AI to Add $15.7 Trillion to Global Economy by 2030 — But at a Rising Environmental Cost

Artificial intelligence is rapidly reshaping economies, industries, and everyday life at a pace few technologies have achieved before. It is accelerating medical research, improving access to education, strengthening logistics networks, helping cities predict infrastructure failures, and enabling industries to operate with greater precision and less waste. Across sectors, AI is increasingly viewed as a powerful tool capable of unlocking productivity, driving innovation, and improving quality of life on a large scale.

Governments and businesses are investing heavily to capture these benefits, seeing AI as a catalyst for competitiveness, efficiency, and long-term growth. Yet behind this promise lies a more complex reality. The systems powering AI require vast physical infrastructure, significant computing power, and rising levels of electricity and water consumption.

As adoption accelerates, a broader conversation is emerging, one that moves beyond the optimism surrounding AI’s capabilities to examine the environmental and resource costs tied to its rapid expansion.

A new report from KPMG Middle East argues that while artificial intelligence could contribute as much as $15.7 trillion to the global economy by 2030, the infrastructure required to sustain that growth is placing unprecedented pressure on electricity grids, water resources, and environmental systems.

The report, The AI and Sustainability Paradox: Maximizing Value, Managing Risk, exposes a widening blind spot at the center of the AI boom: despite rising concern around data center expansion and energy demand, no major data center operator clearly discloses how much of its electricity and water use is directly linked to AI workloads.

That lack of transparency, KPMG argues, is becoming a strategic problem.

The Infrastructure Behind the AI Boom

Artificial intelligence often appears intangible to the public, algorithms running in the background, chatbots responding instantly, predictive systems quietly improving efficiency. But behind every AI model sits a vast physical infrastructure powered by data centers that require enormous amounts of electricity, cooling systems, and water.

According to the report, data centers currently consume approximately 1.5 percent of global electricity, equal to around 415 terawatt-hours in 2024. By 2030, that number is projected to reach nearly 945 terawatt-hours. Water use is expected to follow a similar trajectory. Annual consumption could rise from around 560 billion liters today to approximately 1.2 trillion liters by the end of the decade. The report suggests that while these figures are already significant, the real challenge lies in what remains unknown. A review of eleven major data center operators found that none clearly separates AI-related energy and water consumption from broader operational demand. As a result, policymakers, investors, and businesses are trying to manage one of the fastest-growing industrial transformations in history without fully understanding its environmental footprint.

“We cannot manage what we cannot measure,” said Fadi Al-Shihabi, Partner and Head of Sustainability Solutions at KPMG Middle East. “The most striking finding in our research is not that data centers consume extraordinary amounts of energy and water, we already knew that. It is that the industry has not yet built the disclosure standards to tell us how much of that consumption is driven by AI specifically. Until that gap closes, every projection we publish carries a margin of uncertainty that should make regulators and investors uncomfortable.”

The Promise: Why AI Matters

The report makes clear that AI is not inherently a problem. In fact, much of its value lies in solving resource and efficiency challenges. In education, adaptive learning systems are helping personalize lessons for students in underserved communities. In healthcare, AI-enabled remote monitoring allows specialists to reach patients who may otherwise lack access to care. In agriculture, precision irrigation systems help farmers reduce water waste while increasing crop productivity. Mapped against the United Nations Sustainable Development Goals, AI demonstrates clear potential to improve quality education, support good health, reduce inequality, and strengthen institutions.

For governments and businesses, these applications explain why investment in AI continues to accelerate. But the report also highlights a more complicated reality.

AI’s benefits depend entirely on how systems are governed, designed, and deployed. Poor oversight can reinforce inequality, introduce opaque decision-making, and create societal distrust faster than technology can generate convenience.

The $15.7 Trillion Opportunity, and Who Captures It

The economic upside of AI is substantial.

Analysis from the World Economic Forum suggests that AI could contribute up to 14 percent of global GDP by 2030, equivalent to roughly $15.7 trillion in economic value. Yet those gains are unlikely to be distributed evenly.

According to the International Monetary Fund, economies with strong institutions, advanced digital infrastructure, skilled talent pools, and effective governance frameworks will capture the greatest share of value. 

Countries that lack readiness may remain consumers of AI technologies rather than creators of economic advantage. 

“The $15.7 trillion figure is not a gift to be claimed, it is a reward for readiness,” said Tareq Dreiza, Partner and Head of AI and Technology Enablement at KPMG Middle East. “The economies and organizations that will capture the largest share of AI’s value are those investing today in skills, infrastructure, and governance. The Middle East has the ambition and the capital. The question is whether we build the institutional capacity fast enough to convert that into lasting advantage.”

The report argues that trust is becoming a form of economic infrastructure. Organizations that prioritize transparency, accountability, and responsible data practices are more likely to gain investor confidence and long-term customer loyalty. Those that fail to do so may face regulatory friction, reputational damage, and public skepticism.

The Environmental Trade-Off

The environmental implications of AI are becoming increasingly difficult to ignore.

Data centers supporting modern AI applications can span hundreds of acres, sometimes replacing farmland or ecologically sensitive land. Once operational, they become major consumers of electricity and water, creating pressure on local grids and natural resources. This becomes especially relevant in regions already facing environmental constraints.

For the Middle East, a region simultaneously positioning itself as a global AI hub while managing some of the world’s highest levels of water stress, the issue becomes more than a technical discussion. It becomes a strategic planning question.

Without transparent reporting that distinguishes AI workloads from other data center activity, governments and investors are effectively trying to solve an environmental challenge without access to complete information.

The report suggests that disclosure standards have not kept pace with technological adoption.

The Way Forward

Rather than advocating for slower AI adoption or unchecked acceleration, the report proposes a middle path.

It identifies four areas that require immediate attention:

• Establishing transparent measurement standards that distinguish AI workloads from broader data center activity.
• Building institutional capacity that allows AI adoption to create durable economic value.
• Strengthening international cooperation around ethics, safety, and sustainability.
• Accelerating investment in renewable energy sources capable of supporting digital infrastructure responsibly.

“In a region where water is among our most precious resources, the decisions we make today about where and how to site data centers will shape our sustainability trajectory for decades,” said Al-Shihabi.

“This is not an argument against AI. It is an argument for deploying it with eyes open, powered by renewables, sited responsibly, and held to transparent standards.”

The report concludes that technology alone does not produce sustainable growth.

Governance determines whether productivity gains become lasting economic value, whether efficiency translates into environmental progress, and whether AI’s benefits are distributed broadly rather than concentrated narrowly. Managing AI’s environmental footprint is not simply about limiting damage. It is also about improving how AI systems are designed, trained, and optimized over time.

The next phase of AI development must go beyond mitigation, transparency, and responsible infrastructure. It should also focus on advancing the underlying algorithms that power AI, 

making them more efficient, less resource-intensive, and capable of delivering stronger outcomes with lower energy and computing demands.

As AI continues to evolve, innovation should not only become more powerful, but also more intelligent in how it consumes resources. The objective is not to slow progress, but to ensure that future AI systems are built to be more adaptive, efficient, and sustainable by design.

“Every boardroom conversation we have comes back to the same question: how do we move fast without losing trust?” said Dreiza.

“AI does not succeed on the strength of the algorithm alone. It succeeds when people, employees, customers, regulators, believe the systems are transparent, accountable, and working in their interest.”

The paradox at the heart of AI will not resolve itself. It requires leaders across government, business, and civil society to make deliberate decisions about how this technology is built, deployed, and measured.

The organizations that will define the next decade may not necessarily be those with the largest AI budgets, but those willing to ask more difficult questions: Who benefits? Who bears the cost? What are we measuring? And what are we still missing?

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