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For years, the tech world sold artificial intelligence as a miracle solution that would cut costs, eliminate inefficiency and usher in a new era of productivity.

In response, governments, banks, media organisations and multinational corporations rushed to adopt AI systems, believing they are cheaper than people.

But beneath the glossy presentations and billion-dollar valuations of AI firms lies the uncomfortable truth that AI is not cheap.

In many ways, it is proving to be far more expensive than human capital, while creating serious environmental, economic and social costs.

The popular assumption is that since machines do not demand salaries, pensions, health insurance or annual leave, replacing humans with AI should reduce expenses.

But this argument ignores the enormous infrastructure costs required to power modern AI systems. These AI systems rely on giant data centres packed with advanced chips, cooling systems and constant electricity supply.

According to the International Energy Agency, AI is already driving a sharp increase in global electricity demand from data centres.

Training advanced AI models also costs billions of dollars. Companies must also pay for cloud infrastructure, specialised hardware, cybersecurity, software engineers, data storage and continuous updates.

Even after deployment, AI systems still require expensive human oversight because they frequently produce inaccurate or misleading outputs.

In reality, many firms are discovering that AI does not fully replace workers. Instead, it creates a parallel system where humans must constantly monitor, correct and clean up after the technology. This means organisations often end up paying for both automation and human labour at the same time.

A recent MIT-linked analysis showed that practical barriers and high integration costs are slowing widespread AI adoption across industries.

Technology companies often market AI as faster and smarter than humans. And yes, it is all that. Yet AI systems remain deeply flawed.

Researchers at Massachusetts Institute of Technology found that machine-learning systems frequently fail to reproduce human judgment accurately and can make harsher or distorted decisions. Another study warned that humans can develop “automation bias”, becoming overly confident in AI systems even when the technology is wrong.

The consequences are serious. AI errors in healthcare, finance, journalism, law enforcement or aviation can carry devastating costs. A mistaken financial recommendation can wipe out investments. A flawed medical suggestion can endanger lives. A hallucinated legal citation can collapse a court case.

Human workers make mistakes too. But humans possess context, empathy, ethical reasoning and accountability in ways machines simply do not. When AI fails, responsibility often becomes blurred between software developers, companies and users.

Ironically, the more companies automate critical functions, the more they expose themselves to costly operational and reputational risks.

The environmental damage is the most concerning, and it’s growing

Perhaps the most overlooked cost of AI is environmental destruction. Every AI query requires computing power. Every generated image, chatbot response and automated process consumes electricity and water inside data centres. Researchers have warned that the rapid expansion of generative AI is creating major carbon and water footprints globally.

One study estimated that developing large language models can produce hundreds of metric tons of carbon emissions and consume millions of litres of water. The environmental burden becomes even greater when billions of daily AI interactions are considered collectively. In some communities, the impact is already visible. Reports show AI data centres are contributing to localised warming effects and placing strain on water and power infrastructure.

The IEA has also warned that AI data centres will continue to significantly increase electricity demand worldwide.