Flexible compute: the largest battery the grid already has

Grids are discarding record volumes of clean energy for want of flexibility while data-centre demand grows faster than wires can be built. Virtual power plants (VPPs) — software-dispatched aggregations of distributed resources that trade like power stations — are already operating at tens-of-gigawatts scale, and Bitcoin mining has quietly become the most proven flexible load on the planet, earning real grid revenue at sub-minute response times. The open question is not whether flexible compute works but how far AI workloads — the next order of magnitude of demand — can inherit that flexibility, and what architecture lets one site sell both compute and grid services.

• The bottleneck is flexibility, not wires. In Ireland — the sharpest case — 14% of all-island wind was dispatched down in 2024 (2.2 TWh, per EirGrid/SONI; worth an estimated €450M to consumers, per Aurora Energy Research), with 96% of the Republic's wind curtailment logged against minimum-conventional-generation limits, while data centres already take 22% of national electricity (CSO, June 2025) and head toward ~30% by the early 2030s on EirGrid's projections.
• VPPs are present tense, not future tense. North America runs ~37.5 GW of behind-the-meter VPP capacity (Wood Mackenzie, September 2025); the US DOE projects 80–160 GW by 2030. Kraken passed 2 GW of aggregated residential flexibility in 2025; AGL orchestrates ~1.5 GW; Sunrun's CalReady delivers ~250 MW average per event from 56,000+ California homes.
• Bitcoin mining is a de facto ~20 GW global VPP, dispatched by price. The receipts are real: ERCOT-related power credits paid Riot Platforms $71M in 2023 ($31.7M in August 2023 alone) and $33.7M in FY2024. ERCOT's mining fleet reached ~4.6 GW of price-responsive demand in 2025, though formal registration lags behaviour: ~0.24 GW enrolled as Controllable Load Resources plus ~1 GW in the Emergency Response Service (Potomac Economics).
• AI is not one load but three flexibility classes. Training can checkpoint, power-cap to 60–80% TDP and shift geographically — all in production; latency-bound inference cannot be curtailed without breaching SLAs; batch/async inference behaves like training. A grid-facing control system must treat these as separate workload classes.
• The integration, not the components, is the innovation. A 500 MW co-located campus — 150 MW firm inference / 200 MW semi-flex training / 50 MW batch / 100 MW Bitcoin buffer, first off and first on — operates as a two-quadrant grid resource. In Satscryption's model (v1.6) the power-side stack is roughly break-even on its own: the AI anchor lease drives the returns, and the Bitcoin buffer is what makes the over-built renewable PPA economic.

Infrastructure, energy and data-centre strategy leaders should:

• Treat load flexibility as a monetisable product line, not a compliance concession — price curtailment response, capacity value and ancillary services into every new data-centre business case.
• Bitcoin operators: productise what you already run — firmware-level curtailment, stateless restart, multi-tier hosting and energy-advantaged sites — as grid services and as hosted flexibility for AI tenants.
• AI and data-centre operators: segregate workloads into firm / semi-flexible / batch classes inside the control plane, and engineer training for checkpoint-and-shift from day one. Do not promise the grid flexibility from latency-bound inference.
• Grid operators and policymakers: procure flexibility explicitly — ERCOT's controllable-load programmes are the template; curtailment driven by system-stability limits is a flexibility problem that wires alone will not fix.
• Be willing not to proceed where the economics do not close: without an anchor tenant the campus power stack is near break-even, and without the buffer tier the over-built renewable PPA does not pay.