Your best bet would be to look deeply into performance on ARC-AGI fully-private test set performances (e.g. https://arcprize.org/blog/arc-prize-2025-results-analysis), and think carefully about the discrepancies here, or, just to broadly read any academic research on classic benchmarks and note the plateaus on classic datasets.

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.