Scenarios for 2030

Every chapter before this one decides something about a building you can draw. This one decides which world the building lives in. The same 200 MW campus is a brilliant asset in one 2030 and a stranded liability in another, and the difference is which of a few structurally distinct futures actually arrives. The scenario is the largest uncontrolled variable in any AI-infrastructure thesis, and the one most often smuggled in as an unexamined assumption.

We frame 2030 along three axes that move semi-independently. First, the demand-and-capital regime: supercycle, digestion, or bubble — distinct from the firm-level economics of Chapter 1.8; here it is the sector-wide structural lens. Second, the architectural shape: centralized mega-campus versus distributed inference, the fork that decides where the megawatts go. Third, the industry structure and geographic re-map: who owns the capacity and where it lands. We then confront the power-supply endgame — CFE, SMR, fusion optionism, and the demand-side wildcards that could invalidate the whole demand curve — and close on stranded-asset and systemic risk, the downside each scenario hides.

Start with the demand-and-capital regime, because it sets the boundary conditions for everything else. The three scenarios are not optimist/realist/pessimist moods — they are different beliefs about a single mechanism: does AI revenue grow fast enough to service the capital being deployed before the assets depreciate? McKinsey's own framing spans a $3.7T-to-$7.9T range of AI-capable capex to 2030 against a ~$5.2T midline — a spread wide enough that the constrained and accelerated cases describe genuinely different industries (McKinsey, 2025).

Supercycle is the case where demand keeps outrunning supply: reasoning and agentic workloads inflate tokens-per-task, inference compute compounds, and power — not chips, not capital — stays the binding constraint through 2030. In this world the scarce asset is an energized megawatt, time-to-power is the master variable, and the operators who locked firm power and long-lead gear early win. Digestion is the soft-landing case: the extraordinary base is absorbed, capex growth decelerates sharply (one widely-cited path: ~51% growth in 2026 falling to ~13% in 2027 and ~5% in 2028) without a crash, and the winners are the disciplined operators who did not over-commit at the top. Bubble is the case where the revenue gap wins: the spend behaves like a utility build-out while revenue still behaves like software subscriptions, and the mismatch forces write-downs, cancelled announcements, and a depreciation reckoning. Sequoia's framing put the annual revenue that must materialize to justify the spend in the hundreds of billions and widening; Bain sized a ~$800B annual shortfall by 2030 even after AI-driven savings (Bain, 2025).

The decision that follows is not which scenario you predict — nobody knows — but which one your balance sheet is structured to survive. A supercycle bet is contracted, levered, and over-built on power; a bubble hedge is merchant-light, opex-flexible, and short on irreversible commitments. You cannot be optimized for both at once, and pretending you are is how operators get caught.

The second axis is architectural, and it is orthogonal to the demand regime: even a supercycle splits between two physically different build-outs that pull siting and design in opposite directions. Centralized mega-campus is the gigawatt-scale training factory — one tightly-coupled supercomputer chasing the cheapest firm power and the coldest climate, indifferent to user proximity, with the gigawatt campus as the unit of compute (→ Chapter 16.1). Distributed inference is the fleet of smaller, latency-first sites pushed toward users — metro colos, edge nodes, 5G MEC, branch and venue deployments — because for most inference, latency, privacy, and egress cost dominate, and a small site near the user beats a remote gigawatt campus.

The forecast that makes this fork decisive is the inference transition: by the end of the decade the majority of AI compute is inference, not training, even as frontier training concentrates into 1 GW-plus campuses that become standard (Bain, 2025). That bifurcation is the whole point. If you build only the centralized campus, you have optimized for the shrinking-share workload and a power-first remote site; if you build only the distributed fleet, you cannot host frontier training and you pay a latency-first energy premium of 2-4x. The two design bases share almost nothing — density, redundancy, fabric blocking, and siting driver all invert — which is why this is a fork to decide at scoping, not a dial to tune later. Reasoning and test-time compute, which inflate the decode-heavy share, push the center of gravity toward the distributed-inference side over the decade (→ Chapter 16.3).

Who owns the 2030 capacity is a third axis. Three operator archetypes are consolidating, each carrying a different cost-of-capital and obsolescence exposure. Hyperscalers self-fund the largest share — the top-four US hyperscalers alone are tracking toward roughly $600B of data-center capex in 2026 (CreditSights/Dell'Oro), with the big-five-plus-Oracle cluster cited as high as ~$725B — financing from cash flow and carrying the depreciation debate on their own books. Neoclouds are the pure-play GPU landlords: capital-intensive, debt-financed, margin-pressured, and the most exposed to a utilization or residual-value shock because their entire asset is the depreciating silicon. Sovereign and enterprise buyers — nation-states pursuing compute independence, regulated industries pursuing data residency — are the fastest-broadening demand source, and the one least governed by pure unit economics.

The geographic re-map follows from where firm power and permissive policy actually exist. Power-first training campuses migrate to stranded-generation and cold-climate regions; latency-first inference clusters stay in the metros. Sovereign demand and export controls re-map the map again: the (later rescinded) US AI-diffusion tiering, Gulf-state build-outs converting energy wealth into compute, and the empirical reality that residency is not control — a study of 775 non-US data centers found sovereignty depends on control-of-stack, not just where the concrete sits (arXiv, 2025). The consequence for a 2026 siting decision: the cheapest-power, fastest-permit jurisdiction may carry a geopolitical or export-control tail risk that a spreadsheet does not price. → grid integration in Chapter 15.8.

Every 2030 scenario eventually collides with the same wall: where do the firm, clean megawatts come from, and when? Global data-center electricity demand is on track to roughly double from ~485 TWh in 2025 to ~950 TWh by 2030 (~3% of global electricity), with AI-specific load tripling (IEA, 2026). In the US, data centers move from ~4-5% of electricity today toward 9-17% by 2030 on EPRI's scenarios. The endgame is a ranking of supply options by the one variable that actually gates a build: time-to-megawatt, not headline LCOE.

The ordering that matters is that the clean-and-firm options that look best on a carbon spreadsheet are the slowest to energize. Existing nuclear and restarts are the only firm-clean option available now — the Three Mile Island/Crane restart for Microsoft (835 MW), Amazon-Talen (~2 GW) — but the fleet is finite and largely spoken for. SMRs are real but late: Google-Kairos and Amazon-X-energy deals target first units around 2030 and full build-out toward 2035, and the 2030 dates are widely judged optimistic. Fusion is optionism: Commonwealth Fusion targets grid power by 2030 with SPARC/ARC, but no one should underwrite a 2030 build on a fusion megawatt. That leaves gas-and-grid — behind-the-meter gas (~82-101 GW announced cumulatively, though only single-digit GW under construction) and grid interconnection (3-7+ years, up to ~10 in the worst queues) — as the default bridge that actually carries the load this decade, at a carbon cost the CFE commitments must then offset. → speed-to-power mechanics in Chapter 3.2.

The entire demand curve rests on assumptions that a single shock could invalidate — and the wildcards cut both ways, which is what makes them dangerous to ignore. On the downside: an algorithmic-efficiency step (a DeepSeek-style training- or inference-cost collapse) that deflates the compute needed per unit of value; an enterprise-adoption air-pocket (the MIT NANDA study found ~95% of enterprise GenAI pilots produced no measurable P&L impact on $30-40B of spend); or a regulatory/energy backlash that caps siting. On the upside, the same efficiency gains can increase total compute via the Jevons paradox — cheaper inference begets more inference — and reasoning/agentic workloads are a structural demand multiplier that could keep the supercycle alive longer than the bears expect (→ Chapter 16.3).

The decision consequence is asymmetric. A pure-play, single-generation, debt-financed asset is fragile to every downside wildcard and only benefits from the upside ones if it is already full. A flexible substrate — over-provisioned floor loading and water, modal procurement, contracted-not-merchant revenue, a power deal that flexes — is the cheapest insurance against the wildcard you cannot predict. You are not buying a forecast; you are buying the right to be wrong about the forecast.

Each scenario hides a different stranded-asset failure mode, and naming them is the point of the whole exercise. Stranded silicon is the obsolescence risk: the 2-3 year frontier-economic life vs the 5-6 year book life (CONTESTED) means a generational density step — the ramp from ~132 kW NVL72 racks toward ~600 kW Kyber-class racks — can leave current-generation fleets uneconomic against newer parts and uncompetitive against grid power before they are depreciated. Stranded substations is the power-delivery mirror image: an interconnection slot energized for a campus whose demand never materializes, or a behind-the-meter gas plant carrying carbon liability for a load that digested. Stranded balance sheets is the financial endgame: long-lived debt underwritten against short-lived silicon, the under-depreciation question that turns reported earnings into borrowed future write-downs (→ Chapter 1.8).

The systemic and societal constraints are the outer boundary on all three scenarios. Grid reliability is now a planning constraint, not a footnote — NERC issued a rare Level 3 alert after large-load loss events (~1.5 GW dropped in 82 seconds in one Virginia event), and ride-through is now mandatory. Water, community social license, and the concentration of load in a handful of jurisdictions (Virginia alone projected at 39-57% of state power) are real ceilings that a demand curve cannot wish away. The deepest systemic risk is correlation: every scenario assumes the constraints relax independently, but a power slip, a residual-value shock, and a demand air-pocket are correlated — they tend to arrive together in a downturn, which is exactly when a levered, single-generation, merchant-exposed asset has the least room to survive them. The dual-use framing matters here too: the same failure modes that random faults trigger are attacker-induceable (→ Chapter 11.10).