Deepmind's best-in-class chess and Go AIs are the same code (AlphaZero) just given respectively rules and game state input for either chess or Go and then allowed to train on the target game.
One of the fun works in progress in this space is teaching AIs to play a suite of 80s video games. Getting quite good at several games where the idea is to go right and not die is pretty easy these days, but Deepmind's work can do a broader variety only coming badly unstuck on games where it's hard to discern your progress at all without some meta-knowledge.
You claimed that "different architectures" are needed. Not true. And further you claimed this is true even for "each subdomain". This would have been a fair point in 1989. Traditional chess AIs approach the opening very differently for example, relying on fixed "books" of known good openings. But AlphaZero since it is a generalist doesn't do this, it plays every part of a match the same way.
Now you've gone from asserting that Chess and Go need separate AIs to claiming that since BERT and AlphaZero are different software it makes your point. Humans pretty clearly don't have a single structure that's doing all the work in both playing Go (AlphaZero) and understanding English (BERT) either - so that's a pretty bold bit of goalpost moving.
On a practical level, this is not true. There are different algorithms, different architectures, different hyperparameters required for each of these problems, and often for each subdomain within each of these problems, and often for each specific instance of these problems. It's difficult to draw any kind of holistic picture that combines all of the individual advances in each of these problem instances; that's why progress in AI is so hard to measure, and why a statement like "each of these toy problems...brings us closer and closer to solving the 'real problems'" is probably a bit too coarse-grained to be fair as well.