By Stuart Kerr, Technology Correspondent
🗓️ Published: 11 July 2025 | 🔁 Last updated: 11 July 2025
📧 Contact: liveaiwire@gmail.com | 📣 Follow @LiveAIWire
🔗 Author Bio: https://www.liveaiwire.com/p/to-liveaiwire-where-artificial.html
The Promise of Green Intelligence
Artificial intelligence has been sold as a saviour of sustainability. From energy-efficient smart grids to carbon footprint trackers and climate modelling, AI is pitched as a tool that can help humanity manage and mitigate its environmental crises. Tech companies are keen to show off green credentials, citing machine learning as an accelerator for everything from precision agriculture to clean energy.
But beneath the surface of this optimistic marketing lies an inconvenient truth: the environmental footprint of AI itself is anything but clean. With ballooning demand for training data, soaring compute power, and an insatiable appetite for energy and water, AI's climate impact is rapidly becoming one of the tech world’s dirtiest secrets.
Data Centres: The Hidden Polluters
At the heart of AI's environmental impact is the data centre. These sprawling facilities, packed with GPUs and cooling systems, require enormous amounts of electricity. According to MIT News, training a single large language model can consume as much energy as 100 U.S. households use in a year. And it's not just electricity—water is used in vast quantities to cool the systems, often in drought-prone regions.
In Google's own sustainability report, the company admitted to a 13% rise in emissions, citing surging AI activity. As highlighted in Energy Digital, this growth challenges the narrative that digital transformation naturally leads to decarbonisation.
While companies claim to use renewable energy offsets, these credits often mask the real-time environmental damage of around-the-clock machine learning operations.
The Crypto Parallel
AI's resource consumption mirrors that of cryptocurrency mining—a sector already criticised for its environmental cost. Like crypto, AI rewards scale. Bigger models, better predictions. But as detailed in the ICEF white paper (PDF), this scale comes at a greenhouse gas cost that regulators and environmentalists can no longer afford to ignore.
The OECD's 2024 policy review (PDF) recommends clear reporting standards and sustainability thresholds for AI development. Yet few of the world’s largest AI labs publish complete data on energy use or emissions.
In our own investigation, The Energy Crisis of AI, we explored how tech giants are quietly investing in private energy reserves and grid access to fuel AI ambitions. These backdoor deals rarely make the headlines.
Smart Grids or Smart Excuses?
Companies often point to AI-powered smart grids and predictive maintenance tools as evidence of net environmental gain. And yes, there are real efficiencies to be found—optimised logistics, reduced waste, and smarter urban planning.
However, the overall balance remains unclear. As shown in AI and Climate Change: Can Machine Learning Save the Planet?, most of AI’s energy-saving applications are still experimental or small in scale, while its energy consumption is anything but.
It's a classic case of greenwashing: showcasing eco-friendly use cases while hiding the full carbon cost of the infrastructure required to deliver them.
The Transparency Gap
A major part of the problem is opacity. As noted in Scotland’s Green AI Dream, governments are keen to partner with AI firms to drive innovation, but few demand full lifecycle sustainability disclosures.
Even internal emissions audits, where they exist, are rarely made public. The lack of shared metrics or independent monitoring creates an environment where green claims go unchallenged and unverified.
MIT Sloan argues that sustainable AI is possible—but only if companies commit to rigorous reporting and energy-conscious model design. Techniques like model pruning, federated learning, and low-power inference exist. But incentives are needed to make them the default, not the exception.
Conclusion: AI's Eco-Crisis Needs More Than Optics
The hype around AI as a climate solution masks a growing ecological debt. While it may offer tools to monitor and model climate change, its own environmental burden is increasing, not decreasing.
Until there are enforceable standards, transparent disclosures, and a shift away from scale-at-all-costs mentality, AI risks becoming just another contributor to the crisis it claims to solve.
As detailed in The Hidden Carbon Cost of AI Training, the question isn’t whether AI can be green—it’s whether we’re willing to make it so.
About the Author
Stuart Kerr is the Technology Correspondent at LiveAIWire. He writes about AI’s impact on ethics, infrastructure, climate, and global equity.
📧 Contact: liveaiwire@gmail.com | 📣 @LiveAIWire
