The escalating energy demands of artificial intelligence are drawing increased scrutiny, with OpenAI CEO Sam Altman recently defending the substantial power consumption of systems like ChatGPT. Altman framed the energy use as comparable to the resources required to nurture human intelligence, a statement that has sparked debate and criticism. This discussion comes as OpenAI nears a potential $100 billion funding round, fueled by the continued growth of its popular chatbot, and as the company navigates intensifying competition in the AI landscape.
ChatGPT, which boasts over 800 million weekly users as of February 9, 2026, requires significant energy both for its initial training and for responding to user queries. Altman acknowledged this high consumption, emphasizing the need for innovative energy technologies – including solar and nuclear fusion – to power AI development sustainably. However, his comparison of AI energy needs to human development, made during a speech at an AI summit in India, drew criticism for seemingly diminishing the value of human life and equating the processes of training a person with training an AI system.
Altman’s Energy Comparison Draws Fire
Speaking to attendees at the India summit, Altman reportedly stated, “The people talk about how much energy it takes to train an AI model, but it as well takes a lot of energy to form a human being. It takes about 20 years of life—and all the food you consume during that time—before you become intelligent.” This analogy, as reported by The Indian Express, was presented alongside Altman’s assertion that concerns about AI’s energy footprint are “valid,” but that the solution lies in rapidly adopting new energy sources.
The comparison immediately resonated negatively with many observers. Critics argued that it trivialized the inherent value of human life and ignored the qualitative differences between the development of a biological being and the training of an algorithm. The statement also appeared to downplay the environmental impact of AI, despite Altman’s acknowledgement of the need for sustainable energy solutions. The core of the debate centers on the ethical responsibility of AI developers to minimize the environmental consequences of their creations, particularly as AI systems become increasingly complex and energy-intensive.
Addressing Concerns About Water Usage
Beyond energy consumption, Altman also addressed concerns regarding the water used in cooling the data centers that power AI systems. He dismissed these concerns as “false,” stating that while water usage was previously a valid point of contention, it is no longer a significant issue. He characterized the suggestion that people should avoid using AI due to its water footprint as “completely false” and “absurd.”
However, the claim that water usage is no longer a concern requires further scrutiny. Data centers, even with advanced cooling technologies, still require substantial amounts of water, particularly in arid regions. The environmental impact of water usage varies significantly depending on the location and the source of the water. While advancements in cooling technologies, such as liquid cooling and air cooling, are reducing water consumption, it remains a critical factor in the sustainability of AI infrastructure. According to a 2023 report by the U.S. Geological Survey, data centers accounted for approximately 3% of total U.S. Freshwater withdrawals in 2020, a figure expected to rise with the continued growth of cloud computing and AI.
OpenAI’s Growth and Competitive Landscape
Altman’s comments come at a pivotal moment for OpenAI. The company is reportedly on the cusp of securing funding that would value it at nearly $100 billion. This valuation is largely driven by the success of ChatGPT and the broader adoption of generative AI technologies. As of February 9, 2026, ChatGPT is reportedly “back to exceeding 10% monthly growth,” according to an internal Slack message viewed by CNBC. OpenAI is also preparing to launch an updated version of the Chat model this week, aiming to maintain its competitive edge.
However, OpenAI is facing increasing competition from companies like Anthropic, whose Claude Code has gained traction in the coding space. In December 2025, OpenAI declared a “code red” to improve ChatGPT, temporarily pausing other projects to focus on enhancing its core capabilities. The company recently launched a new Codex model, GPT‑5.3-Codex, and a standalone app for Apple computers, reporting “insane” growth in its coding product. Codex directly competes with Anthropic’s Claude Code, which has seen significant adoption over the past year. Sam Altman, born April 22, 1985, currently serves as CEO of OpenAI and Chairman of Helion Energy, having stepped down from his role as Chairman of Oklo Inc. In April 2025. He was also named among Time Magazine’s “Architects of AI” for their 2025 Person of the Year selection.
The Energy Demands of Large Language Models
The energy consumption of large language models (LLMs) like ChatGPT is a growing concern within the AI community. Training these models requires massive computational resources, resulting in a substantial carbon footprint. The process involves feeding the model vast amounts of data and iteratively adjusting its parameters, a process that can take weeks or months and consume significant amounts of electricity. A 2019 study by Strubell et al. Estimated that training a single large language model can emit as much carbon dioxide as five cars over their lifetimes.
The energy demands are not limited to the training phase. Even after training, running LLMs to respond to user queries requires considerable energy. As the number of users and the complexity of queries increase, so too does the energy consumption. This raises questions about the long-term sustainability of AI development and the need for more energy-efficient algorithms and hardware.
Sustainable Solutions for AI Energy Consumption
Addressing the energy challenges of AI requires a multi-faceted approach. One key strategy is to develop more energy-efficient algorithms and hardware. Researchers are exploring techniques such as model compression, quantization, and pruning to reduce the computational complexity of LLMs. The development of specialized AI chips, such as those being developed by Nvidia and Google, can significantly improve energy efficiency.
Another crucial step is to transition to renewable energy sources. Data centers can be powered by solar, wind, and other renewable energy sources, reducing their carbon footprint. OpenAI and other AI companies are increasingly investing in renewable energy projects to offset their energy consumption. Exploring alternative cooling methods, such as liquid cooling and immersion cooling, can reduce water usage and energy consumption in data centers.
Sam Altman’s recent statements highlight the complex interplay between AI innovation, energy consumption, and environmental sustainability. While acknowledging the need for sustainable energy solutions, his comparison of AI energy needs to human development has drawn criticism for potentially minimizing the environmental impact of AI and devaluing human life. As AI continues to evolve, it is crucial for developers and policymakers to prioritize energy efficiency and sustainability to ensure that AI benefits society without exacerbating environmental challenges.
The debate surrounding AI’s energy consumption is likely to intensify as the technology becomes more pervasive. Continued research and development of energy-efficient algorithms, hardware, and renewable energy sources will be essential to mitigating the environmental impact of AI and ensuring its long-term sustainability. The next major checkpoint will be OpenAI’s planned release of its updated Chat model this week, which will be closely watched for improvements in both performance and energy efficiency.
What are your thoughts on the energy demands of AI? Share your comments below and let us know how you think the industry can address these challenges.
Related reading