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Is the LLM Value Chain the Next Apple Supply Chain?

From the global manufacturing of a device to the chips, energy, data, and software behind an intelligent task.

Zefan Cai · October 18, 2026 · Global industries and companies

Apple’s supply chain offers an appealing point of comparison. A company that controls the device, operating system, and customer relationship organizes a complex global manufacturing network. Suppliers invest in processes, equipment, and capacity around new products, turning technological innovation into sustained growth. Today’s emerging supplier networks, from GPUs, high-bandwidth memory, and liquid cooling to expert data, agent training environments, and evaluation, readily bring that earlier supply chain to mind.

The analogy is useful. But if we use it only to search for “the Apple of AI” or “the next smartphone assembler,” we miss the most consequential changes.

The LLM value chain is developing several centers of bargaining power: computing platforms, advanced manufacturing, cloud and distribution, models, and applications that control business workflows. It adds a continuing layer of inference costs, along with constraints involving power, financing, and utilization. A supplier’s ability to earn profits depends on the bottleneck it resolves, how readily customers can replace it, and how much capital its growth consumes.

This article compares the two chains from the perspective of global industries and companies. Chinese companies are discussed within the global stages in which they operate.

Start with the structure of the two chains

The demand anchors in Apple’s supply chain are relatively clear: device shipments, replacement purchases, the active installed base, and the services consumed through those devices. IDC’s preliminary estimates, published in January 2026, put global smartphone shipments in calendar 2025 at approximately 1.2603 billion units, up 1.9% year over year. Apple shipped an estimated 247.8 million units, for a 19.7% share. These are estimates of branded device shipments, not unit sales disclosed by Apple or consumer activations. Source: IDC

Final demand in the LLM chain may take the form of a subscription, a million tokens, a task completed automatically, or an improvement in advertising conversion or software development productivity. Demand passes through model usage, cloud resource purchases, and data center construction before it becomes an order for chips or equipment. Growth at different stages often arrives at different times.

These differences are matters of emphasis. Apple also operates cloud and AI infrastructure, and AI suppliers also depend on large buyers. Suppliers such as TSMC can retain considerable strength in both chains. The comparison needs to follow actual transactions and relationships of control.

A global company map: what each stage does

The following map shows representative positions. A company can span several stages, and appearing in the same row does not make two companies interchangeable.

These positions can be read alongside Apple’s official supplier list, the company filings cited below, and OCI’s GPU infrastructure description. Supplier lists have their own reporting periods. The list obtained through Apple’s official 2026 reporting portal still states on its cover that it represents 98% of direct spending on materials, manufacturing, and assembly in FY2023. It establishes relationships during that coverage period, not suppliers’ shares of every product in 2026. Rows covering equipment, EDA, cloud, and applications also include indirect suppliers and ecosystem participants; they should not all be treated as direct Apple vendors.

This map exposes the limit of the analogy. Apple’s chain is centered on the product system of one device company. The LLM chain is a network spanning products, computing platforms, clouds, and applications. A data provider and a power equipment manufacturer create value in very different ways.

How Apple retains profits in its supply chain

Apple’s FY2025, the year ended September 27, 2025, provides a clear picture of its profit structure. Source: Apple 10-K

Services generated 42.17% of consolidated gross profit from roughly one-quarter of revenue: 82.314 ÷ 195.201. Hardware establishes the customer relationship; the operating system, distribution, and services allow that relationship to generate revenue after the device is sold. Looking only at iPhone shipments understates Apple’s economic structure.

But a 75.4% gross margin does not mean every app has that profitability. Apple recognizes third-party App Store-related sales on a net basis, including only the commission it retains in Services revenue. Hardware companies and manufacturers often recognize revenue on a different, gross basis. Revenue and gross margins reflect economic position, but also product mix and accounting presentation.

Manufacturing looks very different. In calendar 2025, Foxconn generated NTD 8.103 trillion in revenue, with calculated consolidated gross and operating margins of 6.15% and 3.20%, respectively. Source: Foxconn consolidated financial statements Scale, engineering expertise, and delivery capabilities can be formidable without producing the profit structure of a company that owns the end-user ecosystem.

The relatively low ratio of Apple’s own cash purchases of fixed assets to revenue also reflects this division of manufacturing responsibilities. Apple accesses a vast system of factories and equipment through suppliers. That capital does not disappear because it sits outside Apple’s balance sheet. To understand how little capital a lead company holds itself, we also need to see what its suppliers carry.

Why profits accrue at several points in the AI chain

Start by placing representative companies in one table. These are unadjusted consolidated financial results, not margins for AI businesses alone. Their fiscal years are not perfectly aligned, so the table illustrates economic structures rather than ranking profitability over an identical period.

Sources: Apple, NVIDIA, TSMC, Broadcom, AMD SEC data, Arista SEC data, Micron’s FY2025 comparative figures, Vertiv, and Foxconn. Operating margins are calculated as operating income divided by revenue, using GAAP or the applicable IFRS figures without mixing in adjusted or non-GAAP measures.

The data supports at least three observations.

First, upstream suppliers can retain substantial profits too. NVIDIA’s software, systems, and developer ecosystem; TSMC’s manufacturing processes, yields, and delivery capabilities; and Arista’s networking systems create different forms of switching cost. Apple’s own chain has long included powerful suppliers such as TSMC. The idea that the lead company captures all the profits while every supplier is a low-margin contract manufacturer is incomplete.

Second, entering AI does not turn every company into the same kind of business. Standardization, control over design, the proportion of materials in revenue, customer qualification, scale efficiencies, and supply-and-demand cycles all affect profitability. Delivering AI servers does not entitle a company to the gross margins of a semiconductor platform.

Third, business labels are broader than they may appear. NVIDIA’s FY2026 Data Center revenue was USD 193.7B, approximately 89.7% of total revenue, but it was not all related to LLMs. TSMC’s HPC platform represented 58% of 2025 revenue, while management described the narrower AI accelerator category as only a high-teens percentage. Calling the entire 58% HPC share “AI revenue” substantially overstates the comparable category. NVIDIA, TSMC earnings call

Memory cannot be explained by the HBM label alone either. SK hynix reported 2025 revenue of KRW 97.1467 trillion and operating profit of KRW 47.2063 trillion, and said HBM revenue more than doubled year over year. Other DRAM and NAND products also affected those results. Source: SK hynix This article does not fill in undisclosed absolute HBM revenue or margins with estimates.

The biggest change: who carries the capital spending

Apple places much of the manufacturing capital within its supplier network. The LLM industry is making cloud and compute operators carry increasingly large investments in equipment and facilities directly.

To keep the comparison as consistent as possible, the table uses gross cash purchases of property, plant, and equipment from the cash flow statement. It excludes noncash lease additions, finance lease principal payments, business acquisitions, and multiyear investment commitments. Microsoft’s and Apple’s FY2025 periods do not fully overlap with calendar 2025 for the other three companies.

Sources: Apple, Microsoft, Alphabet, Amazon, and Meta. These are consolidated expenditures and include non-AI businesses. Amazon also invests in logistics and other facilities; Meta’s infrastructure also supports advertising and recommendation systems. The table illustrates who bears the capital burden. It is not a ranking of LLM investment.

Definitions can materially change headline figures. Amazon’s cash capex after deducting asset sales and incentive proceeds was USD 128.320B in the same period. Meta reported approximately USD 72.22B in capex when finance lease principal payments were added to cash PP&E purchases. This table uses USD 131.819B and USD 69.691B, respectively, to maintain the definition of gross cash purchases. Amazon, Meta

By Microsoft’s FY2026, ended June 30, 2026, the same cash purchases line had reached USD 115.948B, or 34.94% of revenue for that period. Its annual report also disclosed that Microsoft Cloud gross margin fell to 66%, citing factors including continued investment in AI infrastructure and growing AI product usage. Microsoft Cloud should not be equated with Azure alone. Source: Microsoft

The capital structure of a specialist GPU cloud is more concentrated. CoreWeave recognized USD 5.131B of revenue in 2025, while cash purchases of equipment and capitalized internal-use software reached USD 10.309B, approximately 2.01 times annual revenue. Its largest unnamed customer contributed 67% of revenue. Source: CoreWeave 10-K This describes one year of expansion, not a permanent requirement to invest two dollars for every dollar of revenue. It shows that specialist compute operators carry equipment financing, delivery, and major-customer risks at the same time.

A GPU shipment is therefore not the end of the economic cycle. Equipment must be installed, networked, powered, and supplied with a steady workload. Pricing and utilization must then cover depreciation, energy, operations, maintenance, and financing costs. A supplier recognizing revenue today does not establish that the final computing service has already earned an adequate return.

Cloud, models, and applications: who owns the customer relationship

Looking only at hardware can turn the LLM chain into another list of electronics manufacturers. At the other end of the commercial cycle, however, the competition is over customer relationships, enterprise data, and workflows.

Cloud platforms already have established revenue sources. In 2025, AWS generated USD 128.725B in revenue and USD 45.606B in operating income, for a calculated operating margin of 35.4%. Google Cloud generated USD 58.705B in revenue and USD 13.910B in operating income, for a 23.7% operating margin. Amazon, Alphabet Both are entire cloud segments, not AI-only businesses. Google Cloud also includes Workspace and other offerings.

Model developers are building their own subscriptions, APIs, and application entry points, but their public figures cannot yet be placed alongside those filings without qualification.

Sources: OpenAI CFO disclosure, OpenAI scale announcement, and Anthropic Series H announcement. This article did not obtain complete public income statements for either company on a basis comparable with listed-company annual reports, and therefore does not insert estimated gross or operating margins.

LLM applications also carry continuing costs: deeper usage can entail more model execution, retrieval, tool calls, retries, and human review. A more useful enterprise metric is therefore total cost per acceptable completed task = total associated inference, tool, retry, and review costs ÷ the number of successfully completed tasks. Lower token prices can reduce costs while encouraging more complex usage. A price decline alone does not establish that total compute demand will fall or profits will rise.

There are consequently at least three possible answers to who resembles Apple. NVIDIA controls a computing developer ecosystem. Model companies such as OpenAI and Anthropic are competing for direct access to users. Microsoft, Google, Amazon, and enterprise software platforms already have extensive customer relationships, distribution, and workflows. An enterprise may use several models while finding it difficult to migrate its existing data permissions and business processes. The eventual division of value will depend on how these different switching costs compete.

Data companies in depth: Silicon Valley, Beijing, and UniPat

To place data companies in the value chain, we first need to establish what customers are buying. Some suppliers provide licensable text, images, and video. Others organize experts to produce demonstrations, preferences, and scoring criteria. Some deliver agent task environments that can be run, reset, and tested; others supply data governance and production software. Their roles resemble content/IP suppliers, engineering service providers, research and validation tools, and enterprise software platforms, respectively. Their revenue structures and competitive advantages differ accordingly.

The following discussion focuses on startups and new entrants. “Silicon Valley” is used broadly to refer to the San Francisco Bay Area’s startup ecosystem, while distinguishing headquarters, offices, legal contact addresses, and remote teams. The Beijing discussion likewise separates registered legal entities, new startups, and established suppliers. Registered experts, total payouts, financing valuations, and revenue are labeled separately.

Silicon Valley and the Bay Area: from talent networks to post-training engineering

Sources: Scale’s company profile and SWE Atlas; Reuters’ Surge/Scale report; Surge’s product catalog; Mercor’s website, data catalog, and CEO interview; Turing interview; Labelbox’s company profile, financing announcement, and Horizon; micro1’s update, legal contact information, and Cortex; Handshake’s company profile and AI business introduction; Lightwheel’s products and job locations. These figures have different dates, sources, and recognition bases. They are not a comparable revenue ranking.

Three developments deserve a closer look.

Surge brings training results into the sales process. Its Trusted Program lets qualifying labs train on the full dataset, evaluate the model, and pay when the agreed metrics improve. Surge This is a provision of a specific program, but it shows suppliers beginning to demonstrate value through what a dataset actually teaches a model. Delivery capabilities include difficult task design, expert judgment, validation on held-out examples, and iteration speed.

Mercor, Turing, micro1, and Handshake turn access to talent into data production capabilities. Model developers need professionals to demonstrate, judge, correct, and design tasks. Mercor’s catalog already includes containerized coding tasks, executable tests, enterprise application environments, and expert rubrics. micro1 connects diagnosis of enterprise agents’ failures to targeted data production and subsequent retesting. Mercor, micro1 But accounts in a talent network are different from qualified experts completing paid tasks this month. Mercor’s gross revenue example also shows that substantial contractor compensation can flow through a platform; that amount should not be compared directly with net software subscription revenue.

Labelbox combines tools, experts, and environments. Horizon delivers enterprise tool simulations, task trajectories, deterministic verifiers, and human preferences. Customer evidence shows a more precise division of work: the appendix of Meta’s GIM paper states that Labelbox was contracted to handle participant recruitment, credential verification, compensation, informed consent, and related work, while the GIM team performed final scoring independently. Original paper, §§G.1–G.2 The supplier provides production organization and quality support; the customer retains research design and acceptance criteria. This is a more concrete description of the transaction than simply “selling data to model companies.”

Beijing: new expert teams and established delivery networks

Beijing’s data ecosystem is also extending from collection and annotation into expert tasks, environments, and evaluation. Newly founded companies should nevertheless be distinguished from more established delivery networks.

Sources: Zhineng Zhishi’s product catalog, Meetchances, and its public corporate profile; TalentsAI’s website, entity privacy policy, and public corporate profile; Longmao’s company profile; Testin’s company and brand history and data products. A Beijing-registered entity does not establish that its entire team works in Beijing. DataBaker provides another example of a company spanning cities: its website says it began in Beijing in 2016 but is now headquartered in Qingdao, so it is not listed here as currently Beijing-headquartered. DataBaker

Zhineng Zhishi stands out because the coding tasks in its public catalog already include fixed starting environments, reference implementations, tests, and execution trajectories. Such assets can support both training and evaluation; their costs include environment configuration, correctness checks, difficulty calibration, and separation of training and test data. TalentsAI’s public evidence is more concentrated on expert credentials, specialized questions, and review processes. Describing tasks as complex does not establish that it has the same environment infrastructure.

Public financial information on these companies remains limited. Rather than rank them by unconfirmed valuations, it is more useful to track repeat purchases, acceptance and rework rates, cost per accepted task, and whether environments and quality-control software can be reused across projects.

UniPat: turning research problems, scoring rules, and execution environments into data assets

UniPat AI deserves separate discussion because it publicly demonstrates a relatively complete technical approach: models expand knowledge and construct candidate problems, experts and models jointly validate the problems and scoring rules, and the resulting assets support training and evaluation. This discussion uses its technical releases to examine the form of its capabilities; benchmark or open-source project scale is not treated as commercial orders.

Sources: UniScientist, SaaS-Bench, PaperBenchX, and the release-date index. These are self-reported metrics for public research projects. This article has not independently rerun the reported model evaluations. Nor did this review obtain verifiable full-year revenue or a headquarters address on the company’s website, so neither financial scale nor a headquarters location is inferred from its university collaborations.

This case shifts the value of a data company beyond the volume of text produced toward three questions: is the task worth learning, can successful performance be judged reliably, and does the environment allow training and evaluation to be repeated? A plausible-looking research report does not establish that an experiment was reproduced correctly. An agent clicking the right button does not establish that the final business state is consistent across systems. Encoding these differences in deliverable tasks, tools, trajectories, and scoring rules is itself engineering work.

An approach such as UniPat’s therefore has a closer counterpart in research-validation tools and engineering services within the Apple chain, combined with the reusability of data assets. Customers need to verify whether those assets produce reliable improvements on new tasks, rather than merely whether the supplier’s public leaderboard results look impressive.

Data production also has financial statements: growth, profits, and customer dependence

Private-company disclosures are insufficient to establish reliable profit margins. Listed data service providers offer a comparison of economic structures, provided they are not presented as new startups. The following table uses USD million (M), rather than the billions used in most other tables in this article.

Sources: Innodata’s 2025 10-K and Appen’s 2025 annual report. Appen is listed in Australia, but these financial statements are denominated in U.S. dollars. Its highlighted 4.5% revenue growth excludes the effect of the previous year’s Google contract and does not replace the unadjusted consolidated growth rate in the table. Appen disclosed that GenAI-related work accounted for 33% of annual revenue; all data services revenue should not be classified as LLM revenue.

The companies’ costs illustrate the importance of human labor and engineering delivery. Innodata’s direct operating costs increased by approximately USD 48.8M in 2025, including a USD 42.5M increase in labor-related costs; its contracts include both time-and-materials and fixed-price arrangements. In Appen’s expense table, crowd services and project employee costs totaled USD 137.773M, approximately 59.7% of revenue. The companies classify costs differently, so their reported “gross margins” are not forced into a single ranking here.

Dependence on major customers also resembles parts of the Apple chain. On January 22, 2024, Appen announced that Google’s relevant global services contract projects would end that March. The announcement put FY2023 Google revenue at USD 82.8M, a preliminary, unaudited figure at the time. Original announcement This does not prove that every customer will bring work in-house or demand lower prices. It does show that a large delivery organization does not automatically secure stable orders.

Customer payment timing affects cash flow too. Innodata’s operating cash flow reached USD 164.441M in the first half of 2026, compared with net income of USD 29.310M. Cash flow included increases in customer prepayments and payables. 2026 interim financial statements Receiving cash before future delivery obligations are fulfilled is different from having already earned an equivalent amount of profit.

Content licensing and enterprise data software: two other economic structures

Reddit, Shutterstock, and Getty Images illustrate the content and licensing chain, but not all of their non-advertising revenue should be labeled “AI training data.” Reddit’s Other revenue was USD 140.026M in 2025, or 6.36% of total revenue, including content licensing and products sold to users. Shutterstock’s Data, Distribution and Services revenue was USD 203.264M, or 20.53% of total revenue, but also included distribution, advertising, and production services. Reddit annual report, Shutterstock annual report

The rights these companies sell differ as well. Continuous access and updates, training use, display within answers, and licenses to use generated outputs are not the same contract. For example, Getty’s 2026 OpenAI announcement concerned a display agreement for search and discovery within ChatGPT. It does not establish a license to train on Getty’s entire library. Getty’s official announcement Data can be licensed repeatedly, but permitted uses, exclusivity, and the chain of author permissions affect its value.

Snowflake and Databricks primarily connect models with governed enterprise data and workflows. Snowflake generated USD 4.684B in FY2026 revenue, for the year ended January 31, 2026, with a calculated consolidated GAAP gross margin of 67.2%, but an operating loss of USD 1.435B. Its period-end net revenue retention rate of 125% measured expansion in product revenue from existing customers, not user retention. Snowflake 10-K This illustrates a different software-platform cost structure and reminds us that high gross margins do not guarantee operating profitability.

On August 13, 2026, Databricks reported a revenue run rate above USD 7B. Its product priorities included the Lakebase database for agents, Genie, and governance and cost control across multiple models. Databricks update This is not an audited figure directly comparable with Snowflake’s recognized full-year revenue. Neither company automatically owns the copyright to customer data merely by managing it, and neither company’s entire platform revenue should be treated as sales of training corpora to model developers.

Bargaining power in data: acceptance, reuse, and customer trust

Data suppliers and Apple suppliers both work with specifications, pilots, quality control, acceptance testing, and major customers. But data is not consumed like one component installed in one phone. A nonexclusive data asset can be licensed repeatedly, and a task environment can be run repeatedly; custom expert labor and environment maintenance also create continuing costs. Growth in future inference tokens therefore cannot be translated mechanically into growth in data purchases.

More useful measures are the full delivery cost per accepted task, first-pass acceptance rates, rework and update costs, asset reuse rights, repeat orders, and cash collections. Synthetic models can lower generation costs while shifting bottlenecks toward task selection, factual verification, graders, and control of evaluation contamination. UniPat’s joint human-model rubric development, Zhineng Zhishi’s executable tests, and Surge’s trial training all address such problems, rather than simply increasing the number of records.

Customer trust is also a commercial asset. Reuters reported in 2025 that Google and OpenAI were then planning to reduce or move away from their relationships with Scale, discussing neutrality concerns following a strategic investment. Scale’s official response pledged not to give Meta access to its internal systems or customers’ confidential information. Reuters, Scale’s response This is evidence about supplier relationships and trust, not evidence of a data leak, nor proof that every relationship subsequently ended.

Seen from this layer, the LLM industry’s critical inputs include both expensive chips and professional judgment and validation conditions that are difficult to produce reliably. How much profit a data company can retain depends on whether it can turn one-off deliveries into reusable capabilities while continuing to meet increasingly demanding customer acceptance standards.

Customer concentration, cash flow, and the overlap between the chains

Many end uses for AI do not mean every supplier has a diversified customer base. CoreWeave’s 67% share from its largest customer is one example; disclosures from electronics manufacturers and optical communications companies show similar patterns.

The following comparison uses four companies within global manufacturing networks, all on a calendar 2025 consolidated basis. Revenue is in RMB 100 million, and is not added to dollar-denominated amounts.

Sources: annual reports from Luxshare, Inspur Information, Zhongji Innolight, and Eoptolink. Net margin attributable to parent shareholders equals net profit attributable to shareholders of the listed company divided by consolidated revenue; it is a different measure from operating margin above. Customer concentration follows each company’s disclosed direct-customer basis. Unnamed customers are not identified as Apple, NVIDIA, or a particular cloud provider.

The differences are substantial. Participating in AI infrastructure markets can mean supplying relatively profitable optical interconnect components or assembling low-margin systems. At the same time, several AI-related suppliers still have highly concentrated customer bases. A downstream world of many models and applications can still channel procurement through a small number of cloud and equipment buyers.

Profits also have to turn into cash. In the first half of 2026, Zhongji Innolight reported net profit attributable to parent shareholders of RMB 13.651 billion, versus operating cash flow of RMB 1.800 billion. Source: interim report Such a gap warrants further examination of receivables, inventory, collection timing, and seasonality. One half-year does not establish that growth is problematic, but rising profits should not automatically be treated as cash collected at the same pace.

The chains are already overlapping in practice. Foxconn’s cloud and networking products represented approximately 40% of 2025 revenue, exceeding the 38% from smart consumer electronics. The category includes servers and communications networks, not AI alone. Source: Foxconn annual report Luxshare’s communications and data center business generated RMB 24.568 billion in 2025, or 7.39% of consolidated revenue. Its segment gross margin of 18.40% exceeded the consumer electronics segment’s 10.64%. Source: Luxshare annual report Communications and data center revenue likewise cannot all be labeled LLM revenue.

Manufacturing capabilities can transfer, but doing so requires new product qualifications, engineering skills, and capital. Connectors, PCBs, optical interconnects, system integration, power delivery, and thermal management enter new order structures in different ways. “Part of the Apple supply chain” and “part of the AI chain” are becoming less useful as mutually exclusive labels.

What the new data from 2026 changes

The annual tables provide a baseline; more recent data tests whether those trends are continuing. The following are published actual results, which must be distinguished from forward guidance.

Sources: NVIDIA, TSMC, Broadcom, Micron, and Apple. Micron’s 80.7% is calculated from GAAP gross profit of USD 107.504B divided by revenue of USD 133.188B. Its materials also provide non-GAAP figures, which are not mixed into this calculation.

Micron’s September 2026 disclosure also stated that agreements covered most of its 2027 HBM bit supply. Initial output from its Singapore HBM packaging facility was expected in early 2027, while other new facilities would take longer. Source: Micron management remarks Agreements are not recognized revenue, and a factory’s first production is not the same as immediately reaching mature capacity. Near-term supply constraints can generate profits, while capacity expansion, product iterations, and price changes can alter the subsequent distribution of value.

Price increases for conventional DRAM and NAND also contributed to Micron’s growth during the period; the improvement in profitability cannot all be attributed to HBM. The profits in these tables reflect both company structures and the supply-and-demand conditions of the period, and do not justify permanent “high-margin” or “low-margin” labels for entire stages of the industry.

Power, geography, and trade

Apple’s chain depends on manufacturing clusters across national borders. Its FY2025 annual report still lists mainland China, India, Japan, South Korea, Taiwan, and Vietnam as the principal locations of manufacturing partners, and warns that changing its supply chain can be expensive, time-consuming, and disruptive. Apple Relocation involves supplier networks, yields, equipment, and engineering organizations. A company’s headquarters country does not establish where it produces.

The AI chain adds a more prominent constraint: computing capacity must secure electricity, substation equipment, networking, and cooling at a specific location.

The IEA’s 2025 Energy and AI report estimates that all data centers globally consumed approximately 415 TWh of electricity in 2024, around 1.5% of global electricity consumption. Its base-case projection for 2030 is approximately 945 TWh. Source: IEA Both figures include non-AI data centers, and the latter is a forecast, not consumption that has already occurred.

Local delivery schedules matter more than the global total alone. The same report notes that building new transmission lines in advanced economies often takes 4–8 years, while a data center may become operational in 2–3 years. Full report, pp. 15, 50, and 95 There is a long chain of conversions between a data center order, a completed building, power-ready capacity, and actual computing revenue.

The product positions of Vertiv, Schneider Electric, Eaton, Delta, and others therefore deserve separate analysis. Liquid cooling cannot replace a grid connection, and an equipment supplier’s backlog is not realized revenue. Rising data center power consumption also cannot be translated directly into the revenue growth of a particular cooling company.

Trade rules change the addressable market as well. A BIS rule published on January 15, 2026, moved certain qualifying advanced computing exports to China and Macau to case-by-case license review, covering the H200 and products meeting the relevant parameters and conditions. This did not eliminate controls generally or guarantee license approval. Source: Federal Register Assessing global companies requires attention to technical specifications, destinations, licensing conditions, and subsequent rule changes.

Capital relationships also merit tracking. OpenAI’s new investment announcement on February 27, 2026, included NVIDIA and Amazon, while Anthropic’s financing and compute partnerships also involve several cloud and hardware companies. OpenAI, Anthropic A company can be an investor, supplier, and distribution partner at the same time. Financing supports expansion, but funding amounts, procurement commitments, and actual payments from final customers are different quantities. Repeatedly adding them together does not establish the size of demand.

What to watch next

The most useful lesson from Apple’s supply chain is how manufacturing at scale, supplier qualification, and ecosystem control combine to create commercial advantages. The LLM chain requires additional scrutiny: whether sustained, productive demand can absorb heavy compute investment, and whether profits from bottlenecks can persist as capacity expands and alternative technologies emerge.

I see the LLM industry as a division of value that is still taking shape. GPU platforms have established strong positions. Advanced manufacturing and memory retain physical constraints. Cloud providers control capital and distribution, while models and applications are still competing for the final customer. These companies depend on one another while using in-house development, alternative sourcing, and product integration to compete for adjacent roles.

Whether one company will emerge with Apple’s combination of control over the device, experience, and ecosystem economics remains an open question. A more useful question is: for each increment of demand, who solves a problem that cannot yet be bypassed, who carries the capital and inventory, and who ultimately retains the customer and the cash flow?

Following those questions makes the similarities, differences, and existing intersections between the two chains clearer.

Sources and calculation notes

This article prioritizes company annual reports, quarterly disclosures, official supplier materials, and original institutional research. For some private companies, it uses reporting and interviews from Reuters and TechCrunch, retaining the reporting dates and the fact that the figures have not been publicly audited. Financial figures are disclosures by the relevant reporting entities; model-company snapshots, product pages, and management plans retain their status as company-reported information. The sources below also provide a way to check reporting periods, financial definitions, and business boundaries.

Gross margin is calculated as (revenue − cost of sales) ÷ revenue; operating margin as operating income ÷ revenue. Net margin attributable to parent shareholders is used only for the manufacturing examples explicitly labeled as such. Percentages are calculated from original amounts and then rounded. Amounts in different currencies are neither converted nor added together, and companies’ revenues may reflect repeated transactions involving the same input. This article does not infer the identities of unnamed customers or use undisclosed contract prices to fill gaps in “AI revenue.”

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