Why the Global Economy Must Start Tracking Data, Computing and Energy Flows

Global supply chains rely as much on the flow of information as they do on the flow of goods.¹ Digital product designs are shared across continents, while algorithms manage inventory and production schedules. Payments and logistics are coordinated through data platforms.

When these data exchanges fail because of outages, congestion or cyber incidents, confusion spreads between suppliers and customers.² Even without physical damage, the financial losses can be significant.

In 2024, for example, a flawed software update by cybersecurity firm CrowdStrike crashed millions of Microsoft Windows devices and caused a global information-technology outage. Flights, hospitals and payment systems were disrupted, costing America’s Fortune 500 companies $5.4 billion. According to Oxford Economics, digital downtime costs the world’s 2,000 largest companies $400 billion each year.

Yet the flow of information is not systematically tracked. Nor is it included in monetary input-output tables, the central tool national statistical offices use to map economic supply chains.³ These tables track the value of material inputs flowing between industries, but do not capture energy, data or computational resources. Companies similarly measure how much they spend on information technology, but not the data, computing power and electricity on which their operations depend.

This gap is becoming increasingly problematic as digital technologies, including artificial intelligence, reshape economies and increase threats to energy grids, digital connectivity and cybersecurity. A major power outage across Spain and Portugal in April 2025 took half a day to restore. Internet traffic fell to 17% of normal levels, electronic payments froze and the outage cost €1.6 billion (US$1.8 billion). Since late 2023, around a dozen submarine communications and power cables in the Baltic Sea have been damaged in suspected sabotage.

Companies and governments therefore need practical statistical tools to track critical digital dependencies.

How data, computing and energy drive economic growth

The flows of energy, computation and information are intertwined. Businesses, particularly those with high power requirements such as data centers, are increasingly choosing locations based on access to clean, affordable and reliable electricity.

In the United States, Microsoft signed a 2024 deal to restart the Three Mile Island nuclear power plant in Pennsylvania to supply its data centers. China’s national East Data West Computing Program is directing data centers towards provinces in the west that have abundant renewable energy.⁵

Global value chains and payment systems also depend on computational resources, including processing power and memory, to generate, analyze and share data. The computing resources required to train state-of-the-art AI models have increased four- to fivefold every year for more than a decade.^{go.nature.com/3tpemjh} The largest AI-focused supercomputers now combine approximately 200,000 processors and consume the equivalent power of 250,000 homes.^{go.nature.com/3tmh73s}

Most economic transactions and markets are now digital. They are increasingly managed through digital tokens that encode ownership, compliance requirements and settlement methods for financial arrangements.

Predictions suggest that these tokens could become the main “commodities” for future transactions, reaching between $2 trillion and $9.4 trillion by 2030, compared with around $600 billion today.⁵

Central banks and the Bank for International Settlements in Basel, Switzerland, are exploring tokenization platforms for cross-border payments. Financial exchanges and regulators are also moving towards tokenized markets for trading assets such as stocks, bonds and derivatives.⁶

These resources reinforce one another through feedback loops. The growing use of AI and tokens is increasing demand for computing power. More computing platforms and data centers increase electricity demand, accelerating energy-infrastructure deployment and grid investment. This generates more data, which must be moved and stored. The value derived from data, traded and settled using tokens, is then fed back into investment in energy systems and digital infrastructure.

The chain from watts to kilowatt-hours, floating-point operations per second (FLOPs), megabytes, tokens and dollars captures a new driver of modern growth that remains largely untracked. The International Energy Agency predicts that global electricity consumption by data centers could more than double, from 415 terawatt hours (TWh) in 2024 to approximately 945 TWh by 2030.⁴

How invisible digital connections increase economic risk

When these dependencies are not tracked, they create real costs. Supply-chain failures can expose vulnerabilities that remain unaddressed because the links between energy, data and computation are not visible in conventional economic accounts.

In March 2025, a fire at a single electrical substation closed London Heathrow Airport for a day, canceling approximately 1,300 flights and disrupting the journeys of more than 200,000 passengers. An official investigation found that a transformer failure known since 2018 had been ignored. The energy network was also unaware of the airport’s dependence on a single supply point.⁷ There was no accounting framework linking the substation to the airport and its downstream supply chain. Dependencies are often revealed only when they fail.

Similarly, a cyberattack that halted production at British carmaker Jaguar Land Rover for five weeks in 2025 was estimated to have cost £1.9 billion (US$2.5 billion), making it the most expensive cyber incident in British history. The disruption spread to more than 5,000 companies in the supply chain. No statistical measures documented the supply chain’s reliance on digital systems connected to a single manufacturer.

Interconnected energy, data and computing networks are critical to resilience because they represent constraints, not merely correlations. Traditional accounting can show who pays whom, but it cannot show where physical and operational bottlenecks are, how they change over time or how constraints at one layer — such as grid congestion, data-center capacity, network disruption or transaction friction — spill over into lost production elsewhere.

The challenge is to measure these factors consistently, compare them across countries and make them auditable by statistical agencies rather than leaving the task to proprietary company dashboards.

Building better statistics for the digital economy

Current statistical systems can account for the value of digital activities, but they do not consistently quantify or attribute the throughput that determines supply-chain resilience. That includes the computational resources supplied and used, the amount of data generated and transferred, how those flows move through national and transnational production networks and how they interact with power constraints and trade-settlement difficulties.

Since 2018, tools developed by the Organisation for Economic Co-operation and Development (OECD) in Paris have helped national accounting methods visualize more of the digital economy. Digital supply-and-use tables track the production, trade and consumption of digital goods, services and transactions. Canada and Australia released such estimates for the first time in 2019. The OECD released a handbook in 2023, and the 2025 revision of the United Nations System of National Accounts includes a dedicated chapter on digitization.⁸ However, these tables remain based on economic value rather than physical constraints.

In a rapidly changing economy, technical quantities often change before monetary values do. These include data-center efficiency, data and energy use and the relationship between computing and data flows. Such quantities have macroeconomic implications. The International Monetary Fund estimates that AI-driven demand could cause US electricity prices to rise by 8.6% if renewable energy cannot keep pace.⁹ Measuring these factors can reveal vulnerabilities, such as dependence on a single data center, cable or payment channel, while also identifying successful approaches that could be replicated.

Analysts need a more comprehensive way to trace computing, network-traffic and payment exposures back to the sectors and demands that drive them.¹⁰ Such maps would create a basis for targeted interventions and direct resilience spending to the areas with the deepest dependencies, rather than relying on blanket measures.¹¹

Governments and businesses could use these maps to harden facilities on which many critical sectors depend, fast-track grid connections in regions constrained by computing capacity or open alternative payment channels during an outage.

These tools should not be one-off research datasets. They should be practical systems that national statistical offices can compile, audit and update. The best approach is to build on existing national accounting systems by adding layers that make information dependencies visible through the same accounting logic.

There are precedents in other fields. When tracking water, energy and carbon became a binding requirement for businesses and economies, supply-chain systems added appropriate extensions while remaining consistent with systems used to measure gross domestic product.

However, each of these extensions tracked its resources as separate inputs to the production environment. The next step is to develop comparable extensions for data, computing and the digital infrastructure that increasingly determines economic resilience.