The evolution of artificial intelligence has been the subject of many discussions, ranging from military security to environmental efficiency. Indeed, AI has increasingly been framed as a dual-use technology: one that promises significant societal benefits while simultaneously raising complex ethical, political, and strategic concerns.
When it comes to using AI as a tool to verify environmental decay or progress, its principal value lies in its ability to identify patterns within vast and complex datasets, including anomalies, correlations, and long-term trends that exceed human analytical capacity. By drawing on historical data to generate predictive insights, AI systems are increasingly positioned as powerful instruments for environmental monitoring and assessment. This has created opportunities for governments, corporations, and individuals to make more environmentally sustainable decisions, while also enhancing efficiency in data collection and analysis. For example, the United Nations Environment Programme (UNEP) has deployed AI-enabled monitoring technologies to detect methane emissions from oil and gas infrastructure, targeting a highly potent greenhouse gas that significantly accelerates climate change.
Countries in the Middle East are rapidly developing and integrating AI technologies as part of long-term national planning strategies aimed at economic diversification and technological leadership. Saudi Arabia, for instance, has placed artificial intelligence at the centre of its Vision 2030 programme, with 70% of strategic goals involving data and AI. Saudi Arabia has institutionalised its AI ambitions through the creation of the Saudi Data and Artificial Intelligence Authority (SDAIA), which oversees national AI strategy and data governance. In late 2024, the Kingdom announced “Project Transcendence,” a $100 billion initiative aimed at positioning Saudi Arabia as a global AI hub through investment in data infrastructure, startups, talent acquisition, and international partnerships. These efforts align with stated goals to rank among the world’s leading AI states and become an exporter of AI technologies by 2030. Alongside this expansion, SDAIA has introduced policy frameworks and ethical guidelines to regulate AI adoption across public and private sectors, reflecting the central role assigned to AI in Saudi Arabia’s post-oil economic strategy.
While these initiatives appear highly promising, they overlook a central tension: the development and deployment of AI systems are themselves resource-intensive processes. The rapid expansion of artificial intelligence carries significant environmental costs, particularly through its energy, water, and material demands. Rising computational demands increase energy consumption and greenhouse gas emissions, particularly as data centres expand and remain heavily reliant on fossil-fuel-based electricity. AI infrastructure also places growing pressure on water resources for cooling, intensifies demand for critical minerals with significant ecological impacts, and may introduce new risks to ecosystems, including marine environments. These factors complicate narratives that present AI as an inherently sustainable solution to environmental challenges and highlight the need for greater transparency and regulation.
In conclusion, although AI offers significant potential for enhancing environmental monitoring and verification, its benefits are inseparable from the environmental and political costs of its deployment. The resource-intensive nature of AI infrastructure challenges claims that it represents an inherently sustainable solution, particularly in contexts of large-scale state-led adoption such as Saudi Arabia. AI should therefore be understood as a dual-use technology whose environmental value depends on transparent governance, effective regulation, and genuine alignment with sustainability goals, rather than technological ambition alone.
