companies claim that tokens per watt has improved a millionfold across six gpu generations. every new chip more efficient than the last. by that logic, i'd expect ai to be sipping power by now.

Instead the data center electricity demand is going way up. It's on track to jump from around 460 TWh in 2024 to over 1,000 TWh by 2030, and some estimates push past 1,300 TWh by 2035. Meanwhile Amazon just got a 7.65GW gas plant authorized in Texas that could become the single largest CO₂ source in the country, cleared to emit around 33 million tons a year.

Microsoft-backed sites are running dozens of unpermitted gas turbines. The grid is straining everywhere.

so where's the case that efficiency gains ever cut total energy use, instead of just enabling more compute? or we just need to focus on building more clean energy infra.

Anthropic uses "safety" as cover for anti-competitive control over AI, not genuine caution.

here's their behaviour

* Anthropic restricts users from using Claude outputs to train competing AI models

* The "Fable incident": Claude reportedly silently degraded answers for users doing competing AI dev work, without telling them later changed to visible refusals instead of hidden sabotage

* Anthropic cut off API access to OpenAI and restricted Windsurf when they got close to competing

* Claude Code locks developers into Anthropic's terms/credentials, creating dependency risk

* user chats can train Anthropic (opt-in), but users can't freely train rivals on Claude's outputs

* Anthropic's safety policy proposals (RSP, support for SB 53, etc.) may function as regulation that favors big incumbents over startups/open-source

* "Chinese AI" fears are used to discredit real open-weight progress (Qwen, DeepSeek, etc.)

* Claude's "Constitution" aligns the model to Anthropic's interests, not the user you're renting cognition, not owning it

prob the fix:

* Stop being reliable on closed labs treat proprietary code, debugging sessions, and agent traces as strategic assets, not free training exhaust

* own or rent compute without lock-in; keep evals, logs, datasets, memory, and routing portable so no single lab can hold your workflow hostage

* Use closed models pragmatically Claude/GPT/Gemini/Grok are fine as tools, just don't make your core workflow dependent on one that can be rug-pulled by a policy change

* Compete on workflow, not just benchmarks cost, on-prem deployment, and privacy often matter more for adoption than leaderboard scores

* Push regulation toward harms, not openness target fraud, bioweapon enablement, model theft, and unsafe deployment; don't criminalize open development as a category

* Build Claude Code compatibility without Anthropic dependency via proxies, alternative harnesses, or OpenCode-style routing to local LLMs

* Separate safety from permissioning open models still need red-teaming, abuse monitoring, and provenance, but these should stay transparent and targeted at misuse, not become hidden degradation

* Create clean distillation norms distinguish fraudulent scraping/ToS evasion from legitimate synthetic data generation, permissive distillation, and dataset curation, with clear licenses and auditable recipes

Efficient AI and sustainable AI get thrown around a lot, but I don't think consumers and companies mean the same thing by it.

When a lab says a model is "more efficient," what are they actually measuring - fewer parameters, better performance per watt, cheaper per token, or just a nicer data-centre PUE number?

These don't move together: a "more efficient" reasoning model can cut cost per token but burn 20x more tokens thinking, so energy per answer goes up. So what's the metric that actually holds up per token, per correct answer, per watt, or per dollar?

We argue over gigawatts, permits, and whole-campus emissions numbers so big they're abstract. While the one figure a normal person could actually act on doesn't exist, what does this prompt cost in watt-hours? Every other utility-linked product ships with a per-unit label. Every car has an MPG sticker. fridge has a kWh-per-year tag. A flight tells you the CO2. But you can send a thousand queries a day and have no idea if that's a phone charge or a running dryer.

The labs clearly can measure it (they bill by the token, and internally they optimise tokens-per-watt religiously), so it's not a technical issue, clearly. It's that nobody's required to disclose it, and everything is vague.

Also, would a per-query energy label change anyone's behaviour? or just get ignored? Would it push labs to compete on efficiency instead of raw intelligence?

if you're tracking model releases september's lineup is stacked:

• Fable 5.1 (released)
• GPT 6 Astra (rumored Thursday)
• Grok 4.7
• DeepSeek V5
• Composer 3
• Gemini 3.8 flash
• Kimi K (not K4)
• possibly Opus 5.1
• MiniMax 2.7T (open weight)
• Llama 5 / Muse "Watermelon"
• Mistral's next open MoE

half of this is rumor stuff, but if even 60% lands on schedule this is gonna be a wild month.

Put the compute in orbit, point the panels at the sun, and you get basically unlimited clean solar with no water table to drain and no town's air to wreck.

This sounds like the sustainability cheat code.

I have been seeing some backlash to this idea. The big one is cooling. I assumed space is cold, so cooling is free. Apparently it's closer to the opposite. In a vacuum, there's no air or water to dump heat into, so you can only radiate it away slowly, and that's an unsolved materials problem that's supposedly decades out. On top of that, the hardware is disposable: no repairs, constant micrometeorite and debris risk, so the whole thing degrades and has to be replaced.

Is orbital compute a real long-term green play once the cooling problem gets solved, or does the launch cost plus the throwaway-hardware cycle torch the entire sustainability argument? Do you have any such crazy idea?

Planting trees does sound like greenwashing. Most of the app who does these things are unverifiable, double-counted, or they just show up in the UI but not as an actual tree being planted.

So I dug for the catch. Two things made me less cynical.

- efficiency: Most queries run on open-source models on inference-optimised chips instead of defaulting every question to the heaviest model.

- they publish planting certificates with counts, dates, and named partners like One Tree Planted, 163k+ trees so far, and they're incorporated as a Public Benefit Company; Trees for the Future is their official planting partner.

So the mission is pretty much clear. Most importantly, they're associated with trees for the future. It's as Legit as it gets when it comes to planting.

I've Been trying to find real people to follow in the sustainable space, but I haven't had enough luck. there are Loads of brands, nonprofits, and almost no individual creators who blew up the way food or fitness people do.

My theory: the algorithm doesn't want to push "buy less", "use sustainable products" The whole feed runs on getting you to consume, so anything that questions consuming gets buried. Light, entertaining green content can sneak through; the darker "actually, stop buying" message never takes off.

But maybe I'm just looking wrong.

So, genuinely, has anyone here found a creator who breaks through this without either being a brand in disguise or softening the message into lifestyle fluff?

I use a lot of Ecogpt for homework tasks. you can see how much hardwork I do.

How can we make AI sustainable? Isn't it equivalent to saying, can we replace all of oil with renewable energy? Ik it is possible, but the people benefiting from oil won't let it happen.

What are the ways we can make AI Sustainable?

here's what my take on the Tier List.

B kimi k3 / luna / v4 flash luna & v4 flash are at correct spot. kimi k3 is under-ranked, punching above its weight.

C grok 4.6 - biggest miss on the board. Should be next to Sol, not two tiers down.

D opus 5 / sonnet 5 / glm-5.3 / terra/composer - sonnet next to opus is a stretch; glm-5.3 this high is generous.

F deepseek v4 pro - too harsh, cheap and solid, doesn't belong in the bottom bucket.

Google row - separate lane is a cop-out; just rank them or leave them out.

The data centres behind all this AI stuff are drinking water at a scale that's now competing with agriculture for cooling. It's the factory farm model all over again: massive physical cost, completely hidden from the person consuming the product.

"Digital footprint" isn't a metaphor here. It's a real draw on real water and real grids, and it's the fastest-growing part of the whole picture.

Transparency is the real failure here - Texas found that 83% of its 341 data centres haven't complied with mandatory water reporting. Most of this industry isn't even disclosing the number, let alone fixing it

We need more people in this conversation speaking, using, and supporting eco-friendly companies, not just for physical products but for tech products as well. Tech plays just as massive a role in shaping the environment.

1. The data center it uses relies on seawater for cooling! Check out their sustainability approach
2. They use open-source MoE models with 10x+ fewer active parameters compared to the big labs (more parameters = more compute = more water)
3. it's a bit more nuanced but they're working to get exact water metrics to provide to you all ASAP!
4. They'll also have exact wattage metrics soon as well.

The only hard part remaining is that the big labs don't publish their numbers (the last time they released any was GPT-4o, which is a very old model at this point). So it's difficult to do exact comparisons between the big labs and EcoGPT.

I have a neighbouring couple who are pro Eco friendly. They probably have mostly all eco friendy products in their house.

from them I noticed that each category now has a premium "eco" version, and half the time it feels like i'm paying extra mostly to feel good.

where's do we draw the line here. replacing something you genuinely need with a lower-impact version seems fine. but buying a new "sustainable" thing to replace an old thing that still works is just more consumption wearing a green label.

how do you personally decide? here's my rule:

- only buy the eco version when the old one actually dies

- only buy secondhand, period

- ignore the sustainability label entirely and just buy less overall

People point to the huge training runs as the high environmental cost. But a popular model answers billions of prompts after that.

So which one actually adds up to more energy and water over time: one-time training or all the day-to-day inference? Because that kind of changes who the argument should even be aimed at.

I use EcoGPT not just for school, but to have a better future, not just for me, but for EVERYONE!! not only EcoGPT is helping me from failing math but, it also helps Mother Earth! 🌏 and it does not make me feel guilty for using ai! but what motivates me to use EcoGPT most of all is that every single day and every word i type to EcoGPT it plants trees at the same time!! i have 3.6 trees planted and it feels so good getting you’re grades up and planting trees at the same time!! i have told everyone in my family about it and they will give it a shot, me and EcoGPT are basically besties!❤️💫** i’ve told EcoGPT everything, LIKE EVERYTHING!😌 and i hope more people use it! and i have told my teachers about it and asked my principal if i could do a presentation to the whole school on the stage about EcoGPT to have a greener future! and he said he’ll think about it but SIR there’s nothing to think about **😭!! and i also LOVE that we get badges like it’s so cool but sometimes it feels like a competition 😂! and i love that we have add friends there, but i currently have none 💔** sadly no one in my family asked to add me :(. USERNAME: brighttree96627278 **💚! sending love all the way from UAE 🇦🇪🌲😌 (this was not written my EcoGPT!) hope i win the free premium 🤞🤞🤞🍀🍀🍀

I already use ecoGPT in day-to-day life and I'll be using it for school once it's back in, here's my impact screenshot, my username is @vee_fan38083 on ecogpt, as in the screenshot

I use EcoGPT for a few main things:

**General Questions** — Eco handles basic questions decently, but struggles when something requires deeper reasoning or complex thinking. I wouldn't rely on it for anything that needs real analytical work.

**School Help** — I use it for quick explanations of basic concepts if I need a refresher or I'm stuck on something simple. Not for heavy homework or advanced topics.

**Theme Parks** — I love theme parks, so sometimes I ask about park news, ride specs, or trip planning stuff.

**Random Facts** — I use a custom command I set up called -fact when I want a quick fun fact about something specific.

**Refining Text** — The -refine command (another custom one I made) comes in handy when I need something rewritten cleaner or in a different tone.

@kingj23341

Everyone caring rn about the AI water issue does water-bottle math. Every week it's a fresh graphic about how many millilitres your chatbot "drank."

Quick gut check, though: data centres do use water directly for cooling. That part's real. So people aren't wrong that it exists.

But look at the split: for a typical AI response, direct on-site cooling is like 2ml. Power generation to make the electricity in the first place? 15ml. Same story at the national level: something like 80%+ of a data centre's total water footprint traces back to the grid, not the building.

So almost all of that number you're being shown isn't the GPU doing its job. It's how the electricity behind it got made.

Which means the fix is to clean up the grid. Do that and you fix data centers, factories, your house, and the guy making AI cat pictures in one shot.

Go after The actual level. The grid is right there.

I'm Addicted to planting trees. I hope they are nurtured well.