1. Something I learned from each article/video
- BBC Video:
I honestly didn’t know that AI tools like ChatGPT rely on actual clean drinking water just to cool the servers that run our messages. I always assumed the main environmental issue was electricity. Hearing that one conversation can contribute to water loss made me rethink how “invisible” the impact of tech use really is. This information came directly from the BBC report’s breakdown of server cooling practices. - Medium Article – Joseph Lawrence:
Lawrence mentioned that some data centers use up to 2 million liters of water a day, which shocked me. I’ve used AI tools all semester and never once thought about the physical resources behind them. Learning this made the issue feel way bigger and more urgent than I originally assumed. - Medium Article – Mansi (Heartline Publications):
One thing that stood out in Mansi’s article was the contrast she made between massive water consumption in data centers and the reality that many communities still lack clean water. That comparison pushed me to think about AI ethics beyond just the environment—it becomes a social justice issue too. - Axios Article:
What surprised me most from Axios was the fact that less than one-third of data centers even track their water usage. That feels irresponsible considering how much strain this puts on local water systems. To me, that lack of accountability is almost more alarming than the water usage itself.
2. What I think each article was really trying to say
- BBC Video:
The BBC’s main point was that AI has a hidden environmental cost that most of us never consider, especially when it comes to water. They made the point clear, but I felt like it barely scratched the surface. It was almost like a wake-up call rather than a deep dive. - Medium (Lawrence):
Lawrence wanted readers to understand the severity of the issue but also see that solutions exist. He balanced the problem with hope by talking about innovations like closed-loop cooling. His tone made me feel like tech companies could fix this if they actually wanted to. - Medium (Mansi):
Her main argument was about inequality. She emphasized how strange it is that we’re using precious drinking water to cool machines while people still struggle to access basic water. I thought it was a powerful ethical point even though she didn’t go into solutions. - Axios:
Axios focused more on accountability, regulation, and community impact. Their message was that we can’t solve something that we’re not even measuring. I appreciated how they connected the issue to real-world consequences for towns and local water supplies.
AI Experiment
1a. Where the Lawrence and Axios articles agree/disagree
- Agreement:
Both articles agree that AI growth is increasing water consumption and that this is a serious environmental risk. They also both acknowledge that the public is mostly unaware of this issue. - Disagreement:
Lawrence takes a more optimistic, innovation-based approach. He focuses on emerging solutions and future technology that can reduce water use.
Axios, on the other hand, is much more critical, stressing the lack of transparency from tech companies and the local strain data centers put on communities. Axios sounds more urgent and policy-focused.
1b. More persuasive article
I personally found the Axios article more persuasive. It felt grounded in real-world examples and local government concerns. The fact that companies aren’t even tracking water use made the issue feel more immediate. It wasn’t just “AI uses water”—it was “AI is using water in a way that harms communities.”
1c. Main takeaway from both articles
Overall, we should walk away understanding that:
- AI has a huge and mostly hidden water footprint
- We need better reporting, better technology, and better regulation
- And if AI continues to expand, this issue will only grow
It’s not just a tech problem t’s an environmental and equity problem.
2. Two prompts I used and why they were interesting
- Prompt 1: “Compare the environmental arguments made in the Lawrence Medium article and the Axios article. Where do they agree and disagree?”
→ This was helpful because AI broke down the differences quickly, and it made it easier for me to understand the contrast in tone and focus. - Prompt 2: “What solutions exist to reduce water usage in AI data centers?”
→ This was interesting because I learned about options like recycled water systems and closed-loop cooling, which I didn’t know existed. It helped me think more critically about practical solutions.
3. What I learned about using AI
- I realized how important it is to give clear, specific prompts. When I did, the responses were detailed and helpful. When I didn’t, the answers felt generic.
- Some of the AI’s responses were surprisingly useful, especially when organizing information.
- Other times, the answers felt overly cautious, like AI avoids strong opinions.
My emotional reaction
I felt curious, a little unsettled, and also impressed. It’s weird how confidently AI responds, even when it simplifies things. But overall it helped me understand the readings faster and see the environmental impact of AI in a way I never thought about before.
Where I think we’re headed & how we should respond
AI is growing fast, and if nothing changes, its environmental footprint is going to become a major issue—especially with water usage. To manage this, we need:
- Clear reporting
- Stronger regulation
- Better technology
- And actual community involvement when companies build these data centers
If we use AI responsibly and push for transparency, we can balance innovation with environmental justice.
This picture shows the cooling systems used in data centers. It connects to my discussion because AI tools like ChatGPT rely on massive amounts of water to keep these servers from overheating. This image helps show the physical side of AI that we do not usually think about.
Hello lollivierre2, I agree with you about how the environmental impact of AI is much deeper and more urgent than most people realize. I especially appreciate how you connected water usage not only to sustainability, but also to equity and community well-being. Your comparison of the articles was clear and showed how different perspectives, optimistic vs critical, shape the overall conversation about AI’s growth. Overall, your reflection shows thoughtful engagement with both the materials and the broader implications of AI on society.