Digitising the Space Economy: Who Will Hold Sovereignty as the Shift from Hardware to Software Accelerates?

The global space economy is currently undergoing a profound structural transformation. Whereas the sector has historically been characterised by its heavy reliance on rocket propulsion capabilities and the vast capital investments required to deploy physical hardware into orbit, the focus is now shifting toward an economic model in which value creation is increasingly decoupled from material mass.

In this context, the contours of what may be described as a “software-defined space economy” are becoming increasingly evident. This shift is driven by the convergence of two core digital infrastructures: digital twins and space-based edge computing. At the same time, declining launch costs, resulting from advances in reusable launch vehicles, have shifted the primary determinant of economic efficiency. Rather than centring on mere access to space, value is now anchored in the operational efficiency of on-orbit assets, their embedded intelligence, and the length of their functional lifespan.

This paper argues that the sector’s future economic value—estimated to reach USD 1.8 trillion by 2035—will not be realised solely through an increase in the number of satellites launched, but rather through the digitisation of their life cycles and the processing of data at the source.

This analysis provides a comprehensive economic deconstruction of these technologies. It examines how “virtual modelling” is reshaping cost structures in space manufacturing, enabling companies such as Varda Space Industries and SpaceX to accelerate development cycles at software speed. It also highlights the roles of artificial intelligence and the Internet of Things (IoT) in establishing space systems capable of autonomous fault processing, thereby maximising returns by extending assets’ operational lifetimes. The analysis concludes by linking gains in operational efficiency to the sector’s overall growth, demonstrating how digital infrastructure forms the material foundation for emerging in-space manufacturing (ISM) markets and next-generation Earth observation services.

Understanding the economic value of digital twins and edge computing first requires placing them within the broader structural transformations reshaping the space economy. For decades, the sector was dominated by government contracts based on the cost-plus model, in which technical reliability was prioritised over cost efficiency. However, the shift toward the commercial exploitation of low Earth orbit (LEO) has introduced a markedly different competitive environment. This transition has necessitated a fundamental reassessment of cost structures, along with a re-evaluation of expected investment timelines and returns on investment (ROI).

An examination of the financial indicators underpinning this new reality reveals a clear valuation gap. While the global space economy is currently estimated at approximately USD 630 billion, forward-looking projections suggest it will nearly triple to USD 1.8 trillion by 2035. This anticipated expansion, however, is not driven by the launch services segment, which has gradually evolved into a commoditised market characterised by declining profit margins. Instead, genuine value creation is concentrated in the midstream and downstream segments, such as operations, data services, and in-space manufacturing, whose operational foundations rest fundamentally on digital infrastructure.

This structural shift is reflected in the market’s transition from a singular reliance on large communications satellites to a diversified ecosystem encompassing mega-constellations, Earth observation (EO) services, and in-space manufacturing (ISM). This diversification has introduced a new benchmark for success centred on asset utility: operators are now compelled to maximise both data productivity and operational longevity for every kilogram launched. In practice, this objective is achieved through the deployment of digital twins and space-based edge computing.

Consequently, the pursuit of maximising utility has severed the traditional link between mass and value. Functional efficiency is no longer determined by increases in a satellite’s weight or physical scale, as was once the norm, but by what it can produce and how long it can remain operational. Software-defined satellites now make it possible to upgrade and reconfigure satellite functions while they are already in orbit—for example, repurposing a sensor designed initially for ocean monitoring to track crops through software updates alone—thereby extending economic viability without the need for a new launch. This growing reliance on virtualised systems, in turn, reduces the capital expenditure (CAPEX) risks associated with developing physical hardware.

Capital flows clearly reflect this technological shift. While government budgets continue to expand, private equity investment is increasingly gravitating toward software-centric start-ups, given the superior profit margins and scalability they offer compared with hardware-dependent counterparts. Public authorities are reinforcing this trajectory: institutions such as NASA are mandating the adoption of digital engineering standards to accelerate procurement processes. This, in turn, reinforces the view that digital solutions are no longer discretionary enhancements, but an urgent economic and regulatory necessity.

Within the space economy, digital twins constitute dynamic, multi-physics simulations of assets and processes. These models are continuously updated with real-time data to reflect the physical and operational state of an asset accurately. The historical origins of this concept can be traced back to NASA’s Apollo programme, in which engineers relied on “mirrored systems” to simulate emergency procedures on Earth before executing them in space.

This conceptual framework evolved further in 2010, when John Vickers formally introduced the term “digital twin.” At that stage, the model moved beyond static simulations or one-way data flows—often described as a “digital shadow”—towards a bidirectional architecture in which the digital model can transmit control commands back to the physical asset to optimise performance. This technological progression enables a shift from reactive management of space assets to a predictive management paradigm, generating additional economic value by anticipating failures before they occur.

The direct economic impact of digital twins is most evident in the design and manufacturing phases through virtual prototyping. This approach fundamentally reshapes traditional industry cost structures by reducing capital expenditure (CAPEX), primarily by limiting the need for physical prototypes and destructive testing. Available data indicate reductions of more than 50% in the number of required prototypes.

Conducting parallel simulation testing within cloud environments further accelerates development timelines while safeguarding manufacturing quality, thereby conferring a decisive competitive advantage. SpaceX’s experience developing Starship, alongside Varda Space Industries’s work on re-entry capsule design, provides applied case studies illustrating how digital simulation is used to validate aerodynamics and structural loads. Through these approaches, such firms can iterate development cycles at speeds comparable to those in software engineering.

The economic benefits of digital twins extend further to the optimisation of in-space manufacturing (ISM), where they function as indispensable tools for controlling production variables in microgravity environments. This is exemplified by Redwire’s experiments in the production of protein crystals and pharmaceutical compounds. Once assets are deployed in orbit, digital twins evolve into comprehensive health records, enabling operators to implement predictive maintenance regimes.

AI-driven data analytics further reinforce this model by detecting early indicators of failure and enabling pre-emptive corrective measures. In doing so, they extend satellite service lifetimes while safeguarding revenue streams. Practical applications—such as the systems developed by AstroVigil—have demonstrated the effectiveness of this approach in reducing unscheduled maintenance activities and maximising the operational availability of space assets.

The concept of space-based edge computing completes the digital architecture established by digital twins. While the latter provides system-level virtual models, the former supplies the computational capacity required to process data. Together, they represent a fundamental architectural shift in space systems engineering, moving the sector away from the traditional bent-pipe data delivery model—which merely retransmits raw data—toward an in-orbit processing paradigm.

This shift responds to pressing economic imperatives imposed by the downlink bottleneck. Modern Earth observation satellites generate vast volumes of data that are costly to transmit through commercial ground stations. At the same time, transmission latency further erodes data value in time-critical applications such as defence and real-time surveillance.

Space-based edge computing addresses these structural constraints by relocating data-processing capabilities to the data source itself. This approach can reduce transmitted data volumes by up to 99%, as only derived insights—rather than raw imagery—are downlinked, thereby substantially lowering the operational expenditure (OPEX) associated with downlink operations.

These cost savings are accompanied by time gains that eliminate latency, thereby opening new markets predicated on real-time responsiveness. Economic analyses further advance the concept of “energy arbitrage,” which posits that orbital data centres can benefit from continuous solar power at highly cost-effective rates—potentially as low as USD 0.002 per kWh—representing a dramatic reduction compared with terrestrial energy costs. Adopting this model entails a clear economic trade-off: accepting higher upfront capital expenditure (CAPEX) to equip satellites with advanced hardware in exchange for substantial long-term reductions in operational expenditure.

The deployment of these computational capabilities poses significant environmental challenges, introducing technical and material complexities. High-energy cosmic radiation can cause disturbances in processor memory, known as single-event upsets. In addition, thermal management in the vacuum of space—where convective heat transfer is absent—requires sophisticated radiative cooling systems that constrain power density. To mitigate the high costs of traditional radiation-hardened processors, the industry is increasingly turning to radiation-tolerant commercial off-the-shelf (COTS) components, supported by fault-tolerant software that corrects memory errors or initiates system reboots when faults are detected. This approach delivers contemporary computational performance at a far more cost-effective price.

The structural convergence between digital twins and space-based edge computing, when integrated with AI and the IoT, yields a comprehensive command-and-control framework for the space economy. This technological synthesis moves the sector beyond incremental improvements in the operational efficiency of individual assets—as discussed earlier—toward the establishment of system-wide autonomy.

The economic applications of this convergence are most evident in the space-based Internet of Things (SatIoT) sector, which seeks to bridge the digital divide by extending coverage to the 85 percent of the planet’s surface that lacks terrestrial cellular networks, including oceans and remote regions. Kinéis’ experience provides an applied model of this approach, as the company is deploying a constellation of nanosatellites to enable hybrid connectivity. This architecture allows devices to seamlessly exchange data across terrestrial networks and space-based links, maximising cost efficiency, extending battery life, and establishing a global data layer that feeds digital twins of supply chains and environmental systems.

The growing congestion of orbital space, driven by the deployment of mega-constellations, introduces acute security and economic challenges, including collision risks associated with the Kessler syndrome. Under these conditions, traditional human-led traffic management becomes impractical. Addressing this dilemma requires the deployment of AI algorithms operating at the edge (Edge AI), enabling satellites to autonomously process tracking data and execute collision-avoidance manoeuvres without recourse to ground-based commands. This autonomous capability delivers tangible savings in operational labour costs at control centres while mitigating the catastrophic financial risks associated with asset loss, thereby enhancing both the safety and insurability of orbital systems.

This trajectory ultimately culminates in the emergence of self-healing systems, in which the predictive capabilities of digital twins are integrated with the decision-making capacity of edge-based AI. This integration enables satellites to autonomously diagnose faults and reconfigure their internal functions—for instance, by automatically rerouting data flows upon detecting transmitter failures—thereby ensuring service availability and protecting revenue streams from disruption.

A rigorous assessment of the transition toward a space economy projected to reach USD 1.8 trillion requires acknowledging that this qualitative shift remains fraught with structural, technical, and strategic risks that could undermine the realisation of anticipated returns. The digitisation of space infrastructure—despite the operational advantages discussed above—significantly expands the security exposure surface. A reprogrammable satellite in orbit ceases to be an isolated physical asset and instead becomes a networked node vulnerable to intrusion. Threat vectors range from attempts to seize control of command-and-control links to the manipulation of sensor data feeding digital twins, and ultimately to the injection of malicious code into edge-processing systems.

In this context, digital twins assume a dual and paradoxical role. While they form part of the targeted infrastructure, they simultaneously emerge as a critical defensive instrument, functioning as cyber test ranges. This is illustrated by the U.S. Air Force’s approach, which uses a digital twin of the GPS Block IIR satellite to conduct penetration testing, assess vulnerabilities, and validate the effectiveness of software patches in a secure virtual environment before physical deployment. This practice has entrenched the cyber–physical approach as a binding standard in military acquisition and systems assurance processes.

Alongside security challenges, the growth of the digital space economy faces a significant regulatory obstacle: the lack of unified interoperability standards. Divergent protocols and data formats across providers—where, for example, a digital twin of a propulsion system developed by one manufacturer may not align with a power-system twin produced by another—impede the creation of comprehensive, integrated digital models for complex spacecraft. Specialised industry bodies, such as the American Institute of Aeronautics and Astronautics (AIAA), are therefore tasked with harmonising ontologies and data-exchange frameworks, a widely recognised prerequisite for achieving scalable industrial growth.

Finally, this transformation confronts a qualitative gap in human capital. A software-defined space economy generates growing demand for a workforce equipped with rare hybrid competencies that combine traditional aerospace engineering with advanced data science. The current shortage of engineers capable of bridging the knowledge divide between orbital mechanics and neural network architectures constitutes a long-term economic challenge that the sector must address to sustain its trajectory of innovation.

In sum, the “New Space” economy extends far beyond mere reductions in launch costs, resting fundamentally on the maximisation of the operational intelligence of space assets. The integration of digital twins and space-based edge computing signals the sector’s transition from an experimental phase to one of industrial efficiency, as the decoupling of value from physical mass enables the space economy to scale in line with non-linear growth dynamics.

Taken together, these technologies systematically dismantle traditional cost structures. Virtual modelling lowers the cost of innovation, edge computing reduces data transmission costs, and automation alleviates operational burdens. The strategic conclusion is therefore clear: future economic sovereignty in this domain will not necessarily accrue to those with the largest rocket propulsion capabilities, but to those who command the most intelligent and efficient software code.

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