하이퍼스케일 시장 트래커(2026년 2분기) : 설비투자가 7,000억 달러를 돌파하며, Alphabet의 이익 증가로 순이익률이 사상 최고치를 기록한 반면, 자유 현금 흐름 마진은 사상 최저치를 기록

Snapshot view of 2Q26

In 2Q26, hyperscale revenues reached $849.2B (single-quarter, +17.7% YoY), with annualized revenues reaching $3.264T (+15.8% YoY). Annualized capex surged to $701.5B (+75.3% YoY), driving capital intensity to 21.5% of revenues, well above the telecom sector’s ~16% level. Net PP&E grew +55.5% YoY. Net profit margin hit a fresh record of 26.1%, boosted heavily by a one-time gain at Alphabet; FCF margin, a cleaner read on cash generation, fell again to 9.5%, its lowest since our database begins in 2011. Headcount grew 6.2% YoY in 2Q26. The full report analyzes what is driving each of these numbers, which companies are winning and losing, and what the data says about where this market goes next.

Why read this report

MTN Consulting has been tracking this market with quarterly deep dives since 4Q17, and our database begins in 1Q11. We have a long history of in-depth coverage of this market, and proprietary tools and consistent, comparable data across revenues, capex, R&D, profitability, employment, and balance sheet metrics for the world’s leading hyperscalers (previously called “webscalers”). We cover Amazon, Alphabet, Microsoft, Meta, Apple, Oracle, Alibaba, Tencent, and more than a dozen other players, including newer entrants like CoreWeave, Nebius, Kuaishou, and Xiaomi. No other analyst firm covers this market with this depth or our data coverage.

We have called this market a bubble for almost two years now, and the analysis reflects that view. The 2Q26 report includes detailed company-by-company breakouts, regional data, vendor revenue benchmarks, and three capex outlook scenarios for 2026-2028. If you are making investment, procurement, or strategic decisions that depend on where hyperscale spending goes from here, this report is built for you.

• Notes:
  • (1) This is MTN Consulting’s 35th quarterly assessment of the hyperscale market, part of a series we launched in 4Q17; our data and analysis spans the 1Q11-2Q26 timeframe, i.e. 62 quarters.
  • (2) The companies in our study include several recent additions: CoreWeave (added in 2024), and Kuaishou, Nebius (Yandex spinoff), and Xiaomi (added in 3Q25).
  • (3) The 4Q25 edition adopts a new term for the sector, swapping “webscale” with the more common “hyperscale”.

Revenue: Topline tops $849B (+17.7% YoY), as HPE, Meta, and Yandex post the biggest gains among established players

Hyperscale revenues reached $849.2B in 2Q26, up 17.7% YoY, with annualized revenues rising to $3.264T (+15.8% YoY). CoreWeave (+112% YoY) and Nebius (+454%) remain the fastest-growing names in our coverage, though both remain small in absolute terms. Among established players, HPE (+33.7%, still absorbing Juniper Networks) posted the largest gain, ahead of Meta (+28.0%) and Yandex (+26.0%) – a name that rarely tops this list. Alphabet (+24.2%), Oracle (+20.6%, increasingly a beneficiary of OpenAI-linked cloud contracts), Amazon (+19.6%), Tencent (+17.9%), and Microsoft (+17.8%) all grew above the sector average. At the bottom of the table, last quarter’s laggards Baidu, JD.Com, and Alibaba all returned to growth, at 1.8%, 3.2%, and 15.4% respectively. The only two companies in our coverage with a revenue decline this quarter were Fujitsu (-5.7%) and Xiaomi (-0.2%).

Advertising still anchors several of the largest names here. Meta remains the most exposed, with ads still generating the overwhelming majority of revenue, even as it keeps trying to diversify into hardware and AI platforms. Alphabet’s non-ad share has climbed past 25% of total revenue. Amazon sits just under 10% of revenue from ads. That advertising exposure is a real risk given concerns about US consumer spending in 2026, concerns that have grown sharper following the energy disruption tied to the Trump administration’s actions in Iran.

The bigger open question for the sector: will AI platforms end up relying on advertising too, given how little traction paid subscriptions have found outside early adopters?

Capex: Annualized spend tops $700B (+75% YoY), and even the industry’s own guidance keeps getting revised upward

Annualized capex reached $701.5B in 2Q26, up 75.3% YoY, with capital intensity rising to 21.5% of revenues – now more than five points above the telecom sector’s ~16%. Amazon, Alphabet and Meta each raised their 2026 capex guidance this quarter: Amazon to ~$220B (from $200B), Alphabet to $195-205B (from $180-190B), and Meta to $130-145B (from $125-145B). Amazon cited higher component pricing and Alphabet faster delivery of capacity. Some of this is real – Oracle’s own contracted backlog reached $664B this quarter, up more than $200B in a single year. But a meaningful share of the sector’s combined backlog still reflects circular financing between the same small set of counterparties. In August 2026, Nvidia agreed to guarantee up to $105B of lease and power payments for OpenAI’s new data center campus in Ohio, where Nvidia will also be the sole chip supplier.

The capex escalation still carries the dynamics of an arms race: no company wants to appear less committed to AI than its rivals, and the scale of investment makes it harder to pull back. With hundreds of billions already committed, stopping would mean writing down assets, admitting the strategy has failed, and giving competitors room to advance. So the spending continues, driven by a mix of genuine ambition, fear of falling behind, and the hope of securing a market position before the economics come under scrutiny.

Power is emerging as a constraint in its own right, though it is too early to say it has overtaken chips as the industry’s biggest bottleneck. The bigger issue is timing: data centers can be built and chips procured within months, while grid connections often take years. Capital is already moving to address the gap. Google, Intersect Power and TPG Rise Climate have committed $20B to a partnership linking new solar, wind and storage capacity directly to data center projects. In June 2026, KKR also launched Helix Digital Infrastructure, a $10B+ platform backed by NVIDIA, Vistra and others, focused on developing hyperscale data centers with power secured upfront. Samsung group companies committed a further $1B to Helix in September. Securing energy supply is becoming part of the AI infrastructure investment itself. The question is whether these private power arrangements can deliver capacity faster than traditional grid connections.

Fiber remains a real constraint too: Meta and Amazon have both signed multi-billion-dollar fiber contracts with Corning this year (Meta’s agreement is worth up to $6B). NVIDIA invested $500M in Corning in May specifically to expand its optical manufacturing capacity. Chip supply remains a concern, with Nvidia CEO Jensen Huang warning that shortages could persist for years. Power and connectivity are also becoming constraints that companies cannot solve simply by spending more. They need to secure energy supplies and network access well before their data centers come online.

Custom silicon remains the industry’s hedge against NVIDIA pricing power. Amazon’s chips business, led by Trainium and Graviton, now has “an annual revenue run rate of over $25 billion” and is growing at triple-digit rates, CEO Andy Jassy said on the July earnings call. He added that revenue commitments nearly tripled in the quarter. Jassy has also said the business could generate $50B a year if Amazon sold the chips externally the way conventional chip vendors do. Amazon estimates Trainium saves it tens of billions of dollars in annual capex versus relying on Nvidia, plus several hundred basis points of margin. Google’s tensor processing unit (TPU) program and Meta’s Broadcom-developed silicon are on similar trajectories. As custom silicon matures, it should eventually take share from Nvidia in Network/IT capex. This quarter’s numbers show no sign of that yet: Network/IT capex grew 80.2%, faster than total capex at 75.3%.

R&D intensity was 13.4% of revenues in 2Q26, against capex intensity of 21.5%. The 8.1-point difference has grown every quarter this year. For most of this sector’s history, R&D spending as a share of revenue ran ahead of capex, a legacy of its software-centric roots. That relationship has now inverted: the sector has shifted to a hardware-first growth model, and capital is not yet flowing back toward software and monetization work. Net PP&E per employee keeps climbing, with net PP&E up 55.5% YoY against 6.2% headcount growth.

M&A spending rose this quarter: annualized acquisition spend reached $103.0B, up nearly 30% from 1Q26 and more than triple the year-ago level. That is still a fraction of the $701.5B annualized capex bill. The underlying logic has not changed: a traditional acquisition takes 12 months or more to close, too slow for an arms race moving this fast. Capital continues to flow disproportionately into infrastructure capex, targeted IP purchases, and acqui-hires alongside the pickup in traditional M&A. The Meta-Scale AI deal (more than $14B for a 49% stake, announced mid-2025) remains the template: immediate access to talent and data-labeling IP, without the integration lag or regulatory friction of a full buyout.

China: Huawei pulls its Ascend roadmap forward; the embargo still isn’t working

Huawei says it is selling every Ascend chip it can make, and rotating chairman Eric Xu said in early October that Ascend has likely overtaken Nvidia in China, based on Huawei’s own tracking. Bloomberg reported on 10 September 2026 that Huawei had raised the suggested price of its Ascend 950DT accelerator by around 60% over three months, to CNY 250,000, as demand outstripped supply. The company is expected to produce only a few hundred thousand units this year, with shortages of high-bandwidth memory (HBM) constraining output. DeepSeek alone plans to deploy at least 160,000 of them at a data center in Inner Mongolia. At Huawei Connect in September, Huawei brought its next chip, the Ascend 960DT, forward by three quarters to 1Q27, and said it is prioritizing Chinese customers. Huawei has also acknowledged that its current chips remain one to two generations behind Nvidia, while independently verified benchmarks against Nvidia hardware remain limited. Claims of performance parity therefore remain largely vendor- or analyst-sourced. DeepSeek is strengthening its ties with Huawei’s silicon ecosystem. On 30 September, it announced that it had developed and open-sourced programming tools for Huawei’s Ascend AI chips, including compute and communication libraries. The work also covers a supernode built around 128 Ascend 950 chips, with both companies optimizing computation and communication. Baidu has separately filed for a Hong Kong listing of its AI-chip unit, Kunlunxin, although the valuation and timing remain unsettled. Baidu has separately filed for a Hong Kong listing of its AI-chip unit, Kunlunxin, although the valuation and timing remain unsettled.

Stanford’s 2026 AI Index puts the gap between the top US and Chinese AI models at 2.7% as of March 2026, measured on the Arena leaderboard. At the same time, the US continues to lead in private AI investment and the production of top-tier models, while China leads in areas including AI publications, citations, patents and industrial robot installations. Whatever the chip-for-chip comparison, export controls have not prevented China from continuing to build out its domestic AI ecosystem.

This matters for capex forecasts in two ways. First, if Chinese models remain competitive at a fraction of the investment, it undercuts the argument that massive US hyperscaler capex is the only path to frontier AI. Second, a high-profile Chinese breakthrough could force a rapid reassessment of US spending plans, especially if it lands alongside weak macro data or disappointing AI monetization.

Capex Outlook: We raise all three scenarios, with 2026 at ~$890B in our base case

Capex closed 2025 at ~$507B, and hyperscalers spent $414.3B in the first half of 2026 alone ($183.3B in 1Q26 and $231.0B in 2Q26). Amazon, Alphabet and Meta have all raised their 2026 guidance since early in the year, to ~$220B, $195-205B and $130-145B. We have raised all three scenarios to match. Each case now starts from a higher 2026 level, and the 2027-28 path keeps the same shape as before. The high case (~$1,020B in 2026, ~$1,052B in 2027, ~$1,010B in 2028) assumes spending keeps accelerating through 2H26, to about $300B a quarter.

The base case (~$890B in 2026, ~$883B in 2027, ~$852B in 2028) assumed more conservative execution: Nvidia losing share to custom silicon, some projects consolidating or delaying, and energy and power constraints slowing deployment. That last assumption has held up: the stalled data center project and grid delays described above are the kind of friction this scenario anticipated. For 2026, it implies 2H26 spending roughly flat with 2Q26.

The low case (~$850B in 2026, declining to ~$701B in 2027 and ~$643B in 2028) assumes a market correction beginning in 2027, after a modest 2H26 slowdown to about $218B a quarter. The likely trigger is a combination of weak macro data, soft AI revenue monetization, and a high-profile Chinese AI breakthrough that resets global expectations. A delayed or canceled OpenAI initial public offering (IPO) is the early signal we watch. OpenAI said on 8 June 2026 that it had confidentially submitted a draft registration statement for a US IPO, and The New York Times reported on 26 June that Sam Altman was pressing advisers for a $1 trillion valuation. In a September interview with Fortune, Altman said that “right now would be an ill-advised moment to go public” and ruled out a 2026 listing. OpenAI is raising private money instead: Bloomberg reported on 29 September that it is targeting at least $30B at a $1.4 trillion valuation. So the delay does not yet point to a funding problem.

Profitability: Net margin reaches a record on a one-time Alphabet gain, while cash profitability keeps sliding

The divergence between net margin and FCF margin has widened, and an Alphabet gain explains part of it. Net profit margin reached a record 26.1% in 2Q26, the highest in our database, which goes back to 2011. A large share of that jump traces to a one-time, non-cash gain on equity securities at Alphabet. Alphabet’s 2Q26 earnings release shows the $99.0B pre-tax gain added $77.1B to net income. FCF margin nets out capex and does not include that gain, and it tells the opposite story: it fell again, to 9.5%, a new low. FCF is operating cash minus capex, so the widening divergence measures what the AI arms race costs. The harder hyperscalers spend, the more they draw down their own liquidity. Microsoft, Tencent and Meta report strong net income while their FCF margins erode, because graphics processing unit (GPU) cluster spending runs ahead of the cash these businesses generate. Amazon and Oracle follow the same pattern, trading liquid cash for infrastructure with long payoff horizons.

On a per-employee basis, Apple leads at $816K in FCF per employee (2Q26 annualized), up from $773K a year ago, followed by Meta at $543K, down from $619K. Meta’s infrastructure spending has started to outpace the cash its ad business generates. Microsoft ($300K) and Alphabet ($268K) follow among the larger players. By FCF margin: Apple (29.3%) leads our coverage, followed by Tencent (23.7%, down sharply from 31.8%) and SAP (22.9%). At the other end, Oracle (-35.2%) is even deeper in its build-ahead phase than a year ago, when it stood at -20.7%. CoreWeave (-179.9%) and Nebius (-434.2%) remain deeply negative as early-stage neoclouds, GPU-focused cloud providers, scale rapidly against contracted backlogs. Alibaba (-7.4%) and Baidu (-10.2%) are both more FCF-negative than a year ago, even as their revenue growth has turned positive. Top-line recovery and cash profitability do not move together.

Regulatory fines remain a minor drag on profitability. On 2 July 2026, the Court of Justice of the European Union dismissed Google’s final appeal against a €4.1B Android antitrust fine. On 23 July, the European Commission fined Google about $1B under the Digital Markets Act, over search ranking and Play Store restrictions. Hyperscalers continue to treat fines largely as a cost of doing business: they fight rulings in court, absorb the fines and use public relations to manage the fallout.

Employment: Headcount growth holds flat at +6.2% YoY, still trailing the long-run trend

Headcount grew 6.2% YoY in 2Q26, essentially flat with last quarter and still running below the 2019-25 CAGR of 7.9%. Total headcount remains a tricky metric for this sector, since the employee base is heavily influenced by logistics, fulfillment, and delivery staff at companies like Amazon, Alibaba, and JD.Com – a single company’s warehouse hiring can move the sector average more than any AI-related headcount trend. Among the more tech-centric hyperscalers, the direction continues to point downward.

The hyperscale business model is built around massive economies of scale, with investment concentrated in areas where the marginal cost of production can approach zero. Many hyperscaler executives would welcome a meaningfully smaller headcount if AI platforms can absorb more of the operational load – and that remains the clear direction of travel for the industry. Revenue per employee and net PP&E per employee have both climbed sharply over the last 2-3 years, and this quarter’s data – particularly the 55.5% jump in net PP&E against 6.2% headcount growth – shows no sign of that trend slowing.

Regional Revenues: Europe grows fastest (+20.4% YoY) and the Americas move above the sector average

Americas revenues reached $396.2B in 2Q26 (single-quarter, 46.7% of total, +19.1% YoY) – a reversal from last quarter, when the region grew below the sector average; it’s now the second-fastest-growing region. Europe reached $155.6B (18.3%, +20.4% YoY) and is now the fastest-growing region, ahead of Asia Pacific and MEA both this quarter and last. Asia Pacific contributed $270.9B (31.9%, +14.3% YoY), the slowest-growing region, still weighed down by Chinese macro headwinds and domestic competition, even as Alibaba, Baidu, and JD.Com all returned to growth this quarter. MEA reached $26.5B (3.1%, +16.8% YoY). Europe’s continued outperformance reflects sustained demand from regulated industries, AI Act compliance-driven cloud migration, and sovereign AI initiatives; the Americas’ rebound suggests US-based hyperscalers’ home market still has plenty of room to absorb fresh AI-driven demand even as they expand abroad.

Table of Contents

• 1. Report highlights
• 2. Outlook
• 3. Analysis
• 4. Key Stats
• 5. Company Drilldown
• 6. Company Benchmarking
• 7. Regional Breakouts
• 8. Raw Data
• 9. Exchange Rates
• 10. About