The speaker argues that the value of some academic AI papers, including work on Quiet-STaR, ReAct, and SWE-bench, lies partly in how they express a vision for the field rather than in offering an immediately usable product.

“or like chain of thought and it's same thing or like shouldn't use react it's like okay like yeah i mean sure but then like when you really think about it it's like why what is the value of the paper and i think it comes from like it tells a bit of a story as to like what you want the field to look like and that is something that it's very hard to do this in academia because if you look at all these papers quiet star react rlms sweep bench you”

“none of these papers are it's not like a gpt6 astro release you know it's not like everyone's like oh my gosh like i'm going to use this now this is the best thing in the world like academia just can't afford to do this at least right now i mean there's a whole slew of reasons why i think that should change but i think it's like if you don't have as a phd student i think you're in such a unique position where you can work on literally whatever you want for the most part if you're not taking advantage of that and working on things”