[D] Is the Covid-19 crisis the rock on which the ML hype wave finally crashes?

People have been predicting the end of the ML Hype for a while, but it didn't seem to go away. Andrew Ng's "A.I. is the new electricity" statement looked like it was true, and the number of ML related stuff on resumes, job descriptions and software requirements, not to mention startups, seemed to keep increasing and increasing, and increasing....

Then came a virus, with a billion years of optimization and search efficiency baked into its RNA.

Some considerations:

• Despite all the hype, production grade ML was still a challenge for most companies outside of the big tech shops and some talented startups.
• With the Covid-19 induced economic meltdown, most companies don't have the money or the resources to fund the projects required to take ML from PoC/Jupyter Notebook status to value generating production applications.
• Most of the startups that are building ML productionizing tools and platforms will run out of funds, clients, or both.
• Moreover, the current economic meltdown makes most historical data on business KPIs, Customer behavior, time series forecasting, etc...is no longer useful as training data. The only data sets that are still useful are those for "hard-core" ML problems like computer vision and NLP, for which completely automated APIs have been already developed and Auto-ML works pretty well, so no real ML talent is needed in deploying them.

All of this tells me that Q2 2020 will mark the end of the ML and Deep Learning hype, and besides a likely multi-year economic depression in the U.S., we are also headed into another AI winter.

I'm not happy about the ML hype dying, it has helped me a lot in my career, and I really really love Deep Learning from a purely conceptual point of view.

But one needs to be realistic in such a job market, should we all start reframing our skill sets and our resumes? I'm kind of hoping somebody will prove my above reasoning wrong.