- Sam Altman dismisses claims about ChatGPT’s water usage as “completely false”
- Experts warn that scaling AI infrastructure incurs huge costs and increases pressure on power, cooling and resources
- The real issue isn’t efficiency—it’s whether AI can grow at this scale without serious environmental impact
Speaking at an event hosted by The Indian Express, OpenAI CEO Sam Altman dismissed claims that AI’s water consumption is high as “completely false,” but acknowledged that it had been a problem in the past when “we used to do evaporative cooling in data centers.”
“Now that we don’t, you see these things on the Internet like, ‘Don’t use ChatGPT, it’s 17 gallons of water for every query,’ or whatever,” Altman said. “This is completely untrue, completely insane, no connection to reality.”
You can find this segment at about 27 minutes in the video of the event:
Look at
Altman conceded that concerns about AI’s overall energy consumption are “fair”, noting that “the world is now using so much AI” and that “we have to move towards nuclear or wind and solar very quickly”.
AI-specific data centers already leave a larger and more complex footprint than traditional facilities, and several groups have raised concerns about their environmental impact — particularly around increasing demand for electricity, water use and the construction of new infrastructure. This expansion also has knock-on effects, including increased demand for components such as RAM, pushing up prices across the industry.
IBM CEO Arvind Krishna has previously questioned whether the current pace and scale of AI data center expansion is financially sustainable. He estimates that equipping a single 1GW site with computing hardware now costs close to $80 billion—and with plans for nearly 100GW of capacity dedicated to advanced AI training, the total potential spend could approach a staggering $8 trillion.
Meanwhile, AI’s new wave of ultra-powerful accelerators is pushing data centers to the breaking point, forcing a rethinking of power, cooling and connectivity. Hardware that felt cutting edge just a few years ago can’t keep up as modern AI workloads require a complete overhaul of everything from rack design to thermal strategy.
News flash: humans also require a lot of energy
In addition to dismissing claims about ChatGPT’s water consumption, Altman also offered a more unusual defense of OpenAI’s overall energy consumption. He argued that discussions surrounding AI’s energy consumption were “unfair” because they do not take into account how much energy it takes to train humans to perform similar tasks.
It also takes a lot of energy to train a human.
Sam Altman, CEO OpenAI
“But it also takes a lot of energy to train a human,” Altman said. “It takes about 20 years of life and all the food you eat during that time to become smart. And not only that, it took the very widespread evolution of the 100 billion people who have ever lived, learning not to be eaten by predators and learning to figure out science and whatever, to produce you.”
He continued: “If you ask ChatGPT a question, how much energy does it take when its model is trained to answer that question relative to a human? And presumably AI has already caught up on energy efficiency, measured that way.”
I can see the argument Altman is making – that human intelligence also comes with an energy cost – but it feels reductive and faintly cynical to reduce the value of a human life to its energy consumption. More importantly, it sidesteps the real problem. The question is not whether humans also use energy (of course they do!), but whether scaling AI to billions of daily queries introduces entirely new levels of demand that we haven’t had to account for before. Comparing the lifetime energy cost of a human to the marginal cost of an AI response may be provocative, but it is not very helpful.
What Altman’s comments highlight is a growing tension at the heart of the AI boom. The technology may be getting smarter and more efficient, but the scale at which it is being deployed is growing even faster, raising new concerns about its long-term environmental impact, including pressure on global water supplies. The UN has already warned that the world has entered an “era of global water bankruptcy”, underscoring how fragile these resources have become.
Those questions don’t go away. As AI adoption accelerates, the real challenge will not only be how effective the technology will be, but whether it can even scale sustainably.
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