No, his example points out what an AI that specifically does not have the sunk cost fallacy is like.
The thing is, an AI wouldn't need to feel a sunk cost effect. It would act optimally simply by maximising expected utility.
For example, say that I'm decide to work on Task A, which will take me five hours and will earn me $200. After two hours of work, I discover Task B which will award me $300 after five hours. At this point, I can behave like a human, and feel bored and annoyed, but the sunk cost effect will make me continue, maybe. Or I can calculate expected return: I'll get $200 after 3 hours of work on Task A, which is %67 per hour, wheras I'll get $...
Taylor & Brown (1988) argued that several kinds of irrationality are good for you — for example that overconfidence, including the planning fallacy, protects you from depression and gives you greater motivation because your expectancy of success is higher.
One can imagine other examples. Perhaps the sunk cost fallacy is useful because without it you're prone to switch projects as soon as a higher-value project comes along, leaving an ever-growing heap of abandoned projects behind you.
This may be one reason that many people's lives aren't much improved by rationality training. Perhaps the benefits of having more accurate models of the world and making better decisions are swamped by the negative effects of losing out on the benefits of overconfidence and the sunk costs fallacy and other "positive illusions." Yes, I read "Less Wrong Probably Doesn't Cause Akrasia," but there were too many methodological weaknesses to give that study much weight, I think.
Others have argued against Taylor & Brown's conclusion, and at least one recent study suggests that biases are not inherently positive or negative for mental health and motivation because the effect depends on the context in which they occur. There seems to be no expert consensus on the matter.
(Inspired by a conversation with Louie.)