AI models are trained and deployed
The Silent Footprint of Intelligence To train large AI models like GPT-5, Gemini, or Claude, trillions of data points are processed using high-end computer clusters called data centers. Data centers hold thousands of GPUs (graphic processing units), which work around the clock for weeks or months. ARead more
The Silent Footprint of Intelligence
To train large AI models like GPT-5, Gemini, or Claude, trillions of data points are processed using high-end computer clusters called data centers. Data centers hold thousands of GPUs (graphic processing units), which work around the clock for weeks or months. A training cycle consumes gigawatt-hours of power, most of which has not been produced using fossil fuels yet.
A 2023 study estimated the cost as equivalent to five cars’ worth of carbon emissions over their lifetime to train one large language model. And that’s just the training — in use, they just continue to require copious amounts of energy for inference (producing a response to a user query). Hundreds of millions of users submitting queries daily, and carbon consumption expands at an exponential rate.
Water — The Unseen Victim
Something that most people don’t realize is that not only does AI consume lots of electricity, it also drains enormous amounts of water. Data centers generate enormous amounts of heat when running high-speed chips, so they must have water-cooling systems to prevent overheating.
Recent news reports suggested that training advanced AI models could consume as much as hundreds of thousands of liters of water, which is often tapped from local water reservoirs around the data centers. Citizens in drought-stricken areas of the U.S. and Europe, for instance, have raised concerns about utilizing local water resources for cooling AI devices by technology companies — the unsavory marriage of cyber innovation and environmental stewardship.
E-Waste and Hardware Requirements
The second often-overlooked consideration is the hardware footprint. Training behemoth models is compute-heavy and requires high-end GPUs and AI-designed chips (e.g., NVIDIA’s H100s), which are dependent on rare earth elements such as lithium, cobalt, and nickel. Producing and extracting these components not only strain ecosystems but also produce e-waste when eventually hardware becomes outdated.
The rapid rate of AI progress has chips replaced on a regular basis — typically in the span of only a few years — leading to growing piles of dead electronics that can’t be recycled.
The Push Toward “Green AI”
In order to answer these questions, researchers and institutions are now advocating “Green AI” — a movement that seeks efficiency, transparency, and sustainability. This is all about making models smarter with fewer watts. Some of the prominent initiatives are:
- Small, specialized models: Instead of training gargantuan systems from the ground up, constructors are taking pre-existing models and adapting them to specific tasks.
- Successful architectures: Model distillation, pruning, and quantization methods reduce compute without sacrificing performance.
- Renewable-powered data centers: Google, Microsoft, and others are building solar, wind, and hydro-powered data centers to offset carbon emissions.
- Energy transparency reports: Certain AI labs now disclose how much energy and water their model training consumes — a move towards accountability.
A Global Inequality Issue
There is also a more profound social aspect to this situation. Much of the big-data training of AI happens in affluent nations with advanced infrastructure, and the environmental impacts — ranging from mineral mining to e-waste — typically hit developing countries the hardest.
For example, cobalt mined for AI chips is often mined in regions of Africa where there are weak environmental and labor regulations. Conversely, small nations experiencing water scarcity or climate stresses have minimal leverage over global digital expansion that drains their shared resources.
Balancing Innovation with Responsibility
AI can help the world too. Models are being used to create more efficient renewable grids, monitor deforestation, predict climate trends, and create better materials. But that potential gets discredited if the AI technologies themselves are high emitters of carbon.
The goal is not, then, to slow down AI development — but to make it smarter and cleaner. Companies, legislators, and consumers alike need to step in: pushing for cleaner code, supporting renewable energy-powered data centers, and demanding openness about the true environmental cost of “intelligence.”
In Conclusion
The green cost of artificial intelligence is a paradox — the very technology that can be used to fix climate change is, in its current form, contributing to it. Every letter you type, every drawing you create, or every chatbot you converse with carries an invisible environmental price.
In the future, it’s not whether we need to create more intelligent machines — but whether we can do so responsibly, with a sense of consideration for the world that sustains both humans and machines. Real intelligence, after all, isn’t just a function of computational power — but of understanding our impact and acting wisely.
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The Case For Transparency Trust is at the heart of the argument for government intervention. AI systems are making decisions that have far-reaching impacts on human lives — deciding who is given money to lend, what news one can read, or how police single out suspects. When the underlying algorithm iRead more
The Case For Transparency
Trust is at the heart of the argument for government intervention. AI systems are making decisions that have far-reaching impacts on human lives — deciding who is given money to lend, what news one can read, or how police single out suspects. When the underlying algorithm is a “black box,” one has no means of knowing whether these systems are fair, ethical, or correct.
Transparency encourages accountability.
If developers make public how a model was trained — the data used, the potential biases that there are, and the safeguards deployed to avoid them — it is easier for regulators, researchers, and citizens to audit, query, and improve those systems. It avoids discrimination, misinformation, and abuse.
Transparency can also strengthen democracy itself.
AI is not a technical issue only — it’s a social one. When extremely powerful models fall into the hands of some companies’ or governments’ without checks, power becomes concentrated in ways that could threaten freedom, privacy, and equality. By mandating transparency, governments would be making the playing field level so that innovation benefits society rather than the opposite.
The Case Against Over-Enforcement
But transparency is not simple. For most companies, training AI models is a trade secret — a result of billions of dollars of research and engineering. Requiring full disclosure may stifle innovation or grant competitors an unfair edge. In areas where secrecy and speed are the keys to success, too much regulation may hamper technological progress.
And then there is the issue of abuse and security. Some AI technologies — most notably those capable of producing deepfakes, code hacking, or bio simulations — are potentially evil if their internal mechanisms are exposed. Exposure could reveal sensitive data, making cutting-edge technology more susceptible to misuse by wrongdoers.
Also, governments themselves may lack technical expertise available to them to responsibly regulate AI. Ineffective or vague laws could stifle small innovators while allowing giant tech companies to manipulate the system. So, the question is not if transparency is a good idea — but how to do it intelligently and safely.
Finding the Middle Ground
The way forward could be in “responsible transparency.”
Instead of mandating full public disclosure, governments could mandate tiered transparency, where firms have to report to trusted oversight agencies — much in the same fashion that pharmaceuticals are vetted for safety prior to appearing on store shelves. This preserves intellectual property but retains ethical compliance and public safety.
Transparency is not necessarily about revealing every line of code; it is about being responsible with impact.
That would mean publishing reports on sources of data, bias-mitigation methods, environmental impacts of training, and potential harms. Some AI firms, like OpenAI and Anthropic, already do partial disclosure through “model cards” and “system cards,” which give concise summaries of key facts without jeopardizing safety. Governments could make these practices official and routine.
Why It Matters for the Future
With artificial intelligence becoming increasingly ingrained in society, the call for transparency is no longer just a question of curiosity — it’s a question of human dignity and equality. Humans have the right to be informed when they’re interacting with AI, how their data is being processed, and whether the system making decisions on their behalf is ethical and safe.
In a world where algorithms tacitly dictate our choices, secrecy breeds suspicion. Open AI, with proper governance behind it, may help society towards a future where ethics and innovation can evolve hand-in-hand — and not against each other, but together.
Last Word
Should governments make transparency in AI obligatory, then?
Yes — but subtly and judiciously. Utter secrecy invites abuse, utter openness invites chaos. The trick is to work out systems where transparency is in the interests of the public without glazing over progress.
The real question isn’t how transparent AI models need to be — it’s whether or not humanity wishes its relationship with the technology it has created to be one of blind trust, or one of educated trust.
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