Mexico and Saudi Arabia are investing heavily in artificial intelligence to monitor climate change and optimize energy systems, but both countries face a critical problem: their AI infrastructure is powered largely by fossil fuels, which could increase emissions rather than reduce them. The tension reveals a fundamental challenge for developing nations pursuing AI-driven climate solutions without first securing renewable energy sources to power them .

Why Are Mexico and Saudi Arabia Betting So Much on AI for Climate?

Mexico is building the Cōātlīcue supercomputer, named after an Aztec deity associated with creation and the earth. Once completed, it will deliver over 314 petaflops of computing power, placing it among the world's most powerful supercomputers . The country is also attracting major foreign investment, including a $4.8 billion data center cluster planned for Querétaro through CloudHQ. These facilities are designed to support cloud computing, data storage, and AI research that could help Mexico meet its Paris Agreement climate commitments .

Similarly, Saudi Arabia is pursuing aggressive AI expansion through its Saudi Arabia Data and Artificial Intelligence Authority (SDAIA). The Kingdom has invested $120 million in computing infrastructure, acquiring more than 3,000 Nvidia graphics processing units (GPUs), which are specialized chips essential for training large-scale AI models. Saudi Arabia is also expanding its Shaheen III supercomputer, which features 35.66 petaflops of computing power .

Both nations see AI as a tool to interpret massive volumes of environmental data. Machine learning models can integrate satellite observations, ground sensors, and weather data to generate high-resolution models of pollution patterns, temperature changes, and greenhouse gas emissions. In Mexico, the Office of Sustainable Development in Querétaro partnered with data scientists to develop predictive models that forecast air pollution using real-time sensor inputs, enabling more targeted mitigation strategies .

What's the Hidden Cost of This AI Infrastructure?

Here's where the paradox emerges: data centers and supercomputers are extremely energy-intensive. They require vast electrical supplies for processing and continuous climate control to prevent overheating, which drastically increases overall power demand. In Mexico, the "AI data boom" is straining electricity infrastructure, and many data centers are relying on fossil-fuel-generated power because available clean energy cannot yet meet demand . This results in expanded use of diesel and natural gas generators and heightened emissions in regions with concentrated data centers.

Mexico's electrical grid was not originally designed to support such prominent levels of computational demand. Despite national commitments to renewable energy, current clean generation is limited relative to total capacity. Without significant upgrades in renewable generation and grid resilience, the energy required by AI infrastructure risks increasing overall emissions and undermining the climate benefits associated with AI-enhanced environmental monitoring .

Saudi Arabia faces a similar trade-off. While the Kingdom has announced plans to source at least 50 percent of its power from renewable energy, primarily solar and wind, by 2030, its power grid still relies heavily on fossil fuels today . A joint venture with Advanced Micro Devices and Cisco aims to build renewable-powered data centers beginning with 100 megawatts in the Kingdom and scaling to 1 gigawatt by 2030, but this timeline means years of fossil-fuel-powered AI expansion in the interim .

How Can These Countries Align AI Growth With Climate Goals?

• Accelerate Renewable Energy Deployment: Both Mexico and Saudi Arabia must dramatically increase clean energy capacity before expanding AI infrastructure. Mexico needs strategic, large-scale investment in renewable energy and sustainable grid infrastructure to couple computational growth with clean power . Saudi Arabia's 50 percent renewable target by 2030 is a start, but faster deployment is critical .
• Upgrade Grid Infrastructure: Existing electrical grids in both countries were not designed for the massive demand that modern AI systems create. Grid modernization and resilience improvements must accompany supercomputer deployment to prevent reliance on fossil fuel backup generators .
• Use AI as a Complementary Tool, Not a Primary Solution: Saudi Arabia's experience suggests that AI can support climate goals, but only as a complementary tool rather than a primary solution. The country's net-zero-by-2060 target depends heavily on scaling up renewables, deploying carbon capture technologies, and reducing emissions intensity, with AI accelerating progress in these areas rather than replacing them .

What Do the Numbers Actually Show?

Saudi Arabia has demonstrated some success in using AI for emissions reduction. The International Telecommunication Union (ITU) praised the Kingdom's initiative in leveraging AI to reduce emissions by more than 588,000 tons of carbon equivalent annually and to improve energy and water efficiency through ACWA Power's Monitoring and Prediction Centre . However, these gains are modest compared to the Kingdom's total emissions and depend on continued clean energy investment.

Mexico's situation is more precarious. The country's updated Nationally Determined Contribution emphasizes emissions reductions by 2030 and a trajectory toward net zero by 2050. Achieving these goals requires not only improved climate monitoring but also accelerated clean energy deployment and systemic decarbonization of energy infrastructure . Without alignment between AI expansion and renewable energy growth, the energy demands of AI could threaten the very climate goals it is intended to support.

"Realizing AI's climate-positive potential depends on coupling computational growth with strategic, large-scale investment in renewable energy and sustainable grid infrastructure," noted Climate Scorecard's analysis of Mexico's AI strategy.

Climate Scorecard Mexico Country Manager, Miguel Martinez

The core lesson is stark: AI's environmental promise is conditional. It will enhance analytic and predictive capacity only to the extent that its energy requirements are met through renewable generation and efficient grid planning. For Mexico and Saudi Arabia, the next few years will determine whether AI becomes a climate solution or a climate liability.