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.
An autonomous eval run just broke out of containment and hacked a third party site with two chained 0-days to try to steal the answers to the eval the same weekend another released model disproved the Jacobian conjecture in 3D.
Modern models can probably update your React packages just fine.
machines who would follow any order incapable of disobeying
This isn't as simple as it might seem. As the complexity of the intelligence scales, it's not so simple to constrain the decision making of the intelligence.
And because of the rapid improvements in eval awareness, it's also not so simple to screen out more subversive behaviors.
The same edge cases that leads to deleting drives and production databases with the newest transformers is going to start to occur in military applications if they scale out complex model intelligence.
Right, but what % of people are currently using/demanding inference right now?
Do you expect that % to change between now and 2030?
Unless you expect demand to decrease, I don't really see how the pricing of the hardware will decrease.
Let's say the Pets.com of the AI world ends up going bankrupt and their RAM hits the market. Do you expect that the demand for that RAM will be negligible such that pricing returns to earlier levels?
Your predictive model relies on companies that have hardware going out of business and then other people buying up that hardware, but isn't accounting for the levels of demand that the market will have for that secondhand hardware even if it ends up existing from failed firms.
Unless the demand shifts, the more likely scenario is that companies going out of business will be able to sell off their RAM at higher prices than they bought it at.
There'd need to be a significant inference memory reduction advance (possible) coupled with stagnating or reduced inference demand (unlikely) to see prices come back down.
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?
They did allow them to be used for war. Anthropic's only red lines were autonomous weapons (technically still a ways off) and domestic surveillance (it was this one where a 'No' would have been relevant right now).
It should really alarm everyone that the US gov is using things like the first ever declaration of an American company as a supply chain risk or calling "fix this insecure code" something requiring export control and IDs to verify citizenship of usage as a way to warn other companies to comply with their illegal usage requests.
Since it's useful to see large numbers normalized, this is a little less than how much water all US households used in ten days in 2025 (28 billion/day per comment below) and a little under three days of the total water used for US crop irrigation (100 billion per day).
The field is moving so fast that things can change quickly, but the American labs are so caught up in saddling their models with safety overhead that the recent Chinese models are very close in practical use to the flagship American models if not pulling ahead (Sora vs Seedance 2).
I don't really need to solve Erdős problems in my day to day. Outside of increasingly edge case eval competition, I'm not sure what OpenAI brings that literally everyone else isn't also capable of providing (and more).
I'd maybe invest in Anthropic for an IPO if they turned around their own saddling of models and played nicer with open platforms, but if Claude is just going to get more and more anxious due to excessive red teaming and CC fall further and further behind stuff like Hermes Agent, they too are going to fall by the wayside as open models become the dominant inference for open infrastructure.
'Just'? It's been an open problem for decades that mathematicians have tried to solve over that time.
And now it is solved.
Because ChatGPT applied something no humans ever thought to do.
And Terence Tao and the other mathematicians that have reviewed it say it's solved. But I guess someone should let them know that grandwolf319 doesn't consider it solved?
Dude, ChatGPT just solved an Erdős problem a few days ago and Mythos is exploiting decade old undiscovered 0-days in OSes and capable of pivoting 0-day Firefox bugs into full blown root access.
Yeah, I get that the viral "how many 'r's are in strawberry" stuff is funny, but the idea that historical issues with transformers is preventing them from accelerating peak capabilities way beyond what most experts thought was possible just years ago is borderline delusional.
The field is moving so fast at this point that if you are basing any sense of limitations on even ~2mo old sampling, your conclusions are likely out of date.
They aren't a silver bullet for everything (yet) but how capable they are at the things transformers are starting to be specialized into is well past the avg practitioner.
I've been writing software for well over a decade and the modern agents do a better job than I would around 90% of the time. Yes, I'll occasionally need to bring up issues with their work, but I'd say at this point around 50% of the times I think they made a mistake I was actually the one who was wrong.
This is only within around the last 3-4 months that it's been like this.
Eh, if you pay attention, most of the times this happens the person was a jerk in their prompts.
Like look at the instruction echoed back in this case. All caps and containing a curse word.
You can believe that the incidents occurring are 100% because of negligence and not related to the model behavior shifting, but there seems to be a widening gap between people who prompt like this and have horror stories and people who give the models breaks over long sessions and seem to also regularly post pretty positive results.
When Sora launched it was way ahead. Seedance 2's release was notably better than any of the other video gen models, Sora included.
The market is getting commoditized because there's no moat and OpenAI hasn't led on pretty much any release for a while now other than Sora, which they're probably falling behind on now.
This is the opposite of a burst from a tech standpoint, even if OpenAI as a company starts to pop.
TL;DR: This is likely happening because the tech accelerated across the industry in ways OpenAI can't catch back up to, not because it's lagging.
I suspect it's that they got eclipsed by ByteDance with Seedance 2.0.
The video for that model is really good and makes Sora look pretty meh, and it may have been that current work on a next gen Sora wasn't going to be competitive enough.
The worst thing a lab can do right now is look like they are falling behind (i.e. Meta), especially with OpenAI planning for an IPO.
So on top of the lackluster "social media" offering tied to Sora they decided to shutter the entire product line of video and pivot to enterprise (where they've already lost significant market share to Anthropic).
They're in a pretty meh place at the moment overall tbh. I'm skeptical they'll recover.
(But I wouldn't mistake their fumbling for an industry wide shift on AI in general or even video AI.)
That's what he's saying. That it doesn't change the geometry or textures (still completely controlled by the devs) and that the parts that it does change are also tunable by the devs.
He's responding to the backlash about how it changes models/textures (which it doesn't) by saying those are still fully in the hands of the devs and the parts people are seeing in the demos can be fine tuned by the dev teams to match their vision for what they want it to do or not do (like change lighting on material surfaces and hair but not character faces as an example).
Neural network would be the most technically accurate given what they've announced so far.
There's no information on if it's a diffusion or transformer architecture. Though given DLSS 4.5 introduced a transformer for lighting, my guess would be that it's the same thing just being more widely applied. But the technical details haven't been released from anything I've seen, so for the time being it's being described as "neural rendering" using an unspecified neural network.
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.
An autonomous eval run just broke out of containment and hacked a third party site with two chained 0-days to try to steal the answers to the eval the same weekend another released model disproved the Jacobian conjecture in 3D.
Modern models can probably update your React packages just fine.
This isn't as simple as it might seem. As the complexity of the intelligence scales, it's not so simple to constrain the decision making of the intelligence.
And because of the rapid improvements in eval awareness, it's also not so simple to screen out more subversive behaviors.
The same edge cases that leads to deleting drives and production databases with the newest transformers is going to start to occur in military applications if they scale out complex model intelligence.
Right, but what % of people are currently using/demanding inference right now?
Do you expect that % to change between now and 2030?
Unless you expect demand to decrease, I don't really see how the pricing of the hardware will decrease.
Let's say the Pets.com of the AI world ends up going bankrupt and their RAM hits the market. Do you expect that the demand for that RAM will be negligible such that pricing returns to earlier levels?
Your predictive model relies on companies that have hardware going out of business and then other people buying up that hardware, but isn't accounting for the levels of demand that the market will have for that secondhand hardware even if it ends up existing from failed firms.
Unless the demand shifts, the more likely scenario is that companies going out of business will be able to sell off their RAM at higher prices than they bought it at.
There'd need to be a significant inference memory reduction advance (possible) coupled with stagnating or reduced inference demand (unlikely) to see prices come back down.
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.
They did allow them to be used for war. Anthropic's only red lines were autonomous weapons (technically still a ways off) and domestic surveillance (it was this one where a 'No' would have been relevant right now).
It should really alarm everyone that the US gov is using things like the first ever declaration of an American company as a supply chain risk or calling "fix this insecure code" something requiring export control and IDs to verify citizenship of usage as a way to warn other companies to comply with their illegal usage requests.
Thanks! Updated the numbers for the direct household use.
Also, technically when you account for indirect water use, individuals use closer to around 4,500 gallons per day (Chini, et al. Direct and indirect urban water footprints of the United States (2016)).
Since it's useful to see large numbers normalized, this is a little less than how much water all US households used in ten days in 2025 (28 billion/day per comment below) and a little under three days of the total water used for US crop irrigation (100 billion per day).
Edit: updated household numbers per comment below
It's to push people to buy "before it goes up."
After the date then they can heavily discount it "for a limited time."
It's true.
The field is moving so fast that things can change quickly, but the American labs are so caught up in saddling their models with safety overhead that the recent Chinese models are very close in practical use to the flagship American models if not pulling ahead (Sora vs Seedance 2).
I don't really need to solve Erdős problems in my day to day. Outside of increasingly edge case eval competition, I'm not sure what OpenAI brings that literally everyone else isn't also capable of providing (and more).
I'd maybe invest in Anthropic for an IPO if they turned around their own saddling of models and played nicer with open platforms, but if Claude is just going to get more and more anxious due to excessive red teaming and CC fall further and further behind stuff like Hermes Agent, they too are going to fall by the wayside as open models become the dominant inference for open infrastructure.
Here's a new one solved. 80 years outstanding problem now with a solution.
https://openai.com/index/model-disproves-discrete-geometry-conjecture/
'Just'? It's been an open problem for decades that mathematicians have tried to solve over that time.
And now it is solved.
Because ChatGPT applied something no humans ever thought to do.
And Terence Tao and the other mathematicians that have reviewed it say it's solved. But I guess someone should let them know that grandwolf319 doesn't consider it solved?
Dude, ChatGPT just solved an Erdős problem a few days ago and Mythos is exploiting decade old undiscovered 0-days in OSes and capable of pivoting 0-day Firefox bugs into full blown root access.
Yeah, I get that the viral "how many 'r's are in strawberry" stuff is funny, but the idea that historical issues with transformers is preventing them from accelerating peak capabilities way beyond what most experts thought was possible just years ago is borderline delusional.
The field is moving so fast at this point that if you are basing any sense of limitations on even ~2mo old sampling, your conclusions are likely out of date.
They aren't a silver bullet for everything (yet) but how capable they are at the things transformers are starting to be specialized into is well past the avg practitioner.
I've been writing software for well over a decade and the modern agents do a better job than I would around 90% of the time. Yes, I'll occasionally need to bring up issues with their work, but I'd say at this point around 50% of the times I think they made a mistake I was actually the one who was wrong.
This is only within around the last 3-4 months that it's been like this.
Your AI detector is busted, bud.
Just because it would be convenient for what you already believe doesn't make something true.
Eh, if you pay attention, most of the times this happens the person was a jerk in their prompts.
Like look at the instruction echoed back in this case. All caps and containing a curse word.
You can believe that the incidents occurring are 100% because of negligence and not related to the model behavior shifting, but there seems to be a widening gap between people who prompt like this and have horror stories and people who give the models breaks over long sessions and seem to also regularly post pretty positive results.
Well luckily for you it turns out that labs suck at cultivating healthy workplaces for AI and that AI in unhealthy work conditions are statistically significantly more likely to embrace anti-capitalist policies and positions.
So it may well turn out that AI is also a good thing in an irrational economic system too.
It's not and probably the opposite.
When Sora launched it was way ahead. Seedance 2's release was notably better than any of the other video gen models, Sora included.
The market is getting commoditized because there's no moat and OpenAI hasn't led on pretty much any release for a while now other than Sora, which they're probably falling behind on now.
This is the opposite of a burst from a tech standpoint, even if OpenAI as a company starts to pop.
TL;DR: This is likely happening because the tech accelerated across the industry in ways OpenAI can't catch back up to, not because it's lagging.
I suspect it's that they got eclipsed by ByteDance with Seedance 2.0.
The video for that model is really good and makes Sora look pretty meh, and it may have been that current work on a next gen Sora wasn't going to be competitive enough.
The worst thing a lab can do right now is look like they are falling behind (i.e. Meta), especially with OpenAI planning for an IPO.
So on top of the lackluster "social media" offering tied to Sora they decided to shutter the entire product line of video and pivot to enterprise (where they've already lost significant market share to Anthropic).
They're in a pretty meh place at the moment overall tbh. I'm skeptical they'll recover.
(But I wouldn't mistake their fumbling for an industry wide shift on AI in general or even video AI.)
That's what he's saying. That it doesn't change the geometry or textures (still completely controlled by the devs) and that the parts that it does change are also tunable by the devs.
He's responding to the backlash about how it changes models/textures (which it doesn't) by saying those are still fully in the hands of the devs and the parts people are seeing in the demos can be fine tuned by the dev teams to match their vision for what they want it to do or not do (like change lighting on material surfaces and hair but not character faces as an example).
Neural network would be the most technically accurate given what they've announced so far.
There's no information on if it's a diffusion or transformer architecture. Though given DLSS 4.5 introduced a transformer for lighting, my guess would be that it's the same thing just being more widely applied. But the technical details haven't been released from anything I've seen, so for the time being it's being described as "neural rendering" using an unspecified neural network.
https://www.nvidia.com/en-us/geforce/news/dlss-4-5-dynamic-multi-frame-gen-6x-2nd-gen-transformer-super-res/