for one, assuming it takes 5 yrs after graduation for an average engineer to reach senior at a tier-1 company (esp. Google) is simply untrue in today's environment. Besides the point that promotion is highly dependent on manager, team, org and timing (think about reorgs and project cancellation/deprioritizations), the average timeline for promotion nowadays takes longer than a few years ago.
You're just wrong, at least w.r.t. Google. Folks who are good and destined for senior in 4 years do not wait for a good manager/team/project - they seek them out and take their career into their own hands.
didn't I put "average" in the comment already? (and to be fair, this is the group the post was talking about when making the calculation). I know and have helped some brilliant engs at google reach l5 with much shorter time frame, but it's not the norm.
given you seem to be from google, there are various stats w.r.t promotion timeline that offers more insights about the average case internally
Author here. Was not expecting this quick post being picked up by HN - thanks for all the comments!
I want to acknowledge that the original title is inaccurate, as many of you have pointed out. It should be "(current) LLMs don't brainstorm novel things really well" rather than just brainstorming.
It's not intended to be a clickbait though - I was kind of mixing two definitions of brainstorming unintentionally. When we refer to the group activity that aims to collect all angles from participants (and common wisdoms), LLMs are really good, and it's something I do on a regular basis. However when it comes to the hope of reaching novel ideas that don't exist before (which some of us will consider what distinguishes brainstorming from group discussion or research study), I would say today's LLMs don't do well. I've seen such issue in business and arts domains, and also someone here mentioned similar experience in video game design.
I would argue that (so far) for any idea LLMs tell us, there exists at least one instance of a similar pattern in the training data (either exact or in a high level). If this is what you need, then great. But some problems require more than that. And I would argue that a lot of important innovations in history didn't follow this pattern. I'm aware of reports on LLMs helping research (e.g. the works shared by Terry Tao), but I don't think they contradict the point here. Will be super happy to be proven wrong though!
> Was not expecting this quick post being picked up by HN - thanks for all the comments!
You submitted the post?!
It got “picked up” so quickly because it is wrong. I think what your post does show is how effectively you can farm HN for supporting arguments.
You provide zero evidence for your claims, nor do you give us any transcripts of your attempts. Every post that matches the structure of yours is usually a summary of how the author has low skill in using LLMs.
Though I am absolutely delighted in the comments and the nih paper referenced was a joy to read.
