That CodeNet would be the SkyNet, essentially. What's shown here looks impressive, but it's the same good old text generator that can produce something that
looks very similar to the dataset used to train it. It can't go beyond the dataset and generate something new. From the mathematical point of view, that generator interpolates samples from the dataset and generates a new sample.
To give an idea how big is the gap between MuseNet and CodeNet, we can consider a simple problem of reversing a sequence: [1,2,3,4,5] should become [5,4,3,2,1] and so on. How many samples do you need to look at to understand how to reverse an arbitrary sequence of numbers? Do you need to retrain your brain to reverse a sequence of pictures? No, because instead of memorizing the given samples, you looked at a few and built a mental model of "reversing a sequence of things". Now, the state of the art ML models can reverse sequences as long as they are using the same numbers as in the dataset, i.e. we can train them to reverse any sequence of 1..5 or 1..50 numbers, but once we add 6 to the input, the model instantly fails, no matter how complex and fancy it is. I don't even dare to add a letter to the input. Reason? 6 isn't in the samples it's learnt to interpolate. And CodeNet is supposed to generate a C++ program that would reverse any sequence, btw.
At the moment, ML is kinda stuck at this pictures interpolation stage. For AI, we don't need to interpolate samples, but need to build a "mental model" of what these samples are and as far as I know, we have no clue how to even approach this problem.