AI and Ecology, Fantasy or Convenient Scapegoat?
It's hard to talk about AI these days. I've rarely, if ever, seen a subject so polarized in tech. You could tell me I have a short memory. The internet sparked plenty of criticism around the destruction of brick-and-mortar retail, print media, and the end of human interaction. Same for mobile, with added, legitimate reproaches about addiction and the ease of surveilling individuals. We could also mention crypto, a massive Ponzi scheme for some, a way to reclaim power from central banks for others. And yet, with AI, I feel like we've crossed a threshold. There would be only two possibilities: Pick a side, friend, and if you don't, others will do it for you. The "safest" bet is not to talk about it at all, but burying my head in the sand feels cowardly, if not impossible when you work in tech. Simply put, I need to stick my head out and try this exercise without resorting to clichés. And since we're in the middle of a heat wave, it seems obvious that the first subject to address is ecology. Is AI as catastrophic as people say? Is the impact of an AI query truly astronomically higher than a Google search? How does it compare with other digital uses? Let's take some time to look at all this. First, let's establish some basics about what we call ecological impact. This impact falls into several categories: To keep things simple, an AI consumes energy at two distinct stages: during training (when the model is created, like Gemini, Llama, Claude, etc.) and during inference (when users actually query the model). When looking at carbon footprints, models vary wildly, but the estimated range for training a single major model sits between 500 and 12,000 tonnes of CO2 equivalent. To put that into perspective: ::callout{type=primary} The massive gap between US and French household equivalents stems from the fact that France’s energy mix relies heavily on nuclear power, which is virtually carbon-free. :: Operational consumption is another moving target. It depends on the complexity of the prompt, the location of the data center (and its corresponding energy mix), the model being used, and so on. But it's estimated to vary between 0.03g , and 1g of CO2 per request. We'll see below how that compares with internet, gaming, streaming etc… To talk about water consumption, we must first address a common misconception: No, we don't destroy water . Earth’s water operates in a closed loop. When water is used in a cooling system, whether in a data center, nuclear power plant or anything else, it's not destroyed. When water evaporates, it eventually falls back as rain. However , evaporation causes water to displace. If water moves more than 800km, the region where it was drawn from has effectively lost it, temporarily but lost nonetheless. In ecology, we distinguish water withdrawal (borrowing water and returning it to the same place after use) and water consumption (drawing water and evaporating or releasing it elsewhere, making it unavailable locally). AI consumes water. On a planetary scale , it's not necessarily a problem. On a local scale , however, it can trigger severe water stress, creating direct competition between residents, agriculture, and data centers. To be fair, technology is advancing. The majority of new data center projects use closed-loop water systems so water isn't evaporated. Some countries (Ireland, Sweden, Finland) take advantage of their cold climate to reduce water needs by 90% and we see other systems emerging. But to look at the flip side, the vast majority of existing data centers use evaporation systems and in any case, these systems require electricity which creates tensions, for example in Sweden or Ireland. Now that we've said all that, what's the consumption for evaporation data centers? Training a recent model is estimated to consume approximately 40 to 80 million liters (a small lake). In a water-stressed region, that can make a difference. And if we look at usage, for a request, it's between 2 and 6.5ml of water per request. ::callout{type=warning} This section is the trickiest for me because it’s the one I’m least familiar with, and honestly, it probably deserves an entire article of its own. So, while we will only scratch the surface here, I promise to dive much deeper into this specific topic in a future post. :: We often focus on electricity and water, but the environmental footprint of mining is one of AI's biggest blind spots. To run AIs or train models, you need ultra-powerful equipment and colossal infrastructure that will require copper, aluminum, cobalt, lithium, nickel, rare earths and I imagine I'm forgetting some. Well, these resources are in limited quantities on earth but I'll discuss that in a future article, recycling in this sector is currently negligible but moreover, the extraction itself is extremely polluting. To make matters worse, we must add that current equipment becomes obsolete much faster. In the AI race, we replace equipment much faster. Certainly, new equipment is more efficient, particularly in terms of energy but this ultra-rapid rotation creates a volume of electronic waste we don't know how to manage. Despite everything, I don't yet know from which angle and with which figures to illustrate all this, especially since these subjects also pull along many other geopolitical subjects (tension over Taiwan, tension over rare earths etc…), so we'll set that aside for future publication. We already have plenty to do with the first two subjects. With these orders of magnitude in mind, is AI " stratospherically " different from the rest? How does it compare with a Google search for example? Or with streaming, video call, an online video game? By comparison, a query to a search engine (Google) is approximately 0.2g of CO2 . Depending on the complexity of the question and the model used, an AI prompt can cost slightly less than a Google search, or up to five times more . So, it is not "stratospherically" higher than a standard web search. Furthermore, if a topic requires you to do multiple Google searches and open several websites to find your answer, the gap quickly narrows, and can even reverse. But we must separate simple uses: "give me the strawberry pie recipe", from complex uses: "analyze this PDF document of several megabytes for me and create an application that displays results with charts". I propose we do an exercise and compare 1 hour of streaming, 1 hour of gaming, 1 hour of video call, and one hour of AI-assisted software development (a relatively power-consuming use). ::callout{type=primary} Why such strong variations when considering AI-assisted development? Because it encompasses vastly different habits. Consumption will be drastically different between an "amateur" coder copy-pasting a few lines from a browser, a "pro" user partially delegating tasks within their code editor, and an "intensive" power-user running automated tools where code generation is almost entirely outsourced. :: In other words: No matter how you look at the data, it is hard to find evidence of a "stratospheric" gap. And to go further, we could look at the impact of AI model creation compared to the ecological impact of creating a video game, or a movie. An AAA video game (big-budget), developed by a team of 150 people, costs between 500 and 3,000 tonnes of CO2 depending on development time, travel, and motion-capture filming. To this, we must add the annual maintenance for live-service games that push out continuous updates and DLCs (like World of Warcraft or Overwatch ). For a big budget film, we can estimate a carbon cost between 3,000 and 4,000t of CO2 , including transport, filming locations, generators, set construction. Granted, training a massive AI model can cost more than a single movie, but the difference isn't orders of magnitude apart. More importantly, we must remember that the world releases thousands of films and video games every year , whereas the creation of new foundational AI models remains relatively rare. Let's be careful here. It would be lazy whataboutism to simply say, "Sure, AI is bad, but look at how much worse everything else is." That is missing the point. The real goal here is to question our consumption habits as a whole. What is certain, however, is that the reality is far more nuanced than the mainstream narrative suggests. Today, the hyper-focus on AI serves as a very convenient distraction, allowing us to forget the environmental cost of our other digital habits. But you don't earn moral virtue points by campaigning against AI while actively indulging in online gaming, streaming blockbusters, or flying to international sports events. If you've followed the numbers well, the ecological impact of AI is relatively close to other impacts in digital (streaming and gaming for example). That doesn't mean it's good. In the world we live in, each additional tension on the planet is to be questioned . But it forces us to realize that all of our digital behaviors need to be reassessed, not just the fact that "I asked ChatGPT a question." I don't pretend to be able to rank these activities against one another. Comparing gaming, streaming, and professional workloads is highly complex. And even within professional uses, And I certainly won't decide, on my own , what constitutes a "good" or "bad" use of technology. But collectively, we might soon be forced to make those choices , not out of kindness, but by constraints (See next chapter). The core issue isn't about outright banning AI. This is precisely what organizations like Shift Project , France’s leading think tank on the energy transition, are trying to convey: we need to look at data volumes and digital use cases in their entirety. The argument isn't that we should abandon AI altogether, but rather that we cannot afford its current, unchecked trajectory Let's take an example: the FIFA World Cup generates between 9 and 15 million tonnes of CO2, which is roughly equivalent to the annual energy consumption of all US data centers combined.. Again, the idea isn't to say, they do worse. We'll get nowhere with that mindset. But I like this example because of the contrast it highlights. Playing football doesn't cost much. Gathering thousands of people across 3 countries and having them fly everywhere is absurd, as is air-conditioning football stadiums, or trying to organize winter games in a desert country. AI operates on the exact same spectrum. There is a massive gulf between a professional, high-utility application, like using AI in biochemistry, mathematics, meteorology, drug discovery, medical imaging, satellite analysis, or precision agriculture, and a purely recreational use aimed at generating thousands of Ghibli-style images just to dump them on social media. Yes, we can, and should, question the latter (and that’s an understatement). Ultimately, understanding these orders of magnitude is what empowers us to make informed choices instead of just parroting the absurdities we hear on TV. Once you know the real numbers, you can weigh your choices accurately. I said earlier that one hour of video call was between 30 and 60g of CO2. Ok, but that might replace a Paris Lyon trip. By car it's between 60 and 90kg of CO2 saved. By train it's about 1kg. Similarly, one hour of streaming costs about 100g of CO2. But if it prevented you from driving 20km to the local movie theater (which would cost around 4.4kg of CO2 in car emissions), streaming turns out to be "not so bad" after all. In the end, once we have the data, it is up to each of us to make those choices. A question I asked myself before writing this article was: If the carbon footprint of AI is actually pretty close to our other digital habits, and assuming it replaces some of them (if I’m using AI, I’m not doing something else), why on earth are we building so many new data centers? Ok, this question might seem naive but it's estimated that data center electricity consumption could double, or even triple by 2030 (See BCG study and this IRIS article). So why? Is it linked to AI? According to articles, partly yes, but only partly. The majority of electricity consumed by data centers (about 2/3) should be dedicated to historical digital uses and acceleration of cloud migrations. Yes AI plays a role, but it's mainly that digital is taking up more and more space. The share of digital in global CO2 emissions went from 2% in 2010 to about 4% today, with an annual increase of about 6% even though the global objective is to reduce our emissions by 5% per year to hope to ++stabilize++ the climate . Where AI genuinely worsens the problem compared to other tech is its rapid hardware obsolescence. However, the root cause is the massive scale-up of all our digital habits: the ubiquity of 4K/8K streaming, cloud gaming, high-fidelity music streaming, and the explosion of connected IoT (Internet of Things) devices. Of course, we should take these data center growth forecasts with a grain of salt. They remain predictions. They could easily be overestimated, just like the predicted "tidal wave" of data that was supposed to arrive with 5G but never quite materialized. Many of these projections are pushed by tech giants that have bet their entire financial futures on these exact growth scenarios. If you are Nvidia, Google, or Oracle, you have no choice but to reassure your shareholders by guaranteeing this growth will happen to justify the colossal investments already made. Honestly, if the AI financial bubble were to burst tomorrow, it might actually be good news for the planet, as it would instantly ease the pressure on our resources. That being said, we are looking at contracts that are already signed, budgets locked in, and massive public announcements, like the Stargate project or Europe's future investments . Every current scenario predicts a 2x to 3x increase in demand. Digital consumption is going up, and AI-related infrastructure is leading the charge. Will these digital habits replace physical ones (like my earlier example of a video call replacing a car trip)? Or are they purely additive ? Evidence suggests they are additive. While some AI applications will certainly accelerate decarbonization in specific industries, that impact remains relatively marginal for now. And, according to the Shift Project, it's mainly that the electrical consumption needed to run all these data centers will exceed our infrastructure capacity, and thus force using thermal sources (gas power plant) to compensate or create usage conflicts . So yes, it's alarming. Again, it's not about banning AI, the subject is much more global than that. How do we make our consumption and pressure on the planet decrease? At our individual scale, we have to audit our own habits. We need to question our obsession with upgrading devices, over-equipping our homes, and engaging in mindless, heavy recreational uses (like generating endless Ghibli images just for a laugh). At a collective level, we will eventually be forced, likely much sooner than we think, to make hard choices. We will have to decide whether to route precious electricity to a data center or to power and heat local homes . But let’s remember one crucial thing: the future isn’t set in stone. If we collectively choose to consume less, we won't need the electricity these companies are dying to sell us. If data centers are multiplying, it's only because there is a planned demand for them. To paraphrase a famous French comedian: "To think that if people just stopped buying, it wouldn't sell anymore!" The Doomers (declinists) who envision an ecological apocalypse, total destruction of employment and placing public opinions under the guardianship of Big Tech controlling AI. And the Bloomers (accelerationists), who advocate blind faith in progress, convinced that AI will liberate humanity, eradicate diseases and generate infinite growth, and for whom slowing research is the real crime. Electricity consumption (which translates into CO2 emissions) Water consumption for cooling data centers Resource extraction, required to build the data centers and user devices themselves There is only a 2x ratio between gaming and professional AI-assisted development. video call is what consumes the least Streaming is remarkably close to professional AI development usage
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