it doesn't take any extra code to predict all the outcomes that you'll never see. Just extra space/time. But those are not the minimized quantity. In fact, predicting all the outcomes that you'll never see is exactly the sort of wasteful space/time usage that programmers engage in when they want to minimize code length - it's hard to write code telling your processor to abandon certain threads of computation when they are no longer relevant.
you missed the point. you need code for picking some outcome that you see out of outcomes that you didn't see, if you calculated those. It does take extra code to predict the outcome you did see if you actually calculated extra outcomes you didn't see, and then it's hard to tell what would require less code, one piece of code is not subset of the other and difference likely depends to encoding of programs.
Series: How to Purchase AI Risk Reduction
Another method for purchasing AI risk reduction is to raise the safety-consciousness of researchers doing work related to AGI.
The Singularity Institute is conducting a study of scientists who decided to either (1) stop researching some topic after realizing it might be dangerous, or who (2) forked their career into advocacy, activism, ethics, etc. because they became concerned about the potential negative consequences of their work. From this historical inquiry we hope to learn some things about what causes scientists to become so concerned about the consequences of their work that they take action. Some of the examples we've found so far: Michael Michaud (resigned from SETI in part due to worries about the safety of trying to contact ET), Joseph Rotblat (resigned from the Manhattan Project before the end of the war due to concerns about the destructive impact of nuclear weapons), and Paul Berg (became part of a self-imposed moratorium on recombinant DNA back when it was still unknown how dangerous this new technology could be).
What else can be done?
Naturally, these efforts should be directed toward researchers who are both highly competent and whose work is very relevant to development toward AGI: researchers like Josh Tenenbaum, Shane Legg, and Henry Markram.