Algorithms matter much more than computing or data availability.
Alpha Zero’s Monte Carlo search tree is a completely different approach. At every point the program plays a number of games against itself, that always start with the current position. In the end it counts the results for an evaluation. In their paper “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm” the authors described this approach in more detail.
In a learning phase (training) Alpha Zero used 5000 “first-generation” TPUs from the Google hardware park to play games against itself. 64 “second-generation” TPUs were used for the training of the neuronal network. And after only four hours of training Alpha Zero played better than Stockfish.
https://en.chessbase.com/post/alpha-zero-comparing-orang-utans-and-apples
