- Sam Altman dismisses ChatGPT’s water usage claims as ‘totally false’
- Experts warn that scaling up AI infrastructure is generating huge costs and increasing pressure on power, cooling and resources.
- The real issue is not efficiency, but whether AI can grow to this scale without serious environmental impact.
At an event hosted by The Indian Express, OpenAI CEO Sam Altman dismissed claims that AI’s water usage is high as “completely false,” but acknowledged that it had been an issue in the past when “we used to do evaporative cooling in data centers.”
“Now that we don’t do that, you see things on the Internet like, ‘Don’t use ChatGPT, it’s 17 gallons of water for each query,’ or whatever,” Altman said. “This is completely false, totally crazy, it has no connection with reality.”
You can find this segment around the 27 minute mark in the event video:
Look
Altman admitted that concerns about AI’s overall energy consumption are “fair,” noting that “the world is using so much AI now” and that “we need to move toward 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 growing electricity demand, water use, and the construction of new infrastructure. That expansion is also having knock-on effects, including increased demand for components like RAM, which is driving up prices across the industry.
IBM CEO Arvind Krishna has previously raised questions about whether the current pace and scale of AI data center expansion is financially sustainable. It estimates that equipping a single 1 GW site with computing hardware now costs nearly $80 billion, and with plans for nearly 100 GW of capacity dedicated to advanced AI training, total potential spending could approach a staggering $8 trillion.
Meanwhile, the new wave of ultra-powerful AI accelerators is pushing data centers to the limits, forcing a rethink of power, cooling and connectivity. Hardware that seemed cutting-edge just a few years ago can’t keep up, as modern AI workloads demand a complete overhaul of everything from rack design to thermal strategy.
Breaking news: humans need a lot of energy too
In addition to dismissing claims about ChatGPT’s water use, Altman also offered a more unusual defense of OpenAI’s overall energy use. He argued that discussions of AI energy consumption were “unfair” because they do not take into account how much energy is needed to train humans to perform similar tasks.
It also takes a lot of energy to train a human.
Sam Altman, CEO of OpenAI
“But it also takes a lot of energy to train a human being,” Altman said. “It takes like 20 years of life and all the food you eat during that time before you become intelligent. And not only that, it took the very widespread evolution of the 100 billion people who once lived and learned not to be eaten by predators and learned to discover science and whatever, to produce you.”
He continued: “If you ask ChatGPT a question, how much energy does it take once its model is trained to answer that question compared to a human? And AI has probably already caught up in terms of energy efficiency, measured that way.”
I can understand the argument Altman is making (that human intelligence also carries an energetic cost), but it seems reductionist and slightly 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 energetic cost of a human life to the marginal cost of an AI response may be provocative, but it is not particularly useful.
What Altman’s comments highlight is a growing tension at the heart of the AI rise. 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 those resources have become.
Those questions are not going away. As AI adoption accelerates, the real challenge will not only be how efficient the technology becomes, but also whether it can scale sustainably.
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