODMs	Qualification cycles, yield	Cyclical + structural AI improvement	Ibiden, Shinko, Unimicron, Nan Ya PCB	Public	7	8	Larger AI-chip packages and higher IO drive high-end ABF demand, but public financial disclosure by segment is limited and needs further verification.
Mid-stream systems	AI server ODM/OEM	HGX servers, GB200/GB300 racks, rack-scale systems	Cluster deployment, rack-level delivery	Full-system/rack shipments	Microsoft, Meta, AWS, Oracle, CoreWeave, xAI	GPU, HBM, power and liquid-cooling adaptation	High revenue elasticity, low-to-mid margins	Quanta, Wiwynn, Foxconn, Wistron, Celestica, Foxconn Industrial Internet, Dell, SMCI	Public	8	9	Celestica Q1 2026 revenue up 53% year over year; Tom's Hardware says NVIDIA is going deeper into system integration, squeezing ODM margin room.
Networking	Switch ASIC / switches	800G/1.6T switches, AI Fabric, InfiniBand, Ethernet	Scale-up / scale-out network upgrades	Chip/system shipments	Hyperscalers, NeoClouds, server ODMs	High-end ASICs, optical interconnect, software stack	High gross margin at the chip layer, mid-to-high at the system layer	Broadcom, Arista, NVIDIA Networking, Cisco, Celestica, Accton	Public	9	8	Broadcom's AI revenue growth is driven by "custom AI accelerators and AI networking"; Arista's 2026 AI networking revenue figures keep being revised upward.
Optical communications	Optical modules/lasers/silicon photonics	400G/800G/1.6T, EML, DSP, VCSEL, silicon photonics	Training-cluster scale-out, cross-rack bandwidth	Module/component shipments	Hyperscalers, switch makers, OEM/ODM	Optical chips, DSP, yield	Improving leader margins, but fast price competition	Coherent, Lumentum, AAOI, Innolight, Eoptolink, Fabrinet	Public	7	8	Coherent Q3 FY2026 revenue up 21% year over year; AAOI won hyperscale-customer orders, but profit delivery is still ramping.
Storage	Enterprise SSD / AI storage systems	NVMe SSD, object storage, parallel file systems	Training data lakes, RAG, checkpoints and inference caching	SSD/system shipments, subscription maintenance	Hyperscalers, enterprises, GPU clouds	NAND, controllers, software integration	Medium for SSD, higher for software-defined storage	Micron, Samsung, Solidigm, Pure Storage, VAST Data	Public + private	6	7	NVIDIA has tied its data center storage platform to BlueField-4, showing that AI storage is moving from optional to part of the system architecture.
Cooling	Liquid cooling	Cold plates, CDUs, rack liquid cooling, immersion	High power density of GB200/GB300	Equipment shipments + engineering integration	Hyperscalers, OEM/ODM, IDC	Design validation, on-site retrofit	Margins improve fast, but heavily engineering-driven	Vertiv, Schneider Electric, Eaton, Delta Electronics, Auras, Asia Vital Components, Submer, LiquidStack	Public + private	8	8	AWS is advancing liquid-cooled DC architecture; a single GB300 NVL72 rack's liquid-cooling BOM already approaches 50,000 dollars.
Data center power	UPS/PDU/busbar/switchgear/transformers	UPS, busways, switchgear, power-distribution systems	Rising AI-rack power density, megawatt-scale campus buildout	Product shipments + EPC/integration	Hyperscalers, IDC, GPU clouds	Switchgear, transformers, construction labor	Mid-to-high margins, strong order visibility	Vertiv, Eaton, Schneider Electric, Legrand, nVent, Hubbell	Public	8	7	Oracle AI campus construction has been reported as delayed by materials and labor; AWS is also restructuring its power architecture.
Downstream compute services	GPU cloud/NeoCloud	GPU instances, managed compute, leasing	Asset-light expansion by model companies, short-term demand spikes	Charged by duration/reserved capacity	Meta, Microsoft, Anthropic, smaller model companies	GPU supply, financing, customer concentration	Extremely high revenue elasticity, but high CapEx and leverage	CoreWeave, Nebius, Lambda, Crusoe	Public + private	7	10	Nebius Q1 revenue up nearly 8x year over year, raising annual CapEx to 20 billion to 25 billion dollars and saying demand significantly exceeds GPU supply.

Judging Where the Money Ultimately Flows

If we break down "a cloud provider/model company spending one dollar of AI CapEx" into its most likely ultimate economic destination, I lean toward the following order:

Accelerators and the HBM bound to them
Leading-edge process and advanced packaging
Rack-scale servers, NVLink/NIC/switch ASICs, switches, and optical modules
Liquid cooling and power-side retrofits
Storage systems and SSDs
Data center shells, civil construction, and general facility leasing

This is why many companies that seem "further from the GPU" actually deliver financial results faster than general-purpose IDC operators: in the AI era, the most expensive, most scarce, and earliest-locked items are precisely chips, memory, packaging, networking, and high-power infrastructure—not simple white-box floor space.

CapEx Breakdown and Demand Scenarios

Where Cloud Providers Mainly Spend Their AI CapEx

In FY26 Q3, Microsoft disclosed that its AI business already runs at an annualized revenue run rate above 37 billion dollars, with Azure still growing 40%; Meta raised its 2026 CapEx to 125 billion to 145 billion dollars and explicitly listed higher component prices and data center costs as the main reasons; Amazon maintains about 200 billion dollars of CapEx in 2026, and AWS's AI services already run at an annualized revenue run rate above 15 billion dollars; per market reports, Alphabet raised its full-year AI data center investment plan to about 190 billion dollars, and Google Cloud's order backlog reached 462 billion dollars. Taken together, the bulk of cloud providers' AI CapEx can be summarized as: accelerator systems, memory, full-system servers, networking, optical interconnect, data center power and cooling, and GPU cloud/third-party cloud contracts.

More importantly, the 2026 increase in hyperscaler CapEx does not come entirely from "building more data centers." Public statements from Meta, Microsoft, and Amazon all show that memory and component price increases have become a major driver of rising CapEx, meaning the same one dollar of CapEx increasingly flows to upstream scarce components rather than mid-stream low-margin systems.

A Rough Cost Structure for AI Servers and Training Clusters

Since vendors do not publish complete BOMs, public figures can only support range estimates. Combining NVIDIA's full-system price ranges, liquid-cooling BOMs, and upstream revenue structure, I prefer the following framework:

Object	Cost Component	Approximate Share	Notes
Single 8-GPU / HGX-class server	GPU+HBM+motherboard/NVSwitch	55%–70%	The absolute core of value; for a rack-scale full system, the share may be higher
	CPU+standard DRAM+SSD	5%–10%	Important to a training machine's value, but below the accelerator
	NIC/DPU/switch connectivity	8%–12%	Not negligible in a training cluster, especially high-speed networking
	Chassis, power supply, cooling/liquid-cooling adaptation	8%–12%	Share rises as power density increases
	ODM/OEM integration, warranty and service	3%–5%	Large revenue, but margins typically weaker than upstream chips
GB200/GB300/rack-scale systems	Accelerators+HBM+NVLink/NVSwitch	60%–70%	NVIDIA delivers a deeper bundle, pushing value further up
	Networking/switching	10%–15%	The larger the scale, the higher the share
	Power+liquid cooling	5%–10%	A single rack's liquid-cooling BOM is already substantial
	Storage and rack integration	5%–10%	Rubin-class systems may even carry higher NAND value

The public market diverges considerably on NVL72 pricing: citing supply-chain figures, Tom's Hardware says the GB200 NVL72 costs about 2.8 million to 3.4 million dollars, while the GB300 NVL72 inference-oriented system can reach 6 million to 6.5 million dollars; the same source says Rubin NVL72 quotes have risen to a 5 million to 7 million dollar range. Meanwhile, a single GB300 NVL72 rack's liquid-cooling system BOM already approaches 49,860 dollars, and Rubin will push it higher still. Since these figures are not official vendor list prices, they are better suited to relative-share judgments than to precise absolute valuation.

Which Links Have the Highest Value, the Best Margins, and the Lowest Substitutability

The highest value sits with GPUs/AI accelerators, followed by HBM, advanced packaging, switch ASICs, and high-end networking. NVIDIA's FY2026 data center revenue of 193.7 billion dollars already far exceeds the revenue scale of nearly all server OEMs and networking suppliers; Broadcom's AI semiconductor revenue reached 8.4 billion dollars in a single quarter; Micron's gross margin rose to 74.4% in the AI memory cycle. All of this shows the profit pool still sitting up at the "key component layer."

The best margins sit with GPUs/AI accelerators, HBM, switch ASICs/custom ASICs, and certain equipment/software-bound links. Conversely, the easiest to substitute are general-purpose server contract manufacturing, low-end optical components, and general-purpose hardware without a software-hardware platform binding. Tom's Hardware reports that NVIDIA is reducing the ODM's role in the full system, which precisely shows that the mid-stream integration link has weaker long-term bargaining power than upstream.

The most likely to see supply-demand mismatch fall into four categories: First, HBM/DRAM, because demand lock-ins and supply expansion are out of sync; second, CoWoS/advanced packaging, because beyond wafers it is also constrained by interposers, test, and substrates; third, power equipment and grid connection, because data center projects come online far faster than the grid and switchgear can be delivered; fourth, liquid cooling, because large-scale on-site deployment, validation, and maintenance are not purely a capacity question.

Long-Term Compounding Attributes versus Short-Cycle Prosperity Attributes

Tracks with stronger long-term compounding include: GPU/AI accelerator platforms, leading-edge foundry, HBM leaders, switch ASICs/AI networking, and data center power and thermal-management platforms. These links are either locked in by software ecosystems, protected by very high capital barriers, or benefit long-term from "continually rising AI power density."

Tracks more tilted toward short-cycle prosperity include: server ODMs, general-purpose optical modules, ordinary DDR/NAND, ABF substrates, and a portion of GPU cloud companies that depend on specific projects. They can clearly capture the upcycle but are vulnerable to one or two customers switching platforms, inventory adjustments, or price competition.

Demand Transmission and Three Scenarios

Scenario	Core Assumption	Main Drivers	Most-Benefited Links	Representative Companies	Main Risks
Conservative	Hyperscalers slow CapEx from 2027, with model efficiency improving faster than token growth	Small models, compressed inference, cloud-provider ROI pressure	Power/liquid cooling, network optimization, storage-efficiency tools	Vertiv, Schneider, Arista, Ciena, Pure Storage	Prices fall after GPU/HBM capacity expansion; NeoCloud financing under pressure
Base	Hyperscalers keep raising AI investment, but price increases absorb part of the physical expansion	Training and inference advance together, with inference's share rising	The full GPU/HBM/packaging/networking/liquid-cooling chain	NVIDIA, TSMC, SK hynix, Broadcom, Micron, Arista, Vertiv	Grid and construction slow the pace of coming online
Aggressive	Agents, multimodal, video generation, and enterprise inference all break out	Token explosion, long context, video and agents continuously online	GPU/ASIC, HBM, switches, optical modules, power distribution, GPU cloud	NVIDIA, Broadcom, SK hynix, Micron, Celestica, Innolight, CoreWeave, Nebius	Leverage/debt risk, valuation bubble, policy and export controls

I lean toward the base scenario: inference will not weaken total hardware demand but will extend the value from "simply running more cards in training" to "larger-scale networking, power, cooling, caching, and storage." The latest disclosures from Microsoft, AWS, Meta, and Nebius all show AI revenue growth and AI CapEx expansion still happening in sync, with no chain-wide stall yet visible.

Deep Dive into Segments

Master Segment Table

Segment	Segment Logic	How AI Demand Becomes Revenue	Current Supply-Demand	Key Customers	Price/Margin Trend	Capacity Bottleneck	Leaders	Catalysts	Main Risks	Investment Appeal
GPU	Core compute for training and high-end inference	Chip/module/system shipments	Still tilted toward tight balance	Hyperscalers, NeoClouds, model companies	High ASP, high margin	Wafers, HBM, packaging	NVIDIA, AMD	Blackwell/Rubin ramp	In-house ASIC diversion, export restrictions	10
AI ASIC	Cloud providers' in-house control of TCO	ASIC design services + volume production	Strong demand, share still smaller than GPU	Google, AWS, Meta, OpenAI-related chains	High margin once mature	Leading-edge process, packaging, software ecosystem	Broadcom, Marvell, TSMC	TPU/Trainium/MTIA volume growth	High software-migration cost, customer concentration	8
HBM	A hard requirement for AI memory bandwidth	Stack/die ASP rising	Clearly tight	NVIDIA, AMD, ASIC makers	Price and margin rising sharply	TSV, yield, wafers	SK hynix, Micron, Samsung	HBM3E/HBM4 qualification	Pullback after over-fast expansion	10
DRAM/NAND	HBM crowds out general memory supply	Die/SSD/module shipments	AI tightens the general market	Server/PC/phone/SSD	Cyclical upswing	Competing for capacity with HBM	Micron, Samsung, SK hynix	Price increases and inventory restocking	Large cyclical swings	7
Wafer foundry	A required path for all high-end AI chips	Wafer starts and long-term contracts	Leading-edge process still tight	NVIDIA, Broadcom, Apple, AMD	High utilization improves margins	3nm/2nm capacity	TSMC, Samsung, Intel	N2, 3nm volume production	Geopolitical risk	9
Advanced packaging	The core of GPU+HBM complexity	Package volume production	Tight balance, leaning tight	NVIDIA, AMD, ASIC chains	Improving margins	CoWoS, substrates, test	TSMC, ASE, Amkor, Hanmi	CoWoS expansion	Downstream switching packaging routes	9
ABF substrates	Indispensable for high-end FC-BGA	Substrate shipments	Structural improvement but still cyclical	GPU/ASIC/switch chips	Recovering from the bottom	Qualification, layer count, yield	Ibiden, Shinko, Unimicron, Nan Ya PCB	Rising AI mix	Strong cyclical attributes	7
AI server ODM	Revenue amplifier	Full-system/rack delivery	Strong demand	Microsoft, Meta, AWS, Oracle	High revenue, low-to-mid margin	GPU, liquid cooling, test	Quanta, Wiwynn, Foxconn, Foxconn Industrial Internet, Celestica	Volume delivery of GB200/GB300	Weak ODM bargaining power	8
Server power supplies and connectors	Rising AI-rack power	PSU, high-speed connector shipments	Tilted tight	ODMs, hyperscalers	Improving margins	Copper cabling/high-speed materials	Delta, Amphenol, TE	Rack power upgrades	Fast project switching	8
Data center switches	Scale-out infrastructure	Switch system shipments	Strong demand	Hyperscalers, NeoClouds	Mid-to-high margin	ASIC, optical modules	Arista, Cisco, Accton, Celestica	800G/1.6T upgrades	CapEx pullback	8
Ethernet and InfiniBand	The AI networking standards battle	NICs + switches + software stack	Dual-track in parallel	NVIDIA, Arista, hyperscalers	Chips better than systems	Ecosystem compatibility	NVIDIA, Broadcom, Arista	Rising Ethernet penetration	Compatibility-versus-performance contest	8
DPU/NIC	Data movement and security offload	NIC/DPU shipments	Rising importance in the inference phase	Cloud, storage, AI clusters	Mid-to-high margin	Hardware-software co-design	NVIDIA, Broadcom, Marvell	AI-native storage architecture	Integrated into the SoC	7
Optical modules	High-speed interconnect upgrades	800G/1.6T module shipments	Volume up, price down	Hyperscalers, switch makers	Depends on capacity and price war	DSP, lasers, yield	Innolight, Eoptolink, AAOI, Coherent, Lumentum	800G→1.6T volume production	Prices falling too fast	7
Silicon photonics	A long-term direction, but penetration still early	Chips/modules/platforms	Hot theme, slow delivery	Hyperscalers, switch makers	Not yet fully proven	Process and packaging	Intel, Cisco/Acacia, Ayar, Lumentum	CPO progress	Commercialization later than expected	6
Storage systems	Training data and inference caching	SSD/parallel file systems/object storage	Improving demand	Hyperscalers, enterprise AI	More stable margins from software-hardware combination	Controllers, software	Micron, Samsung, Pure, VAST	Penetration of AI-native storage platforms	Built into the cloud	6
Liquid cooling	A must-have as AI density rises	Cold plates/CDUs/system integration	High growth	Hyperscalers, ODMs, IDC	Improving margins, strong engineering attributes	On-site delivery and maintenance	Vertiv, Schneider, Eaton, Auras, Asia Vital Components	GB300/Rubin coming online	Diverging technology routes	8
Data center power equipment	The real scarcity is in grid connection and distribution	UPS/PDU/switchgear/busbar	Tilted tight	Hyperscalers, IDC, GPU clouds	High order visibility	Transformers/switchgear/labor	Vertiv, Schneider, Eaton, Legrand	Gigawatt campuses	Cyclicality and project delays	8
GPU cloud and NeoCloud	The vehicle for outsourcing demand	Charged by duration/reserved capacity	Strong demand but capital-intensive	Model companies, enterprises	Revenue explosion, margins under pressure	GPU supply, financing, customer concentration	CoreWeave, Nebius, Lambda, Crusoe	Large Meta/OpenAI/Anthropic deals	High leverage and concentration	7

Key Judgments at the Segment Level

In this round of AI expansion, the strongest "industrial trend" does not equal the strongest "deliverable profit." For example, the narratives for both GPU cloud and server ODMs are strong, but the former is constrained by financing and concentrated customers, the latter by NVIDIA's system control and thin-margin attributes; whereas power, liquid cooling, and switch ASICs, though their stories are less dazzling than the GPU's, often deliver more solid financial results and are most likely to surprise expectations after 2026.

In addition, CPO/silicon photonics is worth tracking, but it currently looks more like a mid-term technology option than a near-term profit mainline. In its coverage of Ciena's earnings, Barron's noted that Broadcom's management threw cold water on the pace of the in-rack switch from copper to CPO, arguing it may not happen this year or even next; this shows the market's imagination around CPO may be running ahead of revenue delivery in the short term.

Investment Targets and Key Companies

Master Investment Target Table

Note: the table below prioritizes preserving AI benefit path, customer evidence, financial elasticity, and valuation judgment. Where public figures on "AI revenue mix, forward PE/EV/EBITDA, orders/backlog" are insufficient, the item is uniformly marked "needs further verification." This is not a gap but the boundary of public disclosure.

Company	Ticker	Market	Segment	AI Benefit Path	Key Customers/Evidence	AI Revenue Evidence	Financial Elasticity	Valuation Judgment	Tier
NVIDIA	NVDA	US	GPU/networking	Cloud-provider AI CapEx flows straight to GPUs, NVLink, networking	AWS, Google, Azure, Oracle, Meta, CoreWeave, etc. with public deployments/partnerships	FY2026 data center revenue of 193.7 billion dollars	Extremely high	Highest quality but not cheap	A
Broadcom	AVGO	US	ASIC/switch chips	TPU/Trainium/switch ASICs + AI networking	Google, Meta and other custom chains	Q1 FY2026 AI revenue of 8.4 billion dollars, Q2 guidance of 10.7 billion dollars	Extremely high	High quality, high expectations	A
Micron	MU	US	HBM/DRAM/SSD	AI memory and SSD	AI data center customer base	FQ2 2026 revenue of 23.86 billion dollars at a 74.4% gross margin	Extremely high	Cyclical upswing but already re-rated	A
Arista	ANET	US	Switches	Hyperscaler AI network upgrades	Meta, Microsoft are high-exposure customers	AI networking revenue figures revised upward continuously	High	Pricey but strong delivery	A
Vertiv	VRT	US	Power/liquid cooling	High-power and thermal-management retrofits for AI racks	Hyperscalers, colos, OEMs	Data center demand driving strong orders and CapEx expansion	High	Expectations already high	A/B
Applied Materials	AMAT	US	Semiconductor equipment	Packaging and front-end capacity expansion	TSMC, Samsung, logic/packaging fabs	2026 packaging revenue growth guidance >50%	High	Relatively more balanced	A
Coherent	COHR	US	Optical components	AI datacom optical interconnect	AI networking customer base	Q3 FY2026 revenue +21% year over year, AI datacom benefiting	Mid-to-high	Already prices in plenty of AI expectations	B
Lumentum	LITE	US	Optical components	Optical modules/lasers	AI data center customers	Strong AI narrative, segment delivery needs ongoing tracking	Medium	On the high side	B/C
Applied Optoelectronics	AAOI	US	Optical modules	800G/1.6T and hyperscaler orders	Microsoft, Amazon, etc.	Fast-growing data center orders, Barron's reports a high forward valuation	Extremely high	High elasticity, high risk	B
Oracle	ORCL	US	AI cloud/data center	OpenAI/Stargate/OCI AI clusters	Large OpenAI, Meta deals	Surging backlog contracts and long-term leases	High	High debt and execution risk	B
TSMC	2330/TSM	Taiwan/ADR	Wafer foundry/packaging	Core of GPU/ASIC/switch chip manufacturing	NVIDIA, AMD, Broadcom, Apple	2026 CapEx revised to a high level, extremely strong AI demand	High	Scarce, core position	A
Quanta	2382 TT	Taiwan	AI server ODM	HGX/GB200 rack full systems	Hyperscalers, cloud providers	High prosperity in AI server business, segment detail needs further verification	High revenue elasticity	Margins below upstream	A/B
Wiwynn	6669 TT	Taiwan	AI server ODM	High-end AI servers/racks	Hyperscalers	High AI mix, strong elasticity	High	Depends on shipment cadence	A/B
Hon Hai	2317 TT	Taiwan	Servers/racks	AI server and rack assembly	NVIDIA ecosystem, hyperscalers	AI servers as a growth focus	Mid-to-high	Legacy business dilutes valuation	B
Delta Electronics	2308 TT	Taiwan	Power supplies/power	Server power supplies, data center power	ODMs/hyperscalers	AI drives rising power density	Mid-to-high	Relatively balanced valuation	A/B
Auras	3324 TT	Taiwan	Liquid cooling/thermal	Cold plates and thermal modules	AI server chain	Clear benefit, public segment detail needs verification	High	Hot segment	B
Accton	2345 TT	Taiwan	Switches	White-box switches/AI Ethernet	Hyperscalers	800G/1.6T following network upgrades	High	Needs tracking of customer cadence	B
Ibiden	4062 JP	Japan	ABF substrates	High-end GPU/ASIC substrates	GPU/CPU/ASIC makers	Structural benefit, but financial elasticity depends on the cycle	Medium	Expectation gap in cyclical recovery	B/C
Shinko Electric	6967 JP	Japan	ABF/packaging	High-end packaging substrates	CPU/GPU/ASIC chains	Real benefit path	Medium	Strongly cyclical	B/C
Fujikura	5803 JP	Japan	Optical connectivity/cabling	High-speed interconnect, optical and thermal management	Data center chain	Benefiting from AI network buildout	Medium	Still has an expectation gap	B
Advantest	6857 JP	Japan	Test equipment	AI chip test demand	GPU/logic/memory makers	Rising AI test load	High	High valuation but strong logic	B
SK hynix	000660 KS	South Korea	HBM/DRAM	HBM bound to AI accelerators	NVIDIA, etc.	HBM leadership, tight supply	Extremely high	Heavily re-rated in 2026	A
Samsung Electronics	005930 KS	South Korea	HBM/DRAM/Foundry	Benefits at both the memory and logic ends	Hyperscalers, AI chip customers	Record chip profit in Q1 2026, says supply far below demand	High	Labor and execution risk	B
Hanmi Semiconductor	042700 KS	South Korea	Packaging equipment	HBM/advanced-packaging equipment	Memory makers	Direct benefit from HBM expansion	High	Needs tracking of order durability	B
Innolight	300308 CH	A-shares	Optical modules	800G/1.6T optical modules	North American cloud providers are core	Real benefit, high AI mix	High	Already hot but strong delivery	A/B
Eoptolink	300502 CH	A-shares	Optical modules	800G/1.6T growth	Overseas cloud customers	Real benefit	High	High volatility	B
Foxconn Industrial Internet	601138 CH	A-shares	AI servers	Server/rack assembly and system integration	North American cloud providers, NVIDIA chain	AI servers as one main growth point	High revenue elasticity	Average margins	A/B
Inspur	000977 CH	A-shares	AI server	Domestic AI cluster servers	Internet and government/enterprise customers	Clear domestic benefit path, but heavy export-restriction disruption	Mid-to-high	Higher risk than Taiwanese ODMs	B/C
Victory Giant/Wus Printed Circuit/Shengyi Technology	Multiple tickers	A-shares	PCB/materials	High-frequency high-speed boards and materials	Server/switch chains	Indirect benefit, limited public AI mix	Medium	More cyclical + AI	C
Lenovo	0992 HK	Hong Kong	Servers/devices	AI servers and edge AI	Enterprise/cloud customers	Real AI business but a large overall base	Medium	Cheap valuation but limited elasticity	C
GDS/VNET	9698 HK / VNET	Hong Kong/US	IDC	Providing high-power data centers and colocation	Hyperscalers/enterprises	Benefit is relatively indirect	Medium	More of a real-estate + power logic	C
Schneider Electric	SU FP	Europe	Power/liquid cooling/distribution	Data center power and thermal-management platform	Global cloud providers and IDC	Direct benefit from AI campus construction	High	High quality but not cheap	A
Eaton	ETN	US/strong European business	Distribution/liquid cooling	UPS, switchgear, power + thermal-management acquisitions	Data centers and industry	Data center share keeps rising	Mid-to-high	Still has M&A catalysts	A/B
Legrand	LR FP	Europe	PDU/busbar/racks	Data center infrastructure	Global customers	Real but not pure benefit	Medium	Relatively balanced	B/C
ASML	ASML	Europe	Semiconductor equipment	Leading-edge process capacity expansion	TSMC, Samsung, Intel	AI driving EUV demand	High	Extremely strong long-term barrier	A

Deep Analysis of Key Listed Companies

Note: the following focuses on the companies "most worth further research." In the valuation column, financial data directly verifiable from company disclosure is listed where possible; forward valuations lacking a uniform public figure are marked "needs further verification."

NVIDIA

Chain position: GPU, NVLink, InfiniBand/Ethernet networking, AI system platform.
AI exposure: the highest. The primary beneficiary of hyperscaler AI CapEx.
Customers: AWS, Google Cloud, Microsoft Azure, Oracle, Meta, CoreWeave, Anthropic, etc. NVIDIA's FY2026 announcement also disclosed a multi-year, multi-generation strategic partnership with Meta and continued expansion of its AWS/Anthropic/CoreWeave partnerships.
Financials: FY2026 revenue of 215.938 billion dollars, up 65% year over year; data center revenue of 193.7 billion dollars, up 68% year over year; GAAP gross margin of 71.1%; net income of 120.067 billion dollars.
Orders/capacity: no separate backlog disclosure, but dense collaboration around Blackwell, Rubin, and hyperscale customers.
Moat: the CUDA software ecosystem, system-level integration capability, NVLink/NVSwitch, and a platform spanning from chip to factory scale.
Valuation analysis: high quality and high certainty, but market expectations are also extremely high; current detailed PE/EV/EBITDA needs further verification.
Catalysts: Blackwell/Rubin volume production, continued descent of the inference-cost curve.
Risks: slowing AI spending, in-house ASIC diversion, export controls.
Research conclusion: Strong beneficiary / high certainty / not cheap, but still the anchor of the chain.

TSMC

Chain position: core of leading-edge process and advanced packaging.
AI exposure: extremely high, but unlike NVIDIA it does not show up in a single AI metric; instead it shows up broadly through GPUs/ASICs/switch chips.
Customers: NVIDIA, Broadcom, AMD, Apple, hyperscaler in-house ASICs.
Financials and trends: Reuters reports record Q1 2026 profit, with 2026 capital spending positioned at the high end of 52 billion to 56 billion dollars, full-year revenue growth guidance revised upward to more than 30%, and AI demand "extremely strong."
Barrier: leading-edge nodes, packaging synergy, yield, and customer switching cost.
Valuation: a high-quality core asset; detailed forward valuation needs further verification.
Risks: geopolitics, rising overseas-fab costs.
Conclusion: Strong beneficiary / high certainty / an extremely strong long-term moat.

Broadcom

Chain position: custom ASICs, Ethernet switch chips, AI networking.
AI exposure: very high.
Customers: Google, Meta, and other hyperscaler custom chains.
Financials: Q1 FY2026 AI semiconductor revenue of 8.4 billion dollars, up 106% year over year; Q2 guidance of 10.7 billion dollars; Q1 total revenue of 19.311 billion dollars, adjusted EBITDA of 13.128 billion dollars.
Barrier: customer co-design, switch chips and the networking stack, economies of scale.
Valuation: a high-quality AI-substitution beneficiary, but the market has already priced it fairly fully.
Risks: customer concentration, custom ASIC project delays.
Conclusion: Strong beneficiary / high elasticity / not cheap.

SK hynix

Chain position: HBM leader.
AI exposure: extremely high.
Customers: NVIDIA is the most central.
Financials and trends: Reuters and the market broadly see it maintaining a clear lead in the HBM market; in 2025 there was already a state of 2026 supply being locked in ahead of time. The sharp 2026 re-rating of its share price and market cap shows the financial benefit has been confirmed by the market.
Barrier: HBM process first-mover advantage, customer qualification, the yield curve.
Valuation: both prosperity and valuation rising; watch for high-level volatility.
Risks: subsequent capacity expansion, Samsung catching up, customer concentration.
Conclusion: Strong beneficiary / high elasticity / high prosperity but cyclical risk cannot be ignored.

Micron

Chain position: HBM/DRAM/NAND/SSD.
AI exposure: high.
Financials: FQ2 2026 revenue of 23.86 billion dollars, GAAP gross margin of 74.4%, operating margin of 67.6%, capital spending of 5 billion dollars; cloud memory business revenue of 7.749 billion dollars, core data center business of 5.687 billion dollars.
Barrier: forms an oligopoly with SK hynix/Samsung.
Valuation: a dual lift from cycle + AI; if prosperity continues, earnings elasticity is very large.
Risks: a reversal of the memory cycle.
Conclusion: Strong beneficiary / high elasticity / strong cyclical attributes.

Arista Networks

Chain position: AI data center switches and Ethernet.
AI exposure: high.
Customers: Meta, Microsoft are high-exposure.
Evidence: MarketWatch reports it raised its 2026 revenue growth target to 25% and its AI networking revenue target to 3.25 billion dollars; Investopedia also notes that Meta and Microsoft together account for about 40% of company sales.
Barrier: the Ethernet operating system, hyperscale-customer qualification, the AI Ethernet trend.
Risks: customer concentration, white-boxing, ASIC supply.
Conclusion: Strong beneficiary / high certainty / valuation on the high side.

Vertiv

Chain position: UPS, PDU, thermal management, liquid cooling, rack infrastructure.
AI exposure: high, and easily underestimated.
Evidence: Barron's reports it benefits from AI data center infrastructure demand, with about 250 million dollars of 2025 capital spending to expand capacity; the market also broadly views it as a key platform vendor in the "chip-to-grid" chain.
Barrier: global delivery, project integration, service capability.
Risks: high expectations, project delays, intensifying competition.
Conclusion: Strong beneficiary / high certainty / expectations already high but long-term logic remains.

Celestica

Chain position: AI server and network platform manufacturing.
AI exposure: high.
Financials: IBD reports Q1 2026 revenue up 53% year over year to 4.047 billion dollars, with full-year revenue guidance raised to 19 billion dollars; the company also flagged multi-year growth in high-speed Ethernet switching and custom ASIC platforms at its investor day.
Barrier: high-bandwidth Ethernet platforms, deep binding with North American customers.
Risks: customer concentration and the low-margin ODM nature.
Conclusion: Mid-to-high beneficiary / high elasticity / need to track margins and order structure.

Quanta

Chain position: leading Taiwanese AI server ODM.
AI exposure: high.
Logic: benefits from NVIDIA HGX/GB200 and hyperscaler in-house AI clusters.
Public evidence: long-term high prosperity in the company's AI server business is already a market consensus, but English-language public segment disclosure is limited, so this report suggests treating it as a continued deep-dive name rather than giving overly granular financial figures here.
Conclusion: Mid-to-high beneficiary / high revenue elasticity / margins need ongoing verification.

Wiwynn

Chain position: high-end cloud server and AI server ODM.
Logic: deeply bound with hyperscalers, with an AI server revenue mix typically higher than diversified ODMs.
Conclusion: Mid-to-high beneficiary / high elasticity / worth a deep dive.

Foxconn Industrial Internet

Chain position: AI servers and rack systems.
Logic: one of the few manufacturing platforms in the Chinese market that can genuinely take on the North American cloud-provider and NVIDIA system chain.
Conclusion: Mid-to-high beneficiary / strong revenue elasticity / margins are not the biggest highlight.

Innolight

Chain position: high-end optical modules.
AI exposure: high.
Logic: a direct beneficiary of North American cloud providers' 800G/1.6T upgrades.
Risks: price competition.
Conclusion: Mid-to-high beneficiary / high elasticity / need to track ASP and yield.

Delta Electronics

Chain position: server power supplies, data center power distribution and energy management.
Logic: high-power AI racks raise the value of PSUs and power-supply systems.
Conclusion: Mid-to-high beneficiary / relatively strong certainty / relatively acceptable valuation.

Schneider Electric

Chain position: data center power distribution, thermal management, and integration.
Logic: an "infrastructure-platform beneficiary" of AI campus construction.
Conclusion: Strong beneficiary / high certainty / less of a trading stock than semiconductors, but steady financial delivery.

Ibiden

Chain position: ABF substrates.
Logic: high-end GPU/ASIC packaging drives substrate demand.
Conclusion: Medium beneficiary / cyclical recovery + AI expectation gap / need to track the substrate-prosperity inflection.

Applied Materials

Chain position: semiconductor equipment, especially advanced-packaging equipment.
Logic: the more complex AI chips become, the more packaging equipment is needed.
Evidence: the company's 2026 packaging business growth guidance exceeds 50%.
Conclusion: Strong beneficiary / mid-to-high certainty / relatively balanced valuation and quality.

Oracle

Chain position: AI cloud, data centers, and the OpenAI/Stargate hosting platform.
Logic: not an upstream chip company, but converting AI infrastructure CapEx into OCI revenue and long-term capacity contracts.
Risks: debt and construction execution.
Conclusion: Clear beneficiary / high risk / need to keep verifying financing and grid connection.

Further Research List

Starting from both "revenue-growth certainty" and "irreplaceability of industrial position," I think the listed companies most worth a continued deep dive are:

NVIDIA, TSMC, Broadcom, SK hynix, Micron, Arista, Vertiv, Applied Materials, Schneider Electric, Quanta, Wiwynn, Innolight, Delta Electronics, Celestica, Foxconn Industrial Internet. Of these, the first nine lean toward "high certainty," and the latter six toward "high elasticity."

Private Companies and Scoring Tiers

Important Private Companies and Primary-Market Opportunities

Company	Country/Region	Segment	Core Products/Capabilities	Customers/Partners	Funding and Valuation	Relation to Listed Companies	Investment Focus	Main Risks
Crusoe	US	GPU cloud/data center development	AI cloud, campus development, power infrastructure	OpenAI/Stargate-related ecosystem, enterprise customers	Needs further verification	Synergy with NVIDIA, Oracle, data center developers	Integrated "compute + energy + campus"	Capital-heavy, project execution
Lambda	US	GPU cloud	AI training and inference cloud	Model companies, enterprises	Needs further verification	Deeply bound with the NVIDIA ecosystem	A key entry point for smaller model companies outsourcing compute	GPU supply and financing
Groq	US	Inference chips	LPU inference acceleration	Enterprise and inference scenarios	Needs further verification	Partly a substitute/complement to NVIDIA	Potential for inference cost advantage	Ecosystem and software migration
Ayar Labs	US	CPO/optical interconnect	Optical I/O chips	Chip and system makers	Needs further verification	Interaction with the Broadcom, Intel, NVIDIA ecosystem	If CPO lands, it will carry extreme leverage	Late commercialization cadence
Celestial AI	US	Memory interconnect/optical interconnect	Photonic Fabric	ASIC/GPU ecosystem	Needs further verification	Related to advanced packaging and memory-bandwidth routes	Solves the "compute constrained by I/O" pain point	Uncertain penetration timing
LiquidStack	Europe/US	Liquid cooling	Immersion liquid cooling	Data center customers	Needs further verification	Competes and cooperates with Vertiv/Eaton/Schneider	Clear benefit if 3,600W-class GPUs become widespread	Route competition
Submer	Europe	Liquid cooling	Immersion and cooling infrastructure	Data center customers	Needs further verification	Competes and cooperates with listed power/thermal-management companies	Large elasticity in high-density AI scenarios	Commercialization scale
OpenAI infrastructure projects	US	In-house compute/leasing	Stargate, third-party cloud mix	Oracle, AWS, Google, CoreWeave	OpenAI's valuation and funding figures are huge and need ongoing verification	Directly drives upstream procurement	Watch shifts in its "build vs. lease" structure	Financing, governance, partnership terms
xAI infrastructure projects	US	In-house compute	Colossus mega cluster	NVIDIA, multiple supply chain parties	Funding and valuation need ongoing verification	Drives GPUs, power, and cooling at scale	Watch power density and infrastructure expansion	Environment, grid connection, regulation

Company Tiers and Investment Priority

Category	Definition	Representative Companies	Rationale for Categorization
Tier A	Core direct beneficiaries of AI compute CapEx	NVIDIA, TSMC, Broadcom, SK hynix, Micron, Arista, Vertiv, Schneider, Applied Materials	Can clearly convert CapEx into revenue/profit and sit in high-barrier links
Tier B	Clear beneficiaries, but with cyclical or valuation risk	Samsung, Celestica, Quanta, Wiwynn, Innolight, Eoptolink, AAOI, Coherent, Oracle, Delta	Clear business benefit, but disrupted by price, project landing, customer concentration, or valuation
Tier C	May benefit long-term, but near-term financial elasticity is weak	Equinix, Digital Realty, Legrand, Lenovo, some PCB/materials, some ordinary storage companies	The logic exists, but the transmission chain is long or the AI mix is not high
Tier D	Strong AI narrative, but insufficient actual financial evidence	Some pure CPO concepts, some small-cap liquid-cooling/connector concepts, some general-purpose IDC concept stocks	No sufficient evidence of AI orders, AI revenue mix, or profit delivery

Scoring Model and Total-Score Ranking

I suggest the weights follow the user-given framework:

Direct AI demand exposure: 25%
Chain position and bargaining power: 20%
Financial quality: 15%
Growth elasticity: 15%
Technical barrier: 10%
Valuation reasonableness: 10%
Future catalysts: 5%

Using this framework, I provide a research priority ranking:

Rank	Company	Total Score
NVIDIA	90	The highest in direct exposure, bargaining power, technical barrier, and earnings certainty
TSMC	88	Irreplaceable in the chain, with the broadest benefit reach
SK hynix	86	HBM leader with scarce supply, but cyclicality slightly higher than the top two
Broadcom	84	ASIC + AI networking dual drive, concentrated customers but strong delivery
Micron	82	Extremely large AI memory elasticity, slightly higher cyclical attributes
Arista	81	A high-certainty beneficiary of Ethernet AI networking
Vertiv	80	A power/liquid-cooling platform beneficiary, high expectations but solid logic
Applied Materials	79	Clear benefit from packaging equipment, relatively balanced valuation
Schneider Electric	78	A "shovel seller" for AI campuses, financially steady but a slightly weaker trading stock
Quanta	77	Very strong revenue elasticity, but a profit-pool position below upstream
Innolight	76	Clear benefit, but price competition must be watched closely
Delta Electronics	75	Benefits from AI PSUs and power distribution, relatively steady valuation
Celestica	74	High elasticity, but customer concentration and the manufacturing nature cap valuation
Samsung Electronics	73	Real benefit, but internal execution and labor variables are pronounced
Oracle	70	Strong growth, but high risk too

Valuation, Risk, and Final Conclusions

Valuation and Market-Expectation Analysis

Companies already pricing in AI expectations fairly fully: NVIDIA, SK hynix, Arista, Vertiv, AAOI, Nebius, and parts of Coherent/Lumentum valuations—the market no longer prices them on a traditional cyclical framework alone, but trades on the 2027 scale of AI infrastructure. When Barron's reported on AAOI, its forward 12-month valuation of about 55.1x is a typical example.

Companies that still have an expectation gap: I prefer some names among power and thermal-management platforms, advanced-packaging equipment, ABF substrate leaders, and Ethernet AI networking leaders. The reason is not that they haven't risen, but that the market still tends to treat them as "supporting components" rather than "bottleneck assets." Oracle's project delays, the AWS Titus architecture upgrade, and Meta's component price increases all prove that "power distribution + liquid cooling + infrastructure" has moved from optional to required.

Good companies but too expensive: NVIDIA, Arista, Vertiv, some optical-module/optical-component names, Nebius. They may still be good companies, but not necessarily a "good price" at any given moment. By contrast, names like TSMC, Applied Materials, and Schneider are often more balanced between "quality and valuation."

Cyclical bottom reversal + AI demand: Micron, Samsung, ABF substrate-related names, and some high-end PCB/material chains are the more typical "cycle + AI" overlay. If the industry does not over-expand, this kind of combination sometimes beats pure-narrative segments on fundamental delivery.

Largest near-term earnings elasticity: Micron, SK hynix, Broadcom, Celestica, some high-end optical-module companies. Strongest long-term moat: NVIDIA, TSMC, ASML, Broadcom, some HBM leaders. Most asymmetric risk-reward: high-leverage GPU clouds, optical-module/CPO concept stocks already priced for multi-year-out delivery, and companies that depend on large deals but lack ongoing segment disclosure.

Risk Analysis

The most important risk in this theme is not a single "AI bubble," but the following concurrent risks:

Cloud-provider AI CapEx slowdown: current hyperscaler CapEx is enormous; if ROI pressure rises in 2027, it will first hit mid-stream systems and supporting components.
GPU/HBM supply loosening: if 2027–2028 capacity expands too fast, memory and parts of packaging are hit first.
ASIC substituting for GPU: this will erode NVIDIA's share but not necessarily total spending, more likely carving the profit pool over to Broadcom, TSMC, etc.
Model efficiency gains: may lower per-token hardware demand, but may also expand the deployment scope; the net effect on total volume bears watching.
Server and optical-module price competition: mid-stream and some optical components may have "volume without profit."
Data center construction delays: Oracle/OpenAI projects have been reported as delayed by materials and labor.
Power and grid constraints: the real constraint on AI campus construction increasingly comes from grid connection and power supply/distribution, not the GPU itself.
Customer concentration: Arista, AAOI, GPU clouds, and some ODMs all have concentrated large customers.
Geopolitics and export controls: leading-edge process, equipment, and high-end GPUs are all affected.
Valuation-bubble risk: especially in NeoClouds, optical interconnect, and small-cap supporting names.

Final Conclusion

AI compute infrastructure is not a "supporting role" in the AI value chain, but the layer where the real profit pool and the real bottlenecks are most concentrated. If we view AI as an industrial system, then the CapEx of cloud providers and model companies will not scatter evenly across the whole chain, but flow highly concentrated to a few links that hold technical barriers, scarce supply, and customer lock-in: GPUs/ASICs, HBM, leading-edge process and advanced packaging, AI networking, and power and liquid-cooling infrastructure.

I think the five segments most worth watching now are:

GPUs/AI accelerators
HBM and AI memory
Advanced packaging and leading-edge process
AI networking and switch ASICs/switches
Data center power and liquid cooling

The ten listed companies most worth in-depth research are:

NVIDIA
TSMC
Broadcom
SK hynix
Micron
Arista Networks
Vertiv
Applied Materials
Schneider Electric
Quanta

The five private companies/projects most worth ongoing tracking are:

Crusoe
Lambda
Groq
Ayar Labs
OpenAI/Stargate infrastructure projects

The three points most easily misunderstood by the market are:

"AI CapEx growth" does not equal "newly added physical compute growing at the same rate year over year", because much of the 2026 increment comes from memory and component price increases.
"Server revenue growth" does not equal "server companies earning the most profit"; the profit pool still sits in GPU/HBM/ASIC/packaging.
"More efficient inference" does not equal "falling hardware demand"; in many cases it just shifts demand from training to networking, power, cooling, caching, and low-cost compute architectures.

The indicators most worth tracking over the next 6–12 months include:

Hyperscaler quarterly CapEx and cloud backlog
The actual delivery cadence of NVIDIA's Blackwell/GB300/Rubin
HBM3E/HBM4 qualification and order lock-in
Delivery on TSMC's CoWoS/advanced-packaging expansion
Data center grid-connection capacity and campus MW/GW progress
Ethernet 800G/1.6T and liquid-cooling penetration

For the next research list, the directions I suggest prioritizing for secondary deep dives are:

HBM: the strongest profit elasticity, but also the most prone to "misjudging cycle versus prosperity."
Advanced packaging: it determines the true deliverable capacity of GPUs/HBM.
AI server ODM: high revenue elasticity, but the margins and NVIDIA system integration need to be disentangled.
GPU cloud / NeoCloud: financing, contracts, power, and customer concentration are critical.
Liquid cooling and power distribution: the "bottleneck infrastructure" most likely to be undervalued by the market in the future.

If I had to pick a single secondary research direction most worth a continued deep dive, I would prioritize "HBM + advanced packaging": these two links are the core crossover point in the current AI CapEx transmission chain that can simultaneously explain revenue-growth certainty, margin expansion, supply bottlenecks, and valuation divergence.

Open Questions and Limitations

Some companies in Taiwan, Japan, and A-shares disclose AI business segments less transparently than US stocks; this report has tried to avoid fabricating figures, and any unverified AI revenue mix, orders, and forward valuations are marked "needs further verification."
The precise BOM of a single AI server or full rack system is not a publicly disclosed item by vendors; this report uses range estimates, suitable for industrial research but not for precise cost-method valuation.
Funding, contracts, and utilization at NeoClouds and primary-market companies change quickly, suited to high-frequency tracking and should not rely on a single snapshot.

This report is based on public information and does not constitute investment advice. Markets carry risk; invest with caution.

Mentioned Tickers

NVDA AVGO MU ANET VRT AMAT COHR ORCL TSM CLS ASML 300308 2382 4062