"In evaluating Google’s claims, it’s valuable to keep in mind Google has a vested financial interest in convincing us that the key to effective use of deep learning is more computational power, because this is an area where they clearly beat the rest of us. If true, we may all need to purchase Google products. On its own, this doesn’t mean that Google’s claims are false, but it’s good be aware of what financial motivations could underlie their statements."

Excuse the snark, but:

"In evaluating FastAI's claims, it’s valuable to keep in mind FastAI has a vested financial interest in convincing us that the key to effective use of deep learning is more machine learning experts, because ML education is their business model. If true, we may all need to purchase FastAI courses. On its own, this doesn’t mean that FastAI’s claims are false, but it’s good be aware of what financial motivations could underlie their statements."

I can think of thousands of ways that Google could increase the computational power required that would be much easier than the AutoML effort (for ex. simply recommending ridiculously deep models that take 100x to train without giving higher performance). They are putting so much effort into AutoML because it just works. A lot of the things included in later parts of this series are very useful (learning rate search, etc.) and decrease the computational power required, but most people just want to drop a dataset and pick the type of model (say, multiclass image classification) and leave the rest for machines to optimize.