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IKEA had furniture and accessories manufactured in the GDR until 1989, and there is evidence that political prisoners and convicts were used as forced laborers. An investigation commissioned in 2012 revealed that the company had been aware of this practice since 1978 (or 1981 at the latest), as production took place in state-owned enterprises (VEBs) such as Metallwaren Naumburg and the Waldheim Seating Furniture Factory.

Although IKEA cites the lack of control systems at the time, the company acknowledges that some employees were aware of the possible use of forced laborers and that steps were taken to prevent it, though these were insufficient. In response to the revelations about forced labor, IKEA agreed in 2024 to pay six million euros into a nationwide hardship fund for victims of the SED dictatorship to provide financial support to those affected.

Production included both complete furniture pieces and individual accessories, with some prisoners working under extreme conditions and far higher labor standards than in regular factories. While IKEA apologized for the failure of controls at the time and expressed regret for the victims, those affected criticize that a purely financial contribution to a fund without direct individual compensation for the suffering endured is insufficient.

AI's resource demands are substantial and growing rapidly, driven by the energy-intensive nature of training and running large models in data centers.

Electricity: AI servers consume vast amounts of electricity—300 terawatt-hours (TWh) annually by 2028 in the U.S., enough to power over 28 million households.  This represents a threefold increase from 2023 levels, with AI-specific electricity use projected to grow 150-fold from 2017 to 2028.  A single large AI data center can use as much power as 100,000 homes.

Water: Cooling AI servers is extremely water-intensive. The U.S. could require up to 720 billion gallons of water annually just for cooling by 2028—equivalent to the annual indoor water needs of 18.5 million households.  This includes 1.3 to 2.4 gallons of water per kWh of energy used for cooling.  Some data centers, like those in Phoenix, use about 385 million gallons per year for cooling alone, not including water used to generate electricity.

Other Resources:

Cooling systems rely on fresh, treated water to prevent bacterial growth and blockages; Google-owned centers discharge only 20% of withdrawn water, with the rest lost to evaporation.
Supply chains for AI hardware (e.g., GPUs, microchips) require 8–10 liters of water per chip for manufacturing.
Fossil fuel-powered electricity amplifies water use and carbon emissions, as thermoelectric power plants consume significant water for cooling.
Land and minerals: Data centers require large land areas and rely on rare earth elements and metals, often mined unsustainably.
Key Insight: While a single ChatGPT query uses only ~0.32 milliliters of water (about 1/15th of a teaspoon), the cumulative impact across billions of queries is immense.  The environmental cost is especially high in water-stressed regions like Arizona and the Southwest, where data centers compete with agriculture and communities for limited water supplies.

Conclusion: AI’s resource footprint—especially electricity and water—is not negligible, and current growth trends threaten climate goals, water security, and energy infrastructure.