It actually comes from Peter Norvig's definition that AI is simply good software, a comment that Robin Hanson made: , and the general theme of Shane Legg's definitions: which are ways of achieving particular goals.

I would also emphasize that the foundations of statistics can (and probably should) be framed in terms of decision theory (See DeGroot, "Optimal Statistical Decisions" for what I think is the best book on the topic, as a further note the decision-theoretic perspective is neither frequentist nor Bayesian: those two approaches can be understood through decision theory). The notion of an AI as being like an automated statistician captures at least the spirit of how I think about what I'm working on and this requires fundamentally economic thinking (in terms of the tradeoffs) as well as notions of utility.