# layers

slug: compute-silicon-and-packaging
name: Compute silicon & packaging
position: inside_server
summary:
The silicon at the heart of the build: GPUs and custom accelerators, the CPUs beside them, the foundries and advanced packaging that put them together, and the substrates, wafers, equipment and materials one step upstream. Almost every dollar of an AI data center is ultimately spent to keep this layer busy.

description_md:
Layer 1 is everything that happens before a chip is a chip: the merchant GPUs and custom ASICs that do the AI math, the CPUs and NICs/DPUs that support them, and the foundry, wafer, packaging, substrate, and test supply chain that turns a design into a working die. The chain runs roughly wafer → fab processing → test → advanced packaging (die plus HBM stacks onto a substrate) → the finished accelerator or CPU that ships into a server board in Layer 4. Almost everything here sells into every tier of data center — an enterprise pilot rack and a gigawatt campus use the same die designs, just in different quantities — so the interesting variation in this layer is not tier, it's concentration. A handful of choke points recur: one foundry runs essentially all advanced-node AI accelerator wafers; one company's film is the effective sole source for the substrate material holding advanced packages together; advanced 2.5D/3D packaging capacity has repeatedly been the binding constraint on how many finished accelerators can ship, not wafer output. For investors, the layer rewards looking past the chip logos at what's underneath them — foundry and packaging capacity commitments, substrate and specialty-chemical supply agreements, and equipment order books — because those are the harder-to-replicate positions and the ones where a shortfall anywhere in the chain caps output regardless of how much demand exists for the finished accelerator.


slug: memory-and-storage
name: Memory & storage
position: inside_server
summary:
Memory and storage attached to the accelerators and servers: HBM stacked on the package, DDR5 on the board, the interface silicon between them, and the NAND, SSDs and nearline drives that hold checkpoints and training data.

description_md:
Layer 2 covers everything that stores or holds data for an AI system, from the memory stacked on the accelerator package down to the bulk drives that hold training sets and checkpoints. The chain runs in order of proximity to compute: HBM sits directly on the accelerator package for the fastest, most capacity-constrained tier; DRAM and memory modules serve the host CPU a step further out, assembled with buffer chips that keep signal integrity as module capacity rises; CXL controllers and PCIe/CXL switches extend that memory beyond a single board when a platform chooses to pool it; and NAND flash, packaged into enterprise SSDs, forms the fast local storage tier that stages data and checkpoints ahead of the bulk, lower-cost HDD tier. AI storage systems sit above all of it as the software and appliance layer that aggregates drives into throughput a large GPU cluster can actually consume. The investable choke points cluster at the two ends of this chain: HBM, where a small number of qualified suppliers sell into an accelerator roadmap they do not control, and the storage-system layer, where specialized vendors built around GPU-direct access patterns compete against both enterprise storage incumbents extending existing lines and hyperscalers building in-house. In between, buffer chips, memory expanders, and SSD controllers are narrower, qualification-driven niches that reset with every new platform generation.


slug: networking-and-interconnect
name: Networking & interconnect
position: inside_server
summary:
How chips, servers and sites talk to each other: the scale-up fabric inside a rack, the switch ASICs and systems of the scale-out network, the optical transceivers and copper cables between them, and the fiber and coherent optics linking buildings and campuses.

description_md:
Layer 3 covers the fabric that moves data between accelerators, servers, and racks once it leaves the package: scale-up links inside a rack or pod, scale-out switching between racks, and the optics, copper, and fiber that physically carry the signal. The chain runs from switch silicon (scale-up and scale-out ASICs) through switch systems and white-box ODMs, out to transceivers, the lasers and DSPs inside them, and the copper or fiber that connects everything — with a parallel track for campus-to-campus and metro links once a build spans more than one building. Two forces are reshaping this layer at once: rising per-GPU bandwidth is pushing scale-up links from copper toward optical and co-packaged designs as reach and cable count become limiting, while the sheer number of accelerators per cluster keeps expanding the scale-out fabric that ties pods together. Choke points sit in a few places worth watching: optics assembly and testing concentrates in a handful of contract manufacturers building modules for the merchant module makers, the laser and DSP layers inside those modules have relatively few qualified suppliers per speed grade, and the shift toward co-packaged optics and higher-radix switch ASICs is a qualification-heavy transition where incumbents in switch silicon and optical components have an edge but are not guaranteed to keep it. Copper still dominates the shortest, highest-bandwidth links for cost reasons, which keeps demand for high-speed cable assemblies growing alongside optics rather than being displaced by them.


slug: servers-racks-and-integration
name: Servers, racks & integration
position: rack
summary:
The server and the rack it lives in: rack-scale systems and their ODMs, GPU baseboards and trays, the high-layer-count PCBs, connectors, VRMs and passives on every board, plus power shelves, busbars, PDUs and the sheet metal that holds it together.

description_md:
Layer 4 covers everything that turns packaged silicon and memory into a rack that can be wired into a cluster: the boards chips sit on, the power delivery that feeds them, the mechanical structure that holds them, and the integration and test work that turns all of it into a shippable, working system. The chain runs from raw board materials (laminate, copper foil, glass cloth) through fabricated high-layer-count PCBs, onto which board-level power, connectors and support silicon are populated, to build the compute trays, switch trays and power shelves that a rack-scale system reference design (Nvidia's NVL72-class, AMD's MI400 Helios-class) specifies. ODMs and EMS providers build and integrate that hardware, running rack-level test before it ships; OEMs badge and sell the finished result. The neighboring layers bracket this one directly: layer 1-2 silicon and memory populate the boards built here, layer 3 interconnect is what the switch trays and connectors carry, and layer 5 liquid cooling hardware is what a rack-scale cabinet is plumbed with before it leaves the integration line. The investable choke points sit in a few places: BMC silicon, where a near-monopoly supplier sits on every board; high-layer-count PCB fabrication and low-loss laminate, where capacity and qualification lag design; and the rack-scale reference design and ODM qualification race itself, which resets with every new GPU generation and rewards whoever ramps fastest.


slug: liquid-cooling-it-side-chip-to-cdu
name: Liquid cooling (IT-side: chip to CDU)
position: rack
summary:
Liquid cooling on the IT side, from the chip to the coolant distribution unit: cold plates, manifolds, quick disconnects, hoses, CDUs, the pumps and plate heat exchangers inside them, the fluids in the loop, and the sensors watching for leaks.

description_md:
Layer 5 covers everything that moves heat off an AI accelerator and out to the coolant distribution unit (CDU) — the IT-side half of a liquid-cooling loop, before the heat is handed to the facility water loop in Layer 6. The chain runs from cold plates bonded to the GPU, CPU, and switch packages, through rack manifolds and quick disconnects, hoses and fittings, up to the CDU itself and the plate heat exchangers, pumps, and filters inside it. Rear-door heat exchangers and immersion systems are alternate architectures that skip or reshape parts of that chain. Fluids and coolant treatment, leak detection and sensing, and rack/row controls run alongside the physical loop and keep it safe and tuned. Air cooling could not keep pace with GPU power density, so this layer went from a niche add-on to a default rack component in a few product cycles. The investable choke points sit at the connection points rather than the pipe itself: quick disconnects and cold plates require tight tolerances and qualification against a specific chip package, so switching suppliers mid-program is costly; CDUs bundle mechanical, thermal, and controls engineering that not every player can scale to facility-class units; and the reference-design ODMs (Foxconn, Wiwynn) building cold plates in-house for their own racks compete directly with the component vendors this layer otherwise depends on. Fluids sit downstream of a real supply shift, with a large single-phase and two-phase producer exiting part of the category.


slug: facility-cooling-and-hvac
name: Facility cooling & HVAC
position: building
summary:
Facility cooling and HVAC: chillers, dry coolers and cooling towers, CRAH and fan-wall units, the facility water loop with its pumps and valves, water treatment, heat reuse, building controls, and fire detection and suppression.

description_md:
Layer 6 is the facility-side cooling plant — everything that moves heat from the building's chilled-water or facility liquid loop out to the atmosphere (or a reuse destination), plus the controls and fire-life-safety systems that keep it running. It picks up where Layer 5's IT-side liquid cooling and the CRAH/CRAC air-cooling of the white space hand off: chillers and their compressors (6.01) chill water, dry coolers, adiabatic units and cooling towers (6.02) reject heat to outside air, and a facility water loop of pumps, valves, piping, heat exchangers and tanks (6.05) ties it together, with air handling and economizers (6.04) doing the equivalent job for air-cooled halls. Water treatment and reuse (6.06) and, at a minority of sites, heat reuse to district heating (6.07) sit alongside that plant. Building management systems and an emerging AI-driven controls layer (6.08) sequence the equipment, and fire detection and suppression (6.09) is a parallel life-safety system with no direct energy role.
The chain's choke points sit less in exotic components than in capacity and site constraints: chiller and cooling-tower/dry-cooler lead times queue behind broad HVAC demand, not just data centers; water availability and permitting increasingly gate where wet-cooled campuses can be sited, favoring water-free designs at a cost premium; and the shift to liquid cooling at the IT layer adds a second facility loop's worth of pumps, piping and heat exchangers rather than eliminating the legacy air-cooling equipment, which mostly shrinks to a residual and backup role. The controls layer is the newest investable seam, as AI-driven optimization software competes with and gets absorbed by incumbent building-automation platforms.


slug: electrical-power-distribution-grid-to-rack
name: Electrical: power distribution (grid to rack)
position: building
summary:
Electrical distribution from the grid to the rack: large power transformers and HV switchgear, MV/LV distribution, busway, UPS and batteries, backup generators and their controls, and the emerging 800 V DC architecture.

description_md:
Layer 7 covers everything that moves power from the utility grid to the rack once it crosses the property line: substations and large power transformers, HV and MV switchgear packaged into E-houses, step-down transformers, LV switchgear and busway, and the floor PDUs and transfer switches that hand load off to the rack (layer 4 picks it up from there). Running alongside that main chain are the systems that keep power available and stable — UPS and its batteries, supercapacitors that absorb the fast load swings synchronized GPU clusters create, backup generators and the switchgear that parallels them into fleets, and the metering that measures all of it. An emerging 800 V DC and solid-state-transformer architecture sits at the edge of the layer, aimed at cutting conversion losses in future rack generations. The chain is sequential and largely non-substitutable: a shortfall at any single stage, from the substation transformer to the rack PDU, caps how much load capacity a campus can actually energize regardless of how much IT equipment is sitting ready. The clearest choke points sit upstream, where large power transformers and HV switchgear are custom-engineered, utility-qualified products with long lead times and concentrated manufacturing capacity; further downstream, the category is broader and better supplied, so competitive position within a product line matters more than scarcity of the product line itself.


slug: power-generation-and-energy-supply
name: Power generation & energy supply
position: outside_fence
summary:
Where the power comes from: gas turbines and reciprocating engines behind the meter, fuel cells, nuclear PPAs and SMRs, the utilities and IPPs signing data-center deals, renewables, storage, transmission, and the gas supply chain.

description_md:
Layer 8 covers everything that turns fuel, fission, or sunlight into electrons before those electrons ever reach a data center's fence line — generation hardware, the utilities and IPPs that contract for it, the transmission and interconnection equipment that moves it, and the gas supply chain that feeds onsite turbines and engines. It hands off to Layer 7 (electrical distribution from the meter to the rack) and Layer 6 (facility cooling), and it sits furthest from the chip of any layer in the taxonomy — which is also why it is the least differentiated: most of this equipment was designed for the broader grid and power industry long before AI load growth, and data centers are simply a new, large, fast-moving customer standing in line behind utilities, industrials, and export terminals. The chain runs from primary energy (gas fields, uranium, sun, wind) through conversion hardware (turbines, engines, reactors, panels, inverters) to the wires and switching gear that carry the output to a site, with utilities and IPPs as the commercial layer that ties generation to a signed data-center deal. The investable choke points cluster at the front of long lead-time equipment queues: gas turbine and HRSG order books, large power transformer and HVDC equipment backlogs, and the handful of firms qualified to build advanced reactor components or enrich fuel. Nuclear and fusion PPAs are optionality bets on entities with long, uncertain timelines rather than near-term revenue. Renewables and storage hardware are comparatively commoditized and price-competitive globally, while transmission EPC and grid-equipment names benefit from a buildout that would be happening with or without AI, just on a longer timeline.


slug: building-shell-site-and-construction
name: Building shell, site & construction
position: outside_fence
summary:
The building and the site: developers, operators and REITs, the hyperscalers and labs that fill them, the architects, contractors and MEP trades that build them, structure and envelope materials, site utilities, security, and commissioning.

description_md:
Layer 9 is everything that has to exist before a rack can be turned on: the land, the building, the people who design and build it, and the site-level systems that aren't power or cooling but still gate occupancy. It runs from the capital allocators at the top (developers, REITs, and the hyperscalers/neoclouds/labs who lease or own the finished building) through the design chain (architecture and MEP engineering, then general contractors, then self-perform MEP and electrical trades) into the physical shell (steel, precast, envelope, raised floor) and the site systems that ride alongside it (prefab and modular skids, water and fiber entrance, physical security, and commissioning). The chain is sequential in a way most of the other layers aren't: land and power interconnection get secured first, design follows, and construction trades queue up behind design freeze — so a slip anywhere upstream pushes every downstream trade. The investable choke points are less about any single component and more about capacity: engineering and self-perform electrical labor, precast and switchgear-adjacent steel fabrication, and the general contractors with a track record of hitting AI-campus schedules are all named repeatedly as the constraint, not the underlying commodity materials. REITs and developers capture the real estate and power economics; the design-build and MEP trades capture a construction cycle that has both gotten larger per project and more repeatable, which favors firms that can standardize a design and redeploy the same crews across many sites.


slug: materials-and-upstream-inputs
name: Materials & upstream inputs
position: outside_fence
summary:
Raw materials several steps upstream that AI demand still moves: copper, aluminum, electrical steel for transformer cores, rare-earth magnets, specialty glass, refrigerants, industrial gases, and export-control-sensitive critical minerals.

description_md:
This layer is the raw-material base every other layer draws from: copper, aluminum, electrical steel, rare-earth magnets, glass and glass fiber, refrigerants and insulating gases, industrial gases, and a handful of minor but essential elements (gallium, germanium, indium, tungsten, beryllium). None of it is data-center-specific — these are commodity and specialty materials also consumed by autos, grid buildout, consumer electronics, and general industry — so data-center demand is a claim on existing global supply rather than a dedicated pipeline. It feeds forward into nearly every layer above it: copper and aluminum become busbars, cable, cold plates and transformer windings (Layers 4, 5, 6, 7); electrical steel becomes the cores inside power and distribution transformers (Layer 6); rare-earth magnets become the motors inside fans, pumps and gensets (Layers 4, 5, 7); glass and glass fiber become optical fiber and the T-glass and glass-core substrates under chip packages (Layers 1, 3); refrigerants and insulating gases serve cooling equipment and switchgear (Layers 5, 6); and critical minerals like gallium and germanium sit inside power semiconductors and optics (Layer 1). The investable choke points sit upstream of the data-center industry itself: mining and refining capacity, smelter and mill lead times, and — for grain-oriented electrical steel and the critical-minerals group in particular — a small number of qualified producers and geographically concentrated, sometimes export-controlled, supply.


slug: software-tools-and-services-touching-the-hardware-optional
name: Software, tools & services touching the hardware (optional)
position: context
summary:
Software, tools and services that touch the hardware: DCIM and digital twins, cluster management, site selection and market intelligence, the capital behind the buildout, test instruments, and operations robotics. Optional context, not parts.

description_md:
Layer 11 covers the software, market-intelligence, financing, and physical-operations tooling that sits around the hardware described in Layers 1–10 rather than inside it. It splits into distinct chains: DCIM and digital-twin platforms that model and manage the facility once it is built; cluster management and GPU orchestration software that schedules workloads onto the compute (listed here for completeness even though it falls outside a parts-focused site); site-selection and power-procurement intelligence that developers and utilities lean on before a shovel goes into the ground; the capital stack — private equity, credit funds, banks, and GPU-backed lenders — that funds construction and equipment purchases; test-and-measurement instruments used to validate silicon, cabling, and power systems through the build and commissioning cycle; and robotics or inspection hardware used in facility operations. None of these are physical parts of a rack in the way a busbar or cold plate is, which is why the taxonomy marks this layer optional. The investable angle differs by sub-chain: DCIM and test instruments are vendor-concentrated software and equipment businesses with recurring revenue; site-intelligence and brokerage firms are mostly private or bundled inside larger real-estate and consulting franchises; the capital and financing chain is where the largest dollar amounts move but the exposure is indirect, through asset managers and banks rather than equipment makers. Coverage of this layer is thinner and more circumstantial than the hardware layers.


# parts

id: 1.01
slug: merchant-gpus-accelerators
name: Merchant GPUs / accelerators
unit_per_rack: 72 GPUs per NVL72 rack (Nvidia rack specification, via taxonomy v0.1 Appendix A)
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
GPUs and other accelerator chips designed by merchant vendors and sold to any buyer, rather than designed in-house by a single hyperscaler; the workhorse silicon for AI training and inference.

description_md:
Merchant accelerators are the chips that do the actual matrix math in AI training and inference — bought off a price list (allocation permitting) rather than designed for one company's fleet alone. They sit downstream of foundry (the advanced-node foundry step) and advanced packaging, which combine the compute die with HBM stacks onto a substrate, and upstream of the server board and tray assembly in Layer 4, where they're paired with server CPUs, NICs, and cooling. Because merchant GPUs are sold to every buyer from a single enterprise pilot through a gigawatt-class campus, this is the part of the catalog investors search first when asking who benefits from AI buildouts.
An investor should watch supplier concentration — a small number of vendors set the pace for each new architecture generation — alongside qualification cycles, since a new generation typically requires re-validation across the software stack, board designs, and cooling before hyperscalers will deploy it at scale. Pricing has moved less on open competition than on allocation: demand has outrun available packaging and wafer capacity for multiple generations running, which has kept lead times elongated and kept the vendors that can secure capacity in a stronger negotiating position than a normal semiconductor upcycle would imply.

ai_delta:
AI training and inference workloads are the primary demand driver for merchant accelerators, which is why unit volumes and generational upgrade cadence have both accelerated well beyond a typical chip replacement cycle.

scale_notes:
Same die designs ship into an enterprise pilot rack and a gigawatt campus alike; only unit count and generation mix change with scale.


id: 1.02
slug: hyperscaler-custom-asics-xpus
name: Hyperscaler custom ASICs
qualifiers: XPUs
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
AI accelerator chips (often called XPUs) designed in-house by a hyperscaler for its own fleet, typically co-developed with an outside ASIC design partner and fabbed by a merchant foundry.

description_md:
Custom ASICs are a hyperscaler's answer to the cost and allocation constraints of merchant GPUs (Merchant GPUs / accelerators): a chip specified in-house, usually with an outside design-services partner (ASIC design services, IP & EDA) supplying implementation and IP blocks, then built through the same advanced-node foundry and advanced packaging supply chain as everyone else's accelerators. Because these parts are proprietary to one buyer, they don't compete on an open price list — they compete on whether a hyperscaler's own workload mix justifies the design cost of not buying merchant silicon.
For investors, the interesting exposure here usually isn't the hyperscaler (who doesn't sell the chip) but the design-services and IP partner, whose revenue scales with design starts and program renewals rather than with unit shipments. Supplier concentration is real: a small number of design houses handle most of the announced custom AI silicon programs, and qualification is effectively a multi-year, single-customer relationship rather than a repeatable sales motion, which makes program wins and losses unusually consequential events for the partner's business.

ai_delta:
Hyperscalers commission custom ASICs specifically to cut the cost of AI training and inference at their own fleet scale, which is why this category exists at all outside of a few prior-generation exceptions.

scale_notes:
A custom ASIc program only pencils out once a hyperscaler's own deployment volume is large enough to amortize the design cost, so presence skews toward hyperscale and campus-scale fleets even though the finished chips can run in smaller server pools too.


id: 1.03
slug: asic-design-services-ip-and-eda
name: ASIC design services, IP & EDA
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
Outside firms and licensors — design-services houses, IP blocks, and EDA software — that hyperscalers and accelerator startups use to turn a chip specification into a manufacturable design.

description_md:
This category sits behind every custom accelerator in the catalog rather than in front of the end customer: it supplies the design implementation, SerDes and CPU IP blocks, and the EDA software used to turn a hyperscaler's or startup's chip specification (see Hyperscaler custom ASICs and Startup accelerators) into something a foundry can actually fab. It doesn't touch the finished silicon directly, but almost no announced AI chip program — merchant or custom — ships without touching at least one firm in this group.
The investor angle is toll-road exposure: revenue here tracks the number of active design programs and IP licensing renewals rather than chip unit volumes, so it can grow even in a generation where accelerator shipments are constrained elsewhere in the chain (foundry or packaging). Concentration is high in a few sub-segments — a small number of firms dominate EDA software and CPU core licensing — which gives those positions unusual pricing power relative to the fragmented design-services side, where competition among design houses for the same customer program is more common.

ai_delta:
Every hyperscaler and well-funded AI startup now wants its own accelerator design, which has pulled design-services, IP licensing, and EDA demand up well beyond what general-purpose chip design alone would generate.

scale_notes:
Not tied to any single facility; revenue tracks the number of active chip design programs across the industry rather than deployed unit count at any one site.


id: 1.04
slug: startup-accelerators-non-gpu-architectures
name: Startup accelerators
qualifiers: non-GPU architectures
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
Non-GPU accelerator architectures from mostly-private startups, built as an alternative to merchant GPUs for specific AI training or inference workloads.

description_md:
This is the long tail of accelerator design: companies building chips with fundamentally different architectures from merchant GPUs (Merchant GPUs / accelerators) or hyperscaler ASICs (Hyperscaler custom ASICs), usually pitched on cost, latency, or efficiency advantages for a specific workload like inference serving. They still depend on the same downstream chain as everyone else — advanced-node foundry capacity and advanced packaging — so their unit economics are exposed to the same choke points as the merchant vendors they're trying to displace, without the volume to negotiate as favorable terms.
Almost all of these companies are private, so public-market exposure is indirect: through the foundries and packaging houses that fab and package their chips, or through eventual IPOs. The investor question is less "who wins" than "which workload segment, if any, actually needs a different architecture than a merchant GPU provides" — a smaller and harder-to-answer question than it looks, since qualification with a customer's software stack is as much the barrier to adoption as raw chip performance, and most AI software has been built and tuned against the incumbent architecture first.

ai_delta:
The search for AI training or inference compute that is cheaper, faster, or more efficient than merchant GPUs for a specific workload is what funds this entire category of startups.

scale_notes:
Deployment tends to concentrate with whichever customer or cluster has qualified a given architecture, rather than spreading evenly; presence at any tier depends on that customer's buying decision.


id: 1.05
slug: server-cpus
name: Server CPUs
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The host CPU in an AI server — from AMD, Intel, or Arm-based designs including Nvidia's own and hyperscaler in-house parts — that orchestrates the GPUs and handles work the accelerators don't do.

description_md:
Even a GPU-dense AI server needs a CPU to boot the system, run the operating system, orchestrate data movement to the accelerators, and handle any workload that isn't matrix math. Server CPUs sit alongside merchant GPUs or custom ASICs (Merchant GPUs / accelerators, Hyperscaler custom ASICs) on the same board, feed data to them over NICs and DPUs (NICs / SuperNICs, DPUs), and are increasingly Arm-based rather than x86, both from merchant vendors and hyperscalers building their own (see the child part below). The category also includes CPU designs, like Nvidia Grace, built specifically as a tightly coupled companion to that vendor's own GPUs rather than a general-purpose part.
For investors, this is a maturer, lower-growth-rate segment than the accelerator itself, but ride share matters: AI servers still buy a CPU per node even as GPU count per node rises, so unit volume tracks server shipments broadly. The competitive dynamic worth watching is the Arm-based share gain, both from merchant vendors and hyperscalers building silicon in-house, versus the incumbent x86 vendors — a share shift more than a category-wide volume story.

ai_delta:
AI servers still need a host CPU to orchestrate GPUs and run non-matrix workloads, so CPU volume rises with every GPU-dense server shipped even though the CPU itself does little of the AI math.

scale_notes:
CPU count per server is roughly fixed regardless of facility size; total volume scales with server count across tiers, not with per-server intensity.


id: 1.06
slug: nics-supernics
name: NICs / SuperNICs
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
Network interface cards, including Nvidia's higher-bandwidth "SuperNIC" variants, that connect a server's CPU and GPUs to the cluster network.

description_md:
NICs move data in and out of a server node; SuperNICs are a higher-bandwidth variant built specifically to keep pace with GPU-to-GPU traffic in an AI cluster rather than conventional data center networking loads. They sit between the server CPU and GPUs (Server CPUs, Merchant GPUs / accelerators) on the board and the switches and cabling in Layer 3, and their bandwidth and port count are set largely by what the GPU generation on the same board needs to feed and be fed at.
Supplier concentration here is meaningful — a small number of vendors, several of them also selling the GPUs themselves, supply the bulk of AI-cluster NIC and SuperNIC silicon — which gives an investor a fairly direct read-through from GPU shipment trends to NIC demand without needing a separate forecast. The qualification bar is set by interoperability with the switch and optics layer above it (Layer 3) as much as by the NIC silicon alone, so generational transitions tend to move in lockstep with the broader cluster networking refresh rather than on an independent cycle.

ai_delta:
Training and inference workloads depend on moving large tensors between accelerators at high bandwidth and low latency, which pulls NIC and SuperNIC speed and count up in step with cluster and GPU generation size.

scale_notes:
At least one NIC ships per server regardless of scale; port count and speed step up as cluster size and GPU generation move from enterprise toward campus- and gigawatt-class builds.


id: 1.07
slug: dpus
name: DPUs
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
Data Processing Units that offload networking, storage, and security functions from the host CPU onto a dedicated, programmable chip.

description_md:
A DPU takes infrastructure work — packet processing, storage virtualization, security functions — off the server CPU (Server CPUs) so those CPU cycles can go to orchestrating GPUs and other workload instead. It typically shares the board with the NIC or SuperNIC (NICs / SuperNICs) and, in some designs, is combined with it on a single card. Adoption is not universal: it's a design choice tied to a given server architecture and hyperscaler infrastructure stack rather than a component every AI server needs.
For investors, DPU exposure is concentrated in a small number of vendors, including at least one hyperscaler that has built its own in-house rather than buying merchant silicon, which caps the addressable market for the merchant suppliers at whichever fleets choose to buy rather than build. The category is worth tracking less for standalone unit growth and more as a signal of which server architectures a given hyperscaler or OEM has standardized on, since DPU attach is usually decided at the platform level rather than per order.

ai_delta:
Offloading networking, storage, and security work from the host CPU matters more as GPU-dense AI servers push CPU cycles toward scarcity, since every cycle not spent on infrastructure work is a cycle available for orchestrating accelerators.

scale_notes:
Attach rate depends on server architecture and hyperscaler infrastructure choices rather than facility size; not universal even in gigawatt-class fleets.


id: 1.08
slug: fpgas-inference-networking
name: FPGAs
qualifiers: inference, networking, test
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
Field-programmable chips used in AI clusters for inference offload, networking functions, and hardware test and bring-up, rather than as the primary training accelerator.

description_md:
FPGAs are reconfigurable logic chips that pick up work a fixed-function accelerator design doesn't fit well: certain inference workloads, custom networking functions that sit near the NIC and DPU layer (NICs / SuperNICs, DPUs), and test and bring-up tasks used across the semiconductor supply chain (Semiconductor test & burn-in) as new AI silicon comes online. They're a smaller, more specialized category than the GPU and ASIC accelerators that dominate the layer, and one where volume is driven by breadth of use case rather than by cluster-wide compute scaling.
Investor exposure is concentrated in a small number of vendors, and the category's growth is tied less to overall AI buildout volume than to how many distinct niches — inference offload, networking, test — continue to favor reconfigurable logic over a purpose-built ASIC. That makes it a steadier, lower-beta way to have AI exposure within compute silicon than the accelerator vendors themselves, at the cost of a smaller addressable opportunity.

ai_delta:
FPGAs pick up AI inference and networking tasks where a fixed accelerator design doesn't fit, and see added demand from the test and bring-up work that a growing volume of new AI silicon generates.

scale_notes:
Deployed in smaller, targeted volumes tied to specific use cases rather than scaling linearly with accelerator count as facility size increases.


id: 1.09
slug: leading-edge-foundry
name: Leading-edge foundry
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The advanced-node semiconductor fabs that manufacture the compute dies for essentially every AI accelerator on the market.

description_md:
Foundry is where a chip design (from Merchant GPUs / accelerators, Hyperscaler custom ASICs, or Startup accelerators) becomes a physical wafer of dies. It sits upstream of silicon wafers as a raw material input and feeds directly into advanced packaging, where the finished die is combined with memory stacks and mounted onto a substrate. Almost every AI accelerator shipping today, regardless of which company designed it, is manufactured at the most advanced available process nodes, which means the finished-chip supply chain funnels through a small number of qualified fabs regardless of how many chip designers there are.
That concentration is the central fact investors need to hold onto for this part: demand growth for AI accelerators is, almost one for one, demand growth for advanced-node wafer capacity, and capacity additions at the most advanced process nodes take years and enormous capital, so the foundry side of the chain moves on a much slower clock than chip design announcements do. Qualification of a new process node is a multi-year undertaking for both the foundry and each customer's chip design, which makes switching suppliers between the handful of qualified providers a rare and consequential event rather than a routine one.

ai_delta:
Every additional AI accelerator design and unit sold is another wafer order at the most advanced process nodes, concentrating incremental demand at a small number of qualified fabs.

scale_notes:
Wafer volume ordered scales with total accelerator unit count across the industry, not with the size of any single data center buildout.


id: 1.10
slug: silicon-wafers
name: Silicon wafers
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The raw silicon wafers that foundries process into finished chips, produced by a small group of specialty wafer manufacturers.

description_md:
Silicon wafers are the physical substrate every chip in this layer starts from, sold to the advanced-node foundry step before any transistor is patterned onto them. It's a raw-materials position upstream of essentially the entire rest of the layer — every merchant GPU, custom ASIC, CPU, NIC, and DPU chip design traces back to a wafer order from one of a handful of specialty producers, most of them concentrated in Japan, Taiwan, Germany, and Korea.
The investor case here is a volume and specification story rather than a differentiation story: wafer demand tracks foundry wafer starts, and AI accelerator dies, being larger and more complex than typical logic chips, add incremental pressure on the most advanced wafer specifications even when unit counts alone wouldn't suggest tight supply. Supplier concentration is high and the qualification bar for a new wafer supplier at an advanced node is steep, so this tends to be a stable, long-cycle relationship business rather than one with frequent competitive displacement.

ai_delta:
Wafer demand rises directly with foundry wafer starts for AI accelerators, and the larger, more complex AI dies add further pressure on the most advanced wafer specifications.

scale_notes:
Wafer starts scale with total foundry capacity booked across all customers industry-wide, not with any single data center's size.


id: 1.11
slug: 2-5d-3d-advanced-packaging-cowos-s-l-r-soic
name: 2.5D/3D advanced packaging
qualifiers: CoWoS-S/L/R, SoIC, EMIB/Foveros, I-Cube
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The 2.5D/3D processes that combine a compute die, HBM memory stacks, and a substrate into one finished package — the step that turns silicon into a shippable AI accelerator.

description_md:
Advanced packaging takes the finished die from the advanced-node foundry step, combines it with HBM memory stacks (see Layer 2) on an interposer or bridge (Interposers, silicon bridges, RDL), and mounts the result on a substrate (ABF / high-layer package substrates) — the step that actually produces a shippable AI accelerator rather than a bare die. It's the part of the chain that has repeatedly been the binding constraint on how many finished accelerators can reach the market, independent of how much wafer capacity exists upstream, because 2.5D/3D packaging capacity has historically been smaller and slower to add than logic wafer capacity.
One company's dominance in this specific process, as the taxonomy itself notes, has made packaging allocation as consequential to accelerator supply as foundry allocation, with a set of OSATs (see the child part below) picking up back-end and overflow work when primary capacity is full. For investors, the packaging step is arguably the single highest-conviction choke point in the whole compute-silicon chain: qualification cycles for a new packaging process are long, the process is capital-intensive to add capacity for, and demand for it has consistently run ahead of what's available.

ai_delta:
Multi-die AI accelerator packages — a compute die plus HBM stacks — require advanced 2.5D/3D packaging that ordinary logic chips never needed, and packaging capacity has repeatedly been the first-order constraint on how many finished accelerators can ship.

scale_notes:
Every merchant and custom AI accelerator needs advanced packaging; volume tracks total accelerator unit count across all tiers rather than any single facility's footprint.


id: 1.12
slug: packaging-and-bonding-equipment
name: Packaging & bonding equipment
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The bonding, dicing, inspection, and molding equipment used to physically assemble and verify advanced chip packages.

description_md:
This is the equipment layer behind advanced packaging (2.5D/3D advanced packaging): the hybrid-bonding, thermo-compression bonding, dicing and grinding, molding, and inspection and metrology tools that OSATs and foundries use to physically build and check a finished package. It doesn't touch the die design at all — it's capital equipment sold to whoever is doing the packaging work, whether that's the foundry itself or a third-party OSAT — and its order book is a forward indicator for future packaging capacity additions rather than current shipment volume.
Investors get a more fragmented but arguably clearer read from this sub-layer than from packaging capacity itself: equipment order backlogs are disclosed on a normal capital-equipment reporting cycle, and a given tool vendor's exposure to HBM-specific bonding equipment (as opposed to general-purpose semiconductor equipment) is usually identifiable. Qualification cycles for a new tool at a customer's line are long, which tends to lock in incumbent equipment suppliers once a packaging process is established, but a shift to a new bonding technique (such as hybrid bonding gaining share over thermo-compression) can reshuffle which equipment vendors benefit.

ai_delta:
Growth in advanced packaging volume for AI accelerators flows directly through to demand for the bonding, dicing, and inspection tools used to build that packaging.

scale_notes:
Capacity investment here tracks industry-wide advanced-packaging volume rather than any single data center build.


id: 1.13
slug: abf-high-layer-package-substrates
name: ABF / high-layer package substrates
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The high-layer-count ABF substrates that an advanced chip package is mounted on, sitting between the interposer and the server board.

description_md:
Substrates are the printed-circuit-like base that a packaged chip — die, interposer, and HBM stacks (2.5D/3D advanced packaging, Interposers, silicon bridges, RDL) — is mounted onto before it plugs into a server board. AI accelerator packages need substrates with more layers and larger surface area than prior chip generations, which pushes them toward the upper end of what existing substrate manufacturing lines can handle. Substrate makers sit downstream of the specialty materials (Substrate materials) that go into the laminate itself and upstream of the finished, shippable accelerator.
The investor-relevant constraint here is capacity at the largest, highest-layer-count end of the product range rather than substrate manufacturing broadly — a supplier with general ABF substrate capacity isn't necessarily qualified for the specific large-format, high-layer-count substrates AI packages need. Supplier concentration is meaningful and geographically clustered, and qualification for a new substrate design tied to a new accelerator generation is a lengthy process run in lockstep with the chip and packaging design itself, not a commodity purchase.

ai_delta:
Larger, more complex AI accelerator packages need bigger, higher-layer-count substrates than prior chip generations, straining the segment of substrate capacity qualified to build them.

scale_notes:
Substrate volume scales with package count, which scales with total accelerator unit count across all tiers.


id: 1.14
slug: substrate-materials
name: Substrate materials
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The films, resins, glass cloth, and other specialty chemicals that go into building an ABF package substrate.

description_md:
These are the raw material inputs to substrate manufacturing (ABF / high-layer package substrates): the resin film, glass cloth, and specialty resins that get layered and cured into a finished substrate. It's a step further upstream than the substrate makers themselves, and the taxonomy notes that at least one input — the ABF film itself — is effectively sole-sourced from a single supplier, which makes this one of the tightest single-point dependencies in the entire compute-silicon chain, upstream of every AI accelerator package that uses it.
For investors, a sole-source or near-sole-source material position is about as concentrated as supply chain exposure gets: it means substrate output, and by extension advanced packaging output, and by extension finished accelerator supply, can be gated by a single material producer's capacity decisions. That's a structural point the taxonomy makes explicitly rather than a market-share estimate, and it's worth treating as a genuine chain constraint rather than a hedge-able diversification risk, since there is reportedly no readily qualified alternative supplier at scale.

ai_delta:
Rising AI substrate volume and larger, higher-layer-count substrate designs pull directly on the limited capacity of the specialty films, resins, and cloth that go into them.

scale_notes:
Material volume tracks substrate production industry-wide; a single input can gate an entire generation's packaging output regardless of which facility tier the finished accelerators end up in.


id: 1.15
slug: glass-core-substrates-emerging
name: Glass-core substrates
qualifiers: emerging
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
Substrates that use a glass core instead of an organic laminate, an emerging alternative aimed at the largest, most demanding AI packages.

description_md:
Glass-core substrates are a still-emerging alternative to conventional ABF substrates (ABF / high-layer package substrates), aimed at the flatness, size, and layer-count limits that the largest AI accelerator packages are starting to run into. They occupy the same position in the chain as ABF substrates — between the interposer and the server board — but represent a materials shift rather than an incremental improvement on the existing substrate supply base, with a different set of suppliers, some overlapping with existing substrate and materials makers and others entirely new to the space.
For investors, this is a call-option-shaped position rather than a current-revenue one: adoption depends on which upcoming accelerator generations qualify glass-core substrates as viable at volume, a decision made jointly by chip designers and packaging houses rather than by the substrate makers alone. Because it's an emerging technology rather than an established one, supplier lists here are the least settled of anywhere in this layer and should be treated as leads to verify rather than confirmed production relationships.

ai_delta:
AI package sizes and layer counts are pushing conventional substrate materials toward their physical limits, which is the specific pressure pulling glass-core substrates from lab work toward qualification.

scale_notes:
Still a qualification-stage technology; presence depends on which packaging generation a given accelerator design adopts rather than on data center site size.


id: 1.16
slug: interposers-silicon-bridges-rdl
name: Interposers, silicon bridges, RDL
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The silicon interposers, bridges, and redistribution layers that route signals between a compute die, HBM stacks, and the substrate inside an advanced package.

description_md:
Interposers and bridges are the wiring layer inside an advanced package: a piece of silicon (or a smaller bridge die) that sits between the compute die and HBM stacks above it and the substrate below (2.5D/3D advanced packaging, ABF / high-layer package substrates), routing the dense set of connections a multi-die AI accelerator package needs. Some of this capacity comes from the same foundries doing the most advanced logic manufacturing, and some from separate interposer-focused suppliers, which makes this sub-category's investor exposure partly overlap with, and partly separate from, foundry exposure itself.
The category's importance to investors is proportional to how many discrete dies a given accelerator package combines — more chiplets and more HBM stacks per package means more interposer or bridge area needed per finished accelerator, independent of overall accelerator unit growth. It's a smaller, more technical sub-position than packaging or substrates broadly, but one where a design shift toward more disaggregated, multi-die accelerator architectures would increase demand per chip even if total chip volume held flat.

ai_delta:
More die per package and more instances of 2.5D/3D packaging per accelerator generation both require more interposer or bridge area, independent of overall unit growth.

scale_notes:
Volume follows advanced-packaging unit count industry-wide rather than the size of any one facility.


id: 1.17
slug: chip-level-thermal-lids-heat-spreaders-tims
name: Chip-level thermal: lids, heat spreaders, TIMs
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The metal lids, heat spreaders, and thermal interface materials applied directly at the die level to move heat off an accelerator package before it ever reaches a cold plate or heat sink.

description_md:
This is the first step of thermal management in the entire catalog, applied before the part sits on a board at all: a lid or heat spreader bonded over the die, with a thermal interface material filling the gap between them, moving heat from the silicon itself out to whatever cooling hardware the server uses next — a cold plate for liquid-cooled designs or a heat sink and fan for air-cooled ones (Layer 5, Layer 6). It sits directly downstream of advanced packaging, since the lid is typically part of the finished package rather than added later on the server line.
As accelerator power density rises, the performance bar on both the lid material and the thermal interface material rises with it — an interface material that was adequate for a lower-power chip generation can become a limiting factor for a hotter one. For investors, this is a smaller, more specialized materials position than packaging or substrates, with a mix of dedicated thermal materials suppliers and larger diversified chemicals and materials companies, where qualification is tied tightly to a specific chip generation's thermal design rather than being a general-purpose materials sale.

ai_delta:
Rising AI accelerator power density raises the performance bar on die-level lids, heat spreaders, and thermal interface materials generation over generation.

scale_notes:
One lid and one set of thermal interface materials per die regardless of facility tier; the specific material chosen can depend on whether the finished server is air- or liquid-cooled downstream.


id: 1.18
slug: semiconductor-test-and-burn-in
name: Semiconductor test & burn-in
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The automated test equipment, probe cards, sockets, and burn-in systems used to verify a chip works before and after it's packaged.

description_md:
Test and burn-in happens at multiple points in the chain — wafer-level probing before a die leaves the foundry, and package-level test and burn-in after advanced packaging is complete — using testers, probe cards, sockets, and dedicated burn-in systems from a distinct set of suppliers from the packaging and equipment side (Packaging & bonding equipment). It's the quality gate between every other step in this layer and a shippable, working accelerator, and some OSATs handling overflow packaging work (see the OSAT child part under advanced packaging) also provide test services directly.
Investor interest in this sub-layer tends to track how much more test time and how many more test insertions a given AI die needs relative to a conventional logic chip — larger, more complex, higher-value dies raise the cost of letting a defective unit through, which pushes toward more thorough (and more expensive) test and burn-in per unit rather than toward higher unit test cost. Supplier concentration is meaningful in testers specifically, with a more fragmented set of vendors for probe cards, sockets, and handlers.

ai_delta:
Larger, more complex, and higher-value AI dies raise the cost of letting a defective unit through, pushing more test time and more test insertions onto each unit shipped.

scale_notes:
Test and burn-in throughput scales with total die volume shipped industry-wide, not with the size of any single data center.


id: 1.19
slug: fab-equipment-upstream
name: Fab equipment
qualifiers: upstream
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The lithography, deposition, etch, and inspection tools that fabs use to manufacture chips — upstream capital equipment rather than a part that ships in any finished accelerator.

description_md:
Fab equipment is the capital equipment layer behind the advanced-node foundry step: the lithography, deposition, etch, and inspection and metrology tools installed on a fab floor to actually pattern and build chips. None of it ships inside a finished accelerator — it's sold to the foundries themselves, and its order book is a forward indicator of future foundry capacity rather than a reflection of current chip shipment volume. A small number of vendors dominate specific tool categories, with lithography in particular concentrated in a single supplier for the most advanced nodes.
For investors, this sub-layer offers a longer, more capital-cycle-driven view of AI accelerator demand than any part further downstream: a fab capacity expansion decision, and the equipment order that funds it, is made years ahead of the wafers that expansion will eventually produce. That makes equipment order backlogs and disclosed capital spending plans a genuinely forward-looking read on where accelerator supply is headed, at the cost of being one more step removed from the finished product than foundry capacity itself.

ai_delta:
Every increment of new advanced-node wafer capacity added for AI accelerators is new fab equipment demand somewhere upstream in the process, ordered years ahead of the wafers it will eventually produce.

scale_notes:
Tool capacity additions are a global, multi-year capital cycle independent of any single AI data center's footprint.


id: 1.20
slug: fab-materials-masks-gases-upstream
name: Fab materials, masks, gases
qualifiers: upstream
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute
summary:
The photoresist, masks, process chemicals, sputtering targets, and specialty gases consumed in volume during chip fabrication.

description_md:
This is the consumables layer of chip fabrication, sitting alongside fab equipment as an input to the advanced-node foundry step: photoresist and masks used to pattern each layer, CMP slurries and other process chemicals, sputtering targets for deposition steps, and the specialty gases used throughout the process. Unlike fab equipment, which is a durable capital good, these are consumed and repurchased continuously in step with wafer output, making this sub-layer's volume a closer real-time read on actual fab utilization than equipment order books are.
Several of the specific sub-categories here (see the child parts) carry meaningful supplier concentration — a handful of firms dominate advanced-node photoresist, for instance — which the taxonomy breaks out separately because the investor-relevant dynamics differ materially by material type. For investors, the general pattern across this whole sub-layer is that AI accelerator dies, being larger and more process-step-intensive than typical logic chips, consume more of each material per finished wafer than a conventional chip would, adding a second source of demand growth beyond wafer start volume alone.

ai_delta:
Fab materials consumption scales with wafer starts, and AI accelerator dies' larger size and greater process-step complexity add further consumption per wafer on top of that.

scale_notes:
Materials consumption tracks fab wafer starts industry-wide; not tied to the size of any single data center.


id: 2.01
slug: hbm-hbm3e-hbm4
name: HBM
qualifiers: HBM3E, HBM4
bottleneck_status: constrained
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
notes: The most supply-constrained component in the stack.
summary:
High-bandwidth memory stacked directly on the same package as an AI accelerator, giving the GPU or ASIC very fast, close-range access to the memory it needs to stay fed.

description_md:
HBM sits on the accelerator package itself, next to the compute die, rather than on a separate module across the board — the physical packaging that puts it there belongs to Layer 1. It is the first thing a new accelerator generation is designed around, since the number of stacks and their capacity set a hard ceiling on how large a model an accelerator can hold in memory before it has to reach further out to DRAM. AI training and inference workloads scale primarily by adding parameters and context length, both of which are bound by per-accelerator memory capacity, so HBM content has risen with every accelerator generation. The taxonomy flags HBM as **the most supply-constrained component in the stack**, and also notes that HBM4 base die is fabricated at a foundry partner for some suppliers or produced in-house by others — a qualification detail worth tracking, since a supplier's base-die sourcing shapes its cost position and lead times. For an investor, the things to watch are supplier qualification status on each new accelerator design win, and how stacking-tool and base-die capacity trail the memory makers' own output.

ai_delta:
Larger models and longer context windows push accelerator designs toward more HBM stacks and higher per-stack capacity with each generation, keeping HBM the tightest link in the memory chain.

scale_notes:
Content is fixed per accelerator package; total HBM demand scales with the number of accelerators deployed rather than with facility size or cooling design.


id: 2.02
slug: dram-ddr5-rdimm-mrdimm-lpddr5x-for-grace
name: DRAM
qualifiers: DDR5 RDIMM/MRDIMM, LPDDR5X for Grace
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
summary:
Off-package system memory — DDR5 modules for host CPUs and LPDDR5X soldered near Nvidia's Grace CPU — that holds the working data a server's processors operate on.

description_md:
DRAM sits a step further from compute than HBM: it serves the host CPU rather than the accelerator, reached over the memory channels described by the buffer chips in the next part, and it is what memory modules (2.03) package into a pluggable form. AI servers lean on host DRAM for data staging, batching, and checkpoint handling ahead of GPU reads, and newer module standards like MRDIMM exist specifically to push more bandwidth through the same socket as core counts rise. A structural pressure worth watching is that HBM and standard DRAM are produced on the same manufacturing lines by the same small set of makers, so wafer capacity a supplier commits to HBM is capacity it is not committing to commodity DRAM — a trade-off that can tighten DRAM supply and pricing even though DRAM itself has not become any more technically exotic. For an investor, this layer is less about a single chokepoint than about how the same three suppliers allocate capacity across HBM, DRAM, and LPDDR product lines as AI demand pulls at all three simultaneously.

ai_delta:
AI servers' data-staging and checkpointing needs raise host DRAM content per node, and HBM production competing for the same fab capacity tightens standard DRAM supply alongside it.

scale_notes:
Quantity tracks node count and the memory-per-socket ratio a platform is designed around, not the facility's physical scale.


id: 2.03
slug: memory-modules
name: Memory modules
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
scale_notes: Quantity tracks socket and channel count per server rather than site scale.
flow: compute|data
summary:
The physical DIMM (or module) that packages raw DRAM die, substrate, and management circuitry into the pluggable unit a server actually sockets.

description_md:
A memory module is the assembly step between raw DRAM (2.02) and a server motherboard: it takes bare die and turns them into a qualified, pluggable part, working alongside the buffer and register chips described next to keep high-rank modules electrically stable. AI-era servers push toward higher-capacity, higher-rank modules to keep memory per socket in step with rising core and accelerator counts, which raises the assembly and qualification bar module makers have to clear with every new platform. For an investor, the module layer is a value-add assembly business distinct from the underlying DRAM trade: margins depend on qualification wins with system builders and on how much of that assembly the large DRAM makers choose to keep in-house versus leave to independent module vendors. As capacity per module rises, the module becomes a more complex, higher-content part rather than a simple pass-through of the chips inside it.

ai_delta:
Higher memory-per-socket requirements in AI servers push module makers toward higher-capacity, higher-rank designs that carry more assembly and qualification content per unit.


id: 2.04
slug: memory-interface-buffer-chips-rcd-db
name: Memory interface / buffer chips
qualifiers: RCD, DB, MRCD/MDB
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
summary:
Small companion chips on a memory module that buffer and re-drive command, address, and data signals so many DRAM chips can be packed per module without losing signal integrity.

description_md:
Buffer and register chips sit on the memory module (2.03) itself, between the CPU's memory controller and the DRAM ranks it is addressing — a purely signal-integrity role that becomes necessary once a module carries enough DRAM chips that direct connections would no longer hold a clean signal. AI-era platforms pushing toward higher-capacity, higher-rank modules make this chip closer to mandatory rather than optional, since it is what lets a module scale in capacity at all without a corresponding loss in speed or reliability. For an investor, this is a narrow analog and mixed-signal niche: the design is tied to memory standard cycles set by the industry standards body, so a new DRAM generation forces a fresh qualification round that resets which suppliers hold the design win, and the pool of qualified suppliers per generation is small.

ai_delta:
Higher-capacity, higher-rank modules needed to keep memory in step with AI-era core and accelerator counts make buffer and register chips a near-mandatory part of the module rather than an optional add-on.

scale_notes:
Presence depends on module design — registered and load-reduced modules carry these chips, simpler unbuffered modules do not — so it tracks platform choice as much as facility scale.


id: 2.05
slug: cxl-controllers-and-memory-expanders
name: CXL controllers & memory expanders
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
summary:
Chips that let a server attach an extra pool of DRAM over a CXL link rather than only through the CPU's own memory channels, expanding usable capacity beyond what fits in local DIMM slots.

description_md:
CXL controllers and expanders sit between the host CPU and a pool of DRAM that is reached over the interconnect fabric described next (2.06), rather than plugged directly into the CPU's own memory channels. Because accelerator memory is expensive and capacity constrained, workloads increasingly want an overflow tier of cheaper, pooled DRAM they can reach without paying for it on every board — and pooling only pays off once a platform is built at a scale where sharing memory across nodes is worth the added complexity, so this part's presence is a design choice rather than a given. For an investor, this is an early-stage market: a handful of dedicated controller vendors compete alongside established interconnect chip makers extending existing product lines into the segment, and qualification tracks new CPU platform launches rather than a steady annual cycle.

ai_delta:
AI workloads' appetite for memory beyond what fits in a single node's DIMM slots is what makes pooled, CXL-attached memory worth the added system complexity.

scale_notes:
Presence depends entirely on whether a platform adopts CXL memory pooling; where it does, it appears from enterprise servers up through the largest clusters.


id: 2.06
slug: pcie-cxl-switches-and-retimers
name: PCIe/CXL switches & retimers
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
summary:
Fabric silicon — switches that fan a single PCIe or CXL root out to many downstream devices, and retimers that clean up the signal over longer runs — connecting accelerators, SSDs, and pooled memory back to the host.

description_md:
This part is the physical and link layer underneath the CXL memory expanders in 2.05, and it also carries general PCIe traffic to SSDs and accelerators elsewhere in a system. AI servers pack far more PCIe and CXL endpoints per node than general-purpose servers — accelerators, NICs, SSDs, and memory expanders all compete for lanes — and a single host cannot drive that many endpoints directly, which is what makes a switch necessary. Longer high-speed board traces at newer PCIe generations also need retiming just to close timing margin, so retimer content per system tends to rise alongside endpoint count and generation. For an investor, the supplier base spans both merchant interconnect chip makers with broad portfolios and specialists built specifically around switching and retiming silicon, and each new PCIe generation is a fresh design-win cycle rather than a carryover of the prior one.

ai_delta:
Packing many more PCIe and CXL endpoints per AI server than a general-purpose server requires drives up switch and retimer content per system.

scale_notes:
Content per server scales with endpoint count and trace length rather than facility size; it appears wherever PCIe fan-out exceeds what the host can drive directly.


id: 2.07
slug: nand-flash
name: NAND flash
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
summary:
Non-volatile flash memory die — the raw storage medium later packaged into SSDs — that holds data and model artifacts when the system is powered off.

description_md:
NAND is the upstream input to the enterprise SSDs and controllers described next (2.08), and it sits below DRAM (2.02) and above HDD (2.09) in the usual data center storage hierarchy: faster and more expensive than spinning disk, but non-volatile and far cheaper per unit than DRAM. AI workloads write checkpoints frequently during training and stage large datasets locally ahead of GPU reads, and inference deployments cache model weights on fast local flash to shorten cold starts — all of which push endurance and capacity requirements above what a typical enterprise server workload needs. For an investor, NAND remains a cyclical, capital-intensive commodity business already consolidated among a small number of large integrated device makers that produce both the die and, through captive or partner controllers, finished drives — so pricing tends to move with the industry's broader supply cycle rather than with AI demand in isolation.

ai_delta:
Frequent checkpointing during training and local weight caching for inference push NAND endurance and capacity needs above typical enterprise storage workloads.

scale_notes:
Quantity tracks the local storage footprint designed into each server, independent of facility scale or cooling approach.


id: 2.08
slug: enterprise-ssds-and-controllers
name: Enterprise SSDs & controllers
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
summary:
Solid-state drives built for data center duty cycles, and the controller chips that manage them — the fast local storage tier that feeds checkpoints, staged datasets, and cached weights to accelerators.

description_md:
An enterprise SSD packages NAND (2.07) with a controller into a drive that plugs into a compute or storage tray, typically reached over the PCIe/CXL fabric in 2.06; a dense form factor sized for AI compute trays has become common as GPU-direct storage architectures pull SSD content up. AI training clusters need read and write throughput and endurance profiles distinct from general enterprise workloads, since many accelerators can be reading from or writing to storage simultaneously during checkpointing or data loading. For an investor, the drive side of this market is largely captive to the NAND makers themselves given the vertical integration described above, while the controller side has a narrower set of independent designers competing for design wins against in-house captive controllers at the largest NAND makers — and each new host interface generation is its own qualification cycle.

ai_delta:
GPU-direct storage architectures and dense compute-tray form factors have pulled SSD content per AI server well above what general enterprise storage designs carry.

scale_notes:
Drive count and capacity per node scale with local storage design rather than facility size; present from a single edge server up through the largest clusters.


id: 2.09
slug: hdd-nearline-object-tiers
name: HDD
qualifiers: nearline / object tiers
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
summary:
Spinning hard disk drives used for the bulk, lower-cost nearline and object storage tier that holds data too large or too cold to justify keeping on flash.

description_md:
HDD sits below NAND and enterprise SSDs (2.07, 2.08) in the storage hierarchy, typically inside the AI storage systems described next (2.10) as the capacity tier for raw and processed datasets, backups, and object stores rather than the latency-critical path a GPU reads from directly. The volume of training data, checkpoints, and generated content that needs affordable long-term retention keeps growing even as flash takes over the performance-sensitive tiers, which sustains nearline HDD demand alongside SSD growth rather than being displaced by it. For an investor, this is a consolidated supplier base of a small number of large-scale drive makers where areal-density roadmaps and qualification with the largest buyers gate competitive position, and the category competes for the same underlying manufacturing investment dollars against flash's continued growth.

ai_delta:
The sheer volume of training data, checkpoints, and generated content needing affordable long-term retention keeps nearline HDD demand growing alongside, not despite, flash's rise in performance-sensitive tiers.

scale_notes:
Presence depends on whether a site runs a dedicated hot/cold storage split; where it does, capacity scales with total dataset and retention volume rather than rack count.


id: 2.10
slug: ai-storage-systems-parallel-file-systems-object
name: AI storage systems
qualifiers: parallel file systems, object, GPU-direct
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|data
summary:
Purpose-built storage software and appliances — parallel file systems, object stores, and GPU-direct pipelines — that aggregate many drives into one system capable of feeding a large number of accelerators at once.

description_md:
This is the system and software layer sitting above the drive tiers below it (2.07 through 2.09), the layer that a training cluster's compute (Layer 4) and networking (Layer 3) actually talk to when reading or writing data. Accelerators sit idle waiting on data if storage cannot sustain the read throughput a large training job demands, so clusters increasingly get built around parallel file systems and GPU-direct storage paths designed specifically for that access pattern instead of general enterprise file and block storage. For an investor, a wave of storage-specific vendors has built products around this workload and now competes with established enterprise storage incumbents extending existing product lines into the same use case, alongside hyperscalers running their own in-house systems; competitive position depends on certified GPU-direct integration with each new accelerator and networking generation rather than on general enterprise storage credentials.

ai_delta:
Large training jobs need read throughput that general enterprise storage was not designed to sustain, which is what has created a distinct category of storage system built around GPU-direct access.

scale_notes:
Presence and scale depend on cluster size and whether training data volume justifies a dedicated parallel file system versus general-purpose storage.


id: 3.01
slug: scale-up-fabric-gpu-to-gpu-inside-the-rack-pod
name: Scale-up fabric
qualifiers: GPU-to-GPU inside the rack/pod
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
The fabric linking GPUs to each other inside a single rack or pod, separate from the switching fabric that connects racks to each other.

description_md:
Scale-up fabric is the shortest, highest-bandwidth network in the data center: it lets GPUs inside the same rack or pod address each other's memory directly, rather than going through the scale-out switch fabric (3.02, 3.03). It sits upstream of the copper spine and backplane cartridges (3.10) that physically wire it together and, at higher speeds, of the optics and co-packaged approaches (3.04, 3.08) being evaluated as a copper replacement. AI training and inference workloads that split a single model across many accelerators depend on this fabric scaling in lockstep with GPU count, since it sets the ceiling on how large a tightly coupled GPU domain can get. An investor should watch whether a proprietary approach keeps its lead over open alternatives being pushed by a wider industry consortium, since that split determines how concentrated the supplier base for switch chips, cables, and connectors stays as pod sizes grow.

ai_delta:
Larger AI models split across more GPUs per training or inference job, which raises the bandwidth and reach demanded of the scale-up fabric linking them inside a rack or pod.

scale_notes:
Present wherever multi-GPU racks or pods exist; the fabric's reach and topology (single rack vs. multi-rack pod) scale with cluster size rather than being an enterprise feature.


id: 3.02
slug: scale-out-switch-asics
name: Scale-out switch ASICs
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
The switch ASICs that route traffic between racks and pods, distinct from the scale-up fabric inside a single rack.

description_md:
Scale-out switch ASICs sit between the rack-level scale-up fabric (3.01) and the switch systems and white-box ODMs (3.03) that build them into deployable hardware. They carry traffic between racks, pods, and clusters — the layer that turns individual GPU-dense racks into a single, larger training or inference domain. AI clusters push both the port count and per-port bandwidth of these ASICs higher as accelerator counts grow, since network bandwidth between racks has to keep pace with the compute inside them or become the bottleneck. An investor should watch supplier concentration at the ASIC level itself, since a small number of designs feed a much larger number of switch-system builders, and watch how Ethernet-based and InfiniBand-based approaches split share as hyperscalers weigh openness against performance.

ai_delta:
AI clusters need far more inter-rack bandwidth than general-purpose data centers, pushing scale-out switch ASICs toward higher port counts and per-port speeds sooner.

scale_notes:
Scales with cluster size: enterprise and small deployments may use merchant switch systems without needing the highest-radix ASICs that only very large clusters require.


id: 3.03
slug: switch-systems-and-white-box-odms
name: Switch systems & white-box ODMs
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
The physical switches — built by branded vendors and white-box ODMs — that house scale-out switch ASICs and connect racks into a cluster fabric.

description_md:
Switch systems take the ASICs from 3.02 and package them into deployable network hardware, connecting to the transceivers and cabling in 3.04–3.11 on one side and to server NICs on the other. The market splits between branded systems from established networking vendors and white-box designs built by ODMs to hyperscaler specifications, a split that mirrors how hyperscalers buy compute servers. AI buildouts favor white-box and merchant-silicon designs where hyperscalers want to control their own network software stack, while branded systems remain common in enterprise and smaller AI deployments that value a single vendor's support. An investor should watch how this branded-versus-white-box mix shifts with buyer type, since it changes who captures margin on a switch system versus who merely captures the ASIC or optics content inside it.

ai_delta:
Hyperscale AI clusters increasingly specify white-box switch systems built to their own designs around merchant silicon, shifting share away from fully branded switch platforms.

scale_notes:
Present at every scale; the mix shifts from branded systems at enterprise scale toward white-box, merchant-silicon designs as deployments approach hyperscale and campus scale.


id: 3.04
slug: optical-transceivers-400g-800g-1-6t
name: Optical transceivers
qualifiers: 400G / 800G / 1.6T
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
Pluggable optical modules that convert electrical signals to light for transmission over fiber between switches and servers, sold at 400G, 800G, and 1.6T speeds.

description_md:
Optical transceivers plug into the switch systems (3.03) and NICs to link them over the fiber and connectors in 3.11, carrying the scale-out traffic that copper cabling (3.09) can't reach as far or as fast. Inside each module sit the lasers (3.06), DSPs and drivers (3.07), and — as speeds rise — silicon photonics (3.08) that do the actual signal conversion; module vendors largely assemble and test these components rather than build them all in-house, which is why optics contract manufacturing (3.05) is its own layer. AI clusters consume far more optical ports per GPU than general-purpose data centers because of the scale-out bandwidth growth described in 3.02, and the industry's steady move from 400G to 800G to 1.6T tracks rising per-GPU network bandwidth requirements directly. An investor should watch module vendor concentration by speed grade, since a small number of merchant module makers plus a GPU vendor's own branded modules account for most volume at the newest speeds, and watch qualification cycles as each new speed grade requires re-qualifying lasers, DSPs, and photonics together.

ai_delta:
AI clusters need far more optical ports per GPU than general-purpose data centers, and the push to higher per-GPU bandwidth accelerates the transition from 400G to 800G to 1.6T modules.

scale_notes:
Volume scales directly with GPU and switch port count; negligible at enterprise scale, the dominant network cost line at hyperscale and above.


id: 3.05
slug: optics-contract-manufacturing
name: Optics contract manufacturing
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
Contract manufacturers that assemble and test optical transceivers on behalf of module brands and switch vendors.

description_md:
Optics contract manufacturing sits between the component layers that feed a transceiver (lasers in 3.06, DSPs and drivers in 3.07, silicon photonics in 3.08) and the finished, branded modules described in 3.04. Rather than every module brand running its own assembly lines, a handful of contract manufacturers build modules — including a GPU vendor's own branded optics — to the module brand's design, similar to how EMS providers build servers to an OEM's specification elsewhere in the stack. AI-driven optics demand has concentrated volume in this small group of manufacturers, since qualifying a new assembly and test line for the highest optical speeds is a slow, capital-intensive process. An investor should watch how tightly a given contract manufacturer is tied to a single large customer's volume, since that concentration cuts both ways: it can mean a durable, high-utilization relationship or a single-customer dependency.

ai_delta:
Surging optical module demand from AI clusters has concentrated assembly volume in the small group of contract manufacturers already qualified at the highest optical speeds.

scale_notes:
Volume follows transceiver demand (3.04); relevant wherever high-speed optics are being built at scale, immaterial below hyperscale-level deployments.


id: 3.06
slug: lasers-eml-cw-dfb
name: Lasers
qualifiers: EML, CW/DFB, VCSEL
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
notes: partial in-house at Innolight.
summary:
The laser sources — EML, CW/DFB, and VCSEL types — that generate the light signal inside optical transceivers and co-packaged optics.

description_md:
Lasers are a component inside the optical transceivers of 3.04 and the silicon photonics and co-packaged optics of 3.08, feeding light into the DSPs, drivers, and TIAs of 3.07 for modulation and detection. Different laser types suit different reach and speed combinations, and some transceiver makers source lasers externally while others build a portion in-house. Rising transceiver volume and the move to higher speed grades both increase laser demand and tighten qualification requirements, since a laser has to be re-qualified for each new speed and reach combination. An investor should watch supplier concentration by laser type, since external laser sourcing for co-packaged optics designs in particular depends on a narrower set of qualified vendors than transceiver-embedded lasers do.

ai_delta:
Rising optical transceiver and co-packaged optics volume from AI clusters increases demand for qualified laser sources across every reach and speed grade.

scale_notes:
Volume follows transceiver and co-packaged optics demand; immaterial outside hyperscale- and campus-scale optical deployments.


id: 3.07
slug: optical-dsps-drivers-tias
name: Optical DSPs, drivers, TIAs
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
The signal-processing silicon inside optical modules — DSPs plus the drivers and TIAs that condition the electrical signal on either side of the laser and photodetector.

description_md:
Optical DSPs, drivers, and TIAs sit inside the transceivers of 3.04 and the co-packaged optics of 3.08, converting between the electrical domain of the switch or GPU and the optical domain of the laser (3.06) and fiber (3.11). DSPs handle the heavier signal-processing work while drivers and TIAs condition the signal closer to the laser and photodetector; the two functions come from overlapping but not identical sets of suppliers. As speed grades rise from 800G toward 1.6T, DSP power consumption and complexity both increase, which is part of why co-packaged optics designs that eliminate some DSP stages are being evaluated as an alternative. An investor should watch DSP supplier concentration at each new speed grade, since qualifying a DSP for a new speed is a slow process that tends to favor incumbents with existing design wins.

scale_notes:
Volume follows transceiver and co-packaged optics demand; immaterial outside hyperscale- and campus-scale deployments.


id: 3.08
slug: silicon-photonics-and-co-packaged-optics-cpo
name: Silicon photonics & co-packaged optics
qualifiers: CPO
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
An approach that integrates optical components directly onto or next to the switch or GPU package, replacing some pluggable transceivers with light routed closer to the silicon.

description_md:
Silicon photonics and co-packaged optics (CPO) is an alternative to the pluggable transceiver model of 3.04, moving optical conversion closer to the switch ASIC (3.02) or GPU package to cut the electrical reach a signal has to travel before going optical — which lowers power draw and can reduce cable count at the highest speeds. It still depends on external laser sources (3.08a) and fiber attach and connectors (3.08b) as separate supply chains from the photonic integration itself. The taxonomy notes that TSMC plays a role here through its COUPE platform, alongside switch vendors building their own CPO switch designs and specialist private companies focused solely on photonic integration. AI clusters are the primary driver of interest in CPO because the power and cable-count savings matter most at the highest per-GPU bandwidths, where the copper spine cartridges of 3.10 start running into reach and cable-count limits. An investor should watch this as an early, qualification-heavy transition: it threatens to disintermediate part of the traditional pluggable-optics supply chain if it scales, but adoption timing and the split between switch vendors building CPO in-house versus buying from specialists remain open questions.

ai_delta:
Rising per-GPU bandwidth is what makes co-packaged optics attractive, since the power and cable-count savings only outweigh pluggable optics' simplicity at AI-scale link speeds.

scale_notes:
An emerging alternative to pluggable optics rather than a universal requirement; presence depends on a design choice by the switch or rack vendor, concentrated at the highest-speed, highest-density deployments where it exists at all.


id: 3.09
slug: copper-dac-acc-aec-cables
name: Copper: DAC / ACC / AEC cables
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
High-speed copper cable assemblies — DAC, ACC, and AEC types — that link GPUs, NICs, and switches over short distances as a lower-cost alternative to optical transceivers.

description_md:
Copper cabling connects the same points the optical transceivers of 3.04 do — GPUs to NICs, NICs to switches — but over shorter reaches, where it stays cheaper and lower-power than going optical. DAC is passive, ACC and AEC add active signal conditioning to extend reach, with AEC in particular depending on retimers (3.09a) to hit longer distances at high speed. Copper remains the default choice for the shortest, highest-bandwidth links in a rack, which keeps demand growing alongside optics rather than being displaced by it, even as the NVLink copper spine described in 3.10 approaches its practical reach limit at the highest speed grades. An investor should watch the mix between passive DAC and active ACC/AEC cabling as speeds rise, since active cables carry more semiconductor content (the retimers) and shift value toward cable assembly makers with in-house or partnered active-cable capability.

ai_delta:
AI racks pack far more high-speed copper links per GPU than general-purpose servers, and rising per-link speeds push volume from passive DAC toward active ACC and AEC assemblies.

scale_notes:
Volume scales with GPU and port count; the dominant short-reach interconnect at hyperscale and above, negligible at enterprise scale.


id: 3.10
slug: nvlink-copper-spine-backplane-cartridges
name: NVLink copper spine / backplane cartridges
unit_per_rack: ~5,000 copper cables per NVL72 (taxonomy v0.1, from Nvidia rack materials)
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
notes: About 5,000 copper cables per NVL72; the reason 1.6T-era racks push toward CPO.
summary:
The copper cable assemblies and backplane cartridges that physically wire the scale-up fabric together inside a rack, most visible in Nvidia's NVL72 architecture.

description_md:
NVLink copper spine and backplane cartridges are the physical layer under the scale-up fabric of 3.01, wiring GPU compute trays to NVLink switch trays inside a rack — the taxonomy notes roughly 5,000 copper cables in an NVL72-class rack. That cable count is itself the reason the industry is evaluating co-packaged optics (3.08) as a replacement at the next speed generation: as per-GPU bandwidth rises, all-copper backplanes start running into reach and density limits that pushing more copper doesn't solve. Several connector and cable assembly makers supply this content, generally under reference-design partner arrangements with the rack architect rather than as a fully open market. An investor should watch this layer as a bellwether for the copper-to-optical transition inside the rack: continued growth in cable count per rack favors incumbent copper suppliers, while an early shift to CPO would cap that growth sooner than volume trends alone would suggest.

ai_delta:
Larger scale-up domains push cable count per rack higher, up to the point where copper's reach and density limits at the highest speeds make an optical alternative necessary.

scale_notes:
Present wherever a scale-up rack architecture uses a copper backplane; cable count scales with rack GPU density and could shrink or disappear in racks that adopt co-packaged optics.


id: 3.11
slug: fiber-connectors-structured-cabling
name: Fiber, connectors, structured cabling
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
The fiber optic cable, connectors, and structured cabling that carry optical signals between transceivers across a data hall or campus.

description_md:
Fiber, connectors, and structured cabling is the physical medium the optical transceivers of 3.04 transmit over, running from switch to switch within a data hall and, via the DCI and coherent optics of 3.12, between buildings on a campus or between sites. MPO/MTP ferrules and connectors (3.11a) are the specific interface that lets fiber be broken out to individual transceiver ports and reconnected during maintenance or upgrades. AI data halls run substantially more fiber per rack than general-purpose facilities because of the higher port count per GPU described in 3.02–3.04, and higher speed grades tighten the tolerances the fiber and connectors have to meet. An investor should watch this as a relatively fragmented, lower-margin layer compared to the active optics above it, where scale and manufacturing footprint matter more than proprietary technology.

ai_delta:
Higher optical port counts per GPU and per rack in AI clusters increase the volume of fiber, connectors, and structured cabling needed per unit of compute deployed.

scale_notes:
Volume scales with optical port count and data hall size; minor at enterprise scale, a substantial physical-infrastructure line at hyperscale and above.


id: 3.12
slug: dci-coherent-optics-campus-to-campus-metro
name: DCI / coherent optics
qualifiers: campus-to-campus, metro
scale_tiers: ai_campus|gw_class
density_modes: any
flow: data
summary:
Coherent optical systems that carry data center interconnect (DCI) traffic between buildings on a campus or between metro sites, distinct from the shorter-reach optics inside a data hall.

description_md:
DCI and coherent optics extend the network beyond the fiber and connectors of 3.11 that serve a single data hall, linking separate buildings on an AI campus or connecting sites across a metro area. This is a distinct technology from the direct-detect optics used inside a data hall (3.04): coherent transmission encodes more information per wavelength to cover longer distances, and the vendor base for coherent systems overlaps only partly with the vendor base for short-reach transceivers. As AI campuses grow beyond a single building and hyperscalers link multiple sites into one logical training cluster, demand for campus-to-campus and metro-scale coherent capacity grows with them. An investor should watch supplier concentration here separately from the rest of the optics stack, since coherent DCI has historically been a smaller, more specialized market than short-reach data center optics.

ai_delta:
Multi-building AI campuses and multi-site training clusters push more traffic onto campus-to-campus and metro coherent links than single-building data centers required.

scale_notes:
Only relevant once a build spans more than one building or site; largely absent below ai_campus scale.


id: 3.13
slug: timing-and-telemetry-silicon
name: Timing & telemetry silicon
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: data
summary:
Precision timing and telemetry chips that synchronize clocks and monitor signal integrity across the network fabric.

description_md:
Timing and telemetry silicon supports the rest of the networking layer rather than carrying data itself: precision clock chips keep switches, NICs, and optical modules synchronized, while telemetry silicon monitors signal quality and network health across the scale-up and scale-out fabrics described in 3.01–3.12. As network speeds rise and topologies grow more complex across larger AI clusters, timing tolerances tighten and telemetry coverage becomes more important for catching degraded links before they cause job failures. An investor should watch this as a small but sticky content line: timing and telemetry chips are typically specified into a switch or module design early and stay there for the life of that design, which favors incumbents with existing qualifications across multiple switch and optics vendors.

ai_delta:
Higher network speeds and larger, more complex AI cluster topologies tighten timing tolerances and increase the value of telemetry coverage across the fabric.

scale_notes:
Present wherever high-speed switching and optics exist; content per port grows with speed grade and cluster complexity rather than with physical size alone.


id: 4.01
slug: rack-scale-systems-nvl72-class-mi400-helios-class
name: Rack-scale systems
qualifiers: NVL72-class, MI400 Helios-class
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: compute|power
summary:
A rack-scale system is the fully integrated cabinet of compute trays, switch trays, power shelves, cooling manifolds and busbar that ships as one wired unit rather than as discrete servers.

description_md:
The rack-scale system is the top-level assembly layer: it takes the GPU baseboards (4.03), switch trays (4.04), power shelves (4.12) and liquid-cooling hardware from layer 5 and integrates them into a single cabinet wired for one scale-up fabric domain (layer 3). Nvidia and AMD publish the reference architecture and mechanical/electrical spec; the ODMs and OEMs listed against this part build to that spec, badge the result, and compete mainly on integration quality, lead time and support rather than on the underlying design. AI training and inference clusters push this part into existence at all: a conventional enterprise server rack has no equivalent, because the point of a rack-scale system is to turn dozens of GPUs into one coherent domain, something only large models require. For investors, the interesting split is between the reference-design owners (Nvidia, AMD), who capture the architecture value, and the ODM/OEM tier, which competes on manufacturing scale and qualification speed with each new rack generation. Each new generation resets the qualification race, so watch which ODMs get design wins early and which fall behind on ramp.

ai_delta:
AI training and inference workloads are the reason rack-scale systems exist at all — no other server class assembles dozens of GPUs into one wired, liquid-cooled domain.

scale_notes:
enterprise deployments typically use standalone or multi-GPU servers rather than a full rack-scale system; the fully wired cabinet becomes the norm once GPU counts justify NVLink or Infinity Fabric domains at hyperscale and above.


id: 4.02
slug: ems-system-integration-and-rack-level-test
name: EMS, system integration & rack-level test
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
Electronics manufacturing services firms assemble, wire and burn-in the finished server or rack before it ships, standing between the ODM board build and the customer's data center floor.

description_md:
EMS providers sit downstream of the board and tray builders (4.03, 4.04): they take built boards, populate chassis, route cabling, install the liquid-cooling loop hardware from layer 5, and run rack-level test and burn-in before a system leaves the factory. Several of the same companies that design and build the boards also run their own integration lines, while pure-play EMS firms take on integration and test work for OEMs that don't want to run factories themselves. AI racks make this step harder and more valuable: a liquid-cooled, multi-tray cabinet with a large number of internal cables and connectors has far more failure points to catch before shipment than a conventional 1U server, and a leak or a bad connection discovered in the field is expensive to fix. That raises the bar for test equipment and floor space, and it means integration capacity — not just chip supply — can gate how fast a rack generation ships. For investors, the signal to watch is which integrators win capacity commitments tied to a new rack generation, since qualification and yield on rack-level test tend to lag the underlying silicon ramp.

ai_delta:
AI rack complexity — liquid cooling, dense internal cabling, tight power budgets — raises the bar for pre-ship test and burn-in well past what a standard server requires.

scale_notes:
enterprise servers get factory test at the unit level; hyperscale-and-up rack-scale systems require dedicated rack-level test cells and longer burn-in cycles.


id: 4.03
slug: gpu-baseboards-and-compute-trays-hgx-bianca
name: GPU baseboards & compute trays
qualifiers: HGX, Bianca, UBB/OAM
unit_per_rack: 18 compute trays per NVL72 (Nvidia rack specification, via taxonomy v0.1 Appendix A)
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The baseboard is the printed circuit assembly that carries the GPUs (and often paired CPUs) and their direct power and interconnect, packaged into a tray that slides into the rack.

description_md:
The compute tray sits between the packaged silicon (layer 1, layer 2 memory) and the rack-scale system (4.01): it's the board that physically carries the GPUs, any paired CPUs, VRMs (4.08), connectors (4.07) and the traces that bring scale-up fabric (3.01) to each chip. Nvidia's HGX and Bianca designs and the OAM/UBB open standard define the mechanical and electrical envelope; the ODMs build to that spec while Supermicro builds boards in-house rather than sourcing from the ODM tier. AI demand is the entire reason this part category exists in its current form — a baseboard carrying multiple high-power GPUs, coherent CPU links and dense high-speed interconnect is a different design problem from a general-purpose server motherboard, with far tighter power delivery, signal integrity and thermal requirements. For investors, the baseboard is where board complexity, layer count (4.05) and connector density all stack up, so it's a useful proxy for how much of a rack's bill of materials sits in high-spec board manufacturing versus commodity components; watch which board builders get first-wave qualification on each new GPU generation, since that tends to set the pecking order for the whole rack cycle.

ai_delta:
A compute tray carrying multiple high-power GPUs with coherent CPU links and dense scale-up interconnect is a fundamentally different design than a general-purpose server motherboard, which is why this category exists as its own part class.

scale_notes:
present in every GPU server regardless of scale; what changes is tray count per rack and whether the tray is air-cooled or fitted with cold plates.


id: 4.04
slug: motherboards-cpu-trays-switch-trays
name: Motherboards, CPU trays, switch trays
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The non-GPU trays in a rack — CPU-only compute trays, host motherboards and the switch trays carrying scale-up switch silicon — built by the same board makers as the GPU trays.

description_md:
This part covers the trays that round out a rack-scale system alongside the GPU baseboard (4.03): CPU-only compute trays for control and orchestration nodes, conventional server motherboards, and switch trays that carry the scale-up switch silicon (3.01) linking GPU trays together. The same ODM tier that builds GPU baseboards builds these, joined by motherboard specialists who don't necessarily compete for GPU-tray business. AI racks need more of this category than a conventional data center rack: a rack-scale cabinet carries dedicated switch trays purely to fan out the scale-up fabric, a tray type that barely existed before rack-scale GPU systems. For investors, this is a lower-margin, higher-volume counterpart to the GPU baseboard — less differentiated, more competitive — so the interesting question is less "who wins" and more whether a builder's exposure here dilutes or complements its GPU-tray economics.

ai_delta:
Rack-scale GPU systems require dedicated switch trays purely to fan out the scale-up fabric between GPU trays, a tray category that has little equivalent in conventional servers.

scale_notes:
switch-tray count scales with the number of GPU trays and the scale-up fabric's radix, so it grows faster than headcount-style server metrics as racks get larger.


id: 4.05
slug: high-layer-count-pcbs-hdi-backdrilled
name: High-layer-count PCBs
qualifiers: HDI, backdrilled, 20–40 layers
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The bare printed circuit boards themselves — high-density interconnect, backdrilled, 20 to 40 copper layers — that GPU and switch trays are built on.

description_md:
This is the physical board substrate beneath the baseboards and switch trays (4.03, 4.04): a laminate stack of 20 to 40 signal and power layers, built with HDI microvias and backdrilling to keep high-speed signal integrity across the board. It sits downstream of the laminate and copper-foil suppliers (4.06) and upstream of everything that gets populated onto it. AI boards push layer count and manufacturing precision well past a standard server motherboard because scale-up interconnect, high pin-count sockets and dense power delivery all need more routing layers and tighter tolerances than a general-purpose board. That raises both the capital intensity of the fabrication step and the qualification burden, since a board with dozens of layers has far more opportunities for a manufacturing defect to show up only under load. For investors, this is a capacity-constrained, capital-heavy tier of the supply chain: the fabricators that can reliably hit the highest layer counts at volume are a shorter list than the board designers who spec them, and that gap tends to widen, not narrow, with each new rack generation.

ai_delta:
AI boards need far more routing layers and tighter fabrication tolerances than a standard server motherboard, because dense scale-up interconnect and high-pin-count sockets have to be routed across the same board.

scale_notes:
layer count and fabrication precision scale with board complexity rather than data center size, so the highest-layer-count boards appear wherever the highest-end GPU trays are built, at any deployment scale.


id: 4.06
slug: copper-clad-laminate-and-pcb-materials
name: Copper-clad laminate & PCB materials
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The laminate — resin, copper foil and glass-cloth reinforcement — that high-layer-count boards (4.05) are fabricated from.

description_md:
This part sits one step upstream of the PCB fabricators (4.05): copper-clad laminate suppliers combine resin systems, copper foil (4.06a) and glass-cloth reinforcement (4.06b) into the sheet material that board makers etch and stack into finished PCBs. Low-loss, low-dielectric-constant resin systems matter specifically for high-speed digital signaling, which is why they show up by name against this part. AI boards' higher layer counts and higher signal speeds push demand toward these low-Dk, low-loss grades rather than commodity FR-4 laminate, and that mix shift matters more to this tier's economics than raw board volume does. For investors, the laminate tier is a materials-science-driven part of the chain: qualification of a new resin or glass-cloth grade for a given board design takes time, so incumbency and long design-in relationships carry real weight, and pricing tends to track the mix shift toward higher-spec grades rather than unit volume alone.

ai_delta:
Higher board layer counts and faster signaling push demand toward low-loss, low-dielectric-constant laminate grades instead of commodity material, shifting the mix even where total board area doesn't grow as fast.

scale_notes:
an upstream material input present wherever high-layer-count boards are fabricated, independent of the data center's own scale tier.


id: 4.07
slug: board-level-connectors-and-sockets
name: Board-level connectors & sockets
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The physical connectors — CPU and GPU sockets, board-to-board and cable connectors, and I/O cages — that link chips, trays and cables together on and between boards.

description_md:
Connectors sit at nearly every junction in the rack: CPU and GPU sockets attach silicon to the baseboard (4.03), board-to-board connectors link trays together, and I/O cages (4.07a) terminate the optical and copper interconnect coming off layer 3. As pin counts, power currents and signal speeds all rise with each GPU generation, socket and connector design becomes a harder mechanical and electrical problem — more contact points, tighter tolerances on insertion force and contact resistance, and more attention to high-speed signal integrity through the connector itself. AI racks also simply need more connectors per system than a conventional server, both because there are more trays to link and because power delivery at rack scale increasingly moves through busbars and whips (4.15) rather than cables, each with its own connector interface. For investors, this tier tends to be sticky once a connector is designed into a socket or board generation, since requalifying a different supplier's socket against a new GPU package is not a drop-in swap; the interesting question by generation is who wins the socket design-in for the next GPU package.

ai_delta:
Higher pin counts, higher currents and faster signaling with each GPU generation make socket and connector design a harder mechanical problem, and AI racks need more connectors per system than a conventional server simply because there are more trays to link.

scale_notes:
connector count per rack rises with tray count and interconnect density rather than with facility size, so it scales with rack generation more than with deployment tier.


id: 4.08
slug: board-level-power-vrms-power-stages-pol-power-semis
name: Board-level power: VRMs, power stages, PoL, power semis
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The voltage regulator modules and point-of-load converters that step rack-level DC power down to the precise, high-current voltages each GPU and chip needs at the board.

description_md:
This part sits between the rack's power shelves (4.12) and busbar (4.15) on one side and the GPU or CPU die on the other: VRMs and power stages take the incoming rail and regulate it down, at high current and tight tolerance, to the voltage a chip actually consumes. GPU-class VRMs are a harder engineering problem than a CPU's because GPU power draw is both higher and more transient — regulation has to respond fast enough that a sudden compute load doesn't cause the voltage to sag or overshoot. That has pulled wide-bandgap semiconductors (4.08a) into board-level power delivery as GaN and SiC devices allow smaller, more efficient power stages at the currents modern GPUs need. For investors, this tier is defined by a handful of suppliers with the analog and power-electronics design expertise to hit these specs reliably, and by how tightly a given VRM design is qualified against a specific GPU generation's transient response profile — a qualification cycle that resets with every new chip generation and tends to favor incumbents with an existing track record on the previous one.

ai_delta:
GPU power draw is both higher and far more transient than CPU power draw, which pushes VRM design toward faster response and higher current density than board-level power delivery has historically needed.

scale_notes:
present on every GPU and CPU board regardless of facility scale; what changes with rack density is the current and transient-response spec each VRM has to hit.


id: 4.09
slug: passives-mlcc-inductors
name: Passives
qualifiers: MLCC, inductors, polymer/tantalum, resistors
bottleneck_status: tight
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The multilayer ceramic capacitors, inductors and other passive components that filter and stabilize power everywhere on a board, populated in far greater numbers on AI boards than on standard servers.

description_md:
Passives are the smallest, most numerous parts on any board: MLCCs, inductors and tantalum or polymer capacitors sit beside the VRMs (4.08) to filter and stabilize the power rails feeding GPUs, CPUs and memory. A single high-power GPU board's power delivery network needs many more of these components than a conventional server board, because more numerous, more transient power rails each need their own local filtering. That volume increase is one of the more overlooked demand multipliers in an AI rack: it isn't a new part category, but sheer count reshapes procurement. High-specification MLCC supply has tightened as a result, with lead times reported to have lengthened well beyond what commodity passives see. For investors, this tier is normally viewed as undifferentiated and price-competitive, but the AI-driven volume and spec shift changes that calculus somewhat: capacity for high-spec, high-reliability grades is a narrower pool than capacity for commodity passives, and that gap is what determines who benefits from the volume increase rather than just absorbing more low-margin unit orders.

ai_delta:
roughly 10–15× the passive count of a standard server; high-spec MLCC lead times around 24 weeks (mid-2026 trade press).

scale_notes:
passive count per board is roughly fixed by design regardless of facility scale; total demand instead scales with the number of boards deployed.


id: 4.10
slug: bmc-and-platform-firmware
name: BMC & platform firmware
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The baseboard management controller chip and its firmware, which independently monitor power, temperature and health on a server board and let it be managed remotely, separate from the host CPU or GPU.

description_md:
The BMC sits alongside the CPU and GPU on the board (4.03, 4.04) but runs its own independent processor and firmware stack, giving operators out-of-band visibility into power, temperature, fan and health telemetry even if the host system is unresponsive. Aspeed holds a near-monopoly position in BMC silicon as reported in the taxonomy, which makes this one of the more concentrated single points in the entire board bill of materials — a status shared by very few other components in the rack. AI racks raise the stakes on this part because liquid cooling, higher power density and tighter thermal margins all make out-of-band monitoring more operationally important than it was in an air-cooled, lower-density server; a leak or thermal excursion needs to be caught by something other than the host OS. Nvidia's BlueField line is positioned in the taxonomy as a future BMC replacement, worth watching as a potential architectural shift rather than a near-term one. For investors, the concentration here is the story: a near-monopoly supplier in a component every board needs is a different risk profile than the competitive layers around it, and any credible second source or architectural replacement is the thing to track.

ai_delta:
Liquid cooling and tighter thermal margins in AI racks make independent, out-of-band health monitoring more operationally critical than it was in lower-density air-cooled servers, since a leak or thermal excursion has to be caught by something other than the host OS.

scale_notes:
present on every server board regardless of scale; its operational importance rises with density and liquid cooling rather than its physical presence changing.


id: 4.11
slug: timing-security-and-misc-board-silicon
name: Timing, security & misc board silicon
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
Clock and timing chips, TPM security modules, and boot flash — the smaller fixed-function silicon that supports the CPU, GPU and BMC on every board without doing the compute itself.

description_md:
This part covers the supporting silicon that doesn't fit neatly into the compute, power or connector categories: precision timing and clock-generation chips that keep high-speed interconnect and memory interfaces synchronized, trusted-platform-module security silicon, and the boot flash that stores firmware for the BMC (4.10) and other on-board controllers. None of these individually consume much board area or power, but each is a required, qualified part of the bill of materials on every board built. AI boards raise the bar on the timing piece specifically, since the higher signaling rates used by scale-up and scale-out interconnect (layer 3) need tighter clock jitter and synchronization tolerances than lower-speed server interfaces required. For investors, this is a diversified, lower-visibility tier — no single company dominates the way it does in BMC silicon — where the more relevant question is less about market share and more about which suppliers get designed into the reference platforms that ODMs build from, since that tends to carry forward across several board generations.

ai_delta:
Higher signaling rates on scale-up and scale-out interconnect need tighter clock jitter and synchronization tolerances than lower-speed server interfaces required, raising the specification bar on timing silicon specifically.

scale_notes:
present on every board regardless of facility scale; specification tightens with interconnect speed rather than with deployment size.


id: 4.12
slug: power-shelves-and-psus-33-kw-class-orv3-shelves-rack-level
name: Power shelves & PSUs
qualifiers: 33 kW-class ORV3 shelves, rack-level
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The rack-mounted power shelves that convert facility AC or DC input into the regulated rail distributed to trays across the rack, built to the Open Rack V3 33 kW-class spec used in rack-scale AI systems.

description_md:
Power shelves sit between the facility's electrical distribution (layer 7) and the busbar or whips (4.15) that carry power the rest of the way to each tray; they're the rack's own bulk power-conversion stage. The Open Rack V3 form factor standardizes a 33 kW-class shelf design shared across ODMs, which lets a rack-scale system swap in shelves from multiple suppliers without re-engineering the rack's power architecture. AI racks need far more power-shelf capacity than a conventional server rack because GPU trays draw far more continuous power than general-purpose compute, and a growing share of that conversion work happens with wide-bandgap power semiconductors (4.12a) rather than silicon, since GaN and SiC devices handle these currents at higher efficiency and in less space. For investors, this tier tracks rack power density directly: as GPU generations draw more power per tray, shelf count and shelf capacity per rack both rise, and the suppliers who qualify early on a new shelf generation tend to hold that position through the generation's full production run.

ai_delta:
GPU trays draw far more continuous power than general-purpose compute, which is why rack-scale AI systems need substantially more power-shelf capacity per rack than a conventional server rack, and why wide-bandgap semiconductors are displacing silicon inside the shelf.

scale_notes:
the Open Rack V3 shelf format is specific to rack-scale systems at hyperscale and above; smaller enterprise GPU servers typically use conventional internal PSUs rather than shared rack-level power shelves.


id: 4.13
slug: fans-air-cooled-servers-hybrid-trays
name: Fans
qualifiers: air-cooled servers, hybrid trays
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: air|hybrid
flow: compute|power
summary:
The axial and centrifugal fans mounted in server chassis and rack trays that move air across heatsinks and boards, still needed even in racks that use liquid cooling for the highest-power chips.

description_md:
Tray and chassis fans sit alongside the heatsinks and vapor chambers (4.17) as the air-cooling half of a rack's thermal design, and they remain necessary even in a liquid-cooled rack-scale system, since cold plates (layer 5) typically cool only the GPUs and a few other hot components while the rest of the tray — memory, VRMs, networking silicon — still relies on airflow. That makes fans a hybrid-cooling-era component: as more of a rack's heat load moves to liquid, fan count per tray can fall, but it doesn't go to zero, and the fans that remain often have to move air through a more crowded, liquid-plumbed chassis than before. For investors, this is a mature, competitive component category without a strong bottleneck, so the more relevant question is the net demand trajectory as liquid cooling adoption rises — fewer fans per liquid-cooled tray, but more total trays being built.

ai_delta:
Even in a liquid-cooled rack, cold plates typically cool only the GPUs, so fans remain necessary for memory, VRMs and networking silicon on the same tray, making air-cooling components a hybrid-era holdover rather than something liquid cooling eliminates outright.

scale_notes:
fan count per tray declines as liquid cooling covers more of the heat load, but does not disappear even in the highest-density liquid-cooled racks.


id: 4.14
slug: chassis-rails-rack-enclosures
name: Chassis, rails, rack enclosures
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
notes: Foxconn and Quanta build NVL72 cabinets in-house.
summary:
The sheet-metal chassis and mounting rails that trays slide into, and the enclosure (the rack cabinet itself) that houses the whole assembly.

description_md:
The chassis and rails are the mechanical structure underneath everything else in this layer: sheet-metal trays hold the boards (4.03, 4.04) in place and mount into rails that let a tray slide in and out of the rack enclosure (4.14a) for service. Some rack-scale ODMs build their own cabinets in-house rather than sourcing a separate enclosure, which is notable because it signals how much of the rack's value they're capturing beyond just tray assembly. AI racks change the mechanical design problem in a few ways: liquid-cooling plumbing has to be routed through the chassis alongside cabling, and higher tray weight from liquid-cooling hardware and denser boards puts more load on the rails and enclosure frame than a conventional air-cooled server rack does. For investors, this tier is largely a mechanical-engineering and manufacturing-scale business rather than a technology one, so the interesting split is between the enclosure makers who sell a general-purpose cabinet platform across many customers and the ODMs who build cabinets in-house specifically to keep that margin themselves.

ai_delta:
Liquid-cooling plumbing routed through the chassis, plus the added weight of cold plates, manifolds and denser boards, puts more mechanical load on rails and enclosures than a conventional air-cooled server rack requires.

scale_notes:
mechanical demands rise with tray weight and plumbing complexity as density increases, even though the part category itself is present at every scale.


id: 4.15
slug: in-rack-busbar-and-power-whips
name: In-rack busbar & power whips
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
notes: Nvidia-spec ±48 V / 1,400 A busbar
summary:
The high-current busbar and cabling that carries power from the rack's power shelves to each tray, including Nvidia's specified ±48 V / 1,400 A busbar used in rack-scale liquid-cooled systems.

description_md:
Busbar and power whips are the last stretch of power distribution inside the rack, carrying current from the power shelves (4.12) to each tray's own board-level power delivery (4.08). A solid or laminated busbar running vertically through the rack, rather than a bundle of individual cables, is what rack-scale systems use to move very high current at the ±48 V level the taxonomy specifies for NVL72-class racks; a bundle of discrete power whips remains the more conventional approach outside that specific architecture. The shift toward busbar reflects how much current a fully populated GPU rack now needs to distribute internally — a level that individual cables handle poorly at scale. For investors, this is a component defined by its current-carrying and thermal-management engineering rather than by exotic materials, and the suppliers building to the Nvidia-spec busbar are working from a reference design that's likely to be revised with each rack generation, which keeps requalification a recurring event rather than a one-time qualification.

ai_delta:
Fully populated GPU racks need to move more current internally than a bundle of discrete cables handles well, which is why rack-scale systems increasingly use a solid busbar rather than conventional power whips.

scale_notes:
the high-current ±48 V busbar architecture is specific to rack-scale liquid-cooled systems at hyperscale and above; smaller enterprise racks more often use conventional power whips.


id: 4.16
slug: rack-pdus
name: Rack PDUs
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: compute|power
summary:
The power distribution units mounted in or beside a rack that meter and distribute power to individual devices, sitting between facility power and the rack's internal busbar or whips.

description_md:
Rack PDUs are the metering and distribution point between a rack's facility-side power feed (layer 7) and its internal power shelves or busbar (4.12, 4.15); in a rack-scale AI system this is often a higher-capacity unit than a conventional enterprise rack PDU because it has to feed a system already drawing far more continuous power. PDUs also increasingly carry monitoring and remote-management functions that plug into rack and facility management software, letting operators track power draw at the rack or even the outlet level, which matters more as power becomes the binding constraint on how many racks a given facility can support. For investors, this is a mature, established product category rather than a technology bottleneck, so growth here mostly tracks rack count and rack power density rather than any change in the underlying part itself; the metering and management layer that increasingly comes bundled with a PDU is a smaller, secondary differentiator worth watching.

ai_delta:
As power becomes the binding constraint on how many racks a facility can support, rack-level and outlet-level metering on the PDU becomes more operationally important than in a lower-density, less power-constrained enterprise deployment.

scale_notes:
present at every scale; capacity per unit and the value of built-in metering both rise with rack power density.


id: 4.17
slug: air-cooled-heatsinks-and-vapor-chambers
name: Air-cooled heatsinks & vapor chambers
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: air|hybrid
flow: compute|power
summary:
The finned aluminum or copper heatsinks and vapor chambers that spread and dissipate heat from chips that rely on airflow (4.13) rather than a liquid cold plate.

description_md:
Heatsinks and vapor chambers sit directly on chips that aren't covered by a liquid cold plate (layer 5.01) — memory, networking silicon, VRMs, and, in air-cooled server configurations, the CPU or GPU itself — spreading heat across a larger surface area for fans (4.13) to carry away. As rack-scale AI systems move the highest-power chips to liquid cooling, this part's role narrows to the components liquid cooling doesn't reach and to the segment of the market still running air-cooled GPU servers rather than rack-scale liquid systems. That narrowing role doesn't mean the part disappears — it means the design problem shifts toward smaller, more thermally demanding heatsinks squeezed into a chassis that also has to route liquid-cooling plumbing, rather than large heatsinks free to use as much airflow and space as needed. For investors, this tier's growth is more tied to the air-cooled and hybrid segment of the server market than to the highest-end liquid-cooled racks, so its trajectory diverges somewhat from the rest of this layer as liquid cooling adoption increases.

ai_delta:
As the highest-power chips in a rack move to liquid cooling, heatsinks and vapor chambers narrow to the components liquid cooling doesn't reach, shrinking in scope even as they get squeezed into more crowded, liquid-plumbed chassis.

scale_notes:
shrinks in scope per tray as liquid cooling covers more of the heat load, but remains present for the components liquid cooling doesn't reach, even at the highest density tiers.


id: 5.01
slug: direct-to-chip-cold-plates
name: Direct-to-chip cold plates
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: heat
summary:
A metal plate with internal microchannels, bonded or clamped directly onto a GPU, CPU, or switch package, that carries coolant across the die to remove heat by conduction rather than airflow.

description_md:
The cold plate is the first thermal interface in the loop: coolant enters through a quick disconnect, flows across channels machined or etched into the plate above the silicon, and exits carrying the heat toward the rack manifold. Every accelerator, CPU, and switch die on a liquid-cooled tray gets its own plate, so the part is qualified against a specific chip package and reference design rather than sold as a generic component. That qualification tie is the main structural feature investors should track: a plate design that wins a reference socket tends to stay for the life of that platform, and switching vendors mid-cycle means re-validating thermal performance against the die. AI GPUs run hotter and denser than prior server chips, which is why liquid cold plates displaced air heatsinks as the default rather than an option, and why two-phase and microfluidic variants are being developed for the next step up in power density. Some rack integrators build cold plates in-house for their own designs, which narrows the market available to independent cold-plate makers even as unit volume grows. Watch for supplier concentration at the top GPU platforms and how much of the volume in-house integrators keep for themselves versus outsource.

ai_delta:
Rising per-die power density pushed liquid cold plates from a niche option to the default thermal interface on AI accelerator packages, direct-to-chip cooling being the entry point for the two-phase and microfluidic variants under development for the next density step.

scale_notes:
Present at any scale that runs liquid-cooled compute; count per rack tracks GPU, CPU, and switch die count rather than facility size.


id: 5.02
slug: rack-manifolds-cdms
name: Rack manifolds
qualifiers: CDMs
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: heat
summary:
The manifold (sometimes called a CDM) that distributes coolant from the CDU supply line to each cold plate in a rack and collects the return flow, usually mounted vertically at the rear or side of the rack.

description_md:
The manifold sits between the CDU's supply and return lines and the individual trays: it splits one large coolant flow into the branch lines that feed each cold plate's quick disconnects, then recombines the return flow for the trip back to the CDU. Because it touches every tray in the rack, the manifold's port count, spacing, and pressure rating are set by the rack's specific tray layout and GPU count, which ties the part to a reference design in the same way cold plates are. As racks pack more accelerators per U and run hotter, manifolds need more ports, higher flow rates, and tighter tolerances on the quick disconnect interfaces at each branch — a manifold designed for a lower-density generation does not simply carry over. Investors should watch how much of this part rack integrators build in-house for their own designs versus source from independent thermal vendors, and whether manifold makers also supply the CDU and cold plate to capture more of the loop.

scale_notes:
Sized to rack tray count and GPU density rather than facility scale; higher-density racks need more ports and higher flow rates per manifold.


id: 5.03
slug: quick-disconnects-uqd-uqdb
name: Quick disconnects
qualifiers: UQD / UQDB
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
scale_notes: Count per rack scales directly with the number of liquid-cooled trays and cold plates.
flow: heat
summary:
Blind-mate connectors (UQD or UQDB) that let a compute tray or cold plate be connected to or disconnected from the coolant manifold without draining or spilling the loop.

description_md:
Quick disconnects let a technician pull a single compute tray for service while the rest of the rack's coolant loop stays pressurized and sealed — a dripless, blind-mate connection at each branch off the manifold. Because a leak or a failed seal at this joint can damage the electronics it is meant to cool, the connector is qualified to tight tolerances against the specific manifold and cold plate it mates with, and reliability under repeated connect/disconnect cycles matters as much as flow rate. As liquid cooling moved from a minority of racks to the default configuration for AI accelerator trays, the number of these connectors per rack scaled with GPU count, and blind, dripless mating became a baseline requirement rather than a premium feature. Investors should watch qualification cycles at the connector level: a design that passes reference-design testing with a given GPU platform tends to be locked in for that platform's life, and few vendors carry the combination of sealing performance and manufacturing scale to serve hyperscale volumes.

ai_delta:
Liquid cooling moving from optional to default on AI accelerator trays scaled the number of blind-mate quick disconnects per rack with GPU count.


id: 5.04
slug: hoses-tubing-fittings-valves
name: Hoses, tubing, fittings, valves
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: heat
summary:
The flexible hoses, rigid tubing, compression and push-fit fittings, and shutoff or balancing valves that carry coolant between manifolds, quick disconnects, and the CDU.

description_md:
These are the plumbing runs that connect the parts around them: hoses and tubing carry coolant from the CDU to the rack manifold and between branch points, fittings join runs of different diameter or material, and valves isolate or balance flow to individual racks or rows. It is a lower-differentiation part of the loop than the cold plate or CDU, but it still has to hold pressure, resist the specific coolant chemistry in use, and avoid permeation or degradation over the life of the deployment — a hose or fitting failure downstream of a manifold can take out multiple trays at once. Demand for this part scales with the total length of liquid-cooling loops being installed, which follows the number of liquid-cooled racks rather than any single design win. Investors should watch material and chemical compatibility requirements as coolant chemistries diversify (glycol-water blends, dielectric fluids, two-phase refrigerants), since not every hose or valve material works across all of them.

scale_notes:
Volume tracks total loop length installed, which scales with the count of liquid-cooled racks and rows rather than facility footprint alone.


id: 5.05
slug: coolant-distribution-units-in-rack-in-row
name: Coolant distribution units
qualifiers: in-rack, in-row, 1–2.5 MW+ facility units
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: heat
summary:
The unit that separates the IT-side (technology) coolant loop from the facility water loop, exchanging heat between them and pumping and filtering the technology-side flow — sized from in-rack and in-row units up to multi-megawatt facility-class units.

description_md:
The CDU is the boundary of Layer 5: everything upstream (cold plates, manifolds, quick disconnects, hoses) is the technology-side loop it feeds, and everything downstream (chillers, cooling towers, the facility water loop) belongs to Layer 6. Inside, a plate heat exchanger keeps the clean, controlled technology-side coolant physically separate from facility water, while pumps circulate the technology-side flow and filters protect the cold plates from particulates. CDUs come in three form factors — in-rack units serving a single rack, in-row units serving several racks, and facility-class units in the 1–2.5 MW-plus range serving a whole row or hall — and the choice among them is a design decision driven by rack density and facility layout rather than a strict scale progression. As GPU racks moved from air to liquid cooling, CDU capacity requirements rose sharply, and controls (temperature setpoints, redundant pumps, leak response) became as important as the mechanical design. Investors should watch which vendors can scale from smaller in-rack or in-row units to facility-class CDUs, since that is a different engineering and manufacturing problem, and note that some server integrators build CDUs in-house for their own rack designs.

ai_delta:
The shift of AI accelerator racks from air to liquid cooling pushed CDU capacity requirements up sharply and pulled facility-class (1–2.5 MW-plus) units into more deployments alongside smaller in-rack and in-row units.

scale_notes:
Form factor is a design choice, not a strict scale tier: in-rack and in-row units suit smaller or mixed deployments, while facility-class (1–2.5 MW-plus) units suit large, liquid-dense halls.


id: 5.06
slug: cdu-internals-plate-heat-exchangers-pumps-filters
name: CDU internals: plate heat exchangers, pumps, filters
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: heat
summary:
The internal components of a CDU — plate heat exchangers, pumps, and filters — that do the actual heat exchange, circulation, and particulate control inside the unit.

description_md:
These are the working parts inside the CDU housing described in 5.05: the plate heat exchanger transfers heat from the technology-side loop to the facility water loop without mixing the two fluids, pumps circulate coolant through the cold plates and back, and filters keep particulates out of the narrow channels in the cold plates and manifolds upstream. Pump and heat-exchanger sizing scales with the CDU's rated capacity, from in-rack units up to facility-class systems, and redundancy (multiple pumps, N+1 configurations) is a common design response to the fact that a pump failure can take an entire loop offline. Some of these component makers also supply plate heat exchangers and pumps into the facility water loop in Layer 6, so the same vendors can appear on both sides of the chiller-to-chip chain. Investors should watch pump reliability and redundancy design as CDU capacity rises, and whether component vendors are captured by CDU integrators or continue to sell into the broader market.

scale_notes:
Component sizing and redundancy (single pump versus N+1) scale with the CDU's rated capacity, from in-rack units to facility-class systems.


id: 5.07
slug: rear-door-heat-exchangers
name: Rear-door heat exchangers
scale_tiers: enterprise|hyperscale_building
density_modes: hybrid
flow: heat
summary:
A heat exchanger mounted on the rear door of a server rack that cools exhaust air with chilled water or refrigerant before it leaves the rack, without cooling the chips directly.

description_md:
The rear-door heat exchanger is an air-assist approach rather than a direct-to-chip one: it sits behind a rack of otherwise air-cooled servers and pulls heat out of the exhaust air using a chilled-water or refrigerant coil before that air re-enters the room, rather than cooling the die directly the way a cold plate does. It is typically deployed where a facility wants to raise the density it can support without redesigning every server for direct liquid cooling, or as a supplement in racks that mix liquid-cooled accelerators with air-cooled networking and storage. Because it works at the rack-exhaust level rather than the die level, it has a lower cooling ceiling than a direct-to-chip loop and is generally positioned as a bridge or hybrid option rather than the primary cooling path for the highest-density AI racks. Investors should watch it as a hybrid-density play: demand depends on how much of the installed base stays on air or mixed cooling rather than converting fully to direct-to-chip.

scale_notes:
Used as a hybrid or bridging measure in mixed air/liquid racks or facilities not built for full direct-to-chip cooling; less common at the highest-density AI campus and GW-class builds, which favor direct-to-chip.


id: 5.08
slug: immersion-cooling-systems
name: Immersion cooling systems
scale_tiers: enterprise|hyperscale_building|ai_campus
density_modes: liquid
flow: heat
summary:
Systems that submerge servers or trays directly in a dielectric fluid, either in open tanks (single-phase) or sealed chambers where the fluid boils and recondenses (two-phase), to cool the whole tray rather than individual dies.

description_md:
Immersion cooling replaces the cold-plate-and-manifold chain entirely: instead of routing coolant to each die through a plate, the whole server tray sits in a bath of dielectric fluid that carries heat away by direct contact. Single-phase systems keep the fluid liquid throughout and rely on pumps and heat exchangers to move heat out of the tank; two-phase systems let the fluid boil at the hot surfaces and recondense at a coil, using the phase change itself to move heat more efficiently. It is a different mechanical and facility design from the rack-based direct-to-chip chain used elsewhere in this layer — servers and racks built for immersion need different chassis, connectors, and serviceability approaches, and retrofitting an existing air-cooled or direct-to-chip hall for immersion is a bigger undertaking than adding cold plates. Adoption has concentrated in specific use cases rather than becoming the default alongside direct-to-chip. Investors should watch it as a higher-conviction, narrower bet than direct-to-chip cooling: fewer vendors, less standardization, and adoption tied to specific operators and use cases rather than broad industry uptake.

scale_notes:
Deployment depends on a design choice to build for immersion rather than direct-to-chip; where used, tank or chamber count scales with server count rather than facility power tier.


id: 5.09
slug: fluids-and-coolant-treatment
name: Fluids & coolant treatment
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid
flow: heat
summary:
The coolant fluids that circulate through direct-to-chip and immersion loops, plus the chemical treatment (inhibitors, biocides) that keeps them stable over the life of the loop.

description_md:
Three fluid families cover this part: single-phase dielectric fluids used mainly in immersion tanks; two-phase fluids that rely on evaporation and recondensation for either immersion or emerging two-phase cold-plate systems; and glycol-water blends with corrosion and biological inhibitors used in conventional direct-to-chip loops feeding cold plates and manifolds. Fluid choice is tied to the cooling architecture chosen upstream — a rack built for direct-to-chip cold plates uses a different fluid than an immersion tank — so this part depends on decisions made at 5.01 and 5.08. The category is in the middle of a real supply shift: one major two-phase and dielectric fluid producer is exiting part of its product line at the end of 2025 for PFAS-related reasons, which pushes qualification work toward the remaining suppliers and toward newer entrants developing alternative chemistries. Investors should watch how much of the exiting volume gets absorbed by other established chemical makers versus smaller, newer entrants, and whether operators standardize on a narrower set of approved fluids as PFAS scrutiny continues.

ai_delta:
The AI-driven shift of racks to direct-to-chip and immersion cooling created new, coolant-specific demand for glycol-water and dielectric fluid chemistries just as a major two-phase and dielectric fluid producer exits part of its line for PFAS-related reasons at the end of 2025.

scale_notes:
Present wherever liquid cooling is deployed; fluid family depends on whether the design uses direct-to-chip cold plates or immersion tanks.


id: 5.10
slug: leak-detection-flow-and-pressure-sensing
name: Leak detection, flow & pressure sensing
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: heat
summary:
Sensors and systems that detect coolant leaks and monitor flow rate and pressure throughout the liquid-cooling loop, feeding alerts to rack and row controls.

description_md:
Because a leak in a direct-to-chip loop sits centimeters from live electronics, leak detection is a safety-critical layer that runs alongside the physical plumbing described elsewhere in this section: cable- or rope-style sensors laid along cold plates and manifolds detect moisture at the point of a leak, while flow and pressure sensors at the CDU and manifold level watch for the drop in flow or pressure that signals a blockage or a failed connection. These feed into the rack/row cooling controls (5.11) that can shut off flow to an affected branch or alert facility staff before a small leak becomes tray-level or rack-level damage. As liquid cooling scaled from pilot deployments to the default configuration for AI racks, the number of monitored points per rack rose with the number of quick disconnects and manifold branches it has to cover. Investors should watch this as a volume-driven category tied to liquid-cooled rack count rather than a design-differentiated one, though reliability and false-alarm rates differentiate vendors competing for hyperscale-scale deployments.

ai_delta:
Liquid cooling becoming the default configuration for AI racks raised the number of leak, flow, and pressure monitoring points needed per rack, tracking the growth in quick disconnects and manifold branches.

scale_notes:
Monitored point count scales with the number of quick disconnects and manifold branches in the rack, which itself tracks liquid-cooled GPU density.


id: 5.11
slug: rack-row-cooling-controls-and-software
name: Rack/row cooling controls & software
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: heat
summary:
Software and controllers that manage coolant setpoints, pump speed, and valve positions at the rack and row level, and that tie into facility-wide data center infrastructure management (DCIM) systems.

description_md:
This is the control layer sitting on top of the physical loop: it reads the flow, pressure, and leak sensors described in 5.10, adjusts CDU pump speed and valve positions in response, and coordinates cooling across racks and rows rather than treating each CDU as an isolated unit. It also feeds data up into facility-wide DCIM software, so operators can see rack-level thermal and coolant status alongside power and IT load. As liquid cooling moved from a handful of specialized racks to the default configuration across a hall, coordinating many CDUs and thousands of leak and flow sensors became a software problem as much as a mechanical one, particularly for facilities that mix direct-to-chip, rear-door, and immersion cooling on the same floor. Vendors that supply the physical CDU or manifold hardware often bundle their own controls software, which raises the question of how much of this category is captured by hardware incumbents versus independent software providers. Investors should watch the degree of integration with broader DCIM platforms as a signal of which vendors are positioned to expand beyond point control into facility-wide software.

scale_notes:
Complexity rises with the number of CDUs, racks, and sensor points coordinated together rather than with facility size alone; larger halls push controls toward row- and facility-level coordination rather than per-rack management.


id: 6.01
slug: chillers-and-compressors
name: Chillers & compressors
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: heat
summary:
Chillers use a vapor-compression or absorption cycle, built around a compressor, to produce chilled water for the facility's cooling loop.

description_md:
Chillers sit at the facility side of the cooling chain, producing the chilled water that circulates through CRAH units (6.03) and, for liquid-cooled halls, through the facility water loop (6.05) toward the CDU interface described in Layer 5. Chiller type — centrifugal, screw, magnetic-bearing, or absorption — governs efficiency at part load, which matters more for data centers than for typical buildings because IT load runs near-constant rather than swinging with occupancy or weather. Densification pushes plants toward larger, fewer chiller blocks and toward warmer chilled-water setpoints that stretch the hours dry coolers (6.02) can carry the load without compressors engaging at all. Compressors are the component most exposed to broader HVAC demand: large chiller orders queue behind commercial and industrial cooling demand as well as data center demand, so lead time is a scheduling variable in its own right. Investors should watch order backlogs at the major chiller OEMs, how much of a campus's cooling load compressors actually carry versus free-cooling hours, and the split between mechanical-first and adiabatic-first plant designs.

ai_delta:
AI training and inference loads keep chiller plants at sustained near-full utilization instead of the intermittent duty conventional HVAC chillers are sized for, favoring larger, efficiency-optimized plants and pulling chiller lead times into the same order queue as other heavy mechanical and electrical equipment.

scale_notes:
N+1 chiller redundancy at enterprise scale, shifting to 2N or fully parallel chiller blocks with independent compressor trains at hyperscale and gw-class campuses.


id: 6.02
slug: dry-coolers-adiabatic-evaporative-units-cooling-towers-fans-and
name: Dry coolers, adiabatic/evaporative units, cooling towers, fans & motors
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: air|hybrid
flow: heat
summary:
Dry coolers, adiabatic/evaporative units, and cooling towers reject heat picked up by the facility water loop to outside air, driven by fans and motors.

description_md:
This equipment sits at the end of the facility cooling chain, rejecting heat that chillers (6.01) concentrated or, in free-cooling designs, heat drawn straight off the facility water loop (6.05). The choice among dry (air-only), adiabatic (evaporative-assisted), and wet cooling-tower designs trades water consumption against fan energy and footprint, a tradeoff drawing more scrutiny as campuses grow and site water rights come under pressure — see water treatment and reuse (6.06). AI campuses' round-the-clock, weather-insensitive load makes free-cooling hours more valuable than in a conventional building, pushing designs toward larger dry-cooler and adiabatic arrays that can carry more of the year without compressors. Fans and motors are a distinct, high-count sub-component here, sized for continuous duty and increasingly specified with variable-speed drives for part-load efficiency. Investors should track cooling-tower and dry-cooler capacity backlogs alongside chillers, and the split between water-intensive and water-free designs on new campuses, since that choice reshapes both the bill of materials and the number of large fans a given site needs.

ai_delta:
Round-the-clock AI load raises the number of full-load hours this equipment runs and makes water-free or low-water dry and adiabatic designs more valuable at sites where water rights or drought risk constrain wet cooling towers.

scale_notes:
Enterprise sites often run a handful of packaged units; hyperscale and campus-scale builds move to arrays of large dry coolers or cooling-tower cells sized in parallel blocks.


id: 6.03
slug: crah-crac-fan-walls-in-row-units
name: CRAH / CRAC / fan walls / in-row units
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: air|hybrid
flow: heat
summary:
CRAH, CRAC, fan-wall, and in-row units circulate conditioned air across IT equipment in air-cooled halls, using either facility chilled water (CRAH) or an internal refrigerant circuit (CRAC).

description_md:
CRAH units draw chilled water from the facility loop (6.05) and blow conditioned air across server intake; CRAC units carry their own compressor instead of relying on facility chilled water. Fan walls and in-row units are denser evolutions of the same function, pushing more air per rack as air-cooled density rose before designs crossed over to liquid cooling. As AI racks move chip heat into liquid loops (Layer 5), CRAH capacity does not disappear — it typically shrinks to cover the residual air-cooled heat load per rack (networking, storage, lower-density equipment) plus emergency and backup air cooling, even in halls that are mostly liquid-cooled. Where a facility stays fully air-cooled, CRAH and fan-wall counts still scale directly with heat load. Investors should watch the shift in mix between CRAH-served air cooling and liquid-side CDUs as the main determinant of demand here, and how much redundant CRAH capacity operators retain per rack even in liquid-first designs.

ai_delta:
Liquid cooling absorbs most of the chip heat load in AI racks, so CRAH and fan-wall demand per rack falls even as total site CRAH capacity keeps growing to serve the residual air-cooled load and act as backup.

scale_notes:
Sole cooling method at air-cooled enterprise and hyperscale halls; shrinks to a residual and backup role per rack once a site is liquid-cooling-first at ai_campus and gw_class scale.


id: 6.04
slug: air-handling-economizers-filtration
name: Air handling, economizers, filtration
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: air|hybrid
flow: heat
summary:
Air-handling units, outside-air economizers, and filtration equipment that condition and clean the air supplied to air-cooled data center halls.

description_md:
Works alongside CRAH and CRAC units (6.03) to bring in and treat air — economizers use outside air directly when ambient conditions allow, cutting mechanical cooling hours, while filtration protects electronics from particulates and, in some locations, gaseous contamination. Economizer-heavy designs are the air-side equivalent of the "free cooling" strategy in the chilled-water loop that dry coolers enable (6.02): both trade on the same logic that more hours without compressors lowers energy draw and the load chillers carry. As AI halls push chip heat into liquid loops instead, the marginal role of air handling narrows toward conditioning the building shell and the residual air-cooled equipment rather than carrying the full IT heat load, though cleanliness filtration standards generally hold regardless of cooling method. Investors should watch how economizer hours and air-handling scope get resized in liquid-first designs versus legacy air-cooled fleets.

ai_delta:
As liquid cooling takes over the chip heat load, air handling and economizer capacity is increasingly sized to the building envelope and residual equipment rather than to full IT load, shrinking its role relative to the facility water loop.

scale_notes:
Sized to the full IT heat load at air-cooled enterprise and hyperscale sites; shrinks to a shell-conditioning and residual-load role once liquid cooling dominates at ai_campus and gw_class.


id: 6.05
slug: facility-water-loop-hardware-pumps-valves
name: Facility water loop hardware
qualifiers: pumps, valves, piping, heat exchangers, tanks
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: heat
summary:
The pumps, valves, piping, plate heat exchangers, and thermal-storage tanks that move and buffer water between chillers, heat-rejection equipment, and the CRAH or CDU interfaces on the IT side.

description_md:
This is the connective tissue of facility cooling — the primary, secondary, and (at scale) tertiary water loops that carry heat from the IT space, via CRAH coils or the CDU heat exchanger described in Layer 5, out to chillers (6.01) and ultimately to dry coolers or cooling towers (6.02). Pumps (6.05a) move the water, valves and actuators (6.05b) modulate flow and isolate zones, piping (6.05c) carries it, plate heat exchangers (6.05d) isolate one loop from another the way a CDU isolates a rack loop from the facility loop, and tanks and thermal storage (6.05e) buffer against a pump or chiller trip and can shave peak cooling demand. Liquid-cooled AI campuses typically add a distinct, often higher-temperature facility loop on top of any legacy chilled-water loop rather than replacing it, effectively duplicating this hardware category. Investors should watch loop redundancy design — N+1 pumps and valves versus fully parallel loops at gw-class scale — and how much incremental piping and heat-exchanger capacity liquid-cooling retrofits add to sites originally built for air cooling.

ai_delta:
Liquid-cooled AI racks add a second, often higher-temperature facility water loop to carry chip heat out to the CDU interface, increasing the pumps, piping, and heat-exchanger capacity a site needs beyond what an air-cooled building of the same size would require.

scale_notes:
Single loop with N+1 pumps at enterprise scale; parallel or 2N loops, with a dedicated liquid-cooling-side loop layered on, at ai_campus and gw_class.


id: 6.06
slug: water-treatment-reuse-consumption
name: Water treatment, reuse, consumption
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: heat
notes: municipal supply and reuse partners are site-specific.
summary:
Chemical treatment, filtration, and reuse systems that condition make-up water and manage the water a facility's cooling system consumes or discharges.

description_md:
Applies to both the closed facility water loop (6.05) and, where wet cooling towers are used, the open evaporative loop (6.02): treatment prevents scale, corrosion, and biological fouling in closed loops, while reuse and consumption management address the water lost to evaporation in wet heat rejection. Data center water consumption has become a public and regulatory flashpoint as campuses grow, pushing some operators toward water-free (dry, air-cooled) heat rejection at a cost premium and others toward reclaimed or non-potable water sources that cut fresh-water draw without giving up evaporative cooling's efficiency. Municipal supply and reuse arrangements are inherently site-specific, so the investable exposure sits less in who controls a given site's water and more in the vendors of treatment chemistry, membranes, and reuse or reclaim equipment that let a wet-cooled site use less fresh water per unit of heat rejected. Investors should watch permitting timelines tied to water use as a growing schedule risk alongside the power-side bottlenecks in Layers 7 and 8.

ai_delta:
AI campuses' scale and continuous operation have made cooling-water consumption a public and regulatory issue in a way conventional data centers rarely triggered, pushing sites toward reclaimed or reuse water, treatment that extends closed-loop cycles, or water-free heat rejection.

scale_notes:
Basic loop water treatment exists at every scale; dedicated reuse and reclaim infrastructure, and consumption permitting, become material design and site-selection factors at ai_campus and gw_class.


id: 6.07
slug: heat-reuse-and-district-heating-interfaces
name: Heat reuse & district heating interfaces
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: liquid|hybrid
flow: heat
summary:
Heat exchangers and interconnection equipment that export waste heat from a data center's cooling loop into a district heating network or other end use instead of rejecting it to atmosphere.

description_md:
Taps the facility water loop (6.05) — typically its warmer, IT-heat-carrying side — and routes it through a heat exchanger, and where the reuse temperature needs boosting, a large heat pump, into a district heating network or process-heat customer, displacing some of the heat-rejection equipment (6.02) a site would otherwise need. Adoption is concentrated where district heating networks already exist and local economics or regulation favor it, so it remains a minority design choice rather than a default. Higher-temperature liquid-cooling loops in AI racks produce warmer reject water than legacy air-cooled facilities, which is exactly the direction that makes heat reuse more thermodynamically attractive, since a heat pump has less work to do raising an already-warm source to district-heating temperature. Investors should treat this as a niche but growing interconnection market tied to specific geographies with district heating infrastructure, not a broad-based demand driver across the layer.

ai_delta:
Liquid-cooled AI racks run their facility loop hotter than legacy air-cooled halls, producing waste heat closer to the temperature district heating networks need and lowering the heat-pump work required to reuse it.

scale_notes:
Presence depends on site-specific access to a district heating network or heat customer rather than on facility size alone; more thermodynamically attractive at ai_campus and gw_class sites running hotter liquid-cooled loops.


id: 6.08
slug: building-management-controls-environmental-sensors
name: Building management, controls, environmental sensors
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: heat
summary:
The building management system, PLCs, and environmental sensors that monitor and automatically control chillers, air handlers, pumps, and dampers across the facility.

description_md:
Sits above the individual cooling equipment in 6.01–6.07 as the control layer that sequences chillers, modulates valve and fan speed, and enforces setpoints, feeding data up into the site's broader DCIM software (Layer 11). PLCs (6.08a) execute the low-level control loops; environmental sensors (6.08b) supply the temperature, humidity, pressure, and flow data those loops act on; and a newer category of AI-driven cooling control software (6.08c) layers optimization on top of the conventional BMS to trim energy use and hold tighter setpoints than a rules-based system would risk. Because cooling is a meaningful share of a data center's non-IT power draw, even modest optimization gains compound at scale, which is what has drawn a wave of specialist AI-controls vendors into this niche alongside the incumbent building-automation platforms. Investors should watch whether the AI-driven optimization layer gets built by incumbents, acquired by them, or stays independent, and how tightly control-layer contracts get bundled with the mechanical equipment itself.

ai_delta:
Continuous, high-density AI loads leave less thermal margin for error than conventional buildings, which has pulled a new category of AI-driven optimization software into the control layer to hold tighter setpoints without tripping the cooling plant.

scale_notes:
Present at every scale; sophistication rises from a basic BMS at enterprise to AI-driven, multi-loop optimization layered over PLCs at ai_campus and gw_class.


id: 6.09
slug: fire-detection-and-suppression
name: Fire detection & suppression
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: heat
summary:
Smoke and heat detection systems and fire suppression equipment — agents, piping, and discharge nozzles — that protect the data hall and electrical rooms.

description_md:
Covers early-warning detection (6.09a), including aspirating smoke detection that can sense a fault before visible smoke appears, and suppression hardware (6.09b) that discharges an extinguishing agent (6.09c) into a data hall or electrical room without damaging electronics — which rules out water-based suppression in the white space in favor of clean gaseous or inert-gas agents. The agent side of this category has been in flux: 3M's Novec 1230 clean agent was discontinued at the end of 2025, pushing suppression-system designers toward alternative fluorinated agents or inert-gas systems for new builds and retrofits. Liquid cooling adds electrical and coolant-leak risk in new places — manifolds, CDUs — that detection and suppression design has to account for alongside the traditional IT-rack and electrical-room risk. Investors should watch which agent chemistry backfills the Novec gap and how much of that transition flows through to system redesigns rather than like-for-like agent swaps.

ai_delta:
Higher power density and the added electrical and coolant-leak surface of liquid cooling widen what detection and suppression systems need to cover per rack, while the discontinuation of 3M's Novec 1230 clean agent forces new designs onto alternative agents.

scale_notes:
Present at every scale as a code requirement; zoning and agent quantity scale with hall size and, for liquid-cooled sites, with added coverage for manifold and CDU areas.


id: 7.01
slug: large-power-transformers-345-765-kv-and-substations
name: Large power transformers (345–765 kV) & substations
ai_delta: lead times of 2–4 years; the single biggest schedule risk for GW campuses.
bottleneck_status: constrained
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
Grid-scale transformers and switchyards that step transmission voltage down to what a data center campus can use, plus the substation civil works around them.

description_md:
The substation and its transformers are the physical interconnect between the utility transmission grid and the campus, converting transmission voltage down to the range that HV switchgear and downstream MV distribution can handle. Everything inside the fence — HV switchgear, MV switchgear and E-houses, transformers all the way down to the rack — depends on this equipment being on site and energized before load can be added. Because a large power transformer is a custom-engineered, long-lead unit built to a specific voltage, MVA, and impedance spec, and because global manufacturing capacity for the core steel and windings involved is tight, transformer availability rather than construction speed increasingly sets the pace at which a campus can bring capacity online. For investors, the things to watch are supplier order books and backlog length, how much manufacturing capacity incumbents are adding versus how much new entrants can qualify, and whether utilities or developers begin pre-ordering transformers speculatively ahead of a firm interconnection to protect their place in the queue. Substation equipment also tends to require utility-grade qualification and testing, which limits how quickly a new supplier can be brought in even once it has capacity.

scale_notes:
Present once a campus is large enough to warrant a dedicated substation; enterprise and most hyperscale-building sites draw from existing utility service instead.


id: 7.02
slug: hv-switchgear-gis-breakers-protection-relays
name: HV switchgear, GIS, breakers, protection relays
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
The high-voltage switching, protection, and fault-isolation gear between the substation transformers and the campus's medium-voltage distribution, including gas-insulated (GIS) designs for compact sites.

description_md:
HV switchgear does the actual switching and fault protection at transmission-adjacent voltages, working alongside the transformers in 7.01 to get power safely onto the campus's medium-voltage backbone (7.03, 7.04). Breakers interrupt fault current, protection relays detect faults and command the breakers, and gas-insulated switchgear (GIS) packages the same function into a sealed, compact footprint useful where land or setback rules constrain a substation; air-insulated switchgear remains the default where space is not the binding constraint. Like the transformers next to it, HV switchgear is a custom-engineered, utility-qualified product line, so it shares the same order-book dynamics: a campus that orders several transformers at once is also ordering matching breaker bays and relay panels, and the industry that has long supplied utilities is now also supplying data centers competing for the same slots. A shift toward SF6-free breaker designs, driven by regulatory pressure on the potent greenhouse gas SF6, adds a qualification cycle on top of ordinary lead time as buyers weigh proven SF6 designs against newer alternatives. For investors, the relevant signals are the same backlog and capacity-expansion metrics as transformers, plus how fast SF6-free designs get qualified by utilities and hyperscalers, since that transition could reshuffle who wins new orders.

ai_delta:
AI campuses ordering multiple large transformers at once are also multiplying HV switchgear and breaker orders, pulling on the same protection-relay and GIS supply chains already stretched by grid buildout.

scale_notes:
Tracks the presence of a dedicated substation; not present at enterprise or typical hyperscale-building scale where utility-owned equipment stops upstream of the site.


id: 7.03
slug: mv-switchgear-e-houses-power-skids
name: MV switchgear, E-houses, power skids
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Medium-voltage switchgear packaged into pre-fabricated electrical houses (E-houses) or skids that distribute power across a campus and can be factory-built and shipped as modules.

description_md:
MV switchgear takes power handed off from the HV side (7.02) or from step-down transformers (7.04) and distributes it across a campus's buildings and yards. Packaging that switchgear into an E-house — a pre-fabricated, walk-in enclosure built and tested off-site — or a smaller power skid lets it be manufactured in parallel with civil construction and craned into place largely finished, which shortens the critical path compared with building an electrical room on site. This modularity has made E-houses a default choice for large, fast-moving campuses even though it adds a factory step and a shipping and rigging logistics chain that a stick-built room does not need. Downstream, an E-house typically feeds MV/LV transformers and LV switchgear serving individual halls or buildings, so its capacity and the number of feeder positions it is built with set a ceiling on how much can be added later without a new E-house. Investors should watch factory capacity and backlog at the firms that build E-houses and skids, since that fabrication step — not the switchgear itself — is often the binding constraint, along with how much of the enclosure and integration work incumbents keep in-house versus outsource.

ai_delta:
E-houses and power skids let a campus add pre-fabricated MV distribution capacity in parallel with site construction rather than waiting on stick-built electrical rooms, and AI campus buildout is now testing how fast factories can turn them around.

scale_notes:
Enterprise sites typically use simpler, non-modular MV switchgear rooms rather than E-houses; the pre-fabricated form factor becomes standard once a campus is adding capacity in repeatable blocks.


id: 7.04
slug: mv-lv-transformers-pad-mount-dry-type
name: MV/LV transformers
qualifiers: pad-mount, dry-type, cast-resin
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
scale_notes: Present at every scale; only the count and size of units per site changes.
flow: power
summary:
Step-down transformers that take medium-voltage distribution down to the low voltage used inside a building, available in pad-mount (outdoor, oil-filled) and dry-type or cast-resin (indoor) designs.

description_md:
These transformers sit between MV switchgear or E-houses (7.03) and LV switchgear and panelboards (7.05), doing the final voltage step-down before power reaches switchboards, busway, and PDUs. Pad-mount, oil-filled units are typically used outdoors or at building perimeters, while dry-type and cast-resin designs go indoors, including inside electrical rooms and E-houses, where the absence of oil simplifies fire code compliance. Unlike the large power transformers in 7.01, these are a higher-volume, more standardized product with a shorter design cycle, but a large campus still needs many of them — every hall, pod, or power block typically has its own — so aggregate demand scales directly with the number of buildings under construction rather than with a single large order. That volume dynamic makes throughput and factory capacity at transformer manufacturers a relevant constraint even though any individual unit is not as schedule-critical as a substation transformer. For investors, signals include order backlogs at mid-size transformer manufacturers, copper and electrical steel input costs and availability, and whether campus developers are standardizing designs across sites to simplify sourcing and shorten factory queues.

ai_delta:
Each new hall or power block needs its own set of these transformers, so campus buildout multiplies unit orders even though any individual transformer is smaller and faster to build than the units in 7.01.


id: 7.05
slug: lv-switchgear-switchboards-panelboards
name: LV switchgear, switchboards, panelboards
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Low-voltage electrical distribution equipment — switchgear, switchboards, and panelboards — that receives power from MV/LV transformers and distributes it onward to busway, PDUs, and building loads.

description_md:
LV switchgear and switchboards sit downstream of MV/LV transformers (7.04) and upstream of busway (7.06) and floor PDUs (7.07), forming the low-voltage backbone that a building's electrical room is organized around. Panelboards handle smaller, more localized circuits — lighting, mechanical loads, and other building systems — branching off the main switchboards. In a data center this equipment also has to coordinate with UPS (7.10) and backup generation (7.13), since normal, UPS-backed, and generator-backed power paths often converge and diverge at the switchboard level through automatic and static transfer switches. Because IT load density keeps rising, switchboards feeding data halls are increasingly built to higher ampacity ratings and with tighter short-circuit and arc-flash engineering than a comparable commercial building would need, which narrows the pool of qualified manufacturers for the largest boards. For investors, the things to track are lead times on custom-engineered switchboards (as distinct from off-the-shelf panelboards, which remain widely available), how much of this equipment is being pulled toward integrated, pre-tested power skids rather than site-assembled boards, and capacity at the firms that build both switchgear and the E-houses around it.

ai_delta:
Larger, denser power blocks push individual LV switchboards toward higher ampacity ratings and tighter integration with UPS and generator controls than a conventional commercial building would need.

scale_notes:
Present at every scale; board size and ampacity rating scale with hall density rather than campus size alone.


id: 7.06
slug: busway-busbar-trunking
name: Busway / busbar trunking
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Rigid, modular busbar systems that carry power along a data hall's overhead or perimeter path with tap-off points, used as an alternative to running individual cables to each row.

description_md:
Busway carries current from LV switchgear or PDUs (7.05, 7.07) along a data hall, typically overhead, with tap-off boxes at intervals that feed individual racks or rows — functionally similar to what LV cable and tray (7.09) do, but as a rigid, pre-engineered, higher-ampacity alternative. Its appeal in a data center is flexibility: adding or moving a tap-off is simpler than pulling new cable, which matters as rack power draw and layouts change between generations of IT equipment. As racks draw more current, busway systems are being specified at higher ampacity ratings and with more tap-off density than in a conventional commercial building, and some designs are extending busway architecture down into the rack itself as a power-distribution backbone. Because busway is a metal-intensive, factory-fabricated product shipped in fixed lengths and site-jointed, manufacturing capacity and copper or aluminum input costs both matter to how quickly it can be delivered at scale. For investors, watch order backlogs at busway manufacturers, the pace at which they are qualifying higher-ampacity product lines for AI-density racks, and the extent to which busway is displacing cable and tray as the default in-hall distribution method rather than remaining a premium option.

ai_delta:
Higher rack power draw favors busway over cable runs because busway supports higher-ampacity tap-offs and is easier to reconfigure as rack layouts and power densities change between generations.

scale_notes:
Common wherever hall layouts are standardized and repeated across many rows; smaller enterprise rooms more often use cable and tray instead.


id: 7.07
slug: floor-pdus-rpps-static-transfer-switches-ats
name: Floor PDUs, RPPs, static transfer switches, ATS
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
scale_notes: Present at every scale; unit count and ampacity per zone scale with hall density.
flow: power
summary:
Floor-mounted power distribution units, remote power panels, and the static and automatic transfer switches that route power between utility, UPS, and generator sources down to the rack level.

description_md:
Floor PDUs and RPPs are the last stage of building-level distribution before power reaches busway or in-rack distribution, stepping down and metering power for a zone of racks. Static transfer switches (STS) and automatic transfer switches (ATS) sit alongside them to switch a load between sources — typically utility and UPS-backed paths, or normal and generator power — without an interruption the IT equipment would notice, which is what lets a facility carry dual, concurrently maintainable power paths. As rack power draw rises, floor PDUs and RPPs are being specified at higher ampacity, and the distance between a PDU and the racks it serves is shrinking, because running high current over long low-voltage cable runs loses more power as heat than moving that distribution closer to the load. This equipment overlaps with UPS and generator suppliers (7.10, 7.13), since transfer switching logic has to be engineered together with those systems rather than sourced independently. For investors, useful signals are how fast vendors are qualifying higher-ampacity PDU and STS product lines for AI-density halls, and whether facilities are standardizing on fewer, larger PDUs per zone versus more numerous smaller units, which changes the addressable unit count per campus.

ai_delta:
Higher rack power draw is pushing floor PDU and RPP ratings up and shortening the distance these units sit from the rack, since longer low-voltage runs at high current become less efficient.


id: 7.08
slug: circuit-protection-fuses-surge-grounding
name: Circuit protection, fuses, surge, grounding
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Fuses, circuit breakers, surge protection devices, and grounding systems that protect electrical equipment and personnel throughout the power chain, from switchgear down to the rack.

description_md:
This is the protective layer threaded through every stage of electrical distribution described above — switchgear (7.02, 7.03, 7.05), transformers (7.01, 7.04), busway (7.06), and PDUs (7.07) all incorporate fuses, breakers, surge protection devices, and grounding systems rather than standing apart from them as separate equipment. Fuses and breakers interrupt fault current before it damages equipment or starts a fire; surge protection devices absorb transient voltage spikes, including those from utility switching or lightning; grounding systems provide the safe path fault current needs and protect sensitive electronics from stray currents. As data centers move to higher voltages, higher ampacities, and higher fault-current levels to support denser racks, the protection devices sized for that equipment become more specialized and higher-rated than standard commercial building products, and coordinating protection devices at different points in the chain — so that a fault trips only the nearest breaker rather than an upstream one — becomes a more demanding engineering exercise. For investors, this is a broad, largely commoditized product category with many qualified suppliers, so the more relevant signal is which suppliers are best positioned on the highest-current, highest-voltage end of the range where AI-scale equipment is pushing demand, rather than overall category growth.

ai_delta:
Higher fault currents and ampacities at AI-density power levels push circuit protection toward higher-rated, more specialized fuses and breakers than a conventional commercial building needs.

scale_notes:
Present at every scale as an embedded component of the switchgear, transformers, and distribution equipment around it rather than a standalone system.


id: 7.09
slug: lv-cable-wire-tray-raceway
name: LV cable, wire, tray, raceway
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
scale_notes: Present at every scale; cable gauge and tray capacity scale with rack density.
flow: power
summary:
Low-voltage power cable and wire, and the tray and raceway systems that route and support it between switchgear, PDUs, and racks.

description_md:
LV cable and wire carry power the last distance from switchgear, transformers, and PDUs (7.04, 7.05, 7.07) to busway or directly to racks, run through tray and raceway systems that physically support and organize the cable overhead or underfloor. As rack power draw rises, the cable gauge needed to carry that current at low voltage loss increases, and heavier cable in turn needs more robust, higher-capacity tray to support it, which is part of why busway (7.06) has become a competing option for some in-hall runs. Cable is a copper- and, increasingly given cost pressure, aluminum-intensive product, and data center buildout is only one of several sources of demand pulling on the same copper supply chain, alongside grid transmission buildout and vehicle and building electrification more broadly. Tray and raceway manufacturing is comparatively less capacity-constrained but still scales with the sheer volume of hall square footage being built. For investors, relevant signals include copper price and availability trends, lead times quoted by cable manufacturers for data-center-grade product, and how much of a given campus's in-hall distribution is shifting from cable and tray to busway, which changes the mix of demand between these two supplier groups rather than the total.

ai_delta:
A single AI data hall requires a large volume of heavy-gauge low-voltage cable and tray to carry the higher currents that dense racks draw, and that demand now competes with grid buildout and electrification for the same copper supply chain.


id: 7.10
slug: ups-static-modular-rotary-drups-and-ups-power-semis
name: UPS (static, modular, rotary/DRUPS) & UPS power semis
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Uninterruptible power supplies that bridge the gap between a utility outage and generator start, built as static (battery-based), modular, or rotary/diesel-rotary (DRUPS) designs, plus the power semiconductors inside them.

description_md:
UPS sits between LV switchgear (7.05) and the IT load, holding output voltage steady through the brief gap between a utility interruption and the moment backup generators (7.13) come online and take over. Static UPS units use battery energy storage (7.11) and power electronics to do this; rotary and diesel-rotary UPS (DRUPS) use spinning mass and, in the DRUPS case, an integrated engine to bridge the same gap without a separate battery bank; modular UPS lets capacity be added in smaller increments as a facility grows. Inside a static UPS, IGBT and silicon-carbide power semiconductor modules do the actual power conversion between AC, DC, and back to AC, and the performance and efficiency of a UPS increasingly depends on those semiconductor choices as much as on the battery or rotary system around them. Beyond simple outage bridging, AI clusters draw power in sharp, correlated swings as large synchronized GPU jobs start, stop, or checkpoint, and utilities and grid operators are increasingly asking data centers to use UPS and related equipment (7.12) to smooth those swings rather than let them propagate onto the grid. For investors, watch UPS order backlogs, capacity additions among the small set of vendors building at data-center scale, and how much of the value is migrating toward the power semiconductor content inside each unit.

ai_delta:
GPU cluster power draw swings sharply and quickly with training and inference workload changes, and UPS systems are increasingly being asked to absorb those swings for grid stability rather than just bridge outages.

scale_notes:
Present at every scale; the architecture shifts from a handful of large static UPS at hyperscale and above toward more distributed or rotary designs depending on site design choices.


id: 7.11
slug: energy-storage-ups-batteries-and-bess
name: Energy storage: UPS batteries & BESS
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Battery systems that store energy for UPS ride-through, plus larger grid-scale battery energy storage systems (BESS) that a campus can use for peak shaving, load smoothing, or backup at a scale beyond a single UPS.

description_md:
UPS batteries (7.10) are sized to bridge the short gap until generators (7.13) take over, using li-ion, VRLA, or newer chemistries depending on cost, footprint, and safety requirements. Grid-scale BESS is a different scale of the same underlying technology, sited alongside or near the campus substation (7.01) rather than inside the building, and used for purposes closer to what a utility or independent power producer would use it for: shifting load away from peak grid demand, smoothing the output of an on-site or co-located generation source, or providing a buffer that lets a campus start operating before its full generation or interconnection capacity is complete. Power conversion systems (PCS) and inverters sit between the battery cells and the AC grid or campus distribution in both applications, converting stored DC energy to usable AC power. Because grid-scale BESS is deployed at a scale that draws on the same lithium-ion and battery-materials supply chain as electric vehicles and utility storage, campus buildout competes for cell and pack capacity with those larger markets. For investors, the split between UPS-scale batteries (a mature, well-supplied market) and grid-scale BESS (a newer, faster-growing use case for data centers specifically) matters more than the category as a whole.

ai_delta:
AI campuses are pairing grid-scale BESS with their interconnection to smooth GPU load swings and to draw down stored energy during peak periods, a use case largely new to data centers rather than inherited from conventional IT backup power.

scale_notes:
UPS batteries are present at every scale; grid-scale BESS is largely an ai_campus and gw_class phenomenon tied to interconnection and load-smoothing needs at that size.


id: 7.12
slug: power-smoothing-supercapacitors-gpu-load-swing-mitigation
name: Power smoothing / supercapacitors
qualifiers: GPU load-swing mitigation
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
notes: plus firmware-side smoothing from Nvidia and Microsoft.
summary:
Supercapacitor-based and firmware-coordinated systems that absorb the fast, large power swings synchronized GPU training and inference jobs create, protecting upstream electrical equipment and the grid from those transients.

description_md:
A large training job running across many GPUs can start, checkpoint, or pause in near-unison, producing power swings sharp enough to stress voltage regulation on the equipment upstream — UPS (7.10), switchgear (7.05), and potentially the grid interconnection itself. Supercapacitors respond on a timescale that battery chemistry cannot match, discharging and absorbing energy fast enough to flatten these transients before they propagate further up the distribution chain, typically working alongside rather than instead of the UPS in 7.10. This is complemented by firmware-side smoothing built into the GPUs and cluster management software themselves, which can stagger workload transitions or throttle power draw to reduce the swing at the source rather than only absorbing it downstream. Because this is a problem largely specific to large, synchronized AI training clusters rather than conventional enterprise IT load, it is a newer product category with a small number of suppliers and no long incumbency to displace, and utilities are increasingly interested in it as a way to let data centers connect to the grid without the swings they would otherwise cause. For investors, this is a young, fast-evolving niche: watch which supercapacitor and power-electronics suppliers get qualified into major GPU cluster reference designs, and how much of the smoothing job software and firmware end up doing instead, which would reduce the addressable hardware market.

ai_delta:
Synchronized GPU clusters can swing power draw by a large share of full load almost instantaneously as a training job pauses, checkpoints, or resumes, a response speed that batteries and generators alone are not built for.

scale_notes:
Relevant wherever GPU clusters are large enough to synchronize load swings across many racks; less applicable to enterprise-scale deployments with smaller, less correlated workloads.


id: 7.13
slug: backup-generation-gensets-fuel-systems-hvo
name: Backup generation: gensets, fuel systems, HVO
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Diesel or gas-fired backup generators, their fuel storage and delivery systems, and renewable diesel (HVO) as a lower-emission fuel alternative, providing power when utility and UPS ride-through are not enough.

description_md:
Generators take over once a UPS's ride-through window (7.10, 7.11) runs out, providing power for as long as fuel supply allows, and are sized and paralleled (7.14) to match a facility's full backed-up load. Day tanks and larger fuel storage and delivery systems keep enough fuel on site and moving to the engines to sustain an extended outage, and aftertreatment systems reduce the emissions from running large diesel or gas engines, which matters for permitting in many jurisdictions. Hydrotreated vegetable oil (HVO), a renewable diesel substitute, is increasingly specified as a way to reduce a generator fleet's emissions profile without changing the engines themselves, though it depends on a separate renewable-fuel supply chain reaching the site reliably. Because a large campus's genset fleet can rival the output of a mid-size power plant, and because the same large-bore engine manufacturing base also serves marine, rail, and industrial customers, a wave of concentrated data center orders can lengthen lead times across all of those markets at once. Rental fleets provide a way to bridge capacity gaps or cover maintenance windows without owning excess generator capacity outright. For investors, watch order backlogs at large-engine manufacturers, permitting timelines for emissions in the jurisdictions campuses are sited in, and how much HVO supply is being contracted ahead of need.

ai_delta:
AI campus generator orders are large enough, and concentrated enough in time, that they compete for the same large-engine manufacturing capacity that marine, rail, and industrial customers use, lengthening lead times across all of those markets.

scale_notes:
Present at every scale, from a single genset at enterprise up to large paralleled fleets at ai_campus and gw_class sites.


id: 7.14
slug: paralleling-switchgear-and-generator-controls
name: Paralleling switchgear & generator controls
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Switchgear and control systems that synchronize and combine the output of multiple generators so they can operate as one larger backed-up power source, and manage the transition between utility, UPS, and generator power.

description_md:
A single generator (7.13) can only supply so much power, so facilities needing more back it up with multiple units run in parallel, synchronized so their combined output behaves as one source rather than several independent ones. Paralleling switchgear does the electrical synchronization, matching frequency and phase before connecting generators together and to the facility load; generator controls handle the sequencing — starting units in the right order, load-sharing between them once running, and shedding non-critical load if capacity is tight. This layer also manages the broader transition logic between utility, UPS, and generator power alongside the transfer switches in 7.07, since a facility moving through an outage has to hand load between sources without an interruption downstream equipment would notice. As campuses scale from a handful of generators to fleets large enough to approach a power plant's output, the paralleling and controls problem becomes proportionally harder — more units to synchronize, more failure modes to plan for, and tighter coordination with the utility interconnection itself. For investors, this is a smaller, more specialized supplier base than generators themselves, and the relevant signal is how well a given controls vendor's systems have been proven at the fleet sizes gw_class campuses are now ordering.

ai_delta:
A gw_class campus's generator fleet can require far more paralleled units than a conventional building, making synchronization and load-sharing controls a proportionally harder engineering problem.

scale_notes:
Scales with the number of generators being paralleled; a single-genset enterprise site typically has little need for this layer.


id: 7.15
slug: metering-power-quality-monitoring
name: Metering, power quality, monitoring
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Meters and power-quality monitoring equipment installed throughout the electrical chain to measure consumption, detect anomalies, and feed the data facility operators use to manage load and bill tenants.

description_md:
Metering and monitoring equipment is layered throughout the electrical chain described above, from utility interconnection points down to individual racks, measuring voltage, current, and power quality rather than switching or converting power itself. At the utility and substation level (7.01), metering settles billing and interconnection compliance; at the building and hall level, it lets an operator see load in real time and plan capacity; at the rack level, in colocation and multi-tenant settings, it is what tenant billing is based on. Power-quality monitoring specifically watches for harmonics, voltage sags, and transients — the kind of disturbance that GPU load swings (7.12) can create — so operators can see stress on upstream equipment before it trips a breaker or damages sensitive electronics. This is a broad, mature product category with many qualified suppliers and low differentiation at the individual meter level, so the more relevant question for investors is which vendors are building the software and analytics layer on top of the raw meter data, since that is increasingly where facility operators expect to spend, rather than on the sensors and meters alone.

ai_delta:
Fast GPU load swings make power-quality monitoring more important than in a conventional data center, since operators need to see transients that could stress upstream equipment (7.12) or trip protection devices (7.08) before they cause a problem.

scale_notes:
Present at every scale; the number of meter and monitoring points scales with the number of tenants and the granularity of billing required.


id: 7.16
slug: 800-v-dc-architecture-and-solid-state-transformers-rubin-kyber-e
name: 800 V DC architecture & solid-state transformers
qualifiers: Rubin / Kyber era
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
An emerging DC power distribution architecture, using 800 V DC in place of the AC busbar and power-shelf approach current racks use, paired with solid-state transformers (SSTs) that could replace multiple stages of conventional AC transformation.

description_md:
Conventional data center power distribution converts AC to DC repeatedly — at the UPS (7.10), again at the rack power shelf, and again inside the server — with efficiency lost at each stage. An 800 V DC architecture pushes that conversion further upstream, distributing DC power at higher voltage closer to the rack so fewer conversion stages sit between the grid and the chip, an approach discussed for the rack generations that follow current designs. Solid-state transformers (SSTs) are the enabling technology on the AC side of that architecture: a power-electronics-based transformer that can do voltage conversion and, in some designs, AC-DC conversion in one stage, rather than the separate magnetic transformer and rectifier stages a conventional design uses. Both technologies depend heavily on the same power semiconductor suppliers already central to UPS (7.10a) and power shelves — silicon carbide and gallium nitride devices in particular — so the supply chain for this next architecture overlaps substantially with equipment already being built today rather than requiring an entirely new supplier base. For investors, this is an early-stage, pre-volume category: the signal to watch is which vendors get their 800 V DC and SST designs into reference architectures for the next generation of racks, since that qualification, more than any near-term revenue, is what will determine their position in the eventual transition.

ai_delta:
Rack power draw is approaching levels where converting AC to DC multiple times between the grid and the chip becomes a meaningful efficiency and space loss, which is the design pressure pushing 800 V DC distribution and solid-state transformers onto the roadmap for the generations after current designs.

scale_notes:
Relevant only at the newest, most power-dense builds designed around next-generation rack architectures; not yet present in currently deployed generations at any scale.


id: 8.01
slug: gas-turbines-onsite-behind-the-meter-and-hrsgs
name: Gas turbines (onsite / behind-the-meter) & HRSGs
bottleneck_status: constrained
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
Combustion turbines burning natural gas (or dual-fuel), paired with heat-recovery steam generators, used to generate power onsite or behind the meter at a data center campus.

description_md:
Gas turbines convert natural gas into rotating mechanical energy that drives a generator, either as a simple-cycle unit or paired with a heat-recovery steam generator (HRSG) in a combined-cycle plant for higher efficiency. In the data center context they sit upstream of the utility interconnection or, for behind-the-meter builds, upstream of the facility's own switchgear and transformers (Layer 7), providing firm power where grid interconnection queues or capacity are constrained. Aeroderivative turbines (adapted from jet engines) are favored for faster startup and modularity at campus scale, while heavy-frame industrial turbines suit larger, grid-connected combined-cycle plants. An investor should watch order-book length at the handful of OEMs that build new units, the smaller pool of specialists that refurbish older units and broker turbine "slots," and the HRSG makers whose capacity is a separate, often overlooked constraint on how fast a combined-cycle plant can actually be built.

ai_delta:
new-build turbine slots are sold out years ahead; refurbished and aeroderivative units command premiums.

scale_notes:
Present mainly at ai_campus and gw_class builds pursuing behind-the-meter or dedicated generation; smaller sites typically buy grid power instead.


id: 8.02
slug: reciprocating-engines-and-engine-microgrids
name: Reciprocating engines & engine microgrids
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Diesel, natural-gas, or dual-fuel reciprocating engines assembled into modular microgrids for onsite or bridge power at a data center site.

description_md:
Reciprocating engines are smaller, faster-to-deploy alternatives to gas turbines, packaged in modular units that can be trucked in and paired into microgrids for bridge power while a site waits on grid interconnection, or as permanent behind-the-meter generation at smaller scale. They sit alongside gensets and gas turbines (Layer 8.01) in a facility's onsite generation mix and feed the same switchgear and transformers described in Layer 7. AI-driven interconnection delays have pulled reciprocating-engine microgrids from a niche backup role into a stopgap for getting racks powered before a permanent grid connection or turbine order arrives. Investors should watch how much of this demand converts into repeat orders for engine OEMs versus staying a one-off bridge, and whether the specialist microgrid integrators can scale delivery without the engine makers disintermediating them.

ai_delta:
AI campuses facing multi-year grid interconnection queues increasingly deploy reciprocating-engine microgrids as bridge power to energize racks before permanent utility service arrives.

scale_notes:
Scales from a handful of modular units for bridge power at a single building up to large microgrid arrays at campus scale; less common at enterprise-scale sites that rely on grid power.


id: 8.03
slug: fuel-cells
name: Fuel cells
scale_tiers: hyperscale_building|ai_campus
density_modes: any
flow: power
summary:
Fuel cells, chiefly solid-oxide units, that generate electricity electrochemically from natural gas or hydrogen without combustion, deployed as onsite or behind-the-meter power.

description_md:
Fuel cells generate electricity through an electrochemical reaction rather than combustion, offering steady baseload output with lower emissions and noise than a turbine or engine, at the cost of higher capital expense per megawatt. Solid-oxide fuel cells are the technology most relevant to data centers, deployed as onsite generation alongside or instead of gas turbines and reciprocating engines (Layers 8.01–8.02), feeding the same facility switchgear covered in Layer 7. Interconnection delays and sustainability commitments have both pushed hyperscalers to sign onsite fuel-cell power deals as a way to energize sites faster than waiting for grid upgrades while avoiding the emissions profile of diesel gensets. An investor should watch order backlogs at the small number of scaled fuel-cell manufacturers, since this remains a concentrated field relative to turbines or engines, and watch whether large multi-hundred- megawatt deployments prove out reliability at a scale the technology hasn't run at before.

ai_delta:
Hyperscalers facing interconnection queues and emissions targets have signed onsite fuel-cell deals to energize AI campuses faster than grid upgrades would allow.

scale_notes:
Deployed at hyperscale_building and ai_campus scale as onsite or behind-the-meter generation; less common at smaller sites given per-megawatt capital cost.


id: 8.04
slug: nuclear-existing-fleet-ppas-and-restarts
name: Nuclear: existing fleet PPAs & restarts
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
Power purchase agreements with existing nuclear plant operators, including deals that fund restarts of previously shuttered reactors, to secure firm, carbon-free power for AI load.

description_md:
This category covers commercial arrangements — not hardware — where a data center operator contracts directly with a utility or independent power producer for output from an existing nuclear plant, sometimes underwriting the restart of a reactor that had been retired. These deals sit upstream of the transmission and interconnection equipment in Layer 8.09 that physically delivers the power, and they compete with the broader pool of utility and IPP deals in Layer 8.07 for the same firm, carbon-free attributes. Existing nuclear capacity is fixed in the near term — no new large reactor has been added to most of these fleets in years — so demand from AI operators for this power is a claim on a scarce, non-expandable resource rather than a new supply chain. An investor should watch how many more restart candidates exist in a given fleet, since each one is a one-time, plant-specific event, and how regulators and other ratepayers respond to large single buyers securing capacity that was previously sold into the broader grid.

ai_delta:
AI operators are contracting directly for existing nuclear output, including funding restarts of retired reactors, to lock in firm carbon-free power without waiting for new generation.

scale_notes:
Relevant at ai_campus and gw_class scale, where a single operator's load is large enough to justify a plant-level power purchase agreement or restart commitment.


id: 8.05
slug: smrs-advanced-reactors-components-fuel
name: SMRs, advanced reactors, components, fuel
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
Small modular reactors and other advanced reactor designs, plus the components, fuel, and enrichment supply chain that would build and run them.

description_md:
This category spans developers racing to commercialize small modular and advanced reactor designs, the specialist manufacturers who would supply reactor-grade components, and the fuel and enrichment chain (including uranium mining) that feeds any reactor, new or existing. None of these designs have been built and licensed at commercial scale for a data center yet, which separates this category from the existing-fleet PPAs in Layer 8.04 — this is a bet on future supply rather than a claim on current output. The output of an SMR would ultimately connect through the same transmission and interconnection equipment described in Layer 8.09. AI demand has been the single biggest driver of new capital and offtake commitments into SMR developers, but licensing, first-of-a-kind construction risk, and fuel supply (particularly HALEU enrichment capacity) remain unresolved for nearly every design in this category. An investor should weigh developer timelines against enrichment capacity, since a shortage of enriched fuel could bind even a successfully licensed reactor design.

ai_delta:
AI operators have become the largest source of offtake commitments and equity capital for SMR and advanced reactor developers, betting on future firm carbon-free supply years ahead of any commercial operation.

scale_notes:
Relevant to ai_campus and gw_class buildouts planning multi-year power roadmaps; presence depends entirely on whether an operator has signed a developer offtake or investment deal.


id: 8.06
slug: fusion-long-dated-ppas
name: Fusion
qualifiers: long-dated PPAs
scale_tiers: gw_class
density_modes: any
flow: power
summary:
Long-dated power purchase agreements with fusion developers, none of which have yet demonstrated commercial electricity generation.

description_md:
Fusion PPAs are contracts for power from developers pursuing net-energy-gain fusion reactors, none of which currently generate commercial electricity — this is the furthest-dated and most speculative category in the layer, sitting conceptually alongside the SMR and advanced reactor bets in Layer 8.05 but with an even longer and less certain path to first delivered megawatt. Several of the largest AI operators have signed these agreements as long-horizon options on future firm power rather than as near-term supply. Because no fusion developer has a working commercial plant, these deals carry essentially all technology and schedule risk on the developer side, and none of it should be read as contracted revenue in the way a signed utility PPA (Layer 8.07) would be. An investor should treat named delivery dates as targets rather than commitments and focus on which developers have independently verified net-energy milestones rather than announced offtake volume alone.

ai_delta:
AI operators have signed long-dated fusion PPAs as options on future firm power, providing capital and demand signals to developers years before any commercial generation is proven.

scale_notes:
Relevant only to the largest, longest-horizon gw_class power roadmaps given the multi-year to multi-decade timelines involved.


id: 8.07
slug: utilities-ipps-and-power-providers-signing-data-center-deals
name: Utilities, IPPs & power providers signing data-center deals
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: power
summary:
Utilities, independent power producers, and grid operators that sign the power purchase agreements, interconnection contracts, and generation deals underlying a data center's supply.

description_md:
This category is the commercial layer that ties raw generation — gas, nuclear, renewables, or storage — to a signed data-center power deal, spanning regulated utilities, independent power producers, and (for context) the grid operators who run the wholesale markets and interconnection queues those deals move through. It sits above every generation technology in this layer (Layers 8.01–8.06, 8.08) and hands off to the transmission and interconnection hardware in Layer 8.09 that physically delivers contracted power to a site. AI load growth has made data centers among the largest new customers many utilities and IPPs have signed in decades, shifting negotiating power, rate-design debates, and interconnection queue priority in ways that vary sharply by region and market structure. An investor should watch which utilities and IPPs are securing large-load tariffs or direct deals versus which are constrained by interconnection queues or regulatory pushback, and note that market structure (regulated utility versus competitive IPP versus vertically integrated player) shapes how much of this demand actually flows through to shareholder returns.

ai_delta:
AI data centers have become some of the largest new individual customers many utilities and IPPs have signed in decades, reshaping interconnection queue priority and large-load rate design.

scale_notes:
Relevant at every scale tier, since even an enterprise-scale site ultimately buys power from a utility or IPP; the size and structure of the deal (standard tariff versus negotiated large-load contract) scales with site size.


id: 8.08
slug: renewables-and-long-duration-storage-hardware
name: Renewables & long-duration storage hardware
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
Solar, wind, geothermal, and hydro generation hardware, plus long-duration energy storage, contracted or co-located to supply data-center power.

description_md:
This category covers the hardware behind renewable and long-duration storage generation — solar panels and inverters, wind turbines, geothermal drilling and plant equipment, hydro equipment, and long-duration storage systems — as distinct from the utilities and IPPs (Layer 8.07) that contract for their output. Renewables typically supply data centers either through power purchase agreements with grid-scale projects or, less commonly, through co-located generation, and they sit alongside gas, nuclear, and fusion (Layers 8.01–8.06) as one option in an operator's generation mix, usually paired with firm capacity to cover intermittency. Solar and wind hardware is comparatively commoditized and globally traded, with pricing set largely by global supply-demand rather than AI-specific dynamics, while long-duration storage and next-generation geothermal remain earlier-stage and less price-competitive. An investor should separate the commoditized, scaled segments (solar panels, wind turbines) from the earlier-stage ones (enhanced geothermal, long-duration storage) where a smaller set of developers is racing to prove out technology and cost curves specifically to serve firm, round-the-clock data-center load.

scale_notes:
Relevant at ai_campus and gw_class scale where an operator can absorb a grid-scale renewable PPA or co-located project; presence depends on an operator's generation-mix and sustainability commitments rather than site size alone.


id: 8.09
slug: transmission-interconnection-and-grid-equipment
name: Transmission, interconnection & grid equipment
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
High-voltage transmission equipment, interconnection hardware, grid-enhancing technology, and the EPC firms and developers that build and expand the grid feeding a data center site.

description_md:
This category covers everything that moves bulk power from a generation source to a data center's point of interconnection: HVDC converter equipment and cable, STATCOMs and synchronous condensers that stabilize voltage, grid-enhancing technology that squeezes more capacity out of existing lines, advanced conductors, transmission structures, the EPC firms that build new lines, and the developers who originate transmission projects. It sits between the generation and utility layers above (Layers 8.01–8.08) and the facility-side electrical equipment in Layer 7, and grid operators are listed here for context on the markets this equipment serves, not as vendors. AI load growth has turned transmission capacity and interconnection queues into one of the most visible bottlenecks in bringing new data center capacity online, pulling forward demand for grid-enhancing technology and advanced conductors as faster-to-deploy alternatives to building entirely new transmission lines. An investor should watch order backlogs at the concentrated set of HVDC and large power-equipment makers, and separate the EPC and developer names (which benefit from transmission buildout regardless of who ultimately owns the line) from equipment makers exposed to specific technology cycles like grid-enhancing tech adoption.

ai_delta:
AI data centers have made transmission capacity and interconnection queues a visible bottleneck, pulling forward demand for grid-enhancing technology and advanced conductors as faster alternatives to new transmission lines.

scale_notes:
Relevant at ai_campus and gw_class scale where a site's load is large enough to require dedicated transmission upgrades or new interconnection infrastructure.


id: 8.10
slug: gas-supply-chain-for-onsite-generation
name: Gas supply chain
qualifiers: for onsite generation
scale_tiers: ai_campus|gw_class
density_modes: any
flow: power
summary:
Pipelines, producers, compression equipment, LNG and virtual-pipeline logistics, and metering that supply natural gas to onsite generation.

description_md:
This category covers the natural gas supply chain feeding onsite gas turbines and reciprocating engines (Layers 8.01–8.02): upstream producers, midstream pipeline operators, compression equipment that moves gas through the system, LNG and virtual-pipeline logistics for sites without direct pipeline access, and the meters and regulators that measure and control delivery at a site. It sits upstream of the generation hardware itself and is the piece of Layer 8 most exposed to broader energy-market dynamics rather than AI-specific demand, since data centers are one buyer among many industrial and utility customers for the same gas infrastructure. Where a site lacks pipeline access, virtual-pipeline delivery by truck or rail becomes the binding constraint on how fast onsite generation can be fueled, rather than turbine or engine availability. An investor should watch whether new onsite generation projects can secure pipeline capacity or interconnects on a timeline that matches turbine delivery, since gas supply and generation hardware lead times don't always move together, and note that most names in this category are exposed to the far larger non-data-center gas market as their primary business.

ai_delta:
Onsite gas generation for AI campuses has added a new source of demand for pipeline capacity, compression equipment, and virtual-pipeline delivery, particularly at sites without existing pipeline access.

scale_notes:
Relevant where a site pursues onsite gas generation (Layers 8.01–8.02) at ai_campus or gw_class scale; not applicable to sites drawing solely from the grid.


id: 9.01
slug: data-center-developers-operators-and-reits
name: Data center developers, operators & REITs
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
The companies that own, develop, and operate the physical data center buildings AI workloads run in, from public REITs and colocation operators to private developers and bitcoin miners that have pivoted to AI hosting.

description_md:
This is the landlord layer: firms that acquire land and power, build or buy the shell and core, and either lease finished capacity to hyperscalers and neoclouds or operate it themselves. It sits upstream of the architecture, contractor, and trade layers below it, which it hires, and downstream of nothing — it's the entity signing the power and land deals that everything else depends on.
AI training and inference campuses are denser, larger, and more power-hungry than the enterprise colocation halls this industry was built around, which has pulled a wave of new entrants into the space: bitcoin miners with existing power interconnects repositioning as AI hosts, and private-equity-backed platforms racing to assemble land-and-power pipelines at a scale public REITs can't match on balance sheet alone. For investors, the questions are about the underlying asset, not the building: how much of a developer's pipeline has signed, creditworthy tenants versus speculative land banking; how exposed a given operator is to a single hyperscaler tenant; and how much of the value accrues to the landlord versus getting passed through in the lease. Power interconnection queues, not construction capacity, are increasingly the gating constraint on how fast this layer can add supply.

ai_delta:
AI campuses require single-site power and land commitments far larger than enterprise colocation, drawing new entrants — repositioned bitcoin miners, PE-backed platforms — into land-and-power development.

scale_notes:
Present at every scale from a single edge cabinet to a multi-gigawatt campus; what changes is ownership structure — enterprise and edge sites are more often owned outright by the tenant, while hyperscale and larger sites increasingly involve a developer/REIT as landlord.


id: 9.02
slug: hyperscalers-neoclouds-and-model-labs-the-tenants-and-owners
name: Hyperscalers, neoclouds & model labs
qualifiers: the tenants and owners
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
The hyperscalers, neoclouds, and AI labs that occupy data center capacity as tenants or owner-operators, and whose capital spending ultimately drives demand for every layer below this one.

description_md:
These are the buyers: the companies that lease or self-build the buildings from Layer 9.01's developers and REITs, fill them with the compute from Layers 1 through 4, and sell the resulting capacity as cloud services or use it to train and run their own models. The category splits into three tiers with different economics — diversified hyperscalers funding data centers from broad cloud and advertising revenue, neoclouds that are effectively single-purpose GPU-leasing vehicles often financed against the hardware itself, and model labs whose compute is frequently backstopped by a hyperscaler partner or dedicated project financing.
This layer doesn't manufacture anything, but it's the layer every other part of the taxonomy is downstream of: capital expenditure guidance from this group is the earliest signal for order books across silicon, networking, power, and construction. For investors, the key distinction is balance-sheet quality and revenue diversity — a hyperscaler's data center spend is one line in a much larger business, while a neocloud's spend is the business, which makes its debt and lease commitments far more sensitive to utilization and to the resale value of GPU generations as they age.

ai_delta:
AI training and inference workloads are the primary driver of the capital spending that funds every other layer of this taxonomy.

scale_notes:
Concentrated at hyperscale-building scale and above; individual labs and smaller neoclouds may lease enterprise-scale capacity as well, but the volume that defines this category sits at campus and gigawatt scale.


id: 9.03
slug: architecture-engineering-and-mep-design
name: Architecture, engineering & MEP design
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
Architecture and engineering firms that design a data center's building envelope and its mechanical, electrical, and plumbing (MEP) systems before construction begins.

description_md:
This is the design layer that sits between the developer/tenant (9.01, 9.02) and the general contractors (9.04) who build to the design. Architecture covers the shell and site layout; MEP engineering specifies the power distribution, cooling, and plumbing systems that Layers 5 through 8 of this taxonomy physically supply, translating a target IT load and density into drawings a contractor can build from and equipment vendors can quote against.
AI campuses have compressed design timelines even as the systems being designed have gotten more complex — liquid cooling loops, higher-voltage electrical distribution, and larger single-site power capacities all require MEP engineering that a decade of enterprise-colocation templates didn't anticipate. Firms that have built repeatable, pre-engineered designs for hyperscale and AI-campus MEP can turn projects faster than those still customizing each site from scratch, which is the main differentiator investors watching this space should track — that and the engineering labor capacity to staff a larger pipeline of concurrent projects at all.

ai_delta:
AI campuses require MEP designs for higher-voltage electrical distribution and liquid cooling loops that a decade of enterprise-colocation templates didn't anticipate.

scale_notes:
Engaged on essentially every purpose-built data center above the smallest edge deployments; the scope of work (and the premium commanded by firms with AI-campus experience) grows with scale and with liquid-cooling complexity.


id: 9.04
slug: general-contractors-and-design-build
name: General contractors & design-build
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
General contractors and design-build firms that construct the data center shell and coordinate the trade subcontractors once architecture and MEP design (9.03) are complete.

description_md:
The GC is the builder of record: it takes the design from Layer 9.03, hires or self-performs the structural, envelope, and site work, and coordinates the specialty MEP and electrical trades in Layer 9.05 to deliver a finished, commissioned building to the developer or tenant. Design-build contracts, where the same firm holds both design and construction responsibility, have become more common on AI campuses specifically because they compress the schedule between design freeze and groundbreaking.
Hyperscale and AI-campus projects are larger, more repetitive across sites, and more schedule-driven than the enterprise colocation builds this industry has historically served, which rewards contractors that can standardize a design and redeploy the same project teams and subcontractor relationships from site to site rather than starting over each time. For investors, the constraint reported across this layer is less about materials than about the number of contractors with a proven track record of delivering AI-campus schedules and the skilled labor to staff several concurrent megaprojects at once.

ai_delta:
AI campuses are larger, more repetitive across sites, and more schedule-driven than the enterprise colocation builds this industry has historically served.

scale_notes:
Present at every scale above the smallest edge site; project size and the premium for proven AI-campus delivery experience both scale up with site size.


id: 9.05
slug: mep-and-electrical-contractors-self-perform
name: MEP & electrical contractors
qualifiers: self-perform
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
Self-performing MEP and electrical contractors who install the power distribution, cooling piping, and electrical systems specified by design (9.03) under the general contractor's coordination (9.04).

description_md:
Where Layer 9.04's general contractor manages the overall build, the trades in this layer do the hands-on installation of the electrical switchgear, cabling, cooling piping, and mechanical systems that turn the shell into a working data center — installing the hardware that Layers 5 through 8 of this taxonomy manufacture. "Self-perform" distinguishes firms that do this work with their own crews from those that only broker it out, which matters for schedule control on projects where the electrical and mechanical scope is the critical path.
Higher-density AI racks mean more electrical capacity and more liquid-cooling piping per square foot than a comparable enterprise build, which increases the skilled-labor hours needed per project even before accounting for the larger total footprint of AI campuses. Skilled electrical and mechanical labor availability is the constraint most consistently named at this layer — a shortage that doesn't scale down with automation the way some other parts of construction can, since this is licensed, on-site trade work. Investors should watch labor pipeline and backlog at the firms with data-center-specific self-perform crews rather than at generalist electrical contractors.

ai_delta:
Higher-density AI racks require more electrical capacity and more liquid-cooling piping per square foot than a comparable enterprise build, raising skilled-labor hours per project.

scale_notes:
Scope per square foot grows with density — a liquid-cooled AI campus requires substantially more electrical and piping labor hours than an air-cooled enterprise hall of the same footprint.


id: 9.06
slug: structure-steel-precast-envelope-site-equipment
name: Structure, steel, precast, envelope, site equipment
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
The raw structural and envelope inputs to a data center building — steel, precast concrete, cement and aggregates, wall and roof systems, raised floor, doors, and the heavy site equipment used to build it.

description_md:
This is the bill of materials for the shell itself, installed by the contractors in Layers 9.04 and 9.05 according to the design from 9.03: structural steel and precast panels form the building, cement and aggregates go into the foundation and slab, envelope systems (roofing, insulated panels) close it in, and raised floor, doors, and hardware finish the interior. Site and civil work and rented heavy equipment (cranes, earthmovers) are the construction inputs that make the rest possible before any of it is installed.
None of these materials are unique to AI data centers — the same steel and concrete go into warehouses and factories — but the AI buildout has added a large, geographically concentrated source of demand on top of existing construction activity, and AI campuses in particular favor precast and modular structural systems that can be fabricated off-site and erected faster than site-built alternatives. Investors should look at regional fabrication and precast capacity near active campus clusters, since this is a category where transport cost limits how far material can travel to meet a schedule-driven project.

ai_delta:
AI campuses favor precast and modular structural systems, fabricated off-site and erected faster than site-built alternatives, on top of a large increase in overall construction volume.

scale_notes:
Volume of structural material scales roughly with building footprint and floor loading requirements, which both grow from enterprise through gigawatt-class campuses.


id: 9.07
slug: prefab-modular-data-centers-and-power-cooling-skids
name: Prefab / modular data centers & power/cooling skids
scale_tiers: hyperscale_building|ai_campus|gw_class
density_modes: hybrid|liquid
flow: shell
summary:
Factory-built modular data center units and prefabricated power and cooling skids that are assembled off-site and installed as complete, pre-integrated blocks.

description_md:
Instead of building power distribution and cooling systems entirely on-site from the components in Layers 5 through 8, this layer packages them into pre-engineered, factory- tested modules — power skids, cooling skids, and in some cases entire containerized compute units — that arrive at the site largely complete and are craned into place, cutting down the on-site labor scope handled by Layers 9.04 and 9.05.
AI campuses' appetite for repeatable, fast-to-deploy capacity favors this approach: a prefabricated module built on a standard design can be manufactured in parallel with site and shell construction rather than sequentially after it, and factory conditions allow tighter quality control on the high-density power and liquid-cooling systems AI racks require. Investors should watch whether a given developer's design standardizes around a particular vendor's modular product, since that creates a qualification and single-vendor dependency similar to other parts of this taxonomy, and whether module manufacturing capacity itself becomes a scheduling constraint as adoption grows.

ai_delta:
AI campuses' need for fast, repeatable deployment favors factory-built modular power and cooling systems that can be manufactured in parallel with site and shell construction.

scale_notes:
Adoption is a design choice rather than a function of size alone — used where speed of deployment and repeatability matter most, which skews toward hyperscale and larger AI campuses, but not universal even there.


id: 9.08
slug: site-utilities-water-and-fiber-entrance
name: Site utilities, water & fiber entrance
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
The utility connections a data center needs beyond electrical power — water supply and wastewater handling, on-site water storage and reuse, and the fiber and carrier entrances that connect it to the network.

description_md:
This layer covers the non-electrical utility hookups that gate occupancy alongside the power interconnection covered in Layers 7 and 8: municipal or well water supply and wastewater discharge, on-site storage tanks and water reuse systems that reduce draw on local supply, and the physical fiber entrance and carrier connections that link the building to the network backbone the servers in Layer 4 depend on.
Liquid cooling and evaporative cooling systems (Layer 6) can significantly increase a facility's water demand relative to older air-cooled designs, which has pushed water rights and local utility capacity into the same category of siting constraint that power interconnection already occupies — some operators respond by investing in on-site reuse and storage specifically to reduce dependence on local water utilities. Fiber entrance is a separate but parallel siting constraint: a site needs diverse, redundant carrier paths before it can be considered qualified for hyperscale tenancy, and that dark-fiber availability varies as much by geography as power does.

ai_delta:
Liquid and evaporative cooling used in AI facilities increases water demand relative to older air-cooled designs, pushing water rights into the same siting-constraint category as power.

scale_notes:
Water demand scales with cooling load and with the share of liquid or evaporative cooling in the design; fiber entrance requirements scale with site criticality rather than size alone.


id: 9.09
slug: physical-security-and-access
name: Physical security & access
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
Physical access control, video surveillance, perimeter detection, and guarding systems that secure the facility and its data halls.

description_md:
This layer covers the security systems installed alongside the building's electrical and mechanical infrastructure: badge and biometric access control, video management systems, mantraps and turnstiles that enforce single-person entry to data halls, perimeter intrusion detection around the site boundary, and the guarding services that staff it. It's a cross-cutting layer that touches the building envelope (9.06), the fencing and site perimeter, and the operational procedures of the tenants in 9.01 and 9.02.
Security requirements at hyperscale and AI-campus facilities are typically driven by tenant-specific compliance regimes rather than by AI workloads themselves, so this category hasn't seen the same demand reshaping visible elsewhere in the taxonomy — the main effect of the AI buildout here is simply more sites needing a full security stack, at a pace investors should expect to track overall data center construction volume rather than any distinct density-driven dynamic.

ai_delta:
AI buildout mainly adds volume — more sites needing a full security stack — rather than changing per-site security requirements.

scale_notes:
Present at nearly every scale; scope (number of mantraps, perimeter length, guard headcount) scales with site footprint and tenant compliance requirements rather than with compute density.


id: 9.10
slug: testing-commissioning-and-certification
name: Testing, commissioning & certification
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: shell
summary:
Independent commissioning agents, load banks, and test equipment that verify a completed data center's power and cooling systems perform to specification before it goes live, along with the certification bodies that set the standards they test against.

description_md:
Commissioning is the final gate between construction (Layers 9.04–9.07) and operation: a third-party agent runs the completed electrical (Layer 7), generation (Layer 8), and cooling (Layers 5–6) systems through a sequence of integrated tests — often using load banks to simulate full IT load before real servers are installed — to confirm the building performs as designed under both normal and failure conditions. Certification bodies set the standards (uptime tiers, energy and sustainability ratings) that commissioning tests against and that tenants often require contractually.
Larger, more complex, and higher-density facilities require more extensive and longer commissioning cycles, since there are more subsystems and more interactions between them to verify — a liquid-cooled AI hall has cooling-loop and power-delivery interdependencies that a simpler air-cooled enterprise room doesn't. Investors should note that commissioning sits directly on the critical path to a building generating revenue, so backlog and staffing at independent commissioning agents can become a schedule bottleneck independent of construction progress itself.

ai_delta:
Higher-density, liquid-cooled AI facilities have more cooling-loop and power-delivery interdependencies to verify, requiring longer and more extensive commissioning cycles.

scale_notes:
Scope and duration of the commissioning cycle grow with facility complexity and density; load bank capacity required scales directly with the IT load being tested.


id: 10.01
slug: copper-busbars-cable
name: Copper
qualifiers: busbars, cable, cold plates, transformer windings
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Refined copper metal and copper products — wire, cable, sheet and strip — used as the primary electrical conductor and a thermal-transfer material throughout the data center.

description_md:
Copper carries current from the utility interconnect down to the chip, and carries heat away from it. It is the conductor in busbars and switchgear (Layers 6, 7), in low-voltage rack power distribution (Layer 4), in the windings inside every transformer (Layer 6), and — as engineered cold plates — the material in direct contact with GPUs and CPUs in liquid-cooled racks (Layer 5). The chain runs from mined ore through smelting and refining to finished wire, cable, sheet and machined parts, so data-center demand sits downstream of mining output, smelter capacity and scrap availability that are set mostly by other industries (grid buildout, electric vehicles, construction).
AI racks pull far more power through a much smaller footprint than legacy enterprise IT, which means more copper cross-section per rack for busbars and windings, and cold plates add a use of copper that air-cooled racks never needed. For investors, the relevant questions are less about data centers specifically and more about global refined-copper supply and price: mine output growth, smelter bottlenecks, and how much of a given producer's book is copper products versus other metals. Companies further down the chain — magnet wire, cable, cold-plate fabrication — are the ones with more direct, visible data-center exposure.

ai_delta:
Denser racks carry more current at lower voltage and add direct-contact liquid cooling, both of which increase the amount of copper needed per rack and per megawatt of IT load.

scale_notes:
Present at every scale as wire and busbar; the cold-plate use is limited to liquid and hybrid deployments, while busbars, cable and transformer windings apply regardless of cooling approach.


id: 10.02
slug: aluminum-heat-sinks-cable
name: Aluminum
qualifiers: heat sinks, cable, structures
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Refined aluminum metal and mill products — sheet, extrusion, casting — used for heat sinks, cable conductors, and structural and enclosure parts across the data center.

description_md:
Aluminum is the lighter, cheaper alternative to copper wherever conductivity per dollar (rather than per unit of cross-section) matters, and the default metal for anything that needs to be light, formable or heat-dissipating. It shows up as heat sinks on air-cooled components, as aluminum-conductor cable for lower-criticality runs, and as the structural and enclosure material in server chassis, racks, containment, prefabricated modules and building envelope framing (Layers 4, 5, 9). Supply runs from bauxite mining through alumina refining to smelting — an energy-intensive step that concentrates production in regions with cheap power — and then to rolling, extrusion and casting into finished shapes.
AI hardware shifts some of this mix: as GPUs move to direct-to-chip liquid cooling, the air-cooled heat-sink use case shrinks on the highest-density components even as overall server and rack volumes grow, while aluminum's structural role in chassis, rails, containment and modular buildings keeps expanding with data-center footprint. For investors, this is standard industrial-metals economics — smelter capacity, energy cost as a share of production cost, and trade policy — with data centers a small, growing claim on a market whose swing factor is autos, packaging, aerospace and construction.

ai_delta:
AI racks still use aluminum for chassis, rails and structure, but the highest-power components shift heat removal to liquid cold plates, so aluminum's role moves from primary thermal path toward structural and secondary-cooling uses as density rises.

scale_notes:
Structural and cabling uses scale with total footprint at every tier; the heat-sink role is most prominent in air-cooled and hybrid deployments and recedes on components that move to direct liquid cooling.


id: 10.03
slug: grain-oriented-electrical-steel-and-amorphous-cores-transformers
name: Grain-oriented electrical steel & amorphous cores
qualifiers: transformers
ai_delta: GOES supply is the input constraint behind transformer lead times.
bottleneck_status: tight
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Grain-oriented electrical steel and amorphous metal alloy used to build the laminated cores inside power, distribution and instrument transformers.

description_md:
The core of a transformer is a stack of thin steel or amorphous-alloy laminations that carries the magnetic flux linking the primary and secondary windings; the grade of that material sets the transformer's efficiency and, increasingly, how long it takes to build one. Grain-oriented electrical steel (GOES) is the standard material for larger power and distribution transformers; amorphous alloy cores are used where no-load losses matter most. This sits directly upstream of every transformer in the power chain — utility and substation transformers, unit substations and pad-mounted transformers feeding the building, and the smaller transformers inside power distribution equipment (Layer 6).
Production is concentrated among a small number of specialty steel mills that run dedicated GOES lines, and a shift toward amorphous cores draws on an even narrower alloy-supply base. Utilities and data-center developers have been buying transformers years ahead of need, and the taxonomy identifies GOES supply itself — not transformer manufacturing capacity — as the binding constraint behind those lead times. For investors, the questions are mill capacity additions (GOES lines are slow and capital-intensive to build), the pace of qualification for new suppliers, and exposure to Cleveland-Cliffs, named in the taxonomy as the sole US GOES producer, versus the larger number of international mills.

scale_notes:
Applies from enterprise scale up, wherever a dedicated power or distribution transformer is used; material draw grows sharply with transformer size and count toward hyperscale, campus and gigawatt-class sites.


id: 10.04
slug: rare-earths-and-permanent-magnets-fans-motors
name: Rare earths & permanent magnets
qualifiers: fans, motors, gensets, wind
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Rare-earth elements and the permanent magnets made from them, used in motors and fans throughout the facility, from server and cooling fans to generator and turbine components.

description_md:
Neodymium-iron-boron and similar rare-earth magnets are what make small, powerful, efficient motors possible — the motors inside server and rack fans, CRAH and fan-wall units, CDU and cooling-loop pumps, and the alternators and starter systems on backup generators and gas turbines (Layers 4, 5, 6, 7). The chain runs from mined and separated rare-earth oxides through magnet manufacturing, a step that has historically been concentrated in a small number of countries and companies.
Data centers are not the largest buyer of rare-earth magnets — that's still autos and wind — but AI infrastructure adds motors in places legacy IT didn't need them (liquid-cooling pumps, denser fan arrays) and draws on the same tight magnet-supply base that other electrified end markets are pulling from. Governments in multiple regions have pushed to build alternative separation and magnet-manufacturing capacity outside the traditional supply base, and some fan and motor makers are qualifying reduced-rare-earth designs as a hedge. For investors, the relevant signals are the pace of non-traditional separation and magnet capacity coming online, magnet-maker qualification cycles with data-center-relevant component makers, and how exposed a given fan, pump or genset supplier is to a single magnet source.

ai_delta:
Liquid cooling adds pump and CDU motors that air-cooled racks didn't need, and denser fan arrays add motor count, so AI infrastructure increases the number of rare-earth magnets drawn per rack even though data centers remain a small buyer relative to autos and wind.

scale_notes:
Present at every scale through fans, pumps and gensets; motor count and magnet draw per site rise with fan-wall and pump density in liquid-cooled hyperscale, campus and gigawatt-class builds.


id: 10.05
slug: specialty-glass-and-glass-fiber-optical-fiber-t-glass
name: Specialty glass & glass fiber
qualifiers: optical fiber, T-glass, glass cores
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Purpose-engineered glass and glass fiber — optical fiber, low-loss T-glass fabric, and glass-core material — used in network cabling and inside advanced chip packages.

description_md:
This part covers two related product lines. Optical fiber, drawn from specialty glass preforms, is the physical medium for the network layer's transceivers and cabling (Layer 3) and for site fiber entrance (Layer 9). Separately, low-dielectric-loss glass fabric (T-glass) and, in newer designs, solid glass cores are used inside the substrate that sits under large AI accelerator packages (Layer 1), where they carry high-speed signals between the die and the board with less loss than standard substrate materials. Both draw on specialty glass-melting and fiber-drawing capacity that is distinct from ordinary glass manufacturing and concentrated among a handful of producers.
AI clusters are unusually fiber-intensive relative to their size — the scale-out and scale-up network fabric that ties thousands of GPUs together runs on far more fiber per rack than a general-purpose enterprise cluster — and the largest packages are the first to adopt glass-core substrates to keep signal loss down as package sizes grow. For investors, the fiber side is a function of optical-fiber and cable capacity and glass-preform lead times; the substrate side is a smaller, newer market where qualification with a handful of substrate and package makers is the gating step rather than raw material availability.

ai_delta:
AI clusters run far more optical fiber per rack than general-purpose compute because of the added scale-out and scale-up network fabric, and the largest accelerator packages are moving to glass-core and T-glass substrates to control signal loss as package size grows.

scale_notes:
Optical-fiber demand scales with network fabric density at every tier and is highest in hyperscale, campus and gigawatt-class clusters; the glass-core substrate use applies only to the largest chip packages, independent of site size.


id: 10.06
slug: refrigerants-and-insulating-gases
name: Refrigerants & insulating gases
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
notes: GE Vernova g3, Hitachi Energy EconiQ, Siemens Energy Blue
summary:
Refrigerant chemicals used in cooling equipment and dielectric gases used to insulate and interrupt current inside medium- and high-voltage switchgear.

description_md:
This part groups two chemically unrelated but similarly regulated product lines. Refrigerants circulate inside chillers and computer-room air conditioning units, the mechanical backbone of air-cooled and hybrid thermal designs (Layer 5); insulating and arc-interruption gases sit inside medium- and high-voltage switchgear, where they let a compact enclosure safely interrupt fault current (Layer 6). Both categories are under active environmental regulation: refrigerants have gone through successive phase-downs of high-global-warming-potential chemicals, and SF6, the traditional switchgear insulating gas, is one of the most potent greenhouse gases in use, prompting several equipment makers to develop lower-impact alternatives (a sub-category of this part).
AI data centers touch both sides — chiller and CRAC capacity scales with facility cooling load even where the racks themselves are liquid-cooled, because heat still has to be rejected somewhere, and the larger, denser electrical distribution behind an AI campus needs more switchgear, and therefore more insulating gas, than a conventional enterprise room. For investors, the questions are regulatory timelines (phase-down schedules set replacement- chemical demand), how quickly SF6-alternative switchgear gets specified into new builds, and which chemical makers hold the intellectual property behind the newer, lower-impact gas blends.

ai_delta:
Larger, denser electrical distribution behind AI campuses requires more medium- and high-voltage switchgear, and therefore more insulating gas, while facility-level heat rejection keeps refrigerant-based chillers in the loop even where the racks themselves are liquid-cooled.

scale_notes:
Insulating-gas demand tracks switchgear count and voltage class, heaviest at campus and gigawatt-class sites and negligible below enterprise scale; refrigerant demand tracks mechanical cooling plant size and is present in hybrid designs too, since chillers still handle facility heat rejection alongside a liquid-cooled loop.


id: 10.07
slug: industrial-and-specialty-gases-fabs-lasers
name: Industrial & specialty gases
qualifiers: fabs, lasers, suppression
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Bulk and specialty industrial gases — nitrogen, oxygen, hydrogen, argon and others — used in chip fabrication, laser manufacturing, and fire suppression systems.

description_md:
Industrial gas companies supply a wide product line that touches the data-center chain at several unrelated points. Bulk and ultra-high-purity gases are consumed inside semiconductor fabs during chip manufacturing (upstream of Layer 1), specialty gas mixes are used in the lasers that manufacture optical components and in laser-based processes elsewhere in the supply chain, and clean gas-based fire suppression systems protect the data hall itself (Layer 9). These are largely separate end markets served by the same handful of large industrial-gas producers, who typically supply fabs and large industrial sites through on-site or pipeline arrangements rather than open-market sales.
AI-driven fab expansion — more wafer capacity for GPUs, HBM and networking silicon — is the larger and more direct driver of gas demand here; the fire-suppression use inside the data center itself scales with the count and size of protected spaces rather than with chip volume. For investors, exposure runs mostly through the fab-buildout cycle (on-site gas supply contracts are long-dated and tied to specific fab expansions) rather than through data-center construction directly, with the suppression-system side a smaller, steadier adjacent market.

ai_delta:
AI accelerator, memory and networking-chip demand is driving fab capacity expansion, and each new or expanded fab brings a long-dated on-site industrial-gas supply contract with it.

scale_notes:
The fab-supply use is independent of data-center site size, since it tracks wafer capacity rather than IT load; the fire-suppression use is present at every tier and scales with the number and size of protected spaces.


id: 10.08
slug: critical-minerals-gallium-germanium-indium-tungsten-beryllium
name: Critical minerals: gallium, germanium, indium, tungsten, beryllium
scale_tiers: edge|enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
notes: export-control-sensitive, China-dominant supply
summary:
A group of minor metals — gallium, germanium, indium, tungsten and beryllium — used in semiconductors, optics, thermal interface materials and specialty alloys, with supply concentrated in China and subject to export controls.

description_md:
Each of these five elements plays a narrow but hard-to-substitute role somewhere in the chain: gallium in compound-semiconductor power devices and substrates, germanium in infrared optics and certain fiber and detector applications, indium in solder and thermal-interface materials used at the chip and cold-plate interface (Layers 1, 5), tungsten in high-density and high-melting-point alloys, and beryllium in specialty alloys and RF components. What ties them together for an investor is not end use but supply structure: production, and especially refining, of several of these elements is concentrated in China, and export licensing requirements have periodically restricted shipments, creating a supply risk that is geopolitical rather than geological.
Reserves of gallium, germanium and indium in particular exist more widely than current refining capacity does, so the near-term constraint is processing capacity and trade policy rather than resource scarcity. AI hardware is a small but growing draw on this group, mainly through power semiconductors, optics and thermal-interface-material formulations rather than bulk consumption. For investors, this is a policy-and-substitution watch: export-control announcements, the pace of non-Chinese refining capacity coming online, and how quickly device makers qualify substitute materials or alternative chemistries where possible.

ai_delta:
AI hardware adds a growing draw on this group through compound-semiconductor power devices, optics and thermal-interface formulations, layering additional demand onto a supply base already concentrated and subject to export licensing.

scale_notes:
These are chip- and component-level inputs rather than site infrastructure, so presence is uniform across site sizes; total draw scales with GPU, power-semiconductor and optics volume rather than with facility footprint.


id: 11.01
slug: dcim-digital-twins-capacity-planning
name: DCIM, digital twins, capacity planning
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Software that models a data center's power, cooling, space, and asset capacity in a live digital twin, giving operators visibility into what a facility can still support.

description_md:
DCIM platforms sit above every physical system in the facility — power distribution, cooling, rack space, network ports — and turn sensor and asset data into a single model operators use to plan capacity and catch problems before they cause an outage. As racks get denser and liquid cooling loops add another set of variables to track alongside power and airflow, the planning problem DCIM tools solve gets harder, and the digital-twin approach (modeling the facility before and during construction, not just monitoring it afterward) becomes more useful for validating a design ahead of costly retrofits.

ai_delta:
Denser AI racks, mixed liquid and air cooling, and faster fit-out timelines increase the complexity DCIM and digital-twin tools are asked to manage.

scale_notes:
Present at every scale; at hyperscale and above it typically expands from asset tracking into full digital-twin modeling used during design and commissioning, not just live monitoring.


id: 11.02
slug: cluster-management-and-gpu-orchestration-out-of-scope-for-a-part
name: Cluster management & GPU orchestration
qualifiers: out of scope for a parts site; listed for completeness
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Software that schedules AI workloads onto GPU clusters and manages the compute fleet, sitting outside the physical parts this site otherwise tracks.

description_md:
Cluster management and orchestration software allocates jobs across GPU clusters, handling scheduling, fault tolerance, and utilization — the layer between the physical compute (Layer 1) and the AI workloads running on it. It is listed here for completeness because it is part of how the hardware in every other layer gets used, but it is a software category rather than a physical part, so it sits outside the scope of what this site otherwise catalogs.

ai_delta:
Larger, more expensive GPU clusters raise the cost of scheduling and utilization mistakes, increasing demand for orchestration software built specifically for AI training and inference.

scale_notes:
Present wherever GPU clusters run; complexity and the case for dedicated orchestration software grow with cluster size rather than with any single facility's density mode.


id: 11.03
slug: site-selection-power-procurement-market-intelligence-brokerage
name: Site selection, power procurement, market intelligence, brokerage
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Firms and platforms that help developers pick sites, secure power, and read market conditions before and during a data center build.

description_md:
Before construction starts, developers need to know where power is available, how long interconnection will take, what land and utility terms look like, and how a given market compares to competing sites — questions answered by a mix of power-market intelligence platforms, brokerage firms, and specialist research shops. This work happens upstream of Layer 9 (building shell, site & construction) and Layer 8 (power generation & energy supply): it determines which sites get built at all and on what timeline. As power availability has become the binding constraint on where AI campuses can go, the intelligence used to find and secure that power has become a more active part of the site-selection process than it was for earlier generations of data centers, where power was rarely the limiting factor.

ai_delta:
Power availability, not land or building cost, is increasingly the binding constraint on where AI data centers can be sited, raising the value of power-market intelligence in site selection.

scale_notes:
Relevant at every scale a new site is being evaluated; the power-procurement piece becomes the dominant consideration at ai_campus and gw_class scale.


id: 11.04
slug: capital-and-financing-context
name: Capital & financing
qualifiers: context
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
The private equity firms, credit funds, banks, and specialist lenders that finance data center construction and equipment purchases, provided here as context rather than as a hardware part.

description_md:
Data center construction and the compute inside it are increasingly financed through purpose-built capital structures — infrastructure funds, private credit, project finance from banks, and lending secured against GPU fleets rather than real estate. This chain does not supply a physical part; it supplies the capital that lets developers and operators buy the parts described in every other layer, from HV switchgear to the compute silicon itself. Because so much AI infrastructure spending now runs through debt and structured finance rather than corporate balance sheets, the health and terms of this financing chain influence how fast capacity can be built out. For investors, exposure here is indirect: through asset managers, banks, and business development companies rather than through any single physical product.

ai_delta:
The scale of AI data center and GPU capital expenditure has pulled in project finance, private credit, and asset-backed lending structures at a scale prior data center cycles did not require.

scale_notes:
Relevant across all scales as a funding source; asset-backed structures like GPU-backed lending become more prominent at ai_campus and gw_class scale where compute purchases are largest.


id: 11.05
slug: test-and-measurement-instruments
name: Test & measurement instruments
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Test and measurement instruments — oscilloscopes, network analyzers, power analyzers, and related gear — used to validate silicon, cabling, and power systems through build and commissioning.

description_md:
Bringing up a data center and the compute inside it requires verifying that chips, boards, copper and optical links, and power systems actually perform to spec before they go into production — work done with oscilloscopes, network and protocol analyzers, power analyzers, and related bench and field instruments. This equipment touches nearly every other layer at some point in the build and validation cycle: signal integrity testing for the interconnect in Layer 3, power quality testing for the electrical chain in Layer 7, and functional test during server integration in Layer 4. Faster interconnect speeds and higher power densities push testing toward tighter tolerances and more specialized instruments, and qualification of new high-speed links or power topologies typically runs through a small set of established test-and-measurement vendors before it reaches volume production.

ai_delta:
Higher-speed interconnect and higher-density power systems in AI racks require tighter test tolerances and more specialized instruments than prior generations of data center hardware.

scale_notes:
Used at every scale during manufacturing, integration, and commissioning; intensity of use scales with the pace and volume of hardware being qualified and deployed rather than with facility size alone.


id: 11.06
slug: robotics-inspection-and-operations-hardware
name: Robotics, inspection & operations hardware
scale_tiers: enterprise|hyperscale_building|ai_campus|gw_class
density_modes: any
flow: supply
summary:
Robots and inspection hardware used for physical operations tasks inside and around data center facilities, such as monitoring and inspection rounds.

description_md:
Mobile robots and inspection hardware are used in and around data center facilities for tasks like routine inspection rounds, thermal monitoring, and security patrols, supplementing the fixed sensor networks that feed DCIM platforms (Layer 11.01). This is a smaller and newer category than the rest of the layer, drawing on general-purpose robotics and inspection hardware makers rather than a data-center-specific supplier base, and its role remains concentrated in monitoring and inspection rather than hands-on maintenance or installation work.

ai_delta:
Denser, higher-value AI racks raise the cost of an undetected thermal or power fault, adding to the case for continuous robotic inspection alongside fixed sensors.

scale_notes:
Adoption is a facility-operations choice rather than a scale requirement; more common at larger sites where the inspection burden across a bigger physical footprint is greater.


id: 1.05a
parent_id: 1.05
slug: server-cpus-hyperscaler-arm-cpus
name: Hyperscaler Arm CPUs
flow: compute
summary:
Arm-based server CPUs designed in-house by a hyperscaler for its own fleet, such as AWS Graviton, Google Axion, and Microsoft Cobalt.


id: 1.11a
parent_id: 1.11
slug: 2-5d-3d-advanced-packaging-cowos-s-l-r-soic-osats-taking-cowos-b
name: OSATs taking CoWoS back-end and overflow
flow: compute
summary:
Outsourced assembly and test (OSAT) providers that take on back-end assembly and overflow packaging work when primary CoWoS capacity is full.


id: 1.20b
parent_id: 1.20
slug: fab-materials-masks-gases-upstream-masks-and-blanks
name: Masks and blanks
flow: compute
summary:
Photomasks and mask blanks that carry the circuit pattern projected onto a wafer during lithography.


id: 1.20c
parent_id: 1.20
slug: fab-materials-masks-gases-upstream-cmp-and-chemicals
name: CMP and chemicals
flow: compute
summary:
Chemical-mechanical planarization slurries and related process chemicals used to polish and clean wafers between fabrication steps.


id: 1.20e
parent_id: 1.20
slug: fab-materials-masks-gases-upstream-gases
name: Gases
flow: compute
summary:
Specialty and bulk gases consumed throughout the fabrication process, from etching to chamber purging.


id: 2.08a
parent_id: 2.08
slug: enterprise-ssds-and-controllers-drives
name: Drives
flow: compute|data
summary:
The physical NVMe SSD units themselves, in the dense form factors common in AI compute trays, installed directly in a server or storage node.


id: 2.08b
parent_id: 2.08
slug: enterprise-ssds-and-controllers-controllers
name: Controllers
flow: compute|data
summary:
The chip inside or paired with an SSD that manages NAND wear-leveling, error correction, and the drive's host interface.


id: 3.07a
parent_id: 3.07
slug: optical-dsps-drivers-tias-drivers-and-tias
name: Drivers and TIAs
flow: data
summary:
Drivers and TIAs specifically — the signal-conditioning chips positioned closest to the laser and photodetector, as distinct from the DSP.


id: 3.08a
parent_id: 3.08
slug: silicon-photonics-and-co-packaged-optics-cpo-external-laser-sour
name: External laser sources
flow: data
summary:
Laser sources supplied externally to co-packaged optics designs rather than integrated on the photonic chip itself.


id: 3.08b
parent_id: 3.08
slug: silicon-photonics-and-co-packaged-optics-cpo-fiber-attach-and-co
name: Fiber attach and connectors
flow: data
summary:
The fiber attachment process and connectors that couple external fiber to a co-packaged optics module.


id: 3.09a
parent_id: 3.09
slug: copper-dac-acc-aec-cables-retimers-inside-aecs
name: Retimers inside AECs
flow: data
summary:
Retimer chips embedded inside active electrical cables (AEC) that regenerate the signal to extend reach at high speed.


id: 3.11a
parent_id: 3.11
slug: fiber-connectors-structured-cabling-mpo-mtp-ferrules-and-connect
name: MPO/MTP ferrules and connectors
flow: data
summary:
The precision ferrules and connectors that terminate and align fiber for MPO/MTP-style multi-fiber connections.


id: 4.02a
parent_id: 4.02
slug: ems-system-integration-and-rack-level-test-rack-test
name: Rack test
flow: compute|power
summary:
Dedicated test cells and instrumentation that exercise a fully assembled rack under load before it ships, distinct from chip- or board-level test earlier in the chain.


id: 4.06a
parent_id: 4.06
slug: copper-clad-laminate-and-pcb-materials-copper-foil
name: Copper foil
flow: compute|power
summary:
The rolled or electrodeposited copper foil bonded to laminate cores that forms the actual conductive layers inside a PCB.


id: 4.06b
parent_id: 4.06
slug: copper-clad-laminate-and-pcb-materials-low-dk-glass-cloth
name: Low-Dk glass cloth
flow: compute|power
summary:
The woven glass-fiber reinforcement, in low-dielectric-constant grades, that gives laminate its mechanical strength and its high-speed electrical properties.


id: 4.07a
parent_id: 4.07
slug: board-level-connectors-and-sockets-osfp-qsfp-cages
name: OSFP/QSFP cages
flow: compute|power
summary:
The metal cages mounted on switch and NIC boards that mechanically receive and shield pluggable OSFP or QSFP optical and copper transceiver modules.


id: 4.08a
parent_id: 4.08
slug: board-level-power-vrms-power-stages-pol-power-semis-gan-and-sic
name: GaN and SiC
flow: compute|power
summary:
Gallium-nitride and silicon-carbide power semiconductors used inside VRMs and power stages to switch at higher frequency and efficiency than silicon at the currents modern GPU boards need.


id: 4.12a
parent_id: 4.12
slug: power-shelves-and-psus-33-kw-class-orv3-shelves-rack-level-gan-a
name: GaN and SiC inside
flow: compute|power
summary:
Wide-bandgap gallium-nitride and silicon-carbide power devices used inside the power shelf's AC-DC and DC-DC conversion stages for higher efficiency and power density than silicon.


id: 4.14a
parent_id: 4.14
slug: chassis-rails-rack-enclosures-enclosures
name: Enclosures
flow: compute|power
summary:
The rack cabinet frame itself — the enclosure that chassis and trays mount into, sold either as a standalone product or built in-house by the rack ODM.


id: 5.01a
parent_id: 5.01
slug: direct-to-chip-cold-plates-two-phase-and-microfluidic
name: Two-phase and microfluidic
flow: heat
summary:
Alternative cold-plate architectures — two-phase plates that use coolant evaporation and microfluidic plates that route channels closer to the die — aimed at higher heat flux than single-phase liquid cold plates handle.


id: 5.06a
parent_id: 5.06
slug: cdu-internals-plate-heat-exchangers-pumps-filters-pumps
name: Pumps
flow: heat
summary:
Pumps inside the CDU that circulate technology-side coolant through the cold plates and back, often configured with redundancy so a single pump failure does not take the loop offline.


id: 5.06b
parent_id: 5.06
slug: cdu-internals-plate-heat-exchangers-pumps-filters-filtration
name: Filtration
flow: heat
summary:
Filtration inside or ahead of the CDU that removes particulates from the technology-side coolant loop before it reaches the narrow channels of manifolds and cold plates.


id: 5.09a
parent_id: 5.09
slug: fluids-and-coolant-treatment-single-phase-dielectric
name: Single-phase dielectric
flow: heat
summary:
Single-phase dielectric fluids that stay liquid throughout the loop and are used mainly in open-tank immersion cooling systems.


id: 5.09b
parent_id: 5.09
slug: fluids-and-coolant-treatment-two-phase
name: Two-phase
flow: heat
summary:
Two-phase fluids that boil at hot surfaces and recondense elsewhere in the loop, used in two-phase immersion chambers and emerging two-phase cold-plate systems.


id: 5.09c
parent_id: 5.09
slug: fluids-and-coolant-treatment-direct-to-chip-coolant-pg25-and-inh
name: Direct-to-chip coolant (PG25) and inhibitors
flow: heat
summary:
Glycol-water coolant (PG25) and its corrosion/biological inhibitors, the standard fluid used in conventional direct-to-chip cold-plate and manifold loops.


id: 6.01a
parent_id: 6.01
slug: chillers-and-compressors-compressors
name: Compressors
flow: heat
summary:
The core rotating component inside a chiller — centrifugal, screw, scroll, or magnetic-bearing (turbocor-type) — that drives the vapor-compression refrigeration cycle.


id: 6.02a
parent_id: 6.02
slug: dry-coolers-adiabatic-evaporative-units-cooling-towers-fans-and-2
name: Fans and motors
flow: heat
summary:
Fans and their drive motors that move air through dry coolers, adiabatic units, and cooling towers to reject heat to the atmosphere.


id: 6.05a
parent_id: 6.05
slug: facility-water-loop-hardware-pumps-valves-pumps
name: Pumps
flow: heat
summary:
Pumps that circulate water through the facility cooling loop, configured from N+1 up to fully parallel trains depending on the loop's redundancy requirements.


id: 6.05b
parent_id: 6.05
slug: facility-water-loop-hardware-pumps-valves-valves-and-actuators
name: Valves and actuators
flow: heat
summary:
Valves and actuators that modulate and isolate flow within the facility water loop, including zone-isolation valves that take a section offline without stopping the whole loop.


id: 6.05c
parent_id: 6.05
slug: facility-water-loop-hardware-pumps-valves-piping
name: Piping
flow: heat
summary:
Piping that carries chilled or facility-loop water between chillers, heat-rejection equipment, and the CRAH/CDU interfaces across a site.


id: 6.05d
parent_id: 6.05
slug: facility-water-loop-hardware-pumps-valves-plate-hx
name: Plate HX
flow: heat
summary:
Plate heat exchangers that isolate one water loop from another — for example, the facility loop from the IT-side loop — so a leak or pressure event on one side doesn't cross into the other.


id: 6.05e
parent_id: 6.05
slug: facility-water-loop-hardware-pumps-valves-tanks-and-thermal-stor
name: Tanks and thermal storage
flow: heat
summary:
Buffer tanks that hold reserve chilled or facility-loop water to ride through a pump or chiller trip and to shave peak cooling demand.


id: 6.08a
parent_id: 6.08
slug: building-management-controls-environmental-sensors-plcs
name: PLCs
flow: heat
summary:
Programmable logic controllers that execute the low-level control loops — chiller sequencing, valve and damper modulation — the building management system directs.


id: 6.08b
parent_id: 6.08
slug: building-management-controls-environmental-sensors-sensors
name: Sensors
flow: heat
summary:
Temperature, humidity, pressure, and flow sensors that feed the data the building management system and PLCs act on.


id: 6.08c
parent_id: 6.08
slug: building-management-controls-environmental-sensors-ai-driven-coo
name: AI-driven cooling control
flow: heat
summary:
Optimization software layered over the conventional BMS and PLC control loops that uses live sensor data to hold tighter setpoints and trim cooling energy use.


id: 6.09a
parent_id: 6.09
slug: fire-detection-and-suppression-detection
name: Detection
flow: heat
summary:
Smoke and heat detection, including aspirating detectors that continuously sample air to catch a fault before visible smoke develops.


id: 6.09b
parent_id: 6.09
slug: fire-detection-and-suppression-suppression
name: Suppression
flow: heat
summary:
Discharge hardware — piping, nozzles, and release systems — that delivers a clean or inert-gas extinguishing agent into the data hall or electrical room without damaging electronics.


id: 6.09c
parent_id: 6.09
slug: fire-detection-and-suppression-agents
name: Agents
flow: heat
summary:
The extinguishing agents themselves — clean fluorinated agents or inert gases — used in gaseous suppression systems, an area in flux following 3M's discontinuation of Novec 1230 at the end of 2025.


id: 7.02a
parent_id: 7.02
slug: hv-switchgear-gis-breakers-protection-relays-relays
name: Relays
flow: power
summary:
Protection relays that detect electrical faults on HV circuits and trigger the associated breakers to isolate them.


id: 7.02b
parent_id: 7.02
slug: hv-switchgear-gis-breakers-protection-relays-sf6-free
name: SF6-free
flow: power
summary:
Breaker and switchgear designs that replace the potent greenhouse gas SF6 as an insulating medium, offered as an alternative to conventional gas-insulated switchgear.


id: 7.09a
parent_id: 7.09
slug: lv-cable-wire-tray-raceway-tray-and-supports
name: Tray and supports
flow: power
summary:
Cable tray, ladder rack, and support hardware that physically routes and secures LV cable and wire runs overhead or underfloor.


id: 7.10a
parent_id: 7.10
slug: ups-static-modular-rotary-drups-and-ups-power-semis-igbt-and-sic
name: IGBT and SiC modules
flow: power
summary:
Silicon and silicon-carbide power semiconductor modules that perform the AC-DC-AC conversion inside a UPS.


id: 7.11a
parent_id: 7.11
slug: energy-storage-ups-batteries-and-bess-li-ion-for-ups
name: Li-ion for UPS
flow: power
summary:
Lithium-ion battery chemistries used in static UPS systems, valued for energy density and footprint versus older lead-acid designs.


id: 7.11b
parent_id: 7.11
slug: energy-storage-ups-batteries-and-bess-nizn
name: NiZn
flow: power
summary:
Nickel-zinc battery chemistry offered as an alternative to lithium-ion or lead-acid for UPS applications.


id: 7.11c
parent_id: 7.11
slug: energy-storage-ups-batteries-and-bess-vrla
name: VRLA
flow: power
summary:
Valve-regulated lead-acid batteries, the older, lower-cost UPS battery chemistry that lithium-ion has been displacing in new installations.


id: 7.11d
parent_id: 7.11
slug: energy-storage-ups-batteries-and-bess-sodium-ion
name: Sodium-ion
flow: power
summary:
An emerging battery chemistry pitched as a lower-cost, less supply-constrained alternative to lithium-ion for UPS and storage applications.


id: 7.11e
parent_id: 7.11
slug: energy-storage-ups-batteries-and-bess-grid-scale-bess
name: Grid-scale BESS
flow: power
summary:
Utility-scale battery installations sited at or near a campus for peak shaving, load smoothing, or capacity buffering, distinct from batteries inside a UPS.


id: 7.11f
parent_id: 7.11
slug: energy-storage-ups-batteries-and-bess-pcs-and-inverters
name: PCS and inverters
flow: power
summary:
Power conversion systems and inverters that convert stored DC battery energy to AC power for UPS and grid-scale BESS alike.


id: 7.13a
parent_id: 7.13
slug: backup-generation-gensets-fuel-systems-hvo-day-tanks-and-fuel-sy
name: Day tanks and fuel systems
flow: power
summary:
Fuel storage and delivery equipment that keeps diesel or HVO moving from bulk storage to the generator engines during extended runtime.


id: 7.13b
parent_id: 7.13
slug: backup-generation-gensets-fuel-systems-hvo-aftertreatment
name: Aftertreatment
flow: power
summary:
Emissions-control equipment fitted to backup generator engines to meet air-quality permitting requirements where they run.


id: 7.13c
parent_id: 7.13
slug: backup-generation-gensets-fuel-systems-hvo-hvo
name: HVO
flow: power
summary:
Hydrotreated vegetable oil, a renewable diesel substitute usable in existing generator engines without modification, specified to reduce a genset fleet's emissions profile.


id: 7.13d
parent_id: 7.13
slug: backup-generation-gensets-fuel-systems-hvo-rental-fleets
name: Rental fleets
flow: power
summary:
Temporary generator capacity rented to bridge gaps during construction, maintenance, or unexpected demand rather than owned outright.


id: 7.16a
parent_id: 7.16
slug: 800-v-dc-architecture-and-solid-state-transformers-rubin-kyber-e-2
name: SSTs (emerging)
flow: power
summary:
Solid-state transformers using power electronics rather than only magnetics to convert or step voltage, an enabling technology for 800 V DC distribution architectures.


id: 8.01a
parent_id: 8.01
slug: gas-turbines-onsite-behind-the-meter-and-hrsgs-hrsgs
name: HRSGs
flow: power
summary:
Heat-recovery steam generators that capture gas-turbine exhaust heat to raise steam for a combined-cycle plant, lifting overall plant efficiency.


id: 8.05a
parent_id: 8.05
slug: smrs-advanced-reactors-components-fuel-developers
name: Developers
flow: power
summary:
Companies developing SMR and advanced reactor designs, ranging from near-term licensing candidates to early-stage concepts, several backed directly by AI operators.


id: 8.05b
parent_id: 8.05
slug: smrs-advanced-reactors-components-fuel-components
name: Components
flow: power
summary:
Manufacturers of reactor-grade components — pressure vessels, reactor internals, and related heavy equipment — that would supply SMR and advanced reactor builds.


id: 8.05c
parent_id: 8.05
slug: smrs-advanced-reactors-components-fuel-fuel-and-enrichment
name: Fuel and enrichment
flow: power
summary:
Companies that supply and enrich nuclear fuel, including specialized HALEU enrichment relevant to several advanced reactor designs.


id: 8.08a
parent_id: 8.08
slug: renewables-and-long-duration-storage-hardware-solar
name: Solar
flow: power
summary:
Solar panel manufacturers supplying utility-scale photovoltaic projects contracted to data center power deals.


id: 8.08b
parent_id: 8.08
slug: renewables-and-long-duration-storage-hardware-inverters
name: Inverters
flow: power
summary:
Manufacturers of solar and battery inverters that convert DC output to grid-compatible AC power for renewable projects.


id: 8.08c
parent_id: 8.08
slug: renewables-and-long-duration-storage-hardware-trackers
name: Trackers
flow: power
summary:
Manufacturers of solar tracker systems that tilt panels to follow the sun and raise a utility-scale project's energy yield.


id: 8.08d
parent_id: 8.08
slug: renewables-and-long-duration-storage-hardware-wind
name: Wind
flow: power
summary:
Wind turbine manufacturers supplying onshore and offshore projects contracted to data-center power deals.


id: 8.08e
parent_id: 8.08
slug: renewables-and-long-duration-storage-hardware-geothermal
name: Geothermal
flow: power
summary:
Geothermal developers and drilling specialists, including enhanced and next-generation geothermal approaches, pursuing firm carbon-free power for data centers.


id: 8.08g
parent_id: 8.08
slug: renewables-and-long-duration-storage-hardware-ldes
name: LDES
flow: power
summary:
Long-duration energy storage developers building systems designed to discharge over many hours, complementing shorter-duration battery storage.


id: 8.09a
parent_id: 8.09
slug: transmission-interconnection-and-grid-equipment-hvdc
name: HVDC
flow: power
summary:
Manufacturers of high-voltage direct current converter stations and cable used to move bulk power over long distances or between asynchronous grids.


id: 8.09b
parent_id: 8.09
slug: transmission-interconnection-and-grid-equipment-statcom-and-sync
name: STATCOM and synchronous condensers
flow: power
summary:
Manufacturers of STATCOMs and synchronous condensers that stabilize grid voltage and provide reactive power support near large loads.


id: 8.09c
parent_id: 8.09
slug: transmission-interconnection-and-grid-equipment-grid-enhancing-t
name: Grid-enhancing tech
flow: power
summary:
Companies supplying sensors and software that raise the usable capacity of existing transmission lines without new construction.


id: 8.09d
parent_id: 8.09
slug: transmission-interconnection-and-grid-equipment-advanced-conduct
name: Advanced conductors
flow: power
summary:
Manufacturers of high-capacity conductor materials that carry more current than conventional transmission cable on the same structures.


id: 8.10a
parent_id: 8.10
slug: gas-supply-chain-for-onsite-generation-pipelines-and-midstream
name: Pipelines and midstream
flow: power
summary:
Midstream companies operating the pipeline networks that move natural gas from production basins to end users, including onsite generation sites.


id: 8.10b
parent_id: 8.10
slug: gas-supply-chain-for-onsite-generation-producers
name: Producers
flow: power
summary:
Upstream natural gas producers extracting the gas that ultimately fuels onsite generation and the broader grid.


id: 8.10c
parent_id: 8.10
slug: gas-supply-chain-for-onsite-generation-compression-and-gas-handl
name: Compression and gas handling
flow: power
summary:
Manufacturers and operators of compression equipment that moves natural gas through pipelines and gathering systems.


id: 8.10d
parent_id: 8.10
slug: gas-supply-chain-for-onsite-generation-lng-and-virtual-pipeline
name: LNG and virtual pipeline
flow: power
summary:
Companies supplying liquefied natural gas and virtual-pipeline (truck or rail) delivery for sites without direct pipeline access.


id: 9.02a
parent_id: 9.02
slug: hyperscalers-neoclouds-and-model-labs-the-tenants-and-owners-neo
name: Neoclouds
flow: shell
summary:
Independent cloud providers that lease or build data center capacity specifically to rent out GPU compute, often financed against the hardware rather than a diversified balance sheet.


id: 9.02b
parent_id: 9.02
slug: hyperscalers-neoclouds-and-model-labs-the-tenants-and-owners-lab
name: Labs
flow: shell
summary:
AI model developers whose compute needs — met through owned, leased, or partner-financed data center capacity — sit at the top of demand for the entire taxonomy below this layer.


id: 9.06a
parent_id: 9.06
slug: structure-steel-precast-envelope-site-equipment-steel
name: Steel
flow: shell
summary:
Structural and rebar steel, including data-center-specific and modular steel products, used to frame the building.


id: 9.06b
parent_id: 9.06
slug: structure-steel-precast-envelope-site-equipment-precast
name: Precast
flow: shell
summary:
Precast concrete panels and structural elements fabricated off-site and erected on-site to speed up shell construction.


id: 9.06d
parent_id: 9.06
slug: structure-steel-precast-envelope-site-equipment-envelope
name: Envelope
flow: shell
summary:
Roofing, wall panels, insulation, and other building envelope systems that enclose and weatherproof the shell.


id: 9.06e
parent_id: 9.06
slug: structure-steel-precast-envelope-site-equipment-raised-floor
name: Raised floor
flow: shell
summary:
Raised access flooring installed in data halls to route cabling and, in air-cooled designs, underfloor airflow.


id: 9.06g
parent_id: 9.06
slug: structure-steel-precast-envelope-site-equipment-site-and-civil
name: Site and civil
flow: shell
summary:
Site preparation, grading, paving, and civil engineering work performed before and during vertical construction.


id: 9.06h
parent_id: 9.06
slug: structure-steel-precast-envelope-site-equipment-equipment-and-re
name: Equipment and rental
flow: shell
summary:
Heavy construction equipment — cranes, earthmovers, and rental fleets — used by contractors during the build.


id: 9.07a
parent_id: 9.07
slug: prefab-modular-data-centers-and-power-cooling-skids-containerize
name: Containerized "AI factories"
flow: shell
summary:
Fully containerized, self-contained compute units — sometimes marketed as complete "AI factories" — that ship with compute, power, and cooling pre-integrated.


id: 9.08d
parent_id: 9.08
slug: site-utilities-water-and-fiber-entrance-dark-fiber-and-carrier-e
name: Dark fiber and carrier entrance
flow: shell
summary:
Dark fiber routes and carrier-neutral network entrances that connect the facility to internet and interconnection backbones.


id: 9.09a
parent_id: 9.09
slug: physical-security-and-access-perimeter-intrusion-detection
name: Perimeter intrusion detection
flow: shell
summary:
Microwave, radar, and fiber-based sensing systems that detect intrusion along a facility's perimeter before it reaches the building.


id: 9.10a
parent_id: 9.10
slug: testing-commissioning-and-certification-cx-agents
name: Cx agents
flow: shell
summary:
Independent commissioning agents who plan, execute, and document the testing that verifies a facility's systems before handover.


id: 9.10b
parent_id: 9.10
slug: testing-commissioning-and-certification-load-banks
name: Load banks
flow: shell
summary:
Portable and permanent load banks used to simulate IT load on power and cooling systems during commissioning tests.


id: 9.10c
parent_id: 9.10
slug: testing-commissioning-and-certification-test-equipment
name: Test equipment
flow: shell
summary:
Handheld and bench test equipment used to verify electrical and mechanical systems during commissioning and ongoing maintenance.


id: 9.10d
parent_id: 9.10
slug: testing-commissioning-and-certification-certification-and-standa
name: Certification and standards
flow: shell
summary:
Standards bodies and certification programs (uptime tiers, safety listings, green building ratings) that define the benchmarks commissioning and design are tested against.


id: 10.01a
parent_id: 10.01
slug: copper-busbars-cable-miners
name: Miners
flow: supply
summary:
Mining companies that extract copper ore, the first link in the chain feeding smelting, refining and eventual use as busbars, cable, cold plates and transformer windings.


id: 10.01b
parent_id: 10.01
slug: copper-busbars-cable-refining-and-products
name: Refining and products
flow: supply
summary:
Smelters, refiners and product manufacturers that turn mined copper into wire, cable, sheet, strip and machined parts used across the data center.


id: 10.06a
parent_id: 10.06
slug: refrigerants-and-insulating-gases-sf6-alternatives
name: SF6 alternatives
flow: supply
summary:
Lower-global-warming-potential dielectric gas blends developed as substitutes for SF6 in medium- and high-voltage switchgear insulation and arc interruption.


id: 11.04a
parent_id: 11.04
slug: capital-and-financing-context-gpu-backed-lending
name: GPU-backed lending
flow: supply
summary:
Lending structured against pools of GPUs as collateral, used to finance compute purchases without relying solely on developer or operator balance sheets.


