Sorry, but it's nothing of the sort. The approach is primitive, obsolete, and its results are very poor.
I've posted this three times already but the arxiv preprint includes an evaluation against a formal benchmark dataset, APPS. On that more objective measure of performance, the best performing variant of AlphaCode tested, solved 25% of the easiest tasks ("introductory") and less than 10% of the intermediary ("interview") and advanced ("competition") tasks.
What's more, the approach that AlphaCode takes to program generation is primitive. It generates millions of candidate programs and then it "filters" them by running them against input-output examples of the target programs taken from the problem descriptions. The filtering still leaves thousands of candidate programs (because there are very few I/O examples and the almost random generation can generate too many programs that pass the tests, but still don't solve the problem) so there's an additional step of clustering applied to pare this down to 10 programs that are finally submitted. Overall, that's a brute-force, almost random approach that is ignoring entire decades of program synthesis work.
To make an analogy, it's as if DeepMind had just published an article boasting of its invention of a new sorting algorithm... bubblesort.
