AI: A Zero-Sum Game for Sustainability?

As part of my PhD ambitions, I am doing my desk research on the intersection between AI and Sustainable Development. It's impossible to ignore the elephant in the room: the exponential increase of energy requirements of data centers. The question for me seems to be whether the drastic increase in energy requirements, possible waste produced, and the impact on water and land caused by AI can be justified in terms of its potential for positive impact on sustainable development? Or is it a zero-sum game? And if so, how do we manage it? Artificial Intelligence and Sustainable Development | Image generated by Gemini Consider these statistics from a 2024 article in Forbes (https://www.forbes.com/sites/arielcohen/2024/05/23/ai-is-pushing-the-world-towards-an-energy-crisis/) :

• Energy Demand: Data centers already account for 3% of global energy consumption in 2024. This is projected to more than double by 2030, with individual AI servers using up to 14 times more energy than a traditional server.
• Water Consumption: AI is incredibly thirsty. Just two companies—Google and Microsoft—consumed 32 billion liters of water in their data centers in 2022 for cooling systems alone.
• Individual Models: Training a single large language model like GPT-4 required over 50 gigawatt-hours of electricity, which is 50 times the amount needed for its predecessor, GPT-3. And don't forget GPT-5 was recently released in early Aug 2025 (https://openai.com/index/introducing-gpt-5/)

The question of balance is not just a philosophical one; it’s an urgent and very real business imperative. The future of AI hinges on our ability to manage its resource requirements. There is a peer-reviewed paper published in Nature in 2020 that provides some insights that I thought were worth sharing. In 'The role of artificial intelligence in achieving the Sustainable Development Goals ' (https://www.nature.com/articles/s41467-019-14108-y) a study by Vinuesa et al. employed a consensus-based expert elicitation process to systematically evaluate how AI can influence the achievement of the 17 SDGs and their 169 targets outlined in the 2030 Agenda for Sustainable Development. It's a heavy read but these are the highlights. The research found that AI has a dual potential: it can enable the accomplishment of 134 targets (79%) across all SDGs but may also inhibit 59 targets (35%). This is a crucial and nuanced perspective that moves us beyond taking polarised positions.

The Two Sides of the Coin: AI's Impact on Our World The paper breaks down AI's influence into three pillars: Society, Economy, and Environment.

Society

• The Good: AI could positively impact 82% of targets by supporting essential services like food, health, water, and energy, and by underpinning low-carbon systems and smart cities.
• The Bad: But AI also has a dark side. The high energy requirements of AI technologies can compromise climate goals. AI may also trigger inequalities by raising job requirements, and its development, often based on the values of wealthy nations, could perpetuate biases and threaten human rights.

Economy

• The Good: AI offers benefits to 70% of targets, mainly through increased productivity.
• The Bad: The paper warns that AI can also widen the economic gap if data analysis resources are not equally available to all nations. AI-driven automation may replace jobs with those that require higher skills, disproportionately rewarding the educated and shifting income from workers to company owners.

Environment

• The Good: AI shows positive potential for 93% of targets in this group, particularly in understanding climate change, modeling its impacts, and improving ecosystem health.
• The Bad: The high energy demands of AI applications, if powered by non-carbon-neutral sources, could undermine climate action efforts. There is also a concern that increased access to ecosystem information via AI could potentially drive over-exploitation of resources.

The Social impact of AI Of particular interest to me is the social impact of AI particularly on the problems created by potential AI bias. Of particular interest to me in my research is the social impact of AI, and specifically, the profound problems created by potential AI bias. It’s not just a technical issue; it's a strategic and ethical minefield that organizations must learn to navigate. The research is clear: The risks are real, and they span every aspect of a business and society.

• The Problem of Bias in Development: AI is often developed by a non-diverse workforce in wealthy nations, leading to systems that are trained on a narrow set of values. If deployed in regions lacking ethical scrutiny and democratic control, this technology can enable nationalism, hate towards minorities, and biased outcomes.
• Perpetuating Societal Inequalities: AI, trained on news articles and languages that contain societal biases, will inadvertently learn and reproduce those biases. This can lead to discriminatory challenges in everything from online job advertising to criminal justice, where algorithms can act as a "mirror," reflecting and amplifying existing prejudices.
• The "Big Nudging" Threat: AI has the power to exploit psychological weaknesses to "steer decisions," a concept known as "big nudging." This can damage social cohesion, democratic principles, and even human rights by manipulating public opinion and creating political polarization.
• Exacerbating Economic Inequality: AI can exacerbate inequalities within and between nations, especially if data analysis resources are not equally available to all. Automation may replace jobs with those requiring more skills, disproportionately rewarding the educated and shifting corporate income from workers to owners.

Gaps In Research Back to my PhD ambitions, the paper finally identified some research gaps which is of interest to me:

• Bias in Research Community: The tendency for the AI research community and industry to publish positive results, and the need for longer-term studies to uncover detrimental aspects.
• Funding Priorities: The risk that AI projects with the highest profit potential get funded, potentially neglecting those with high SDG potential but lower economic return.
• Real-world Impact Assessment: The challenge of extrapolating findings from controlled lab environments with limited datasets to real-world, large-scale impacts, highlighting the need for novel methodologies.
• Regulatory Insight vs. Oversight: The urgent need for policymakers to gain sufficient insight into AI challenges before formulating effective oversight policies.

As this paper was published in 2020, I now have to continue my research to understand how these gaps have been filled (if it has) particularly in the Malaysian context.

So what? What does it matter? Most conversations around AI and sustainability are either overly optimistic (AI will save us!) or deeply pessimistic (AI is an energy monster). There are advocates and opposition on both positions. This black and white thinking represents a dangerous approach which will polarize views. Don't forget. Embedded within the concept of sustainable development if we were to use the classical definition of sustainable development which is 'meeting the needs of the present without compromising the ability of future generations to meet their own needs'. (I wrote about this definition and its implication in my last paper which you can read here -> https://rezaali.my/what-sustainable-development-is-really-about/) This definition pushes and forces us to find a balance. Yes. Sometimes that balance is hard to find, but we must still look for it. I think this paper gives us a good start.