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Middle Eastern and North African governments should classify hyperscale artificial-intelligence data centres as critical, dynamic electricity loads, not ordinary commercial customers. The World Economic Forum (WEF) says their concentrated, high-density computing demand can change rapidly enough to affect grid stability, requiring coordinated power, water, digital-infrastructure and land-use planning.
The warning comes as the region prepares for a potential hyperscale build-out. WEF estimates that MENA could capture more than $1 trillion in economic and sustainability value by 2030, but only if utilities and regulators redesign how these facilities are connected, cooled, supplied and managed.
Why AI data centres are different from normal commercial loads
AI training and inference run on dense accelerator racks that draw substantial power for long periods. Workloads can also rise or fall sharply as computing jobs start, finish or are shifted between facilities. That combination of sustained demand and rapid variation is unlike the smoother profiles assumed by many conventional grid-planning models.
In a statement published by WEF on 1 May 2026, Energy Transition Lead Jessica Obeid and WEF MENA Manager Hassan Abulenein wrote: “Data centres should be recognized as critical, dynamic loads that materially affect grid stability, rather than treated as standard commercial customers.” The classification would change interconnection studies, reliability assumptions, reserve requirements and operating agreements.
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MENA’s capacity gap is arriving before the AI build-out
WEF says installed electricity capacity in MENA would need to increase by 40% by 2030 to meet expected demand, while current trajectories indicate only about 15% growth. Hyperscale AI facilities would add large, geographically concentrated requirements to that shortfall rather than simply spreading ordinary data-centre consumption across the region.
There is, however, a potential speed advantage. WEF’s 2026 analysis says a new data centre in MENA could potentially connect to the grid in two to five years, compared with more than 10 years in mature markets. Faster delivery does not remove reliability constraints: it makes early coordination more important because a site can be commissioned before generation, transmission and water systems have been expanded around it.
Cooling turns electricity planning into a water-planning problem
Cooling can consume more than 40% of total data-centre electricity globally, according to WEF. MENA’s extreme heat increases the cooling burden and reduces the number of hours when outdoor conditions can provide “free” cooling. In some MENA markets, cooling already accounts for up to 70% of residential electricity consumption, illustrating the seasonal pressure that large computing campuses could add.
Water use is tied to the same decisions. Evaporative systems can reduce electricity use in some conditions but consume water; desalination supplies water at an energy cost. Siting and operating an AI campus therefore affects three linked resources:
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- Electricity: computing, pumps, chillers and desalination all add load.
- Water: cooling demand competes with municipal and industrial uses in a water-stressed region.
- Grid flexibility: sudden workload changes can coincide with hot-weather peaks, when cooling demand is already high.
How WEF’s resource–intelligence matrix evaluates facilities
WEF proposes judging hyperscale sites on two dimensions. Energy optimization measures how power is sourced, diversified and secured. Intelligence optimization measures how real-time data, automation and predictive analytics control demand and equipment.
| Evaluation area | What a high-performing facility does | Why it matters in MENA |
|---|---|---|
| Energy sourcing and security | Uses renewable or otherwise diversified supply, storage and resilient connections | Reduces exposure to a single grid path or fuel source and eases interconnection bottlenecks |
| Operational intelligence | Applies live telemetry, automated controls, predictive maintenance and workload forecasting | Helps operators respond to volatile AI demand before it threatens reliability |
| Cooling and water | Uses climate-adapted designs, closed-loop or dry cooling where feasible, and treated wastewater when suitable | Limits competition with municipal water and lowers cooling-related electricity use |
| Grid flexibility | Participates in demand-response markets and can shift or reduce non-urgent workloads | Provides the system operator with a controllable resource during peaks or contingencies |
| Transparency and resilience | Reports resource performance and maintains predictive, redundant operating plans | Allows regulators and utilities to verify reliability and environmental impacts |
WEF identifies a power usage effectiveness (PUE) below 1.3 as a typical marker of facilities at the upper end of its matrix. PUE compares total facility energy with the energy used by computing equipment; the figure is a performance indicator, not a guarantee that a site has low absolute consumption.
What governments and utilities need to change
Classify hyperscale sites in reliability and interconnection rules
Grid planners should model AI campuses as critical, dynamic loads, including sustained demand, workload-driven spikes and the consequences of losing a large site or transmission path. Interconnection studies should test ramp rates, reserve needs and local voltage or congestion effects rather than relying on a generic commercial-load category.
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Make one approval process cover power, water and land
WEF calls for energy, water, digital policy and land-use decisions to be coordinated. A site that has spare electrical capacity may still be unsuitable if cooling water is constrained, desalination would create excessive additional load, or transmission expansion is not feasible. Regional coordination can also help align renewable projects, cross-border networks and data-centre corridors.
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Use diversified and behind-the-meter supply carefully
Behind-the-meter solar paired with storage, hybrid generation and other diversified sources can improve reliability and relieve grid-connection bottlenecks where they are technically and economically appropriate. These systems should complement, not bypass, utility reliability standards and emissions or water requirements.
Replace water-intensive cooling where conditions favor it
Closed-loop and dry-cooling systems can reduce freshwater demand; treated wastewater may be an alternative where local treatment capacity and health rules permit. The correct design depends on climate, rack density, water availability and the electricity required by each cooling method, so regulators should assess the full energy-water trade-off rather than mandate one technology everywhere.
Pay for flexibility, not only for consumption
AI-enabled demand optimization, predictive maintenance and demand-response participation can turn some computing demand into a controllable grid resource. Contracts should define which training or inference jobs can be shifted, how quickly a site must respond, and what operating limits protect hardware and service commitments.
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- Map the grid: assess connection timelines, transmission redundancy, short-circuit strength, seasonal congestion and the ability to add firm capacity.
- Audit the energy portfolio: document renewable access, storage options, backup generation, fuel logistics and exposure to a single supplier.
- Quantify the water balance: model annual and peak cooling demand, available treated wastewater, desalination requirements and drought restrictions.
- Model local climate: include extreme-heat conditions, seasonal free-cooling potential and the effect of high ambient temperatures on equipment efficiency.
- Build operational intelligence: deploy telemetry, automated controls, workload forecasting and predictive-maintenance systems before commissioning.
- Agree on flexibility: establish demand-response rules with the utility, including measurable load-reduction capability and restoration procedures.
- Report performance: track PUE, water use, supply diversity, outage performance and response events so regulators and communities can evaluate the site.
The policy choice facing MENA
MENA can use its comparatively short potential connection timeline to attract AI infrastructure, but rapid construction without integrated planning would transfer costs to electricity consumers, water systems and grid operators. WEF’s October 2026 report, Powering AI at Scale: Optimizing Hyperscale Data Centre Infrastructure in MENA, argues that the region’s opportunity depends on treating digital infrastructure as part of the energy-and-water system from the first feasibility study, not as a separate customer added after the grid plan is complete.
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