The real cost of AI: human and environmental harm | Psyll

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1 month ago by

Jarosław Szulc

PRO

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@jarek

Article

Technology

AI & Machine Learning

The real cost of AI: human and environmental harm

AI's hidden costs are staggering: child labor, water depletion, toxic e-waste, deepfakes, and algorithmic bias threatening democracy and human cognition.

"The real danger is not that computers will begin to think like men, but that men will begin to think like computers."

Sydney J. Harris

Preface: the comfortable lie

Artificial Intelligence is routinely presented as an ethereal, magical force - a disembodied "cloud" capable of solving humanity's most complex problems, from curing diseases to reversing climate change. The images are always the same: glowing neural networks on black backgrounds, smiling doctors reviewing AI-assisted diagnoses, clean energy grids humming alongside wind turbines, diverse teams of engineers building a more equitable future. Images of power without consequence. Innovation without cost.

This techno-optimistic narrative - industriously maintained by a handful of trillion-dollar corporations, their well-funded PR agencies, and a media ecosystem dependent on their advertising revenue - is one of the most consequential deceptions of our age.

Behind the sleek chat interfaces, automated homework helpers, and hyper-realistic AI-generated images lies an immense and expanding infrastructure of energy-devouring data centers, exploitative labor pipelines, and algorithmic systems that actively degrade human cognition, fracture societies, and amplify inequalities that have persisted for centuries. AI is not immaterial. It is built on the aggressive extraction of natural resources, the systematic exploitation of vulnerable populations, and the ruthless monetization of human attention, creativity, and psychological weakness.

What follows is an attempt to strip away that comfortable mythology and examine, sector by sector and consequence by consequence, what the unchecked expansion of Artificial Intelligence is actually doing to our world - and what it will do to the generations who must inherit it.

The environmental toll - a thirsty, hungry, and toxic cloud

The myth of the weightless cloud

The metaphor of "the cloud" is one of the tech industry's most successful public relations triumphs. It implies something weightless, natural, infinite - water vapor drifting benignly overhead. In reality, the cloud is composed of massive, warehouse-sized data centers that are among the most energy-intensive and resource-hungry structures ever built by human beings.

A single hyperscale data center - such as those operated by Amazon Web Services, Microsoft Azure, or Google Cloud - can occupy more than a million square feet of floor space. It requires a dedicated electrical substation. Its cooling systems demand millions of gallons of fresh water per day. Its construction requires the extraction and processing of rare earth minerals from some of the most environmentally fragile and politically unstable places on the planet. And it operates continuously, around the clock, every single day of the year, generating heat, waste, and electromagnetic pollution with no seasonal respite.

The AI boom has not merely expanded this infrastructure. It has sent it into overdrive.

The staggering energy appetite of large language models

Training and running Large Language Models (LLMs) requires processing unfathomable amounts of data across tens of thousands of specialized Graphics Processing Units (GPUs) and custom AI accelerator chips. The computational hunger of these systems is pushing global energy grids to their absolute limits.

A landmark study by researchers at the University of Massachusetts Amherst found that training a single large-scale AI Natural Language Processing model with neural architecture search emitted nearly 626,000 pounds of carbon dioxide equivalent - roughly five times the total lifetime emissions of an average American car, including the carbon cost of manufacturing the vehicle itself.

And that figure represents a single training run. Models are trained, refined, retrained, fine-tuned, evaluated, and retrained again. A single model reaching production may represent dozens or hundreds of such runs. Then it is deployed at global scale - millions to billions of inference requests daily, each one consuming electricity.

The numbers are no longer projections. The International Energy Agency confirmed that global data center electricity consumption reached approximately 415 terawatt-hours in 2024 - representing around 1.5% of the world's total electricity use. Electricity consumption from AI-focused data centers has been surging at a pace far outstripping other sectors. The IEA's updated projections see this...