The result shows that the LLM has a hard time generalizing to new (basic!) problems that were not in its training set; this suggests it is memorizing solutions rather than understanding the mechanisms... It's the undergrad who has crammed Leetcode solutions for two weeks before their interview, rather than a student with a deeper understanding who can successfully address new problems.
Having a friend who is objectively pretty dumb but has memorized the entire existing literature of your field of study is still pretty damned useful, however. You just need to understand what kinds of questions you can trust them with.
Along these lines, my own best use of LLMs is looking up jargon and old results. I see some complicated problem, propose a method for attacking it, and ask my friend who has read every stats paper ever digitized whether they have seen this kind of analysis before, what it's called, why it's a bad idea, and what people do instead. The answers point me to relevant literature by giving me the right jargon and method names to search for using Good-Old-Fashioned-Google.
They just have a particular set of strengths and weaknesses, same as any tool. Figuring out their strengths and limitations is how you use them well. And dismissing them for not fully solving AGI is short-sighted.
I am once again reminded of Searle's "Chinese room" thought experiment [0] in which it is argued that computers cannot understand Chinese, merely memorize and regurgitate a canned set of responses to given prompts, and execute procedures by rote; that their computations cannot be "about" a subject matter (such as Chinese, or programming) the same way the operation and contents of our minds are "about" something.
While currently this is a relatively weak strength of genAI - assuming technological improvement of this technique over time, isn't it just as possible that the data quality will converge positively rather than negatively over time, in the future? That is to say, the web would be consistently "refined" as time goes on, by predominant VLLM?
Assuming that the internet is even "filled" as you say in the first place (personally I don't think organically-generated content is ever going to be pushed out of the internet, but that's my opinion, and I'll entertain the opposite case for the sake of the discussion). It also assumes that people are using models trained on the current state of internet "slurry" in the first place - that we are continually ingesting more internet YoY into these models. If we come up with a better model that needs less data to produce high-quality content, neither my nor your assertion is even relevant. Same case if the internet just decides to use small, low-quality models trained on only a portion of the internet.
But if the internet is continually recycling the entirety of itself through a model that has tens of millions of dollars of funding and research focused on directly improving the quality of it's answer metrics, it's not necessarily 100% locked into a downward quality convergence slide. Especially if we assert that humans /will/ continue to be consistently putting more organic data into the internet over time. It's a pessimistic take.
We never used to have such an elaborate or accessible thing before. Kids and senior citizens likely engage constantly without even realizing it - and adopt one another's, idk the words, but I'm going for something akin to dialects/accents/language/opinions/etc
We're all a bunch of parrots