One of my points is that you bury a great deal of hidden complexity and intelligence in 'simply maximize expected utility'; it is true sunk cost is a fallacy in many simple fully-specified models and any simple AI can be rescued just by saying 'give it a longer horizon! more computing power! more data!', but do these simple models correspond to the real world?

(See also the question of whether exponential discounting rather than hyperbolic discounting is appropriate, if returns follow various random walks rather than remain constant in each time period.)