(A frequent Jupyter Notebook user here. For data exploration, and teaching deep learning - then Colab is indispensable.)

The main question is: what are the alternatives, for data exploration (and sharing its results). Similarly, for data science tool demos, Notebooks shine.

IMHO the problem is not in the notebooks, but in how they are being used (i.e. the workflow). By writing scripts in py files, and using notebooks only to show their results (processed data, charts, etc) we get the best of both worlds.

The only build-in problem with Jupyter Notebooks is JSON, mixing input and output (and making it pain to work with version control). But here RMarkdown (and a few other alternatives) work well.

It eases most of the pain regarding version control. You can use it as a 'git filter', so only inputs would be shown in diffs and committed (and also works with interactive adding!), while keeping outputs in your working tree.