You can't solve Texas Hold'em by hand, though, because there are too many parameters.
The same technique they used there for RPS can be used on poker to create strong strategies
I too wish it were about Poker, as the title suggests.
If the goal is to build a general-purpose AI, this approach seems like a dead-end. The distinguishing feature of a general-purpose AI is knowing what to do when it encounters novel situations. In contrast, the CFR algorithm above sounds more like a training program where the "AI" teaches itself using empirical results, what to do for every single scenario.
Such an empirical approach may work well for scenarios that have been frequently encountered in the past, but when dealing with novel scenarios, it seems to me that a deductive approach is what's truly needed.
In the same vein, you might want to look up "fictitious play" as a related topic for finding Nash equilibria in two player games by iterating through best-response strategies.