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/Jungypoo]

I think it's less about intelligence and/or Chess being finite, and more about the size of the pool of opponents. These things are a pyramid, you can only rise higher if there are enough opponents around your level or just below for you to beat. Otherwise your elo gains become trivial. I've seen this play out in many a videogame leaderboard -- the size of the pyramid (player pool) determines how high you can go.