To make an analogy, right now using a LLM filter to read the literature is like reading Scientific American or New Scientist - fun, interesting, entertaining and not always right on the detail.

Let's say, for example, you wanted to build your own cutting edge LLM - would you just ask an LLM on how to do so? Or would you need to do more, and would a simple literature/internet search be just as effective as a starting point?

Note that in my experience - when you are a world expert in some tiny area ( like when doing a PhD ), you realize that quite a large proportion ( ~50% ) of the stuff published in the area you really know about is either wrong in whole or part, and another good proportion doesn't really move the field on.

So back to the original question - how did OpenAI get a lead in LLM - the story I heard was they talked to leading academic's about who were the best people in the field and tried to hire them all.

ie to paraphrase Richard Feymann on the Emperors nose question - you don't really find out the true answer by averaging over loads of ill-informed opinions - much better to carefully examine the nose/data source yourself.

Industry people (usually) shouldn't apply academic research directly because the majority of peer-reviewed published papers are irreproducible bullshit. Of course there is an occasional jewel in the muck so industry people with the skill (or luck) to identify those can get a jump on their competitors.

Industry would not gotten to this stage in LLMs without academic. Your ignorance is not an excuse for spouting bullshit.

I wouldn't go so far as a sibling commenter and so that most academic research is irreproducible bullshit. But academic research does tend to be chewing-gum-and-baling-wire products that are meant to hold together just long enough to get the necessary results. The rate-limiting step of turning academic research into useful products is "let's flip through all the academic research to find interesting papers," it's "figure out how to make this very-barely-works academic product usable on anything other than the exact things they did for the results section."

And, to be blunt, I have never seen anyone pitch an AI project to do that. AI pitches, even today, are almost invariably solving problems that are already decently solved (search is essentially a solved problem). And most of their proponents have shown no willingness to the practitioners telling them what the actual problems they need better solutions for.