Despite the provocative title, I am not here complaining about my academic life. Rather, this is the title of a math book that recently came out the press, written to criticize the misuse of mathematical modeling in environmental science. Apparently a lot of major decision makings have been screwed up by theoretists who claim to have found the key mechanism of many environmental problems, such as in optimally controlling the ecological system within a small geographical confine, or the more cliche task of meteorological prediction. I only saw the description of this book through the AMS book review sent to me for free in my departmental mailbox, but what’s puzzling is that academicians don’t seem to question these realistic aspects of theoretical modeling when they got accepted for publishing. There is a certain laxity of standards pervalent throughout the applied math regime, which presumably results from the very incompetence of the referees in the field. I have heard a professor in my department complaining about sheer mistakes in papers that he encountered recently. And in my personal experience, such fallacious results have also popped up more often than is statistically significant. But it is generally understood that when a piece of mathematics has been ironed out, it is not to be rescinded under any circumstance, because there are always numerous other fields to explore and it’s not like any particular result will have a major impact on everyday life. What I think is necessary, however, is that the standard of precision should be kept at a minimum tolerance, so that no corruptive trend starts to take shape within the peer review system. But what’s more important is that good precision greatly facilitates the readers, and potentially makes it a lot easier for outside people to share a glimpse of the excitement and promote interdisciplinary momentum. It somehow forces researchers to write in a more ploebian, or at least more systematic style so that the intended audience can be significantly widened, without compromising the density of information.