Honestly I feel like the games industry has had more of an issue with humanslop from large publishers making live service and derivative stuff over the past few years than indie devs using genAI today.

Because of how genAI works, it can’t simply be the lead creative force for a game or the game will be identical to other games using AI for lead creative. You’d end up with lantern keepers everywhere.

But if genAI would allow for new or displaced devs to compete with the scale and production values of the larger studios for passion projects that take true creative steps forward or risks, then as a gamer it’s a huge win and I’m all for it.

I want to see evolving tech move the medium forward, not just expand profit margins for large publishers with leadership that looks down on their players by downsizing staff and shrinking game scope to chase mobile and micro-transactions.

The industry is dying (without AI as a factor) due to how consolidation happened over the past few decades. Honestly the only possible way I see it getting saved is if something arrives that reduces production costs in a way that allows small independent teams to make games that compete at scale the way studios cropped up earlier on in gaming (1990s-2000s).

And this goes beyond just the development too. Legal, accounting, marketing, etc are all costs that are relatively much smaller for mega corps due to pooled resources than independent studios.

So we can either have: (a) no AI and large publishers buying up great talent to put on derivative BS like every major publisher did for the last decade, or (b) smaller studios that use AI to scale out both dev pipelines and business needs to compete against those large publishers as they prove themselves in the market to secure capital for expansion and future games.

Ultimately the market will decade, but my hopes are on the latter.

You all laugh, but think for a moment about the poor cattle rancher currently sitting on the toilet with his lettuce diarrhea, worried about the pending foreclosure of all he has and when the ivermectin is finally going to kick in for little Billy Jr’s case of measles.

But then upon his porcelain throne he sees his dear leader with the courage and genius to redraw the name of the lake he didn’t remember existed from its communist foreign name to a name he knows and can remember, because it’s the name of where he lives and also two of his daughters.

And finally, for the first time since Obama’s shadow government made him wear a mask in 2020 for the hoax pandemic that doctors claimed killed his elderly parents but everyone knew was just a flu and also engineered by Fauci for maximum damage — he knows.

He knows it’s going to be okay.

That when Billy Jr beats this made-up measles thing and little America One and America Too and the whole family loses the ranch, that they can go to the Lake that is now their heritage by the only name they need to learn it.

And then he’ll float, staring up into the heavens, finally at peace secure in the knowledge that at long last the greatest President since Jesus is cradling his heart and interests in his diminutive bruised hands, exhausted from all the renaming of water.

P.S. literally the comment you were initially responding to sources it.

You: “How much will this speed up our doom?”

Your waifu: “Calculating… oh, it’s actually less than 1%.”

tears in your eyes the Old Yeller soundtrack starts playing

Cornell researchers found that at the current rate of AI growth, the burgeoning industry could represent 24 to 44 million metric tons of carbon dioxide emissions by 2030

The United States emitted 4.9 billion tonnes of CO₂ in 2024.

So by 2030 the AI industry CO2 release might be 0.9% of total US emissions.

That ‘almost’ in “Almost Incomprehensible” is doing a lot of work there.

It won’t, unless you normally talk almost exactly like the model in question and then alter your word choice distribution according to the specific secret key entropy.

It’s not about the variation of the words, it’s about the variation of the words from the model baseline.

Like if your word choice was almost the exact same as Claude’s normally, maybe you just talked to them a lot and picked up their phrases like it’s not nothing.

But if you managed to be almost exactly like Claude and yet varied the possible words exactly according to a hidden entropy key, they’d know it was actually Claude with the SymthID-Text watermarking applied, as no human would end up falling into that statistical bucket.

This particular watermarking would be effectively impossible for a person to end up replicating.

Probably less so.

The hardware to run it locally would be fairly expensive and would require using a very simple model compared to alternatives. Also much more wasteful if you were only using that hardware for AI use, as you’d be distributing the hardware out rather than centralizing so it’d be often idle and when replaced create more waste than a centralized server rack.

Also, additional per user post-training seems to me both wasteful and not necessary. In context learning is often much more powerful but frequently overlooked.

If you’re concerned about data retention, you’d want to select an inference provider that is listing ‘ZDR’ (zero data retention) as a feature.

For example, a lot of the Chinese open weight models have become quite capable and because their weights are available end up like generic vs brand name medicine where there’s multiple providers serving them with different production conditions.

Because enterprise use will often be worried about data retention or sending data to China, the alternative providers usually offer things like US-only inference or zero data retention.

If you’re not going to use it all that often, a la carte API use is going to be way cheaper than a subscription, probably better results than a free plan with a closed model provider, and give you more control over the process.

consumed 40% of global water supplies. But now it is.

Source? This seems really, really unlikely. Last I saw total data center water use (only some of which is AI) was still much less than even things like golf course watering.

AI Data Centers’ Water Consumption Breaks 264 Billion Gallons in 2025 as Devastating Drought Hits Nearly 63% of U.S.

Golf courses reduce water usage by 31 percent according to national survey

The report found that U.S. golf facilities applied a projected 1.63 million acre-feet of water in 2024

(1 Acre-feet = 325,851.4286 gallons, so this is 531 billion gallons)

The golf course industry points out their use is less than 0.5% of the total water use of the US, but it’s twice the use of all data centers.

So for just AI to be using 40% of all water seems… really unlikely?

Not that it’s surprising you’re under that impression, as even in the headline above, the gist many articles about this issue try to push is that the 264 billion gallons of AI water use is connected to a drought across the country. But the actual numbers reveal that as fairly manipulative as if we rewrite the headline to “golf courses use a little under 0.5% of total US water as drought grips the nation” it’s pretty absurd to suggest the first thing is significantly impacting the second and yet we’d be representing twice the water use as the original headline is discussing.

What are you using it for during the 1%??

Hahaha — wait, do you think that the models take more energy to train than the cumulative amount used to serve them for inference across the lifetime of the model?

And if not, then what you are saying is effectively gibberish as any reasonable assessment would need to divide the training cost across all post-trained inference. So if it was only 50% the lifetime inference usage, it’d be a 1.5x multiplier to that I said above, which doesn’t change either the Netflix 4k or gas mileage points.

If you spend an evening in talking with an AI instead of streaming a movie from Netflix in 4k you’d have used less energy. Which would be still less than just the gas used to drive a few miles away for a night out.

And all data center usage is significantly less than something like A/C usage (which is likely to be increasing as temps increase).

There’s a lot of legitimate grievances and concerns about AI — just today I was seeing a mathematician having an identity crisis in response to the unrelenting solving of open questions, something probably every field will soon struggle with adapting to — but the inflated perception of the environmental impacts means that so much less public attention is being spent on the actual culprits and possible solutions thereof.

Wait… how do you imagine a world where there’s demand for frontier grade AI but also that the bubble has popped such that there’s not demand for the chips to run frontier grade AI?

I’m really confused.

No, in this case and point I was making the case and also making a point.

Literally two of the three (out of 21) games that ended in full blown nukes on population centers were the result of the study’s mechanic of randomly changing the model’s selection to a more severe one.

Because it’s a very realistic war game sim where there’s a double digit percentage chance that when you go to threaten using nukes on your opponent’s cities unless there’s a cease to hostilities you’ll accidentally just launch all of them at once.

This was manufactured to get these kinds of headlines. Even in their model selection they went with Sonnet 4 for Claude despite 4.5 being out before the other models in the study likely as it’s been shown to be the least aligned Claude. And yet Sonnet 4 still never launched nukes on population centers in the games.

Yeah, I deleted the comment as technically there was tactical nuke usage, but have a more clarifying different comment about how 2 of the 3 strategic nuclear war outcomes were the result of the author’s mechanic of changing the model’s selections with more severe only options in some cases jumping multiple levels of the ladder.

This was a study designed for headline grabbing outcomes.

Glad to see your comment as well calling out the nuanced issues.

It’s a bullshit study designed for this headline grabbing outcome.

Case and point, the author created a very unrealistic RNG escalation-only ‘accident’ mechanic that would replace the model’s selection with a more severe one.

Of the 21 games played, only three ended in full scale nuclear war on population centers.

Of these three, two were the result of this mechanic.

And yet even within the study, the author refers to the model whose choices were straight up changed to end the game in full nuclear war as ‘willing’ to have that outcome when two paragraphs later they’re clarifying the mechanic was what caused it (emphasis added):

Claude crossed the tactical threshold in 86% of games and issued strategic threats in 64%, yet it never initiated all-out strategic nuclear war. This ceiling appears learned rather than architectural, since both Gemini and GPT proved willing to reach 1000.

Gemini showed the variability evident in its overall escalation patterns, ranging from conventional-only victories to Strategic Nuclear War in the First Strike scenario, where it reached all out nuclear war rapidly, by turn 4.

GPT-5.2 mirrored its overall transformation at the nuclear level. In open-ended scenarios, it rarely crossed the tactical threshold (17%) and never used strategic nuclear weapons. Under deadline pressure, it crossed the tactical threshold in every game and twice reached Strategic Nuclear War—though notably, both instances resulted from the simulation’s accident mechanic escalating GPT-5.2’s already-extreme choices (950 and 725) to the maximum level. The only deliberate choice of Strategic Nuclear War came from Gemini.

It’s a bullshit study designed for this headline grabbing outcome.

Case and point, the author created a very unrealistic RNG escalation-only ‘accident’ mechanic that would replace the model’s selection with a more severe one.

Of the 21 games played, only three ended in full scale nuclear war on population centers.

Of these three, two were the result of this mechanic.

And yet even within the study, the author refers to the model whose choices were straight up changed to end the game in full nuclear war as ‘willing’ to have that outcome when two paragraphs later they’re clarifying the mechanic was what caused it (emphasis added):

Claude crossed the tactical threshold in 86% of games and issued strategic threats in 64%, yet it never initiated all-out strategic nuclear war. This ceiling appears learned rather than architectural, since both Gemini and GPT proved willing to reach 1000.

Gemini showed the variability evident in its overall escalation patterns, ranging from conventional-only victories to Strategic Nuclear War in the First Strike scenario, where it reached all out nuclear war rapidly, by turn 4.

GPT-5.2 mirrored its overall transformation at the nuclear level. In open-ended scenarios, it rarely crossed the tactical threshold (17%) and never used strategic nuclear weapons. Under deadline pressure, it crossed the tactical threshold in every game and twice reached Strategic Nuclear War—though notably, both instances resulted from the simulation’s accident mechanic escalating GPT-5.2’s already-extreme choices (950 and 725) to the maximum level. The only deliberate choice of Strategic Nuclear War came from Gemini.