Chess and Go are closed systems, the whole knowledge about how to play them well can in theory be derived just from a few basic rules. The same is not true for programming.
Plus, in theory Chess can be solved by exploring the whole solution space (which is finite, even though insanely large) and heuristics can make this practical by reasoning about which branches can probably be cut off. At that point, having more and more processing power and memory helps make the task feasible.
Not that I want to downplay these achievements, they were certainly very significant, but it's still entirely different from "solving programming" (whatever that means).
You have made a distinction between programming and chess and go. There was a period when people thought chess could be solved by AI, but go couldn't, because go was too highly dimensional. That distinction has been proven not to be meaningful.
Let's maybe start with the fact that in Chess and Go it is straightforward to find out if the game is over and if so, who has won, whereas with programming, it's not even clear what it means for a problem to have been "solved", unless you formalise it to an extent that is basically never done (and which is also usually more expensive than just programming the solution).
Solving Chess/Go and programming are really not much alike.
Plus, in theory Chess can be solved by exploring the whole solution space (which is finite, even though insanely large) and heuristics can make this practical by reasoning about which branches can probably be cut off. At that point, having more and more processing power and memory helps make the task feasible.
Not that I want to downplay these achievements, they were certainly very significant, but it's still entirely different from "solving programming" (whatever that means).