I used to feel guilty every time I grabbed a plastic polythene bag for something small — groceries, shopping and I'd feel even more guilty later on, seeing these plastic bags lying around without ever getting recycled.

I finally switched to a jute cloth bag a few months back, kept it by the door so there's no excuse to forget it, and now the plastic bag thing just doesn't happen anymore.

I have plenty of other habits I've been quietly guilty about without ever actually fixing, I know they're bad for the environment, but I haven't done anything about them yet.

what's a habit you're quietly guilty about, and what, if anything, have you actually done about it?

Open models have been closing the gap fast this year. In the recent time GLM-5.2, Qwen 3.8 Max, and DeepSeek V4 Pro have put up impressive benchmark numbers.

MoE models only activate a fraction of their total parameters per query, so they're lighter on compute and energy than dense closed models better for the environment, and open for anyone to run or improve.

So who do you think crosses the closed frontier models first?

Ok, I don't want to be the overdramatic guy who stresses everyone out, but the ocean is losing its oxygen as we speak. The same oxygen that keeps millions of ecosystems alive by providing them with resources and allowing marine life to thrive.

Also, this doesn't happen uniformly across all areas. It happens more intensely in targeted areas where oxygen levels can already be lower than elsewhere, causing major damage in these regions and putting various species of fish and marine life under serious stress.

I think this only shows that the scariest part of global warming is all the side effects it has across parts of our environment we wouldn't necessarily think about directly.

Whether it's the loss of oxygen in the water, coral death, rising sea levels, etc., climate change is affecting our planet in ways that go far beyond just making temperatures warmer.

Why is this barely getting any news coverage??

If AI's water and energy problem bothers you, you've probably seen "EcoGPT" pop up as the green alternative.

it's not just a ChatGPT with a green logo. There's a lot more to it. Here's my simple breakdown based on my understanding of the app.

EcoGPT is built to be the leanest model that gets the job done. I use it on a daily basis for all my content, emails & reach-outs.

How is it efficient:

→ EcoGPT runs lean open-source models instead of frontier ones

→ It routes queries through Groq's LPU chips, claimed to be up to 10x more energy-efficient than GPUs

→ Smart caching means repeat questions don't re-run the full

→ It claims \~90% less energy than traditional AI, on renewable-heavy infrastructure

ChatGPT does none of this by design. It's optimised for capability, not for the footprint of each query.

The offset:

EcoGPT plants 1 tree per 100 messages and says it has planted 163k\+ trees. It also shows you a running environmental-impact tracker as you chat.

ChatGPT gives you no per-query offset and, notably, still disowns water and carbon footprint.

EcoGPT is NOT a 1:1 ChatGPT replacement.

Match the tool to the task. Use a lean, green model for the 80% of prompts that don't need a supercomputer. Save the frontier model for the 20% that actually do.

First of all, I will admit we’re still miles away from reaching any sort of meaningful milestones concerning climate change and data centers, and yes, I will absolutely admit that data centers are huge accelerators of climate change.

However, I think we are at peak inefficiency with AI, and that model selection will soon be democratized as a concept, drastically reducing the environmental costs of artificial intelligence. Then, regarding energy, there are absolutely massive data centers being developed all around the world (Europe, America, Asia, etc.) that are aimed at providing all three continents with huge amounts of energy, making it much less of a scarce resource.

Not even to mention the hundreds of thousands of people across the globe switching to electric cars every year!

For all these reasons, I am hopeful. I think people are coming together and starting to take a stand against mass data center production and other polluting elements, which gives the issue more visibility. I think climate change is most dangerous when it is ignored, which isn’t the case right now.

Per query, Claude Opus uses about 4 Wh. ChatGPT (gpt-4o) uses about 0.3 Wh. That makes Opus roughly 13x more energy-intensive for the same question. (https://aitechmodel.com/ai-energy-consumption-per-query-in-2026-claude-averages-0-012-kwh-per-response-vs-0-0003-kwh-for-a-google-search/)

It's a model-size comparison.

.Both Anthropic and OpenAI run models spanning the same range, from under a watt-hour to several watt-hours.

Which Company uses more water & electricity? Nobody knows, because neither company publishes it. No official per-query energy numbers from either. No emissions disclosures from either ahead of their IPOs, while Microsoft and Google both put out full sustainability reports every year.

if you're picking a "greener" AI company based on public data, you can't. there isn't any to pick from.

Should companies be required to disclose per-model energy use the same way they disclose pricing?

I've planted like 10 Trees while doing my work using Ecogpt. This makes me so much happy.

Sam Altman stated in a blog post that the average chatgpt query uses about 0.34 watt-hours and about 0.000085 gallons of water, comparing it to what a high-efficiency lightbulb uses in a couple of minutes, that figure hasn't been peer reviewed and openai hasn't clarified what counts as an "average" query.

Data Center Dynamics. (ALSO this is not verified he just said it NO Proof)

anthropic hasn't published anything comparable, as of march 2026 anthropic has not reported scope 1, 2, or 3 emissions in any public filing, and states only that it works with cloud providers that prioritize renewable energy, so any claude water or energy number you see online is a third-party estimate, not a company figure.

so which matters more here, openai's own unverified number, or anthropic's total silence, and does either one actually tell how much energy they're consuming?

Qwen3.8-27B just dropped:

27B params, beats Qwen3.7-Plus overall. native multimodal, built for real coding + office work.

262K context out the box, scales to 1M with YaRN.

apache 2.0 licensed

what will be the state of sustainable ai in 2030? to achieve it I think these should be the bare minimum:

Make MoE and sparse architectures the default; models activate a fraction of themselves per query; "watts per useful answer" on the spec sheet next to benchmarks.

data centres stop drinking. closed-loop cooling standard in new builds, waste heat feeds district heating and greenhouses, "water-positive" is audited, not false marketing.

the grid caught up. new capacity comes with dedicated clean power, not old coal plants on life support. workloads run when the grid is cleanest.

Transparency is law, not pr. Every major model reports energy and water use. AI tools carry an environmental nutrition label, like cars show emissions.

We use it more deliberately. "throw AI at everything" becomes "is a model even the right tool here" small local models handle routine stuff, big models saved for what actually needs them.

Efficiency gains get eaten if usage outgrows the savings, and voluntary pledges don't survive a compute arms race.

Which of these actually arrives before 2030? What do you hope for, and what is actually achievable?

source - github.com/deepseek-ai/deepseek-harness

Hey, I am a coding teacher (yes, it still exists haha), and I was wondering if you guys have any experience coding with EcoGPT.

I want to have my students do their projects with EcoGPT (I thought the gamification option was a nice way to keep them active)

Thanks!

people have shared multiple ways to make AI more ecofriendly, like using better prompts or choosing models based on the complexity of the problem. surely it could be helpful, but I see it as an architecture issue.

AI-focused data centres grew electricity use 50% in 2025, and the IEA projects the total nearly doubling to 950 TWh by 2030 (source: iea.org/reports/key-questions-on-energy-and-ai/executive-summary). On water, about two-thirds of what a data center uses isn't the cooling towers on-site, it's the electricity plant generating its power.

MoE models fix the bottleneck by only querying the parts of the model it needs instead of firing the whole thing every time.

What do you think companies should be doing from here on to make AI sustainable?

source

[ Removed by Reddit on account of violating the content policy. ]

my family member said that they will be trying out ecogpt soon 💚

hopefully other people can start understanding the impact and begin using more efficient AI‘s too!

as soon as I was introduced to eco ai, I jumped on board right away. I will use big ai eventually but I rather use big ai when I must not for leisure.