The capital required to build the AI era has arrived. The question now being answered — unevenly, and with consequence — is whether the power exists to run it.
A perspective from Open Doors Partners
Amazon’s projected capital expenditure for 2026 exceeds the entire annual investment budget of the US energy sector. One company, in a single year, is committing more to infrastructure than all of American energy exploration, refining, and delivery combined. That comparison is not offered as a statement about Amazon. It is offered as a statement about the scale of what is being built, and the particular constraint that scale has now encountered.
The five largest US cloud and AI infrastructure providers have committed collectively to between $600 and $630 billion in capital expenditure this year. Morgan Stanley estimates roughly $2.9 trillion in global data centre construction will flow through the global economy by 2028, with more than 80 percent of that spending still ahead. The Stargate project — a joint undertaking between OpenAI, SoftBank, and Oracle — has announced commitments toward a 10-gigawatt US buildout, with nearly $400 billion in capital pledged within the first three years.
These figures are consequential. But they are not the most consequential thing happening in this market.
Throughout 2024 and into 2025, the primary challenge facing AI infrastructure was financing. Matching the capital intensity of frontier AI workloads with instruments suited to long-duration, asset-heavy deployment required new thinking. The market answered with institutional depth. Industry-wide debt issuance reached $182 billion in 2025, nearly doubling the prior year. GPU-collateralised financing emerged as a new instrument class: Nscale secured $1.4 billion in delayed-draw facilities backed by compute hardware. Google issued roughly $29 billion in debt for data centre expansion, including a century bond. The full spectrum of institutional capital, from secured and unsecured to structured and securitised, moved into AI infrastructure financing. Morgan Stanley has identified a $1.5 trillion financing gap for the global buildout; sovereign and private capital are both moving to close it.
Capital found its form. What remains constrained is not money. It is electrons.
Microsoft disclosed an $80 billion backlog of Azure orders it cannot fulfil due to power constraints. Demand exists. Capital exists. Infrastructure cannot be deployed because the grid cannot absorb it. Meta has secured agreements for more than 6 gigawatts of nuclear-linked generation, sufficient to power five million homes, specifically to obtain firm, dispatchable power for AI workloads. Three of the largest hyperscalers have committed to tripling global nuclear production by 2050. The capital intensity of these businesses, Oracle at 57 percent of revenue and Microsoft at 45 percent, is at levels that have no historical precedent in technology. These are not technology companies that need electricity. They are, structurally, energy companies that produce compute.
The IEA’s projections make the constraint legible. Data centres consumed 415 terawatt-hours globally in 2024, roughly 1.5 percent of world electricity. By 2030, that figure is projected to reach 945 terawatt-hours, equivalent to Japan’s current total consumption, reached in six years. In the United States specifically, AI data centre power demand is projected to reach 123 gigawatts by 2035, a 30-fold increase from current levels. BloombergNEF revised its 2035 global data centre power demand forecast upward by 36 percent in the span of seven months, to 106 gigawatts. The revision is itself a signal: the constraint is not static. It is accelerating.
The geography of AI infrastructure is reorganising around this reality. Northern Virginia, the traditional centre of US data centre concentration, is approaching saturation. Development is shifting to central and southern Virginia, Georgia, and Texas, where available grid capacity offers operational room. Internationally, Southeast Asia and the Middle East are gaining share not on the basis of demand proximity or labour cost, but on the basis of what the constrained US grid cannot reliably offer: power at scale. The selection criteria for where AI infrastructure is built have changed. Power availability now ranks above connectivity, real estate cost, and in some cases regulatory environment.
Sovereign capital has recognised this dynamic before most institutional frameworks have caught up to it. Saudi Arabia has doubled its US investment target to approximately $1 trillion, with AI infrastructure as a primary focus. Sovereign wealth funds, including GIC, Temasek, and MGX, are embedded as co-investors in the largest AI model development rounds. Kuwait Investment Authority is named in Brookfield’s $100 billion AI infrastructure programme. The pattern is consistent: sovereigns are moving from enabling infrastructure investment to directing it, acquiring ownership positions in the physical systems on which the next decade of economic activity will run.
This is the second-order implication that private markets have been slower to price than credit markets have been. The asset underneath the AI infrastructure trade is not primarily the GPU cluster. It is the land-plus-power configuration that determines whether that cluster can be built and operated at scale. The valuation framework that applies to companies occupying structural positions in this configuration is not the framework venture has historically applied. These businesses carry capital intensity ratios that belong to infrastructure and industrial underwriting, the logic of utilities, transmission networks, and energy systems, not to growth-multiple technology investment. The firms that recognise this distinction early are underwriting a different asset than the firms that have not yet made the shift.
The constraint has moved. Capital arrived, found its instruments, and is now deployed at a scale the prior decade of technology investment would not have predicted. What is being underwritten now, at the institutional level, at the sovereign level, at the level of firms building durable positions in this category, is control over the physical systems that make the AI era operable.
The buildout is real. The question being answered, imperfectly and with consequence, is who controls the electrons.
Open Doors Partners LLC does not publicly offer or solicit investment opportunities. All investments are made through private placements under Rule 506(b) of Regulation D. Accredited investors only. Past performance is not indicative of future results.