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!

A claim you are making not only without any evidence to substantiate it, but in direct contradiction to the evidence that your claim is untrue. If it were true then Linus would not be allowing the use of AI. To claim otherwise is absurd. Some people aren't smart enough to figure out that they aren't smart and you have proved to be one of those people. Off you go now ...

That is quite literally the opposite of what he wrote: "Except that too much AI slop is the reason why those were brand new bugs." AI is finding old bugs, not creating "brand new" ones. AI slop is not a term that is used to describe code analysis. It applies to the creation of new code.

There is a reason why Linus, who is much smarter than you, says what he says and does what he does. Nothing about kernel development has changed. There is not now, nor has there ever been, a way to determine who wrote the code. The code lives and dies on its merits and gets reviewed. It neither gets accepted or rejected because it is AI code. Code is code. There is an entire system of development at play of which you have literally no understanding. I suggest you learn how to read an article summary before trying to offer your "expertise" on a subject in which you clearly have no experience or understanding.

I'm not sure you are paying attention. I am the one dismissing a claim that was made without evidence, not the one making the assertion. I went so far as to say that if anyone has any evidence to support the claim I would truly like to hear/see it. You provided *zero* evidence to support the claim. The claim made was about technological progress being made in the AI domain, not financial analysis of the AI boom.

So you suggest that I should go to a financial expert for my information to determine if AI is still improving exponentially or it has stopped improving? I'm sure it is a good source of information, for financial analysis, but I think I'll trust AI experts to speak on the veracity of this claim: "The plain fact is that LLM inference systems are plateauing, with exponentially more power needed for smaller and smaller gains. The limit they are hitting is real and far below AGI."The plain fact is that LLM inference systems are plateauing, with exponentially more power needed for smaller and smaller gains. The limit they are hitting is real and far below AGI." If you can point to something that supports this claim feel free to do so. Since it isn't a true statement you cannot, however, do so. Feel free to point to evidence that exponential growth has suddenly stopped and nobody in the AI community knows about this important development.