The central downside to Dell’Oro’s forecast is an AI-demand and funding reset: if OpenAI, Anthropic, or other frontier-model providers fail to turn extraordinary usage growth into durable, high-margin cash flows, capacity commitments could be deferred, resized, or cancelled. Because the DCPI forecast assumes nearly 200 GW of added data-center capacity through 2030, even a partial reduction in the AI build plan would materially affect power, cooling, and electrical-equipment orders.

The OpenAI/Anthropic risk:

The potential issue is not that either company vanishes overnight. It is that they—and the hyperscalers and GPU-cloud firms supporting them—may discover that the revenue and gross-margin trajectory does not justify the scale of previously contracted compute.

The risk chain is: AI monetization miss→lower compute utilization / pricing→capex deferrals→fewer energized MW→lower DCPI revenue.

The exposure is unusually concentrated. Advanced AI demand is dominated by a small number of hyperscalers and frontier-model providers; McKinsey estimates that 60–65% of AI workloads in the United States and Europe will be hosted on hyperscaler infrastructure by 2030. Thus, a retrenchment by a few large buyers can have an outsized effect on the physical-infrastructure supply chain.

Why OpenAI is a focal point:

OpenAI’s downside case would be a mismatch between compute obligations and customer monetization:

• Consumer AI usage may remain high, but paid conversion, enterprise seat expansion, API volume, or willingness to pay for frontier-model performance may disappoint.

• Inference costs may not decline fast enough relative to prices, leaving growth without attractive contribution margins.

• New models may yield diminishing commercial differentiation, shortening product cycles and weakening customers’ willingness to pay premium prices.

• Its financing requirements could become harder to meet if capital markets reassess terminal valuations, the cost of debt rises, or strategic partners limit exposure.

Some reporting and commentary point to very large continuing compute costs and funding needs relative to reported revenue, but the precise economics are opaque because OpenAI remains private and uses non-GAAP and run-rate measures inconsistently across reports. That opacity is itself a risk: DCPI vendors can see announced projects and committed capacity, but cannot fully observe the ultimate cash-flow support for the tenant’s demand.

Why Anthropic is not a complete hedge:

Anthropic’s enterprise orientation and reported revenue growth could diversify the sector’s demand base, but it does not eliminate systemic risk. It faces many of the same conditions:

• Revenue is substantially concentrated in a relatively early enterprise-AI adoption cycle.

• Enterprise customers can trial models broadly but consolidate suppliers quickly if performance differences narrow.

• Model-price competition could reduce revenue per token or per API call faster than cost-per-token declines.

• Large training runs are discretionary. A pause in the cadence of frontier-model releases would immediately reduce the urgency of new GPU clusters and associated electrical/cooling plant.

Recent reports describe unusually rapid revenue expansion and positive adjusted operating income for Anthropic, but the sustainability and definition of those measures are not independently transparent in the way public-company financial statements are. The relevant question for DCPI is not just whether Anthropic grows revenue, but whether its long-term contracted compute load and its own capital support remain sufficient to sustain multi-year facility commitments.

Other downside mechanisms:

An AI crash is the sharpest downside scenario, but Dell’Oro’s bullish outcome also depends on several more gradual assumptions.

Dell’Oro itself reportedly frames the immediate risk as delivery—equipment lead times, construction labor, grid interconnection, and community consent—rather than demand. Those bottlenecks can cut near-term revenue even if AI demand is real, because DCPI is recognized when facilities are physically delivered and commissioned, not when a GPU cluster or capacity plan is announced.

Efficiency is a double-edged sword:

Dell’Oro’s premise benefits from high rack density: AI systems require more substantial electrical architecture and move cooling from conventional air systems toward liquid cooling. McKinsey notes that direct-to-chip cooling can address roughly 60–120 kW racks, and that immersion can support still higher densities; those architectures increase DCPI content per rack and often per MW.

But efficiency can reverse the volume implication. Better accelerators, model distillation, mixture-of-experts approaches, lower-precision inference, and power-system improvements can reduce electricity and infrastructure required per unit of AI output. The IEA explicitly models a “High Efficiency” pathway in which technology and software efficiency gains materially restrain data-center electricity demand, while its “Headwinds” case assumes slower AI uptake and capacity growth that plateaus beyond 2030, with efficiency offsetting much of the effect of increased IT use.

The key analytical distinction is:

• Revenue per MW can rise because AI racks need liquid cooling, high-capacity UPS, switchgear, busways, and sophisticated controls.

• Total MW deployed can fall if model efficiency improves or commercial demand disappoints.

Dell’Oro’s $120 billion outcome requires both substantial net new MW and elevated DCPI content per MW. A positive outcome on only the second factor would not fully protect the forecast.

What would signal trouble:

For a forward-looking DCPI thesis, monitor leading indicators rather than announced headline capex:

• OpenAI and Anthropic: paid enterprise adoption, API demand, realized—not merely annualized—revenue, gross margin, cash burn, and financing terms.

• Hyperscalers: capex guidance, AI-service revenue, remaining performance obligations, capacity utilization, and disclosure of power or data-center commitments.

• GPU-clouds and colocation firms: customer concentration, lease pre-commitments, cancellations, financing costs, and the ratio of contracted versus speculative capacity.

• Physical deployment: utility interconnection queues, energized MW rather than planned MW, transformer/switchgear order cancellations, and data-center construction starts.

• Economics: inference price declines versus cost declines, GPU utilization, and evidence that enterprise AI deployments generate measurable productivity or revenue returns.

A particularly bearish signal would be simultaneous model-price deflation, falling GPU utilization, and delayed data-center energization. That combination would mean the sector is not merely supply constrained; it would imply that the financial rationale for capacity has weakened.

Bottom line:

A failure by OpenAI or Anthropic to meet expectations could trigger a classic capital-cycle correction: capacity was ordered on expectations of demand, but the cash flows needed to validate the investment arrive later, at lower margins, or not at all. In that scenario, DCPI’s most vulnerable segments are discretionary greenfield power and cooling deployments attached to single large AI tenants or thinly capitalized GPU-cloud providers.

However, a single lab’s disappointment would not necessarily collapse the entire market. DCPI demand also comes from hyperscaler internal workloads, enterprise AI, cloud migration, conventional data growth, colocation expansion, and infrastructure upgrades. The most likely downside is therefore a lower and lumpier growth path, with project delays and inventory/order corrections, rather than zero growth. The more severe Dell’Oro downside requires a broad AI-ROI failure that causes multiple frontier labs and hyperscalers to retrench at the same time.