If self-driving car ML is "data scientists replacing a driver with computers", architecture search is computers replacing data scientists. $1.2M is roughly 3-4 data scientists' annual compensations at a company like Google or Uber.
Finding hyperparameters for an existing architecture is much easier because you would typically deal with a much lower cardinality in hyperparameter space. SigOpt is helping with that. Google has an internal solution that is able to take advantage of parameters within their job scheduling infrastructure (they actually have a bunch of them, the one I'm talking about is called hyperband [0]). CERN, as far as I know, was using hyperopt [1]. Avant uses an open-source version of SigOpt that we wrote in 2016 called loop [2].
[0] - https://arxiv.org/pdf/1603.06560.pdf [1] - http://hyperopt.github.io/hyperopt [2] - https://github.com/avantoss/loop