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Across the globe, the rapid deployment of AI infrastructure is running up against physical limits. Rather than technology, AI data centers currently face constraints caused by access to power, water for cooling, and delays in receiving building permit approvals that in some cases now stretch for seven years.
Partner at Arthur D. Little.
Providing a potential alternative, orbital data centres are moving from being purely theoretical to technically feasible. While they won’t meet every need, they do offer a way to bypass terrestrial bottlenecks, when they are expected to come online in the next 5-7 years.
Understanding AI data center constraints
The future growth of AI relies on access to sufficient compute power, delivered through global, large-scale data centers. Deploying this infrastructure relies on speed, but three key constraints are dramatically slowing down data center construction.
Given their enormous energy needs, availability of power is the first critical bottleneck. For example, EU data centers are expected to represent 4% of the region’s electricity demand (~108 TWh) by 2030 - more than the current annual electricity consumption of the Netherlands.
Power constraints are dominating data center–building timelines, especially in major hubs. In Northern Virginia, USA, new-connection waits can be up to seven years.
Thermal management introduces the second crucial constraint. Water-based cooling systems substantially increase local consumption, and water-stress exposure affects numerous data center regions, leading to operational and reputational risks for developers.
Finally, regulatory hurdles compound these physical limitations. Community resistance to data centers has grown, dramatically extending project timelines and increasing stakeholder management costs.
These constraints matter because AI economics reward speed. AI model generations turn over every 12-18 months, meaning that infrastructure that arrives after the model-refresh cycle delivers diminished returns. Developers are therefore looking for new options to overcome these challenges, including through orbital data centers.
What are orbital data centers?
Orbital data centers are compute hardware (processors, memory, storage) hosted by satellites in Low Earth Orbit (LEO). These operate at altitudes of 400- 1,400 km above the Earth’s surface and travel around the Earth every 90- 120 minutes.
A recent demonstration successfully tested an H100-class GPU payload in space, marking a tangible step toward space-based AI infrastructure.
It is important to understand that the vision for orbital data centers is not hyperscale facilities in space.
Rather, it’s a modular, networked layer of satellites designed for workloads where orbit provides structural advantages, such as near-continuous solar exposure for power, a passive thermal environment for cooling, lower communication latency than deep space deployments, proximity to space-generated data, and/or geopolitical resilience.
All of this means that for workloads where these factors matter more than millisecond latency, LEO satellites offer a way to bypass terrestrial bottlenecks.
The critical building blocks for orbital data centers
Even as the hardware advances, the success of orbital data centers requires systems engineering rigor across six important building blocks:
1. Continuous solar power at scale Certain orbital regimes (e.g., dawn-dusk Sun-synchronous orbits) can provide near continuous solar exposure. However, high-specific-power, radiation tolerant solar arrays, and resilient energy storage are needed to handle transients and contingency eclipse events.
2. Effective thermal management Even for satellites illuminated by the Sun, a few minutes of shadow occur during each orbit, leading to a temperature spread from +120°C to -250°C. Thermal management — both within the satellite and in releasing heat into space — is therefore critical.
Efficient thermal management, including heat spreading, conservative power density, and intelligent workload scheduling, becomes key to performance.
3. Resilient, modular compute platforms Radiation hardening, redundancy, and autonomous operation are baseline requirements. Because AI economics depend on a regular cadence of hardware-refreshes, platforms need upgrade pathways, swappable units, and servicing strategies to maintain high utilization rates.
4. High-throughput network links Data needs to move efficiently from orbital data centers. For non-geostationary modules, optical inter-satellite links are needed to exchange data before transmitting it to Earth. Robust, scalable ground gateways are also required to receive large data volumes and route insights securely.
5. Reusable heavy-lift access to bring down launch costs Launch costs currently account for about 40% of total required investment.
Reusable launch systems like SpaceX’s Starship, which targets sub-$100/ kg versus historical rates of $2,000-$10,000/ kg, are fundamentally reshaping orbital data center economics by making deployments at scale commercially viable.
6. In-orbit assembly and servicing Large orbital data centers require robotic assembly of modular units and periodic hardware refresh. This mandates standardized docking interfaces and autonomous operations to scale. Minimizing these in-orbit services may help reduce time to market but may increase the number of satellites needed.
How users will adopt orbital compute
While they seem like science fiction, orbital data centers may sound more visionary than they actually are.
Rather than fully migrating to space, operators will deploy a new data channel (much like the one emerging in mobile communications with OneWeb, Starlink, and Kuiper) and use orbital capacity only where it removes a greater bottleneck than it introduces, especially in three areas:
- Satellite operators and defense users can execute preprocessing and inference in orbit, shrinking downlink volumes while accelerating targeting, alerts, and situational awareness.
- Providing sovereign, off-planet storage of critical archives and immutable audit logs for extreme continuity protection and tamper resistance.
- For batch-compute workloads that prioritize energy availability over millisecond responsiveness.
Understanding the challenges and opportunity
While orbital data centers provide a tangible opportunity for AI, constraints remain. For starters, launch costs, platform mass, utilization rates, and operational lifetime must exceed the costs of terrestrial delays due to power, water, and permitting issues.
Second, autonomous fault management, debris management, credible servicing pathways, and upgrade strategies will determine how often hardware requires refreshing and therefore effective cost per compute hour.
Third, orbital availability, spectrum allocation, and cybersecurity frameworks will shape deployment speed, permissible actors, and operational boundaries. As mentioned previously, latency is an issue, limiting the types of workload that can be deployed in space.
Combining terrestrial and orbital data centers
Increasingly, the data center industry’s constraints are not about technology. Orbital compute will not eliminate every bottleneck, but for specific workload types, it offers a way to avoid power queues, heat limits, and permitting timelines by converting physical constraints into architectural opportunities.
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