If you want your post to have some substance, you ought to address the issue of a "practical tool" vs AI oracle and why the former is less dangerous. HK had a few points about that. Or maybe I don't grasp your point about the difference in the utility function.
(One standard point is that, for an Oracle to be right, it does not have to be a good predictor, it has to be a good modifier, so presumably you want to prove that your approach does not result in a feedback loop.)
That is meant to be informative to those wondering what Holden was talking about. I do not know what do you mean by 'some substance'.
edit: Actually, okay, I should be less negative. It probably is a case of accidental/honest self deception here, and the technobabbling arose by honest attempt to best communicate the intuitions. You approach problem from direction - how do we make a safe oracle out of some AGI model that runs in your imagination reusing the animalism to predict it. Well you can't. That's quite true! However, the actual software is using bran...
Presently, the 'utility maximizers' work as following: given a mathematical function f(x) , a solver finds the x that corresponds to a maximum (or, typically, minimum) of f(x) . The x is usually a vector describing the action of the agent, the f is a mathematically defined function which may e.g. simulate some world evolution and compute the expected worth of end state, given action x, as in f(x)=h(g(x)) where h computes worth of world state g(x), and g computes the world state at some future time assuming that action x was taken.
For instance, the f may represent some metric of risk, discomfort, and time, over a path chosen by a self driving car, in a driving simulator (which is not reductionist). In this case this metric (which is always non-negative) is to be minimized.
In a very trivial case, such as finding the cannon elevation at which the cannonball will land closest to the target, in vacuum, the solution can be found analytically.
In more complex cases multitude of methods are typically employed, combining iteration of potential solutions with analytical and iterative solving for local maximum or minimum. If this is combined with sensors and the model-updater, and actuators, an agent like a self driving car can be made.
Those are the utility functions as used in the field of artificial intelligence.
A system can be strongly superhuman at finding maximums to functions, and ultimately can be very general purpose, allowing it's use to build models which are efficiently invertible into a solution. However it must be understood that the intelligent component finds mathematical solutions to, ultimately, mathematical relations.
The utility functions as known and discussed on LW seem entirely different in nature. Them are defined on the real word, using natural language that conveys intent, and seem to be a rather ill defined concept for which the bottom-up formal definition may not even exist. The implementation of such concept, if at all possible, would seem to require a major breakthrough in the philosophy of mind.
This is an explanation of an important technical distinction mentioned in Holden Karnofsky's post.
On the discussion in general: It may well be the case that it is very difficult or impossible to define a system such as self driving car in terms of the concepts that are used on LW to talk about intelligences. In particular, the LW's notion of "utility" does not seem to allow to accurately describe the kind of tool that Holden Karnofsky was speaking of, in terms of this utility.