It is very clear when you look at academic papers actually targeting problems specific to reasoning / intelligence (e.g. rotation invariance in images, adversarial robustness) that all the big companies are doing is just fitting more data / spending more resources on human raters and other things to boost performance on (open) metrics, but that clear actual gains in genuine intelligence are being made only by milking what we know very well to be a limited approach. I.e. there are trivially-basic problems that cannot be solved by curve-fitting models, which makes it clear most current advances are indeed coming from curve(manifold) fitting. It just isn't clear how far we can exploit these current approaches and in what domains this kind of exploitation is more than good enough.
EDIT: Are people unaware Google Scholar is a thing? It is trivial to find modern AI papers that can be read without requiring access to a research institution. And e.g. HuggingFace collects trending papers (https://huggingface.co/papers/trending), and etc.
But the bizarre thing is, even though the productivity of SWE's is increasing I dont believe there will be much happening in regards to lay offs due to the fact that there isn't complete trust in LLMs; I dont see this changing either. In which case the LLM producers will need to figure out a way to increase the value of LLMs and get users to pay more.
I am hopeful about LLMs for SWE, but the progress is currently contextual.
Even if LLMs could write great code with no human oversight, the world would not change over night. Human creativity is necessary to figure out what stuff to produce that will yield incremental benefits to what already exists.
The humans who possess such capability stand to win long-term; said humans tend to be those from the humanities and liberal arts.
And, again, this is ignoring all the technical debt of produced code that is poorly understood, weakly-reviewed, and of questionable quality overall.
I still think this all has serious potential for net benefit, and does now in certain cases. But we need to be clearer about spelling out where that is (webshit, boilerplate, language-to-language translation, etc) and where it maybe isn't (research code, legacy code, large codebases, niche/expert domains).