- Sam Altman Dismisses ChatGPT’s Water Consumption Claims as ‘Totally False’
- Experts warn that scaling AI infrastructure comes with huge costs and increasing pressure on power, cooling and resources.
- The real issue is not efficiency: it is whether AI can be developed at this scale without serious environmental impact.
Speaking at an event hosted by The Indian Express, OpenAI CEO Sam Altman dismissed claims that AI water consumption is high, calling them “totally false”, but he acknowledged that it had been an issue in the past when “we were doing evaporative cooling in data centers”.
“Now that we don’t do that anymore, 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. “It’s completely false, totally insane, no connection with reality.”
You can find this segment, lasting approximately 27 minutes, in the event video:
Look on it
Altman conceded that concerns about AI’s overall energy consumption were “fair,” noting that “the world is using so much AI now” and that “we need to move very quickly toward nuclear or wind and solar.”
AI-specific data centers already leave a larger and more complex footprint than traditional facilities, and several groups have expressed concerns about their environmental impact, including increased electricity demand, water consumption and the construction of new infrastructure. This growth also has ripple effects, including increased demand for components such as RAM, which is driving up prices across the industry.
IBM CEO Arvind Krishna has previously expressed doubts about the financial viability of the current pace and scale of AI data center expansion. It estimates that equipping a single 1GW site with hardware now costs nearly $80 billion – and with plans for nearly 100GW 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 the breaking point of data centers, forcing a rethink of power, cooling and connectivity. Hardware that seemed cutting edge just a few years ago can no longer keep up as modern AI workloads demand a complete overhaul of everything from rack design to thermal strategy.
News flash: humans also need 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 around AI energy consumption were “unfair” because they don’t take into account the amount of energy 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,” Altman said. “It takes about 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 have ever lived and learned not to get eaten by predators and to understand 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 likely, the AI has already caught up based on energy efficiency, measured that way.”
I can understand the argument Altman makes – that human intelligence also has an energy cost – but it seems reductive and slightly cynical to reduce the value of a human life to its energy consumption. More importantly, it avoids the real problem. The question is not whether humans also use energy (of course!), 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 can be provocative, but it is not particularly useful.
What Altman’s comments underscore is a growing tension at the heart of the AI boom. Technology may be getting smarter and more efficient, but the scale at which it is 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”, highlighting how fragile these resources have become.
These questions will not go away. As AI adoption accelerates, the real challenge will not only be how effective the technology becomes, but also whether it can scale sustainably.
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