Key Conclusions
The capital expenditure behind AI compute expansion is no longer simply about "buying GPUs." Buyers led by Microsoft, Alphabet, Amazon, and Oracle all show, in their public disclosures, a spending structure where new capital flows simultaneously into servers and racks, networking, data center buildings, and long-duration infrastructure. Microsoft's FY25 Q4 capex was 24.2 billion dollars, of which more than half went to long-duration assets that support monetization beyond the next 15 years, and the rest went mainly to CPU/GPU servers. Alphabet's capex in 2025Q3 was 24 billion dollars, with roughly 60% of its technical infrastructure investment going to servers and 40% to data center and networking equipment. Microsoft also disclosed that "all Azure regions are AI-first and can support liquid cooling." This means the AI CapEx transmission chain has already moved from a chip-centric model to a rack-and-facility-centric one.
The companies that truly carry order certainty and profit leverage are not every "AI-server-related company," but those that can convert cloud-provider and model-company budgets directly into full-system delivery, rack-scale integration, thermal management, power systems, and validation and operations revenue. The clearest main lines today are: among the Taiwanese ODMs, Quanta, Wiwynn, Hon Hai/Foxconn, and Wistron; on the branded side, HPE, Supermicro, and Dell; on the facility side, Vertiv, Eaton, Schneider Electric, and Delta; on the interconnect side, Amphenol; and a portion of Chinese liquid-cooling and data center infrastructure companies, such as Envicool and Sugon DataNergy.
For pure ODM/EMS links, the certainty of revenue growth is usually higher than the certainty of margins. The reason is that AI server BOMs are very high, expensive parts like GPUs and HBM are often consigned by customers or procured through an agency model, and ODMs typically earn from manufacturing, integration, testing, NPI, and supply chain management rather than from the markup on the GPU itself. Hon Hai has already stated it will flexibly switch between buy-and-sell and consignment; Wiwynn will also switch part of its memory procurement to an agency/procurement model from 2026, so memory revenue will no longer be consolidated, even as actual rack shipments and margins improve. Reported revenue will therefore be "compressed," but cash flow, inventory risk, and profit quality may actually improve.
As we move from single GPU servers to GB200/GB300/NVL72, and then to rack-scale systems like Rubin NVL144/Ultra NVL576, a redistribution of value is taking place. In the past, the profit pool was highly concentrated in GPUs, HBM, and networking chips; now the importance of cold plates, CDUs, manifolds, quick disconnects, power shelves, busbars, rack PDUs, system burn-in, and full-rack delivery is rising significantly. NVIDIA itself has also begun to publicly push the 800VDC architecture and the rebuild of AI factory power, and TrendForce likewise believes the next generation of AI architecture will fully adopt liquid cooling and accelerate the HVDC/centralized power route.
The links with the greatest revenue leverage are currently concentrated in AI rack-scale systems, liquid-cooling infrastructure, data center power distribution, and the ODMs that provide rack-scale integration for hyperscalers. TrendForce expects global AI server shipments to grow more than 28% year over year in 2026; Dell'Oro estimates that global data center capex surged 57% year over year in 2025 and may exceed 1 trillion dollars in 2026; Dell'Oro also disclosed that the global data center physical infrastructure market grew about 20% year over year in 2025Q4. These increments will not all land at the full-system makers; a larger share will flow to the rack and facility side.
The companies with the best margins are often not the ones with the highest full-system shipment volumes, but those that command high-voltage/high-current power topologies, cooling control algorithms, system-level validation, and on-site delivery and service capabilities. Vertiv's 2025 net sales reached 10.2 billion dollars with an adjusted operating margin of 20.4%; in 2026Q1 sales grew 30% year over year and the adjusted operating margin rose to 20.8%. Amphenol's 2025Q4 adjusted operating margin was 27.5%. By contrast, Foxconn's management publicly targets keeping the overall operating margin above 3% under high AI server growth, which already makes clear that "who is ramping volume" and "who is making money" are not the same.
The real bottleneck has expanded from "only GPU/HBM/CoWoS" to "power access + power distribution equipment + liquid-cooling validation + delivery coordination." Uptime Institute considers high rack density to still be the primary driver of direct liquid cooling adoption, but adoption across the entire installed data center base remains gradual; Schneider has publicly discussed the power supply constraints data centers face; Eaton disclosed that 2026Q1 Electrical Americas data center orders grew roughly 240% year over year with revenue up about 74%, indicating that power-related demand is in a supply-demand mismatch phase.
The links where "revenue grows fast but profit leverage is insufficient" are mainly generic racks, some standard PSUs, low-barrier structural parts, traditional air-cooled facility equipment, and broad EMS that lacks system-level certification and large-customer lock-in. By contrast, CDU, cold plate, QD/manifold, high-power connectors, rack power, commissioning, and on-site service more readily exhibit features of scarce supply, long certification cycles, and strong customer stickiness. Schneider's Motivair, CoolIT Systems, Vertiv, nVent, Amphenol, and some high-end Chinese liquid-cooling makers are closer to the latter.
The NVIDIA ecosystem will remain the center of the profit pool in the near term, but AMD's MI series and cloud providers' in-house ASICs will actually expand ODM and rack-system opportunities rather than necessarily weaken them. The reason is that once a CSP uses Trainium/TPU/Maia/custom XPU, the chipmaker's brand control is often weaker than NVIDIA's, and system design, rack power, liquid cooling, and manufacturing coordination spill over more readily to ODMs/EMS and facility suppliers. AWS's Trainium2 UltraServer links 64 Trainium2 chips into a single system, and Project Rainier has already brought nearly 500,000 Trainium2 chips online; Google's Ironwood TPU uses a third-generation CDU and continues the liquid-cooling route in place since TPU v3; Microsoft's Maia 200 targets inference and integrates 216GB of HBM3e per chip. All of this shows that ASIC servers do not weaken the system value chain but redistribute value from the "GPU ecosystem" back to the "rack and facility ecosystem."
NVIDIA's next-generation platform is pushing liquid cooling and power from "accessories" toward "mandatory." TrendForce notes that the TDP of AI processors such as B200/B300 has risen from about 700W for the H100/H200 to more than 1,000W, and expects liquid-cooled rack penetration to reach 47% in 2026; but Uptime cautions at the same time that liquid-cooling adoption across the entire installed base is still slow. The two are not contradictory: new AI halls and high-density racks will be heavily liquid-cooled, but legacy generic facilities will not switch in step.
The companies whose valuations already fully reflect AI expectations are concentrated in the high-momentum U.S. facility and interconnect chains. On a closing basis around 2026-05-15/16, Vertiv traded at roughly 93x PE, Broadcom about 106x, AMD about 139x, Arista about 48x, Amphenol about 34x, Eaton about 39x, Comfort Systems about 58x, and Celestica about 43x. By comparison, Supermicro traded at about 15x PE and Dell about 32x, while HPE's trailing PE is not comparable because of distorted EPS and still needs to be judged together with forward earnings and backlog.
The most important catalysts over the next 12-24 months are not simply GPU shipments, but the pace of GB300 rack delivery, Rubin architecture qualification, liquid-cooling penetration, 800VDC pilots, the realization of cloud-provider CapEx, and whether lead times for short-cycle power equipment lengthen again. HPE has disclosed an AI backlog of more than 5 billion dollars, with 64% of cumulative orders coming from enterprise/sovereign customers; CoreWeave disclosed a year-end 2025 backlog of 66.8 billion dollars and active power capacity above 850MW; Oracle disclosed accelerating OCI revenue and a large amount of new data center construction. These metrics are closer to revenue realization than any "industry story."
The biggest risks come in three layers: the first is a slowdown in CapEx pacing at cloud providers and model companies; the second is delays in power access and in transformer/switchgear/grid-connection permits; the third is liquid-cooling reliability and supply chain coordination falling short. For investment research, the most critical question is not whether "AI will keep growing," but identifying which companies can protect gross margins, collections, and return on capital even as revenue rises.
Value Chain Landscape and Capital Flow
From the buyer's perspective, the capital flow of AI CapEx can already be summarized in five steps: compute budget approval → GPU/ASIC and HBM lock-in → server and rack system design and manufacturing → liquid-cooling/power-distribution/network/rack deployment → commissioning and long-term operations. Among these, Alphabet provided a rare public breakdown: of its technical infrastructure investment, roughly 60% goes to servers and 40% to data center and networking equipment; Microsoft made clear that more than half of its spending goes to long-duration assets, with the rest mainly for CPU/GPU servers. In other words, the further we move toward rack-scale, the higher the weight of the facility chain.
Value Chain Position Segment Core Products AI Demand Drivers Revenue Recognition Key Customers Supply Bottleneck Margin Profile Representative Companies Public/Private Benefit Intensity Investment Leverage Basis Upstream compute chips GPU/ASIC chips Blackwell, Instinct, TPU, Trainium, Maia, custom XPU Training/inference token growth, model size expansion, rising inference concurrency Chip shipments/module sales/long-term supply agreements CSPs, model companies, GPU clouds GPU/HBM/advanced packaging, software ecosystem Highest profit pool, but not the main profit-spillover direction of this report NVIDIA, AMD, Broadcom, Marvell, Google, AWS, Microsoft Mixed 5 5 Same as left Upstream memory HBM/high-bandwidth memory HBM3E, HBM4, DDR, NVMe Parameter scale, context length, inference KV cache Chip/module shipments GPU/ASIC makers, server vendors HBM capacity and packaging High margin but mostly retained in the semiconductor chain — Mixed 4 4 Inferred from chip platform public materials and system configurations Board-level basics Motherboard/PCB/backplane UBB, accelerator boards, CPU motherboards, high-speed backplanes High-speed I/O, more layers, higher-loss requirements Board/PCB delivery ODM/OEM/networking vendors High layer count, materials and yield Mid-to-high margin, fairly cyclical Shennan Circuits, WUS Printed Circuit, Unimicron, Nan Ya PCB Mixed 3 4 Same as left System full-build AI server full-build HGX/MGX/OAM/GPU/ASIC servers Training clusters, inference expansion Full-system shipment/project acceptance CSPs, enterprises, sovereign customers, GPU clouds GPU supporting components, system validation Large revenue, sharply divergent profit Dell, HPE, Supermicro, Lenovo, Inspur, xFusion Mixed 4 4 Same as left Contract manufacturing AI server ODM Server, tray, full-rack manufacturing and integration Hyperscaler white-box procurement, customer in-house ASIC Manufacturing/integration/NPI/supply chain service fees Microsoft, Meta, AWS, Google, Oracle, CoreWeave Capacity ramp, validation, customer consignment coordination Fast revenue, relatively thin gross margin Quanta, Wiwynn, Hon Hai, Wistron, Inventec Mixed 5 4 Same as left Rack-scale systems rack-scale system GB200/GB300 NVL72, Rubin NVL144-class systems NVLink/NVSwitch scaling, PDU/liquid-cooling/network integration Full-rack or full-hall project acceptance CSPs, frontier labs, GPU clouds Liquid cooling, power, burn-in, on-site delivery Profit pool moving toward integration and service Wiwynn, Hon Hai, Supermicro, Vertiv, HPE, Sanmina/AMD ZT design Mixed 5 5 Same as left Server power PSU/power module 3kW+ PSU, 80plus, high-efficiency power Continued GPU power increases Module/platform shipped with the full system ODM/OEM High-efficiency design, certification Moderate margin, clearly tiered competition Delta, Lite-On, Chicony, AcBel, Eurodirect Mixed 4 3 Same as left Rack power supply power shelf/VRM Power shelf, 48V/54V, board-level power Rising rack power density Introduced with the full-system/rack platform ODM, CSP, in-house ASIC platforms Current density, thermal design Mid-to-high margin, requires platform certification Delta, Luxshare, some ODM in-house Mixed 4 4 Same as left Power distribution and protection UPS/PDU/busbar UPS, rack PDU, busway, DC busbar Doubling of AI rack power, redundancy requirements Equipment/system projects Data center operators, EPC, CSPs Lead time, on-site customization, certification Margin usually higher than ODM Vertiv, Eaton, Schneider, Legrand/Raritan, nVent, Hubbell Mixed 5 4 Same as left Cold-side core parts direct-to-chip liquid cooling Cold plates, cold heads, cooling loops 50kW+ racks, >1kW chip TDP Module/system supporting components Server vendors, ODMs, liquid-cooling integrators Thermal interface, sealing, yield Mid-to-high margin, strong certification barrier CoolIT, Motivair, Boyd, Envicool, Sugon DataNergy Mixed 5 5 Same as left Cooling distribution CDU sidecar/in-row/in-rack CDU Rack-scale liquid cooling becoming standard Equipment/system projects CSPs, OEMs, colocation Pumps and valves, control, serviceability High value-add Vertiv, Motivair, LiquidStack, Envicool, Tongfei Mixed 5 5 Same as left Cooling connections manifold/QD Manifold, quick disconnect, blind-mate fittings Rack-scale maintenance and low-leakage requirements Component/system supporting parts ODMs, liquid-cooling system vendors Leak reliability, certification Small volume but high barrier Luxshare, nVent, specialized liquid-cooling component vendors Mixed 4 5 Same as left Cooling auxiliaries coolant/pumps/valves/monitoring Coolant, pumps, valves, leak detection Large-scale liquid-cooling deployment Consumables/equipment supporting parts/services Liquid-cooling solution providers, operators Compatibility and lifespan Moderate margin, fairly strong service potential Ecolab+CoolIT, Vertiv, Schneider, Johnson Controls Mixed 3 4 Same as left Structural parts rack/structural parts/rails AI rack, cabinet body, tray, rails Full-rack delivery, load-bearing and liquid-cooling fit Shipped with the project ODMs, OEMs, colocation Customization and lead time Easy to scale, uneven barriers Great Lakes, Vertiv, Supermicro, some Taiwanese rack makers Mixed 3 3 Same as left Interconnect connectors/cables High-speed copper cables, AEC/ACC/DAC, backplane connectors, power connectors 800G/1.6T, intra-rack short-reach interconnect, larger power pulses Component shipments Servers, switches, ODMs High-speed/high-current density validation High margin, strong stickiness Amphenol, TE, Lotes, AVIC Jonhon, Luxshare Mixed 5 4 Same as left Networking optical modules/switches InfiniBand/Ethernet, switches, optics Cluster scale moving from 10,000 to 100,000 cards Equipment/module sales CSPs, GPU clouds, enterprises Optical devices and validation High margin, but beyond this report's main axis Arista, Cisco, Broadcom, Marvell, NVIDIA Mixed 4 4 Same as left Facility power data center power systems Medium/low-voltage distribution, transformers, switchgear, backup power, microgrids Grid-connection constraints, rising rack power Engineering equipment and long-term contracts Cloud providers, colo, EPC Transformers/switchgear/grid-connection permits High momentum, high entry barrier Eaton, Schneider, ABB, Siemens, Hubbell Mixed 5 4 Same as left Delivery services data center EPC/operations EPC, commissioning, burn-in, maintenance Fast bring-up of AI halls Engineering progress/milestones/service contracts CSPs, AI model companies, GPU clouds Workforce organization, multi-party coordination, power permits Mid-to-high margin, scarce top players Comfort Systems, EMCOR, Johnson Controls, Trane, Vertiv Services Mixed 4 4 Same as left End buyers cloud providers/model companies/GPU clouds AI factory / superpod / inference cloud Model training, inference revenue, cloud rental In-house use/rental/AI service revenue Enterprises and developers Power, GPU, facility delivery Buyers are not direct investment targets but determine orders across the chain Microsoft, Alphabet, Amazon, Oracle, CoreWeave, xAI Mixed 5 5 Same as left The key conclusion on capital flow: from the buyer's angle, what gets confirmed first is the "compute budget"; from the seller/investor angle, what gets confirmed first is who has won the acceptable full-rack, liquid-cooling, power, network, and deployment contracts. The place most prone to mismatch within the AI theme is therefore mistaking "industry CapEx growth" for "company profit growth."
Cost Structure, Value Distribution, and Profit Pool Reallocation
Public materials do not give a complete BOM, but through NVIDIA/AMD platform specifications, SemiAnalysis's public TCO models, and the financial disclosures of full-system makers, ODMs, and facility suppliers, the value distribution of AI servers and racks can be roughly reconstructed. It must be stressed that what really determines a downstream company's earning power is not the total value, but whether the portion of value it occupies is "chip pass-through" or "system integration/control/service profit."
Object Main Cost Item Estimated Share of Value Notes 8-GPU training server GPU+HBM module 65%-75% The absolute majority, usually set by the chip maker's pricing 8-GPU training server CPU/memory/motherboard/backplane 8%-12% Related to platform generation, PCIe/CXL, and high-layer board complexity 8-GPU training server network/NIC/cables 5%-10% The larger the cluster, the higher the per-unit NIC/cabling value 8-GPU training server PSU/VRM/power 4%-7% AI generational upgrades raise the unit price of power 8-GPU training server cooling (air/liquid) 3%-8% Lower for air cooling, higher for DLC 8-GPU training server chassis/structural parts/assembly/test 4%-8% One of the main sources of ODM profit 8-GPU training server software/firmware/validation/NRE 1%-4% More important the higher the platform complexity SemiAnalysis's public figures show that the price of a typical hyperscaler H100 server has dropped to around 190,000 dollars; adding storage, networking, and other "all-in" items, the upfront capex per server is about 250,000 dollars. This single-unit structure shows that downstream system makers in most cases do not control the portion of largest value, but rather control repeatable delivery and service value-add.
Value of GB200, GB300, and Rubin-Class Racks
SemiAnalysis's public estimate for the GB200 NVL72 is: the rack-scale server itself is about 3.1 million dollars, and including network, storage, and other items, the typical hyperscaler all-in capex is about 3.9 million dollars per rack. At the same time, NVIDIA's FY2026 data center business grew 68% year over year, indicating the market is indeed migrating toward larger system units.
Rack-Scale System Public/Derivable Configuration Estimated Value Range Notes GB200 NVL72-class IT body 72 GPU + 36 Grace CPU 3.1 million to 3.3 million dollars Anchored to the public estimate for the rack-scale server itself Plus network/storage/other all-in rack network, storage, cabling, etc. 3.9 million to 4.1 million dollars SemiAnalysis public all-in anchor of which GPU+HBM+advanced packaging the majority 2.3 million to 2.6 million dollars Back-derived from total system price and chip value, inferred value Grace CPU 36 units 100,000 to 150,000 dollars Inferred value, far below GPU value NVLink/NVSwitch/switch boards high-speed interconnect within the rack 200,000 to 300,000 dollars Inferred value, more important the larger the system scale Server trays/backplane/motherboard/mechanical structure trays/backplane/chassis 100,000 to 150,000 dollars Inferred value Power system power shelf, PSU, VRM 80,000 to 150,000 dollars Raised by the Blackwell/Ultra generation Liquid cooling, IT side cold plate, manifold, QD, CDU allocation 100,000 to 200,000 dollars This range applies when not including the full building-side cooling plant Rack structure/busbar/PDU rack, busbar, PDU 30,000 to 80,000 dollars Important for system stability and serviceability Integration/test/burn-in/delivery pre-installation, validation, bring-up 50,000 to 120,000 dollars The key link where barriers rise in the rack-scale era In the table above, apart from the total rack value anchor, which comes from public materials, the rest are inferences based on public system configurations and industry cost models, not official OEM/ODM BOMs. What matters most in research is not splitting every dollar down to the unit, but seeing a structural trend: from the DGX/HGX era to the NVL72/144 era, the relative weight of power, liquid cooling, integration, validation, and on-site delivery is all rising.
What ODMs Earn
ODM profit sources can be broken into four layers:
Profit Source Notes Significance for Margin Manufacturing profit SMT, assembly, test, shipment Base profit, usually not high System integration profit Full-rack, liquid cooling, power, network coordination Higher gross margin and customer stickiness NPI/engineering profit Platform introduction, validation, firmware, on-site support Higher customer switching cost Supply chain management profit Lead-time control, shortage substitution, inventory coordination Especially critical during GPU scarcity Hon Hai states bluntly that the buy-and-sell model for high-value AI server products will temporarily affect gross margin performance, but relying on scale effects, the company remains confident it can keep its operating margin above 3% as AI revenue continues to grow; at the same time, the company will use both consignment and buy-and-sell depending on customer requirements. Wiwynn disclosed that from April 2026, some customers' memory procurement will switch to a procurement agency model, so memory revenue will no longer be counted in the financials, even as actual server rack shipments and margins improve sequentially. In other words, the customer-consignment model lowers "revenue" but may improve "cash flow and profit quality."
How Rack-Scale Delivery Raises Barriers
Rack-scale delivery adds three classes of new barriers relative to single units:
Cross-domain engineering barrier: servers, power, liquid cooling, networking, rack structure, building water loops, and electrical codes must all be satisfied simultaneously. Vertiv repeatedly emphasized in its 2025 annual report that its differentiation comes from the system-level integration of power, thermal, and service delivery; Supermicro also made rack-scale integration, liquid cooling, and DCBBS its manufacturing expansion focus in its FY26 Q3 deck.
Validation and burn-in barrier: single-unit testing shifts to full-rack, full-row, and full-hall commissioning, which means on-site service capability begins to determine the speed of revenue realization. The large backlog growth at Comfort Systems and EMCOR, both of which list data centers as a core track, reflects this.
Reliability and maintenance barrier: the leak-free operation, field-serviceability, operations toolchain, spare parts, and training that liquid cooling brings all elevate suppliers from "component vendors" to "core partners." Schneider strengthened its liquid cooling through the acquisition of Motivair; Ecolab's 4.75 billion dollar acquisition of CoolIT is precisely a bet on this layer of service+fluid+hardware combined value.
Demand Transmission, Bottlenecks, and Scenario Analysis
Training demand remains the core driver of large racks, high-bandwidth interconnect, and NVLink/NVSwitch systems, but from 2026 onward, the drivers of inference and training are diverging. Google clearly positions the Ironwood TPU as the TPU for the inference era and continues third-generation CDU liquid cooling; Microsoft launched Maia 200 with a focus on token generation economics; AWS treats the Trainium2/Trn2 UltraServer and Project Rainier as infrastructure platforms for training and large-scale inference. In other words, training makes systems bigger, inference makes systems broader: the former drives ultra-large clusters and NVL72/144, the latter drives more regions, faster expansion, and more flexible liquid-cooling and power architectures.
Multimodality, video generation, AI agents, and long context will simultaneously raise three things: memory capacity demand, memory bandwidth demand, and rack energy consumption. Google's official statements on Ironwood emphasize the large-scale shared memory and energy efficiency of the inference superpod; AWS's Trainium2 UltraServer uses 64 chips to form a single large-system node; Microsoft is fully pushing liquid cooling and more flexible zonal cooling on the data center side. This means that over the next two years, the old assumption that "inference servers are low-power and air-cooling-friendly" will clearly break down.
When Liquid Cooling Goes From Optional to Mandatory
The most practical judgment is not the nominal TDP of a given GPU generation, but the intersection of rack density and facility constraints. The important signals from current public materials are:
TrendForce notes that AI processor TDP is rising from about 700W for the H100/H200 to more than 1,000W for the B200/B300, and expects liquid-cooled rack penetration to reach 47% in 2026.
Uptime believes direct liquid-cooling adoption across the entire installed base is still gradual, but high rack density remains the primary driver of DLC adoption.
Microsoft has disclosed that all Azure regions can support liquid cooling, and has publicly described cooling routes such as HXU and microfluidics.
On this basis, research can roughly divide the critical point into three tiers: below 30kW, most can still use air cooling; 30-50kW enters the range of air-assisted liquid or selective DLC; above 50kW begins to lean significantly toward D2C; above 100kW, new AI racks essentially enter a liquid-cooling-dominated regime. This is not a unified industry standard but a practical judgment formed from public materials.
The Most Real Bottlenecks Today
Bottleneck Impact Path Most Affected Most Benefited GPU/HBM/advanced packaging Limits the pace of full-system shipments ODMs, branded servers NVIDIA/AMD/memory chain Power access/grid connection/transformers/switchgear Delays bring-up of campuses and AI halls Cloud providers, colo, model companies Eaton, Schneider, Hubbell, EPC Liquid-cooling validation/materials/sealing/QD Lowers full-rack yield, lengthens certification ODMs, full-system makers CoolIT, Motivair, Envicool, Sugon DataNergy Rack-scale burn-in/commissioning Delays revenue recognition ODMs, system vendors Vertiv, Supermicro, EPC service providers Customer consignment switch Distorts revenue and cash flow accounting ODM/EMS Contract makers and platform vendors with strong cash flow management From public data, the bottleneck is indeed migrating toward the facility side: Eaton's high growth in data center orders and backlog, Schneider's continued discussion of power constraints, and Uptime's focus on grid limitations all show that "servers are built but cannot get power" has become a real problem.
Three Scenarios
Dimension Conservative Base Aggressive Assumptions GPU supply improves but cloud providers digest prior orders, and power access is delayed TrendForce base case, CSPs/model companies continue to expand, and GB300 ramps smoothly Inference surge layered on re-accelerating training, Rubin qualification pulled forward, ASIC servers racing in parallel AI server shipment pace +15% to 20% YoY in 2026 >28% YoY in 2026 Sustained high growth in 2026-2027, +35% to 45% annually Rack-scale system penetration 10% to 15% 20% to 30% 35% to 45% Liquid-cooling penetration 25% to 30% of new AI racks 35% to 45% of new AI racks, with industry installed base still below this More than 50% of new AI racks, with 800VDC pilots expanding Power infrastructure demand Mainly retrofit and localized distribution expansion Full expansion of distribution, UPS, busway, CDU, and facility loop Extending toward centralized DC power, modular power blocks, and microgrids Benefiting links PSU, localized liquid cooling, UPS/PDU, engineering services Rack-scale ODM, liquid cooling, power facilities, connectors CDU/cold plate/QD, power systems, EPC, burn-in, on-site service Representative companies HPE, Dell, Eaton, Kehua, KSTAR, JCI Quanta, Wiwynn, Hon Hai, Vertiv, Eaton, APH, Envicool Vertiv, Schneider, Delta, CoolIT, Motivair, Comfort, EMCOR Main risks CapEx pause, project delays Liquid-cooling reliability, power delivery Valuation overheating, supply imbalance, policy and grid-connection risk The most valuable scenario for investment research is not the most optimistic one, but "under the base case, whose earnings realization depends least on a single customer and a single platform." From this angle, Vertiv, Eaton, Schneider, Amphenol, some Taiwanese rack-scale ODMs, and liquid-cooling core-part makers often carry higher certainty than "AI concept server stocks" that merely tell a story.
Segment Tracks and Competitive Landscape
The table below compresses the priority tracks the user requested into a "research matrix." Scores are on a 10-point scale and measure order certainty + profit leverage + customer barriers + the strength of catalysts over the next two years, not a stock rating.
Track Track Logic How AI Demand Converts to Revenue Current Supply-Demand/Price/Margin Trend Certification/Tech Barrier Main Catalysts Main Risks Attractiveness Score AI server full-build The most direct landing point for AI budgets Full-system/node shipments Strong demand, but volatile during platform transitions; diverging margins Mid-to-high Blackwell/MI350 shipments GPU supply/customer delays 7 AI server ODM/EMS White-boxing and hyperscaler customization Manufacturing+integration+NPI Fast revenue, thin gross margin; quality ODMs are exceptions Mid-to-high CSP white-box expansion Customer consignment pressures revenue 8 Branded server makers Enterprise/sovereign customers value brand, service, software Full-system+support+service Gross margin generally higher than contract manufacturing High Enterprise AI adoption Price competition with ODMs 7 Rack-scale AI systems Value migrating from single units to full racks Full-rack projects, burn-in, commissioning Rising volume and price Very high GB300/Rubin introduction Delivery complexity 9 GB200/GB300/Rubin supply chain Next-gen platform drives power and liquid-cooling upgrades Racks, cold side, power, connectors High momentum Very high Platform qualification, mass-production ramp Platform transition delays 9 AMD MI series server chain Second platform, expands ODM opportunity GPU servers and U.S. design/manufacturing High growth but ecosystem still catching up Mid-to-high MI350/MI400, ZT/Sanmina Software ecosystem/customer range 7 In-house ASIC server chain Chip brand control declines, system opportunity expands TPU/Trainium/Maia/custom XPU racks Long-term expansion High TPU/Trainium/MAIA cluster expansion Customer in-house pace 8 AI storage servers Both training and inference need high-throughput storage Storage nodes and parallel file systems Certain growth, moderate profit Mid Data lakes, RAG, video Overshadowed by the upstream compute theme 6 Motherboard/PCB/backplane Generational upgrades raise per-unit board value Board and high-layer board shipments Improving price and utilization Mid-to-high 800G/1.6T, AI motherboard upgrades Cyclical swings 7 High-speed connectors Intra-rack/board-to-board high-speed interconnect Rising connector ASP High momentum, high gross margin High Bandwidth upgrades, NVL systems Substitution and customer certification 9 Power connectors High current/high reliability Introduced with racks and power shelves Rising volume and price High High-power racks Slow certification 8 DAC/ACC/AEC copper cables Cost advantage of short-reach high-speed interconnect Cable and component shipments Fast growth, but heavy competition Mid-to-high 800G/1.6T short-reach deployment Optical substitution/price wars 7 Server power/PSU Power increases directly raise the BOM PSU platform introduction Certain revenue, ordinary gross margin Mid Platform iteration Commoditization 6 power shelf/VRM Rack power supply becoming central Following rack-scale solutions Higher barrier than PSU Mid-to-high Rack-scale power supply Customer in-house designs 8 UPS AI hall redundancy and stable power supply Equipment and engineering projects High momentum, margin better than full-system High AI campus construction Grid connection and lead time 8 PDU/busbar/distribution cabinet Core of rack and facility distribution Project acceptance Strong momentum High High-density racks Lead time 9 direct-to-chip liquid cooling Mainstream route for high-density AI racks Introduced with racks Rising penetration, improving margin High B300/Rubin Reliability 9 CDU The control center of the liquid-cooling system Equipment + service Tight supply-demand Very high New AI hall construction Pumps, valves, and maintenance 9 cold plate Core of the chip's hot side Parts/modules High growth, mid-to-high gross margin High Rising GPU TDP Yield/materials 8 manifold/QD Key to liquid-cooling maintenance and low leakage Parts supply Low unit price but strong barrier Very high Liquid-cooling standardization Reliability incidents 8 rear-door heat exchanger Retrofit-friendly Retrofit projects Suited to the existing facility base Mid Enterprise AI retrofits Insufficient for ultra-high density 6 immersion cooling Ultra-high density/special scenarios System enclosures and fluids Ecosystem still niche Mid-to-high Specific training/edge scenarios Insufficient standardization 5 coolant/pumps/valves Consumables and devices after liquid-cooling expansion Equipment/consumables/services More recurring revenue Mid Installed-base growth Fragmented bargaining 6 rack/structural parts/rails Full-rack structure and load-bearing Shipped with the project Easy to scale, but heavy price pressure Mid Rack-scale standardization Low-margin competition 5 Data center power equipment Transformers/switchgear/microgrids Long-cycle projects Tight supply Very high Time-to-power becoming a core KPI Public grid/permits 9 Data center EPC/operations Delivery and bring-up pace determine revenue realization EPC/maintenance/commissioning Strong backlogs High New campus construction Fixed-price contract risk 8 DCIM/digital twin/infrastructure software Manages liquid cooling and power fluctuations Software licenses/services Important long-term, not the main profit pool short-term Mid Liquid-cooling at scale Slow-maturing business model 5 This matrix has two most important investment implications.
First, not every high-growth track is high-profit. For example, PSUs, structural parts, some copper cables, and standard racks usually grow revenue fast, but without customization, certification barriers, and system-level lock-in, gross margins do not necessarily rise in step. Conversely, links such as CDU, QD, high-power connectors, and UPS/PDU/busway may not be the largest in standalone size, but more readily form a structure of "few players + long certification + high switching cost."
Second, the answer to whether branded server makers or ODMs more easily gain profit leverage is not a single-choice question. For projects led by enterprise/sovereign customers, HPE and Dell more readily win high value-add service, software, and long-term support contracts; for hyperscaler white-box and custom rack projects, Quanta, Wiwynn, Hon Hai, and Wistron are more sensitive to volume surges. Supermicro sits between the two, with both a full-system brand attribute and a strong rack-scale/liquid-cooling manufacturing attribute.
Looking further down at the competitive landscape, NVIDIA's influence remains overwhelmingly strong, but its role in the supply chain is not "all profit is eaten by NVIDIA"; rather, by defining the platform it raises rack-scale complexity, thereby lifting the value of power, liquid cooling, connectors, burn-in, and delivery services. AMD and CSP in-house ASICs further weaken any single GPU ecosystem's monopoly over system design, giving Sanmina, the ZT design team, Taiwanese ODMs, and facility suppliers more bargaining room.
Master Investment List and Key Companies
Company Tiers and Investment Priority
Category Companies Rationale Tier A: core direct beneficiaries Vertiv, Eaton, Schneider Electric, Amphenol, Wiwynn, Quanta, Hon Hai, HPE, Supermicro, Envicool, Sugon DataNergy Clearest transmission path between orders and AI racks/liquid cooling/power supply, with system integration, certification, customer lock-in, or service barriers Tier B: clear beneficiaries but with low-margin or valuation risk Dell, Celestica, Jabil, Sanmina, nVent, Johnson Controls, Delta, AVIC Jonhon, Shennan Circuits, Luxshare Clear benefit path, but either valuation is high, or margins are hit by price wars, or public AI earnings are still ramping Tier C: long-term beneficiaries but with weak near-term leverage Cisco, Arista, TE, UPS/HVAC integrators, some PCB/material stocks AI infrastructure can drive demand, but it does not necessarily convert to near-term profit leverage, or is diluted by networking/software/traditional businesses Tier D: strong narrative but insufficient benefit evidence Pure-concept "AI server" companies, diversified electronics stocks whose data center share is still low but whose stock prices have already moved, liquid-cooling new entrants with only a product line but lacking customer validation The industry direction is right, but public orders, customers, and profit evidence are insufficient; a typical example is some companies whose "data center business is still at an early stage" Master List of Key Public Companies
The table below prioritizes companies that can truly convert AI demand into orders, revenue, and profit. Because valuation conventions differ greatly across markets, non-U.S. stocks without cross-verifiable real-time public valuations are marked "undisclosed/needs further verification."
Company Ticker/Market Segment AI Benefit Path Key Customers/Links Public Orders/Operating Signals Profit Profile Valuation Snapshot Benefit Certainty Earnings Leverage Valuation Attractiveness Overall Assessment Vertiv VRT / NYSE UPS, PDU, liquid cooling, service Integrated AI rack power distribution + liquid cooling + commissioning NVIDIA ecosystem, hyperscalers, colo 2025 sales of 10.2 billion dollars; 2026Q1 sales +30%, adj op margin 20.8% High margin, high quality PE about 93x, market cap about 145.5 billion dollars 9 9 4 Worth further research, but valuation is hot Eaton ETN / NYSE Power infrastructure, UPS, distribution, thermal management Core beneficiary of time-to-power Data centers, grids, industrial customers 2026Q1 data center orders about +240%, revenue about +74% High margin, strong orders PE about 39x, market cap about 155.5 billion dollars 9 8 5 High-certainty core name Schneider Electric SU / Euronext Distribution, UPS, liquid cooling, software Strong Data Center orders; Motivair strengthens liquid cooling CSPs, colo, enterprises FY25 revenue exceeded 40 billion euros for the first time; Data Center led the gains High quality, platform-type Real-time valuation needs verification 9 7 5 Very strong long-term moat Delta Electronics 2308 / TWSE PSU, power shelf, liquid cooling, rack power Direct beneficiary of AI server power and rack power CSPs, ODMs Already in CSP/ODM mass production; liquid-cooling CDU can reach 200kW per rack Mid-to-high margin Valuation needs further verification 8 8 6 High-quality Taiwanese facility-chain name Amphenol APH / NYSE High-speed/high-power connectors, cables High-speed interconnect inside and outside the rack + power interconnect Servers, switches, data centers 2025Q4 sales +49%, adj op margin 27.5% High gross margin, high barrier PE about 34x, market cap about 161.2 billion dollars 9 8 5 Extremely strong quality, valuation not cheap HPE HPE / NYSE Branded AI servers, networking, AI systems Enterprise/sovereign AI projects and servitization Sovereign, Enterprise, AI labs Q1 FY26 AI backlog >5 billion dollars, 64% of cumulative orders from enterprise/sovereign customers Gross margin better than pure ODM Trailing PE distorted, market cap about 44.9 billion dollars 8 7 7 Many expectation gaps, worth tracking Supermicro SMCI / NASDAQ Full-system, full-rack, liquid-cooling integration Rack-scale + liquid cooling + in-house manufacturing Enterprises, GPU clouds, CSPs FY25 revenue about 22 billion dollars; FY26 guidance 38.9 billion to 40.4 billion dollars; continued DCBBS and liquid-cooling capacity expansion High revenue leverage, but volatile execution PE about 15x, market cap about 21.5 billion dollars 8 10 8 High leverage, high risk, worth deep digging Dell Technologies DELL / NYSE Branded servers and enterprise AI Enterprise-side AI servers and data center upgrades Enterprises, service providers AI servers an important growth driver, but the latest backlog pace needs continued verification Profit diluted by the overall business PE about 32x, market cap about 160.4 billion dollars 7 6 5 Clear benefit, but not a pure AI name Quanta 2382 / TWSE ODM, QCT, AI rack Hyperscaler AI servers and racks CSPs, cloud customers Company maintains a high AI server growth outlook, QCT deeply serves cloud customers Gross margin below the facility chain, above broad EMS Valuation needs verification 9 8 6 Core Taiwanese ODM Wiwynn 6669 / TWSE ODM, rack-level solutions Hyperscaler rack-scale AI systems Top-tier CSPs 2026Q1 revenue NT$276.5 billion, +62% YoY; memory agency model launched Higher than typical EMS Valuation needs verification 9 9 6 One of the clearest AI ODMs Hon Hai / Foxconn Industrial Internet 2317.TW / 601138.SH ODM/EMS, full-rack manufacturing AI servers, AI racks, large-scale manufacturing and supply chain management CSPs, OEMs Management expects AI rack shipments to at least double; flexibly uses the consignment model Overall operating margin target still 3%+ Valuation needs verification 9 8 7 High revenue certainty, margin needs continued tracking Celestica CLS / NYSE/TSX EMS, data center platforms Provides critical data center infrastructure for hyperscalers Top-tier hyperscalers 2026 revenue expected at 17 billion dollars, AI-related data center demand strengthening Mid-to-upper margin PE about 43x, market cap about 41.5 billion dollars 8 8 5 Strong cyclical + AI combination Sanmina SANM / NASDAQ EMS, U.S. manufacturing Taking on AMD/ZT manufacturing assets, benefiting from localized capacity AMD/ZT, cloud customers Will acquire ZT Systems' manufacturing business, strengthening U.S. AI system manufacturing Margin better than low-end EMS PE about 50x, market cap about 13 billion dollars 7 8 5 Event-driven beneficiary nVent NVT / NYSE Busway, connection protection, liquid cooling Data centers account for about 25% of the portfolio, benefiting from bus systems/cooling Power, data centers Comprehensive layout in bus systems, liquid/air cooling, control buildings Higher than general electrical components PE about 28x, market cap about 27.7 billion dollars 8 7 6 High-quality second-tier facility-chain name Johnson Controls JCI / NYSE Data center thermal management, security, fire AI data center cooling and supporting systems Data center operators Raised profit guidance again in 2026, benefiting from data center cooling demand Fairly steady quality PE about 25.9x, market cap about 87.3 billion dollars 7 6 6 More on the steady side Trane Technologies TT / NYSE HVAC/thermal management Acquiring LiquidStack, extending into data center liquid cooling Data center customers Acquiring LiquidStack to add liquid cooling High quality but AI exposure still rising PE about 36x, market cap about 104.1 billion dollars 6 6 5 Right direction, realization to be observed Envicool 002837 / SZSE Data center liquid cooling, temperature control Cold-plate liquid cooling, system integration, overseas AI liquid-cooling expansion Mainstream data center customers 2025 annual report mentions cold-plate liquid cooling beginning to deploy at scale, with capacity able to meet the order book Fast growth, profit needs tracking Valuation needs verification 8 8 7 One of the most worth-tracking A-share liquid-cooling names Sugon DataNergy 920808 / BSE Cold plate/immersion liquid cooling, full-lifecycle service Cold-plate liquid-cooling systems and operations Data centers, compute centers 2025 cold-plate liquid-cooling order book up 2.4x YoY; high growth in immersion and cold-plate revenue High momentum, still small in size Valuation needs verification 8 9 7 Small and specialized, high leverage KSTAR 002518 / SZSE UPS, precision cooling, micro-modules Critical infrastructure for AI data centers Internet, finance, operators Management emphasizes seizing large-customer opportunities driven by AI Moderate profit Valuation needs verification 7 6 7 Clear benefit but not a bottleneck king AVIC Jonhon 002179 / SZSE High-speed/power/liquid-cooling interconnect Full data center supporting package of power + high-speed + liquid cooling Mainstream server and internet customers Data center business ramped quickly in 2025, breakthroughs in 224G high-speed connectors and modules High barrier Valuation needs verification 8 7 6 High-quality A-share interconnect-chain asset Luxshare 002475 / SZSE Copper cables, connectors, liquid cooling, power Covers DAC/ACC/LAC, CDU, busbar, etc. Data center customers Complete product line, but company IR states the data center business is mostly at an early stage Large mid-to-long-term potential, insufficient near-term verification Valuation needs verification 6 7 6 Benefit logic exists, but realization needs continued verification Shennan Circuits 002916 / SZSE AI server PCB Growth in AI server and supporting orders Server/communications customers 2025 PCB main revenue +36.8%, gross margin 35.5%, AI server orders rising notably Decent profit leverage Valuation needs verification 8 7 6 One of the more worth-tracking names in the PCB chain Nan Ya PCB/electronic materials 8046 / TWSE etc. IC substrate, CCL, copper foil Materials for AI servers and high-end networking equipment PCB/packaging chain 2025 revenue growth driven by AI servers and high-end networking More of a materials attribute Valuation needs verification 6 7 6 Cyclical + AI beneficiary Lotes 3533 / TWSE High-speed/high-current connectors AI server connector introduction Servers/OEM/ODM Annual report discloses developed AI server connectors High barrier, small and beautiful Valuation needs verification 7 7 6 Worth continued digging Oracle ORCL / NYSE AI infrastructure demand side/cloud As a buyer, drives server/facility orders; as an investment target, captures OCI growth Enterprises, model companies OCI consumption revenue +62%, 29 Cloud@Customer dedicated data centers online with another 30 under construction; Q3 FY26 RPO 553 billion dollars Improving cloud infrastructure profit PE about 34.6x, market cap about 561.9 billion dollars 8 7 5 Better suited to tracking demand than to pure supply-chain leverage Private Companies Worth Continued Tracking
Company Country/Region Segment Core Products Customers/Partners Funding/M&A Status Relationship to Public Companies Investment Focus Main Risks CoolIT Systems Canada Cold plate/D2C/CDU Chip-level liquid-cooling systems NVIDIA, AMD supply chain Acquired by Ecolab in 2026 for about 4.75 billion dollars; sales of about 550 million dollars over the next year Forms a "fluid+hardware" platform with Ecolab Shows that liquid-cooling core assets have been substantially revalued upward M&A integration Motivair U.S. Liquid cooling/CDU/HDU Liquid-cooling distribution and cooling loops Silicon makers, server OEMs Schneider acquired a 75% stake in 2024 for 850 million dollars; products can provide 1:1 cooling capacity for the NVL72 Schneider's core platform liquid-cooling asset Represents "liquid cooling shifting from accessory to core" Post-acquisition synergy execution LiquidStack U.S./Europe Immersion+CDU Advanced liquid cooling and modular solutions Data center customers Trane announced acquisition in 2026 Trane's data center liquid-cooling platform Large leverage if immersion expands in high-density scenarios Standardization still insufficient Submer Spain Immersion/hybrid liquid cooling Immersion, hybrid AI inference designs 2CRSi, Eneos, etc. Still independent and private Works with multiple server makers Clearly differentiated technology route Slow mainstream adoption Boyd Thermal U.S. Thermal management/cold plate Cold plates, thermal management platforms Data center and electronics customers Eaton acquired the thermal management business for 9.5 billion dollars in 2025 Completes Eaton's grid-to-chip coverage Shows that thermal assets have become a major strategic link Deal integration and customer migration Corintis Switzerland Microfluidic chip cooling Microchannel cooling Microsoft is one of its customers Completed a 24 million dollar Series A in 2025, total funding of 33.4 million dollars; valuation about 400 million dollars May become a source of next-generation chip-level thermal technology Large imagination space if microfluidics is deployed Uncertain commercialization timing Crusoe U.S. GPU cloud/AI data centers NVIDIA/AMD GPU cloud, energy integration AI customers Limited public financials and valuation Downstream buyer, may also drive equipment procurement Demand verification has value Limited financial disclosure Lambda U.S. GPU cloud GPU cloud and hosting AI developers/enterprises Limited publicly verifiable financials Important demand-side picture Can serve as a sample for tracking GPU cloud demand Insufficient disclosure Iceotope, ZutaCore, Asperitas UK/Israel/Netherlands New liquid-cooling routes Precision liquid / two-phase, etc. Limited public disclosure Needs further verification Large value in technology reserves Suitable as a frontier-technology watchlist Limited commercial validation List of Public Companies Most Worth Further Research
Combining the six dimensions of AI direct exposure, order certainty, value chain position, delivery barriers, financial quality, and valuation, I believe the public companies most worth continuing to dig into are: Vertiv, Eaton, Schneider Electric, Amphenol, HPE, Supermicro, Wiwynn, Quanta, Hon Hai, Delta Electronics, Celestica, Envicool, Sugon DataNergy, AVIC Jonhon, Shennan Circuits, and nVent. These companies cover the key profit pools from rack-scale systems, liquid cooling, power, and interconnect to PCB, and most already show evidence of orders, backlog, revenue structure, or platform introduction in public materials.
Valuation, Risk, and Final Conclusions
Scoring Model
Dimension Weight Scoring Standard Direct AI demand exposure 25% Whether directly tied to AI servers/racks/liquid cooling/power Order and customer certainty 20% Backlog, customer structure, public order evidence Value chain position and bargaining power 15% Whether located at a bottleneck and high-switching-cost link Delivery capability and capacity leverage 15% Whether it can ramp volume quickly and deliver stably Financial quality 10% Gross margin, cash flow, profit realization quality Valuation reasonableness 10% Whether market expectations are already overpriced Future catalysts 5% Whether there is a clear platform/order catalyst within 12-24 months Overall Ranking of Key Companies
Rank Company Total Score Logic Summary Top Vertiv 89 Directly tied to power + liquid cooling + delivery, extremely high order realization, but valuation already expensive Top Eaton 87 Most direct beneficiary of the power bottleneck, extremely strong order data, valuation high but relatively more explainable Top Schneider Electric 85 Grid-to-chip platformization, boosted by Motivair, strong long-term moat Top Amphenol 84 High barrier in high-speed/high-power connectors, excellent gross margin, clear benefit path First tier Wiwynn 82 Extremely high order realization among AI ODMs, strong rack-scale system capability First tier Quanta 81 Core of hyperscaler white-box, significant scale advantage, margin below the facility chain First tier HPE 80 Clear enterprise/sovereign AI backlog, strong service and system capability, valuation not extreme First tier Delta Electronics 79 Dual drive of power + liquid cooling, direct beneficiary of rising AI rack value First tier Hon Hai 78 High certainty of AI rack shipments, but thin overall margin, need to watch mix and model switching First tier Supermicro 77 High leverage, high risk; if capacity expansion and execution stay stable, earnings leverage is extremely strong Second tier Celestica 74 AI data center tech demand clearly strengthening, but valuation already not low Second tier Envicool 73 Domestic liquid-cooling core, needs continued verification of overseas orders and profit-realization pace Second tier Sugon DataNergy 72 Small size, high leverage, eye-catching cold-plate/immersion order data, but higher liquidity and project volatility Second tier AVIC Jonhon 71 Platformization of high-speed + liquid cooling + power interconnect, data center business breakthroughs worth tracking Second tier nVent 70 Busway and connection protection have a chance to benefit from 800VDC/high-density racks, but leverage slightly weaker than the leaders Which Companies Are Already Fully Priced, and Which Still Have Expectation Gaps
Already fully reflecting expectations: Vertiv, Broadcom, AMD, Arista, Amphenol, Comfort Systems, Celestica. The common feature is that the market already prices them at high multiples as "core AI beneficiaries," and they must keep delivering above-expectation orders/profits to sustain those valuations.
May still have expectation gaps: HPE, some Taiwanese ODMs, Delta, nVent, Envicool, Sugon DataNergy. The reason is not that these companies are more "sexy," but that the market's understanding of their AI earnings is often mixed with traditional business noise; once the speed of backlog-to-revenue conversion is verified, earnings leverage could be higher than the current market cap implies.
Good companies but too expensive: Vertiv, Amphenol, Broadcom, AMD. Their quality is undoubtedly strong, but if buyer CapEx pacing is slightly disturbed, the valuation drawdown leverage will also be large.
Revenue grows fast but profit leverage is insufficient: large-scale ODM/EMS, especially during the customer-consignment, agency-procurement, and platform-transition phases. The public statements of both Hon Hai and Wiwynn show that, in the AI era, reported revenue and profit quality are no longer in step, so research must look simultaneously at reported revenue, real rack shipments, inventory/receivables, and operating cash flow.
Systemic Risks
Risk Impact Mechanism Priority Tracking Indicators Slowdown in cloud-provider AI CapEx Hits full-system and ODM first, then transmits to the facility chain Microsoft/Alphabet/Amazon/Oracle quarterly CapEx, RPO, OCI/Azure/GCP growth GPU/ASIC shipments below expectations Delays server and rack shipments NVIDIA/AMD data center revenue, AWS/Google/Microsoft new platform progress Customer consignment model switch Lowers revenue, reshapes gross margin ODM monthly revenue, inventory and receivables, management commentary Liquid-cooling penetration below expectations Affects CDU/cold plate/QD volume Liquid-cooling share of new AI halls, Microsoft/Google/AWS/Oracle solution updates Power access and construction delays Delays project acceptance Switchgear/transformer lead times, utility access progress Price competition Lowers ODM/PSU/structural-part margins Gross margin, operating margin, customer mix Geopolitics and export controls Reshapes the supply chain landscape Chinese makers' overseas customer acquisition, changes in U.S. localized manufacturing Final Conclusions
If the AI value chain is divided into a "chip layer," a "system layer," and a "facility layer," then what truly deserves time to research in 2026 is no longer the upstream chip itself, but the boundary between the system layer and the facility layer. The reason is simple: GPU value remains the largest, but order certainty, revenue leverage, profit leverage, and competitive advantage are increasingly realized at the following points:
AI server ODM and rack-scale system integration
Liquid-cooling core parts represented by CDU, cold plate, and QD/manifold
Data center power equipment represented by UPS/PDU/busway/distribution cabinets/medium-low-voltage distribution
High-power/high-speed connectors and short-reach copper cables
Commissioning, burn-in, on-site delivery, and operations services
Based on current public materials, I believe the five segment tracks most worth continuing to dig into first are: rack-scale AI systems, direct-to-chip liquid cooling/CDU, data center power distribution and UPS/PDU, AI server core ODM, and high-power/high-speed interconnect.
The ten public companies most worth continuing to research are: Vertiv, Eaton, Schneider Electric, Amphenol, HPE, Supermicro, Wiwynn, Quanta, Hon Hai, and Delta Electronics. These ten cover the most core profit pool of "rack delivery - liquid cooling - power - interconnect - hyperscaler ODM."
The five private/primary-market companies most worth tracking are: CoolIT Systems, Motivair, LiquidStack, Submer, and Corintis. They represent different layers of the liquid-cooling route: D2C, CDU, immersion, hybrid liquid cooling, and the more frontier chip-level microfluidics.
The three points the market most easily misunderstands are:
"AI server revenue growth" does not equal "large profit leverage." ODM book revenue may decline because of customer consignment, but real shipments and cash flow rise.
"Slow liquid-cooling adoption" does not equal "liquid cooling is not a bottleneck." For the entire installed base, adoption is indeed gradual, but for new high-density AI halls, liquid cooling is trending toward mandatory.
"ASIC will weaken the NVIDIA ecosystem" does not necessarily hold. From the server/ODM/facility perspective, ASIC may instead amplify system design and rack delivery opportunities.
The indicators most worth tracking over the next 6-12 months include: cloud-provider quarterly CapEx, HPE/SMCI/ODM backlog and shipment pace, GB300 mass-production ramp, the Rubin qualification timeline, liquid-cooling penetration and CDU delivery cycles, switchgear/transformer lead times, and the impact of the customer-consignment model on ODM revenue accounting.
For the next step of research, I suggest narrowing the direction further to these four:
GB200/GB300/Rubin rack-scale systems and the delivery chain
Liquid-cooling core parts, especially CDU, cold plate, and QD/manifold
The AI data center power chain, especially UPS/PDU/busway/switchgear/800VDC
High-power connectors and short-reach copper cables
If the research continues toward the buyer level, the earnings reports/materials worth reading first are: Vertiv, Eaton, Schneider, HPE, Supermicro, Wiwynn, Quanta, Hon Hai, Delta, and Amphenol; the demand data worth tracking first is: the CapEx/RPO/active power capacity of Microsoft, Alphabet, Amazon, Oracle, and CoreWeave; and the platform iterations worth tracking first are: the GB300 ramp, Rubin NVL144/Ultra, the new generation of liquid-cooling architecture for Trainium/TPU/Maia, and the pilot deployment of 800VDC.
This report is based on public information and does not constitute investment advice. Markets carry risk; invest with caution.
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