Trying not to get pulled into this argument…as I´m travelling and iNatting! … but :
This feels like disinformation to leave this hanging in this thread at this point.
See Andy Masley posts linked above by me and @upupa-epops …which in turn pulls from MIT review amongst others.
The upper bound is more like 3wh so 0.003 kWh per prompt
A fraction of 0.14kWh
See Andy Masley specific breakdown of why Washington post number is an outlier here.
I´m also gonna drop in some of Andy´s lovely visuals as everyone seems to want to keep continuing with the environmental impact trope regardless of all the information we have to counter this argument. Maybe the quick and easy on the eye comparisons will help.
That’s not disinformation, it’s a scientific paper that can be improved upon. There’s another preprint with numbers closer to yours (depending on the model): https://arxiv.org/abs/2505.09598
But it hasn’t passed peer review yet. And while some GPT-4 models are in the range you say, it also notes that some models are actually much worse. There are many factors that influence how much resources are used by these models, but the fact is that there isn’t enough transparency from genAI companies, so all we have are estimates. I’m disinclined to trust the opinion of someone from Effective Altruism, especially if that’s the upper bound that they conveniently found (or rather chose):
The reason I chose 3 Wh as the number for my post wasn’t that I think it’s the definitive final answer for the cost of a ChatGPT prompt, it’s that every serious attempt at estimating the average ChatGPT prompt’s energy use I’ve seen finds that it’s below 3 Wh, so I ran with that as a reasonable upper bound.
I would take those plots with massive a grain of salt. The numbers are suspect and I don’t see a clear provenance from some of his assumptions. And even if they were sound, I don’t see how comparing to other activities is relevant. Is the use of genAI going to decrease or stop those activities? How many queries will people actually make in the same time frame that they would eat a hamburguer? Trying to shift the consumption of resources to individuals is disingenuous when the problem with these models is their so-called “efficiency”, which they achieve at scale (the amount of queries and the number of users, mainly).
To me one link from one techbro is not sufficient evidence of the climate impact being overblown, unfortunately. Do you have a peer reviewed source to back the claim that it’s not as bad?
But anyhow, the link you yourself added gave 0.43 Wh for a short prompt. This is massively lower than the 3Wh upper bound Andy Masley uses, and a huge huge discrepancy with the 140Wh you mention / the Washington Post suggest which is where the vast majority of anti-genAI viral posts on social media seems to have come from…( and in turn, a significant amount of the bias in response to the iNat grant ).
Also, fwiw… I don´t see that a genAI implementation would even necessitate the equivalent to a ChatGPT prompt. Once an ID tip was made for a species, it could be stored and repurposed. Again, lets wait and see the demo.
So again, I see no evidence to suggest this will be more energy intensive than the existing CV or costs of data storage per obs and all the other energy costs associated with iNat. Not that I have any data to back that up but… my hunch is that it´s similar. I hope iNat can quantify all these things down the line. Again, lets wait and see what this shows.
To all those screaming…why compare against the CV again?!
If the only arguments people against the grant have are things that already exist within iNat under CV… or are arguments that would be integral to iNat even if it were entirely human-lead without any AI… then the arguments that genAI itself is specifically a problem fall flat and reveal the deeper bias in play from those arguing against it´s usage.
Cherry-picking data is not great, because I also said:
Such as:
GPT-4.1 nano remains the most efficient overall, requiring only 0.454 Wh for long prompts (approximately 7,000 words of input and 1,000 words of output). In contrast, o3 consumes 39.223 Wh, while DeepSeek-R1 and GPT-4.5 consume 33.634 Wh and 30.495 Wh, respectively, which is over seventy times the energy use of GPT-4.1 nano. To contextualize, a single long query to o3 or DeepSeek-R1 may consume as much electricity as running a 65-inch LED television (≈130W) for roughly 20–30 minutes. Although o3 and DeepSeek-R1 rely heavily on chain-of-thought prompting, GPT-4.5 stands out for its relatively high energy use, despite not being a multi-step reasoning model. This suggests inefficiencies rooted in model architecture.
Claude-3.7 Sonnet ET presents a notable exception. While it supports chain-of-thought reasoning, it consumes only 17.045 Wh for long-form input, which is less than half the energy of o3. Similarly, GPT-4o, OpenAI’s current default model, demonstrates strong energy efficiency, requiring just 1.788 Wh for long prompts and 0.42 Wh for short ones. Interestingly, GPT-4o mini, although substantially smaller in parameter count, consumes slightly more energy per query than GPT-4o due to its deployment on less efficient A100 hardware instead of H100s or H200s, illustrating that deployment infrastructure can overshadow model size in determining real-world energy use.
I just shared one, here’s the link again: https://arxiv.org/html/2505.09598v2. I suggest reading the full article when you’re back from vacation. I’m disengaging from this discussion as I don’t feel it’s being done in good faith.
For the staff: The preprint I shared has insights about the deployment infrastructure and their efficiency, and how that impacts resource consumption. I’m not sure how much is within reach for you, but the discussions there hopefully help you make informed decisions about models and hardware:
Our findings indicate that infrastructure is a crucial determinant of AI inference sustainability. While model design enhances theoretical efficiency, real-world outcomes can substantially diverge based on deployment conditions and factors such as renewable energy usage and hardware efficiency. For instance, GPT-4o mini, despite its smaller architecture, consumes approximately 20% more energy than GPT-4o on long queries due to reliance on older A100 GPU nodes. Similarly, DeepSeek models exhibit disproportionately high water footprints, not solely due to model characteristics but due to data center inefficiencies. These observations suggest that true sustainability will depend on integrating more efficient hardware, sustainable cooling strategies, renewable energy sourcing, evaluation practices, and deployment infrastructures.
The discussion about energy use is interesting, but irrelevant to the potential use of AI on iNaturalist, because iNaturalist is not going to be using ChatGPT or other commercial large language models, and is not going to be generating responses on demand. Up to now, they’ve done the training for AI (computer vision and geomodel) on three computers.
Comparisons to hamburgers are unhelpful, since beef is literally the most environmentally damaging food we produce, so almost anything will look better than a hamburger. It’s difficult to believe arguments that genAI in general doesn’t have a big environmental impact when we see numerous news items about how coal-fired power plants are being kept online to deal with the anticipated surge in electricity demand. Sure, individual queries do not use much energy, but when ChatGPT is already the fifth-most visited site in the world (ahead of the site formerly known as Twitter), and with plans to integrate genAI into every aspect of our lives, the energy demand of AI is growing rapidly. Even with current energy use of genAI equivalent to that used by many thousands of home per year, the MIT Technology Review article* makes the important point that “These estimates don’t capture the near future of how we’ll use AI.”
I despise having generative AI forced down my throat and disable it wherever I can (I use DuckDuckGo rather than Google in part because you can just switch the AI nonsense off). My greatest concern is Google – one of the worst tech companies in existence – getting some cred just by being associated with the “nicest place online”. Be careful what company you keep, iNaturalist.
At the same time, I’m willing to give iNaturalist staff the benefit of the doubt. It’s entirely possible that the tool will be useful, interactive, and editable, and that the energy use involved will be far less that that of storing low-quality photos of mallards and houseplants. Let’s wait and see, and decide on the merits of what is proposed.
*Updated on 2 July to acknowledge the article had already been cited in the discussion, which I had overlooked/forgotten
I do not oppose users being able to get additional information compiled by the AI. Information about identification or about general biology, whatever, assuming it’s labeled as AI. (I think concerns about computer time for generating AI would be reduced if the program stored answers so that, for example, once it’s compiled data on American Robins it can just retrieve what it compiled last time, maybe updating once a month or so.)
Why do I oppose having AI identify observations and provide additional information? You know we already have trouble with people looking at CV suggestion just and accepting it. Sometimes they even pick the first of a list of half a dozen suggestions though the CV didn’t give any one of them priority. Now, along comes AI not only giving suggestion(s) but explaining why they’re right. If the information were supplied without their explicitly asking for it, do you suppose most people would read all the information? And meaningfully evaluate it? Not often enough! I think AI ID’s with explanations would have even more credibility than the simple CV lists do now and would more often be chosen inaccurately.
Also, as an identifier I would become annoyed as hell if the computer were always throwing up reams of “useful” data about organisms I can identify and am just trying to identify; please leave me alone. I assume than any attempt to increase the AI component of iNaturalist would allow us to opt out of it (or better, to opt in only if we want it), but I can’t stress too strongly how important having options is. I mean, one reason iNaturalist is so successful is that it is (kind of) easy to use and not excessively annoying. Do you think this would be a welcoming site for me if it insisted on explaining why each American Robin observation is an American Robin?
I would especially be annoyed by AI summaries because I know there is a lot of false or out-of-date information out there for AI to skim through. When I need to look up information or pictures to compare or learn from, I have a feel for how credible the sources are – which books I should check, which websites have reliable information, which have good pictures but bad descriptions, which have a taxonomy that iNaturalist now treats as out of date, which I should use cautiously or not at all. I don’t have a idea how credible an AI compilation is because (1) I can’t know where the data comes from and (2) I can’t know what the AI has done to it. (Maybe you don’t think those are problems. Maybe you don’t work with taxonomy and identification of plants.)
Going straight to an AI explanation not only bypasses all that evaluation, it bypasses my ability to go out and figure out how credible information sources are. We all need to learn how to do such evaluation, all the more because of AI throwing a screen of credible seeming veil over everything (not just iNaturalist).
(I recently read a research article about how students actually searching through sources and writing their own reports gained useful skill that those using AI for this did not. Gee, aren’t we shocked.)
Now, I do think that identifiers’ comments on iNaturalist observations are usually accurate! But they’re uneven (many for some taxa, none for most) so I doubt an AI generated descriptions would be limited to them. But maybe.
I admit that my confidence in AI is not enhanced by the unwanted but seductively succinct AI summaries that I now get at the start of each Google search, summaries whose validity I cannot evaluate unless I already know the subject (in which case, why would I google it?) or I check other sources (in which case, why do I need the summary?).
I think we need to ask not only “How can AI be not too harmful/annoying?” but “What do we really need doing that AI can do better than what we have now without being too harmful/annoying?” and identification isn’t one of them, though compiling information (if from reliable sources) might be.
Apologies if I seemed brusque!.. not intentional or personal… just yes, trying not to spend too much time here haha…and probably not the best at retaining diplomatic tone in forum comments. Especially in the morning. Being neurodiverse also may not help.
The use of a smiley emoji was aimed at the Washington Post reporting, not you…if that was the part that was offensive. As a European I have zero knowledge of the credibility of them as a source more generally - I guess I was just presuming them to be unreliable given general science reportage in newspapers…
Looking at the WP article a bit more, I see there is a source for the original Ren paper which can be found here. It´s a pre-print.
As Masley stated, there is no mention of 140Wh in the paper, and in fact I only see mention of 4Wh in the Ren paper, which is similar to Masley.
The other paper you linked to is cool to see, not one I´d come across.
I just don´t see it backing up the Washington Post claim though, which is still far higher… but even for the 39Wh estimate…that´s on 7000 word inputs! …how many people ever prompt ChatGPT with 7000 word prompts?!
I use it daily, but the most I´ve ever put in would be 500-1000 and thats only three or four times in the last year I think - 99% of my prompts are a sentence long.
To me it seems like that paper offers an extreme use-case to push the models to the max and test the architecture as you state…rather than an example of general usage such as the MIT review, Masley and others use. As such, I guess I didn´t really see my comment as cherry-picking tbh - I just saw 3Wh short prompts as being typical usage. I could argue those using the Washington Post number of 140Wh are more guilty of cherry-picking tbh, given its a long way from the other general use estimates.
Andy Masley´s main point is …that when it comes to climate activism, there are fights worth fighting and there are distractions - genAI individual prompting right now is by and large not a big deal compared to other electricity usage / activities the vast majority of the general public engage in. We should choose our battles. (He´s also vegan, so perhaps the exact use of hamburgers is in part due to that )
Note he´s explicitly not ruling out the bigger issue of AI energy use and data centres more broadly / in the future. And neither am I fwiw!.. I think there is sooo much to be justly concerned about in regard to AI impact on society in the coming years…it honestly scares the hell out of me. But the implementation here… in an unknown manner …of a demo … in the hands of an organisation that already uses AI for good purpose…which will most likely be minimal in additional environmental cost … is simply unworthy of the furore it has created imo.
For everyone concerned about genAI environmental impacts, where do you stand on general energy/data usage of iNat?
If the cost of 1gb of data storage is 7kWh as this Stanford article states.
That´s 7000Wh for 1024Mb = 7Wh for 1Mb give or take.
So using the genAI prompt cost of 3Wh that’s roughly equivalent to a 1 x 500Kb photo upload here. Whilst a laptop using 50 watts for 1 hour to be on the site would consume 50Wh.
Of the staunchly anti-genAI due to environmental cost folks…are you really so rigorous about the minutiae of your energy costs ?.. how conscious are people of their file sizes when uploading? How conscious are you of the energy usage of your computer when using it for iNat? Or of energy usage of your computer and cloud storage more generally?
Genuinely curious where your lines are in the sand when it comes to online energy usage.
I’ve hidden posts that are arguing about the word “bribe” that’s being used in a video/post by someone not on iNat or on this forum, it became a semantic back-and-forth and was not constructive, I’m sorry.
You don’t need to apologize for hiding unconstructive posts that would likely lead to endless arguing. Some of those posts would have possibly been flagged later anyways if they weren’t hidden now.
I feel like if all of iNaturalists digital infrastructure was run off coal power. It still wouldnt be that large in the grand scheme of things largely because of scale. INaturalist is only a single digital nature oriented organization. Maybe somebody could do the math, would be interesting.
Your deleting your account in 24 hrs and your asking this? This sounds like you are missing much information and context.
This is incorrect, it was largely done becuase journal comment sections aren’t designed for huge discussions of 300+ comments in just 2 days. If it countinued that page would get laggier and laggier just like the Gerald observation also with 100s of comments.
Again you seem to be lacking context and have missed posts from staff in here. This is not surprising or of fault of your own as even here at a venue for large conversations, going through this huge discussion and finding exact comments is difficult without knowledge of filters and knowing exactly what your looking for.
I do still think that staff have not done that great of a job communicating though. Another journal post should have been made. There are useful staff comments in here buried amongst 100s of other comments.
It is possibly annoying to some users that iNat relies partly on AWS (yuck!), and tolerates uploading tens of wasteful 2048px heavy files. Still, can’t really figure the logical articulation with uncritical acceptance of any additional future use of resources (even if minimal, as aptly pointed).
For those who were still reluctant, this debate on energy/resource consumption possible contributes new arguments for leaving iNat. Of the “all or nothing” kind – embrace it in full or else move along. (Fortunately, there’s at least one platform better suited to resource-conscious naturalists out there; essentially free of “evil” AWS/Google, not interested in GenAI yet, and with tighter upload limits, too. Not as pretty and populous and funny as here though.)
This isn’t going to stay quiet, many people are upset about this and are already talking about it, here on the forum and on other sites, including youtube. Staff in particular should be aware of how people are taking this so that if it’s wrong, they can correct it whenever they get around to explaining to us what exactly it is they’re planning to do with generative AI on this site.
Let’s assume you didn’t read the bylaws, as otherwise that’s very disingenuous: the bylaws make clear that the board of iNaturalist is self-perpetuating. Unless you are already on the board or are a staff member, there are zero ways to influence or make the changes you might want to see.
To put it another way: for those of you who have had some higher education, think of the professors you most admired and respected. Were they in the habit of giving you the answers? The educators we respect the most tend to be the ones who act more as guides while we find the answers for ourselves.
I agree. The conversation has become repetitive. I find it most interesting that the best ideas came out during the period when the number of posts declined to a few per day – almost seemed like a quality vs. quantity thing.
And also: which ones seem to have plagiarized each other. Seriously, there’s a lot of that on the internet: multiple low-quality websites (but perhaps SEO to get their banner ads seen) which can be near word-for-word duplicates of each other’s content. If you weren’t careful, you could get the impression that the “information” on them was widely accepted because of the number of places it appears.
I find it especially ironic, given this bar graph, that some people are linking to YouTube videos to make their case against GenAI. Doubly so given that YouTube is itself notorious as a repository of non-peer reviewed and even outright unreliable information, and no serious researcher would cite YouTube as a source unless the study was about media usage. Arguments about whether YouTube or GhatGPT is worse in this regard are (thankfully!) beyond the scope of the present thread.
“we should improve society somewhat” “Yet you participate in society! Curious!” Alright lol if we’re going to pretend that using youtube is the same thing as endorsing the creation of brand new planet killing machines, then that is just an argument for everyone deleting their iNaturalist accounts, now isn’t it?
“Don’t create another machine that will kill the environment while greenwashing the machines that already kill the environment” “And yet machines that kill the environment already exist!”
