A professional esports friend recognized the goal of an ARC-AGI-3 game instantly and finished the first level in three seconds without a single wasted move, showing that rapid human mastery of these games comes from bootstrapping on years of prior gaming pattern-recognition rather than solving from scratch.
Jon Kotar recounts showing an ARC-AGI-3 game to a professional esports player, who solved the first level with superhuman efficiency almost instantly — illustrating how much human 'general' problem-solving actually leans on transferable prior experience. ✦ AI generated
Jon Kotar · Machine Learning Street Talk · 2026-07-01 · original ↗
starts at this moment · 36:58
I have a friend that uh is a professional esports player. I showed him one of the games and he completed the first level without spending an extra move that he didn't have to. It was immediate to him what the goal was. He recognized the pattern and I showed him the game. Within 3 seconds, he finished the first level with sub uh super human performance, let's say.
verbatim transcript · starts at 36:58
36:58quite high level and fractionated and pattern based and it's quite specialized. So some humans can solve certain tasks because they have a certain perspective. They have certain experiences and whatnot and and they can just they they can combine those fractured representations together to come to an answer, but maybe they're just in possession of those representations and another human isn't. >> Uh I have a friend that uh is a
37:19professional esports player. I showed him one of the games and he completed the first level without spending an extra move that he didn't have to. It was immediate to him what the goal was. He recognized the pattern and I showed him the game. Within 3 seconds, he finished the first level with sub uh super human performance, let's say. And okay, he has been playing professionally games for 5 years, right? But and he's
37:45been trained on this, but like exactly as you say, like there's something we can bootstrap on and we do bootstrap on and it does work. So maybe replicating that with LLMs is enough. >> Yeah, I think gaming is a really good example. compliment a good friend of mine is called DDK and he does um commentary on Counter-Strike and stuff like that and it's when I when I watch
38:05him do commentary it seems super situational. So like in in Quake 3, you know that there's you got to time the mega health and then there's the red armor and then there's a there's a position over here and what you get is like the emergence of these complex situational phenomena in the game. And this is this doesn't seem anything like what Shalet is talking about, you know,
38:23because there's two worlds, right? there's the sort of the emergent complexity world and then there's the the sort of reductionist you know kind of core knowledge world. I think in this world we can still use intelligence and we can still ab like acquire abstractions and descriptions for these high level phenomena but but what do we do? Do do we analogize them in terms of low-level knowledge we already have or
38:44are they something new? It it seems like a different modality. >> No, I don't really have a good answer to that. I just only I think I can base my answer on on these examples and I completely agree with you that these that there are some abstractions like that but I I don't know I don't know like but you know like Conway's game of life um it is still path dependent right