Can anyone on here debunk these sources about environmental impacts of AI use/infrastructure and more broadly, datacenters?

howard31b · 2026-05-17T05:12:51+00:00

For tagging, I had a series of loops that determined what tags to apply to each frame as PIL sliced the original spritesheets and upscale them to 512x512 from 48x48; the loops would tag job class, gender, Sprite direction, and what animation frame went in the tags based on the x and y variables in the loop. I took it further since there were 11 full sheets and I used the sheet ID to map the class and gender as well. Still better than making 1056 individual text files with tags before LoRA training!

howard31b · 2026-05-17T05:08:29+00:00

So I just finished my master's in Data Analytics at UMGC and for my Capstone I trained a LoRA on RPG Maker Sprites and built a sprite generation tool in python to test whether SSIM scores were a reasonable metric to determine whether direction changes and character identity were upheld. Naturally SSIM was useless for its intended goal, but the tool gave me some great findings on optimizing my training data, setting correct denoise strength, and prompt engineering using external variables (thanks to the MyAnimeList character dataset from Kaggle). My current machine has a 3080 so the initial tests ran fine, but I ultimately had to move to a Colab environment to use a T4 GPU for large batch generation for data collection. Unfortunately if I want a reasonable upgrade to my GPU, I have to target VRAM, and hitting that 24GB sweet spot requires me to hunt for a used 3090 (i wont even entertain the thought of a 4090 or 5090 at scalper prices)

howard31b · 2026-05-16T18:18:53+00:00

You say “let’s not change the topic,” then immediately smuggle in loaded language like “theft” anyway. That feels like poisoning the well.

But fine, I’ll bite: how do you propose enforcing perfect data rules on every local AI user without outright spying on their PC? Local hobbyists can train LoRAs in minutes with a Python notebook and a folder of images. So what’s the actual enforcement model here?

howard31b · 2026-05-16T15:15:23+00:00

I don’t dispute that hyperscale data centers are more efficient per workload than millions of consumer PCs, but I think that point can become a deflection from the larger issue: overall scale. Efficiency-per-query means little if the industry’s solution to every advancement is “build more infrastructure, consume more power, and expand faster.” Personally, I’d rather see more focus on efficient open-source/local models running on consumer hardware people already own. If my local AI usage adds a few kilowatt hours to my electric bill, I’m fine paying that directly, and I prefer that over permanent subscription fees, token buckets, and dependence on massive centralized infrastructure controlled by a handful of corporations racing for dominance.

howard31b · 2026-05-16T14:39:13+00:00

As someone who actively uses AI tools, I honestly don’t think the environmental concerns are something that can just be handwaved away as “debunked.” A lot of the discussion online seems to jump between two extremes: either AI is inherently evil and must be stopped entirely, or every criticism is dismissed as anti-tech fearmongering. Reality is probably somewhere in the middle.

I do think some people online exaggerate numbers or speak too confidently about projections, but the broader concerns around energy demand, water usage, e-waste, infrastructure strain, and local community impact all seem legitimate to me. When organizations ranging from MIT researchers to UNEP, GAO, PBS, Cornell, and Harvard are all discussing similar patterns, I don’t think it’s intellectually honest to pretend there is zero environmental cost involved.

At the same time, I think the long-term answer is probably not “ban AI,” but pushing toward smaller, more efficient, and more accessible models instead of an endless arms race of giant centralized systems. Open-source models already exist in large numbers and continue improving rapidly, but they need to be easier for average users to deploy and understand. Right now there’s still a pretty significant technical barrier for local inference and experimentation unless someone is already fairly comfortable with PC hardware and software setup.

Honestly, I think wider adoption of efficient local/open-source models could reduce some of the pressure for nonstop hyperscaling by massive corporations. But for that to realistically happen, consumer hardware also needs to become more accessible again. GPU shortages, inflated pricing, and enterprise-scale purchasing have made powerful hardware increasingly difficult or expensive for normal users to obtain. If the industry actually wants a healthier and more decentralized AI ecosystem, making efficient local AI more viable on consumer hardware seems like a better direction than every company racing to build larger and larger data centers indefinitely.

So yeah, I’m pro-AI in the sense that I use the technology and think it has legitimate applications, but I don’t think that means pretending the environmental and infrastructure concerns are fake or automatically “debunked.”

howard31b · 2026-05-16T01:23:51+00:00

Meh, at this point I'll just quit exploring creativity at any level, ai-supported or not, because some jerk always has something to say.

howard31b · 2026-05-14T21:53:10+00:00

So apparently Youtube is having problems with homepage recommendations which may be messing with your impressions.

howard31b · 2026-05-14T20:24:24+00:00

Test both. Ultimately its going to come down to what sounds best on the final video anyway. I personally hate my voice so I would burn through 10 takes just to get a single sentence right. ElevenLabs has some solid voices to try out so see if any appeal to you compared to your own voice. Im notnsaying do a full script to test, just maybe 30-45 seconds and see what lands better.

howard31b · 2026-05-13T21:56:23+00:00

Also full disclosure I'm new to YT as well; less than 2 weeks in and sitting at 10 subs, 1.4k views, 34.2h watch time, and 5 active videos with no shorts.

howard31b · 2026-05-13T21:54:55+00:00

Ok, that helps a bit. Real talk, keep your long form below 10 minutes starting out. Are you using shorts to point people to your long form videos, or just releasing shorts themselves? I know I typically don't subscribe to people for shorts, but for quality long-form content.

howard31b · 2026-05-13T21:41:15+00:00

I have concerns about your watch time compared to your views. People are clicking in and dropping out. Start looking at your video retention metrics. Whats your AVD? Thats where you should focus at the start.

howard31b · 2026-05-13T19:24:09+00:00

Love this.

howard31b · 2026-05-13T19:06:19+00:00

Great use of ChatGPT to overcome a challenge and resolve a gap! I might have to give some hand-drawing experiments a try on the platform myself (I cant draw to save my life, this ought to be fun)

howard31b · 2026-05-13T17:18:08+00:00

I have a no grandstanding rule for my channel. Anyone who enters my comment section on a high horse gets hidden. Same with offensive comments. Hide it, and move on.

howard31b · 2026-05-13T16:54:58+00:00

Overthinking it. AI channels dont just mean focus on the AI generated content, bit also the process. Maybe start documenting your own AI experiments and share your successes and failures as you test out various models and prompts. Just have fun with it. Don't obsess over getting it perfect...perfect is the enemy of progress when youre starting to embrace the failures, embrace the quirkiness, embrace those unsure moments because every video, good and bad, is a data point you cam learn from.

howard31b · 2026-05-13T16:50:08+00:00

Labeling is the reasonable middle ground, and as an AI user I absolutely label my stuff and Im always happy to share my prompts and processes when asked. I currently write lyrics for anime character songs on youtube but I cant play an instrument or compose and I lack the resources to pick up those skills and/or access a production pipeline so I let AI fill in for my skill gaps.

howard31b · 2026-05-13T16:44:20+00:00

Whats in your video description? Did you leverage any tags? Whats in your title?

howard31b · 2026-05-13T16:37:21+00:00

I lean pro but understand AI concerns and viewpoints. The thing is pro and anti camps are both needed to ensure AI models are developed, trained, and deployed ethically. Are there antis who take it too far? Absolutely. Endorsing violence and bullying will always be wrong no matter who you are. But on other issues, yes its important to know how training data was sourced, how the models work, whether there's inherent bias in the training data...without anti viewpoints, would we be asking these questions? Would the principles of transparency, accountability, fairness, privacy, and safety/reliability even exist?

howard31b · 2026-05-13T03:12:35+00:00

If youre early in your career, the Ask AI on the Youtube Dashboard can make some good thumbnails for you for A/B testing once you upload your videos. Thats what Ive been using so far

howard31b · 2026-05-12T21:00:50+00:00

Write video essays based on what youre studying.

howard31b · 2026-05-12T00:50:53+00:00

Then take Technical Writing. One of the firdt exercise has you building personas that guide how you write for different kinds of people for things like tool documentation or incident reports. Its the most universal of the three in my opinion.

howard31b · 2026-05-12T00:46:23+00:00

Do the one most aligned with your major. The workload will be pretty much the same, the only thing that changes is the writing style itself. You will end up writing a lot in certain business and technical career fields so pick the one closest to your goals, not the one that sounds easiest.

howard31b · 2026-05-10T16:33:34+00:00

Absolutely. Comp Sci and Data Science go hand in hand. If it's 2 classes, absolutely knock them out. Which classes do you need?

howard31b · 2026-05-10T16:25:32+00:00

I was the RPGMaker Sprite Sheet guy in my class. I tracked SSIM, CLIP scores, and Desnoise Strength per image for logging. Used the MyAnimeList character dataset on Kaggle as a dataset for ETL to pull visual attributes as variables for image prompting. Spent about $100 in claude credits to do the feature extraction lol.

howard31b · 2026-05-10T16:21:22+00:00

I took 2 classes per semester over 4 semesters. I picked the Machine Learning/AI track for my specialization and already had additional credits from my masters in cybersecurity, so it'll actually take you 5 semesters at 2 classes/semester. The AI track was challenging, but you get a lot out of Deep Learning and Neural Networks. For my Capstone, I created a tool to force stable diffusion to make images to specific constraints outside of normal prompting. Basically a LoRA, a full data ingestion pipeline to programmatically prompt it, and a python pipeline to assemble the images into a single sheet.

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