Well, chess is not a "solved computational problem". We have chess engines that can play better than every human, but there is no system that has mapped the entire chess game tree. That's what is usually meant by "solved" in the context of game-playing AI.

MuZero, one of the successors of AlphaGo, learned to play chess (and a few other games besides) better than its predecessors while only receiving feedback about the end-state of a game (win, loss, draw), and not even the rules of the game, so it basically learned to play just by initially playing many (many) illegal moves. No "description of the game" needed. That didn't lead to AGI.

Progress in game-playing AI has not led to any progress outside game-playing. It's easy to see why. For chess and other classic board games there are specialised algorithms (minimax and Monte-Carlo Tree Search) that are sufficient to dominate any opponent, given enough computing power. Those algorithms are useless outside of board games (even for card games they're no good and dominant players use different approaches). For computer games such as Atari, where AI players often have superhuman performance, the complexity of the game world is tiny, compared to the real world, and techniques (Reinforcement Learning, basically) do not transfer easily from one to the other.

AI pioneers, like Alan Turing, John McCarthy, Claude Shannon and Donald Michie were interested in game playing as a gateway into understanding of the human mind, because it was assumed that the only way to play chess, and other classic board games, was to play them like the human mind plays them. It turns out, that's not the case. You can play very strong chess with a brute force search in alpha-beta minimax, and there is no human whose mind can process alpha-beta minimax well enough to beat a computer. That still tells us nothing about how the human mind works, or how to replicate it. What we have learned from all those successes in game-playing AI is that it's no use as a way towards general AI.

>> We do have a near endless supply of human generated data.

Yes, but what kind? We train systems on very narrow tasks: object identification in images, text prediction, speech recognition, document classification, etc. Those are not examples of "human intelligence", by which we could train a system to simulate "human intelligence", as a distinct process. They are examples of very specialised tasks that humans can perform using our intelligence, but it's not clear that intelligence is even needed for many of those tasks. That's why, for example, we have damn good image classifiers, but they are not AGI.

The problem with statistical machine learning algorithms is that they have to be trained to do one thing, and one thing only. They're one-trick ponies. If you want them to do multiple things together, you have to train them with examples of all those things at once. See for example Google's Gato, which had to be trained on multiple modalities as one big batch of token sequences. Google claimed that Gato is a "generalist agent", but it, too, was only capable of performing a restricted set of tasks, the ones it was trained on, and no others. Humans, by contrast, don't need to be trained on all modalities at once: we can learn to read, and later learn to play ball, and later still to drive. And each of those things is a hundred different tasks engaging different "modalities" (our senses). No AI system can do that.

AGI is not that easy as it perhaps might appear.