Unfortunately while this advice sounds useful, it isn’t. It might be useful if you measure impact is the fake metrics like citation count but if you want to measure with actual tangible impact on the real world, you have a rude awakening before you. To me there are 2 ways a paper can create impact:

1. The paper represents such an impressive leap in performance over existing methods in AI, that it is obviously impactful. Unfortunately, this way of generating impact is dominated by industry. No one can expect Academia to train O1, SAM, GPT5 etc. AI rewards scale, scale requires money, resources and manpower and Academia has none. In the early days of AI, there were rare moments when this was possible, AlexNet, Adam, Transformers, PPO etc. Is it still possible? I do not know, I have not seen anything in the last 3 years and I’m not optimistic many such opportunities are left. Even validating your idea tends to require the scale of industry.

2. The paper affects the thought process of other AI researchers and thus you are indirectly impactful if any of them cause big leaps in AI performance. Unfortunately here is where Academia has shot itself in the foot by generating so many damn papers every year (>10,000). There are just so many, that the effect of any 1 paper is meaningless. In fact the only way to be impactful now is to be in a social circle of great researchers, so that you know your social circle will read your paper and later if any of them make big performance improvements, you can believe that you played a small role in it. I have spoken to a lot of ML researchers, and they told me they choose papers to read just based on people and research groups they know. Even being a NeurIPS spotlight paper, means less than 10% of researchers will read your paper, maybe it will go to 50% if it’s a NEURIPS best paper but even that I doubt. How many researchers remember last year’s NEURIPS best paper?

The only solution to problem 2, is radical. The ML community needs to come together and limit the number of papers it wide releases. Let us say it came out and said that yearly only 20 curated papers will be widely published. Then you can bet most of the ML community will read all 20 of those papers and engage with it deeply as they will be capable of spending more than a day at least thinking about the paper. Of course you can still publish on arxiv, share with friends etc but unless such a dramatic cutdown is made I don’t see how you can be an actually impactful AI researcher in Academia when option 1 is too expensive and option 2 is made impossible.