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  • Dell’Oro: AI Buildouts and Memory Inflation Push 1Q 2026 Data Center Capex Higher

    Dell’Oro: AI Buildouts and Memory Inflation Push 1Q 2026 Data Center Capex Higher

    Market research firm Dell’Oro Group reported that worldwide data center capital expenditure moved higher in the first quarter of 2026, attributing the increase to two forces working in tandem: continued buildouts of AI infrastructure and inflation in memory costs. The finding, published June 10, 2026, comes from the firm’s ongoing tracking of data center IT and infrastructure spending.

    The headline pairing matters. It signals that the capex surge is being driven not only by more servers, accelerators, and facilities being deployed, but also by each unit of that equipment costing more — a distinction with real consequences for how the numbers should be read.

    Executive Summary

    Dell’Oro Group’s first-quarter 2026 reading extends a multi-year run of elevated data center spending tied to artificial intelligence. Capex — capital expenditure, the money operators sink into servers, networking gear, storage, and the facilities that house them — climbed again in the quarter, with AI infrastructure named as the primary engine and memory cost inflation as a significant amplifier.

    The memory angle is the notable wrinkle. High-bandwidth memory (HBM) and conventional DRAM are essential inputs to AI servers, and when their prices rise, total spending rises even if unit volumes were flat. Dell’Oro’s framing suggests both effects are in play: operators are buying more, and paying more per unit of what they buy.

    For the infrastructure industry, the read-through is that the AI spend cycle is broadening rather than cresting. Spending strength that persists into 2026 — after two years in which skeptics repeatedly called a peak — keeps demand signals strong for chipmakers, memory suppliers, server OEMs, colocation providers, and the power and cooling ecosystem behind them.

    Broadening, Not Peaking

    Every quarter of continued capex growth is a data point against the “AI bubble about to deflate” thesis — and a data point that must itself be scrutinized. A first-quarter increase in 2026 means the hyperscalers and large AI builders entered the year still accelerating, not digesting. Historically, capex cycles in IT infrastructure end with a visible plateau in quarterly spending before the decline; Dell’Oro’s reading indicates that plateau has not yet arrived.

    The word “broadening” is doing real work here. Early AI capex was concentrated in a handful of hyperscale cloud providers. As the cycle matures, spending typically spreads to second-tier cloud operators, GPU-cloud specialists, enterprises building private AI capacity, and sovereign or national AI initiatives. A quarter in which growth continues at scale is consistent with that widening base of buyers, though the release headline alone does not break out who spent what.

    Memory Inflation: Growth With an Asterisk

    The second driver Dell’Oro names — memory cost inflation — deserves careful reading. Memory (DRAM for general computing, and especially high-bandwidth memory stacked directly alongside AI accelerators) has been in tight supply as AI demand outstripped what the small number of memory manufacturers could produce. When memory prices rise, every AI server costs more, and aggregate capex inflates mechanically.

    That means dollar-denominated capex growth overstates the growth in deployed computing capacity. An analyst comparing 1Q 2026 spending to a year earlier is partly measuring more infrastructure and partly measuring more expensive infrastructure. For memory suppliers this is a windfall; for buyers it is margin pressure; for anyone using capex as a proxy for AI capacity coming online, it is a reason to discount the headline number somewhat. Dell’Oro’s decision to name inflation explicitly as a driver is a useful piece of intellectual honesty in a market prone to reading every big number as pure demand.

    Winners Along the Supply Chain

    The beneficiaries of this spending pattern are ordered by scarcity. Memory manufacturers sit at the top: rising prices on constrained supply flow almost directly to their revenue. Accelerator vendors and the server OEMs that integrate them continue to ride volume growth. Behind the IT equipment, the physical layer — data center developers, colocation operators, power equipment makers, and cooling specialists — benefits from every incremental megawatt the AI buildout requires, and their revenue tends to lag IT capex, meaning a strong 1Q 2026 for equipment implies continued facility demand into 2027.

    The squeezed parties are buyers without pricing power. Smaller cloud providers and enterprises paying inflated memory prices face a worse cost position than hyperscalers, who negotiate supply agreements at scale. If memory inflation persists, it acts as a regressive tax on the smaller end of the AI market — one more force concentrating AI capacity among the largest players.

    The Risk Ledger

    None of this eliminates cycle risk. Capex is a leading indicator of expected demand, not proven demand: the spending only pays off if AI services generate revenue commensurate with the infrastructure behind them. Input-cost inflation adds a second risk — cycles fed partly by price increases can unwind sharply when supply catches up and prices normalize, as memory markets have done repeatedly across their history. And the physical constraints on the buildout, chiefly electric power availability, remain unresolved in many markets.

    The balanced read: 1Q 2026 confirms the AI infrastructure cycle remains in its expansion phase, while the memory-inflation component is a reminder to separate dollars spent from capacity gained before drawing conclusions about either demand or durability.

    Background

    Data center capex has been the defining economic story of the AI era. Since large language models triggered an infrastructure race in 2023, the biggest cloud and AI companies have committed historically unprecedented sums to accelerated computing — spending that flows through chipmakers and server vendors into land, buildings, power, and cooling. Independent trackers like Dell’Oro Group, which has analyzed telecom and data center equipment markets since 1995, provide the industry’s scorecard for whether that race is accelerating or cooling.

    Memory has emerged as the cycle’s chokepoint. Production of high-bandwidth memory is concentrated among a handful of manufacturers, and AI demand has kept supply tight, pushing prices upward across memory categories. That inflation now shows up directly in aggregate capex figures — making 2026 the year analysts must ask not just how much the industry is spending, but how much of that spending buys new capacity versus simply covering higher input costs.

    Source: AI Infrastructure Buildouts and Memory Cost Inflation Drove Data Center Capex Higher in 1Q 2026, According to Dell’Oro Group — Dell’Oro Group’s first-quarter 2026 data center capex report announcement, published June 10, 2026.

  • The ‘Memory Tax’: Dell’Oro Flags HBM and DRAM Costs in AI Infrastructure

    The ‘Memory Tax’: Dell’Oro Flags HBM and DRAM Costs in AI Infrastructure

    Market research firm Dell’Oro Group has published analysis describing a growing “memory tax” on AI infrastructure — the rising share of system cost attributable to high-bandwidth memory (HBM) and DRAM in AI servers and accelerators. The note, surfaced April 27, 2026, frames memory as an increasingly material and often under-examined component of AI capital spending.

    Executive Summary

    Dell’Oro Group, an analyst firm that tracks data center and telecom infrastructure markets, is calling attention to memory — specifically HBM, the stacked memory packaged alongside AI accelerators, and conventional DRAM used in servers — as a fast-growing cost component in AI infrastructure. The “memory tax” framing suggests that as AI models and the clusters that train and serve them grow, memory is consuming a larger slice of every infrastructure dollar.

    The framing matters because most public discussion of AI capital expenditure centers on GPUs and, increasingly, on power and data center construction. If memory costs are rising as a share of the bill of materials — the itemized cost of the components inside a server — then budget models built around accelerator pricing alone will understate the true cost of AI capacity. That has implications for cloud providers, enterprises buying AI servers, and the memory suppliers positioned to benefit.

    Readers should note what is available here: a headline and thesis from a recognized analyst firm, without the underlying figures, forecast horizon, or methodology visible in the source material. The direction of the claim is consistent with the widely reported tightness in memory supply driven by AI demand, but the magnitude is not substantiated in what we can see.

    Why Memory Became a Line Item Worth Naming

    AI accelerators are unusual among chips in that their usefulness is bounded as much by memory as by raw compute. Training and serving large models requires moving enormous volumes of data to the processor quickly, which is why modern accelerators are packaged with HBM — DRAM dies stacked vertically and connected to the processor over a very wide, short interface. HBM is expensive to manufacture, supply is concentrated among a small number of suppliers (SK hynix, Samsung, and Micron are the established producers), and each new accelerator generation ships with more of it.

    Conventional DRAM matters too: the host servers around the accelerators, plus the storage and networking tiers of an AI cluster, all consume memory. When one demand source — AI — pulls hard on a supply chain with long lead times and few producers, prices tend to rise across the board. Dell’Oro’s “memory tax” label captures the effect from the buyer’s side: a cost that arrives embedded in system prices whether or not the buyer itemizes it.

    Who Pays, and Who Collects

    If memory’s share of AI system cost is growing, the immediate beneficiaries are the memory manufacturers, for whom HBM commands substantially better margins than commodity DRAM historically has. Accelerator vendors sit in the middle: memory is a cost input to their products, but strong demand has so far allowed system prices to carry it. The buyers — hyperscale cloud providers, AI labs, and enterprises — absorb the tax directly in capital expenditure, and indirectly it flows into the price of cloud GPU capacity and AI services.

    There is a second-order effect worth watching. Rising memory prices do not stay confined to AI hardware. General-purpose servers, storage systems, and consumer devices draw on the same DRAM supply base, so a sustained AI-driven squeeze can raise costs for infrastructure buyers who are not purchasing AI systems at all. For data center operators and IT planners, that argues for treating memory pricing as a market variable in refresh budgets, not a constant.

    An Analyst Thesis, Not a Dataset — Yet

    It is worth being precise about the evidentiary weight of what has surfaced. Dell’Oro is an established infrastructure research firm, and the thesis aligns with observable market conditions. But the material visible here is a headline-level framing: it does not disclose how large the memory share of AI system cost currently is, how fast it is growing, or over what forecast period. “Growing” is directionally plausible and quantitatively unverified in this source.

    That distinction matters for anyone using the claim to make decisions. A memory share that rises from, say, a modest slice to a dominant one would reshape supplier negotiations and cloud pricing; a gradual drift would be a planning footnote. Until the underlying figures are public, the responsible reading is that memory costs deserve a named line in AI infrastructure budgets — and that the size of that line needs data the summary does not provide.

    Background

    The AI infrastructure buildout that accelerated from 2023 onward has been discussed mostly in terms of GPUs, power, and data center construction, but every AI accelerator ships with a large complement of high-bandwidth memory, and every cluster consumes conventional DRAM in its servers and supporting systems. Memory is a historically cyclical market dominated by a small number of manufacturers — SK hynix, Samsung, and Micron — and AI demand has become a defining force in its current cycle.

    Dell’Oro Group, founded in the 1990s and based in Silicon Valley, publishes recurring research on data center capex, servers, and network infrastructure. Its analysts’ framing of trends — in this case, memory as a “tax” on AI infrastructure — often shapes how vendors and buyers talk about market economics before detailed figures circulate publicly.

    Source: The Growing Memory Tax on AI Infrastructure — Dell’Oro Group, analyst commentary on rising HBM and DRAM costs in AI infrastructure economics, published April 27, 2026.