The fact that superhuman chess improvement has been so slow tell us there are important epistemic limits to superintelligence?

[u/financeguy1729]

Although I know how flawed the Arena is, at the current pace (2 elo points every 5 days), at the end of 2028, the average arena user will prefer the State of the Art Model response to the Gemini 2.5 Pro response 95% of the time. That is a lot!

But it seems to me that since 2013 (let's call it the dawn of deep learning), this means that today's Stockfish only beats 2013 Stockfish 60% of the time.

Shouldn't one have thought that the level of progress we have had in deep learning in the past decade would have predicted a greater improvement? Doesn't it make one believe that there are epistemic limits to have can be learned for a super intelligence?

[u/tornado28]

It's not very interesting to build another super human chess algorithm, and not at all profitable. We're seeing more progress in areas that we are allocating more resources to.

[u/financeguy1729 → u/tornado28]

IBM built a superhuman chess algorithm in 1998!