I understood the video just fine. You don't understand the difference between performance and capability. You are also confusing training time with quality of training. AI models (models,not LLMs) continue to improve in *capability*, and nobody has suggested they have stopped improving or found any reason to believe that this exponential upward trend in capability has suddenly plateud.

So the "hype" was real (news flash ... if it was real it wasn't hype) but now ... Just a few months ago ... this year ... much more capable models didn't get released? That's an absurd claim. It sounds like you simply didn't know what the word capable means, and think it is about improvement in a single domain. Capability is about models used across domains. If a model doesn't improve at all in a single application domain (say code generation) the capability still improves dramatically if it improves in multiple other domains. You are also still making the same mistake they are making by equating the LLM with the model. They are talking about scaling, which has to do with model training time ... not capability ... then concluding that because scaling doesn't reduce training time exponentially that scaling doesn't improve model capability exponentially. Again, nobody ever claimed that it does.

First of all you don't know what they are doing. Secondly, you failed to grasp the major failure in your reasoning again. If the way they do it is inaccurate that in no way accounts for any aberration in the data.

So your theory is that the OS fingerprinting became significantly more unreliable for a short period of time, and then returned to the previous degree of unreliability? I'm going to go out on a limb and say you didn't think that line of reasoning through.

In order to de-shittify Windows you would have to first de-shittify Microsoft, and that is never going to happen by cause Microsoft didn't get shittified, it has always been a shit company.

You should ask Gemini to read the summary for you next time so that you won't seem stupid.

If there is one thing I love about posts from you, it is that whenever I see a reply from you in my inbox I know I'm about to be entertained by a clown. LOL

I forgot to mention in my most recent post ... The extraordinary claim is the one made by you, to wit that the exponential improvement of AI models that has been going on for years and still continues has plateud and that you are the only one who knows about it. I'm always amazed at how truly stupid you actually are.

Remember' Chat-Gpt is just a word predictor! It doesn't think, and it will never be of any use! Only an idiot would be filled into thinking it does anything other than generate words! Thank you so much for trying to make a post to make me look foolish, and in doing so, unwittingly proving that you now admit that you have been clueless in every exchange we have had regarding AI. ROTFLMAO! Off you go now ...

I just watched the video. It doesn't at all say what you claim it says. It doesn't even remotely claim that AI model capabilities have stopped improving exponentially. I'm not sure where you got that idea. Advancements aren't limited to making bigger LLMS. Imagine if you will that you have a brain that has never been trained ... a baby. Over time it first learns to recognize objects and gains object permanence, then it learns the alphabet, then words, then sentence structure. It eventually gets to the point where it has learned basic math skills. It gets exposed to more and more ideas and concepts, and *learns* more and more. It learns physics, biology, neurology, and gains other medical knowledge. The size of the brain doesn't increase, but it gets closer and closer to being an expert on all of these subjects. That, loosely speaking describes the difference between making a neural network bigger and increasing its capabilities. My explanation is a bit simplistic, but that isn't a bad thing. The idea is to get you off of this idea that there is a linear correlation to neural network size and the capabilities it has after learning over time. You can empirically prove this to yourself by having a conversation with OpenAI or Google models from 2024 and then with the latest releases running them locally on *your* hardware. The hardware is the same. It is no more capable. The *models* however, are worlds apart.

Again ... Scaling is adding more hardware resources to a single running instance of a model to gain improvement. It has never been true that you get an exponential improvement that way, and nobody ever claimed it did. The purpose of building data centers isn't scaling. It is to allow more model instances to run concurrently. Why would I start by focusing on the wrong studies?

I'm going to look more deeply at some of the links you provided, however I can also already tell you that you are confusing scaling (throwing more hardware at a given model) with the exponential improvement of model capabilities with each new model that has been occurring exponentially since before 2025, and that capability increase has accelerated in 2026, not plateud. You are also confusing LLMs with AI. LLMs are just one component of a complete AI stack. Given the same exact hardware the capabilities are improving exponentially and continue to do so as I type this.

It appears you haven't even seen the summary. ROTFLMAO

Yes. I got sidetracked in between the beginning and end of the post. Mea Culpa. The fact remains that a claim was made that the journalist may have lied, despite there being zero reason to believe he would, or without a agreed of evidence he did. The quote is evidence that Gates said it, not evidence that the journalist lied.

Understandable. The OP did in fact say what you were saying. It was evanh who went on to make the ridiculous statement to which I replied. Have a great day!