Obviously it's higher. If it was any lower, they would've made a huge announcement out of it to prove they're better than the competition.

Sam Altman looks like an SNL actor impersonating Sam Altman.

It's safe to assume that any metric they don't disclose is quite damning to them. Plus, these guys don't really care about the environmental impact, or what us tree-hugging environmentalists think. I'm assuming the only group they are scared of upsetting right now is investors. The thing is, even if you don't care about the environment, the problem with LLMs is how poorly they scale.

An important concept when evaluating how something scales is are marginal values, chiefly marginal utility and marginal expenses. Marginal utility is how much utility do you get if you get one more unit of whatever. Marginal expenses is how much it costs to get one more unit. And what the LLMs produce is the probably that a token, T, follows on prefix Q. So P(T|Q) (read: Probably of T, given Q). This is done for all known tokens, and then based on these probabilities, one token is chosen at random. This token is then appended to the prefix, and the process repeats, until the LLM produces a sequence which indicates that it's done talking.

If we now imagine the best possible LLM, then the calculated value for P(T|Q) would be the actual value. However, it's worth noting that this already displays a limitation of LLMs. Namely even if we use this ideal LLM, we're just a few bad dice rolls away from saying something dumb, which then pollutes the context. And the larger we make the LLM, the closer its results get to the actual value. A potential way to measure this precision would be by subtracting P(T|Q) from P_calc(T|Q), and counting the leading zeroes, essentially counting the number of digits we got right. Now, the thing is that each additional digit only provides a tenth of the utility to than the digit before it. While the cost for additional digits goes up exponentially.

So, exponentially decaying marginal utility meets exponentially growing marginal expenses. Which is really bad for companies that try to market LLMs.

Is it this?

Sam Altman has gone into PR and hype overdrive lately. He is practically everywhere trying to distract the media from seeing the truth about LLM. GPT-5 has basically proved that we've hit a wall and the belief that LLM will just scale linearly with amount of training data is false. He knows AI bubble is bursting and he is scared.

It's the same tech. It would have to be bigger or chew through "reasoning" tokens to beat benchmarks. So yeah, of course it is.

If anyone has ever wondered what it would look like if tech giants went all in on "brute force" programming, this is it. This is what it looks like.

When will genAI be so good, it'll solve its own energy crisis?

intense electricity demands, and WATER for cooling.

They literally don't know. "GPT-5" is several models, with a model gating in front to choose which model to use depending on how "hard" it thinks the question is. They've already been tweaking the front-end to change how it cuts over. They've definitely going to keep changing it.

I have to test it with Copilot for work. So far, in my experience its "enhanced capabilities" mostly involve doing things I didn't ask it to do extremely quickly. For example, it massively fucked up the CSS in an experimental project when I instructed it to extract a React element into its own file.

That's literally all I wanted it to do, yet it took it upon itself to make all sorts of changes to styling for the entire application. I ended up reverting all of its changes and extracting the element myself.

Suffice to say, I will not be recommending GPT 5 going forward.

Photographer1: Sam, could you give us a goofier face?

*click* *click*

Photographer2: Goofier!!

*click* *click* *click* *click*

is there any picture of the guy without his hand up like that?

When you want to create the shiniest honeypot, you need high power consumption.

Pump it Sammy, pump it harder!!

So like, is this whole AI bubble being funded directly by the fossil fuel industry or something? Because the AI training and the instantaneous global adoption of them is using energy like it's going out of style. Which fossil fuels actually are (going out of style, and being used to power these data centers). Could there be a link? Gotta find a way to burn all the rest of the oil and gas we can get out of the ground before laws make it illegal. Makes sense, in their traditional who gives a fuck about the climate and environment sort of way, doesn't it?

How can anyone look at that face and trust anything that mad man could have to say.

"Just a few more trillion dollars bro, then itll be ready..." Like a junkie.

So more energy use for what the people that are into AI are calling a worse model. Is someone going to get fired for this?

But it also could be lower, right?