Researchers Realize ASI Is Now Essential Path To Field Progress
if you're a top researcher in a AI-soluble field, consider applying to a lab and learning ML. you can both apply your skills contributing to general LLM progress, and, on the side, do field-specific work: data collection, reward shaping, harnesses, etc. which give great returns
will depue@willdepuefurther, researchers will just come to realize that the path to progress in their field goes through ASI now, that human progress today is slow and progress in AI is lightning fast
#254will depue@WILLDEPUEto clarify, i don't think openai will pick and choose what academic fields to make progress in explicitly, but they already do implicitly
will depue@willdepueit’s not so much that GOMAX will pick and choose what to make progress on — for the most part) but they will pick and choose what evals to track, what north star proxies for 'general intelligence' they’ll focus on, and what big announcements to push for. this effects where compute & time is spent access to the latest models will also be increasingly gated. you want GPT-6.2-SuperPro with max inference compute, you’ll need to be inside the lab. don’t forget, you get infinite tokens and access to the full research cluster if you’re there. and, don’t forget, is research cluster utilization is abysmal. anyone who’s run a research cluster knows how hard it is to constantly have experiments running. sometimes up to 20-30% of a cluster could be doing nothing. that’s millions, maybe billions in compute, just sitting there idle. if you’re a researcher, nobody minds if you take unscheduled GPUs and put a few zettaflops into solving discrete geometry problems on the weekends! i think that the OpenAI foundation/other philanthropic organizations will help fund open access to compute. but anyone inside the labs will maintain a permanent significant advantage to those outside: unreleased models, free tokens, model customization