Training large AI models like those used for generative AI consumes significant energy, rivaling the power demands of small countries. While the exact energy footprint remains difficult to calculate due to companies' reluctance to disclose data, estimates suggest training a single large language model can emit as much carbon dioxide as hundreds of cars over their lifetimes. This energy consumption primarily stems from the computational power required for training and inference, and is expected to increase as AI models become more complex and data-intensive. While efforts to improve efficiency are underway, the growing demand for AI raises concerns about its environmental impact and the need for greater transparency and sustainable practices within the industry.
The Civaux-1 nuclear reactor in France consumed more electricity than it generated during the first two months of 2025. This was due to ongoing maintenance and testing following extended outages for repairs related to stress corrosion cracking discovered in 2021. While the reactor was occasionally connected to the grid for testing, it operated at very low power levels, resulting in net electricity consumption as the plant's systems still required power to function.
Hacker News users discuss the misleading nature of the linked chart showing French nuclear power generation in 2025. Several commenters point out that the chart displays scheduled maintenance periods, where plants are offline and consuming power for upkeep, not generating it. This maintenance is crucial for long-term reliability and explains the apparent negative power output. Some highlight the importance of distinguishing between planned downtime and operational issues. Others note the long lead times required for such maintenance, emphasizing the need for careful planning within the energy sector. A few discuss the broader context of French nuclear power and its role in their energy mix.
The post argues that individual use of ChatGPT and similar AI models has a negligible environmental impact compared to other everyday activities like driving or streaming video. While large language models require significant resources to train, the energy consumed during individual inference (i.e., asking it questions) is minimal. The author uses analogies to illustrate this point, comparing the training process to building a road and individual use to driving on it. Therefore, focusing on individual usage as a source of environmental concern is misplaced and distracts from larger, more impactful areas like the initial model training or even more general sources of energy consumption. The author encourages engagement with AI and emphasizes the potential benefits of its widespread adoption.
Hacker News commenters largely agree with the article's premise that individual AI use isn't a significant environmental concern compared to other factors like training or Bitcoin mining. Several highlight the hypocrisy of focusing on individual use while ignoring the larger impacts of data centers or military operations. Some point out the potential benefits of AI for optimization and problem-solving that could lead to environmental improvements. Others express skepticism, questioning the efficiency of current models and suggesting that future, more complex models could change the environmental cost equation. A few also discuss the potential for AI to exacerbate existing societal inequalities, regardless of its environmental footprint.
UK electricity bills are high due to a confluence of factors. Wholesale gas prices, heavily influencing electricity generation costs, have surged globally. The UK's reliance on gas-fired power plants exacerbates this impact. Government policies, including carbon taxes and renewable energy subsidies, add further costs, although their contribution is often overstated. Network costs, covering infrastructure maintenance and upgrades, also play a significant role. While renewable energy sources like wind and solar have lower operating costs, the upfront investment and intermittency require system balancing with gas, limiting their immediate impact on overall prices.
HN commenters generally agree that UK electricity bills are high due to a confluence of factors. Several point to the increased reliance on natural gas, exacerbated by the war in Ukraine, as a primary driver. Others highlight the UK's "green levies" adding to the cost, though there's debate about their overall impact. Some argue that the privatization of the energy market has led to inefficiency and profiteering, while others criticize the government's handling of the energy crisis. The lack of sufficient investment in nuclear energy and other alternatives is also mentioned as a contributing factor to the high prices. A few commenters offer comparisons to other European countries, noting that while prices are high across Europe, the UK seems particularly affected. Finally, the inherent inefficiencies of relying on intermittent renewable energy sources are also brought up.
Summary of Comments ( 294 )
https://news.ycombinator.com/item?id=44039808
HN commenters discuss the energy consumption of AI, expressing skepticism about the article's claims and methodology. Several users point out the lack of specific data and the difficulty of accurately measuring AI's energy usage separate from overall data center consumption. Some suggest the focus should be on the net impact, considering potential energy savings AI could enable in other sectors. Others question the framing of AI as uniquely problematic, comparing it to other energy-intensive activities like Bitcoin mining or video streaming. A few commenters call for more transparency and better metrics from AI developers, while others dismiss the concerns as premature or overblown, arguing that efficiency improvements will likely outpace growth in compute demands.
The Hacker News post titled "AI's energy footprint" discussing a MIT Technology Review article about the environmental impact of AI generated a moderate number of comments, exploring various facets of the issue. Several commenters focused on the lack of specific data within the original article, calling for more concrete measurements rather than generalizations about AI's energy consumption. They highlighted the difficulty in isolating the energy use of AI from the broader data center operations and questioned the comparability of different AI models. One compelling point raised was the need for transparency and standardized reporting metrics for AI's environmental impact, similar to nutritional labels on food. This would allow for informed decisions about the development and deployment of various AI models.
The discussion also touched upon the potential for optimization and efficiency improvements in AI algorithms and hardware. Some users suggested that focusing on these improvements could significantly reduce the energy footprint of AI, rather than simply focusing on the raw energy consumption numbers. A counterpoint raised was the potential for "rebound effects," where increased efficiency leads to greater overall use, negating some of the environmental benefits. This was linked to Jevons paradox, the idea that technological progress increasing the efficiency with which a resource is used tends to increase (rather than decrease) the rate of consumption of that resource.
Several comments delved into the broader implications of AI's growing energy demands, including the strain on existing power grids and the need for investment in renewable energy sources. Concerns were expressed about the potential for AI development to exacerbate existing environmental inequalities and further contribute to climate change if not carefully managed. One commenter argued that the focus should be on the value generated by AI, suggesting that even high energy consumption could be justified if the resulting benefits were substantial enough. This sparked a debate about how to quantify and compare the value of AI applications against their environmental costs.
Finally, a few comments explored the role of corporate responsibility and government regulation in addressing the energy consumption of AI. Some argued for greater transparency and disclosure from companies developing and deploying AI, while others called for policy interventions to incentivize energy efficiency and renewable energy use in the AI sector. The overall sentiment in the comments reflected a concern about the potential environmental consequences of unchecked AI development, coupled with a cautious optimism about the possibility of mitigating these impacts through technological innovation and responsible policy.